Elasmobranch
Fisheries
Management
Techniques
2004
APEC Fisheries
Working Group
Elasmobranch
Fisheries
Management
Techniques
Edited by
John A. Musick
Ramón Bonfil
2004
Produced for
APEC Secretariat
35 Heng Mui Keng Terrace
Singapore 119616
Tel: (65) 6775 6012 / Fax: (65) 6775 6013
Email: info@mail.apecsec.org.sg
Website: www.apec.org
(c) 2004 APEC Secretariat
APEC#203-FS-03.2
ISBN 981-04-9682-6
THE EDITORS
John A. (Jack) Musick, Ph.D. holds the Marshall Acuff Chair in Marine Science at the Virginia
Institute of Marine Science (VIMS), College of William and Mary, where he has served on the faculty
since 1967. He earned his B.A. in Biology from Rutgers University in 1962 and his M.A. and Ph.D. in
Biology from Harvard University in 1964 and 1969, respectively. While at VIMS he has successfully
mentored 32 masters and 39 Ph.D. students. Dr Musick has been awarded the Thomas Ashley
Graves Award for Sustained Excellence in Teaching from the College of William and Mary, the
Outstanding Faculty Award from the State Council on Higher Education in Virginia, and the Excellence
in Fisheries Education Award by the American Fisheries Society. He has published more than 100
scientific papers and 7 books focused on the ecology and conservation of sharks, marine fishes and
sea turtles. In 1985 he was elected a Fellow by the American Association for the Advancement of
Science. He has received Distinguished Service Awards from both the American Fisheries Society and
the American Elasmobranch Society, for which he has served as president and chair of the Conservation Committee. Dr Musick also has served as president of the Annual Sea Turtle Symposium (now
the International Sea Turtle Society), and as a member of the World Conservation Union (IUCN)
Marine Turtle Specialist Group. Dr Musick currently serves as co-chair of the IUCN Shark Specialist
Group and on two national, five regional, and five state scientific advisory committees concerned with
marine resource management and conservation.
Ramón Bonfil, Ph.D. is a marine biologist and fisheries scientist with the New York-based Wildlife
Conservation Society who has studied sharks since 1984. He initiated his career at the National
Fisheries Institute in his native country, Mexico, studying the commercial shark fisheries of Yucatán.
Ramón left this position to obtain an M.S. in Fisheries Biology and Management at the University of
North Wales, UK, and a Ph.D. in Resource Management Science at the University of British Columbia in Canada. His work has covered several areas of knowledge about sharks including taxonomy,
determination of age and growth, reproduction, worldwide fisheries, the benefit of Marine Protected
Areas as management tools, population dynamics modeling, mathematical evaluation of stock assessment models, ecosystem modeling, and others. Dr Bonfil has been invited as visiting scientist by the
Far Seas Fisheries Research Institute of Japan and by the United Nations Food and Agriculture
Organization (FAO). With FAO, he published the first comprehensive review of shark fisheries of the
world, has served as scientific and technical editor for the ongoing second version of the worldwide
catalogue of sharks and other FAO publications, and is currently writing an identification guide for
sharks and rays of the Red Sea. His current work includes stock assessment analyses for Atlantic
Ocean sharks, ecosystem-based fisheries management, and a conservation research project using
satellite technology to track great white sharks off South Africa. Dr Bonfil also participates in shark
related work within International Commission for the Conservation of Atlantic Tunas (ICCAT) and
International Council for the Exploration of the Sea (ICES). Dr Bonfil’s expertise has been recognized
nationally and internationally; he is a member of the Shark Specialist Group of IUCN since 1992, a
member of the Conservation Committee of the American Elasmobranch Society, serves in the Advisory Panel for Highly Migratory Fishes of NMFS, has been invited to numerous international specialist
meetings, and has served as referee for several scientific journals.
CONTRIBUTORS
Ramón Bonfil
Marine Program
International Conservation Programs
Wildlife Conservation Society
Bronx, New York
Alexia C. Morgan
Florida Museum of Natural History
Division of Fishes
University of Florida
Gainsville, Florida
George H. Burgess
Florida Museum of Natural History
Division of Fishes
University of Florida
Gainsville, Florida
John A. Musick.
Head, Vertebrate Ecology and Systematics Programs
Virginia Institute of Marine Science
College of William and Mary
Gloucester Point, Virginia
Christina L. Conrath
Shark Research Program
Virginia Institute of Marine Science
College of William and Mary
Gloucester Point, Virginia
Paul Rago
Population Dynamics Branch
National Marine Fisheries Service
Woods Hole, Massachusetts
Kenneth J. Goldman
Department of Biology
Jackson State University
Jackson, Mississippi
Edward J. Heist
Fisheries & Illinois Aquaculture Center
Southern Illinois University
Carbondale, Illinois
Robert J. Latour
Department of Fisheries Science
College of William and Mary
Virginia Institute of Marine Science
Gloucester Point, Virginia
Colin A. Simpfendorfer
Center for Shark Research
Mote Marine Laboratory
Sarasota, Florida
John D. Stevens
Marine Research
Commonwealth Scientific and
Industrial Research Organisation (CSIRO)
Hobart, Tasmania
Australia
Terence I. Walker
Marine & Freshwater Resources Institute
Queenscliff, Victoria
Australia
DEDICATION
This book is dedicated to the shark fishers and fishery
managers. May they have the wisdom and will to achieve
and maintain sustainable shark fisheries.
ACKNOWLEDGMENTS
Thanks are due to Melanie Harbin whose efforts in editorial management were invaluable; to Ruth Hershner, who
was responsible for the layout and final production of this publication; and to Julia Ellis, who assisted with
editing and management of the final product. In addition, we thank Enric Cortés, John Graves, William Hamlett,
Robert Latour, and Colin Simpfendorfer for their efforts in providing scientific reviews of manuscripts. Lastly,
thanks are due to Sonja Fordham, Stetson Tinkham, Colin McIff and the APEC Fisheries Working Group, without
whom this project would not have been possible. This manual is a contribution from the IUCN Shark Specialist
Group and the National Shark Research Consortium, and is also Virginia Institute of Marine Science contribution
#2562
TABLE OF CONTENTS
Page
Chapter 1 .................................................................................................................................... 1
Introduction: Management of Sharks and their Relatives (Elasmobranchii)
John A. Musick
Chapter 2 .................................................................................................................................... 9
The Purpose of Stock Assessment and the Objectives of Fisheries Management
Ramón Bonfil
Chapter 3 ...................................................................................................................................21
Taxonomy and Field Techniques for Identification, with Listing of Available Regional Guides
John D. Stevens
Chapter 4 ...................................................................................................................................57
Tagging Methods and Associated Data Analysis
Robert J. Latour
Chapter 5 ...................................................................................................................................79
Genetics: Stock Identification
Edward J. Heist
Chapter 6 ...................................................................................................................................97
Age and Growth
Kenneth J. Goldman
Chapter 7 ................................................................................................................................. 133
Reproductive Biology
Christina L. Conrath
Chapter 8 ................................................................................................................................. 165
Mortality Estimation
Colin A. Simpfendorfer, Ramón Bonfil and Robert J. Latour
Chapter 9 ................................................................................................................................. 187
Demographic Models: Life Tables, Matrix Models and Rebound Potential
Colin A. Simpfendorfer
Chapter 10 ............................................................................................................................... 205
Fishery Stock Assessment Models and their Application to Sharks
Ramón Bonfil
Chapter 11 ................................................................................................................................ 241
Fishery-Dependent Sampling: Total Catch, Effort and Catch Composition
Alexia C. Morgan and George H. Burgess
Chapter 12 ............................................................................................................................... 265
Fishery-Independent Sampling
Paul Rago
TABLE OF CONTENTS (continued)
Chapter 13 ............................................................................................................................... 285
Elasmobranch Fisheries Management Techniques
Terence I. Walker
Chapter 14 .............................................................................................................................. 323
Shark Utilization
John A. Musick
Index ....................................................................................................................................... 337
CHAPTER 1.
INTRODUCTION: MANAGEMENT OF SHARKS AND THEIR
RELATIVES (ELASMOBRANCHII)
John A. Musick, Virginia Institute of Marine Science, College of William and Mary, PO Box 1346,
Gloucester Point, VA 23062 USA
Sharks and their relatives, the rays (subclass Elasmobranchii) are a group of about 1,100 species
of mostly marine fishes (Compagno, 2001). Most sharks and rays that have been studied have slow
growth, late maturity and very low fecundity compared to bony fishes (Camhi et al., 1998). These attributes result in very low intrinsic rates of increase (Smith et al., 1998) and very low resilience to fishing
mortality (Hoenig and Gruber, 1990). Thus, most shark and ray populations can withstand only modest
levels of fishing without depletion and stock collapse (Camhi et al., 1998; Musick, 1999; Cortes, 2000), and
decline more rapidly and are not able rebound as quickly as other fishes to population reductions (Sminkey
and Musick, 1995; 1996). Consequently, management must be implemented at the inception of shark
fisheries (Musick, 1999). However, this has not been the case for the vast majority of shark fisheries that
have developed around the world (Bonfil, 1994). Rather, the overwhelming pattern has been one of no
management, rapid stock decline and collapse, with decades to recovery if recovery occurs at all (Anderson, 1990; Hoff and Musick, 1990).
Successful sustainable fisheries for sharks are possible, particularly for smaller species that
mature early and have a relatively large number of young. The fishery for gummy sharks (Mustelus
antarcticus) in Australia stands as a good example. Success in this fishery has come through knowledge
of the biology of the species and active management measures (mostly through regulation of mesh size in
the gillnet fishery) (Walker, 1998; Stevens, 1999). Even sharks with very low intrinsic rates of increase
may be harvested sustainably if sufficient information exists on their demography and an effective
management strategy can be enforced. Simpfendorfer (1999) reported on the sustainable dusky shark
(Carcharhinus obscurus) fishery in Western Australia, which is focused on a limited catch (500-700 mt/
yr) of young-of-the-year fish, with protection of all other age classes.
Although many sharks and rays have been of lower economic value in fisheries, the economic
impact of stock collapse may be similar to more productive species because the population recovery time
and economic loss last much longer (Musick, 1999). Well-documented cases of collapsed shark fisheries
are the porbeagle (Lamna nasus) fishery in the North Atlantic (Anderson, 1990; Campana et al., 2001),
the tope or soupfin shark (Galeorhinus galeus) fishery off California and Australia (Ripley, 1946; Olsen,
1959), various basking shark (Cetorhinus maximus) fisheries (Parker and Stott, 1965), the spiny dogfish
(Squalus acanthias) fisheries both in the North Sea and off British Columbia (Holden, 1968; Ketchen,
1986; Hoff and Musick, 1990), and most recently the large coastal shark fishery off the east coast of the
U.S. (Musick et al., 1993; NMFS, 1999). While the reasons behind the collapse of some of these fisheries
1
range from stock depletion to economic constraints or market changes (Ketchen, 1986; Myklevoll, 1989;
Bonfil, 1994; 1999), the pattern of long periods for stock recovery prevails, and at least the stock of
California soupfin shark has not recovered to its former level after more than 50 years despite the lack of
fishing.
Although directed fisheries have been the cause of stock collapse in many species of elasmobranchs, a more important threat to long-lived sharks and rays is mortality in mixed-species fisheries and
bycatch in fisheries targeted at other species (Bonfil, 1994; Musick, 1999). In those fisheries, species with
higher production rates continue to support the fishery while species with lower rebound potential are
driven to stock collapse or extirpation (Musick, 1999; Stevens et al., 2000). Thus the sand tiger
(Carcharias taurus) and dusky shark (Carcharhinus obscurus) populations, which have very low
intrinsic rates of increase, collapsed in the western North Atlantic shark fin fisheries in the late 1980s and
show only modest signs of recovery (after ten years of fishery regulation), while the more productive
sandbar shark (Carcharhinus plumbeus), although depleted, continues to drive the fisheries (Musick et
al., 1993; Musick, 1999). Similarly, the barndoor skate (Dipturus laevis) is taken as bycatch in the New
England and Canadian Atlantic ground fisheries and its decline and local extinction would have been
unnoticed were it not for the fishery-independent data sets (where individual species are recorded) that
were analyzed by Casey and Myers (1998). Several other large species of skates may be threatened with
extinction (Dulvy and Reynolds, 2002). Imprecise reporting of fishery statistics where several species are
lumped together as one category (i.e., “sharks” or “skates”) can mask basic changes in community
structure and profound reduction in populations of the larger, slower growing species (Dulvy et al., 2000).
Thus the traditional paradigm that fisheries will become commercially extinct before the targets of those
fisheries become biologically extirpated does not apply in many cases.
Several species of elasmobranchs depleted by fisheries have recently come under protection of
national regulations. The barndoor skate, two species of sawfishes (Pristis pectinata, P. perotteti) and
the sand tiger (Carcharias taurus), dusky (Carcharhinus obscurus), and night (Carcharhinus
signatus) sharks were added in 1999 to the U.S. National Marine Fisheries Service (NMFS) Candidate
List for Threatened and Endangered Species because of large documented declines caused by overfishing
(Diaz-Soltera, 1999). Pristis pectinata has since been listed as Endangered. The sand tiger, dusky and
several other species of sharks became protected under the NMFS Fishery Management Plan (FMP) for
Sharks of the Atlantic Ocean (NMFS, 1999). The sand tiger and great white sharks are also protected by
regulations in South Africa and Australia (Camhi et al., 1998). In recent years the status of elasmobranch
species has come under closer scrutiny worldwide by the World Conservation Union (IUCN) Shark
Specialist Group (SSG), and 62 shark species out of 226 species assessed are currently recognized as
threatened with extinction (IUCN, 2003). The number of threatened species will certainly increase as all
the sharks and batoids are assessed (>1100 species).
2
In addition to the obvious concern over possible extinction of some elasmobranch species and the
ensuing economic hardship due to the collapse of the fisheries, a further problem is the negative effects
that strong declines in apex predators can have on ecosystems. The removal of sharks occupying the role
of top predators in their ecosystems can have not only the expected effect of releasing control over their
main prey, but sometimes unexpected second and third degree effects on non-prey species through trophic
linkages (Stevens et al., 2000; Schindler et al., 2002).
International concern over the sustainability of shark fisheries started to build in the late 1980s and
early 1990s as shark fisheries expanded globally in response to lucrative shark fin markets in southeast
Asia (Bonfil, 1994; Rose, 1996). In 1994 the 9th Conference of Parties (CoP) to the Convention on
International Trade of Endangered Species (CITES) adopted a resolution on “The Status of International
Trade in Shark Species.” The resolution called upon the United Nations Food and Agriculture Organization
(FAO) to review information on the global status of shark stocks and the effects of international trade on
them. The FAO with appropriate international expert consultation developed an International Plan of
Action for the Conservation and Management of Sharks (IPOA-Sharks) which was adopted in 1999. For
the purpose of the IPOA-Sharks, the term “shark” includes all chondrichthyans (sharks, batoids, and
chimaeras). The guidelines (FAO, 2000) for the IPOA-Sharks state that nations contributing to fishing
mortality of shark stocks should participate in their conservation and management, that shark resources be
used sustainably, and that waste and discards be minimized. Shark fishing nations were called upon in the
IPOA-Sharks to prepare National Shark Assessment Reports (Appendix 1) and to implement National
Shark Plans (Appendix 2). Unfortunately, when progress on the IPOA-Sharks was reviewed by the FAO
Committee on Fisheries (COFI) in February 2001 and by CITES in 2002 it was found that only a small
number of shark fishing nations had submitted Shark Assessment Reports or Plans. Many of the countries
that had submitted these documents had not adequately addressed the issues raised in the IPOA nor did
they propose sufficient action to begin precautionary sustainable shark fisheries management (IUCN, 2002
a;b).
The objectives of the present manual are to provide the information necessary for fisheries
managers to effectively address the IPOA Sharks, thus leading to sustainable shark fisheries. We attempt
to provide a step-by-step approach to collecting the information necessary for proper stock assessment
and sustainable shark management. Each chapter progresses from simple to more complex techniques.
We begin in Chapter 2 by explaining the objectives of fisheries management and the methods that may be
used to achieve those objectives. Then, in Chapter 3 we describe how to identify sharks and rays. In
Chapter 4 we describe the value and methodology of tagging studies in shark management and in Chapter
5 we provide similar treatment for genetic techniques. Chapter 6 explains how to determine age and
growth and Chapter 7 describes techniques to study reproductive biology. Chapter 8 describes how to
estimate mortality. In Chapter 9 we review demographic population models and in Chapter 10 stock
3
assessment and population dynamics models are explained. Chapters 11 and 12 describe, respectively,
fisheries-dependent and fisheries-independent sampling procedures. Chapter 13 reviews options that may
be available for managing elasmobranch stocks. Lastly, in Chapter 14 we provide a brief overview of
elasmobranch utilization.
This paper is a contribution from the National Shark Research Consortium and is also contribution
# 2563 from the Virginia Institute of Marine Science.
REFERENCES
ANDERSON, E. D. 1990. Fishery models as applied to elasmobranch fisheries, p. 473-484. In: Elasmobranchs as living resources: advances in the biology, ecology, systematics, and the status of
fisheries. H. L. Pratt, Jr., S. H. Gruber, and T. Taniuchi (eds.). U.S. Department of Commerce,
NOAA Technical Report NMFS 90.
BONFIL, R. 1994. Overview of world elasmobranch fisheries. FAO Fisheries Technical Paper No 341.
__________. 1999. The dogfish (Squalus acanthias) fishery of British Columbia, Canada and its
management, p. 608-655. In: Case studies of the management of elasmobranch fisheries. R.
Shotton (ed.). FAO Fisheries Technical Paper 378. FAO, Rome.
CAMHI, M., S. FOWLER, J. MUSICK, A. BRÄUTIGAM, AND S. FORDHAM. 1998. Sharks and their
relatives: ecology and conservation. Occasional Paper of the IUCN Species Survival
Commission Occas. Pap. No. 20.
CAMPANA, S., L. MARKS, W. JOYCE, AND S. HARLEY. 2001. Analytical assessment of the porbeagle shark
(Lamna nasus) population in the northwest Atlantic with estimates of long-term sustainable yield.
Research Doc 2001/067 Canadian Science Advisory Secretariat. Ottawa, Canada.
CASEY, J. M., AND R. A. MYERS. 1998. Near extinction of a large, widely distributed fish. Science 281:
690-692.
COMPAGNO, L. J. V. 2001. Sharks of the world. An annotated and illustrated catalogue of shark species
known to date. Vol. 2. Bullhead, mackerel and carpet sharks (Heterodontiformes, Lamniformes
and Orectolobiformes). FAO Species Catalogue for Fishery Purposes No 1. Food and Agriculture
Organization of the United Nations, Rome.
CORTÉS, E. 2000. Life History Patterns and Correlations in Sharks. Rev. Fish. Sci. 8(4): 299-344
DIAZ-SOLTERA, H. 1999. Endangered and threatened species; revision of candidate species list
under the Endangered Species Act. Federal Register 64(120): 33166-33467.
DULVY, N. K., J. D. REYNOLDS, J. D. METCALFE, AND J. GLANVILLE. 2000. Fisheries stability, local extinctions and shifts in community structure in skates. Conserv. Biol. 14:1-11.
__________, AND J. D. REYNOLDS. 2002. Predicting extinction vulnerability in skates. Conserv. Biol.
16:440-450.
FAO. 2000. Fisheries Management: 1. Conservation and Management of Sharks. FAO Technical Guidelines for Responsible Fisheries 4 (Supplement 1).
4
HOENIG, J. M., AND S. H. GRUBER. 1990. Life-history patterns in the elasmobranchs: implications for
fisheries management, p. 1-16. In: Elasmobranchs as living resources: advances in the biology,
ecology, systematics, and the status of the fisheries. H.L. Pratt, Jr., S.H. Gruber, and T. Taniuchi
(eds.). U.S. Dept. of Commerce, NOAA Technical Report NMFS 90.
HOFF, T. B., AND J. A. MUSICK. 1990. Western North Atlantic shark fishery management problems and
informational requirements, p. 455-472. In: Elasmobranchs as living resources: advances in the
biology, ecology, systematics, and the status of the fisheries. H.L. Pratt, Jr., S.H. Gruber, and T.
Taniuchi (eds.). U.S. Dept. of Commerce, NOAA Technical Report NMFS 90.
HOLDEN, M. J. 1968. The rational exploitation of the Scottish-Norwegian stocks of spurdogs
(Squalus acanthias L.). Fish. Investig. Minist. Agric. Fish. Food. U.K. Ser.2, 25(8).
IUCN SPECIES SURVIVAL COMMISSION’S SHARK SPECIALIST GROUP AND TRAFFIC. 2002a. CITES Animals
Committee Document (AC 18 Doc 19.2.). http://www.cites.org/eng/cttee/animals/18/
agenda.shtml
__________. 2002b. The Role of CITES in the Conservation and Management of Sharks. http://
www.cites.org/common/notifs/2002/ESFO42A.pdf
__________. 2003. The threatened states of sharks and related species. http://www.flmnh.ufl.edu/fish/
organizations/SSG/SSGDefault.htm
KETCHEN, K. S. 1986. The spiny dogfish (Squalus acanthias) in the Northeast Pacific and a history of its
utilization. Can. Spec. Publ. Fish. Aquat. Sci. No 88.
MUSICK, J. A. 1999. Ecology and conservation of long-lived marine animals, p. 1-10. In: Life in the slow
lane: ecology and conservation of long-lived marine animals. J.A. Musick (ed.). Am. Fish. Soc.
Symp. 23. Bethesda, Maryland.
_________, S. BRANSTETTER, AND J.A. COLVOCORESSES. 1993. Trends in shark abundance from 1974 to
1991 for the Chesapeake Bight region of the U.S. Mid-Atlantic Coast, p. 1-18. In: Conservation
biology of elasmobranchs. S. Branstetter (ed.). U.S. Dept. of Commerce, NOAA Technical
Report NMFS 115.
MYKLEVOLL, S. 1989. Norway’s porbeagle fishery. Working Document presented at the ICES Study Group
on Elasmobranch Fisheries. Dublin, Ireland, 26-28 April 1989. (mimeo).
NMFS (NATIONAL MARINE FISHERIES SERVICE). 1999. Final fishery management plan for the Atlantic tunas,
swordfish and sharks. NOAA, NMFS, Silver Spring, MD.
OLSEN, A. M. 1959. The status of the school shark fishery in south-eastern Australia waters.
Aust. J. Mar. Freshwat. Res. 10: 150-176.
PARKER, H. W., AND F. C. SCOTT. 1965. Age, size and vertebral calcification in the basking shark,
Cetorhinus maximus (Gunnerus). Zool. Meded. (Leiden) 40:305-319.
RIPLEY, W. E. 1946. The soup-fin shark and the fishery. Fish. Bull. 64: 7-37.
5
ROSE, D. A. 1996. Shark Fisheries and Trade in the Americas, Volume 1: North America. TRAFFIC.
Cambridge U.K.
SCHINDLER D. E., T. E. ESSINGTON, J. F. KITCHELL, C. BOGGS, AND R. HILBORN. 2002. Sharks and tunas:
Fisheries impacts on predators with contrasting life histories. Ecol. Appl. 12 (3): 735-748.
SIMPFENDORFER, C. A. 1999. Demographic analysis of the dusky shark fishery in southwestern Australia,
p. 149-160. In: Life in the slow lane: ecology and conservation of long-lived marine animals. J.
A. Musick (ed.). Am. Fish. Soc. Symp. 23. Bethesda, Maryland.
SMINKEY, T. R., AND J. A. MUSICK. 1995. Age and growth of the sandbar shark, Carcharhinus
plumbeus, before and after population depletion. Copeia 1995: 871-883.
SMINKEY, T. R., AND J. A. MUSICK. 1996. Demographic analysis of the sandbar shark, Carcharhinus
plumbeus, in the western North Atlantic. Fish. Bull. 94: 341-347.
SMITH, S. E., D. W. AU, AND C. SHOW. 1998. Intrinsic rebound potentials of 26 species of Pacific sharks.
Mar. Freshwat. Res. 41: 663-678.
STEVENS, J.D. 1999. Variable resilience to fishing pressure in two sharks: the significance of different
ecological and life history parameters, p. 11-15. In: Life in the slow lane: ecology and
conservation of long-lived marine animals. J.A. Musick (ed.). Am. Fish. Soc. Symp. 23.
Bethesda, Maryland.
__________, R. BONFIL, N. DULVY, AND P. WALKER. 2000. The effects of fishing on sharks, rays, and
chimaeras (chondrichthyans), and the implications for marine ecosystems. ICES J. Mar. Sci. 57:
476-494.
WALKER, T. I. 1998. Can shark resources be harvested sustainably?: a question revisited with a review of
shark fisheries. Mar. Freshwat. Res. 49(7):553-572.
6
CHAPTER 2.
THE PURPOSE OF STOCK ASSESSMENT AND THE
OBJECTIVES OF FISHERIES MANAGEMENT
Ramón Bonfil, Wildlife Conservation Society, 2300 Southern Blvd., Bronx, NY 10460 USA
2.1
BASIC CONCEPTS AND THE IMPORTANCE OF MANAGEMENT AS THE
ULTIMATE GOAL OF FISHERIES SCIENCE
2.2
2.3
THE PURPOSE OF STOCK ASSESSMENT IN FISHERIES SCIENCE
2.2.1
Quantitative predictions, dynamics and uncertainty
2.2.2
The concept of MSY and its evolution from an objective to a reference point
2.2.3
Model complexity and the importance of cross-comparison in stock assessment
THE DIFFERENT OBJECTIVES OF FISHERIES MANAGEMENT AND THEIR
INTERPLAY
2.3.1
Biological and conservation objectives
2.3.2
Economic objectives
2.3.3
Social objectives
2.3.4
Recreational objectives
2.3.5
Fisheries management as a balancing act and the importance of explicit objectives
2.4
ACKNOWLEDGMENTS
2.5
REFERENCES
7
8
2.1
BASIC CONCEPTS AND THE IMPORTANCE OF MANAGEMENT AS THE
ULTIMATE GOAL OF FISHERIES SCIENCE
The intention of this chapter is to briefly introduce some important concepts that will be
needed throughout the rest of the manual and whose understanding is essential for the correct practice
of fisheries work and the successful management of fisheries. The chapter also provides a general
framework for the rest of the manual and gives context to the different and interlaced roles of stock
assessment and management, which are sometimes mixed and confused. Given the importance and
scarcity of good management in present-day fisheries, it is never redundant to clarify and emphasize
these basic concepts and put the different components of fisheries work into perspective. The overall
feeling and some of the sections of this chapter are inspired by the book of Hilborn and Walters (1992)
and readers are encouraged to give a thorough read to this excellent source for more in-depth information.
Fisheries Science is the multidisciplinary study of fisheries. Which disciplines are part of
Fisheries Science is to a point a matter of opinion, but a preliminary list would include fisheries biology,
marine ecology, stock assessment, natural resource economics, social sciences, fishing technology,
oceanography, statistics, and computer modeling.
A fishery is defined as the set composed of a particular stock (for a definition of stock see
Chapters 4 and 5) plus the fishing activities related to its harvest, inclusive of fishermen, vessels, gears
and even associated facilities. Often the word stock refers to a population or part of the population of
a single species but in the frequent case of multispecific fisheries it includes a group of at least two
similar or diverse species.
Stock assessment is the part of Fisheries Science that studies the status of a fish stock as well
as the possible outcomes of different management alternatives. It tells us if the abundance of a stock
is below or above a given target point and by doing so lets us know whether the stock is overexploited
or not; it also tells us if a catch level will maintain or change the abundance of the stock. But stock
assessment is not the goal of Fisheries Science.
The ultimate objective of Fisheries Science is to inform management. This statement
embodies the real meaning of the work of fisheries scientists and technicians, whose fundamental
objective is neither to learn how fish grow, where they go or how fast they reproduce, nor to investigate how much fishermen catch, how or where they catch it, or how much money they make. The
real and ultimate goal of fisheries science is to provide the information needed for the adequate
management of fisheries. Ultimately, if the collective work of all those working in Fisheries Science
does not translate into management decisions and their implementation, then we are wasting time and
money.
9
This does not mean that fish biology, stock assessment and other disciplines are not extremely
important; in fact they are, but it is essential to keep in mind that they are a very important means to
an end. The relevance of all the knowledge we can obtain about the biology of the resources and the
dynamics of capture fisheries is that this information is needed to underpin the proper management of
the fishery, including target and non-target species, detrimental effects of fishing on ecosystems, and
also the human communities depending on fishery resources. It follows from the above that it is
worthwhile for governmental agencies charged with fisheries research and management to prioritize
and invest resources in fisheries biology and stock assessment of resources for which this work is
going to be actually used to do fisheries management. This is a very important fact often ignored in
many parts of the world. On the other hand, basic monitoring of unexploited or less important resources can be invaluable several years down the line when fisheries exploitation expands or its
associated effects are felt. In this case, it is usually academic and independent research institutions
that can carry out the basic monitoring that might be unaffordable to government agencies. It is also
often overlooked that a prerequisite to successful management is the existence of the proper institutional and legal structures. Without management institutions, management plans with clearly stated
objectives and management rules there can be no effective decision making and implementation for
fisheries management.
2.2
THE PURPOSE OF STOCK ASSESSMENT IN FISHERIES SCIENCE
Stock assessment makes use of diverse types of information to give managers advice about
the status of a fishery and the possible outcomes of management actions. This includes aspects not
only related to the resource abundance such as whether the stock is depleted or close to its maximum
biomass, but also in regards to other important aspects of fish population dynamics such as the current
levels of mortality and expected levels of future recruitment, or even economically relevant features
such as likely changes in catch per unit effort.
Stock assessment has been defined in many ways, often in terms of its objectives. Sparre and
Venema (1992) proposed that the basic purpose of stock assessment is “to provide advice on the
optimum exploitation of aquatic living resources”. Probably the best modern definition comes from
Hilborn and Walters (1992): “Stock assessment involves the use of various statistical and mathematical
calculations to make quantitative predictions about the reactions of fish populations to alternative
management choices.” The last definition is especially relevant because it explicitly says two important
things, that quantitative predictions are needed in the process and that the objective is to provide
advice to management about choices.
2.2.1
Quantitative predictions, dynamics, and uncertainty
In order to be of practical use, modern fisheries stock assessment must be able to make
quantitative predictions. To state that a fishery resource is “abundant” or “overfished” without further
10
detail is of limited use for shaping management decisions if the level of abundance or depletion is not
expressed as a quantity such as “the fishable stock is at 30% of its original virgin biomass”. Equally
important, stock assessment should be able to make quantitative predictions of the outcomes of
different management regulations, such as how likely it is that an overexploited stock will recover to a
target level in a specified time-frame under different catch or effort quotas. This is why modern stock
assessment work is by necessity a quantitative discipline. While decades ago it was difficult to make
these types of quantitative predictions, computers now allow us to do calculations we would hardly be
capable of doing 20 years ago, and as time passes numerical methods are becoming more rigorous and
powerful for stock assessment.
One of the most important roles of stock assessment is to understand the dynamics of
fisheries. This follows because biological resources, fishermen and the environment are changing
entities; they are dynamic not static. Furthermore, fisheries will necessarily respond dynamically over
time to management actions as well as to external factors such as environmental forces. Understanding all of these dynamics in order to make good predictions is the ultimate role of stock assessment.
Uncertainty is an intrinsic characteristic of stock assessment. First, natural systems have a lot
of random variability that translates into uncertainty and which can be due to variations in fish growth
(Fargo and Kronlund, 2000) and reproductive output, as well as to environmental effects (abiotic and
biotic) on biological and ecological processes (Parsons et al., 1998). Other sources of uncertainty are
the variations in the behavior of fishing fleets and gear, the errors and biases in data collection, and the
often incomplete or less than ideal quality of the data sets available for performing stock assessment.
Uncertainty also arises from the choice of model used for stock assessment; some models are better
suited to capture the underlying dynamics of a given resource than others but it is often impossible to
determine which model is more correct for a particular stock. Considering all of the above, it is not
surprising to find that the results of fisheries stock assessment are never precise estimates of biomass
or mortality, but are in reality estimates that contain a certain degree of uncertainty and doubt. Dealing
with uncertainty, acknowledging it and incorporating it into the decision-making process is something
extremely important but that only recently has begun to be put into practice. Further reading on the
need to embrace uncertainty and new methods to achieve this can be found in Punt and Hilborn
(1997), Hilborn and Lierman (1998), McAllister et al. (1999), and Patterson et al. (2001).
2.2.2
The concept of MSY and its evolution from an objective to a
reference point
The traditional concept of the dynamics of fishery resources is that there is an underlying
model according to which as fishing effort increases, catch will increase up to a maximum, and if
effort continues to grow then catches (also known as yield) will decrease. This leads directly to the
11
concept of maximum sustainable yield (MSY) which has been the holy grail of fisheries (Larkin,
1977).
The specific shape of the
yield curve shown in Figure 2.01
MSY
does not matter. The important
principle always holds: zero effort
means zero catch; too much effort
Average
catch
leads to small or almost zero
catch. Also, in theory there should
be a point at which catch has a
maximum—at least on average—
and supposedly once the curve
reaches the top, the MSY level
Fishing effort
has been found. For decades,
Figure 2.01 A graphical representation of the
Maximum Sustainable Yield (MSY) concept.
finding MSY and keeping fisheries
at this prescribed level of catch
and effort became the sole
objective and obsession of fisheries science, as eloquently put by Larkin (1977).
There are several problems with this concept, the first practical problem being that natural
systems have a lot of random variability. In practice, real data will always reflect this variability as
“noise.” The great danger of focusing stock assessment work solely in finding MSY and its associated
optimum effort (fopt defined as the effort level that produces MSY) is that we can seldom be totally
sure that we have witnessed the MSY level. Even if managers try to be very careful and cautious by
developing a fishery at a very slow pace it will never be guaranteed that the stock will not be overexploited or that opportunities will not be wasted. An excellent example of the difficulties in finding MSY
comes from work on Atlantic yellowfin tuna (Thunnus albacares) published by FAO and cited by
Hilborn and Walters (1992). When scientists performed the first assessment of this resource in the
mid-1970s, they thought they had already arrived at the MSY level and calculated this at about 50,000
t. However, due to lack of effective management the fishery continued to grow and a second analysis
10 years later suggested a different MSY level of more than 100,000 t, clearly indicating that the first
assessment lead to a “false” MSY. The question remaining was if the second assessment was also an
underestimate.
The real problem in the above example and most real fisheries is that in all cases and especially in situations of noisy data we would have to go beyond MSY to make sure that we have actually
12
found it. In other words until yield does not substantially decrease for a good period of time at increased effort levels we cannot be sure that MSY has been observed. This effectively means that we
can never prevent overexploitation, at least not a small amount of it in the best case. This is an important principle identified by Hilborn and Walters (1992): “You cannot determine the potential yield
from fish stocks without overexploiting them.” The secret is not to overexploit the stock beyond
recovery in our effort to find MSY. An additional practical problem is that once fisheries have actually
passed the MSY point and gone into the overexploitation phase, more problems arise. In such cases,
the fishery is already in the overcapacity side of the curve. This leads to another sad but important
principle stressed by Hilborn and Walters (1992): “The hardest thing to do in fisheries management
is to reduce fishing pressure.”
In an ideal situation a new fishery should start with all the mechanisms in place to assure, a)
detection of MSY quickly after passing this point (i.e., a good monitoring and data acquisition system
should be in place), and b) there should be mechanisms in place from the onset of exploitation , to
reduce effort effectively without detrimental effects (high taxes that can be later used to buy back
boats or compensate for the lost catches and revenue of each boat).
Nowadays, MSY is a theoretical concept that should hold on average, but it is mostly useful as
a general concept that helps us to guide our work; it is not the aim of fisheries assessment. In
present times the MSY concept is used to derive management targets and limits or biological reference
points (BRPs). Biological reference points are levels of total biomass, spawning stock biomass, fishing
mortality rate or other measurable characteristics of a fish population and a fishery, which are either
the target of management or a limit beyond which the fishery will not be permitted to go. Two common BRPs are the biomass at which the population can produce the maximum sustainable yield (BMSY)
and the fishing mortality needed to achieve MSY (FMSY). For additional reading about these and related
concepts readers should refer to Clark (1991), Jacobsen (1992), Smith et al. (1993), Caddy and Mahon
(1995), and Hayes (2000). A further important consideration is that MSY and the reference points
based on it assume that environmental conditions are constant. However, human-induced (habitat
destruction, species depletion) and environmentally driven phenomena (climatic “regime shifts”), can
all produce changes in MSY. This issue has commonly been either ignored or mishandled in fisheries
science.
2.2.3
Model complexity and the importance of cross-comparison in stock
assessment
Predictions are always based on the use of a model, whether the model is explicit or implicit.
Even the simplest prediction about what will happen to a stock if effort is increased implies a set of
assumptions or conceptual model. Formally, a model is just a representation or abstraction of a given
13
system or process, which in the case of quantitative disciplines such as fisheries stock assessment
takes the form of equations or sets of equations. The type and complexity of models depends on the
field of research and the particular problem to be analyzed. In terms of Holling’s (1978) classification,
problems in population modeling generally lie in the area of low quality/quantity of relevant data.
However, it is important to emphasize that the complexity of a model (understood as the number of
variables included) is not always directly related to its performance and usefulness.
Models available for stock assessment (see Chapters 9 and 10 for more details) range from
the simple holistic models that intend to capture all biological processes in a simple equation such as
surplus production models, to the detailed and elaborate age-structured, spatially-structured, multi-stock
or even multi-species models that include several sets of equations and which intend to give a more
realistic representation of fish population dynamics. But while intuition tells us that complicated and
detailed models should be better than simple ones because they more accurately represent “reality,”
research has shown that simple models can often perform better because they require fewer parameters to be estimated, and very frequently the uncertainty surrounding the estimation of some of these
parameters only reduces the ability of models to produce useful information (Ludwig and Walters,
1985; 1989; Ludwig et al., 1988). Readers are encouraged to investigate this topic in more detail by
referring to chapter 3 of Hilborn and Walters (1992) for an excellent discussion and further references
on this topic. Starfield and Bleloch (1986) give an excellent accessible introduction to model building.
Perhaps the most important message that readers should take home is that while analyzing a
fishery, it is imperative to avoid using a single “best” method; the idea that any given model is the best
and only model to be used for fisheries stock assessment is dangerously wrong. Instead, it is best to
employ a carefully chosen suite of methods—considering the available data—and if possible including
both simple and complex models. This will allow the cross-comparison of alternative results that helps
detect coincidences and patterns as well as inconsistencies, often highlighting errors in data or guiding
the acquisition of additional key information through additional research. In a similar fashion, conflicting
results using the same model with different data sets should be carefully analyzed for possible biases in
the data. Stock-assessment scientists should ask themselves why there might be differences in predictions about the status of the stock or about the outcomes of different management alternatives across
models. An objective picture of the situation can only be obtained when we question the conclusions
from one analysis with those of a different one and critically use the different results to gauge our
conclusions and to identify which pieces of the puzzle are missing. Only this complete process will
allow us to improve the data and methods, and therefore increase the capacity to perform better
assessments in the future. The same principle applies also to different data sets that could be available
to perform a particular stock assessment. Sound stock assessment is achieved only through healthy
cross-comparison and exhaustive questioning of the results of alternative models and data sets.
14
Finally, it should be mentioned that the complex and often politically charged topic of model
choice in stock assessment can nowadays be dealt with through the use of Bayesian approaches
(Hammond and O’Brien, 2001) and decision analysis techniques (Punt and Hilborn, 1997; McAllister
and Kirkwood, 1998). These methods offer quantitative ways to choose between different models and
management options taking into account the uncertainty involved, and are the best way to make
management decisions based both on the outcomes of the stock assessment and the probabilities of
success of the proposed management options.
2.3
THE DIFFERENT OBJECTIVES OF FISHERIES MANAGEMENT AND
THEIR INTERPLAY
What is the purpose of fisheries management? While early fisheries management had implic-
itly or explicitly MSY as it most important objective (Gulland, 1968) presently MSY is considered only
a biological concept and benchmark to guide management. Although MSY still plays an important role
as a guiding light for fisheries management, often specific and multiple objectives of fisheries management may be more important than obtaining maximum yield in the long term (Alverson and Paulik,
1973). According to Hilborn and Walters (1992), the most widely accepted fundamental purpose of
fisheries management is “to ensure the sustainable production over time from fish stocks, preferably
through regulatory and enhancement options that promote economic and social well-being of the
fishermen and industries that use the production”.
In the modern world of fisheries, management tries to balance multiple objectives that span
beyond biological concerns. Oftentimes these multiple objectives are in opposition to each other, such
that it is not possible to achieve all of them simultaneously. Managers have to make quantitative
decisions about how many fish can be caught, what is the number of boats that will be allowed to enter
a fishery, or what is the minimum size of a fish or a gillnet mesh that should be allowed. They also
have to make decisions about how much should be spent on research, enforcement of regulations,
administration, etc. Within this context, fisheries assessment is about giving advice on the status of the
resource and the likely results of alternative measures. Once this is done, the choice of which action to
take remains (usually a given amount of fish or quota that can be caught by many different combinations of effort and number and size of boats), and this is where choices have to be made by managers,
usually on economic and social grounds. More precisely, fisheries management objectives can be
broken down into at least the four categories presented below.
2.3.1
Biological and conservation objectives
By default the biological objective of fisheries management is obtaining MSY, or in other
words achieving biological yield maximization. This concept has already been explained above. The
standard indicator of biological yield is the annual weight or number of fish caught.
15
Resource conservation, as well as biological and genetic diversity, are also important biological
objectives with an increasingly important role in fisheries management. Explicit directives to avoid
putting stocks of target and non-target species at risk of extinction, and to develop plans for their
recovery in case they are already endangered, play a very important role in fisheries legislation in
many parts of the world. This is exemplified in the 1996 Magnuson-Stevens Fishery Conservation and
Management Act of the USA. Even more recently, ecosystem-health objectives are beginning to take
a very important role in fisheries management (Sainsbury et al. 2000; Stevens et al. 2000). Several
fishery management plans already incorporate ecosystem objectives and it is just a matter of time until
ecosystem-based objectives replace some of the more traditional biological objectives such as obtaining single-species MSY levels. However, that topic is beyond the scope of the present manual.
2.3.2
Economic objectives
In economic terms, to obtain the maximum amount of fish (MSY) is not the main objective.
Fisheries are an economic activity and thus should aim for economic rent and more specifically for
profit maximization; that is the maximization of total revenue minus the total costs. Thus, the concept
of maximum economic rent (MER) is an economic analogue to MSY. The MER level is defined as the
point on the revenue curve (simply the yield curve times the unit value of fish landed) where the
difference between the total costs of fishing (typically a straight inclined line) and revenues is greatest.
However, as shown in figure 2.02 the point of the curve where we find MER will be by definition
always at an effort level that is lower than MSY. It is clear from this that it is impossible to attain MSY
and MER at the same time and this is a typical example of a likely conflict between multiple objectives
in fisheries management (Figure 2.02). Further reading on economics and fisheries management can
start with Crutchfield (1965) and MacKenzie (1992).
Total costs
MSY
MER
Variable costs
Economic
value
Fixed costs
Exploitation rate
Figure 2.02 A graphical representation of the Maximum Economic Rent (MER) concept and a
comparison with MSY.
16
2.3.3
Social objectives
Social objectives are concerned with employment and equity. Fisheries are not only about
landing fish and making money out of it, but also about employing people and making sure that those
involved in the fishery make a living that is adequate and sustainable. In many coastal communities of
the world it is common that fishing is the most important source of employment. In such situations,
having a large number of not-so rich fishermen might be more desirable than having a few very rich
ones. Also, it is often important to preserve community structure and traditional lifestyles. Communities
that have been fishing for a few hundred years and hold traditional fishing rights, such as the case with
many indigenous groups, must be taken into consideration as part of management. From the social
point of view, the total number of jobs related to the fishing activity is often the standard indicator, as
well as the distribution of income among fishers and the maintenance of traditional lifestyles. Excellent
further reading in topics related to economic and social issues in modern fisheries can be found in
Fairlie (1995).
2.3.4
Recreational objectives
In some parts of the world, fish stocks have to be shared between commercial fisheries and
recreational fisheries. Although both sectors are pursuing fish, their objectives are often very different.
For recreational purposes, both the catch and the effort (number of successful fishing trips) might be
important objectives. The total number of fish available to be fished is usually more important to a
sport fishery than the total biomass of fish available, and in the specific case of trophy fish (such as
marlins, swordfish or tunas), the size of the fish will be of outmost importance. In such a case it might
be an objective for the fishery to have a few large fish rather than many small ones. The standard
indicators for recreational fisheries include the estimated total value of recreational effort (dollars per
day times days fished), and the number and size of the recreational catch.
2.3.5
Fisheries management as a balancing act and the importance of
explicit objectives
Fisheries management is about making difficult decisions among multiple choices. The decisions go beyond choosing between multiple stock assessment model/data results with different degrees
of uncertainty, but also include choosing or balancing between conflicting objectives. While the obvious
dilemma between whether to aim for MSY or MER has already been mentioned above, perhaps the
major and most difficult dilemma faced today by fishery managers throughout the world is the conflict
between economic performance and social issues. Fisheries throughout the world are grossly overcapitalized; massive subsidies are responsible for the persistence of a situation in which too many
vessels and too many fishermen chase fish stocks that could be fished by fewer vessels and crews in
a much more economic efficient way (Greboval and Munro, 1999). However, should hundreds of
17
thousands or perhaps even millions of jobs across the coastal areas of the world be lost in the name of
economic efficiency? And where are resources going to come from to give alternative jobs or pensions
to those displaced? Balancing these opposing objectives is a major challenge for fishery managers. It is
precisely for this reason that the explicit statement of the objectives for fisheries management is an
extremely important step, but one that is unfortunately often overlooked in fisheries science. The major
risk of not having explicit objectives is that management then faces getting lost in a sea of political
waves driven by which interest group flexes more power at any point in time. This will probably lead
only to disaster in the long term. On the other hand, Hilborn and Walters (1992) have pointed out
correctly that it might not be desirable to set very rigid and detailed objectives that might be impossible
to reach, thereby leaving management at an impasse when legislation does not allow for frequent and
efficient review of management objectives. Given the likelihood that objectives will eventually collide
with each other even if the have not been explicitly stated, it is more important that a healthy and open
discussion of the overall general objectives of management for each fishery is held as early as possible. However, it is important to clarify at this point that it is not the job of biologists and sometimes
not even managers to define what the objectives of fisheries management will be. This should ideally
be a collective decision by a management advisory body that includes all stakeholders and interested
groups, from fishers and local communities, to government agencies and non-governmental organizations.
2.4
ACKNOWLEDGMENTS
The author acknowledges the useful comments and suggestions of E. Babcock and J. Musick.
2.5
REFERENCES
ALVERSON, D. L., AND G. J. PAULIK. 1973. Objectives and problems of managing aquatic living resources. J. Fish. Res. Board Can. 30:1936-1947.
CADDY, J. F., AND R. MAHON. 1995. Reference points for fisheries management. FAO Fish . Tech. Pap.
347, FAO, Rome.
CLARK, W. G. 1991. Groundfish exploitation rates based on life history parameters. Can. J. Fish. Aquat.
Sci. 8:734–750
CRUTCHFIELD, J.A. (ed.). 1965. The fisheries problems in resource management. University of Washington Press. Seattle.
FAIRLIE, S. (ed.). 1995. Overfishing: its causes and consequences. Ecologist. 25(2/3).
FARGO, J., AND A.R. KRONLUND. 2000. Variation in growth for Hecate Strait English sole (Parophrys
vetulus) with implications for stock assessment. J. Sea Res. 44 (1-2): 3-15.
GREBOVAL, D., AND G. MUNRO. 1999. Overcapitalization and excess capacity in world fisheries: Underlying economics and methods of control, p. 1-48 In: Managing fishing capacity. Selected papers
on underlying concepts and issues. FAO Fish. Tech. Pap. no. 386. FAO, Rome (Italy).
18
GULLAND, J. A. 1968. The concept of maximum sustainable yield and fishery management. FAO Fish.
Tech. Pap. 70. FAO, Rome.
HAMMOND, T. R., AND O’BRIEN, C. M. 2001. An application of the Bayesian approach to stock assessment model uncertainty. ICES J. Mar. Sci. 58:648–656.
HAYES, D. B. 2000. A biological reference point based on the Leslie matrix. Fish. Bull. 98:75–85.
HILBORN, R., AND C. J. WALTERS. 1992. Quantitative fisheries stock assessment: Choice, dynamics and
uncertainty. Chapman and Hall.
__________, AND M. LIERMANN. 1998. Standing on the shoulders of giants: learning from experience in
fisheries. Rev. Fish Biol. Fish. 8:273-283.
HOLLING, C. S. (ed.). 1978. Adaptive environmental assessment and management. Wiley, Chichester.
JAKOBSEN, T. 1992. Biological reference points for North-East Arctic cod and haddock. ICES J. Mar.
Sci. 49(2):155-166.
LARKIN, P. A. 1977. An epitaph to the concept of maximum sustainable yield. Trans. Am. Fish. Soc.
106:1-11.
LUDWIG, D., AND C. J. WALTERS. 1989. A robust method for parameter estimation from catch and effort
data. Can. J. Fish. Aquat. Sci. 46:137-144.
____________,
AND
__________. 1985. Are age-structured models appropriate for catch-effort
data? Can. J. Fish. Aquat. Sci. 42:1066-1072.
____________, C. J. WALTERS, AND J. COOKE. 1988. Comparison of two models and two estimation
methods for catch and effort data. Natural Resource Modeling 2(3):457-498
MCALLISTER, M. K., AND G. P. KIRKWOOD. 1999. Applying multivariate conjugate priors in fisherymanagement system evaluation: how much quicker is it and does it bias the ranking of management options? ICES J. Mar. Sci. 56:884–899.
__________, P. J. STARR, V. R. RESTREPO, AND G. P. KIRKWOOD. 1999. Formulating quantitative methods to valuate fishery-management systems: what fishery processes should be modelled and
what trade-offs should be made? ICES J. Mar. Sci. 56:900–916.
MACKENZIE, W. C. 1992. An introduction to the economics of fisheries management. FAO Fisheries
Technical Paper 226. FAO, Rome.
PARSONS, L.S., H. POWLES, AND M.J. COMFORT. 1998. Science in support of fishery management: New
approaches for sustainable fisheries. Ocean Coast. Manage. 39 (1-2):151-166.
PATTERSON, K., R., COOK, C. DARBY, S. GAVARIS, L. KELL, P. LEWY, B. MESNIL, A. PUNT, V. RESTREPO, D.
W. SKAGEN, AND G. STEFÁNSSON. 2001. Estimating uncertainty in fish stock assessment and
forecasting. Fish Fish. 2: 125–157.
PUNT, A., AND R. HILBORN. 1997. Fisheries stock assessment and decision analysis: the Bayesian
approach. Rev. Fish Biol. Fish. 7:35–63.
19
SAINSBURY, K. J., PUNT, A. E., AND A.D.M. SMITH. 2000. Design of operational management strategies
for achieving fishery ecosystem objectives. ICES J. Mar. Sci. 57:731–741.
SMITH, S. J., J. J. HUNT, AND D. RIVARD (eds.). 1993. Risk Evaluation and Biological Reference Points
for Fisheries Management. Can. Spec. Publ. Fish. Aquat. Sci., no. 120.
SPARRE, P., AND S. VENEMA. 1992. Introduction to tropical fish stock assessment. Part 1. Manual. FAO
Fish. Tech. Paper 306/1 rev 1. FAO, Rome.
STARFIELD, A. M., AND A. L. BLELOCH. 1986. Building models for conservation and wildlife management. Macmillan Publishing Company, New York.
STEVENS, J. D., R. BONFIL, N. K. DULVY, AND P. A. WALKER. 2000. The effects of fishing on sharks,
rays, and chimaeras (chondrichthyans), and the implications for marine ecosystems. ICES J.
Mar. Sci. 57:476–494.
20
CHAPTER 3.
TAXONOMY AND FIELD TECHNIQUES FOR IDENTIFICATION:
WITH LISTING OF AVAILABLE REGIONAL GUIDES.
John D. Stevens, CSIRO Marine Research, PO Box 1538, Hobart, Tasmania 7001, Australia
3.1
SHARKS, RAYS AND CHIMAERIDS: WHAT ARE THEY, AND HOW ARE THEY
CLASSIFIED
3.1.1
Diversity
3.2
GLOSSARY/TERMINOLOGY
3.3
CHARACTERS USED FOR IDENTIFICATION
3.3.1
Field identification
3.3.2
Laboratory identification
3.4
TAKING PHOTOGRAPHS
3.5
SPECIMEN COLLECTION, PRESERVATION AND CATALOGUING
3.6
DISSECTION
3.7
FAMILY KEY
3.8
ORDERS AND FAMILIES
3.8.1
Order Hexanchiformes (frilled, sixgill and sevengill sharks)
3.8.2
Order Squaliformes (dogfish sharks)
3.8.3
Order Pristiophoriformes (sawsharks)
3.8.4
Order Squatiniformes (angel sharks)
3.8.5
Order Heterodontiformes (bullhead or horn sharks)
3.8.6
Order Orectolobiformes (carpet sharks)
3.8.7
Order Lamniformes (mackerel sharks)
3.8.8
Order Carcharhiniformes (ground sharks)
3.8.9
Order Torpediniformes (electric rays)
3.8.10 Order Pristiformes (sawfishes)
3.8.11 Order Rhiniformes (sharkrays)
3.8.12 Order Rhynchobatiformes (wedgefishes or sharkfin guitarfishes)
3.8.13 Order Rajiformes (skates)
3.8.14 Order Myliobatiformes (stingrays)
3.8.15 Order Chimaeriformes (chimaeras)
3.9
ACKNOWLEDGMENTS
3.10
REFERENCES AND LIST OF REGIONAL IDENTIFICATION GUIDES
21
22
3.1
SHARKS, RAYS AND CHIMAERIDS: WHAT ARE THEY, AND HOW ARE THEY
CLASSIFIED?
Sharks, rays and chimaerids comprise the class Chondrichthyes that are separated from the
other major class of living fishes, the Osteichthyes (comprising about 95% of the modern fish fauna),
in having a skeleton made entirely of cartilage (the Osteichthyes have a bony skeleton). All
chondrichthyans also have small tooth-like denticles on their skin and internal fertilization mitigated by
male claspers (modified pelvic fins). About 57% of them give birth to live young; the remainder lay
large eggs contained in a horny capsule.
The chondrichthyans are divided into elasmobranchs, the sharks, skates and rays, and
holocephalans or chimaeras. The elasmobranchs have 5-7 gill openings on each side of the head, a
body largely covered by dermal denticles and teeth that are continuously replaced and embedded in the
gums. Chimaeras have a single gill opening, a largely naked skin and teeth that are fused into plates
that grow with the animal. They have a large head, large pectoral fins, two dorsal fins (the first
preceded by a long spine), a weak caudal fin that may have a long terminal filament and they may
have an anal fin that is barely separated from the caudal fin. Adult male chimaerids have extra claspers on their head and in front of the pelvic fins. Currently, there is no uniform agreement on the higher
classification of the chondrichthyans, and there are many alternate schemes. This chapter follows
Compagno (1999a, b) and McEachran et al. (1996), although with some differences, in separating the
elasmobranchs into two superorders of sharks (Selachei), the Squalomorphii and Galeomorphii that
together contain the eight orders of living sharks, and one superorder of batoids (Rajimorphii) with six
living orders. Sharks are mostly fusiform in shape (a few are ray-like), have one or two dorsal fins,
(sometimes with a spine at their origin), usually have an anal fin, and most have a well-developed
caudal fin. Rays are derived from sharks and have become dorso-ventrally flattened, mostly for life on
the bottom (although a few are shark-like in shape). Rays have their gills on the underside of the head
and their enlarged pectoral fins are joined to the head in front of the gill slits. They have one or two
dorsal fins (occasionally none) without fin-spines, no anal fin and a thin, often whip-like, tail.
3.1.1
Diversity
Compagno (2001) lists 60 families within the living orders of chondrichthyans. There are
nearly 500 species of living sharks, over 600 species of batoids and 50 species of chimaeras, with new
species constantly being described.
Chondrichthyan fishes exhibit great diversity inhabiting most of the seas on earth (although
only a few species live in cold polar waters) from the intertidal zone to the deep abyss, and a few also
inhabit freshwater lakes and rivers and hypersaline habitats. Diversity is greatest in shallow, tropical
23
regions, particularly in the Indo-Australian area. In the northwest Australian region, which has about
178 species, some 23% of known species are ubiquitous, about 15% are endemic and the remainder
have more regional distributions (Last and Seret, 1999). Endemism is almost entirely of demersal
species, and in the tropical eastern Indonesian-Australian region it is most pronounced on the continental slope, except in northwest Australia where more than 60% of the endemics are demersal shelf
species (Last and Seret, 1999). The distributional status of a number of problematic taxonomic genera
such as Squalus, Centrophorus, Mustelus and Himantura, as well as several deep-water groups,
may change when thorough systematic studies are carried out on a regional or global basis.
Chondrichthyans vary greatly in maximum size with sharks ranging from 20-1200 cm total length, rays
from 25-880 cm long and up to 670 cm disc width and chimaerids from 50-200 cm long. Sharks vary in
shape from the “typical” carcharhinids to the bizarre hammerheads and threshers. Some sharks are
ray-like and some rays are shark-like in shape. They vary in color from drab browns and greys to the
highly ornate patterning of some of the wobbegongs and stingrays. Most are predators, but there is a
diversity of feeding mechanisms from giant planktivores to the semi-parasitic cookie-cutter sharks.
3.2
GLOSSARY/TERMINOLOGY
anal fin: the unpaired fin on the underside of the body behind the anus in sharks (Figs. 3.1a, 3.3)
anterior: the front or head end (Fig. 3.1a)
barbel: a slender, fleshy, tentacle-like sensory structure on the underside of the snout of some sharks
(Fig. 3.1a)
caudal: pertaining to the tail region
caudal fin: the tail fin (Figs. 3.1a, 3.2, 3.3)
Figure 3.1a Terminology for a generalized shark.
24
Figure 3.2 Terminology for a generalized ray.
Figure 3.3 Terminology for a generalized chimaera.
25
caudal keel: a longitudinal, fleshy ridge along the side of the caudal peduncle (Fig. 3.1a)
caudal peduncle: the posterior part of the body supporting the caudal fin (from the insertions of the
second dorsal and anal fins to the anterior of the caudal fin)
chondrocranium: the cartilaginous skeleton enclosing the brain and inner ear
clasper: paired cylindrical extensions of the pelvic fins of males used in mating (Figs. 3.1a, 3.2, 3.3)
cusp: a projection (point) on a tooth; many teeth have just one large cusp but some have additional side
cusps
dermal denticles: the tooth-like scales of sharks, rays and chimaeras
diplospondylous: elasmobranchs have two types of vertebrae; diplospondylous vertebrae extend
posteriorly from the back of the body cavity, and have two centra per myotome. In most shark
species, the transition from monospondylous to shorter diplospondylous vertebrae begins above the
pelvic fins
disc: the combined head, trunk and enlarged pectoral fins of some sharks and rays with dorsoventrally
flattened bodies (Fig. 3.2)
dorsal: the upper surface of the body or head (Fig. 3.1a)
dorsal fin: the unpaired fin or fins along the upper surface of the back (Figs. 3.1a, 3.2, 3.3)
endemic: confined to a localized area (e.g., a species endemic to southern Australia is not found
anywhere else)
fusiform: shaped like a spindle or cigar; tapered at both ends
gill slit: a long, narrow gill opening in sharks and rays (Figs. 3.1a, 3.2, 3.3)
head length: distance from the tip of the snout to the most posterior gill slit
insertion: (of a fish’s fin) the most posterior point of a fin base
interdorsal ridge: ridge running along the mid-dorsal surface between the dorsal fins
keel: a fleshy or bony ridge (Fig. 3.1a)
labial furrows: the fold behind the corners of the mouth which provide slack in the skin for protrusion
of the jaws (Fig. 3.1a)
lateral: refers to the sides
lunate: crescent-shaped; refers to the caudal fin when the upper and lower lobes are about the same
size
meristics: pertaining to serially repeated structures such as vertebrae, teeth and other structures that
can be counted (like spiral valve turns)
monospondylous: elasmobranchs have two types of vertebrae; monospondylous vertebrae extend
posteriorly from the chondrocranium, and have one centrum per myotome. In most shark species,
the transition from longer monospondylous to shorter diplospondylous vertebrae begins above the
pelvic fins
26
morphometrics: a character based on measurement. In fish, measurements are taken as a straight
line, not around the curve of the body
nasal flaps: skin flaps extending from the nostrils
nictitating eyelid: an eyelid which can be pulled up or down (varies between families) over the whole
eye (Fig. 3.1b)
nictitating membrane
Figure 3.1b Shark eye showing lower eyelid.
nostril: external opening of the nasal organs, usually pore-like in fishes (Fig. 3.1a)
origin: of a fish’s fin, the most anterior point of a fin base
pectoral fins: paired fins just behind or just below the gill opening of sharks and chimaeras
(Fig. 3.1a, 3.3), part of the disc in rays (Fig. 3.2)
pelvic fins: paired fins on the underside of the body (at the posterior of the body cavity) of sharks and
chimaeras (Fig. 3.1a, 3.3), and near the tail in rays (Fig. 3.2)
posterior: the hind or tail end (Fig. 3.1a)
precaudal pit: a notch on the dorsal or ventral surface of the caudal peduncle just in front of the
caudal fin of some sharks (Fig. 3.1a)
proboscis: elongated, flexible extension of the snout (Fig. 3.3)
rostrum: a rigid projection of the snout
skin fold: an area where skin is bent over upon itself, forming a fleshy ridge (Fig. 3.2)
snout: the part of the head in front of the eyes of fishes (Figs 3.1a, 3.2)
spiracle: a respiratory opening behind the eye in sharks and rays (Figs. 3.1a, 3.2)
spiral valve: section of the intestine arranged with tight spiral turns, or broad turns like a scroll of
paper, to increase the surface area for absorbtion
stinging spine: the large, serrated, sword-like bony structure on the tail of some rays (Fig. 3.2)
symphysials: small teeth at the center of the jaws that are noticeably different in size and shape from
the adjacent laterals
terminal filament: filamentous section at the end of the caudal fin in some chimaerids (Fig. 3.3)
thorn: a sharp, tooth-like structure on the skin of a skate or ray (Fig. 3.2)
tooth row: the line of functional and replacement teeth derived from a single germinal area that is
usually at approximately right angles to the jaw cartilage.
tooth plate: fused (often beak-like) teeth of chimaerids
27
tooth series: the line of teeth parallel to the jaw axis, all of them in different rows
total length: longest length of a fish, measured as a straight line from the snout tip to the tip of the
upper caudal fin (excluding the terminal filament of chimaerids) (Fig. 3.1a)
vent: anus/urogenital opening
ventral: refers to the lower surface or underside of the body (or head) (Fig. 3.1a)
3.3
CHARACTERS USED FOR IDENTIFICATION
3.3.1
Field identification
When in the field, whether it is at sea or sampling fish markets, there is some basic equipment
that should be carried for identifying sharks and rays. This should comprise a camera, notebook, forms,
vernier calipers, tape measure, calculator, sharp knife and selected identification sheets from regional
guides. A digital camera can be particularly useful, as can be a tape recorder, and all these items can
easily be carried in a backpack. Where possible, it is easiest to operate in pairs; this means someone
can keep clean hands for taking notes, photographs, etc. For any regional identification study it is
important, where possible, to build up both a photographic and specimen collection (see later sections).
The collection of material will vary on the individual situation. Trips onboard research or commercial
fishing vessels offer the best chance for getting fresh material. Local fish markets also provide excellent opportunities for good quality material and in undeveloped countries with poor data collection
systems can also provide information on the fishing methods and gear being employed (particularly
where vessels land directly to the market). It is important to set up a protocol (particularly in tropical
locations where specimens dry out and deteriorate rapidly) for photographing, measuring and retaining
specimens in a quick and efficient manner. Identification forms, tailored for the individual, should be
designed to make the recording of measurements and meristics easier.
Characters used for identification vary with the group, but generally color and markings, fin
positions and shape, presence of an anal fin, number of gill slits, possession of dorsal fin spines, proportional body measurements, vertebral counts, tooth shape and counts are important in the sharks. In
batoids, tooth characters are less useful while disc and tail shape, color and markings, position of the
dorsal fins, structure of the mouth and nostril region, and distribution and shape of dermal thorns and
denticles are important. In the chimaerids, color, head shape, fin position and shape, relative heights of
the dorsal fin and spine, tooth plate structure and presence of an anal fin are important diagnostic
characters. Some characters vary between the sexes, and so it is important to record the sex of the
individual. Males can be distinguished by their claspers, paired cylindrical extensions of the pelvic fins
used in mating. In mature individuals the claspers are elongated and rigid. Immature males have short
soft claspers that are sometimes overlooked.
Color varies with life stage and many species (particularly triakids, carcharhinids and
sphyrnids), which have a metallic bronzy sheen in life, become a drab grey after death. Photographs
28
(see later) are particularly important in documenting color, fin positions and body proportions. Proportional body measurements are expressed as percentages of total length (TL) in sharks, most batoids
and chimaerids (although the long caudal filament is excluded) and disc width in rays. Total length is
measured as a straight line (not over the body curve) from the tip of the snout to the tip of the upper
caudal fin lobe (Fig. 3.1a). Total length can vary depending on how the upper caudal lobe is positioned;
usually it is pulled back parallel to the body axis in species with a weak lower caudal lobe. In sharks
with more equally lobed caudal fins, the upper lobe is pulled back while still maintaining a “normal” tail
position. Other length measurements frequently used for sharks are fork length, tip of the snout to the
fork in the tail (Fig. 3.1a) and precaudal length, tip of the snout to the origin of the upper caudal fin.
Total length is used for most rays, but in the dasyatids, gymnurids, myliobatids, rhinopterids and
mobulids disc width, the maximum width across the body (Fig. 3.2), is measured as the tail is often
damaged. Fin and body measurements should follow schemes proposed by Compagno (2001) and
should include both longitudinal (parallel to the body axis) and point to point measurements. Measurements on most small species can be made with a combination of vernier calipers, a measuring board
and possibly a standard 40 cm ruler. For large species, a combination of vernier calipers, large spring
calipers, a 1 m wooden or steel rule and tape measure or folding measuring board can be used.
Usually it is only necessary to make a few measurements to check diagnostic characters, but for
unusual or possibly new species a full set of measurements should be taken (see Compagno, 1984).
Pre-designed forms should be used for recording this information. Waterproof paper, although expensive, can be useful.
Vertebral counts can be made easily in the field, even on relatively large specimens, with the
aid of a large, sharp, wide-bladed butchers knife. Protocol for precaudal (mono and diplosondylous)
and caudal counts should follow Compagno (1984) and Garrick (1982). Precaudal counts are taken to
the anterior edge of the precaudal pit. To make a count the tail should first be severed at the precaudal
pit. The precaudal count can then be made by placing the specimen on its side and, starting at the tail,
filleting it by running the knife along the vertebral column continuing forward right into (or through) the
chondrocranium. Usually only minimal scraping of flesh from the column is required before counting is
possible. Counts should not include the half vertebra fused to the back of the chondrocranium. When
making the caudal count it is important not to damage the delicate terminal vertebrae. It is best to run
the knife about half way along the column from the cut end, and then firmly grasp the flap of cut skin
and flesh and strip off the remainder by pulling on it. In small specimens, the count may have to be
completed in the laboratory using a microscope. Tooth counts and tooth shape are most important in
the carcharhinids. Counts should follow the protocol in Garrick (1982) essentially being expressed as
the number of laterals (left and right side) and symphysials in the upper jaw over those in the lower
jaw.
29
For example:
13-1-13 for Carcharhinus leucas
12-1-12
Teeth counts for carcharhinds can usually be made in situ; sometimes slitting the mouth corners can
help in accessing the extreme lateral teeth. Where there is any uncertainty, jaws can be removed,
cleaned and examined in the laboratory. It is a good idea to compile a reference collection of jaws (see
section 5). In the carcharhinids the shape of the upper laterals can be diagnostic, although differences
between species are often subtle. Garrick (1982) and the series of papers by Bass, D’Aubrey and
Kistnasamy (1973-76) on South African sharks provide excellent drawings and photographs of
carcharhinid teeth. Compagno (1984, 2001) includes useful drawings of teeth for most species for
which they are diagnostic.
3.3.2
Laboratory identification
Some characters and techniques are more practically carried out in the laboratory. Where it is
possible to retain and transport specimens for measuring, electronic calipers linked to a pre-designed
spreadsheet can greatly facilitate time-consuming proportional measurements. Where specimens need
to be retained for a collection vertebral counts can be made by X-ray. Pins can be used to mark the
position of the precaudal pit. Exposure rates and film will vary depending on the type of machine
available. Tooth counts on newly-born carcharhinids, or species with many tooth rows such as the
scyliorhinids (mainly required for new species descriptions) are best carried out in the laboratory using
a microscope. If several duplicate specimens are available it is easiest to remove and dry the jaws
before counting (although some distortion of tooth rows can occur). If specimens must be retained
intact, removal of all mucous, blotting dry, the use of water-soluble dyes and pins as reference marks
can aid examination under the microscope. Spiral valve counts (number of turns or flaps in the intestine, which is immediately posterior to the stomach) can be useful in some groups. This is most easily
carried out by removing it from the specimen, opening lengthways with scissors, washing out all the
contents and mucous, and then counting (this can also be done in the field). In some cases, characters
such as dermal denticles, clasper structure and occasionally chondrocranium structure may be required. Preparation techniques for these can be found in Compagno (1988).
3.4
TAKING PHOTOGRAPHS
The left side of sharks and chimaerids should be photographed; dorsal and ventral views of
batoids should be taken. A shot of the underside of the head back to the level of the pectoral-fin origins
should be taken for sharks, and for some families a dorsal view; more detailed shots of teeth, fin
markings, mouth regions etc., can be taken as necessary. Specimens should be washed clean and
layed out on a plain matte white background so that their fin origins and inner margins are clearly
visible. For ventral shots, where white is a common skin color, a darker matte background may be
required. Thick plastic material is relatively easy to carry and clean in the field. Plasticine, small
30
stones, pieces of paper, wood, etc., can be used to prop-up the fins or stabilize the head. Bright sunlight
can cause problems with reflection and shadows, and a shady area is preferable. With very large
specimens, fitting them into the field of view can be a challenge and may call for innovative solutions
such as climbing onto the roof of a truck or taking shots from a balcony or ladder. Always include a
scale, preferably a colored rule. It is a good idea to also include a label with the species name or a field
code, this can be cropped out later if necessary. Maintain a register of all photographs taken. Where
available, digital cameras are an advantage as results can be checked immediately. It is very helpful to
compile a photographic collection to accompany a regional collection of specimens.
3.5
SPECIMEN COLLECTION, PRESERVATION AND CATALOGUING
Any serious attempt to document regional chondrichthyan faunas should involve compiling and
maintaining a reference collection of specimens. Specimens should be collected as fresh as possible,
washed, photographed, measured, labeled and fixed in 10% formalin made up with seawater (40-44%
concentrated formaldehyde = 4% formalin). All specimens over about 15 cm TL should be injected in
the body cavity with concentrated formalin using a large gauge hypodermic needle. Waterproof paper
labels recording the species identification along with a field number in pencil (entered in a register with
date, collection location, identifier, length and sex of the specimen) should be attached to the specimen.
(Plastic waterproof paper, such as Phase 3, tends to split but can be used if encased in a self-sealing
plastic bag; we use Nalgene polypaper, which doesn’t tear.) Labels are best attached through the
upper caudal-fin lobe of sharks, close to the caudal fin of chimaerids and towards the margin of the
“wings” of batoids using plastic T tags fired from a tagging gun (type used by clothing companies such
as Monarch 3020 from Canada). Specimens should be fixed in containers that allow them sufficient
space to prevent them being bent or distorted. For smaller specimens, 30 litre polythene drums with
large diameter screw-on (or snap-on) lids are ideal; for larger sharks fiberglass or polyethylene tanks
(approximate dimensions 1.5 m long, 0.5 m wide and 0.8 m deep) with sealing lids are required (these
may have to be specially manufactured). After fixation in formalin for four weeks, specimens should
preferably be transferred to 70% ethanol after first washing in water. A layer of muslin covering the
specimens in the tank will help to prevent those at the top from drying out. Fluid levels should be
monitored periodically, every month if tanks are stored outside in tropical areas. For large specimens, it
may not be possible to retain the whole animal in which case the head, and possibly fins of sharks
should be kept. For large rays, the wings can be removed. Particularly for carcharhinids, jaw collections can be valuable. Jaws should be cut out of the shark and all flesh removed by paring away the
muscle and skin with a sharp knife or scalpel. When clean, the jaw should be held open (two pieces of
wood across the jaws work well) and dried in the shade (if placed straight into the sun they may
distort).
31
3.6
DISSECTION
Dissections for vertebral and spiral valve counts, and preparation of jaws have been covered
in previous sections. Those interested in more complicated dissections and preparations such as those
for clasper elements and chondrocrania should consult Compagno (1988).
3.7
FAMILY KEY
The illustrated family key provided here is taken from Daley et al. (2002). It should be noted
that there are some minor differences between the systematic scheme followed in the key, and that in
section 3.8 which follows Compagno (1999b) and McEachran et al. (1996). In particular, the Squalidae
have been separated into several families (section 3.8) that have not been recognized as such in many
of the regional guides cited herein and used for identification by fisheries workers.
KEY TO FAMILIES
Step 1 Five to seven gill openings on each side of head (figs 1, 4), last two openings sometimes very
close together and appearing as one. Go to Step 2
fig 1
fig 4
head of shark
undersurface of head
One external gill opening on each side of head (fig 2). Go to Step 43
fig 2
head of chimaera
Step 2 Snout saw-like, flattened and armed
with lateral teeth (figs 3, 5). Go to Step 3
Snout not saw-like, no lateral teeth. Go to Step 4
Step 3 Gill slits on undersurface of head (fig 4); no barbels on snout (fig 3)
sawfishes (Pristidae) fig 3
fig 3
fig 5
Gill slits on sides of head (fig 5); barbels present on snout (fig 5).
sawsharks (Pristiophoridae) fig 5
32
Step 4 Body dorsoventrally flattened, ray-like (fig 6); eyes on top of head, except in devilrays
(fig 29), eagle rays (fig 30) and cownose rays (fig 31). Go to Step 5
fig 6
fig 7
Body more or less streamlined, shark-like (fig 7); eyes on sides of head (fig 7).
Go to Step 19
Step 5 Gill openings partly on sides of head (fig 8); pectoral fins clearly detached from head (front
part of fin extending forward of fin origin) (fig 8). angel sharks (Squatinidae) fig 9
fig 8
fig 9
undersurface of head
Gill openings entirely on undersurface of head (fig 10); pectoral fins wholly or partly joined to
head (fig 10).
Go to Step 6
fig 10
fig 11
undersurface
Step 6 Two distinct dorsal fins (fig 11); first dorsal fin originating closer to insertion of
pelvic fins than to tip of tail (fig 11). Go to Step 7
Dorsal fins 0–2; origin of first dorsal fin closer to tail tip than to insertion of pelvic fins when
two fins are present (fig 10). Go to Step 11
Step 7 Disc large relative to tail, its maximum width more than twice tail length behind pelvic-fin tips
(fig 13); dorsal fins close together (fig 14). Go to Step 8
33
fig 12
fig 13
fig 14
fig 15
Disc smaller relative to tail, its maximum width about equal to or less than tail length behind
pelvic-fin tips (fig 12); dorsal fins widely separated (fig 11). Go to Step 9
Step 8 Caudal fin much larger than dorsal fins, about the same size as pelvic fins (fig 13). torpedo
rays (Torpedinidae) fig 13
Caudal fin barely larger than dorsal fins, much shorter than pelvic fins (fig 14). coffin rays
(Hypnidae) fig 14
Step 9 Caudal fin with a well-developed, angular lower lobe (fig 15); pectoral and pelvic fins not
overlapping (fig 15). sharkfin guitarfishes (Rhynchobatidae) fig 15
Lower lobe of caudal fin not well defined (fig 16); pectoral and pelvic fins touching or over
lapping (fig 16). Go to Step 10
fig 16
34
Step 10 Snout wedge-shaped, forming a sharp angle at tip (fig 16) or snout broadly rounded; thorns
or fine denticles present on body or tail (surface rough); no electric organs. shovel
nose rays (Rhinobatidae) fig 16
Snout broadly rounded; body surface entirely smooth; electric organs present
fig 17. numbfishes (Narcinidae) fig 17
fig 17
Step 11 Pelvic fin divided into two distinct lobes (fig 18); no enlarged stinging spine on tail (fig 18).
Go to Step 12
fig 18
fig 19
undersurface
undersurface
Pelvic fin with one lobe (fig 19); 1–2 enlarged, serrated stinging spines usually present on tail
(deep scar visible when spine absent) (fig 19). Go to Step 13
Step 12 Thorns or fine denticles (rough to touch) present on at least part of dorsal surface (fig 20);
snout in front of eyes less than 8 times eye diameter; tail slender but not thread-like (fig 20).
skates (Rajidae, in part) fig 20
fig 20
35
Entire dorsal surface smooth (except for outer disc thorns of male) (fig 21); snout in front of
eyes more than 8 times eye diameter; tail very short, thin and thread-like (fig 21). leg skates
(Rajidae, in part)
fig 21
Step 13 Six pairs of gill slits (fig 22). sixgill stingrays (Hexatrygonidae)
fig 23
fig 22
fig 23
undersurface
Five pairs of gill slits (fig 19). Go to Step 14
Step 14 Anterior part of head not extended beyond disc (fig 24); eyes located on top of head and
well inward from disc edge (fig 24). Go to Step 15
fig 24
fig 25
fig 26
Anterior part of head extended beyond disc (fig 25); eyes located on side of head (fig 25).
Go to Step 17
Step 15 Disc very broad, width more than 15 times length (fig 26); tail extremely short and threadlike (fig 26). butterfly rays (Gymnuridae) fig 26
Disc width less than 15 times length (fig 27); tail moderately (fig 28) to very (fig 27) long.
Go to Step 16
Step 16 No caudal fin (fig 27); central disc and dorsal surface of tail normally with some thorns or
small rounded projections (fig 27). stingrays (Dasyatidae) fig 27
36
fig 27
fig 28
Caudal fin present (fig 28); no thorns or small rounded projections on disc or tail (completely
smooth). stingarees (Urolophidae) fig 28
Step 17 A long, paddle-like flap projecting forward from each side of head (fig 29); teeth minute, in
many rows, more than 10 rows in each jaw. devilrays (Mobulidae) fig 29
No long, paddle-like flap projecting forward from each side of head, instead with a single,
fleshy, lobe (fig 30) or pair of broadly rounded lobes forming the snout (fig 31); teeth large,
plate-like, less than 10 rows in each jaw. Go to Step 18
fig 29
fig 31
fig 30
37
Step 18 Undersurface of snout uniformly rounded (fig 30); floor of mouth with small fleshy
projections. eagle rays (Myliobatidae) fig 30
Undersurface of snout with two lobes separated by a deep central notch (fig 31); floor of
mouth without small fleshy projections. cownose rays (Rhinopteridae) fig 31
Step 19 A single dorsal fin (fig 32); 6–7 pairs of gill openings (fig 32). Go to Step 20
Two dorsal fins (fig 33); 5 pairs of gill openings (fig 33). Go to Step 21
fig 32
fig 33
Step 20 Mouth at tip of snout (fig 34); first gill openings connected around throat (fig 35); no
notch on underside of upper caudal-fin lobe (fig 34). frilled sharks
(Chlamydoselachidae) fig 34
fig 34
Mouth on undersurface of head (fig 37); first gill openings not connected around throat (fig
36); notch on underside of upper caudal-fin lobe (fig 37). sixgill and sevengill sharks
(Hexanchidae) fig 37
fig 35
fig 36
undersurface of head
undersurface of head
fig 37
fig 38
Step 21 Anal fin absent (figs 38–40). Go to Step 22
fig 39
fig 40
38
Anal fin present (fig 41), sometimes small (fig 42). Go to Step 24
Step 22 First dorsal fin originating behind pelvic-fin origins (fig 38); dorsal fins located near caudal
fin and almost touching each other (fig 38); denticles extremely large. bramble sharks
(Echinorhinidae) fig 38
First dorsal fin originating in advance of pelvic fins (fig 40); dorsal fins well separated and
located well forward of caudal fin (fig 39); denticles not greatly enlarged. Go to Step 23
Step 23 Trunk laterally compressed, almost triangular in cross-section; fins tall, height of first dorsal
fin more than or about equal to head length (fig 39). prickly dogfishes (Oxynotidae) fig 39
Trunk rounded or oval in cross-section; fins much lower, height of first dorsal fin much less
than head length (fig 40) dogfishes (family Squalidae) fig 40
Step 24 Head hammer-shaped (fig 41); eyes located on outer edge of head (fig 41) hammerhead
sharks (Sphyrnidae) fig 41
Head not hammer-shaped. Go to Step 25
fig 41
Step 25 Length of caudal fin equal to or more than half total length (fig 42); body not spotted or
banded. thresher sharks (Alopiidae) fig 42
fig 42
fig 43
Caudal fin much less than half total length (fig 43) (caudal fin also long in Stegostoma but
body spotted and/or banded, see fig 52). Go to Step 26
Step 26 Dorsal-fin spines present (fig 43) horn sharks (Heterodontidae) fig 43
Dorsal-fin spines absent. Go to Step 27
Step 27 Snout extending above mouth as long, flattened, blade-like shelf (fig 44); nostrils close
to mouth (fig 45). goblin sharks (Mitsukurinidae) fig 44
39
fig 44
fig 45
undersurface of head
Snout not as above (extended slightly in some catsharks, family Scyliorhinidae, fig 71), but
nostrils well forward of mouth). Go to Step 28
Step 28 Whole mouth forward of front edge of eye (fig 46). Go to Step 29
fig 46
fig 47
head
head
Mouth partly beneath or behind front edge of eye (fig 47). Go to Step 35
Step 29 Mouth at snout tip and very broad (fig 48); caudal fin forked, upper and lower lobes tall (fig
48); no notch on underside of upper caudal-fin lobe (fig 48). whale sharks
(Rhincodontidae) fig 48
fig 48
Mouth smaller, not right at snout tip (fig 49); upper and lower lobes of caudal fin low
(fig 49); a notch on underside of upper caudal-fin lobe (fig 49). Go to Step 30
fig 49
40
Step 30 No fleshy lobe or groove on outer edge of nostril (fig 50). Go to Step 31
Fleshy lobe and groove present on outer edge of nostril (fig 51). Go to Step 32
fig 50
fig 51
undersurface of head
undersurface of head
Step 31 Caudal fin very long, almost as long as body (fig 52); ridges present on side of body
(fig 52). zebra sharks (Stegostomatidae) fig 52
fig 52
fig 53
Caudal fin shorter, much less than half length of body (fig 53); no ridges on side of body.
nurse sharks (Ginglymostomatidae) fig 53
Step 32 Origin of anal fin forward of origin of second dorsal fin (fig 54); anal fin more than its base
length from caudal fin (fig 54). collared carpet sharks (Parascylliidae) fig 54
fig 54
Origin of anal fin well behind origin of second dorsal fin (fig 55); anal fin next to caudal fin
(fig 55) and sometimes barely distinguishable from it (fig 59). Go to Step 33
fig 55
fig 56
tail
front view of head
41
Step 33 Body strongly flattened (top to bottom) anteriorly (fig 56); skin flaps present along side of
head behind nostrils (fig 56); enlarged canine teeth at tip of both jaws. wobbegongs
(Orectolobidae) fig 58
Body more or less cylindrical anteriorly (fig 57); no skin flaps along side of head behind
nostrils (fig 57); teeth small, those at tip of jaws not distinctly larger than those next to them.
Go to Step 34
fig 57
fig 58
front view of head
Step 34 Tail long, distance from anus to lower caudal-fin origin greater than distance from snout to
anus (fig 59); insertion of second dorsal fin well in front of anal-fin origin (fig 59). longtail
carpet sharks (Hemiscylliidae) fig 59
fig 59
Tail shorter, distance from anus to lower caudal-fin origin less than distance from snout to
anus (fig 60); insertion of second dorsal fin over or slightly behind origin of anal fin
(fig 60). blind sharks (Brachaeluridae) fig 60
fig 60
Step 35 Caudal-fin lobes almost the same size, upper lobe less than 15 times longer than lower lobe
(fig 61). Go to Step 36
fig 61
fig 62
caudal fin
caudal fin
Caudal-fin lobes of unequal length, upper lobe more than 15 times longer than lower lobe
(fig 62). Go to Step 37
42
Step 36 Gill openings very long, extending on to both dorsal and ventral surfaces (fig 63); first gill
openings almost continuous on throat; more than 150 rows of small hook-like teeth in both
jaws (fig 65). basking sharks (Cetorhinidae) fig 63
fig 63
fig 64
Gill openings shorter, confined to sides (fig 64); first gill openings widely separated on throat;
less than 40 rows of sharp blade-like teeth in each jaw (fig 66). mackerel sharks
(Lamnidae) fig 64
fig 65
fig 66
teeth of upper jaw
Step 37 Mouth huge and at tip of snout, lower jaw extending to snout tip (fig 67); very large sharks.
megamouth sharks (Megachasmidae) fig 67, not featured
Mouth located on undersurface of head, distance from snout to mouth distinctly longer than
eye diameter (fig 68). Go to Step 38
fig 68
fig 69
head
Step 38 Eyes very large, more than half greatest height of snout (fig 69); gill openings extending onto
dorsal surface of head (fig 69); caudal keels present (fig 69). crocodile sharks
(Pseudocarchariidae) fig 69
Eyes smaller, less than half greatest height of snout (fig 70); gill openings not extending onto
dorsal surface of head (fig 70); caudal keels absent in most species. Go to Step 39
43
fig 70
Step 39 Eyelid fixed, not capable of closing over eye. grey nurse sharks (Odontaspididae) fig 70
Eyelid capable of closing over eye (nictitating). Go to Step 40
Step 40 First dorsal-fin origin well behind pelvic-fin origin (fig 71). catsharks (Scyliorhinidae)
fig 71
fig 71
First dorsal-fin origin well in front of pelvic-fin origin (fig 72). Go to Step 41
fig 72
fig 73
caudal fin
Step 41 No precaudal pit (fig 73); leading edge of upper lobe of caudal fin smooth (fig 73). hound
sharks (Triakidae) fig 72
Precaudal pit present (fig 74); leading edge of upper lobe of caudal fin usually rippled
(fig 74). Go to Step 42
fig 74
fig 75
caudal fin
Step 42 Posterior edge of second dorsal fin deeply concave (fig 75); spiracles present
(fig 75). weasel sharks (Hemigaleidae) fig 75
Posterior edge of second dorsal fin not deeply concave (fig 76); spiracles mostly absent.
whaler sharks (Carcharhinidae) fig 76
44
fig 76
fig 77
Step 43 Snout long and flexible with a hoe-shaped tip (fig 77); caudal fin arched upward (fig 77).
elephant fishes (Callorhinchidae) fig 77
Snout straight, bluntly rounded or pointed (figs 78, 79); caudal-fin axis straight (figs 78, 79).
Go to Step 44
fig 78
Step 44 Snout relatively short, tip bluntly rounded (fig 78). shortnose chimaeras
(Chimaeridae) fig 78
Snout very long, tip pointed (fig 79). spookfishes (Rhinochimaeridae) fig 79
fig 79
3.8
ORDERS AND FAMILIES
3.8.1
Order Hexanchiformes (frilled, sixgill and sevengill sharks)
These sharks are easily identified by the combination of six or seven pairs of gill slits on each
side, a single dorsal fin and an anal fin. The order contains two families. The Chlamydoselachidae
(frilled sharks) includes one living species, which has an elongate, eel-like body, six pairs of gill slits and
a reptilian-like head with terminal mouth and long tricuspid teeth. The family Hexanchidae (sixgill and
sevengill sharks) have a fusiform body, ventral mouth with comb-like lower teeth and six or seven
pairs of gill slits. The family contains four medium to large (14-48 m) sharks that mainly live near
the bottom in deep water in temperate and tropical regions (one species inhabits shallow bays and
estuaries).
45
3.8.2
Order Squaliformes (dogfish sharks)
Squaliform sharks are identified by the combination of a fusiform body, short snout (not sawlike), five gill slits, no anal fin and usually spines in front of the dorsal fins (minute or absent in a few
species). There are seven (Compagno, 1999b) living families; the Echinorhinidae, Squalidae,
Centrophoridae, Etmopteridae, Somniosidae, Oxynotidae and Dalatiidae. The echinorhinids (bramble
sharks) contain two relatively rare species (2.6-4.0 m) of deep water, bottom-living, temperate and
tropical sharks which have two small, posterior-placed, spineless dorsal fins (origin of first behind
pelvic-fin origins) that are close together, and enlarged, thorny denticles on the body. There are four
species of small (mostly < 1 m) oxynotids (prickly dogfishes) that live near the bottom in deep water of
temperate and tropical regions. These bizarre shaped sharks have a hump-backed body almost triangular in cross section, ridges between the pectoral and pelvic fins, two high, sail-like dorsal fins with
spines, and very rough skin. The squalids (dogfishes) contain two genera (Cirrhigaleus and Squalus)
and about 12 mostly small (< 1.2 m) species. Squalids have two relatively low dorsal fins usually
preceded by spines, the origin of the first anterior to the pelvic-fin origins. The centrophorids (gulper
sharks) comprise about 14 species within two genera of small to medium sized (up to 1.6 m) sharks,
the etmopterids (lantern sharks) contain five genera and about 38 small (mostly < 1 m) species and the
dalatiids (kitefin sharks) contain seven genera and about 10 mostly very small (with the exception of
Dalatias which attains about 1.8 m) species. The somniosids (sleeper sharks) comprise four genera
and about 16 species and include Somniosus spp. some of which attain about 7 m in length and are
among the largest of sharks. The genera Centrophorus, Etmopterus and Squalus contain many
species that are very difficult to identify, with many new forms being reported as new areas are
sampled. Snout length, dorsal fin shape, color markings (particularly of juveniles), denticle patterns,
spine thickness and height of the spine relative to the dorsal fin are important characters. Revision on a
global (or at least large regional) scale is required to fully resolve their taxonomy at the species level.
3.8.3
Order Pristiophoriformes (sawsharks)
This order contains one living family the Pristiophoridae (sawsharks) comprised of two genera
and about seven relatively small (< 1.5 m) species. Sawsharks are unmistakable among the sharks
because of their blade-like snout armed with rostral teeth (resembling a saw); they also have barbels
on the underside of the saw, sub-cylindrical to slightly flattened (but not ray-like) bodies, two dorsal
fins without spines, five or six pairs of gill slits and no anal fin. Sawsharks should not be confused with
the batoid family Pristidae (sawfish) that have the pectoral fins joined to the head in front of the
ventrally placed gill slits, no barbels on the saw, and which grow much larger (up to 7 m).
3.8.4
Order Squatiniformes (angel sharks)
Squatiniform sharks are easily identified by the combination of no anal fin and a dorso-ventrally flattened, ray-like body with broad pectoral fins and a terminal mouth. However, unlike batoids
46
the gill slits are on the sides of the head and the pectoral fins join the head behind the gill slits (although
they project forward of them as a lobe). The order contains the single living family Squatinidae (angel
sharks) that is comprised of about 14 globally distributed species.
3.8.5
Order Heterodontiformes (bullhead or horn sharks)
Heterodontiform sharks are identified by the combination of an anal fin and two dorsal fins
preceded by spines. The order contains a single living family Heterodontidae (bullhead or horn sharks)
comprising eight species of warm temperate and tropical, medium-sized (up to 1.6 m) sharks from the
Pacific and western Indian Ocean. Other distinctive characters are a large, blunt head with a prominent ridge above each eye, a small, nearly terminal mouth, rough skin and molar-like rear teeth.
3.8.6
Order Orectolobiformes (carpet sharks)
Orectolobiform sharks are identified by the combination of an anal fin, two dorsal fins without
spines, five gill slits on each side of the head and a mouth that is well in front of the eyes. They
comprise a diverse group of mainly Indo-Pacific, benthic sharks including the small, shallow-water
epaulette shark found on coral reefs, the flattened and ornately colored wobbegongs and the giant,
planktivorous whale shark. The order contains seven living families, the Parascylliidae (collared carpet
sharks), Brachaeluridae (blind sharks), Orectolobidae (wobbegongs), Hemiscylliidae (longtail carpet
sharks), Stegostomatidae (zebra sharks), Ginglymostomatidae (nurse sharks) and Rhincodontidae
(whale sharks).
The Rhincodontidae contains a single, huge (reaching 12 m), circum-tropical, plankton-feeding
species that has a very wide, almost terminal mouth, minute teeth, long gill slits with internal filter
screens, longitudinal ridges on the body, a semi-lunate caudal fin (except in very small juveniles) and a
color pattern of light spots and stripes on a dark background. The Stegostomatidae also contains a
single distinctive species that has a long, blade-like upper caudal lobe (about as long as the rest of the
shark), rough skin with a color pattern of dark spots on a yellow background (juveniles with yellow
stripes on a dark background), small mouth connected to the nostrils by grooves, barbels, longitudinal
ridges on the body and two dorsal fins close together. The Ginglymostomatidae contains three species
of medium to large sharks that have small mouths connected to the nostrils by grooves (but no lobes or
grooves on the outer margin of the nostrils), barbels, two relatively large, posteriorly placed dorsal fins
and a caudal fin with a weak ventral lobe. The orectolobids are distinctive, dorso-ventrally flattened (in
front of the dorsal fins) sharks with skin flaps along the sides of the head, enlarged canine teeth and
ornate color patterns. There are three genera and seven recognized species. However, the group is
more complex and requires more detailed work with several probable new species taken recently in
the Indo-Pacific region. The remaining three families contain small, often superficially similar species
that can also be confused with some of the catsharks (family Scyliorhinidae). However, in the
47
catsharks the mouth is located partly beneath the eyes. In the parascylliids (two genera, seven species), the anal-fin origin is in advance of the second dorsal-fin origin, and the anal fin is separated from
the caudal fin by a distance greater than the anal base length. The anal and caudal fins are almost
touching in the brachaelurids (one genus, two species) and hemiscylliids (two genera, 12 or 13 species)
and the anal-fin origin is well behind the second dorsal-fin origin. The brachaelurids have a short tail
(distance from vent to lower caudal-fin origin less than distance from snout to vent) while the
hemiscyllids have a long tail (distance from vent to lower caudal-fin origin greater than distance from
snout to vent).
3.8.7
Order Lamniformes (mackerel sharks)
This order of mainly large (1.1-6.0 m) sharks is comprised of five families and can be identified from the following combination of characters: an anal fin, two spineless dorsal fins, a mouth
located partly beneath the eyes, relatively large teeth, no barbels or grooves connecting the nostrils and
mouth, and no nictitating membrane over the eyes.
Of the six families, the Alopiidae (thresher sharks) are unmistakable having an enormously
elongated, scythe-like upper caudal-fin lobe that is equal in length to the rest of the body (the zebra
shark also has a long upper caudal lobe, but has barbels, and grooves connecting the nostrils and
mouth). There is one genus and three species of thresher sharks. The Cetorhinidae (basking sharks),
contains a single living species of huge (up to 10 m) plankton feeders. Basking sharks have a stout,
fusiform body, conical snout (elongate and proboscis-like in juveniles < 4 m), huge gill slits that almost
encircle the head (with internal filter screens), caudal keels and a lunate caudal fin. Basking sharks are
grey-brown above and paler ventrally, and superficially resemble large white sharks (family
Lamnidae), but have minute teeth.
The family Odontaspididae (sand tiger sharks) contains two genera and three species of large,
stout-bodied sharks with conical snouts, long awl-like teeth with lateral cusplets, two large dorsal fins,
a large anal fin, and an asymmetric caudal fin with a short ventral lobe. The families
Pseudocarchariidae (crocodile sharks), Megachasmidae (megamouth sharks) and Mitsukurinidae
(goblin sharks) each contain a single living species of readily identifiable sharks. The goblin shark
(attaining 3.8 m) has a bizarre head with an elongated snout forming a flat, blade-like rostrum, and
very protrusile jaws, and the filter-feeding megamouth (attaining 5 m) has a bulbous, blubbery whalelike head and a very wide, terminal mouth. The crocodile shark is smaller (up to 1.1 m), has a fusiform
body, conical snout, very large eyes, long gill slits, a low first dorsal fin, asymmetric caudal fin, and is
dark brown above and paler ventrally.
The family Lamnidae (mackerel sharks) consists of three genera and five species of highprofile sharks, the white, mako and porbeagle that are of considerable importance to fisheries. These
48
large (3-6 m) species have conical snouts, fusiform, spindle-shaped bodies, awl-shaped or triangular
teeth, minute second dorsal and anal fins, caudal keels, and lunate caudal fins. The shape of the teeth,
body coloration and number of caudal keels are important for identification at the species level.
3.8.8
Order Carcharhiniformes (ground sharks)
This diverse order of sharks contains many commercially important species within its eight
families. They can be identified by the following combination of characters; an anal fin, two spineless
dorsal fins, a mouth located partly beneath the eyes, five gill slits, relatively large teeth, no barbels or
grooves connecting the nostrils and mouth, and a nictitating membrane over the eyes. The families
Leptochariidae (barbeled houndsharks) and Pseudotriakidae (false catsharks) each contain a single
living species, and together with the Proscylliidae (finback catsharks) comprising four genera and
seven species, are relatively obscure groups. The Scyliorhinidae (catsharks) is by far the largest family
of sharks with 15 genera and more than 110 mostly small, bottom-living species. The Carcharhinidae
(requiem sharks) comprise 12 genera and about 50 species of small to large “typical looking” sharks.
The Triakidae (houndsharks) are also relatively speciose with nine genera and about 40 species, while
the Hemigaleidae (weasel sharks) have four genera and five species and the Sphyrnidae (hammerheads) have two genera and nine species.
The Sphyrnidae are easily recognized by their bizarre, hammer-shaped heads. Variations in
head size and shape are important for species identification. The Pseudotriakidae are also very distinctive with a very long, low first dorsal fin and over 200 rows of teeth in each jaw. The Scyliorhinidae
are best separated from the other families by the position of their first dorsal-fin base, which is opposite or behind the pelvic-fin base. The Hemigaleidae and Carcharhinidae have precaudal pits and an
undulating or rippled dorsal margin to the upper caudal fin, while the other families have no precaudal
pits and a smooth dorsal caudal margin. These two families are difficult to tell apart and while
hemigaleids have a small spiracle, usually absent in carcharhinids, reliable diagnosis mainly relies on
the structure of the intestinal valve. The carcharhinids have a scroll-type valve and the hemigalids a
spiral one. The Proscylliidae have labial furrows that are very short (confined to mouth corners) or
absent, and comb-like posterior teeth. Triakids and leptochariids have much longer labial furrows and
posterior teeth that are not comb-like. In the leptochariids, the upper labial furrows are very long
(length more than half the mouth width) and there are barbels on the nasal flaps. The traikids have no
nasal barbels (except in Furgaleus) and the upper labial furrows are shorter (length less than half the
mouth width).
The genus Carcharhinus (Carcharhinidae) contains about 30 species that are difficult to
identify for those not experienced with the group. Important characters are fin shape and positions,
color markings on the fins, the presence of an interdorsal ridge, tooth and vertebral counts and the
49
shape of the upper teeth in the middle of the jaw. Species in the genus Mustelus (Triakidae) are also
notoriously difficult to identify with partial overlap of many of the morphological, morphometric and
meristic characters used to separate them. An increasing number of “regional forms” have been
discovered recently.
3.8.9
Order Torpediniformes (electric rays)
The four families Torpedinidae (torpedo rays), Hypnidae (coffin rays), Narcinidae
(numbfishes) and Narkidae (sleeper rays) are small to medium-sized (0.15-1.8 m) rays with large,
oval, rounded or shovel-shaped discs, naked skin without denticles, short stout tails with usually two
(occasionally one or none) dorsal fins and a broad caudal fin. The thick pectoral disc has two kidneyshaped electric organs on its ventral surface. In the numbfishes (four genera and at least 17 species),
the tail length from behind the pelvic-fin tips is about equal to, or a little longer than the maximum disc
width while in the other three families the disc is wider than the tail length. The coffin rays, that
comprise one species endemic to Australia, have a pear-shaped disc, pelvic fins joined together to form
a smaller second disc and a very short tail only extending slightly beyond the pelvic fins. The caudal fin
is about the same size as each of the two dorsal fins, and much smaller than the pelvic fins. In the
torpedo rays (one genus, at least 15 species) the caudal fin is much larger than either of the two dorsal
fins and about the same size as the pelvic fins. The sleeper rays (four genera, nine species) have a
large round pectoral disc and a strong tail with only a single or no dorsal fin.
3.8.10 Order Pristiformes (sawfishes)
The family Pristidae (sawfishes) consists of two genera and about six species of mostly large
(up to 7 m) tropical marine and freshwater species. These are unmistakable batoids with the snout
highly modified into a “saw” bearing large lateral rostral teeth. Unlike the superficially similar
sawsharks (family Pristiophoridae), sawfish have their gill slits situated ventrally (rather than laterally)
on the head, have no barbels on the saw and their relatively small pectoral fins join the head in front of
the gill slits. The number of rostral teeth, shape of the caudal fin and position of the first dorsal fin
relative to the pelvic fins are important characters for identification at the species level.
3.8.11 Order Rhiniformes (sharkrays)
McEachran et al. (1996) consider this order to comprise one family (Rhinidae) containing a
single species of large ray (Rhina ancylostoma) attaining a length of at least 2.7 m. It has a broadly
rounded head distinctly demarcated from its pectoral fins, falcate shark-like fins, almost lunate caudal
fin and horny ridges on its back bearing thorns and spines. Other authors have placed Rhina together
with Rhynchobatus in the Rhinidae (Compagno, 1999b) or in the Rhynchobatidae (Last and Stevens,
1994).
50
3.8.12 Order Rhynchobatiformes (wedgefishes or sharkfin guitarfishes)
The single family Rhynchobatidae (sharkfin guitarfishes) contains mainly inshore rays with a
flattened body, a mostly wedge-shaped or oval disc and a broad, shark-like tail with two large dorsal
fins and a large caudal fin. In the shovelnose rays (Rhinobatos spp.), the first dorsal-fin origin is
behind the pelvic fin, the caudal fin has a weak ventral lobe and a well-developed dorsal lobe with a
straight posterior margin. Diversity is highest in the Indo-West Pacific region with four genera and
about 40 species, but more are likely to be discovered as these rays are not well known in many areas.
The sharkfin guitarfishes have the first dorsal-fin origin in front of the pelvic-fin insertions, both caudal
lobes are well developed and the posterior margin of the dorsal lobe is concave. There are two genera
and more than five species which mostly occur in the Indo-West Pacific; however, the taxonomy of
the genus Rhynchobatus requires more study.
3.8.13 Order Rajiformes (skates)
The taxonomy of the skates is very complex with two subfamilies, some 26 genera and around
200 species. These bottom living rays have enlarged pectoral fins forming a disc that varies in shape
from nearly circular to rhomboidal. Their pelvic fins are deeply notched forming two lobes, and they
have a fairly narrow tail with two (rarely one or none) small dorsal fins near the tiny caudal fin. Most
species have enlarged thorns around the eyes, along the dorsal midline or on other parts of the body.
The shape of the disc and snout, the presence and shape of the cartilage supporting the snout, relative
lengths of the pelvic-fin lobes and the pattern of thorns are important characters at the species level.
The genus Anacanthobatus (leg skates) consists of about 18 species that have their pelvic fins
separated into a mobile leg-like front lobe, and normally have smooth skin.
3.8.14 Order Myliobatiformes (stingrays)
This is a complex grouping which McEachran et al. (1996) consider contains three suborders
(Platyrhinoidei, Zanobatoidei and Myliobatoidei) and two superfamilies (Hexatrygonoidea and
Dasyatoidea); in the interests of simplicity this account differs slightly from their classification. The
families Urolophidae (stingarees), Hexatrygonidae (sixgill stingrays), Dasyatidae (stingrays),
Gymnuridae (butterfly rays), Myliobatidae (eagle rays), Rhinopteridae (cownose rays) and Mobulidae
(devilrays) usually have one or more stinging spines on the dorsal surface of the tail, a large pectoral
disc and a stout to very slender tail with a caudal fin and single dorsal fin variably present or absent.
The hexatrygonids are unique among the batoids in having six pairs of gill slits; there is a single genus
and about seven species most of which are known only from a single specimen. More work is required
to resolve the validity of these species. The gymnurids (two genera and at least 12 species) have a
very wide (width more than 1.5 times its length), butterfly-shaped disc and a very short filamentous
tail. Mobulids are the largest of all rays (attaining at least 7 m width). The two genera and about 10
51
species are easily recognized by their wide, angular, wing-like discs, prominent fleshy lobes projecting
forward like scoops on each side of the head, terminal (or nearly so) mouth with minute teeth (they are
plankton feeders) and filamentous tails. Myliobatids (four genera and about 22 species) and
rhinopterids (one genus and about 10 species) also have wing-like disc shapes and filamentous tails,
but a single bulbous fleshy lobe extends around the snout in myliobatids; in rhinopterids this is indented
to give it a distinctive bilobed forehead. Dasyatids have a circular to rhomboidal disc with a whip-like
tail that usually has stinging spines but lacks dorsal, anal or caudal fins. However, they may have
membranous skin folds on the dorsal and or ventral midlines of the tail; the central disc and dorsal tail
surface usually has thorns or tubercles. Dasyatids are represented by at least five genera and more
than 60 species that occur in marine and freshwater habitats; their often large size (some species > 2
m disc width) makes them difficult to study and more taxonomic work is required on the group. Disc
and snout shape, color patterns (which may change subtly with size), the presence of membranous
skin folds on the tail, and the pattern of denticles on the disc and tail are important characters for
identification at the species level. Urolophids resemble dasyatids in body shape, but have shorter tails
with a well developed caudal fin, and usually no thorns or tubercles on the disc or tail. There are three
genera and about 40 species of urolophids. Disc and tail shape, structure of the nostrils, presence of a
dorsal fin and color pattern are important for identifying species. However, some species are difficult
to identify on external characters alone.
The family Platyrhinidae (thornback rays) comprise two genera and two species of inshore
batoids that have round or heart-shaped discs, long, stout, shark-like tails, two large dorsal fins (situated anteriorly on the tail), no stinging spines, and large thorns on the disc and tail. Compagno (1999b)
placed the family Platyrhinidae within the order Rhinobatiformes.
3.8.15 Order Chimaeriformes (chimaeras)
Diagnostic features of the chimaeras were given in section 3.1; the order contains three
families, the Callorhinchidae (elephant fishes), Chimaeridae (shortnose chimaeras) and
Rhinochimaeridae (longnose chimaeras). There is one genus and three species of silvery colored
elephantfishes that are easily recognized by their long snouts terminating in a flexible hoe-shaped
structure, relatively short-based second dorsal fin, large anal fin and well-developed caudal fin that has
no caudal filament and is arched upwards from the body axis. Shortnosed chimaeras (two genera and
at least 22 species) have a relatively short snout with a bluntly rounded tip and a caudal-fin axis that is
straight. Most species occur in deep water and are dark brown to purply-black in color; the systematics of the group needs more attention with several new forms reported recently. Longnose chimaeras
(three genera, at least seven species) also have a straight caudal-fin axis, but they have a very long
snout with a pointed tip; they range in color from pinkish-white to black.
52
3.9
ACKNOWLEDGMENTS
I would like to thank Ross Daley (CSIRO Marine Research, Hobart) for the drawings used in
the glossary/terminology section, and Dr Peter Last (CSIRO Marine Research, Hobart), Dr Ramon
Bonfil (Wildlife Conservation Society, New York) and Dr John Musick (Virginia Institute of Marine
Science, Gloucester Point) for comments on the text.
3.10
REFERENCES AND LIST OF REGIONAL IDENTIFICATION GUIDES
ANDERSON, R. C. 1993. The shark fisheries of the Maldives. Ministry of Fisheries and Agriculture,
Republic of Maldives and FAO, Madras, India.
APPLEGATE, S. P., L. ESPINOSA, L. B. MENCHACA, AND F. SOTELO. 1979. Tiburones méxicanos.
Subsecretaría de Educación e Investigación Tecnológica, Dirección General de Ciencia y
Tecnología de Mar, México.
BASS, A. J., J. D. D’AUBREY, AND N. KISTNASAMY. 1973. Sharks of the east coast of southern Africa. I.
The genus Carcharhinus (Carcharhinidae). South African Association for Marine Biological
Research. Oceanographic Research Institute Investigational Report (33).
__________. 1975a. Sharks of the east coast of Southern Africa. II. The families Scyliorhinidae and
Pseudotriakidae. South African Association for Marine Biological Research. Oceanographic
Research Institute Investigational Report (37).
__________. 1975b. Sharks of the east coast of southern Africa. III. The families Carcharhinidae
(excluding Mustelus and Carcharhinus) and Sphyrnidae. South African Association for
Marine Biological Research. Oceanographic Research Institute Investigational Report (38).
__________. 1975c. Sharks of the east coast of southern Africa. IV. The families Odontaspididae,
Scapanorhynchidae, Isuridae, Cetorhinidae, Alopiidae, Orectolobidae and Rhiniodontidae.
South African Association for Marine Biological Research. Oceanographic Research Institute
Investigational Report (39).
__________. 1975d. Sharks of the east coast of southern Africa. V. The families Hexanchidae,
Chlamydoselachidae, Heterodontidae, Pristiophoridae, and Squatinidae. South African Association for Marine Biological Research. Oceanographic Research Institute Investigational
Report.
__________. 1976. Sharks of the east coast of southern Africa. VI. The families Oxynotidae,
Squalidae, Dalatiidae and Echinorhinidae. South African Association for Marine Biological
Research. Oceanographic Research Institute Investigational Report (45).
CADENAT, J., AND J. BLACHE. 1981. Requins de Méditerranée et d’ Atlantique (plus particulièrement de la
Côte Occidentale d’Afrique). Faune Tropicale (ORSTOM: Paris) 21.
53
CARPENTER, K. E., AND V. H. NIEM. 1998. FAO species identification guide for fishery purposes. The
living marine resources of the Western Central Pacific. Volume 2. Cephalopods, crustaceans,
holothurians and sharks. pp. 687-1396.
__________. 1999. FAO species identification guide for fishery purposes. The living marine resources of the Western Central Pacific. Volume 3. Batoid fishes, chimaeras and bony fishes
part 1 (Elopidae to Linophrynidae). pp. 1397-2068.
CASEY, J. G. 1964. Angler’s guide to sharks of the northwestern United States: Maine to Chesapeake
Bay. U.S. Fish and Wildlife Service, Bureau Sport Fisheries and Wildlife Circular 179.
CASTRO, J. I. 1983. The sharks of North American waters. Texas A & M University Press, College
Station.
CASTOR-AGUIRRE, J. L., AND PÉREZ, H. E. 1996. Listados faunisticos de México VII. Catálogo
sistemático de las rayas y especies afines de México (Chondrichthyes: Elasmobranchii:
Rajiformes: Batoideiomorpha). Instituto de Biología, México.
CHEN, J. T. F. 1963. A review of the sharks of Taiwan. Biological Bulletin Department of Biology
College of Science Tunghai University (Ichthyological Series) 19.
COMPAGNO, L. J. V. 1984. FAO species catalogue. Vol. 4, Sharks of the world. An annotated and
illustrated catalogue of shark species known to date. Part 1 – Hexanchiformes to
Lamniformes: viii, 1–250. Part 2 – Carcharhiniformes: x, 251–655. FAO Fisheries Synopsis
125: 4. FAO, Rome.
__________. 1988. Sharks of the order Carcharhiniformes. Princeton University Press, Princeton,
NJ.
__________. 1999a. Systematics and body form, p. 1-42. In: Sharks, skates, and rays: the biology of
elasmobranch fishes. W. C. Hamlett (ed). The John Hopkins University Press, Baltimore and
London.
__________. 1999b. Checklist of living elasmobranchs, p. 471-498. In: Sharks, skates, and rays: the
biology of elasmobranch fishes. W. C. Hamlett (ed.). The John Hopkins University Press,
Baltimore and London.
__________. 2001. Sharks of the world. An annotated and illustrated catalogue of shark species
known to date. Volume 2. Bullhead, mackerel and carpet sharks (Heterodontiformes,
Lamniformes and Orectolobiformes). FAO Species Catalogue for Fishery Purposes No.1,
Vol.2.
__________ , D.A. EBERT, AND M. J. SMALE. 1989. Guide to the sharks and rays of southern Africa.
Struik Publishers, Cape Town.
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COX, G., AND M. FRANCIS. 1997. Sharks and rays of New Zealand. Canterbury University Press,
Christchurch.
DALEY, R. K., J. D. STEVENS, P. R. LAST, AND G. K. YEARSLEY. 2002. Field guide to Australian sharks &
rays. CSIRO Australia.
ESCHMEYER, W. N., E. S. HERALD, H. HAMMANN, AND K. P. SMITH. 1983. A field guide to Pacific coast
fishes of North America: From the Gulf of Alaska to Baja, California. Houghton Mifflin,
Boston.
FAO website listing regional fisheries identification guides: http://www.fao.org/icatalog/inter-e.htm
FOURMANOIR, P. 1961. Requins de la cote ouest de Madagascar. Memoires de l’Institut de Recherche
Scientifique de Madagascar (Serie F) 4.
__________. 1976. Requins de Nouvelle–Caledonie. Nature Caledonienne.
FOWLER, H. W. 1941. The fishes of the groups Elasmobranchii, Holocephali, Isospondyli, and
Ostariophysi obtained by United States Bureau of Fisheries steamer ‘Albatross’ in 1907 to
1910, chiefly in the Philippine Islands and adjacent seas. Bulletin. United States National
Museum (100) 13.
GARRICK, J. A. F. 1982. Sharks of the genus Carcharhinus. National Oceanic and Atmospheric
Administration Technical Report, National Marine Fisheries Service Circular 445.
GOHAR, H. A. F., AND F. M. MAZHAR. 1964. The elasmobranchs of the north–western Red Sea. Publications of the Marine Biological Station Al-Ghardaqa (Red Sea) 13.
JOHNSON, R. H. 1978. Sharks of tropical and temperate seas. Les editions du Pacifique, Papeete.
KATO, S. S., S. SPRINGER, AND M. H. WAGNER. 1967. Field guide to eastern Pacific and Hawaiian
sharks. United States Fish and Wildlife Service Circular 271.
LAST, P. R., AND J. D. STEVENS. 1994. Sharks and rays of Australia. CSIRO Australia.
LAST, P. R., AND B. SERET. 1999. Comparative biogeography of the chondrichthyan faunas of the
tropical South-East Indian and South-West Pacific Oceans, p. 293-306. In: Proceedings of the
5th Indo-Pacific Fish Conference (Noumea, 3-8 November 1997). B. Seret and J.-Y. Sire
(eds.). Paris: Societe Francais d’Ichtyologie & Institut de Recherche pour le Developpement.
MASUDA, H., K. AMAOKA, C. ARAGA, T. UYENO, AND T. YOSHINO, (eds.). 1984. The fishes of the Japanese archipelago. Tokai University Press, Tokyo.
MCEACHRAN, J. D., K. A. DUNN, AND T. MIYAKA. 1996. Interrelationships of the batoid fishes
(Chondrichthyes: Batoidea), p. 63-84. In: Interrelationships of fishes. M. L. J. Stiassny, L. R.
Parenti and G. D. Johnson (eds.). Academic Press, San Diego, London.
MECKLENBURG, C. W., T. A. MECKLENBURG, L.K. THORSTEINSON, AND C.J. ARTHUR. 2002. Fishes of
Alaska. American Fisheries Society, Bethesda, MD.
55
MONKOLPRASIT, S. 1984. The cartilagenous fishes (Class Elasmobranchii) found in Thai waters and
adjacent areas. Department of Fishery Biology, Kasetsart University, Bangkok.
NAKABO, T., (ed.). 2002. Fishes of Japan. Tokai University Press, Japan.
RANDALL, J. E. 1986. Sharks of Arabia. IMMEL Publishing, London.
SMITH, J. L. B., AND M. M. SMITH. 1963. The fishes of Seychelles. Department of Ichthyology, Rhodes
University, Grahamstown.
SCHWARTZ, F. J., AND G. H. BURGESS. 1975. Sharks of North Carolina and adjacent waters. Information
Series, North Carolina Department of Natural and Economic Resources, Division of Marine
Fisheries, Morehead City, North Carolina.
STAFFORD-DEITSCH, J. 1999. Red Sea sharks. Trident Press, London.
TAYLOR, L. 1993. Sharks of Hawaii: their biology and cultural significance. University of Hawaii Press,
Honolulu.
TINKER, S. W., AND C. J. DELUCA. 1973. Sharks and rays: A handbook of the sharks and rays of Hawaii
and the central Pacific Ocean. C. E. Tuttle Company, Vermont.
56
CHAPTER 4.
TAGGING METHODS AND ASSOCIATED DATA ANALYSIS
Robert J. Latour, Virginia Institute of Marine Science, College of William and Mary, PO Box 1346,
Gloucester Point, VA 23062 USA
4.1
INTRODUCTION
4.2
TAG TYPE AND PLACEMENT
4.3
4.2.1
Petersen disc tag
4.2.2
Internal anchor tag
4.2.3
Rototag
4.2.4
Dart tag
DATA COLLECTION AND ANALYSIS
4.3.1
Delineation of nursery areas, habitat utilization, stock identification
4.3.2
Length/weight relationship
4.3.3
Growth rates
4.3.4
Gear selectivity
4.3.5
Movement
4.3.6
Survival/mortality
4.3.7
Spatial and temporal distribution, relative abundance
4.3.8
Species composition, size composition, sex ratio
4.4
ASSUMPTIONS OF TAG-RECOVERY STUDIES AND AUXILIARY STUDIES
4.5
ARCHIVAL TAGS
4.6
SUMMARY
4.7
REFERENCES
57
58
4.1
INTRODUCTION
Tagging methods have a long history of use as tools to study animal populations. Although the first
attempts to mark an animal occurred sometime between 218 and 201 B.C. (a Roman officer tied a note
describing plans for military action to the leg of a swallow, and when the bird was released, it returned to
its nest which was in close proximity to the military outpost in need of the information), it is uncertain
when fish were first marked (McFarlane et al., 1990). An early report published in The Compleat Angler
in 1653 by Isaak Walton described how private individuals tied ribbons to the tails of juvenile Atlantic
salmon (Salmo salar) and ultimately determined that Atlantic salmon returned from the sea to their natal
river (Walton and Cotton, 1898; McFarlane et al., 1990). Since the late 1800s, numerous fish tagging
experiments have been conducted, with an initial emphasis on salmonids, followed soon after by successful
attempts at tagging flatfish and cod. Pelagic species, namely Pacific herring (Clupea harengus pallasi)
and bluefin tuna (Thunnus thynnus), were successfully tagged in the early 1900s, while elasmobranch
tagging studies did not commence until the 1930s. Since 1945, large-scale tagging programs have been
initiated all over the world in an effort to study the biology and ecology of fish populations.
Modern tagging studies can be separated into two general categories. Tag-recovery studies are
those in which individuals of the target population(s) are tagged, released, and subsequently killed upon
recapture, as in a commercial fishery; while capture-recapture studies are designed to systematically tag,
release, and recapture individuals on multiple sampling occasions. The former study-type often facilitates
the establishment of a cooperative tagging program in which fish are tagged by both scientists and volunteer fishermen. The primary advantage of a cooperative program is the sheer volume of fish that can be
tagged each year, since it is possible to combine the efforts of scientists and a large number of volunteer
recreational and commercial fishermen. The latter study-type typically leads to the creation of agency- or
institution-based tagging program in which only those scientists directly involved with the study tag fish.
When starting a tagging program, the choice of whether to design a tag-recovery study (that may
or may not be cooperative) or a capture-recapture study largely depends on the objectives of the tagging
program. For example, although tag-recovery studies tend to be much less labor intensive than capturerecapture studies, the analysis of tag-recovery data does not easily yield estimates of population size,
which is often of interest to fisheries managers. Similarly, the quality of the data associated with a cooperative tag-recovery study can sometimes be suspect, since the level of tagging experience and overall
commitment to the tagging program in terms of the precision of the data being collected at the time of
tagging can vary significantly among fishermen. However, in some situations, it may not be possible to
develop a tagging program without the help of volunteer fishermen, since a single agency may not be able
to assume the cost associated with capturing and tagging hundreds or possibly thousands of fish each year.
The intent of this chapter is to serve as an overview of tagging studies and their use as tools for
increasing our biological understanding of elasmobranch populations and ultimately the information from
which we base management decisions. In a practical sense, however, it is virtually impossible in a single
59
chapter to adequately discuss all of the various aspects of tagging studies and the analysis of tagging data.
As such, this chapter will focus on issues related to tag-recovery programs and the analysis of tagrecovery data, primarily because the cost effectiveness of these types of studies has rendered them a very
common approach for inferring life history characteristics of aquatic populations. The chapter begins with
a discussion of the various tag types that can be used to mark individuals, followed by a treatment of the
various types of analysis methods that can be used to derive information from tag-recovery data. Not
included in the chapter is a stand-alone section on the design of tag-recovery studies, largely because it is
difficult to accommodate all types of data collection and subsequent analyses using a single study design.
That said, however, it is extremely important to base the development of a tag-recovery program on a
clearly and rigorously defined study design. I have chosen to address the details associated with sampling
and data collection procedures periodically throughout the text, and in accordance with the type of data
and analysis being discussed. For more information on the design of capture-recapture studies and the
associated methods for data analysis, efforts should be made to consult the comprehensive monographs
developed by Burnham et al. (1987) and Pollock et al. (1990).
4.2
TAG TYPE AND PLACEMENT
No single tag type (and therefore tagging technique) is appropriate for all species of sharks, or in
some instances, all life stages within a particular species. As such, great consideration should be given to
the choice of tag type when developing a tagging program. Factors that can be used to assist with the
selection of a tag include but need not be limited to (Wydoski and Emery, 1983; McFarlane et al., 1990;
Kohler and Turner, 2001):
•
The objectives of the tagging study or program.
•
The effect of the tag on the life history characteristics of the species under study, namely, reproduction,
survival, and growth.
•
The durability, longevity, and stability of the tag.
•
The stress associated with the capture, handling, and tagging process.
•
The size and number of individuals to be tagged.
•
Ease (or lack thereof) of tag application.
•
Cost of purchasing the tags and conducting the tagging experiment.
•
The amount and type of cooperation required among agencies, states, or countries for the tagging
study to be successful.
For studies involving teleost species, the number of different tag types that have been used to mark
individuals is fairly extensive (McFarlane et al., 1990). Although a similar diversity among tag types can be
documented for studies involving shark populations, the Petersen disc, internal anchor tag, Rototag, and
dart tag tend to be the most widely used (Kohler and Turner, 2001).
60
4.2.1
Petersen disc tag
The Petersen disc tag, which was developed by Petersen (1896), is one of the first tags ever used
to study fish populations. Although the Petersen disc tag has undergone several modifications over the
years, in essence, the tag is comprised of two plastic discs that are placed on each side of the individual
and connected by either a wire or a pin running through either the dorsal fin or the musculature at the base
of the dorsal fin (Figure 4.01). The tag information is generally printed on the discs. Petersen disc tags
were used in many of the early shark tagging studies, which studied the growth and movement of a variety
of shark species in the Pacific (Holland, 1957; Kato and Carvallo, 1967; Bane, 1968).
There are two key drawbacks associated with the use of Petersen disc tags. Specifically, they are
ORI tag
Petersen disc
Jumbo Rototag
Reward
2003-12
Dart tag
Call 1-800-555-4444 for reward
2003-89
Internal anchor
tag (body cavity)
Reward
2003-2
Internal anchor
tag (button)
Figure 4.01 Types of internal and external tags typically used to tag sharks. The appropriate
anatomical location for attachment is indicated for each tag-type.
prone to fouling by barnacles and algae and they can severely limit body and fin thickness by restricting
growth, especially when used for long-term tagging studies. This restriction of growth can lead to splitting
and deterioration of the dorsal fin, particularly with immature sharks since their cartilaginous dorsal rays
tend to be softer than those of mature sharks, and also because they will experience a more dramatic
growth rate over time when compared to mature individuals (Kohler and Turner, 2001).
4.2.2
Internal anchor tag
Rounsefell and Kask (1943) discuss the development of the internal anchor tag, which was
designed to overcome some of the problems associated with the use of Petersen disc tags, particularly the
restriction of growth. There are two types of internal anchor tags. The first tag, which is sometimes
referred to as a “body cavity tag”, is small and rectangular in shape, and is inserted completely into the
body cavity through a small incision in the lower half of the body wall (Figure 4.01). All pertinent informa61
tion is printed on the tag, which is typically made of plastic. The second tag is sometimes referred to as a
“button” tag and is comprised of a vinyl streamer attached to an elongated plastic disc (Figure 4.02). The
disc serves as the anchor and again it is inserted into the body cavity through a small incision in the body
wall, with the streamer protruding external to the individual. The tag information is usually printed on both
the plastic disc and the streamer (Figure 4.01).
Each type of internal anchor tag has been used for a variety of shark tagging studies (Olsen, 1953;
Grant et al., 1979; Hurst et al.,
1999). An advantage of internal
anchor tags is that they can be
retained for many years, which is
desirable given the longevity of
many shark species. In terms of
tag recovery, however, body cavity
tags are only detectable once an
individual is gutted. This characteristic renders it impossible to
conduct a capture-recapture study
Figure 4.02 A “button” internal anchor tag. The tag is comprised
of a vinyl streamer attached to an elongated plastic disc. The disc
serves as the anchor and it is inserted into the body cavity through
a small incision in the body wall, with the streamer protruding
external to the individual.
using this tag type. Button tags are
more visible than body cavity tags,
despite the fact that the streamers
are susceptible to fouling and
abrasion. The application of some type of antibiotic salve or antiseptic solution to the tagging wound is
recommended when using either type of internal anchor tag.
4.2.3
Rototag
Davies and Joubert (1967) describe the early use of Rototags, which were originally manufactured by Daltons of Henley-on-Thames, UK for livestock tagging but have been adapted for marine and
wildlife tagging studies. The Jumbo Rototag (Figure 4.03) and the ORI tag (which is a modified Jumbo
Rototag) are typically applied with an applicator through a hole in the leading edge of the first dorsal fin
created by a leather punch (Figure 4.01). Both tag types are made from a high-grade nylon, with the
Jumbo Rototag being semirectangular in shape and the ORI tag more circular in shape. Early experiments
with the Jumbo Rototag indicated that the tag was susceptible to vertical movement due to the hydrodynamics of swimming (Davies and Joubert, 1967). The suspicion that this vertical movement caused
swelling and irritation prompted the design of the ORI tag.
As with the Petersen disc tag, the Jumbo Rototag and ORI tag are susceptible to fouling and can
negatively influence growth. Nevertheless, these tags have been used in numerous tagging studies of
shark species (Kato and Carvallo, 1967; Thorson and Lacy, 1982; Stevens, 1990; Kohler et al., 1998).
62
Until 1988, they were the primary
tag used in the common skate
(Dipturus batis) tagging program
conducted off the west coast of
Scotland by the Science Department
of Glasgow Museums, and are also
used by the Central Fisheries Board
of Ireland for their blue shark tagging
program.
4.2.4
Dart tag
The origin of the dart tag can
be traced back to early tagging
studies of marine pelagic fish, particularly tunas (McFarlane et al.,
Figure 4.03 Jumbo rototag showing tag number and mailing
address [from the NMFS Cooperative Shark Tagging Program
website (http://na.nefsc.noaa.gov/sharks/intro.html)].
1990). The dart tag was developed
primarily to facilitate the safe and effective tagging of individuals in the water, since many pelagic species
attain sizes that are too large to be handled onboard a vessel. Relative to the original design, the dart tag
was modified for use on sharks (Casey, 1985) and a variety of types of dart tags have been used by
numerous tagging programs over the years (Kohler and Turner, 2001). Fundamentally, a dart tag is
comprised of a streamer, which can be made of monofilament line, vinyl, or nylon line that is attached to
either a stainless steel, plastic, or nylon pointed head (Figure 4.01, Figure 4.04a). All pertinent tag information is either printed on the streamer itself or on a legend that is enclosed by a capsule and attached to the
streamer. Application of a dart tag is usually accomplished using a stainless steel tagging needle, which is
used to drive the pointed head of the tag into the dorsal musculature of the individual (Figure 4.04b).
Efforts are generally made to apply the tag at an angle so that streamer lies alongside the individual while
it swims. For sharks, the optimal location for a dart tag is next to the base of the first dorsal fin.
The main advantage of using a dart tag is its ease of application. Relative to the Petersen disc tag,
Rototag, and internal anchor tag, very little time is needed to successfully mark an individual with a dart
tag. This characteristic combined with the fact that minimal training is necessary to become proficient at
applying a dart tag has rendered it the most commonly used tag type in shark tagging studies (Kohler and
Turner, 2001). Specific large-scale and longstanding tagging studies that utilize the dart tag include the
NMFS Cooperative Shark Tagging Program (Kohler et al., 1998; Kohler and Turner, 2001) and the
Australian Cooperative Game-Fish Tagging Program (Pepperell, 1990).
4.3
DATA COLLECTION AND ANALYSIS
Tag-recovery studies facilitate the collection of a variety of types of information on the species
under study. These data can be used to infer delineation of nursery areas, habitat utilization, stock identification, length/weight relationships, growth rates, gear selectivity, patterns of movement, survival/mortality,
63
a.
b.
Figure 4.04 (a) An “M” type dart tag displaying tagging needle and legend [from the NMFS
Cooperative Shark Tagging Program website
(http://na.nefsc.noaa.gov sharks/intro.html)];
(b) application of a dart tag to an individual
along side a vessel [photo by J. A. Musick].
spatial and temporal distribution, relative abundance, species and size composition and sex ratio (Kohler
and Turner, 2001). The following subsections contain a more detailed presentation of these data types and
their associated methods of analysis. With respect to deriving survival/mortality information from tagrecovery data, my discussion is brief since a more complete treatment of the topic is provided in section
8.3.2 of Chapter 8.
While many of the aforementioned types of data are fairly simple and straightforward, it is still
important that they be collected under a rigorously defined sampling design. A commonly applied design is
a stratified random sampling design where the strata are defined according to variations in water depth,
salinity, water temperature or latitude/longitude. Although data collected haphazardly can provide anecdotal information about a particular species, subsequent analyses of those data will not yield accurate
inferences about the population as a whole. The choice of a sampling design and the subsequent sampling
gear often depend on a variety of factors, most notably the objective(s) of the study, the topography and
size of the study area, and the general life history characteristics of the species under study. Despite these
factors, a concept that is essential for deriving population level inferences is that the data collected are
representative of the target species in the study area. Hence, sampling should take place during all seasons (unless the target species are not year-round residents) and over all spatial locations or habitat types
that the target species occupies within the study area. Clearly, temporal and spatial information may not be
available for species and areas that are not well studied, which implies that a very non-tailored and systematic sampling design must be adopted. Also, efforts should be made to sample with a gear-type that is
relatively non-selective; that is, one that will capture a wide variety of species and that will capture males
and females of all sizes with approximately equal probability. In practice, this need may render a longline
more appropriate than a gillnet.
64
4.3.1
Delineation of nursery areas, habitat utilization, stock identification
It is possible but often very difficult to use data reflecting the location of tag recoveries to effectively delineate the nursery area of a species. Provided that an adequate number of young-of-the-year
(YOY) could be tagged and, of those, an adequate number of tag recoveries are tabulated, information on
the location of tag recoveries can be used to determine the habitat utilization and extent of the nursery
area for YOY individuals. In addition, if a representative sample of a species in a particular location is
tagged (i.e., individuals of varying sizes from both sexes in the area), it may be possible to determine the
habitat range of the whole population. Moreover, if several population level ranges have been delineated,
inferences about the degree to which various stocks mix and ultimately stock identification can be inferred. However, the generally low tag-recovery rates observed with most elasmobranch species combined with inaccurate reporting of recapture location from fishers can render it difficult to accurately
characterize habitat ranges.
An alternative approach to using the locations of tag recoveries to delineate the range of a population is to infer about habitat utilization from the spatially explicit catch data obtained from sampling efforts
designed to capture individuals for tagging. Note that data resulting from supplemental sampling efforts
that are designed to “canvas” the suspected range or study area will likely be needed. This approach was
used by Grubbs (2001) to characterize the nursery ground of YOY sandbar sharks (Carcharhinus
plumbeus) in Chesapeake Bay. Although it was known that the Bay served as a nursery area for YOY
sandbar sharks, the exact geographical area within the Bay utilized by YOY sandbar sharks was not
known. Hence, Grubbs (2001) added stations to the sampling protocol of an existing longline survey in
such a manner as to systematically sample for the presence of YOY sharks from the Bay mouth northward. The northernmost latitude of the nursery area was determined by noting the location where the
catches of YOY sandbar sharks became zero.
A second alternative approach that can be used to delineate habitat utilization and discern degrees
of site fidelity involves the use of acoustic telemetry (see section 8.3.3 of Chapter 8 for more information
on telemetry). To conduct a telemetry study, high-power, ultrasonic transmitters must be surgically or
externally implanted in a representative sample of the target species. Receivers are then used to monitor
transmitter output for the purpose of intermittently tracking the movements and space utilization of tagged
individuals. Prior to conducting the study, a tracking protocol that specifies the length of the tracking
session, the number of fish tracked each session, and frequency at which position information is obtained
should be developed. If previous telemetry studies have been conducted for the species under study, it is
recommended to adopt the same tracking protocol so that the data are comparable. Morrissey and Gruber
(1993) used acoustic telemetry to examine the spatial and temporal patterns of activity of juvenile lemon
sharks (Negaprion brevirostris) in the Bahamas. The study was the first to utilize nonarbitrary sampling
and successfully characterized patterns of movement and degree of site fixity in any elasmobranch
species. The study also examined the correlation between size of habitat range and body size.
65
4.3.2 Length/weight relationship
The observed length and weight measurements taken at the time of first capture can be used to
establish a number of predictive relationships. For example, it is often useful to develop conversions among
the various length measurements, which can usually be accomplished using simple linear regression:
L1 = α + βL2 ,
(4.1)
where L1 and L2 are the two length measurements (e.g., fork and total length (FL, TL), or FL and
precaudal length (PCL), etc.) for which a predictive relationship is desired, and α and β are the standard
simple linear regression parameters that are to be estimated. Prior to applying equation 4.1, it is recommended to plot the length measurements against each other to ensure that a linear trend is present. Efforts
should also be made to develop length conversion relationships for males and females separately, as well
as for the sexes combined. As an example, see the FL/TL relationship derived by Natanson et al. (1999)
for tiger sharks (Galeocerdo cuvier) in the western North Atlantic.
In addition to predictive relationships among various types of length measurements, it is also
possible to use the size data collected at the time of first capture to establish a length/weight predictive
relationship. This type of relationship is typically derived using the following power function (Figure 4.05).
W = αLβ,
(4.2)
where W and L represent weight and length, respectively, and α and β are regression parameters (not to
be confused with those of equation 4.1). Nonlinear regression techniques (Bates and Watts, 1988) can be
used to estimate α and β , and it is generally recommended to fit equation 4.2 to sex-specific as well as
combined length/weight data. Stevens (1990) applied equation 4.1 to length/weight data obtained at the
time of tagging for tope sharks (Galeorhinus galeus), blue sharks (Prionace glauca), and porbeagle
sharks (Lamna nasus) off the coast of England.
Despite the fact that equation 4.2 is frequently used to relate length and weight data, it should be
noted that it might not always be the most appropriate model. When attempting to derive a predictive
relationship between any variables, it is reasonable to fit several models to the data. Alternative models
for length/weight relationships might include a linear, quadratic, or change-point model, which is a piecewise function that is designed to fit two or more models each to separate portions of the data (Chappell,
1989). By fitting a suite of models to the data, it is then possible to use model selection techniques, notably
likelihood ratio tests and/or Akaike’s Information Criterion (AIC) and related measures (Burnham and
Anderson, 1998) to assess model performance and ultimately identify the model that best fits the data.
4.3.3 Growth rates
If fishers record the date and length when tagged fish are recaptured, then information on growth
increments can be obtained and ultimately used to estimate the parameters of the von Bertalanaffy (1938)
growth function (VBGF). An obvious advantage to this approach is that a VBGF can be defined in the
absence of age data. The VBGF takes the form (Figure 4.06):
66
20
18
16
Figure 4.05 General shape of
the power function typically used
to relate length and weight under
the assumption that α = 0.000005
and β = 2.9. Although these
parameter values are not based
on actual length/weight data, they
closely resemble the estimates
obtained by Stevens (1990) for
tope in the eastern North Atlantic.
Weight (kg)
14
12
10
8
6
4
2
0
0
20
40
60
80
100
120
140
160
180
200
Length (cm)
lt = l∞ (1 − e − k (t −t0 ) ) ,
(4.3)
where lt is the length of an individual at age (or time) t, l∞ is the theoretical maximum attained length, k is
the growth coefficient, and t0 is the hypothetical age (or time) that an individual is of length zero. Note that
equation 4.3 can be developed for males and females as well as for the sexes combined (see Chapter 6
for more details on growth).
A significant body of literature exists on the procedures of estimating growth parameters from
recovery data (Gulland and Holt, 1959; Fabens, 1965; Cailliet et al., 1992; Wang, 1998). What follows is a
description of the method developed by Gulland and Holt (1959) primarily because it is fairly straightforward, however, efforts should be made to use several methodologies when analyzing growth increment
data. Tests can then be performed to statistically compare the results from different methods.
Gulland and Holt (1959) noted that the length of an individual at time t+a would be:
350
300
250
Length (cm)
Figure 4.06 General shape of
the von Bertanalffy growth curve
under the assumption that l4 =
300, k = 0.20, and t0 = -0.75.
Although these parameter values
are not based on actual age/
length or length increment data,
they do not differ substantially
from estimates derived by
Natanson et al. (1999) for tiger
sharks in the western North
Atlantic.
200
150
100
50
0
0
5
10
Age (years)
67
15
20
lt + a = l∞ (1 − e − k ( t −t0 + a ) ) .
(4.4)
Therefore, the growth increment from time t to time t+a, denoted by δl, is given by:
δl = (lt +a −l t ) = l∞ e − k (t −t ) (1 − e − ka ) ,
0
(4.5)
and the growth per unit time, denoted by g, is:
g = l ∞ e − k ( t −t0 )
(1 − e − ka )
.
a
(4.6)
If x represents the midpoint of the length interval (lt, lt+a), then x = ½( lt + lt+a), and after some algebraic
manipulations, the following equation holds:
l ∞ e − k ( t −t0 ) =
2(l∞ − x)
.
1 + e −ka
(4.7)
Substitution of equation (4.7) into equation (4.6) yields:
g = (l∞ − x)
2(1 − e − ka )
.
a (1 + e −ka )
(4.8)
Thus, equation 4.8 implies that the growth over a fixed time period and the midpoint of the corresponding
length interval are linearly related. Hence, linear regression techniques can be used to derive estimates of
k and l∞. The parameter t0 cannot be estimated from tag-recovery data alone, since it requires an estimate
of absolute size at age (Natanson et al., 1999). Given an estimate of the average size at a particular age
(or time), the VBGF can be rearranged to yield an estimate of t0:
⎛l −l
⎛ 1 ⎞⎡
t 0 = t + ⎜ ⎟ ⎢log e ⎜⎜ ∞ t
⎝ k ⎠⎣
⎝ l∞
⎞⎤
⎟⎟⎥ .
⎠⎦
(4.9)
In practice, t0 is usually estimated by letting t = 0 and lt be the average size at birth (Natanson et al.,
1999).
Depending on the number of tag-recoveries and, hence, the amount of length increment data
available, it may be possible to derive growth parameter estimates for the males, female and sexes combined of a single species in a particular region, multiple species in a particular region, and/or for a single
species in several geographically distinct parts of its range. If multiple growth curves are available, it is
recommended to use statistical techniques to formally compare the derived growth information. In general,
two types of comparisons are typically of interest (Wang and Milton, 2000):
1.
Within-species comparisons of growth parameters when two sets of estimates are obtained from
different time periods, areas or sexes.
2.
Between-species comparisons of growth parameters.
68
A major problem when trying to statistically compare growth parameters from two groups of fish is that
estimates of the VBGF parameters tend to be correlated. The presence of covariances among parameter
estimates implies that traditional univariate statistical procedures cannot be used to perform the aforementioned within- or between-species comparisons of growth parameters. To overcome this problem, Wang
and Milton (2000) suggested comparing growth parameter estimates using a generalized T2-statistic. To
test the hypothesis H0: G1 = G2 versus the alternative HA: G1≠ G2, where G1 and G2 are column vectors of
VBGF parameters estimates for two groups of fish and
l∞(1)−l∞(2)
G1−G2 =
k(1)−k(2)
,
(4.10)
t0(1)−t0(2)
the T2-statistic is calculated as
T2 = [G1 - G2]′V-1[G1 - G2] ,
(4.11)
where [G1 - G2]′is the transpose of [G1 - G2], and V is the variance-covariance matrix of [G1 - G2]. The
distribution of the T2-statistic is approximately chi-squared with 2 degrees of freedom. The corresponding
critical value χ2 (α),where α is the desired level of significance.
4.3.4 Gear selectivity
Selectivity can be defined as the probability of capture at a given age/size relative to the probability of capture at the age/size of maximum vulnerability. Determining the selectivity of a particular gear for
different sized individuals is often a key component of fishery stock assessments. In the strictest sense, all
fishing gears used to capture fish are selective to some degree. For example, individuals of varying sizes
are generally not captured with equal probability by a gillnet, since the girth of some individuals may be
substantially larger than the mesh size of the net. Longlines and hook-and-line gear are also selective,
since mouth size relative to hook size influences the probability of capture.
In general, gear selectivity is very difficult to estimate largely because it is not easy to quantify
how swimming speed influences the probability of capture. However, over the years several approaches
have been used to estimate the selectivity of various gear types, particularly gillnets (Olsen, 1959; Regier
and Robson, 1966; Kirkwood and Walker, 1986; Borgstrom and Plahte, 1992; Helser et al., 1998). With
respect to tag-recovery data, Myers and Hoenig (1997) developed a method for estimating the selectivity
of a variety of gear types from the tag recoveries associated with several separate tagging experiments
(since a single tagging experiment often does not provide enough recoveries to estimate selectivities
reliably). The method involves fitting a generalized linear model (McCullagh and Nelder, 1989) to the data
to estimate the size, gear, and experiment effects from a collection of experiments. Specifically, if rigl
represents the observed number of tag recoveries from tagging experiment i captured with gear-type g of
length l, then the expected number of tag recoveries is given by the following expression:
E[ri,g,l] = Ni,lRi,gUi,gSg l,
(4.12)
69
where N is the number of individuals tagged, R is the product of the fraction of individuals that survive the
tagging process, the proportion of tags not shed, and the proportion of recovered tags that are reported
(which is assumed to be constant over length), U is the exploitation rate, and S is the selectivity (which is
assumed to be constant over the experiments included in the analysis). If the probability of capturing a
tagged individual is modeled as pi,g,l = Ri,gUi,gSg l, the generalized linear model takes the form:
log(πi,g,l) = log(Ri,g) + log(Ui,g) + log(Sg,l).
(4.13)
Equation 4.13 possesses the three features of a generalized linear model: the function is linear, the
expected value of the dependent variable is related to the linear combination of the explanatory variables
via a link function (in this case the log link), and the error distribution is in the exponential family (in this
case a binomial error since the probability of observing rigl tag recoveries is a binomial random variable).
Inherent to the method are the assumptions that tag-induced mortality, natural mortality, tag loss,
and tag-reporting rate are independent of fish length for each gear type and that growth and natural
mortality are small enough to be ignored during the analysis. To avoid violation of the latter assumption,
Myers and Hoenig (1997) recommend only considering tag-recoveries associated with individuals that
were at liberty for only a short period of time. Although this method has never been applied to elasmobranch tag-recovery data, Myers and Hoenig (1997) applied it to 137 tagging experiments of Atlantic cod
(Gadus morhua) and showed that the selectivity of otter trawls changed from the 1960s to the 1980s and
that the selectivity pattern assumed in several of the cod stock assessments was incorrect.
4.3.5 Movement
One of the principal objectives of most elasmobranch tag-recovery studies is to derive information
on movement. Over the years, there have been numerous studies documenting the patterns of movement
and space utilization for shark species worldwide. For example, Francis (1988) described the inshoreoffshore movements of rig (Mustelus lenticulatus) in New Zealand, Gruber et al. (1988) and Morrissey
and Gruber (1993) collectively described patterns of movement and home range for lemon sharks in the
Bahamas, and Casey and Kohler (1992) characterized the movement of shortfin mako sharks (Isurus
oxyrinchus) in the western north Atlantic. Many more examples of studies that derived information on the
movement of sharks from tag-recovery data can be found in the literature (see Kohler and Turner (2001)
for comprehensive list of these studies).
Efforts aimed at documenting patterns of activity and space utilization from tag-recovery data
typically begin by calculating the distance traveled and the time at liberty for each recaptured individual.
From those calculations, population-level estimates of movement can be determined by calculating the
mean and median distance traveled and the total range of distances (minimum and maximum) traveled. In
general, data associated with individuals that were recaptured within a short time of tagging are typically
70
excluded from distance calculations, largely because it is important to allow newly tagged individuals
enough time to become fully mixed into the overall tagged population (mixing ensures that tagged population is representative of the total population). However, the decision to exclude these “immediate” recaptures does often depend on the objectives of the study. Although there is no “official” amount of time to
allow for mixing, Francis (1988) omitted all recaptures that were within 20 days of the time of tagging in
the movement analysis of rig.
As with the growth increment data, if there is a sufficient number of tag recoveries, it may be
possible to develop relationships between distance traveled and time at liberty for the males, female and
sexes combined of a single species in a particular region, multiple species in a particular region, and/or for
a single species in several geographically distinct parts of its range. If multiple characterizations of movement are available, it is recommended to use statistical techniques to formally compare the derived movement information. Two types of statistical analyses can be used to perform these comparisons:
1.
A simple t-test, which tests for statistical differences between the mean distances traveled by two
groups (e.g., males and females of a particular species; sexes combined for two species; a
species in two regions of its geographic distribution, etc.).
2.
Analysis of variance (ANOVA), which tests for statistical differences between the mean distances traveled by several groups (e.g., males and females of species in several locations of its
geographical distribution).
A two sample t-test can be used to test the hypotheses H0: d1 = d2 versus HA: d1 ≠ d2, where d1
and d2 represent the mean distance traveled for the two groups being compared, respectively. An equivalent form of the hypotheses is H0: d1 - d2 = 0 versus HA: d1 - d2 ≠ 0, and the t-value for testing these
hypotheses is:
d1 _ d2
t = _____________ ,
1+ 1
Sp
n1 n2
(4.14)
where n1 and n2 represent the sample sizes of the two groups, respectively, and sp is the pooled standard
deviation, which is calculated as a weighted average of the two sample variances S12 and S22:
Sp =
(4.15)
(n1_1)s12 + (n2 _ 1)s22 ,
n1 + n2 _ 2
The test statistic calculated from equation 4.14 can be compared to the critical value and H0 is rejected if
t< - tα/2,v or if t<tα/2,v, where α is the significance level and v = n1+n2 –2 is the degrees of freedom. The
two-sample t-test assumes that both samples are randomly chosen from normal populations with equal
variances (Zar, 1999). In practice, it is difficult to know if these assumptions will be met, however, several
studies have shown that the t-test is robust enough to endure considerable departures from its theoretical
assumptions, particularly when the sample sizes are equal or nearly equal (Zar, 1999).
71
As stated previously, the above t-test is appropriate for situations when two means are being
compared, however, to test the hypotheses H0: d1 = d2 = … = dk, where k is the number of groups being
compared, versus HA: not H0, the procedure of ANOVA must be used. ANOVA is a large area of statistical methods and is not described in detail in this chapter. For more information on ANOVA, it is recommended to consult a statistical methods textbook (e.g., Zar (1999)). For an example of ANOVA being
used to compare the mean distances traveled by several groups of a shark species, see Francis (1988).
4.3.6 Survival/mortality
Brownie et al. (1985) developed a series of models for multiyear tag recovery studies that can be
used to estimate age- and year-specific finite rates of survival (S) and tag recovery (f). More recently,
Pollock et al. (1991) and Hoenig et al. (1998) showed it is possible to convert tag-recovery rates to finite
exploitation (u), when information on the short-term tag retention, tag-induced mortality, and tag-reporting
rate is available. Estimates of year-specific total instantaneous mortality (Z) can be obtained from yearspecific finite rates of survival, and if information on the instantaneous rate of natural mortality (M) is
known, the year-specific estimates of Z can be used to recover year-specific estimates of instantaneous
fishing mortality (F) rates. Also, if the timing of the fishery is known, year-specific estimates of finite
exploitation can also be used to derive year-specific estimates of F (in the case of a continuous Type II
fishery, information on M will again be needed). A detailed discussion of these analyses is presented in
section 8.3.2 of Chapter 8.
4.3.7 Spatial and temporal distribution, relative abundance
Data reflecting the time and location of capture for tagging over the course of a year can be used
to develop a rudimentary understanding of seasonal habitat utilization, and thus, the spatial and temporal
distribution of the target species. In addition, the catch data derived from sampling efforts serves as a
spatial and temporal index of relative abundance for each species. One approach that can be used to
better understand the observed patterns of relative abundance involves correlating the spatially explicit
relative abundances with data that delineates habitat type (if not already available, this type of information
may need to be collected at the time of first capture). Although stand-alone correlations between catch
and habitat type are informative, it is often difficult to fully understand the observed patterns of relative
abundance without additional auxiliary data. Information on abiotic factors such as depth, water temperature, salinity and dissolved oxygen can also be used to help explain the observed patterns of distribution
and ultimately form a more complete understanding of the ecological preferences of the target species.
4.3.8 Species composition, size composition, sex ratio
Information on the species composition in a specific location or region and the sex ratio of a
particular species are two basic but important types of data that can be collected by simply processing the
catch of the gear used to collect individuals for tagging. In addition, when individuals are tagged onboard a
vessel, information on size composition can easily be obtained by taking sex-specific measurements of
72
length, which includes TL, FL, and PCL, and weight. Under circumstances when individuals are too large
to be handled and tagging takes place in the water, it may only be possible to take length measurements. In
areas where elasmobranchs are not well studied and information is lacking, collecting these types of data
can be viewed as the first step toward developing an understanding of the life history characteristics of the
species inhabiting a particular region.
4.4
ASSUMPTIONS OF TAG-RECOVERY STUDIES AND AUXILIARY STUDIES
When attempting to use tag-recovery data to infer about growth rates, gear selectivity, patterns of
movement, and survival/mortality, it is generally necessary to make the following assumptions:
1.
The tagged sample is representative of the target population.
2.
There is no tag loss or, if tag loss occurs, a constant fraction of tags is lost from each cohort and
all tag loss occurs immediately after tagging. Also, the probability of immediate tag loss is not sexor size-dependent.
3.
The time and location of tagging and tag recovery are correctly recorded.
4.
The lengths and weights of individuals are measured without bias at the time of tagging.
5.
The lengths of individuals are measured without bias at the time of tag recovery.
6.
Survival rates are not affected by tagging process or, if they are, the effect is restricted to a
constant fraction dying immediately after tagging. Also, the probability of immediate tag-induced
mortality is not sex- or size-dependent.
7.
The fate of each tagged individual is independent of the other tagged individuals.
8.
Tagging does not affect growth.
9.
There are no significant size-selection processes for individuals within similar age ranges.
10. All tagged individuals within a cohort experience the same annual survival and tag-recovery rates.
11.
The decision made by a fisher on whether or not to return a tag does not depend on when or
where the individual was tagged.
Although tag-recovery studies can be plagued by a variety of factors, it is possible to conduct auxiliary
studies to assess the possibility of violating a few of the aforementioned assumptions. Specifically, to
determine the rates of immediate tag loss and tag-induced mortality (assumptions 2 and 6), newly tagged
individuals can be held in cages or holding pens for a short period of time (Gruber et al., 2001;
Latour et al., 2001). Rates of chronic or long-term tag loss (assumption 2) are best assessed by double
tagging individuals (Latour et al., 2001). Although estimates of the tag-reporting rates associated with
commercial and recreational fishers are not needed for the types of analyses described herein, knowledge
of these tag-reporting rates can be extremely useful, particularly when trying to derive survival/mortality
information. Rates of tag reporting are best estimated by conducting a high reward study (Henny and
Burnham, 1976; Pollock et al., 2001). Additional remedies to some more of the problems of tag-recovery
studies as they pertain to survival/mortality estimation are discussed in section 8.3.2 of Chapter 8.
73
4.5
ARCHIVAL TAGS
Archival, or data storage tags are designed to intermittently record data on (among others) the
depth of an individual, ambient temperature, and light intensity. The data from these tags is downloaded
when the tagged fish is recaptured and the tag is recovered. These types of tags were first used on
southern bluefin tuna (Thunnus maccoyii) in Australia in the early 1990s, and have recently been used to
study elasmobranchs. Specifically, the Centre for Environment, Fisheries and Aquaculture Science
(CEFAS) Lowestoft Laboratory, which is located in the United Kingdom, has used archival tags to study
the movements of thornback rays (Raja clavata) in both the Irish Sea and Thames Estuary (Arnold and
Dewar, 2001). Similarly, Australia’s Commonwealth Scientific and Industrial Research Organisation
(CSIRO) has used archival tags to study the position of school sharks on the continential shelf off South
Australia (West and Stevens, 2001). One problem associated with an archival tagging study is the expense, since for many species, tag-recovery rates are too low to justify the cost of the tags. However, the
data from archival tags do have the potential to solve some important ecological questions (Arnold and
Dewar, 2001).
Pop-up archival satellite tags were developed in part to alleviate some of
the problems associated with low tag-recovery rates. In summary, these tags
combine data storage tags with satellite transmitters and are designed to detach
themselves from fish at a predetermined time (Figure 4.07). Ultimately they float to
the sea surface and communicate their location via a satellite link. The first pop-up
satellite tags were deployed in 1997 to further assist with ongoing efforts directed
at studying long-term movements of Atlantic bluefin tuna (Block et al., 1998).
Some of these tags were programmed to record temperature information on hourly
time scales, while others were programmed to take measurements on daily time
scales. Deployment time of these tags ranged from 3 to 90 days. Lutcavage et al.
(1999) also used pop-up satellite tags to study bluefin tuna in the North Atlantic.
Tags have also been successfully placed on other large pelagic species, including
yellowfin tuna, albacore, blue and striped marlin, and white, basking, thresher and
salmon sharks (Arnold and Dewar, 2001; Boustany et al., 2002).
There is a growing perception among researchers that some of the methods used to attach pop-up archival satellite tags to marine fishes are unreliable.
This perception originated from documented case studies were tags detached from
individuals prior to the predetermined time, thereby compromising the success of
the tagging study. However, the exact cause of the early release of these tags is
Figure 4.07 Wildlife not known. Pop-up satellite tags are typically attached to pelagic teleosts via a dart
Computers Pop-up
that is inserted into the dorsal musculature of the individual. For sharks, tags can be
Archival Transmitting (PAT) tag.
attached using a dart or by attaching the tag to a rototag-like apparatus through a
74
hole in the first dorsal fin. To improve the retention and overall performance of pop-up satellite tags, a
variety of darts have been developed, ranging in terms of both shape and material used for construction.
At present, however, a universally accepted attachment method has not been identified, so for each
tagging study, great care should be directed at evaluating the potential effectiveness of each attachment
method as it pertains to the species under study.
4.6
SUMMARY
This chapter is designed to assist researchers with the development and implementation of a tag-
recovery program for elasmobranch species. As previously described, it is possible to initiate either an
angler-based cooperative program or an agency-based program, and in most cases, the objective(s) of the
study and available funding typically dictate the appropriate choice. Also, there are advantages and
disadvantages associated with each type of program that should be given consideration during the design
phase. Described in this chapter are several data analysis methods that can be used to infer various
aspects of the biology and life history of elasmobranch species. A wide variety of methodologies are
described in part to demonstrate the utility and usefulness of a tag-recovery program. Some inferences
can be drawn in the absence of data reflecting tag recoveries (e.g., habitat utilization, species and size
composition, sex ratio, etc. derived from catch data), while others require analysis of data from both first
capture and tag recovery (e.g., movement, growth, survival/mortality, etc.). Of particular importance to the
validity of any type of data analysis and to the overall success of a tag-recovery program is an assessment
of the potential for assumption violation. As a result, efforts should be directed at conducting auxiliary
studies to determine if the defined sampling, handling, and tagging protocol minimizes the potential for
assumption violation.
4.7
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78
CHAPTER 5.
GENETICS: STOCK IDENTIFICATION
Edward J. Heist, Fisheries and Illinois Aquaculture Center, Southern Illinois University at Carbondale,
Carbondale, IL 62901 USA
5.1
INTRODUCTION
5.2
ESTIMATING STOCK STRUCTURE WITH MOLECULES
5.3
MOLECULAR MARKERS
5.4
5.3.1
Allozymes
5.3.2
Mitochondrial DNA
5.3.3
Microsatellites
5.3.4
Other molecular markers
5.3.5
Tissue collection
SELECTED CASE STUDIES
5.4.1
Gummy shark
5.4.2
Blacktip shark
5.4.3
White shark
5.4.4
Shortfin mako
5.5
CONCLUSION
5.6
GLOSSARY OF GENETIC TERMS USED IN THIS CHAPTER
5.7
REFERENCES
79
80
5.1
INTRODUCTION
When members of a fish species are segregated into multiple reproductive stocks, allele frequen-
cies at neutral genetic markers diverge under genetic drift such that the variance in gene frequencies
reflects the magnitude of reproductive isolation among these stocks. Thus, gene frequency differences
among geographic samples can be used to indirectly estimate patterns of gene flow and hence stock
structure of the species. Molecular markers have been used to infer stock structure in fishes for over forty
years (Utter, 1991). A brief glossary of genetic terms is included at the end of this chapter for those
readers who may be less familiar with the subject.
Application of molecular markers to the estimation of stock structure in marine elasmobranchs
can be very challenging for several reasons. Genetic stock structure is less pronounced in marine species,
which experience few barriers to migration, than in freshwater species (Ward et al., 1994). Stock structure is especially weak in highly motile pelagic fishes (Waples, 1998). Furthermore, sharks exhibit relatively low levels of genetic variation at some molecular markers, perhaps owing to a slowed mutation rate
and/or low long-term effective population sizes (Smith, 1986, Martin et al., 1992). Markers that are not
sufficiently variable will not provide the necessary data for a statistically powerful test of stock structure
and fish from two geographic regions that are fixed for the same allele may not necessarily be members
of the same stock.
The choice of molecular marker depends on the quality and type of tissue available as well as the
equipment and expertise. Even a small amount of reproductive migration among stocks is sufficient to
prevent genetic divergence at neutral molecular markers. Thus, stocks that are independent from the
fisheries perspective may exhibit negligible genetic differentiation (Waples, 1998). Traditional tag/recapture
studies performed in concert with molecular genetics studies can provide more information than either
approach can individually.
5.2
ESTIMATING STOCK STRUCTURE WITH MOLECULES
The degree to which stocks are reproductively isolated is typically estimated using various
estimator’s of Sewell Wright’s FST statistic (Wright, 1931). In the case of a codominant locus that exhibits
only two alleles FST is equal to
FST =
Ht − Hs
Ht
(5.1)
where Ht is the expected heterozygosity in the population based on the mean allele frequency across
populations and expectations of Hardy-Weinberg equilibrium (i.e., Ht = 2pq where p = the frequency of
one allele and q = 1-p) and HS is the mean heterozygosity within populations. Thus, the greater the
variance in allele frequencies among populations the greater the deficit of heterozygosity within each
population, and FST can be determined directly from the variance in allele frequencies as
FST =
81
Var ( p )
pq
(5.2)
where Var(p) is the variance in the frequency of an allele among subpopulations. Expected values of FST
range from zero when each sample possesses identical gene frequencies and hence there is a single
genetic stock, to unity when isolated stocks are fixed for alternate alleles.
Either of these measures is sensitive to sampling error and in the absence of distinct stocks will
result in positive FST values, the magnitudes of which are inversely proportional to sample size. Waples
(1998) observed that in highly migratory species, such as many sharks, the magnitudes of FST estimates
resulting from sampling error alone may be larger than the parametric FST values among stocks. Various
unbiased estimators of Wright’s FST include Weir and Cockerham’s θ estimator, which includes corrections
for several types of sampling error and sometimes produces negative FST estimates when the true value of
FST is very small or zero (Weir and Cockerham, 1984). Many recent studies employ analysis of molecular
variation (AMOVA) (Excoffier et al., 1992), which provides an unbiased estimator of FST known as ΦST
and also permits partitioning of genetic variation to multiple hierarchical levels. These estimators are
computationally demanding but are incorporated into a variety of freely available software packages (see
below). Statistical tests of the hypothesis that ΦST = 0 (and hence samples are drawn from a single
genetic stock) are calculated using algorithms that either model or resample the data and determine the
significance level of ΦST as the likelihood that a larger ΦST value could be produced via a random allocation of the genotypes or alleles (Rousset, 2001).
Several software packages are freely available for analyzing molecular genetic data including
Arlequin (http://lgb.unige.ch/arlequin/) (Schneider et al. 2000), Genepop (http://wbiomed.curtin.edu.au/
genepop/) (Raymond and Rousset, 1995), and GDA (http://lewis.eeb.uconn.edu/lewishome/software.html)
(Lewis and Zaykin, 2001). The capabilities of these and several other programs were recently reviewed
by Labate (2000). Arlequin can be downloaded in Microsoft Windows, Macintosh or Linux format and can
handle haploid (e.g., mtDNA) as well as diploid (allozyme and microsatellite) data. Genepop can be
downloaded to run in a windows environment or can be run directly from the web page. GDA is only
available in windows format and determines significance of θ by bootstrapping across loci, which is only
applicable to studies that employ a large number of loci.
Under the assumptions of the island model of migration (Wright, 1931), which assumes a large
number of discrete populations with equal amounts of migration among each population, FST can be related
to migration as
FST =
1
4N e m + 1
(5.3)
where Nem is the product of the effective population size and the migration rate. Nem can be thought of as
the effective number of migrants, that is the number of reproductive animals exchanged among populations. It may seem counterintuitive that the magnitude of FST would be related to the number of migrants
and not migration rate. However, the degree to which allele frequencies among isolated populations
diverge due to genetic drift is inversely proportional to the effective population size. Thus populations with
82
a large Ne require a smaller migration rate to produce the same magnitude of genetic variance among
populations (FST). The above relationship is derived with several simplifying assumptions that are unrealistic for shark populations (e.g., equal migration among each of the many populations). However, deviations
from these assumptions have only minor effects on the relationship between FST and Nem. For example,
the more realistic case of increased migration among geographically proximate locations and a small
number of populations produces slightly lower FST values for the same rate of migration (Mills and
Allendorf, 1996).
Mitochondrial (mt) DNA is potentially a more powerful marker than nuclear DNA. Because
mtDNA is maternally inherited as a haploid molecule it has approximately ¼ the effective population sizes
of a nuclear marker (Birky et al., 1983). The relationship between FST and migration is
FST =
1
2N e m f + 1
(5.4)
where N e m f refers to the effective migration rate of females only. In species with equal rates of male
and female migration the magnitude of FST will be greater for mitochondrial markers than nuclear markers.
Furthermore, because of the smaller effective population size mtDNA reaches equilibrium levels of FST
more quickly and thus a recently established pattern of stock structure will be more accurately represented by mitochondrial data than by nuclear DNA data. In species that exhibit female reproductive
philopatry and outcrossing with males from widespread localities, such as several species of marine
mammals and sea turtles, mtDNA exhibits stronger differentiation than nuclear markers (Karl and Bowen,
1992; Palumbi and Baker, 1994; Gladden et al., 1999). However, the differences in the rates of genetic
drift, mutation, and intraspecific variation among mitochondrial and nuclear markers are sufficient to
produce vast differences in estimates of FST between the marker types without any differences in maleand female-mediated gene flow (Buonaccorsi et al., 2001). Thus larger FST values for mitochondrial
markers relative to nuclear markers do not necessarily indicate female philopatry against a backdrop of
male roaming.
5.3
MOLECULAR MARKERS
Several types of molecular markers have been applied to the estimation of stock structure in
sharks and many other types used in other marine fishes have yet to be employed in elasmobranchs. The
choice of marker depends on the experience of the researcher and the types of equipment available and
also on the types and quality of tissue that are available. It would be impossible to provide specific protocols in such limited space, but fortunately several excellent published volumes contain protocols for these
and other techniques including Hillis et al. (1996), Ferraris and Palumbi (1996) and Hoelzel (1998).
5.3.1 Allozymes
Allozymes were the first molecular markers to gain widespread use for distinguishing among
stocks of fishes (Utter, 1991). Allozymes are distinct allelic forms of enzymes that are separated by
charge and in some cases three-dimensional shape on a separatory medium, typically starch gels, poly83
acrylamide gels or cellulose acetate plates, and visualized with histochemical stains that indicate the
migration of molecules with specific enzyme activities (Murphy et al., 1996; May, 2003). Allozymes
degrade rapidly after death, especially at high temperatures, and the use of allozymes as molecular markers requires fresh or frozen tissue (maintained at -20°C or preferably colder). Because tissue types vary in
enzyme expression, it is often useful to collect multiple tissue types (e.g., white muscle, heart, liver, brain)
to score a large number of loci. Thus allozyme electrophoresis is not the best technique where lethal
sampling and immediate freezing (e.g., with dry ice or liquid nitrogen) of tissue samples are not possible.
Resolution of allozyme banding patterns requires considerable interpretation (Buth, 1990). Homozygotes for different alleles produce single bands with varying motilities while heterozygotes take on an
appearance that is determined by the subunit structure of the active enzyme. Monomeric enzymes produce two-banded heterozygotes while dimeric and tetrameric enzymes (those possessing two and four
peptides per active enzyme) exhibit three- and five-banded heterozygotes. Many enzymatic reactions are
catalyzed by products of multiple loci heteropolymers which can further complicate the banding patterns.
Resolution of allozyme patterns as discrete bands rather than smears requires the screening of multiple
running buffer conditions to identify the optimal conditions for each locus.
Several studies of allozymes have detected low levels of variation in sharks. In the first published
study of allozymes in sharks Smith (1986) reported relatively low variation in seven species. Low levels of
allozyme variation and geographic heterogeneity in carcharhinid sharks were observed by Lavery and
Shaklee (1989) and by Heist et al. (1995). Relatively high levels of heterozygosity and heterogeneity were
found in Pacific angel sharks (Squatina californica) (Gaida, 1997) and gummy sharks (Mustelus
antarcticus) (Gardner and Ward, 2002).
Resolution of allozyme loci can be more of an art than a science and variation in the methodology
and experience among labs result in differences in the amount of variation that can be resolved on
allozyme gels. Gardner and Ward (1998) found that on average 25.5% of allozyme loci in gummy shark
were polymorphic with a mean heterozygosity of 0.099. For the same species over a somewhat smaller
geographic range MacDonald (1988) detected variation in only one of 32 presumed loci (3%) with a mean
heterozygosity of 0.006 in the same species. Certainly some of this discrepancy must be due to the
increased resolution of the study by Gardner and Ward.
The relative simplicity of the materials needed to perform the allozyme technique (i.e., many rigs
are “homemade”) make allozymes an attractive tool for labs with little research funding. However, as
PCR-based techniques are becoming more affordable, the low variation and high tissue quality demands of
allozymes make techniques that score variation at the DNA-level more attractive. Plans for manufacturing
allozyme equipment can be found in Aebersold et al. (1987) and Murphy et al. (1996).
5.3.2 Mitochondrial DNA
Mitochondrial DNA of elasmobranchs and other fishes is a single closed loop of double stranded
DNA approximately 16,500 base pairs (bp) in length and presumably inherited only from the maternal
84
parent (Billington, 2003). The haploid, uniparental inheritance of mtDNA results in a fourfold reduction in
the effective population size and therefore an accelerated rate of genetic drift, which in turn increases the
rate and magnitude of genetic differentiation among isolated fishery stocks (Birky et al., 1983). Data
derived from sequencing or restriction fragment length polymorphism (RFLP) analysis of mtDNA permit
estimation of the relative divergence time of any two mtDNA haplotypes and can be used to provide
evidence of deep historic divisions or cryptic species (Figure 5.01).
If relatively large quantities (several grams) of fresh or ultrafrozen tissue and an ultracentrifuge
are available, mtDNA can be isolated in its pure circular form and subjected to restriction enzymes that
cleave the circular DNA at specific four- to six-base motifs. The resultant population of restriction fragments can be resolved on agarose or polyacrylamide gels and visualized using radiolabeling or UV illumination of ethidium bromide stained bands (Figure 5.01). This is the technique that was performed by Heist
et al. (1995; 1996a, 1996b) on sandbar (Carcharhinus plumbeus), shortfin mako (Isurus oxyrinchus)
and Atlantic sharpnose (Rhizoprionodon terraenovae) sharks. In the sharpnose shark study, whole
molecule mtDNA prepared from tiger shark was used to probe Southern blots of Atlantic sharpnose shark
hearts that did not provide sufficient whole-molecule mtDNA.
Figure 5.01
Mitochondrial
DNA variation in shortfin mako
(Isurus oxyrinchus). Lane “S”
is a size standard lane. Numbers at left refer to the size (in
base pairs) of each size standard. Whole molecule mtDNA
digested with the restriction
enzyme BstE II produces two
haplotypes (A and B). Haplotype “A” differs from “B” in
that a fragment of approximately 7000 base pairs in “B” is
digested into two smaller
fragments of approximately
4400 and 2600 in “A”.
With the advent of PCR more studies are employing restriction digestion or sequencing of discrete
regions of mtDNA. Perhaps the most useful region for analyzing stock structure in elasmobranchs is the
D-loop or control region, which contains the largest stretches of noncoding DNA in the elasmobranch
mtDNA genome, and in many fishes studied it exhibits the highest nucleotide substitution rate presumably
due to the lack of purifying selection. In my lab we routinely use a primer designed by Martin and Palumbi
(1993) located in the cytochrome-b protein coding region (CB6H 5’ CTC CAG TCT TCG RCT TAC
AAG where “R” represents equal quantities of A and G) and a mammalian primer designed in the highly85
conserved 12S ribosomal gene (282 5’ AAG GCT AGG ACC AAA CCT) (J. C. Patton, unpublished data)
to amplify the entire D-loop region in a variety of sharks. The resultant PCR product can then be analyzed
using restriction enzymes or direct sequencing. The widespread availability of inexpensive thermal cyclers
and gel rigs make PCR-RFLP a viable method of analysis for labs with a limited research budget.
The genetic diversity present in mtDNA can be represented as haplotype diversity which is
estimated as
n(1 − ∑i =1 xi2 )
l
hˆ =
(5.5)
n −1
where h is the haplotype diversity, n is the number of individuals scored, x i is the frequency of each allele,
and l is the number of unique haplotypes detected (Nei and Tajima, 1981). This equation is essentially the
same as that for estimating heterozygosity at a diploid locus and can be thought of as the likelihood that
two randomly sampled haplotypes differ. Because haplotype diversity is affected by the number of bases
surveyed (i.e., amount of sequence data or number of restriction enzymes employed) a more universal
gauge of variation is nucleotide sequence diversity (π) which can be estimated as
πˆ =
n
∑ xˆ i xˆ j πˆ ij
n −1
(5.6)
where x̂i and x̂ j are the frequencies of haplotypes i and j and πˆ ij is the genetic distance between each
pair of haplotypes (Nei and Tajima, 1981). AMOVA (Excoffier et al., 1992) can then be used to estimate
ΦST by partitioning the genetic diversity into among and between sample components. The REAP software package (McElroy et al., 1991), which is available at http://bioweb.wku.edu/faculty/mcelroy/, can be
used to estimate π and to construct a distance matrix between haplotypes for AMOVA.
Sharks possess relatively low levels of intraspecific mtDNA heterogeneity, presumably due to the
low rate of mtDNA evolution relative to that of other vertebrates (Martin et al., 1992). Levels of nucleotide sequence diversity based on whole-molecule RFLP in sharks range from 0.036% in sandbar shark
(Heist et al., 1995) to 0.347% in shortfin mako (Heist et al., 1996a). In order to detect a sufficient amount
of variation one must either perform the whole-molecule technique with a large number (e.g., eight or
more) restriction enzymes or perform direct sequencing. In our lab we are sequencing the entire mtDNA
D-loop in blacktip sharks (C. limbatus) to produce a haplotype diversity of 0.71 (Keeney et al., In Press
“A”).
5.3.3 Microsatellites
DNA microsatellites are among the most recent types of markers developed for estimating stock
structure and are highly repetitive segments of nuclear DNA that are amplified via PCR and typically
resolved on polyacrylamide gels (O’Connell and Wright, 1997). Microsatellite alleles differ in size based
upon differences in the number of repeat units present. Alleles differ in size by multiples of the core
86
repeat motif (typically two to four bases) and thus very high resolution is required to score microsatellites.
Typically PCR products are end-labeled with radionuclides (e.g., 32P or 33P) and resolved via autoradiography (Figure 5.02) or fluorescently tagged and resolved on automated DNA sequencers. Either of these
techniques may be beyond the capabilities of labs with limited budgets and/or without access to radionuclides.
Figure 5.02
Microsatellite
DNA variation in nurse shark
(Ginglymostoma cirratum).
Lane “S” is a 128 base pair
size standard. Individuals 1
through 6 are heterozygous
(genotypes shown below
bands). Individual 7 is
homozygous for allele 128.
The major hurdle to scoring microsatellites in any species is the development of PCR primers that
will amplify polymorphic loci. To date polymorphic microsatellite loci have been developed in sandbar
shark (Heist and Gold, 1999), white shark (Carcharodon carcharias) (Pardini et al., 2000), lemon shark
(Negaprion brevirostris) (Feldheim et al., 2001a; Feldheim et al., 2001b), shortfin mako (Schrey and
Heist, 2002) and nurse shark (Ginglymostoma cirratum) (Heist et al., 2003). Primers developed in one
species often work on congeners and sometimes members of related genera but either fail to amplify or
amplify only monomorphic products in other families or in more distantly-related taxa. Of sixteen polymorphic microsatellite loci developed from the blacktip shark between five and eleven loci were polymorphic
in each of ten other species of Carcharhinus, and several loci were polymorphic in tiger shark, lemon
shark, blue shark (Prionace glauca), Atlantic sharpnose shark and two species of hammerhead sharks
(Sphyrna spp.) (D. Keeney, In Press “B”). Primers developed in shortfin mako amplified polymorphic
microsatellites in salmon (Lamna ditropis), porbeagle (L. nasus) and white sharks (Schrey and Heist,
2002).
Microsatellite data are analyzed much like allozyme data although the very high heterozygosity
and large number of alleles (e.g., 20 or more) can cause a deflation of FST (Hedrick, 1999). Microsatellites
evolve via mutational increases and decreases in the number of times the core motif is repeated in each
allele. Thus microsatellites exhibit a finite number of alleles and alleles are often shared even among
completely isolated gene pools (e.g., among species). The maximum value FST can be expected to achieve
is equal to homozygosity, which for loci with 20 or more alleles may be less than 0.05. Thus, the maximum
value that can be achieved for FST is comparable to the expected amount of error associated with mea87
surements involving small sample sizes (Waples, 1998). An obvious way to alleviate some of this problem
is to employ loci with moderate numbers of alleles and moderate heterozygosities and to obtain sufficiently
large sample sizes to reduce the amount of noise in estimating FST.
A common problem that attends the high genetic diversity of microsatellites and the statistical
power of modern estimators is the detection of very small but nevertheless statistically significant FST
values. Low but significant FST values can arise through a small amount of gene flow (e.g., 1-10 individuals
per generation) between stocks that are essentially discrete in terms of recruitment, or it can be an artifact
of sampling (e.g., inclusion of close relatives in a sample) and scoring (e.g., null alleles) and thus constitutes a statistical (type I) error. Dizon et al. (1995) warned that the consequences of failing to reject the
null hypothesis of FST = 0 when it is false (type II error) may be more deleterious to the management of a
species than falsely concluding that multiple stocks are present and recommended that power analyses be
used to adjust the rejection (α) level upward to a level that balanced the effects of both types of statistical
error. Feldheim et al. (2001b) concluded that a statistically significant (p < 0.05) θ value of 0.016 based on
highly polymorphic (Heterozygosity = 0.69 to 0.90) microsatellite loci was too low to consider lemon
sharks from the Florida, the Bahamas and Brazil as distinct stocks. Tagging data (Kohler et al., 1998)
indicate that lemon sharks move between the Bahamas and Florida, but no lemon sharks tagged in either
Florida or the Bahamas moved to the Caribbean or beyond. Thus, it seems very unlikely that lemon sharks
from Florida and Brazil do not comprise distinct fishery stocks. While gene flow has apparently been high
enough to prevent evolutionary divergence among lemon sharks in the western Atlantic, statistically
significant differences in allele frequency, regardless of their magnitude, indicate that samples are drawn
from different populations (Knutsen et al., 2003).
5.3.4 Other molecular markers
Several other types of molecular markers are used to assess stock structure in fishes but have yet
to be applied to elasmobranchs. Random Amplified Polymorphic DNA (RAPD) employs one or more
short primers (typically about ten bases) to amplify a population of fragments that are resolved on agarose
or polyacrylamide gels (Hadrys et al., 1992). The degree to which bands are shared among individuals can
be used to assess the relatedness of individuals within and among populations. While this method is
attractive because it does not require taxon-specific primers like mtDNA RFLP and microsatellites do,
there are several serious shortcomings that have prevented this technique from gaining widespread
acceptance as a tool for analysis of stock structure. PCR is a finicky process that often produces inconsistent results, especially with short primers and low annealing temperatures. Whether a faint band is present
or absent may depend on the quality of the tissue used to prepare the DNA or the dynamics of the specific
PCR reaction that produced the profile. If tissue quality varies among sample locations, there can be a
systematic bias in the data leading to an erroneous conclusion of stock structure.
Another available technique, Amplified Fragment Length Polymorphism (AFLP) analysis, is
performed by attaching oligonucleotide adapters to nuclear DNA restriction fragments and amplifying with
88
longer PCR primers that anneal mostly to the adapters but also the first one to three bases of the genomic
DNA (Vos et al., 1995). While this approach is far more work than RAPD, the data are more repeatable
because of the use of longer PCR primers and higher annealing temperatures. Both RAPD and AFLP
produce dominant data (i.e., there is generally no way to distinguish between bands that are present in
heterozygous or homozygous dosages), and as a result statistical treatment of the data are not as powerful
as those for codominant data (e.g., allozymes and microsatellites).
5.3.5 Tissue collection
The kinds of tissue samples available and the method of preservation determine what kinds of
molecular markers can be used. PCR-based methods are most forgiving and can even be performed on
dried fins (Shivji et al., 2002). For PCR-based analyses we routinely collect fin clips by excising approximately ½ cm2 from the trailing edge of the first dorsal fin using a scalpel. The thin trailing edge of the fin
produces far better yields of DNA than do muscle tissue or thick skin from other parts of the body. Fin
clips can be stored in either 95% ethanol or 20% dimethyl sulfoxide saturated with NaCl. Tissues are
stable in either medium at room temperature for several months, however long-term storage of ethanolpreserved tissues is best done at 4°C or colder. Tissues for whole molecule RFLP need to be kept fresh or
frozen once and not subjected to freeze-thaw cycles as each freezing cycle produces ice crystals that
linearize the mitochondria making mtDNA purification very difficult. Tissues for allozymes are most
demanding in that enzymes degrade rapidly after death. Tissues need to be frozen (preferable in dry ice or
liquid nitrogen) and maintained as cold as possible until homogenized for electrophoresis.
Many sharks undergo seasonal and reproductive migrations and may segregate by sex and life
stage. Thus, a careful choice of where, when and from which animals to collect tissue can influence
the outcome of a study. For example in the study of blacktip sharks described below (Keeney et al.,
submitted), all tissues were collected from neonate sharks near or within continental shelf nursery areas.
Thus, any signal that resulted from reproductive philopatry could be filtered from the noise of adult movement. Such studies can be biased because a sample from a single nursery may contain siblings, which
would tend to inflate estimates of gene frequency differences among samples. However, because sharks
like the blacktip shark have low fecundities and do not reproduce every year, the number of potential
sibling pairs is low and comparisons across sequential years can be used to determine whether a sampling
of siblings is influencing estimates of FST.
5.4
SELECTED CASE STUDIES
5.4.1 Gummy shark
The gummy shark is a small coastal species continuously distributed around the southern two-
thirds of Australia. Gardner and Ward (1998) found statistically significant differences in allozyme allele
and mtDNA haplotype frequencies in gummy sharks collected from the southern and southeastern coasts
of Australia including Tasmania. Measures of GST (an analog of FST) were significantly greater than the
values expected due to sampling error for three of seven polymorphic loci and for RFLP haplotypes of
89
whole-molecule mtDNA. Both molecular markers indicated that gummy sharks from the southern coast of
Australia, ranging from Bunbury to Eden and including Tasmania, comprised a single stock while gummy
sharks from the east coast of Australia from Eden north comprised one or more additional stocks. Vertebral counts did not differ throughout southern Australia. However, there appeared to be a gradual increase
in the number of precaudal vertebrae corresponding to decreasing latitude on the east coast. Thus, despite
the continuous distribution and great potential for movement in M. anarcticus, there exist multiple fishery
stocks in Australian waters. Subsequently, Gardner and Ward (2002) reported data from additional
Mustelus including M. lenticulatus from New Zealand and two putative undescribed species from
Australia. Allozyme, mtDNA, and vertebral count data all confirmed the presence of four species of
Mustelus in the waters of Australia and New Zealand.
5.4.2 Blacktip shark
The blacktip shark is a migratory species that is the most important component of the US longline
shark fishery operating in the southeastern United States in the Atlantic Ocean and Gulf of Mexico.
Neonate blacktip sharks from the west coast of Florida migrate south in the fall, presumably to southern
Florida, and have been shown to return to specific nursery areas in subsequent years (Hueter et al.
submitted). Whether adult females return to their natal nurseries for parturition is unknown. A study of
mtDNA sequences and microsatellites in young-of-the-year blacktip sharks collected from four nursery
areas, west coast of Florida, South Carolina, Texas and Mexican Yucatan, revealed significant heterogeneity in mtDNA (FST = 0.111, p < 0.001) but not microsatellite loci (FST < 0.001, P = 0.316) (Hueter et al.,
submitted). Neither marker revealed significant differences among three Florida nurseries separated by
less than 250 km. Thus, blacktip sharks comprise multiple fishery stocks in US and Mexican waters, and
while females may tend to return to natal nurseries, the fidelity to do so is not high enough to result in
significant structuring among proximal nurseries.
5.4.3 White shark
The white shark is a wide-ranging globally distributed species with populations clustered around
localities with abundant marine mammals. Pardini et al. (2001) compared mtDNA and nuclear
(microsatellite) markers in white sharks from South Africa, Australia and New Zealand. The mtDNA data
indicated two divergent clusters of haplotypes that were nearly clustered into two highly divergent clades.
One clade (type A) was found in 48 of 49 individuals surveyed in Australia and New Zealand while the
other clade was found in 39 individuals from South Africa and in one of the 49 individuals surveyed in the
Australia/New Zealand sample. FST analogs (θ) based on five microsatellite loci were all non-significant.
Based on the discrepancy in estimates of stock structure between nuclear and mitochondrial data Pardini
et al. (2001) concluded that female white sharks are much more philopatric than males.
5.4.4 Shortfin mako
The shortfin mako is a highly migratory cosmopolitan species found throughout the Atlantic,
Pacific and Indian Oceans. Heist et al. (1996a) examined whole molecule mtDNA RFLP data in 120
90
shortfin makos from the North Atlantic (US and Canada), South Atlantic (Brazil), North Pacific (California) and South Pacific (Australia) and found small but significant differences in haplotype frequencies
between the North Atlantic and all other samples. Subsequently, Schrey and Heist (2003) examined
microsatellites in 433 mako sharks including the individuals from Heist et al. (1996a). They also reanalyzed the data from Heist et al. (1996a) using a more powerful statistical approach. Among ocean
basins, FST estimates from the mitochondrial data were significant and two orders of magnitude larger than
the estimates of FST based on microsatellites. A power analysis indicated that if the amount of heterogeneity present in the mtDNA data accurately represented the magnitude of gene flow of both sexes a statistically significant FST would have been detected using microsatellites, assuming that the stock structure was
stable long enough for nuclear markers to reach equilibrium. The discrepancy in the levels of resolution in
mtDNA and microsatellites is likely due to sex-biased dispersal, but they could also be influenced by
differences in the resolving powers of the two markers. The shortfin mako results differed from those of
white sharks (Pardini et al., 2002) in that no strong phylogeographic signal is present in the mtDNA data,
only minor frequency differences among locations. Shortfin mako does not comprise a single worldwide
population, but there has been a sufficient amount of historical migration among ocean basins to make
detection of stock structure using molecular markers (and especially nuclear DNA markers) very challenging.
5.5
CONCLUSION
Using molecular markers to estimate stock structure in sharks can be very challenging owing to
the great potential for migration among shark stocks, the difficulty in detecting genuine but small differences in gene frequencies in the presence of recent or episodic migration among stocks, and inappropriate
(too low or too high) levels of variation provided by some molecular markers. Nevertheless several
studies have ably demonstrated stocks in sharks and even in highly migratory species across seemingly
continuous distributions. Comparisons between markers with different modes of inheritance (e.g., nuclear
vs mitochondrial) may indicate differences in male- versus female-mediated gene flow. Because many
sharks are viviparous k-strategists that produce well-formed young at a time and place conducive to
survival, stocks that overlap during part of the year may segregate into discrete stocks for mating and/or
parturition. Thus, a careful selection of where and when tissues are collected (e.g., from neonates in
nursery areas) coupled with a wise choice of a molecular marker can provide very valuable information
about the stock structure of sharks that can not be obtained from other methods. Molecular detection of
stock structure is a complementary technique to tagging and morphology based studies of stock structure.
While tagging reveals gross movements of individuals, genetics measures the flow of genes over many
generations and can be used, for example, to study fidelity to nursery or breeding grounds in animals
whose distributions may sometimes overlap with those of other stocks. Morphological and life history
differences may be due to different environmental influences and hence may or may not be reflected in
gene frequency differences at neutral loci.
91
5.6
GLOSSARY OF GENETIC TERMS USED IN THIS CHAPTER
Allele – Alternate forms of a gene at a particular locus. Each diploid organism may possess either one
(homozygote) or two (heterozygote) alleles at a locus; however, there may be more than two
alleles in a population.
Codominant markers – Markers that exhibit both alleles in a heterozygous state. Codominant markers
are more powerful than dominant markers in which a heterozygous individual is indistinguishable
from an individual homozygous for the dominant allele.
Fixed allelic differences – The absence of shared alleles between two populations.
FST –
An index of the magnitude of allele frequency difference among populations. At a locus with two
alleles the maximum value of FST is unity and occurs when each population bears only a single
allele not found in any other population. If allele frequencies are identical across populations,
FST = 0.
Genetic drift – Random change in gene frequencies due to random stochastic sampling of alleles from
generation to generation.
Heterozygosity – The fraction of individuals that exhibit two different alleles at a locus or alternately the
fraction of loci over which an individual exhibits two different alleles.
Heterozygous – Possessing two different alleles at a locus.
Homozygous – Possessing two identical alleles at a locus.
Locus – A particular location on a chromosome where a gene or other DNA sequence resides. Diploid
organisms possess two copies of each locus that may exhibit either the same (homozygote) or
different (heterozygote) alleles.
Mitochondrial DNA (mtDNA) – DNA found in the mitochondria in cells. In animals including sharks
mtDNA is a double stranded molecule approximately 16500 base pairs in length. Mitochondrial
DNA is inherited strictly from the female parent and thus is a haploid (one copy per cell) marker.
Molecular marker – A polymorphic heritable trait that can be scored for variation within or between
species.
Neutral genetic markers – Polymorphic genetic traits that are presumed not to be influenced by natural
selection and thus are sensitive only to mutation, migration, and genetic drift. Most models that
relate gene frequency differences with stock structure assume that the markers examined are
selectively neutral.
Nuclear DNA – The vast majority of DNA in animal cells is found in the nucleus. Nuclear DNA is
inherited equally from both parents and thus is a diploid (two copies per cell) marker.
Polymerase Chain Reaction (PCR) – A technique for producing millions of copies of a chosen segment
of DNA by repeatedly annealing sequence-specific primers on either side of the region of interest
and performing (typically) thirty or more cycles of DNA synthesis. This procedure allows the
characterization of a particular segment of nuclear or mitochondrial DNA using only minute
amounts of tissue.
92
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96
CHAPTER 6.
AGE AND GROWTH OF ELASMOBRANCH FISHES
Kenneth J. Goldman, Department of Biology, Jackson State University, PO Box 18540,
Jackson, MS 39217 USA
6. 1
INTRODUCTION
6.2
VERTEBRAL OR FIN SPINE COLLECTION AND PREPARATION
6.3
6.2.1
Field sampling and storage
6.2.2
Cleaning, cutting and mounting
AGE DETERMINATION
6.3.1
Ageing protocols
6.3.2
Staining methods
6.3.2.1 Crystal violet protocol
6.3.2.2 Silver nitrate protocol
6.4
6.5
6.3.3
Reader agreement, precision and bias
6.3.4
Back-calculation methods
VALIDATION
6.4.1
Indeterminate methods (verification)
6.4.2
Determinate methods (validation)
GROWTH MODELS
6.5.1
von Bertalanffy growth function
6.5.2
Gompertz growth function
6.6
SAMPLING COVERAGE
6.7
WEB-BASED RESOURCES
6.8
ACKNOWLEDGMENTS
6.9
REFERENCES
97
98
6.1
INTRODUCTION
The ability to perform age determinations based on the examination of hard anatomical parts is
of fundamental importance in fisheries research. Precise and accurate age information is the key to
obtaining quality estimates of growth and other vital rates such as natural mortality and longevity, and
is essential for successful fisheries management. The effect of inaccurate age determinations on
population dynamics studies can lead to serious errors in stock assessment resulting in overexploitation
(Hoenig and Gruber, 1990; Hoff and Musick, 1990; Officer et al., 1996; Musick, 1999; Campana,
2001). Fish age and growth are also critical correlates with which to evaluate many other biological
(and pathological) processes, such as productivity, yield per recruit, prey availability, habitat suitability
and even feeding kinematics (DeVries and Frie, 1996; Campana, 2001; Robinson and Motta, 2002).
While age and growth are always used together in phraseology, it is important to remember that each
term has its own distinct meaning, which was eloquently stated by DeVries and Frie (1996):
“Age refers to some quantitative description of the length of time that an
organism has lived, whereas growth is the change in body or body part
size between two points in time, and growth rate is a measure of change
in some metric of fish size as a function of time.”
Concentric growth bands have been documented in the vertebral centra of most elasmobranchs for over 80 years (Ridewood, 1921). Counts of opaque and translucent banding patterns in
vertebrae, dorsal spines, caudal thorns and neural arches have provided the only means of information
on growth rates in these fishes as they lack the hard parts, such as otoliths, scales and bones typically
used in age and growth studies of teleost fishes (Cailliet et al., 1986; Cailliet, 1990; Gallagher and
Nolan, 1999; McFarlane et al., 2002). Unfortunately, the vertebral centra of many elasmobranch
species (such as numerous deep water species) are too poorly calcified to provide information on age,
most species have no dorsal spines and there may be no tangible relationship between observed
banding patterns and growth (Caillet et al., 1986; Cailliet, 1990; Natanson and Cailliet, 1990;
McFarlane et al., 2002). These circumstances continue to cause difficulties in making age estimates
for many species.
Centrum banding patterns may be related to physiological changes induced by changes in
environmental parameters such as temperature and photoperiod (Cailliet et al., 1986; Branstetter,
1987). However, some species such as the little skate, Leucoraja erinacea (Natanson, 1993), and the
Pacific angel shark, Squatina californica do not reflect such relationships (Natanson and Cailliet,
1990; Cailliet et al., 1992). Vertebral growth is inevitably linked to food intake, and a lack of food for
short periods of time can cause subtle bands to appear in vertebral centra of some species (J.
Gelsleichter pers comm., pers. obs.). Considerable variability exists in the amount and pattern of
calicification within and among taxonomic groups of elasmobranch fishes, and much of the variation
observed in several species has not yet been explained (Branstetter, 1990; Branstetter and Musick,
99
1994; Wintner and Cliff, 1999). These factors make it inherently risky to assume that the vertebral
banding pattern of one species is representative of another species or under all conditions, necessitating a species-specific approach.
The age determination process consists of the following steps: collection of hard part samples,
preparation of the hard part for age determination, examination (age reading), assessment of the
validity and reliability of the resulting data and interpretation (modeling growth). The purpose of this
chapter is to provide a concise overview of basic methodologies and statistical analyses that can be
used to quantify age and estimate rates of growth from vertebral centra and dorsal fin spines in
elasmobranch fishes. I provide a few web-based references at the end of the chapter, and cite additional literature sources throughout that can be obtained to conduct specific staining techniques and
age validation methods that are more expensive, complex and technology based. Additional methods of
assessing the age-length relationship can also be conducted, but as the purpose of this chapter relates
solely to age and growth via hard part analysis, alternative methods such as size mode or length
frequency analysis and monitoring captive growth are not covered herein. (See Gulland and Holt,
1959; Francis, 1988; Cailliet et al., 1992; Natanson et al., 2002 and Chapter 4 this volume for size
mode and length frequency analysis; see Van Dykhuizen and Mollet (1992), Mollet et al. (2002) and
Mohan et al. (in press) for monitoring captive growth).
6.2
VERTEBRAL OR FIN SPINE COLLECTION AND PREPARATION
Whole vertebral centra, as well as transverse and sagittally (i.e., longitudinally) sectioned
centra have been used for ageing elasmobranchs (Figure 6.01). Transverse sectioning will prevent
bands on opposing halves from obscuring each other when illuminated from below. However, determining the age of older animals can still be problematic as bands become more tightly grouped at the
outer edge of vertebrae, and may be inadvertently grouped and counted together thereby causing
underestimates of age (Cailliet et al., 1983a; Branstetter, 1987). As such, sagittally sectioned vertebrae
should be used for ageing unless it can be unequivocally demonstrated that identical ages can repeatedly be obtained from a given species using whole centra.
transverse
longitudinal
or saggital
Figure 6.01 Diagram of the two sectioning
planes that can be used on vertebral centra
(courtesy of G.M.Cailliet, Moss Landing Marine
Laboratory).
100
Dorsal fin spines have been another useful hard part for ageing some elasmobranchs, most
notably dogfish sharks of the family Squalidae (Ketchen, 1975; Nammack et al., 1985; McFarlane and
Beamish, 1987a). Spines from the second dorsal fin are preferred for ageing as the tips of first dorsal
fin spines tend to be more worn down, which leads to an underestimation of age.
Novel approaches to ageing various elasmobranchs continue to arise, and researchers may
want to begin collecting additional hard parts from specimens in the field to be experimented with in
the lab. For example, Gallagher and Nolan (1999) used caudal thorns along with vertebral centra to
determine age in four bathyrajid species, demonstrating high precision in ages between the two parts,
and Gallagher et al. (in press) further elaborated on the structure and seasonal growth processes in the
caudal thorns of the broadnose skate, Bathyraja brachyurops. Comparing counts in more than one
hard part is a common age verification technique used in teleost ageing studies. However, it is not
often conducted on elasmobranchs due to the lack of multiple hard parts for comparison. The use of
thorns as a reliable hard part for ageing, where appropriate, has the potential to greatly aid in our
understanding of the life histories of several species of skate and ray. Additionally, McFarlane et al.
(2002) have provided preliminary evidence that neural arches stained with silver nitrate may be useful
in assessing the ages of sharks with poorly calcified vertebral centra (see section 6.2.2.2).
6.2.1
Field sampling and storage
Upon capture, precaudal, fork, and total length (PCL, FL and TL, respectively) of sharks
should be measured on a straight line, while disc width at its widest point and total length should be
measured for skates and stingrays (see Chapter 3, section 3.1 this volume). While disc width is likely
to be the more statistically useful length measurement for skates and rays, total length can be taken for
comparison (and used in growth models if it provides better statistical results). Sex should be recorded
and clasper length of males should be measured (see Chapter 7 this volume). Weights should be
obtained from all specimens prior to the removal of any tissues, organs or hard parts.
The location in the vertebral column from which samples are taken for ageing can have a
statistically significant effect on increment counts (Officer et al., 1996). This emphasizes the importance of standardizing the vertebral sampling region for all ageing studies, allowing for precise, valid
comparisons among individuals within a population and for more accurate comparisons between
populations. A section numbering between 10 and 15 of the largest (usually thoracic) vertebrae should
be removed from the fish. The largest vertebrae may be located in slightly different areas depending
upon the species, but they are typically located directly in front of or under the first dorsal fin in sharks,
and at the thickest body point in skates and rays. The vertebral section should be bagged, labeled and
stored frozen until ready for preparation (see section 6.1.2). If freezing is not an option, vertebrae can
be fixed in 10% formalin for 24 h and preserved in alcohol.
101
Second dorsal fin spines should be removed by cutting horizontally just above the notochord to
ensure that the spine base and stem are intact. Spines can be bagged, labeled and frozen until returned
to the lab or placed immediately in 70-95% ethyl alcohol or 95% isopropyl alcohol.
6.2.2
Cleaning, cutting and mounting
Vertebral samples need to be thawed if frozen, or washed if preserved in alcohol, and cleaned
of excess tissue and separated into individual centra. While the removal of all muscle tissue is required,
I recommend that the neural arches (Figure 6.02) be removed from only ½ of the vertebral sample,
and that the vertebrae with neural arches attached, along with a subsample of the fully cleaned
(whole) centra, be kept frozen. Neural arches may be useful for ageing if centra are not (see section
6.3.2.2), and additional centra will be needed if staining is necessary. Haemal arches (sometimes
referred to as transverse processes) should be removed. If manual cleaning is not sufficient to remove
all of the surrounding tissue, or if working with dried vertebrae, several options are available to assist in
complete cleaning of vertebral sections. However, soaking them in a 5% sodium hypochlorite solution
is a simple and effective method. Soak times can range from five minutes to one hour depending on
the size of the vertebrae and should be followed by soaking centra in distilled water for 30 to 45
minutes (Johnson, 1979; Schwartz, 1983). This method also assists in removal of the vertebral fascia
between centra and does not affect the staining process, should any be conducted. Centra are typically
permanently stored in 70-95% ethyl alcohol or 95% isopropyl alcohol; however, a sub-sample of centra
should be permanently stored in a freezer as long-term exposure to alcohol may reduce the resolution
of the banding pattern (Allen and Wintner, 2002; Wintner et al., 2002). Centra that are to be analyzed
should remain in one of the above alcohol solutions for at least 24 h prior to any further preparation
(i.e., being sectioned). Vertebrae should not be permanently stored in formalin as it may damage
centra making them unreadable, nor should they be stored dry (in air) as this may result in cracking.
Ages can be obtained in most cases from cracked vertebrae, however, accurate centrum measurements may be difficult to obtain from them.
Vertebral sectioning is typically done with a low-speed diamond-bladed saw (e.g., Isomet
rotary diamond saw), but can be made with small handsaws and even scalpels when working with
very small centra. Each centrum should be sagittally sectioned immediately adjacent to the center of
its focus (Figure 6.02) (so that the center of the focus is at the edge of the cut) and then cut again
approximately 1.5 mm off-center. Accuracy and precision in these cuts (i.e., always including the
center point of the focus) will reduce centrum measurement error among individuals. A double-bladed
saw can be used to eliminate the problem of cutting a small section off of one-half of a vertebral
centrum (Figure 6.03). Spacing between blades should be no less than 0.6 mm to allow for some
sanding and/or polishing. Large vertebrae can be hand-held for cutting, whereas imbedding small
102
Figure 6.02 Photograph of
an individual vertebral centrum
showing neural and haemal
arches, spinal cord and focus
(courtesy of S.E. Campana,
Bedford Institute of Oceanography).
Figure 6.03 Photograph
showing a vertebral centrum being sectioned (sideto-side) with a double
bladed saw (courtesy of S.
E. Campana, Bedford
Institute of Oceanography).
vertebrae in resin (thermoplastic cement) and then cutting may prove easier. If not using a rotary saw,
small vertebrae can be sanded in half, mounted, sanded thin and polished. A grinder may be used to
section large vertebrae, which can then be mounted, sanded thin and polished.
If working with lamniform or other vertebrae with small numbers of radials, pressing the
sagittally cut (bowtie-shaped) sections between two pieces of Plexiglas and placing weight on the top
sheet during drying will prevent warping, which can effect increment and centrum radius measurements. Sectioned vertebrae should be air-dried for 24 h (under a ventilation hood if possible), and then
103
mounted onto microscope slides. The focus side of the vertebral section must consistently be placed
face down on the slide when mounting in order to avoid adding to centrum measurement error that will
lead to subsequent analysis error. Any typical slide-mounting medium (e.g., PermountTM) will suffice
for attaching vertebral sections. After the mounting medium is completely dry (24-36 h), sections
should be sanded with wet fine grit sand paper in a series (grades 320, 400 and finally 600 for polishing) to approximately 0.3-0.5 mm and air-dried. A binocular dissecting microscope with transmitted
light is generally used for identification of growth rings and image analysis (see section 6.3).
It is important to the age-determination process that at least the majority of vertebral sections
include the calcified radials of the intermedialia, but this is not always easy (Figure 6.04). For example,
the radials of the intermedialia of carcharhinid sharks are relatively hard, robust and numerous, making
centra nearly solid. In contrast, the radials of the intermedialia in lamnoid sharks are less numerous,
softer and quite fragile. Large interstitial spaces between radials can prevent intermedialia from being
present in a sectioned centrum. Conducting several preliminary “test cuts” should reveal the best
location to make a sagittal cut that will include intermedialia. Once the best location is found, all cuts
need to be consistent (i.e., made in the same location on each centrum) in order to minimize error in
centrum measurements, which are critically important for centrum edge analyses and back-calculations. In the experience of the author, the best “cut” to obtain the radials of the intermedialia has most
frequently been obtained from a side-to-side cut from the vertebral centrum vs. a top-to-bottom one
(Figure 6.03).
Second dorsal fin spines can be permanently stored dry or in 70-95% ethyl alcohol or 95%
isopropyl alcohol, but should be air-dried for at least 24 h before reading. Spines can be read whole
(without further preparation), by wet-sanding the enamel and pigment off the surface and polishing the
spine or from the exposed surface resulting from a longitudinal cut (Ketchen, 1975; McFarlane and
Figure 6.04 Sagittal section
of a vertebral centrum from
a 10 yr old salmon shark,
Lamna ditropis, showing the
typical banding pattern in this
species. CR = centrum
radius. PB = pre-birth ring,
B = birth ring, and arrows
indicate rings or age (photograph K.J. Goldman).
104
Beamish, 1987a). Spines should also be cross-sectioned as this has provided age assessments for
some squaloids and chimaeras (Sullivan, 1977; Freer and Griffiths, 1993; Clark et al., 2002a and b;
Calis et al., in press).
6.3
AGE DETERMINATION
The most commonly distinguishable banding pattern in sectioned centra when viewed micro-
scopically is one of wide bands separated by distinct narrow bands (Figure 6.04). The terms opaque
and translucent are commonly used to describe these bands, and they tend to occur in summer and
winter, respectively. However, the opacity and translucency of these bands varies considerably with
species, light source and methodology (Cailliet et al., 1986; Cailliet, 1990; Wintner et al., 2002; pers.
obs.). It should not be assumed that the opaque and translucent nature of vertebral bands in different
species will be similar; however, the pattern of wide/narrow banding tends to be very consistent
(Figure 6.04). In temperate waters, the wide bands represent faster fish growth during the summer
months when water temperatures are warmest, and the narrow bands represent slower growth during
the colder winter months. An annulus is usually defined as the winter band. The difference in appearance between summer (wide) and winter (narrow) growth bands provides the basis for age determinations. In many species, this so-called winter band actually forms in the spring (Sminkey and Musick,
1995). While tropical teleosts have sometimes proven more difficult to age (due to the lack of seasonality and relatively consistent photoperiod), this does not appear to be the case with tropical elasmobranchs, such as the lemon shark, Negaprion brevirostris (Brown and Gruber, 1988).
In elasmobranch vertebral sections, each pair of wide/narrow bands that extends across one
arm of the corpus calcareum, across the intermedialia and across the opposing corpus calcareum arm
is considered to represent an annual growth cycle; the narrow bands, hereafter referred to as “rings”
or “annuli”, are what are counted (Figure 6.04). It must be noted that counting these rings, at this point
in the process, carries with it the assumption that each one represents a year’s growth; however, the
validity of this assumption must be tested (see section 6.4). (The term annulus is defined as a ring-like
figure, part, structure or marking, but annuli must be shown to be annual in their deposition). The age
determination process (i.e., enumeration of rings, measurements and back-calculations) for spines is
virtually identical to that for vertebrae (Figure 6.05); however, Ketchen’s (1975, see also Nammack et
al., 1985) method for calculating age from worn spines should be used instead of discarding them. This
method uses an age to spine-base-diameter regression for unworn spines to allow an estimation of age
for individuals with worn spines. The best-fit regression line is used to obtain the number of years that
are to be added to the age of an individual based on the diameter of a spine at its “no wear point” (see
Ketchen, 1975 for details on worn spine criterion and specific examples).
While transmitted light is the most commonly used method of illuminating sectioned centra, I
strongly recommend comparing transmitted light with reflected light, translucent and other filtered light,
105
as well as ultraviolet (UV) illumination even if staining or tetracycline injection has not been conducted
(see sections 6.2.2 and 6.3.2, respectively). Altering the intensity of each type of light and making
finite adjustments to the optical focus of the microscope can
often provide visual enhancement of the banding pattern.
6.3.1 Ageing protocols
Age and growth studies require interpretation of
banding patterns in the hard parts of fishes. As such, they
incorporate several sources of variability and error. While the
individuals used in an ageing study provide a source of
natural variability, variability between sexes and among
geographic locations may also exist (Parsons, 1993; Carlson
and Parsons, 1997; Yamaguchi et al., 1998). Other potential
sources of variability and error include the method used to
Figure 6.05 Photograph of spiny
dogfish, Squalus acanthias, second
dorsal fin spines showing annuli.
First spine was aged at 42 yrs.;
second spine aged at 46 yrs (courtesy of G.A. McFarlane, Pacific
Biological Station).
count growth increments, effects of within- and betweenreader variability and bias, effects of staining, variation in
increment counts from different hard parts and variation in
increment counts from within the same region of the vertebral column and from different regions of the vertebral
column (Officer et al., 1996; Campana, 2001). Developing an ageing protocol brings consistency in the
ageing process leading to better precision thus minimizing error. The most important aspect of any
ageing protocol is that it produces repeatable ages within and between readers (i.e., precision). Ageing
protocols have two key components: 1) determination of which marks on vertebral centra or spines
will be counted (see section 6.3 and below), and 2) checking for reader agreement and precision, and
testing for bias within and between readers after age determinations are completed (see section 6.3.3).
A standard part of every ageing protocol, whenever possible, should be to have two readers independently age all centra two times in blind, randomized trials without knowledge of each specimen’s length
or disc width (see section 6.3.3).
One of the more common problems in age determination occurs due to deviations in typical
growth patterns observed in vertebral centra, which can lead to inaccurate counts. These deviations
can result from false checks or split bands occurring within the corpus calcareum, the intermedialia or
both, and the vertebral intermedialia of many species possess a great deal of “background noise”. As
such, it is important that these accessory bands be recognized as anomalies when assigning an age to a
specimen. Checks tend to be discontinuous, weak or diffuse, and inconsistent with the general growth
pattern of true annuli. Developing some familiarity with the typical “look” of the banding pattern in a
given species’ centra to aid in distinguishing checks from annuli is recommended. If the ageing study is
106
an ongoing one, regular review of reference collections and comparing summaries of age-length data
from one season to the next also helps maintain accuracy, precision and reduce bias in age determinations (Officer et al., 1996; Campana, 2001). In addition, because the intermedialia of the centrum in
many species is not very robust, it may warp in a concave manner during the drying process. When
this occurs, the rings near the outer edge of the intermedialia become “bunched up” and indistinguishable. The rings on the corpus calcareum also become more tightly grouped at the outer edge, particularly in larger/older animals; however, they have a tendency to remain distinguishable due to the
stronger (more robust) nature of the structure (see Figure 6.04). For these reasons, the corpus
calcareum should always be used as the primary counting and measuring surface, with the distinct
rings in the intermedialia and any additional features (see below) used as “confirmation” of a ring or
annulus.
Additional difficulties in ageing elasmobranch fishes can include determining the birthmark and
first growth ring. Birthmarks are usually represented by an angle change along the centrum face of
whole vertebrae or along intermedialia-corpus calcareum interface with an associated ring on the
corpus calcareum in sectioned centra (Figures 6.04 and 6.06), but this feature may not be distinct in
either. While the birthmark usually can be found on the whole centrum surface (i.e., the outside wall of
the corpus calcareum), the variability in this mark is such that it may appear distinctly only within the
sagittally cut section (Figure 6.04). Additionally, “pre-birth rings” have been reported in some species
(Branstetter and Musick, 1994; Nagasawa, 1998; Goldman, 2002) (Figure 6.04). Once the angle
change is located, pre-birth rings can easily be distinguished from the first growth ring. The first
growth ring may consist of minimal growth around the focus of a vertebra, can be faint relative to
other annuli (Campana, 2001), and can also differ in its opacity or translucency (Wintner and Dudley,
2000; Allen and Wintner, 2002). Being able to consistently locate a birthmark and (particularly) the first
annulus are obviously of critical importance to accurate age assessment. Knowledge of the pupping
(or hatching) time of a given species can help in determining if the first annulus is expected to be very
small (first winter is soon after birth) or large (first winter is a considerable time after birth).
The vertebral centra of some species may also possess features that can assist in ageing
specimens. For example, sagittally cut vertebral sections of some species reveal distinct notches along
either the inside or outside edge of the corpus calcareum at each ring providing an additional ageing
feature (Figure 6.06). This can be particularly useful in ageing vertebral sections where the cut has
excluded the radials of the intermedialia and in distinguishing growth checks from annuli.
If examination of vertebral centra reveal no discernable banding patterns or reveal rings that
are difficult to interpret, centra (either whole or sectioned) can be stained to attempt enhancement of
growth bands for enumeration.
107
6.3.2
Staining methods
Numerous techniques have been used in
attempts to enhance the visibility of growth bands in
elasmobranch vertebral centra. The list includes
alcohol immersion (Richards et al., 1963), xylene
impregnation (Daiber, 1960), histology (Ishiyama,
1951; Casey et al., 1985; Natanson and Cailliet,
1990), X-radiography (Aasen, 1963; Cailliet et al.,
1983a and b; Natanson and Cailliet, 1990), X-ray
spectrometry (Jones and Green, 1977), cedarwood
oil (Cailliet et al., 1983a; Neer and Cailliet, 2001),
alizarin red (LaMarca, 1966; Gruber and Stout, 1983;
Cailliet et al., 1983a), silver nitrate (Stevens, 1975;
Schwartz, 1983; Cailliet et al., 1983a and b), crystal
violet (Johnshon, 1979; Schwartz, 1983; Carlson et
al., 2003), graphite microtopography (Parsons, 1983;
Parsons, 1985; Neer and Cailliet, 2001), a combina-
Figure 6.06 Sagittal section of a vertebral
centrum from a 2 yr old smooth dogfish,
Mustelus canis, showing the distinct
notching pattern (white arrows) that accompanied the distinct banding pattern (courtesy
of C. Conrath, Virginia Institute of Marine
Science).
tion of cobalt nitrate and ammonium sulfide (Hoenig
and Brown, 1988) and the use of copper, lead and iron based salts (Gelsleichter et al., 1998a). Many of
these studies used multiple techniques on a number of species for comparison, particularly Schwartz
(1983) and Cailliet et al. (1983a). These studies show that the success of each technique is often
species specific and that slight modifications in technique may enhance the results.
In addition to their effectiveness, the various techniques mentioned vary in their simplicity, cost
and technological requirements. Histological processes have proven useful, but require specialized
equipment, a number of chemicals and are relatively time consuming. However, the resulting staining
process resulted in no color change in vertebral sections after 15 yrs (Casey et al., 1985). X-radiography has proven useful in many studies, but has the obvious necessity of an appropriate X-ray machine
and film processing capabilities, and while X-ray spectrometry may hold promise (Jones and Geen,
1977; Casselman, 1983), it is time consuming and expensive. Simpler, less expensive and time-efficient
staining techniques, such as crystal violet, silver nitrate, cedarwood oil, graphite microtopography and
alizarin red should be used first prior to considering other, more elaborate methods. While these
techniques have been tried, many have not yet been thoroughly evaluated. For example, the cobalt
nitrate and ammonium sulfide stain suggested by Hoenig and Brown (1988) is easy to use, time
efficient and provided quality results for two species (Figure 6.07), but has not been extensively
applied. A microradiographic method using injected fluorochrome dyes to aid in resolving individual
108
Figure 6.07 Vertebrae stained using the cobalt nitrate and
ammonium sulfide method of Hoenig and Brown (1988). The
top image is a smooth dogfish, Mustelus canis, centrum, the
middle and bottom images are of lemon shark, Negaprion
brevirostris, centra (courtesy of J.M. Hoenig,
Virginia Institute of Marine Science).
hypermineralized increments was applied to captive gummy
sharks, Mustelus antarcticus, with success (Officer et al.,
1997), but this method has also not been extensively applied.
This method may also have application as a validation technique,
but this needs to be investigated.
Two of the simplest staining techniques are crystal violet
and silver nitrate, which are described below. The appropriate
literature (provided herein) should be acquired for detailed
directions for other staining or enhancement techniques as well
as modifications of the techniques presented. The wide-ranging
subtle differences between studies using the same staining
technique and the use of whole vs. sectioned vertebrae make
presenting a single formula difficult. As such, a general timeline range for the methods is presented
and may require some tinkering for the best results. Mini-modifications are made by many researchers
in attempts to accentuate the vertebral rings in the centra of their study species.
6.3.2.1 Crystal violet protocol
Perhaps the simplest staining technique involves the use of crystal violet (Figure 6.08). An
advantage of this technique is that it can be performed on fresh vertebrae as well as those stored in
alcohol. After each vertebra has been cleaned of excess tissue, it is soaked in a 0.01% solution of
Figure 6.08 Sagittal
section of a vertebral
centrum from a 3 yr old
fine-tooth shark,
Carcharhinus isodon,
stained with crystal violet
(courtesy of J.K. Carlson,
NOAA/NMFS/SEFSC
Panama City Laboratory).
109
crystal violet. Johnson (1979) suggested soak times ranging from 0.2 to 4.0 hrs depending on the size
of vertebrae, but this was for teleost fishes. Schwartz (1983) used soak time ranging from 10-15 min
for 12 different elasmobranch species (10 min for sharks < 70 cm FL, 15 min for sharks > 100 cm
FL). Carlson et al. (2003) used similar soak times as Schwartz (1983) for sectioned finetooth shark,
Carcharhinus isodon, vertebrae (Figure 6.08), and on whole centra for the blacknose shark,
Carcharhinus acronotus (J.K. Carlson, pers. comm.). The best ring definition may occur if vertebrae
are initially overstained and then destained for no more than 1 min in 50% isopropyl alcohol (Schwartz,
1983).
6.3.2.2 Silver nitrate protocol
The silver nitrate technique replaces calcium salts in the centrum with silver, providing bands
that darken when illuminated with ultraviolet light (Figure 6.09). As with crystal violet, this technique
can be performed on fresh vertebrae as well as those stored in alcohol. All connective tissue must be
removed from the centrum to ensure chemical substitution. While Cailliet et al. (1983a) soaked vertebral centra in 88% formic acid for 2-4 min to remove any traces of bleach they had used in the
cleaning process and etch the centrum surface for staining, this may not be required, as neither
Stevens (1975) or Schwartz (1983) conducted this step. Regardless of whether this step is taken, all
centra should be repeatedly washed for 5-15 min in distilled water prior to applying the stain. Centra
can then be placed in 1% silver nitrate solution for 1-3 min and simultaneously illuminated with an
ultraviolet light source for anywhere between 2-4 min and depending on the species and size of the
centrum (Stevens, 1975; Schwartz, 1983), although times used by Cailliet et al. (1983a) ranged from
3-15 min. Submerging whole centra in solution is recommended for ensuring the extreme edges of the
vertebra are stained. Checking the centrum every 30 s or so will allow determination of the proper
immersion time and prevent over-staining, which can easily occur (Schwartz, 1983). Centra should
then be rinsed with distilled water to remove excess silver nitrate, and may then be read or sagittally
sectioned and read.
Cailliet et al. (1983a) used a dissecting scope with reflected illumination focused laterally on
the centra to make counts; however, either reflected or transmitted light can be used for sectioned
vertebrae. After counts are completed, centra should be soaked in a 5% sodium thiosulfate solution for
2-3 min, rinsed with distilled water and stored in 70% isopropyl alcohol (Stevens, 1975; Schwartz,
1983; Cailliet et al., 1983a and b). This process fixes the chemical substitution, but may also eradicate
very narrow rings. Counts should be made before and after fixation to estimate the bias caused by the
process (Cailliet et al., 1983b)
Calcium deposits have been documented in the neural arches of elasmobranch fishes
(Peignoux-Deville et al., 1982; Cailliet, 1990), but they had not been used for ageing. McFarlane et al.
110
(2002) recently introduced the
first attempt using this structure
for ageing elasmobranchs by
silver nitrate staining the neural
arches of sixgill sharks,
Hexanchus griseus. The
results from this preliminary
Figure 6.09 Images of two vertebrae stained with silver nitrate.
Left-hand image is a sagittal sectioned centrum of an 11 yr old
leopard shark, Triakis semifasciata (courtesy of G.M. Cailliet,
Moss Landing Marine Laboratories) and the right-hand image is
of a 4 yr old spot-tail shark, Carcharhinus sorrah (courtesy of
J.D. Stevens, CSIRO Australia).
study indicate that neural arches
may provide another ageing
structure for elasmobranch
species where their vertebral
centra are poorly calcified, but
the method has not been validated. Attempts are currently underway to refine this method by determining the most appropriate sectioning methods and thickness, staining times and solution concentration (McFarlane et al., 2002). The technique is also being applied to several other elasmobranchs with
poorly calcified vertebrae (McFarlane, pers. comm.).
6.3.3
Reader agreement, precision and bias
Precise and accurate age estimation is a critical component of any ageing study. It is important
to keep in mind that the consistent reproducibility of age estimates from vertebral centra will achieve
high precision, but that these age estimates may not be accurate (i.e., reflect the true or absolute age),
and that precision should never be used as a substitute for accuracy. Accurate age determination
requires validation of absolute age not just the frequency of increment formation in vertebral centra or
spines (Beamish and McFarlane, 1983; Cailliet, 1990; Campana, 2001) (see section 6.4).
Two readers independently ageing all centra two times in blind, randomized trials without
knowledge of each specimen’s length or disc width allows two calculations of between-reader agreement and precision, and helps prevent reader bias that can be caused by “predetermination” of age
based on knowledge of length (i.e., prevent subjectivity). When there is a disagreement between
readers, a final age determination should be made by the two readers viewing the centrum together, as
a single age is needed from each specimen for input into growth models. If no consensus can be
reached, the sample should be eliminated from the study.
The most commonly used methods for evaluating precision in age determination have been the
average percent error (APE) technique of Beamish and Fournier (1981) and the modification of their
method by Chang (1982). However, Hoenig et al. (1995) and Evans and Hoenig (1998) have demonstrated that there may be differences in precision that these methods obscure because the APE
111
assumes that the variability among observations of individual fish can be averaged over all age groups
and that this variability can be expressed in relative terms. Also, APE does not result in values that are
independent of the age estimates. APE indices do not test for systematic differences, do not distinguish
all sources of variability (such as differences in precision with age) and do not take experimental
design among studies into account (i.e., number of times each sample was read in each study) (Hoenig
et al., 1995). Within a given ageing study, however, APE indices may serve as good relative indicators
of precision within and between readers provided that each reader ages each vertebra the same
number of times. However, even this appears only to tell us which reader was less variable, not which
one was better or if either were biased. The comparison of precision between ageing studies would
appear to have limited value, and I can find no references that compare precision estimates for a given
species (APE or otherwise) to other studies, although a conversion factor relating the two precision
estimators has been derived based on 14 papers that used both APE methods (Campana, 2001).
Comparing precision between studies would seem to hold importance only if the study species is the
same, but caution should be used if samples are from different geographic areas.
A simple and accurate approach to estimating precision is to 1) calculate the percent reader
agreement (PA=[No. agreed/No. read]•100) within and between readers for all samples, 2) calculate
the percent agreement plus or minus one year (PA +/- 1 yr) within and between readers for all
samples, 3) calculate the percent agreement within and between readers, with individuals divided into
appropriate length or disc width groups (e.g., 5-10 cm increments) as an estimate of precision (this
should be done with sexes separate and together), and 4) test for bias using one or more of the methods below. The criticism of percent agreement as a measure of precision has been that it varies widely
among species and ages within a species (Beamish and Fournier, 1981; Campana, 2001). However,
there is validity in using percent agreement with individuals grouped by length as a test of precision
because it does not rely on ages (which have been estimated), but rather on lengths, which are empirical values. Age could be used if, and only if, validation of absolute age for all available age classes had
been achieved (see section 6.4).
Several methods can be used to compare counts (ages) by multiple readers such as regression
analysis of the first reader’s counts vs. the second reader’s counts, a paired t-test of the two readers’
counts and a Wilcoxon matched pairs signed-ranks test (DeVries and Frie, 1996). Campana et al.
(1995) stated the importance of a separate measure for bias, and that bias should even be tested for
prior to running any tests for precision. They suggest an age bias plot, graphing one reader vs. the
other, which is interpreted by referencing the results to the equivalence line of the two readers (45o
line through the origin) (Figure 6.10). Similarly, Hoenig et al. (1995), and Evans and Hoenig (1998),
state that comparisons of precision are only of interest if there is no evidence of systematic disagree-
112
Figure 6.10 An age
bias plot, graphing
one reader vs. the
other, showing good
agreement and no
bias. Chi-square tests
of symmetry were
also conducted on
these data and gave
no indication that
differences between
and within readers
were systematic
rather than due to
random error (from
Goldman, 2002).
ment among readers or methods, and suggest testing for systematic differences between readers using
Chi-square tests of symmetry such as Bowker’s (Bowker, 1948), McNemar’s (McNemar, 1947), and
their Evans-Hoenig test to determine whether differences between and within readers were systematic (biased) or due to random error. This is of particular importance if initial percent agreement and
precision estimates are low. I recommend these tests of symmetry for testing for bias regardless of
precision as they place all age values in contingency tables and test the hypothesis that values in a
given table are symmetrical about the main diagonal, and because they can be set up to test among all
individual age classes or groups of age classes. The test statistic (the Chi-square variable) will tend to
be large if a systematic difference exists between the two readers.
6.3.4
Back-calculation methods
Back-calculation is a method for describing the growth history of each individual sampled, and
numerous variations in methodology exist (see Francis (1990) for a thorough review). Back-calculations estimate lengths-at-previous-ages for each individual and should be used if sample sizes are small
and if samples have not been obtained from each month. Regression methodologies are ill advised
because they discard information and frequently produce back-calculated lengths that overestimate
fish length at capture (Francis, 1990), and they will not be presented here. Back-calculation formulas
that follow a hard part or body proportion hypothesis are recommended (Campana, 1990; Francis,
1990; Ricker, 1992). The proportional relationship between animal length or disc width and the radius
of the vertebral centrum among different length animals within a population is used as a basis for
empirical relationships regarding population and individual growth, as is the distance from the focus to
each annulus within a given centrum (see below and section 6.3.1). Centrum radius (CR) and distance
113
to each ring should be measured as a straight line from the central focus to the outer margin of the
corpus calcareum (Figure 6.04) to the finest scale possible. If using a compound video microscope
with the image analysis system (e.g., UTHSCSA Image Tools 1997 or Optimus - Media Cybernetics
1999), distances can be measured to the nearest 0.001 mm. If no image analysis system is available,
measurements should be made with an ocular micrometer (which is easily inserted into an eyepiece of
the microscope). Lengths or disc widths should then be plotted against CR to determine the proportional relationship between somatic and vertebral growth (Figure 6.11), which will assist in determining
the most appropriate back-calculation method.
Four different proportion-based back-calculation methods are presented here that can be used
to compare to sample length-at-age data, depending on the relationship between CR and length.
(Length and disc width are interchangeable in the following equations, but length will be the term
used). The results of the method best representing sample data should be used in subsequent growth
models (see section 6.5).
1)
The Dahl-Lea direct proportions method (Carlander, 1969):
This is the most commonly applied proportions method. While it should theoretically only be
conducted when the linear fit to the relationship between CR and length passes through the origin, it
may still provide the most accurate results when compared to sample length-at-age data. Hence, it
should be conducted, but at least one of the other three methods below should also be conducted for
comparison. The Dahl-Lea direct proportions equation is:
Li = (Lc/CRc)•CRi
(6.1)
where Li = length at ring ‘i’, Lc = length at capture, CRc = centrum radius at capture, and CRi =
centrum radius at ring ‘i’.
2)
Linear-modified Dahl-Lea method (Francis, 1990):
This method should be applied if the relationship between CR and length is best described by a
linear equation and the CR-length relationship does not pass through the origin (Figure 6.11). Parameter estimates from the specific linear fit are incorporated into the back-calculation estimates. The
linear-modified Dahl-Lea equation is:
Li = Lc•[(a+bCRi)/(a+bCRc)]
(6.2)
where ‘a’ and ‘b’ are the linear fit parameter estimates (Figure 6.11).
3)
Quadratic-modified Dahl-Lea method (Francis, 1990):
This method should be applied if the relationship between CR and length is best described by a
quadratic fit (Figure 6.14), as parameter estimates from the specific quadratic fit are incorporated into
the back-calculation estimates. The quadratic-modified Dahl-Lea equation is:
Li = Lc•[(a+bCRi+cCRi2)/(a+bCRc+cCRc2)]
where ‘a’, ‘b’, and ‘c’ are the quadratic fit parameter estimates (Figure 6.11).
114
(6.3)
4)
Size-at-birthQuadratic Fit:
PCL = -0.583•CR2 25.189•CR - 63.944
r 2 = 0.94
modified Fraser-Lee
Both Ricker (1992)
and Campana (1990)
suggested that the point of
origin of proportional backcalculations should be
Precaudal length (cm)
method (Campana, 1990):
Linear Fit:
PCL = 10.553•CR + 20.964
r 2 = 0.90
related to a biologically
derived intercept (i.e.,
length at birth). This equation is recommended for use
anytime the linear fit to the
relationship between CR
and length does not pass
Centrum radius (mm)
Figure 6.11 Relationship between centrum radius and precaudal
length for eastern North Pacific salmon sharks, Lamna ditropis,
showing significant fits given by linear and quadratic equations (sexes
combined, n=182). PCL = precaudal length, CR = centrum radius
(from Goldman, 2002).
through the origin. The “size-at-birth-modified” Fraser-Lee equation is:
Li = Lc+[(CRi–CRc)•(Lc–LBirth)/(CRc–CRBirth)]
(6.4)
where LBirth = length at birth and CRBirth = centrum radius at birth.
Providing biological and statistical reasoning behind the choice of a back-calculation method is
extremely important for obtaining accurate life history parameter estimates from a growth function
(e.g., von Bertalanffy) when using back-calculated data. While one method may show itself to be
more statistically appropriate for back-calculation, researchers should conduct several methods for
comparison to available sample length-at-age data to verify that statistical significance equates to
biological accuracy. Biological accuracy can be determined by plotting the sample mean length-at-age
data against the difference between mean back-calculated length-at-age estimates and the sample
mean length-at-age data to see which method provides the best results (Goldman, 2002). This plot will
show which mean back-calculation length-at-previous-age estimates (from each method) most accurately reflect mean lengths-at-age of sampled individuals.
6.4
VALIDATION
Estimates of age, growth rate and longevity in sharks assume that the vertebral rings are an
accurate indicator of age. While this is probably true for most species, few studies on elasmobranch
growth have validated the temporal periodicity of band deposition in vertebral centra and even fewer
have validated the absolute age (Cailliet et al., 1986; Cailliet, 1990; Campana et al., 2002; Natanson et
al., 2002). Cailliet (1990) stated that the process of evaluating growth zone deposition in fishes can be
115
categorized into the terms “verification” and “validation,” where verification is defined as “confirming
an age estimate by comparison with other indeterminate methods,” and validation as “proving the
accuracy of age estimates by comparison with a determinate method,” and these definitions are
adhered to herein.
Obtaining the absolute age of individual fish (complete validation) is the ultimate goal of every
ageing study, yet it is the frequency of growth ring formation for which validation is typically attempted. The distinction between validating absolute age and validating the periodicity of growth ring
formation is important (Beamish and McFarlane, 1983; Cailliet, 1990; Campana, 2001). Validation of
the frequency of growth ring formation must prove that the mark being considered an annulus forms
once a year (Beamish and McFarlane, 1983). However, it is the consistency of the marks in “number
per year” that really matters, be it one or more than one. Two or more marks (rings) may make up an
“annulus” if, and only if, consistent multiple marks per year can be proven. Strictly speaking, validation
of absolute age is only complete when it has been done for all age classes available, with validation of
the first growth increment being the critical component for obtaining absolute ages (Beamish and
McFarlane, 1983; Cailliet, 1990; Campana, 2001).
Validation can be achieved via several methods such as chemically tagging wild fish, markrecapture studies of known-age individuals and bomb carbon dating (see section 6.4.2) (the latter two
can also be used to validate absolute age). A combination of using known-aged individuals, tag and
recapture, and chemical marking is probably the most robust method for achieving complete validation
(Beamish and McFarlane, 1983; Cailliet, 1990; Campana, 2001; Natanson et al., 2002). While this is a
rather daunting task to accomplish with most elasmobranch species, the current necessity to obtain
age-growth data for fisheries management purposes dictates that it be attempted. The most frequently
applied method used with elasmobranchs has been chemical marking of wild fish (see section 6.4.2)
even though recaptures can be difficult to obtain for many species. As validation has proven difficult in
elasmobranchs, verification methods such as centrum edge analysis and relative marginal increment
analysis are frequently employed.
6.4.1
Indeterminate methods (verification)
Centrum edge analysis and relative marginal increment analysis are simple, indeterminate
methods that can be used to verify the temporal periodicity of ring formation in vertebral centra. Each
uses the centrum edge in a different manner to assess the timing of band deposition. While relative
marginal increment analysis may be slightly more robust, as the technique makes all age classes
comparable on a relative scale, it is advantageous to conduct both methods, particularly if electron
microprobe spectrometry can be applied (see below) (Cailliet, 1990; Wintner and Dudley, 2000;
Wintner et al., 2002).
116
Centrum edge analysis compares the opacity and translucency (width and/or density) of the
centrum edge over time in many different individuals to discern seasonal changes in growth. The
centrum edge is categorized as opaque or translucent, and the band width is measured or graded, then
compared to season or time of year (Kusher et al., 1992; Wintner and Dudley, 2000; Wintner et al.,
2002). A more detailed centrum edge analysis can be conducted by analyzing the levels of calcium and
phosphorous at the centrum edge using X-ray or electron microprobe spectrometry (Cailliet et al.,
1986; Cailliet and Radtke, 1987), which according to Cailliet (1990) has only been conducted in a single
study on recaptured nurse sharks that had been injected with tetracycline (Carrier and Radtke, 1988 in
Cailliet, 1990).
Relative marginal increment analysis (RMI)—sometimes Marginal Increment Ratio (MIR)—
is a useful, direct technique with which to assess seasonal band and ring deposition. The margin, or
growth area of a centrum from the last (ultimate) growth ring to the centrum edge, is divided by the
width of the last (previously) fully formed (penultimate) annulus (Branstetter and Musick, 1994;
Natanson et al., 1995; Wintner et al., 2002). Resulting RMI values are then plotted against month of
capture to determine temporal periodicity of band formation. Age-zero animals cannot be used in this
analysis since they have no fully formed increments.
6.4.2
Determinate methods (validation)
Validation of absolute age is extremely difficult to achieve with elasmobranch fishes, hence the
few studies that have attempted validation in these fishes have focused on validating the temporal
periodicity of ring (growth increment) formation. The tetracycline validation method is a standard
among fisheries biologists for marking free-swimming individuals (Cailliet, 1990; DeVries and Frie,
1996; Campana, 2001) to test the assumption of annual periodicity of growth rings. Oxytetracycline
(OTC), a general antibiotic that can be purchased through veterinary catalogs, binds to calcium and is
subsequently deposited at sites of active calcification. It is typically injected intramuscularly at a dose
of 25 mg kg -1 body weight (Tanaka, 1990; Gelsleichter et al., 1998b) and an external identification tag
is simultaneously attached to each injected animal. OTC produces highly visible marks in vertebral
centra and dorsal fin spines of recaptured sharks when viewed under ultraviolet light (Holden and
Vince, 1973; Smith, 1984; McFarlane and Beamish, 1987a and b; Brown and Gruber, 1988; Tanaka,
1990; Kusher et al., 1992; Gelsleichter et al., 1998b; Natanson et al., 2002; Simpfendorfer et al., 2002;
Smith et al., 2003) (Figure 6.12). The combination of body growth information and a discrete mark in
the calcified structure permit direct comparison of time at liberty with growth band deposition, such
that the number of rings deposited in the vertebra or spine since the OTC injection can be counted and
related to the time at liberty. Although there may be problems associated with using captive growth as
a surrogate to growth in the wild and with recapturing animals that have been at large for long enough
117
periods of time, this method has been used on a number of species in the field and laboratory (Cailliet
et al., 1986; Branstetter, 1987; Cailliet, 1990; Cailliet and Goldman, in prep.). While growth in captive
animals may be influenced by constant environmental parameters (e.g., water temperature and
photoperiod) and food availability, laboratory studies can provide valuable information on growth rates
(Tanaka, 1990; Mohan et al., in press) assist in verifying or validating the timing of growth ring deposition (Branstetter, 1987; Goldman, 2002), and the results may resemble growth rates observed in field
experiments (Branstetter, 1987).
Several other chemical markers such as fluorescein and calcein
have been used to validate growth ring
periodicity in teleost otoliths, but very
few studies have evaluated these in
elasmobranchs (Gelsleichter et al.,
1997; Officer et al., 1997). Gelsleichter
et al. (1997) found that while doses of
25 mg kg -1 body weight (typical dose
for teleosts) induced physiological stress
Figure 6.12 Sagittally cut vertebral section of OTC
injected captive sand tiger shark, Carcharias taurus.
White arrows indicate three clearly visible OTC marks
at the sight of ring formation (photograph K. J.
Goldman).
and mortality in elasmobranchs, doses of
5-10 mg kg -1 body weight produced
suitable marks without causing physiological trauma or death. Based on this
evaluation, any alternative chemical markers tested should consider that doses for teleosts might be too
high for elasmobranchs.
Bomb carbon dating is a technique that has recently been applied to age validation in elasmobranchs. A rapid increase in radiocarbon (14C) occurred in the world’s oceans due to atmospheric
atom bomb testing in the 1950’s and 1960’s (Druffel and Linick, 1978). Its uptake was virtually synchronous in marine carbonates including corals and fish otoliths, which allowed the period of increase
to serve as a dated marker in structures exhibiting growth bands (Druffel and Linick, 1978; Weidman
and Jones, 1993; Kalish, 1995; Campana, 1997; Campana, 1999). Hence, all fish born prior to 1958
contain relatively little 14C, all those born after 1968 possess elevated levels of 14C and individuals born
in the interim period have intermediate levels of 14C. Matching the 14C chronology in the fish hard part
with the published 14C chronologies for the region allows interpretation of age and validation. While
this method has been used for ageing several teleost fishes, Campana et al. (2002) reported the first
application of bomb radiocarbon to validate ages in long-lived sharks, specifically the porbeagle shark,
Lamna nasus, and (preliminary results for) the mako shark, Isurus oxyrinchus. This method may
118
provide one of the best approaches to age validation of long-lived fishes; however, it is not viable for
short-lived species or younger individuals, and appropriate reference chronologies are not available for
some environments (Campana, 2001). Bomb carbon dating requires at least some of the fish in the
sample to have been born (or hatched) prior to 1965; it is expensive and requires the use of relatively
high technology equipment such as a mass spectrometer, which may make this method unavailable for
many researchers. It may, however, be a key technique in resolving certain ageing discrepancies such
as questions regarding single vs. double ring formation annually in some species, if vertebrae from the
appropriate time period can be obtained.
6.5
GROWTH MODELS
A number of models and variations of models exist for estimating growth parameters in fishes,
of which the von Bertalanffy (1938) and Gompertz (1825) growth models are the most commonly
applied (see Ricker, 1979; Summerfelt and Hall, 1987; and Haddon, 2001 for thorough reviews). The
von Bertalanffy growth function has mostly been used to describe fish growth, while the Gompertz
curve is often used to describe larval and early life growth of fishes and growth in many invertebrates
(Zweifel and Lasker, 1976; Ricker, 1979). These two models are presented in standard form below;
however, weight can be used in place of length in the von Bertalanffy model, and length may be
substituted for weight in the Gompertz model. Many statistical packages include modules (i.e., functions) that can be used to calculate the best fitting growth parameters for the available length-at-age or
weight-at-age data pairs from the equations given in sections 6.5.1 and 6.5.2. For example, a nonlinear
least squares regression algorithm (e.g., ‘nls’ in S-Plus, Mathsoft Inc., 2000), a maximum likelihood
function or the PROC NLIN function in SAS can be used to fit the von Bertalanffy and Gompertz
curves to the data (SAS Institute Inc., 1999), and programs such as PC-YIELD (Punt and Hughes,
1989) can calculate a wide range of growth models for comparison (Wintner et al., 2002). Additionally,
FISHPARM (Prager et al., 1987), a fishery based statistics program, is simple to use and provides
quality statistical results for the two models presented herein. Both models can also be fit to data on a
spreadsheet via a non-linear regression using the “solver” function in Microsoft Excel.
Sample size can have considerable influence on growth model results. Additionally, pooling the
sexes into one sample can mask differences in sex-specific growth, so growth parameters should be
estimated for the sexes separately and combined and tested for significant differences (see below). If
smaller and-or medium size age classes are not well represented in the sample size of one or both
sexes, lengths at previous ages should be back-calculated from centra measurements for all animals.
Sample (observed) length-at-age data, back-calculated length-at-age data, mean back-calculated
length-at-age data, and a combination of back-calculated lengths-at-age and sample data should then
each be separately fitted with the appropriate growth function, and the resulting parameter estimates
119
compared. As long as large animals are well represented in the sample size, close parameter estimates
from these four growth curves would indicate that a relatively reliable overall sample size (n) has been
obtained. The best parameter estimates in these cases will be those with the smallest standard error;
however, significant differences between curves can be tested with a likelihood ratio test. A likelihood
ratio test should always be conducted if the resulting four curves have not produced very similar
parameter estimates and if standard errors are high.
Several methods exist for evaluating differences in growth curves (Gallucci and Quinn, 1979;
Kimura, 1980; Bernard, 1981; Kappenman, 1981; Francis, 1996; Wang and Milton, 2000). While
several techniques can provide reasonable results, a likelihood ratio test will always equal or surpass
other methods in accuracy and reliability and should be used to determine whether significant differences exist between growth curves, such as to see if female and male growth parameter estimates are
significantly different or if a single set of growth parameters better describe the data (Kimura, 1980;
Cerrato, 1990; Quinn and Deriso, 1999; Haddon, 2001). Likelihood ratio tests can be conducted in a
multitude of programs, such as Microsoft Excel following the Kimura (1980) method (Haddon (2001)
provides an excellent step-by-step instruction guide to the Kimura, 1980 method) or using the PROC
NLIN function in SAS (SAS Institute Inc., 1999).
6.5.1
von Bertalanffy growth function
The von Bertalanffy (1938) growth function has been widely used since its introduction into
fisheries by Beverton and Holt (1957), and although it has received much criticism over the years, it is
the most widely used growth function in fisheries biology today (Roff, 1982; Haddon, 2001). It maintains its attractiveness, in part, because its approach to modeling growth is based on the biological
premise that the size of an organism at any moment depends upon the resultant of two opposing
forces: anabolism and catabolism. Additionally, it is convenient to use and allows for much easier
comparison between populations and several alternate forms of the model can be fit to the age-length
data. [Haddon (2001) presents a variety of growth models including generalized models as possible
alternatives.]
The von Bertalanffy (1938) growth function is:
Lt = L (1-e-k (t-to))
(6.5)
4
where Lt = predicted length at age ‘t’, L = asymptotic or maximum length, k = the growth coefficient,
4
and to = age or time when length theoretically equals zero. The growth rate, k, is often misrepresented
in its description; it should be remembered that when fitted, this curve represents the average growth
rate of population members (i.e., the ‘k’ coefficient is best described as the average rate at which an
organism in the population achieves its maximum length [or size] from its length at birth). The exponent to is an extrapolation from available data and is not biologically interpretable.
120
Small sample size, particularly of small and/or large individuals can cause poor parameter
estimates using the von Bertalanffy model (Cailliet and Tanaka, 1990; Francis and Francis, 1992). In
lieu of using to (due to its lack of biological meaning) some researchers suggest using an estimate of
length at birth (Lo) rather than to as a more robust method (Goosen and Smale, 1997; Carlson et al.,
2003; H. Mollet, pers. comm.). This method was first introduced by Fabens (1965) as an alternate
equation to the von Bertalanffy growth model. While only a few studies have used Faben’s (1965)
equation to estimate growth parameters in elasmobranchs, it has provided more realistic parameter
estimates for some species when the sample size was small (Goosen and Smale, 1997), and extremely
similar results to the von Bertalanffy model when sample size was adequate (Carlson et al., 2003).
This appears to be an excellent alternative to the von Bertalanffy model and should be applied where
appropriate for comparison to other models.
The Fabens (1965) equation is:
Lt = L (1-be-kt) = L - (L - Lo)e-kt,
4
4
4
(6.6)
b = (L - Lo)/L = ekto,
4
4
where Lo is the length at birth.
6.5.2
Gompertz growth function
The Gompertz (1825) growth function is an S-shaped model function (similar to the logistic
function—for use of the logistic function and several alternatives to the Gompertz function, see Ricker
1975 and Ricker 1979). The estimated instantaneous growth rate in the Gompertz function is proportional to the difference between the logarithms of the asymptotic disc width or length and the actual
disc width or length (Ricker, 1975; 1979). This growth function has been used most often for skates
and stingrays (Mollet et al., 2002) and may be better suited to elasmobranchs that hatch from eggs, but
it may also be the most appropriate model for some shark species (Wintner et al., 2002). This model
may offer a better option when the volume of an organism greatly expands with age, such as
myliobatiform rays where considerable thickness is added to the animal over time, but not so much
disc width or length (W. Smith, pers. comm.). The body mass may be distributed differently than
would be readily detectable by length measurement and by the von Bertalanffy model. Additionally,
captive growth rates (particularly when starting with young, small animals) may be better estimated by
this function as newly captured specimens may not grow in their typical fashion due to physiological
stress or a reduction in feeding that often accompanies that stress, which may cause growth rates to
slow (Mollet et al., 2002).
Three commonly used integral forms of the Gompertz growth function (Gompertz, 1825 as
presented by Ricker, 1979) are:
1)
wt = W0 exp(k(1-exp(-gt)))
121
2)
wt = W
3)
wt = W
4
4
exp(k(exp(-gt))), and
exp(-exp(-g(t-to)))
where: wt = biomass at any time t, W0 = hypothetical size (weight or length) at t = 0 (not to), W 4=
Maximum estimated weight, k = dimensionless parameter such that “kg” is the size-specific instantaneous rate of growth at t = 0, g = instantaneous growth rate when t = to, where to = the time at which
the absolute growth rate starts to decrease (i.e. the inflection point in the curve), and t = age. Equations 2 and 3 are simply alternate expressions of equation 1, but the same three parameters (w, k, and
g) are solved.
6.6
SAMPLING COVERAGE
The goal of every age and growth study is to accurately and thoroughly describe (through
validation) the age-length relationship of a species. In order to achieve that goal, a solid experimental
design beginning with field sampling and ending with the calculation and comparison of growth rate
estimates is necessary. Thorough sampling coverage is imperative to the successful outcome of ageing
studies. The dramatic effect that sample size can have on growth parameter estimates makes it
imperative that a representative sample of the population be obtained for ageing studies. It is obvious
that a larger number of specimens (i.e., larger sample size) is beneficial to gaining a thorough understanding of the age and growth process of any species. The desired content of this sample is to have
specimens from both sexes for each month of the year for the entire size and geographic range of the
species. This can be a difficult sample base to obtain for many elasmobranchs, but the goal should be
to come as close to it as possible. Back-calculations can help to “fill in” the gaps for a low sample size
of young and middle age classes, but it must be remembered that while there is considerable value in
using back-calculated data, this is a false increase in sample size, as back-calculation data are not
independent values.
A wide range of techniques is available for conducting ageing studies on elasmobranch fishes
(Casselman, 1983; Cailliet et al., 1986; Cailliet, 1990; Cailliet and Goldman, in prep), and studies should
utilize as many of the available techniques as possible. I have tried to encompass the majority of
options available in this paper, but resources cited herein should be consulted for more detailed information on specific topics. Researchers more frequently follow the advice given by Beamish and
McFarlane (1983) (and reiterated in Cailliet et al., 1986 and Cailliet, 1990) regarding the need to
combine techniques, such as the use of OTC marking and tag-recapture data from the field and-or
laboratory to validate relative (timing of increment formation) and absolute age. However, much work
still needs to be done on this group of fishes. These practices along with the development of new
techniques will be needed to further elucidate the nature of growth increment deposition in the vertebral centra, dorsal fin spines, neural arches and caudal thorns and to validate age. Research on physi-
122
ological aspects related to age and growth, such as the function of the endocrine system in calcium
regulation and the deposition of growth increments, should simultaneously be undertaken, as we know
little about these mechanisms as they relate to growth in elasmobranchs (Cailliet, 1990; Gelsleichter
and Manire, 1997).
6.7
WEB-BASED RESOURCES
•
Canadian Shark Research Laboratory, Bedford Institute of Oceanography, Nova Scotia
Canada:
http://www.mar.dfo-mpo.gc.ca/science/shark/english/index.htm
•
NMFS/NOAA/SEFSC Shark Population Assessment Program, Panama City, FL, U.S.A.:
http://www.sefscpanamalab.noaa.gov/shark/shark_final_1.htm
•
NOAA/NMFS/NEFSC Apex Predator Program, Narragansett, RI, U.S.A.:
http://na.nefsc.noaa.gov/sharks/
6.8
ACKNOWLEDGMENTS
It has perhaps been my greatest pleasure, both personally and professionally, to work with
John A. Musick for the past seven years, and I thank him for allowing me to participate in this important and timely publication. Many thanks to Ramón Bonfil and to Melanie Harbin for assistance during
the writing of this chapter. My special thanks to John Carlson, Wade Smith, Greg Cailliet, Gordon
(Sandy) McFarlane, John Stevens, Steve Campana, Henry Mollet, John Hoenig, Christina Conrath,
Jason Romine, Lisa Natanson and Jim Gelsleichter for providing images to choose from and for
numerous discussions on the ageing process in elasmobranchs. Lastly, I thank my lovely wife Consuelo
for her support and encouragement.
6.9
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CHAPTER 7.
REPRODUCTIVE BIOLOGY
Christina L. Conrath, Virginia Institute of Marine Science, College of William and Mary, PO Box
1346, Gloucester Point, VA 23062 USA
7.1
7.2
7.3
7.4
7.5
7.6
7.7
INTRODUCTION: MODES OF REPRODUCTION
7.1.1 Oviparity
7.1.2 Aplacental viviparity
7.1.2.1 Aplacental yolk sac
7.1.2.2 Oophagy and adelphophagy
7.1.2.3 Placental analogues: histotrophe and trophonemata
7.1.3 Placental viviparity
BASIC ANATOMY
7.2.1 Male
7.2.2 Female
MATURITY
7.3.1 Assessing maturity
7.3.1.1 Male
7.3.1.2 Female
7.3.2 Determining the size or age at maturity
7.3.2.1 Male
7.3.2.2 Female
7.3.3 Age at first maturity vs. age at first reproduction
TIMING OF THE REPRODUCTIVE CYCLE
7.4.1 Mating
7.4.2 Male reproductive cycle
7.4.2.1 Gonad size indices
7.4.2.2 Histological examination of the reproductive tract
7.4.3 Female reproductive cycle
7.4.3.1 Ovulation cycle
7.4.3.2 Gestation cycle and time of birth
7.4.3.3 Reproductive interval
7.4.3.4 Reproductive cycle examples and embryonic diapause
FECUNDITY
SPERM STORAGE IN FEMALE ELASMOBRANCHS AND OVIDUCAL
GLAND STRUCTURE
ADDITIONAL RESOURCES
7.7.1 Literature
7.7.2 Web-based resources
7.7.3
7.8
Field data collection
REFERENCES
133
134
7.1
INTRODUCTION: MODES OF REPRODUCTION
Several reproductive specializations are found within the elasmobranchs. All elasmobranchs
utilize internal fertilization and produce a relatively small number of large eggs. Elasmobranch fecundity generally ranges from one to two offspring produced per year up to a possible maximum of 300 in
the whale shark (Compagno, 1990; Joung et al., 1996). Elasmobranch reproductive strategies include
oviparity, aplacental viviparity, and placental viviparity (Wourms, 1977). Oviparous species enclose
eggs in an egg case and deposit them into the environment, where embryos will develop external to the
body of the mother. Embryos remain in the egg case to develop for a period of time ranging from less
than two months to over one year (Compagno, 1990). Viviparous species retain eggs within the uteri
where the embryos will develop. The yolk sac of placental viviparous species interdigitates with the
uterine wall to form a placenta in which nutrients from the mother are transferred to the embryo
directly. In most species the egg envelope is retained and incorporated into the uteroplacental complex
(Hamlett et al., 1985). Gestation for viviparous species ranges from less than six months to greater
than two years (Compagno, 1990). Viviparous species may have either lecithotrophic or matrotrophic
development. Lecithotrophic development occurs when embryos derive their nutrition solely from yolk
reserves and occurs in many aplacental viviparous species. Matrotrophic development occurs when
embryos supplement the yolk reserves by obtaining maternally derived nutrients during gestation and
also occurs in many aplacental species and all placental viviparous species (Wourms and Lombardi,
1992). The advantage of matrotrophy may be the increase in juvenile size at birth and therefore
increased survivorship of young. Another important consideration in the evolution of elasmobranch
reproductive strategies is the presence or absence of uterine compartments. Uterine compartments
are formed in all species with placental development and some species with aplacental development
and are proposed to be an important step in the evolution of placental viviparity (Otake, 1990).
7.1.1
Oviparity
Oviparity occurs in all batoids of the family Rajidae and six families and over 100 species of
sharks in the orders Heterodontiformes, Orectolobiformes and Carcharhiniformes (Compagno, 1990;
Compagno, 2001). In oviparous species, eggs are enclosed within an egg case and deposited in the
environment. Two types of oviparity occur, extended oviparity and retained oviparity. Almost all
oviparous species have extended oviparity in which large egg cases are fertilized, enclosed in an egg
case, deposited and, after a period of up to 15 months, hatch out. In this reproductive mode almost all
of the embryonic development occurs within the egg case outside of the mother’s body. Retained
oviparity occurs much more rarely and refers to species in which cased eggs are retained in the
oviduct and development proceeds for a longer period before the eggs are released into the environment. One form of retained oviparity occurs in some scyliorhinid catsharks when multiple egg cases
are retained within the oviduct before being released (Compagno, 1988; Compagno, 1990).
135
The egg case generally has tendrils and sticky filaments that aide in attaching the egg to some
sort of structure or substrate where the eggs incubate. The egg case also hardens after being deposited to protect the embryos from predation. All oviparous chondrichthyan eggs are laid in pairs
(Mellinger, 1983). Oviparous embryos tend to be relatively smaller than viviparous embryos as growth
of the embryo is constrained by the amount of yolk initially present in the yolk sac (Hamlett, 1997).
Compagno (1990) suggests egg-laying elasmobranchs may select appropriate substrates for egg
deposition as occurs in the bullhead shark in which the female picks up the egg after it is laid and
wedges it into rocks or marine vegetation. Development time within the egg case is likely dependent
on external temperatures. Differences in the length of the incubation period of eggs laid by female
thornback rays, Raja clavata, held at different temperatures have been noted in at least two aquarium
experiments (Clark, 1922; Ellis and Shackley, 1995).
7.1.2
Aplacental viviparity
Embryos from species with aplacental viviparous development are retained within the mother
for the duration of development, but no placental connection is formed between the mother and the
embryo. A wide range of developmental forms occur within this reproductive mode and Wourms
(1977) separated them into three groups: those dependent entirely on yolk reserves, those which feed
on other eggs or embryos, and those which possess placental analogues. Animals within the first group
are considered lecithotrophic as the embryo receives no extra nutrition from the mother, and animals in
the last two groups are considered matrotrophic as the embryo’s nutrition is supplemented with either
ovulated eggs or uterine milk (histotrophe).
7.1.2.1 Aplacental yolk sac
Embryos from species within this group are entirely dependent on yolk reserves to complete
development. This type of development is the most common reproductive strategy employed by
sharks. It occurs in the orders Hexanchiformes, Squaliformes, Pristiophoriformes, Squatiniformes,
Rhinobatiformes, Pristiformes, Torpediniformes and some species in the orders Orectolobiformes and
Carcharhiniformes (Compagno, 1990). This form of development offers protection from predators for
a longer period of time than oviparous development (Hamlett, 1997).
7.1.2.2 Oophagy and adelphophagy
Oophagy occurs when embryos within the uterus hatch out of the egg capsule after a few
months and then consume additional eggs that continue to be ovulated while the embryo develops.
Oophagy is thought to occur in all sharks in the order Lamniformes (Compagno, 1990; Gilmore, 1993),
specific examples described within the literature include the bigeye thresher shark, Alopias
superciliosus, the pelagic thresher shark, A. pelagicus, the shortfin mako shark, Isurus oxyrinchus
and the porbeagle shark, Lamna nasus (Moreno and Moron, 1992; Francis and Stevens, 1999; Liu et
136
al., 1999; Mollet et al., 2000). In the sand tiger shark, Carcharias taurus, the first embryo to develop
in each uterus consumes all the other embryos within that uterus (adelphophagy or intrauterine
cannabilism), as well as additional ovulated eggs (Gilmore et al., 1983). This type of development may
facilitate the development of very large embryos and may prepare the embryo for a predatory life style
(Wourms, 1977). Yano (1992) found that embryos of the false catshark, Pseudotriakis microdon, also
ingest yolk material from other ova but that they transfer ingested yolk to an external yolk sac rather
than forming the extended stomach of lamniform oophagous embryos. Female slender smooth-hounds,
Gollum attenuatus, form egg capsules which contain 30-80 ova, and only one ovum within each egg
capsule develops with all other ova ingested and packed to an external yolk sac (Yano, 1993). While
ova are ingested by the slender smooth-hound embryo during development, this form of reproduction
may or may not be considered oophagy as after the initial consumption of ova within the egg sac, the
embryo then develops without any additional ova or maternal investment.
7.1.2.3 Placental analogues: histotrophe and trophonemata
This type of development occurs in all rays of the order Myliobatiformes. Trophonemata are
long villous extensions of the uterine epithelium that secrete histotrophe or “uterine milk” which can be
ingested or absorbed by the embryo. The quantity and composition of the histotrophe varies widely
between species. Trophonemata envelope the embryo and may occasionally enter the embryo through
the spiracles. As yolk reserves are depleted, trophonemata increase in size and release uterine secretions rich in proteins and lipids (histotrophe) (Wourms, 1981). White et al. (2001) found that trophonemata of the stingaree, Urolophus lobatus, increase in length and enter the gill, spiracles and mouths
of developing embryos in the uterus about six months after ovulation when yolk reserves from the
external yolk sac have been utilized. Trophonemata are also formed in the Atlantic stingray, Dasyatis
sabina, and increase in length in the late stages of gestation while the developing young are bathed in
histotrophe (Snelson et al., 1988). The transfer of nutrients has been found to be much more efficient
in species with trophonemata than in species with a yolk sac placenta (Wourms, 1981).
7.1.3
Placental viviparity
Placental viviparity occurs when during the course of embryonic development after an initial
period of reliance on yolk from a yolk sac, the yolk sac attaches to the uterine wall and forms a yolk
sac placenta and the associated yolk stalk forms the umbilical cord. In most species the egg envelope
is retained and incorporated into the uteroplacental complex (Hamlett et al., 1985). Thirty percent of
viviparous sharks form a yolk sac placenta (Hamlett, 1997). This type of reproductive development
only occurs in sharks of the order Carcharhinformes (Compagno, 1990), and can occur within the
same family or genus as aplacental viviparous species. The genus Mustelus includes several aplacental viviparous species such as the spotted estuary smooth-hound, M. lenticulatus, the gummy shark,
M. antarcticus, and the starspotted smooth-hound, M. manazo, and several placental viviparous
137
species such as the dusky smooth-hound, M. canis, and the spotless smooth-hound, M. griseus
(Francis and Mace, 1980; Teshima, 1981; Lenanton et al., 1990; Conrath and Musick, 2002). Wourms
and Lombardi (1992) estimate the yolk sac placenta has evolved independently 11-20 times within the
elasmobranchs. This has led to a large diversity in placental structure. After ovulation placental species
undergo a period of dependency on yolk reserves that may last for several weeks to months before the
placenta is formed. Teshima (1981) divides the placental species into two groups, those in which it
forms in mid-gestation and those in which it forms soon after ovulation.
7.2
BASIC ANATOMY
7.2.1
Male
The male reproductive system is composed of the testes, genital ducts (ductus efferens,
epididymis, ductus deferens and seminal vesicle), accessory glands and secondary sex organs (Figure
7.01). Male reproductive organs and tissues have been described and defined using various terminologies, and this account will follow the terminology of Hamlett (1999). The testes are paired structures
supported by a mesorchium and in some species enveloped by the epigonal organ. A pre-germinal fold
runs the length of the testis and is the origin of the spermatogenesis process. The testes are the
location of spermatogenesis and also play a role in creating and secreting steroid hormones. Pratt
(1988) identified three types of testes in elasmobranchs: radial, diametric, and compound, defined by
their pattern of seminiferous follicle origin and propagation. The epididymis is connected to the testis
via the ductus efferens, which are fine tubules which cross the mesorchium at the anterior edge of the
testis. Mature sperm are discharged from the testis through the ductus efferens (Wourms, 1977). The
efferent ducts join the epididymis, which expands to form a long tube with complex convolutions. The
epididymis is continuous with the next section of the genital duct, the ductus deferens also known as
the vas deferens or Wolffian duct. The ductus deferens is continuous with the seminal vesicle or
ampulla ductus deferens. The ductus deferens and seminal vesicle function as storage areas for
seminal products, and in some species sperm is packaged into either spermatozeugmata or spermatophores here (Wourms, 1977). The ureter becomes entwined with the terminal portion of the seminal
vesicle, and both end in the anterior wall of the urogenital sinus. The urogenital sinus vents into a
common cloaca by means of a single large papilla. Two accessory glands are present, Leydig glands
and the alkaline gland. Leydig glands are a series of branched tubular glands that secrete seminal
fluids into the epididymis and ductus deferens. The alkaline gland of batoids may be involved in sperm
protection (Hamlett, 1999).
The secondary sex organs include the claspers and the associated siphon sacs. Claspers are
modified regions of the pelvic fin that act as copulatory organs to transfer sperm and seminal matrix
from the male to the female (Figure 7.02). All elasmobranchs have internal fertilization and possess
138
claspers, but clasper structure varies widely. All claspers have a dorsal longitudinal groove through
which semen passes to the female during mating. The clasper consists of two intermediate elements—
known as the joint and beta cartilages that extend down from the metapterygium of the pelvic fin, the
main stem cartilage to which two marginal cartilages are fused, and four terminal cartilages, the claw,
rhipidion, the distal basal and the spur. The two marginal cartilages help to form the clasper groove
with a terminal end opening, the hypopyle, and an anterodorsal opening, the apopyle (Compagno,
1988). A good diagram of clasper skeletal structure can be found in Compagno (2001). Most male
elasmobranchs possess siphon sacs which are subcutaneous muscular, epithelium-lined bladders
situated on each side of the midline between the skin and belly
musculature. Each sac ends blindly anteriorly and opens into the
clasper groove posteriorly through the apopyle (Gilbert and Heath,
1972). Gilbert and Heath (1972) examined the structure and function
testis
of the siphon sacs in piked dogfish, Squalus acanthias, and dusky
smooth-hounds, Mustelus canis, and determined that the siphon
sacs’ function is to hold seawater, which is used to wash sperm from
the clasper groove into the oviduct of the female.
ep
dd
Figure 7.02 Male and female little skates, Leucoraja erinacea,
female on the left, male on the right.
sv
7.2.2 Female
The female reproductive system is composed of either a
Figure 7.01 The male
reproductive tract of a
spiny dogfish, Squalus
acanthias, ep = epididymis,
dd = ductus deferens, and
sv = seminal vesicle.
paired or single ovary and oviducts, which are differentiated into an
ostium, the anterior oviduct, the oviducal gland, the isthmus, a dilated
terminal region/uterus, a cervix and the urogenital sinus (Hamlett and
Koob, 1999) (Figure 7.03). The ovary and the oviducts are in close
139
association but are not continuous. The female reproductive tract begins as paired ovaries and oviducts, but in many adults the reproductive tract becomes asymmetrical as the animal develops. In
many viviparous sharks species only the right ovary develops fully, and in many ray species the right
ovary and oviduct are reduced to varying degrees. The ovaries are attached to the body wall by a
mesovarium (Wourms, 1977). Pratt (1988) described two types of ovaries: one found in lamniforms in
which the ovary was hollow and contained within the epigonal organ, the other found in other elasmobranch species in which the ovary was external and borne on the flat surface of the epigonal organ or
suspended directly from the mesovarium. The ovary functions in the generation of germ cells, the
acquisition and accumulation of yolk and the biosynthesis and secretion of hormones. The ovary
consists of oocytes, developing follicles and embedded loose connective tissue stroma. The epigonal
organ is present in most species and supports the ovary or ovaries. The ostium is the anterior funnelshaped opening of the oviduct which functions to collect the ovulated eggs. The oviducal gland is a
specialized portion of the oviduct where egg capsule and egg jelly formation occur and where fertilization may take place although it may occur in the upper oviduct (Hamlett
et al., 1998). (The oviducal gland is described more completely in
section 7.6.) An isthmus may occur before the widening of the oviduct
into a posterior oviducal section or a uterus and may function to isolate
the contents of the uterus. The uterus in oviparous species is specialized for egg capsule formation and provides structural modifications for
movement of the capsule through the uterine lumen (Koob and
Hamlett, 1998). In viviparous species the uterus is highly developed and
modified for retention of eggs and the developing embryos. The cervix
occurs at the junction of the uterus and urogenital sinus and is a constriction in this area. The uteri independently join the urogenital sinus
(Hamlett and Koob, 1999).
7.3
MATURITY
7.3.1
Assessing maturity
The meaning of the term maturity in recent elasmobranch
Figure 7.03 The female
reproductive tract dissected out of a spiny
dogfish, Squalus
acanthias, og = oviducal
gland, ova = ovary,
i = isthmus, e = uterine
embryo, ys = embryonic
yolk sac, ut = uterus, and
c = cervix.
literature ranges from defining the onset of maturation to the period of
time when a female elasmobranch undergoes parturition and produces
a litter of pups. Since in many elasmobranch species the period between the beginning of the maturation process until pupping can last a
period of years, it is important to specifically define the term maturity in
an elasmobranch reproductive study. For the purposes of this manual a
140
mature animal is defined as one that is immediately capable of mating and producing viable offspring
or one that has already done so. Therefore, in order to be considered mature, an animal must have
previously mated or possess fully developed gametes and all of the secondary structures necessary for
successful mating and fertilization. For female elasmobranchs that have not previously mated this
requires the presence of fully developed ova in the ovaries that are ready to be ovulated. For male
elasmobranchs that have not previously mated this requires not only the presence of mature sperm
within the reproductive tract but also the presence of fully developed claspers and siphon sacs. Maturity in sharks is determined by either observation of the reproductive tract organs or secondary sex
structures or by noting the presence or absence of reproductive products within the reproductive tract.
7.3.1.1 Male
In order for male elasmobranchs to successfully mate they must have fully developed and
functional claspers, and they must have mature sperm ready to be transported by the claspers into the
female. Therefore male maturity can be assessed by determining if the claspers are calcified and if
sperm products are found within the seminal vesicles of the reproductive tract. Clark and von Schmidt
(1965) considered males mature when the clasper head (rhipidion) could be spread open, the clasper
proximal to the head was rigid due to calcification of the supporting cartilage, the base of the clasper
rotated easily, and when the siphon sacs were fully elongated. Clasper calcification can be a simple
and quick way to determine if male elasmobranchs are mature; however, maturity assessments based
on calcification alone may be inaccurate as claspers may have developed before spermatogenesis is
complete. Pratt (1979), in a reproductive study on blue sharks, Prionace glauca, stated that many
sharks with claspers that appeared mature lacked sperm aggregations and had small ductus deferentia
and were therefore still immature.
Histological evidence or direct observation will confirm the presence of sperm within the
reproductive tract. Sperm products can be located and viewed by cutting a cross section of the
reproductive tract, or smears
of the reproductive tract can be taken, stained, and viewed under a
ut
microscope to determine if viable sperm or sperm products are present. Pratt (1979) found the most
accurate way to determine maturity in male blue sharks was to note the presence or absence of sperm
in the ampulla ductus deferens (seminal vesicle). He found a field test to look for the presence of
mature sperm aggregates could be done by cross-sectioning the thickest part of the kidney of the male
blue shark. When the cross-section was made four ducts were visible; the largest two were the
seminal vesicles, and the presence or absence of spermatophores in a white supportive tissue could be
observed with the aid of a magnifying glass. This technique was used to assess the male maturity of
smalleye hammerheads, Sphyrna tudes, and Pacific angelsharks, Squatina californica (Natanson
and Cailliet, 1986; Castro, 1989). Pratt and Tanaka (1994) stated that mature male elasmobranchs in a
141
resting stage may not possess sperm within the ampullae of the reproductive tract but that the size and
the shape of the ampullae should be a good indicator of maturity as mature males will have large
ampullae. Assessments based on the presence of sperm in the reproductive tract alone may also be
somewhat inaccurate as sperm may be present within the reproductive tract before the claspers are
fully functional. Clark and von Schmidt (1965) found that individuals of at least two species (the
blacktip shark, Carcharhinus limbatus, and the tiger shark, Galeocerdo cuvier) possessed mature
sperm that were produced and present in the seminal vesicles before the claspers and siphon sacs
were fully developed. The best approach to determining maturity in male elasmobranchs should
therefore combine an examination of clasper calcification and development with a simple field or
laboratory test to determine if sperm are present within the seminal vesicles of the reproductive tract
or to determine if the seminal vesicles are enlarged indicating a previous mating event.
7.3.1.2 Female
Female elasmobranchs are considered mature if there is evidence of a current or previous
pregnancy or evidence that they will be ready to reproduce within a short period of time. For females
that are not or have not been pregnant previously, maturity can be determined by assessing the condition of the ova in the ovary and the size of the oviduct. Mature females will have well developed yolky
eggs in the ovary, and the oviduct may start to expand and detach from the body wall. Females that
have previously been pregnant will have an expanded oviduct containing expanded oviducal glands and
well developed uteri. Female maturity can therefore be determined by assessing the condition of the
reproductive tract and noting the presence or absence of well developed ova in the ovary, eggs or
embryos within the reproductive tract, or expanded oviducts. Bass et al. (1973) defined female sharks
with distinct ova in the ovary and an expanded uteri to be mature. In doubtful cases the presence or
absence of an intact hymen was used to show if the female was still an adolescent or was in between
pregnancies. The hymen is a circular transverse fold that separates the vagina from the cloaca; in
virgin elasmobranchs the vagina is sealed by a membrane which is an extension of the hymen (Pratt,
1979). The condition of the reproductive tract has been used to determine maturity in female sandbar
sharks, Carcharhinus plumbeus, and piked dogfish, Squalus acanthias (Springer, 1960; Jones and
Geen, 1977).
In many studies an intermediate maturing stage is identified. During this stage the oviduct
begins to expand or the ova within the ovary begin to undergo vitellogenesis. Jones and Geen (1977) in
their reproductive study of piked dogfish, Squalus acanthias, defined a maturing phase and plotted the
proportion of animals in this stage versus length to determine the size at the onset of maturity.
Natanson and Cailliet (1986) also define three stages of maturity for the Pacific angelshark, Squatina
californica—immature, maturing and mature—based on the condition of ova in the ovary and the
142
condition of the oviduct. Also, in many studies mature females are classified according to what stage
of the reproductive cycle they are currently undergoing. Jones and Geen (1977) defined three stages
of mature females: those between pregnancies, those with candles within the uteri, and those with free
embryos within the uteri. Determining maturity in female elasmobranchs is largely dependent on
observation, and, therefore, assessing maturity will be most accurate when a large enough number of
immature, maturing and mature animals can be observed.
7.3.2
Determining the size or age at maturity
Size or age at maturity is usually determined by either analyzing the growth of reproductive
organs relative to size or age or by quantifying the proportion of mature animals at each length or age
group and determining the length at which 50% of a class is mature.
7.3.2.1 Male
Clasper length measurements have been used in many studies to estimate the size at maturity
because there is a known correlation between the development of secondary sex characters and the
reproductive organs, and maturity. Clasper length is most commonly measured from the posterior
margin of the anus to the tip of the clasper (clasper inner length) or from the base of the pelvic fin to
the tip of the clasper (clasper outer length) (Compagno, 1984). The length of the clasper as a proportion of the precaudal, fork or total length is plotted against the corresponding length. This usually
results in a plot that shows a sharp increase in the slope for a range of lengths before leveling off. This
portion of the plot with the steeper slope corresponds to the range of lengths at which the shark is
becoming mature (Figure 7.04). While the most common reproductive measurement to plot against
length is the clasper length in male elasmobranchs, the size or weight of other reproductive structures
like the testis and siphon sac are often used and plotted in the same manner (Parsons, 1981; Teshima,
1981; Yano, 1993).
The other method commonly used to determine the size at maturity for male elasmobranchs is
to use a maturity ogive. Using this method, the reproductive condition of a large enough sample size of
males of different sizes is first determined as in section 7.3.1.1. Then the proportion of mature animals
found in each length group is determined. These data can then be fitted with a logistic regression and
the length at the point of the curve corresponding to 50% is often used as an indicator of the size at
which these animals mature. The logistic equation can take the following form: proportion mature at a
specific length or age = 1/(1+ea+(b*length or age)), where a and b are coefficients estimated by fitting the
data to the logistic curve. This equation can then be solved to determine at what length or age 50% of
the population is mature (Conrath and Musick, 2002) (Figure 7.05).
143
Proportion Mature
Figure 7.04 The relationship
between clasper length (as % total
length) and total length of M.
canis.
Total Length (cm)
Proportion Mature
a
b
Figure 7.05 a - Maturity
ogives for total length (TL)
of male and female M.
canis, b - maturity ogives for
age of male and female
M. canis.
Age (years)
144
7.3.2.2 Female
The size of oviducal gland or other structures of the female reproductive tract is often used to
assess the size at which animals become mature. The size or weight of the ovary, or the size of the
oviducal gland, uterus, or other reproductive structure is often plotted against the length of the animal
to determine if there is a size range at which the structure in question begins to develop very quickly
before growth tapers off again. Similar to the clasper length versus total length plot discussed above
the length range at which an elasmobranch population matures is determined by a change in the slope
of the plot (Wass, 1973; Parsons, 1981; Castro et al., 1988; Yano, 1993).
As with male elasmobranchs, the other method used to determine length at maturity in female
elasmobranchs is to create a maturity ogive. The same procedures and equations as explained above
in section 7.3.1.2. are used for females (Figure 7.05) (Conrath and Musick, 2002).
7.3.3
Age at maturity vs. age at first reproduction
It is important to distinguish the difference between the age or length at first maturity with the
age or length of first reproduction. This distinction becomes very important in demographic models.
The time a fish matures is generally understood as the size or age of first mating, and this needs to be
distinguished from the size or age at which the animal actually produces pups. If the species being
considered is oviparous or has a very short gestation time there may be no discrepancy or only a slight
one. However, if the animal has a gestation time of several months or longer, the delay must be
accounted for in the model, and the fecundity term should not be included at the size or age when the
animal is first mature but at the age or size when the animal actually undergoes parturition. Dusky
smooth-hound, Mustelus canis, females mature at four to five years of age, and therefore in a agebased model a fecundity term would not be added until age five to six as gestation lasts nearly one
year.
7.4
TIMING OF THE REPRODUCTIVE CYCLE
Wourms (1977) defined three types of reproductive cycles exhibited by elasmobranchs:
reproduction continuously throughout the year, a prolonged annual cycle that is not well defined with
one or two peaks in activity, or a well defined annual or biennial cycle. Chen et al. (1996) found
encapsulated fertilized eggs in the uteri of female blacktip sawtail catsharks, Galeus sauteri, all year
round indicating this species reproduces throughout the year without a well-defined breeding season.
This may be characteristic of some deep sea elasmobranch species as this also occurs in the deep sea
black dogfish, Centroscyllium fabricii (Yano, 1995). The small-spotted catshark, Scyliorhinus
canicula, is proposed to have a very extended breeding season, but peak reproductive activity occurs
during the winter and spring months (Sumpter and Dodd, 1979). Dusky smooth-hounds, Mustelus
canis, have a very well-defined annual reproductive season with an 11 to 12 month gestation followed
145
by ovulation of the next year class of eggs within a period of days to weeks (Conrath and Musick,
2002).
7.4.1
Mating
Determining what time of year a species mates can be quite difficult and is often inferred by
assuming mating occurs sometime between parturition of one year class of embryos and ovulation of
the next year class of eggs to be fertilized. The timing of mating can also be inferred from viewing
female specimens with mating scars. Pratt (1979) found that the skin of mature female blue sharks
was twice as thick as that of male blue sharks to accommodate the biting that occurs during mating.
Pratt and Carrier (2001) found that biting by males during mating seems universal among elasmobranchs, and therefore during the mating season female elasmobranchs frequently bear mating marks
on their bodies with the most common being tooth cuts and abrasions on the pectoral fins. They further
found that in some elasmobranch species there is a sexual dimorphism of the teeth with males having
teeth designed to make courtship biting more effective. Tricas and LeFeuvre (1985) proposed that
biting in the whitetip reef shark, Triaenodon obesus, functions as a precopulatory releasing mechanism for females and to maintain contact during copulation. The timing of the reproductive cycle
before and after mating is generally considered separately for male and female elasmobranchs.
7.4.2
Male reproductive cycle
The timing of the reproductive cycle of male elasmobranchs is generally determined by using
various gonad size indices, through histological examination of the testes, or by noting the presence and
amount of sperm products in the reproductive tract throughout the year. Since the contribution of the
male to the reproductive effort in elasmobranchs primarily ends with mating and inseminating the
female, the following two sections of the chapter are primarily concerned with the timing of mating.
The next two sections are not included in the previous mating section as most of the techniques listed
track the reproductive condition of the male throughout the year or reproductive cycle.
7.4.2.1 Gonad size indices
A gonadosomatic index (GSI)—or some other relationship between the size of the male
reproductive organs and the total size of the animal—is often used to determine when sperm and
sperm products are being produced. The GSI is the testis weight expressed as a percentage of the
total body weight, GSI = (testis weight/total body weight) * 100. By comparing the GSI from mature
males caught during various times of the year, a mating season can be estimated by assuming mating is
occurring when the GSI reaches its highest value. This will correlate to the time of year when sperm
production has reached its highest level. Peak GSI values may not always coincide exactly with mating
season, as sperm products must move down the reproductive tract before mating can occur and sperm
may be stored in the reproductive tract for a period of time. Simpfendorfer (1992) found that the peak
GSI for male Australian sharpnose sharks, Rhizoprionodon taylori, occurred approximately a month
146
before the mating season. While a GSI can give valuable information about the timing of sperm
production in male elasmobranchs, caution should be used when trying to use this data as an approximation of when mating season begins. GSI data is best used combined with other supporting data, like
examinations of sperm presence and quantity in the lower portions of the reproductive tract or other
evidence of mating activity like the presence of sperm products in the female or the presence of
courtship wounds on the female. Stevens and Wiley (1986) defined the mating season by determining
the monthly GSI of two carcharhinid shark species and also examined the quantity of sperm in the
seminal vesicles, mating scars of females captured during the appropriate time of year and the mean
maximum ova diameter of the females.
7.4.2.2 Histological examination of the reproductive tract
A more detailed way to track the formation of sperm in the testis through time is by making
histological sections of the testis. The functional unit of the testis is defined by Callard (1991) as, “the
germ cell clone plus associated Sertoli cells within a closed spherical unit bounded by a basement
membrane.” Parsons and Grier (1992) name this unit the spermatocyst and define the sequence of
development from the germinal zone to the degenerate zone which includes zones of spermatocysts in
various stages of development. Parsons and Grier (1992) define seven stages of development as do
Maruska et al. (1996) also. While these two papers differ slightly in the definition of stages, both track
the spermatogenesis process from loosely organized germ cell, to spermatogonia, spermocytes,
spermatids, and mature spermatozoa.
In order to use this technique a section is removed from the middle of the testis and preserved
in either 10% formalin or Bouin’s solution. The section is processed using standard histological techniques and stained with hematoxylin and eosin. The section is rinsed in a series of water washes,
placed in a tissue cassette, and the Bouin’s fixed tissues are rinsed with a solution of 50% ethanol
(ETOH) saturated with lithium carbonate to remove soluble picrates, then rinsed in 70% ETOH. The
cassettes are then placed in a tissue processor to dehydrate them and infiltrate them with paraffin. A
rotary microtome is used to cut 5 µm thick sections of the tissue, which are then stained with hematoxylin and eosin and cover-slipped with a synthetic mounting media. The testis section is then viewed
under a compound microscope and the proportion of the testis occupied by each different stage is
measured along a straight-line distance across the cross section of the testis, starting from the germinal
zone. Or the number of spermotocysts occupied by each stage can be counted across the straight-line
distance and compared for various times of the year. The mean proportion of the testis occupied by
each stage or the mean number of spermatocysts in each stage throughout different months of the
year can then be compared to determine if there is a recognizable seasonal pattern in testis development. Parsons and Grier (1992) suggest using caution with this technique as the peak testicular
development may not coincide with the mating season.
147
Mating season has been determined for the male piked dogfish, Squalus acanthias, (Jones
and Geen, 1977) and two smooth-hounds, Mustelus griseus and Mustelus manazo (Teshima, 1981)
by examining what percent of the ampullae contain each defined spermatogenic stage throughout the
year. The timing and duration of spermatogenesis was determined for the Port Jackson shark,
Heterodontus portusjacksoni, by examining the migration of the degenerative zone throughout the
year (Jones and Jones, 1982).
In the dusky smooth-hound, Mustelus canis, the mean proportion of the testis occupied by the
seven stages defined by Maruska et al. (1996) was measured and compared for different months of
the year to determine if there was a recognizable seasonal pattern in testis development. A cross
section of the testis is shown in Figure 7.06, and the stages of the sperm development, modeled after
Maruska et al. (1996), are shown in Figure 7.07. Stage one consists of spermatogonia and loosely
organized germ cells not yet bound by a basement membrane into a spermatocyst. During stage two a
layer of spermatogonia and associated Sertoli cells divide and surround a central lumen and are
bounded by a basement membrane forming the spermatocyst. In stage three the spermatogonia
undergo mitosis to become primary spermatocytes, which will then undergo the first meiotic division to
become secondary spermatocytes. In stage four the secondary spermatocytes have undergone the
second meiotic division to become spermatids. Stage five consists of immature sperm, which are
spermatids that have undergone spermiogenesis and possess a head and tail region, but individual
sperm have not organized into bundles yet. During stage six these spermatozoa organize into tightly
shaped packets arranged spirally along the outside of the spermatocysts. Unlike Maruska et al. (1996),
the seventh “degenerate” stage was classified by Conrath and Musick (2002) as the area of the testis
posterior to stage six, which consists of empty spermatocysts, free spermatagonia and free spermatozoa. During September through October the majority of the testes were primarily occupied by
spermatocysts in the spermatocyte stage (stage 3). During November the majority of the testes were
occupied primarily by spermatocysts in the spermatid stage (stage 4). By March and continuing
through May the majority of the testes
were occupied by spermatocysts in the
mature spermatozoa stage (stage 6). Thus
mating most likely occurs sometime
between the months of May and September for this species (Figure 7.08).
Figure 7.06 Cross section of a M. canis
testis, stained with hematoxylin and eosin.
148
Figure 7.07 Sperm stages of the testis: Stages 1 – 7, SG = spermatogonia, SC = spermatocytes,
ST = spermatids, IS = immature sperm, MS = mature spermatozoa, ES = empty spermatocyst,
SG = spermatogonia.
149
7.4.3 Female
1 1
45
30
15
0
reproductive cycle
M a
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A
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b
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a
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a
rc
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In female
ril
2
45
30
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elasmobranchs the
timing of reproductive
M a
y
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44
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observation of the
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the year (ovulation
cycle), and by tracking
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the size of pups within
6
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30
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the uterus throughout the
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7 7
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Ap
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br
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Ju
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M
ay
of the ovulation and
Figure 7.08 The mean proportion of the testis occupied by each
stage for May through April (N=62, error bars are standard error).
gestation cycles can help
determine the reproductive resting interval.
7.4.3.1 Ovulation cycle
The ovulation cycle is determined by measuring the largest developing ova in the ovary and
comparing their size throughout the year. Usually anywhere from two to five of the largest ova in the
ovary are isolated and their diameter is measured using calipers. Then a mean maximum ova diameter
(MOD) is calculated and compared for various animals captured throughout the year. Capape et al.
(1990) studied two angel shark species and plotted the diameter of oocytes and uterine ova against
time to determine the timing of reproductive events. For dusky smooth-hounds the maximum ova
diameter was measured and the mean MOD was calculated for each month of sampling. Ova sizes
increased until May and then became much smaller by July, indicating ovulation occurs between May
and July (Conrath and Musick, 2002) (Figure 7.09a).
7.4.3.2 Gestation cycle and time of birth
For viviparous species the timing and length of gestation is usually determined by following
through time the size of eggs and embryos found within the uterus. The length and timing of gestation
150
has been determined by compar18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
N = 141
throughout the year for Atlantic
sharpnose sharks,
Rhizoprionodon terraenovae
and dusky smooth-hounds,
Mustelus canis (Parsons, 1981;
40
ay
(Figure 7.09b). This can also be
M
Ap
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De
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ce
m
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Conrath and Musick, 2002)
Ju
n
b.
ing the length and weight of
uterine eggs and embryos
Ju
l
Maximum ova diameter (mm)
a.
used to determine the size of
N = 1598
embryos at birth and the timing of
35
birth. If each period of the year is
Total length (cm)
30
adequately sampled, the largest
25
20
size embryos will give a minimum
15
size estimate for size at birth, and
10
the time between the capture of
5
females with the largest uterine
0
n
Ju
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Fe
De
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ar
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Ap
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ay
Figure 7.09 a - Mean maximum ova diameter (MOD), June
through May, b - mean M. canis pup length for May through
April (error bars are standard deviation).
embryos and of females with the
smallest uterine eggs can give
some indication of when parturition is occurring. This approach
will give a minimum estimate of
size at birth and may underestimate the size at birth and not accurately reflect the timing of mating if
all time periods are not sampled adequately. A more accurate way to determine the size at birth and
timing of birth is to compare the size of the largest embryos found within the uterus with the size of the
smallest free living animals captured throughout the year. This approach has been taken for determining the size at birth of blue sharks, Prionace glauca, and of spotted estuary smooth-hounds, Mustelus
lenticulatus (Pratt, 1979; Francis and Mace, 1980). Females containing the largest embryos should be
captured just prior to and possibly during the period of time when the smallest free living animals are
captured depending on the length of the parturition period. In studies of the common guitarfish,
Rhinobatos rhinobatos, and the finetooth shark, Carcharhinus isodon, the timing of parturition has
been estimated or verified by comparing the time between the capture of females with the largest
embryos and the capture of the smallest free living specimens (Abdel-Aziz et al., 1993; Castro, 1993).
151
7.4.3.3 Reproductive interval
Another factor of interest in studying elasmobranch ecology is determining the length of the
reproductive cycle. In addition to having varying lengths of gestation, many species of elasmobranchs
have a resting period between pregnancies, which may last up to two years. For species with a welldefined reproductive cycle, the proportion of pregnant females at any given time can be used for a
preliminary indication of whether or not the female undergoes a resting phase between pregnancies. If
all or nearly all the mature females are pregnant, this may indicate that there is no resting phase.
Jensen et al. (2002) proposed that the porbeagle, Lamna nasus, has a yearly reproductive cycle based
on the fact that all females that were sampled in the month of December were gravid. However, this
approach must be used with caution as pregnant females may have a different migratory pattern than
nonpregnant females. This appears to be the case with sand tiger sharks, Carcharias taurus, sampled
by Lucifora et al. (2002), who found that pregnant females occur in Brazilian waters and nonpregnant
females occur in Argentinean waters. They propose a one-year resting phase for this population with
pregnant and nonpregnant mature females having different migrations.
A more accurate assessment for viviparous species, excluding perhaps oophagous species,
can be made by comparing the development of ova and of embryos in the uterus in each individual. A
resting cycle is usually apparent by determining if the ovulation and gestation cycles are concurrent or
if they are sequential. In the dusky smooth-hound the ovulation and gestation cycles are concurrent;
ova are developing at the same time that pups within the uterus are developing, with both maximum
ova diameter and pup length reaching a maximum value during the month of May (Conrath and
Musick, 2002) (Figure 7.09a and 7.09b). Dusky smooth-hounds likely have a very short “resting
period” of days to possibly as much as one month. Simpfendorfer and Unsworth (1998) found that the
whiskery shark, Furgaleus macki, has a biennial cycle with a one year resting phase by noting the
presence of two groups of mature females, one pregnant with undeveloped ova in the ovary and one
not pregnant with developed ova in the ovary. They did propose the possibility that larger female
whiskery sharks are able to produce a litter of pups yearly with no resting phase.
7.4.3.4 Reproductive cycle examples and embryonic diapause
A wide variety of reproductive strategies are employed by elasmobranch species in regards to
the timing of the reproductive cycle; several examples are given here to emphasize the diversity of
these strategies. In some populations of elasmobranchs more than one reproductive cycle per year is
postulated. Two of the Dasyatis rays, D. centroura and D. marmorata, have very short gestation
times of only three to four months and are therefore thought to possibly have two or three gestation
cycles per year (Capape, 1993; Capape and Zaouali, 1995). The Eastern Australian shovelnose ray,
Aptychotrema rostrata, also has a short gestation of three to five months but gestation and vitellogenesis do not proceed at the same time and the gestation period is followed by a resting phase. This
152
species only produces one litter of pups per year (Kyne and Bennett, 2002). The smalleye hammerhead, Sphyrna tudes, has a gestation of approximately 10 months, and as the ovarian and gestation
cycles run concurrently is postulated to have a yearly reproductive cycle (Castro, 1989). The piked
dogfish, Squalus acanthias, has the longest gestation period known in any fish (23 months) but
ovulation and gestation cycles run concurrently so female piked dogfish reproduce every two years
(Jones and Geen, 1977). The blacktip shark, Carcharhinus limbatus, also has a two-year reproductive cycle, but gestation only lasts 12 months, after which these sharks undergo a resting period before
vitellogenesis and oogenesis are resumed (Castro, 1996). The tope shark, Galeorhinus galeus, has a
three-year reproductive cycle with females found in one of three reproductive conditions: either a)
gravid, b) first year nongravid with small ovarian follicles, appearing to be in a resting stage with slow
vitellogensis, or c) second year nongravid with larger ovarian follicles. Gestation for this species lasts
12 months, with a one-year resting phase, followed by a year of vitellogenesis constituting a threeyear reproductive cycle (Peres and Vooren, 1991).
Diapause for the purposes of this manual will be defined as a pause in the development of
fertilized eggs or young embryos within the uterus during development. This phenomenon has been
documented in at least three species of elasmobranchs. Simpfendorfer (1992) found that the Australian
sharpnose shark, Rhizoprionodon taylori, has embryos that undergo an approximately seven month
diapause. He proposes that this diapause may have been part of the evolution of smaller embryo size
and larger litter size within this population. He also proposes the reason for this diapause may be so
that embryos are born when water temperatures are maximized and conditions for juvenile growth are
optimal. Fertilized uterine eggs of the masked stingaree, Trygonoptera personata, undergo a fivemonth period of embryonic diapause (White et al., 2002). The authors suggest embryonic growth for
this species is delayed until water temperatures are at their highest. The bluntnose stingray, Dasyatis
say, ovulates in May or June but uterine embryos do not begin to develop until the following April
(Snelson et al., 1989).
7.5
FECUNDITY
The fecundity of elasmobranch species is often determined by simply counting the number of
eggs and embryos within the uterus of viviparous species. Two potential difficulties arise using this
method of determining fecundity. First, in some species reproductive failure occurs during gestation,
and the number of pups actually surviving to gestation may be considerably smaller than the initial
number of ovulated eggs present in the uterus. This occurs in the stingaree species, Urolophus
lobatus. White et al. (2001) determined the mean number of embryos decreased to less than half of
original values throughout the yearly reproductive cycle and attributed this change to embryos being
aborted during pregnancy. Therefore it may be preferable to count the number of later term embryos
as this may more accurately reflect the number of pups that will survive to gestation. A second poten153
tial difficulty with simply counting uterine eggs or embryos is that many elasmobranchs will abort some
of these during the stress of capture, especially if embryos are close to parturition size. For viviparous
species placental scars can be counted in the uterus to determine if embryos were aborted or to
determine fecundity of animals that are recently postpartum. Therefore counts of embryos in the
uterus may be negatively biased and this should be taken into consideration. In situations where the
probability of embryos being aborted is unknown and cannot be corrected for by counting placental
scars, estimates of fecundity using this method should be considered the lower limit of fecundity.
Fecundity can also be estimated by counting the number of developing ova in the ovary when
uterine counts of eggs and embryos are not possible, but this is a less reliable method. Wetherbee
(1996) estimated the fecundity of the southern lanternshark, Etmopterus granulosus, by counting the
number of large ova in mature females. In some species uterine and ovarian fecundity have been
found to be very similar. Little differentiation between ovarian and uterine fecundity has been shown
for populations of the shortspine spurdog, Squalus mitsukurii, two angel shark species, Squatina
squatina and S. oculata, and the tope shark, Galeorhinus galeus (Capape et al., 1990; Peres and
Vooren, 1991; Wilson and Seki, 1994). However, in other species ovarian fecundity is notably higher
than uterine fecundity indicating that some of the developing ovarian eggs will be reabsorbed. A
significant difference in ovarian and uterine fecundity has been noted for populations of the finetooth
shark, Carcharinus isodon, and the common guitarfish, Rhinobatos rhinobatos (Abdel-Aziz, 1993;
Castro, 1993). While counting developing ovarian eggs will likely give a good estimate of fecundity, in
many species not all ovarian eggs are ovulated, and (if possible) using uterine counts of eggs and
embryos will be a more accurate indicator of fecundity. However, it is important to note that using
ovarian eggs to estimate fecundity will likely lead to an overestimate of fecundity, and using uterine
eggs or embryos to estimate fecundity may lead to an underestimate of fecundity.
Determining fecundity for oviparous species is more difficult. Fecundity as previously mentioned can be estimated by counting the number of developing eggs in the ovary, but many oviparous
species have a very extended breeding season with eggs continuing to develop throughout the year,
and this approach may lead to an underestimation of eggs produced. One method of estimating fecundity for oviparous species is to determine the ovulation rate and the duration of the egg laying period
and to use these values to calculate the number of eggs laid by the female during the period. Sumpter
and Dodd (1979) studying the small-spotted catshark, Scyliorhinus canicula, determined an extended
breeding season with a peak in egg laying occurring in winter and spring but did not calculate an
ovulation rate. Many estimates of fecundity and egg-laying frequency are determined by keeping
animals in captivity. Chain catsharks, Scyliorhinus retifer, small-spotted catsharks, and thornback
rays, Raja clavata, have all been kept in captivity to determine ovulation rates and egg-laying periods.
154
Estimated annual fecundities for these species range between 20 to 140 eggs per year with egg laying
rates (of egg pairs) varying from every 2 days for the thornback ray to every 15.3 days for the smallspotted catshark (Holden, 1975; Mellinger, 1983; Castro et al., 1988; Ellis and Shackley, 1995).
In many species of elasmobranchs there is a positive relationship between fecundity and the
size of the female. Presumably, as a female becomes larger this increase in total length and girth
results in a larger space in the body cavity to accommodate pups. A positive linear relationship is
reported in many species of sharks including populations of scalloped hammerheads, Sphyrna lewini,
piked dogfish, Squalus acanthias, and tope sharks, Galeorhinus galeus (Chen et al., 1988; Hanchet,
1988; Peres and Vooren, 1991). The relationships of fecundity and length of a species tend to have low
r2 values and generally, while length is often related to fecundity, it tends to be a poor predictor of
fecundity. Negative bias in fecundity estimates based on uterine counts as previously discussed, may
obscure correlations between length and fecundity. Fecundity has a significant positive relationship
with both age and length in the dusky smooth-hound, Mustelus canis (Figures 7.10a and 7.10b)
(authors in this study were careful to confirm fecundity estimates in questionable cases by counting
placental scars). Fecundity is more
likely due to the variability in ages
of larger animals. Both relationships
have a low r2 value indicating the
data do not fit the relationship
closely and that neither age nor
Fecundity (# pups per liter)
closely related to length than to age
length are very accurate predictors
of fecundity for this species
(Conrath and Musick,
7.6
SPERM STORAGE IN
FEMALE ELASMOBRANCHS
AND OVIDUCAL GLAND
STRUCTURE
Sperm storage was first
Fecundity (# pups per litter)
2002).
proposed to occur when aquarium
female specimens of skates of the
genus Raja continued to lay
fertilized eggs after periods of
Figure 7.10 a - The relationship between fecundity (number
of pups per litter) and total length (TL), b - the relationship
between fecundity (number of pups per litter) and age for
female M. canis.
155
separation from male specimens (Clark, 1922). The storage of sperm in the oviducal gland of the
female has been shown by Pratt (1993) to occur in at least nine species of sharks in the western North
Atlantic. He proposes three types of fertilization occurring in elasmobranchs: no storage or fertilization
occurs immediately following mating, short-term storage in species in which ovulation is prolonged
over long periods, and long-term storage for repeated fertilization.
Sperm storage is assumed to occur in species where there is a time lag between mating and
ovulation and has been proposed for several species of elasmobranchs. Peres and Vooren (1991)
found up to five months passes between mating and ovulation in the tope shark, Galeorhinus galeus.
Simpfendorfer and Unsworth (1998) propose a six month period of sperm storage for the whiskery
shark, Furgaleus macki. White et al. (2001) also propose a three month period of sperm storage in
the lobed stingaree, Urolophus lobatus.
Sperm storage can be examined by sectioning the oviducal gland and examining it using
histological techniques. Hamlett et al. (1998) describe four fundamental zones of the elasmobranch
oviducal gland based on the morphology of the epithelium: the proximal club zone, the papillary zone,
the baffle zone, and the terminal zone. The jelly coats that surround the egg are produced within the
proximal club and papillary zones and various types of egg investments are produced within the baffle
zone (Hamlett and Koob, 1999). To determine if sperm is present in the oviducal gland, the posterior
third of the preserved oviducal gland is sectioned and stained using standard histological techniques, or
a sperm smear is taken from this area of the oviducal gland and stained. Pratt (1993) found that most
spermatozoa are usually located in the thin walled tubules of the lower oviducal gland. These were
subsequently identified by Hamlett et al. (1998) as terminal zone tubules. Hamlett et al. (2002) noted
the presence of bundled sperm throughout gestation in the terminal zone of the oviducal gland of the
dusky smooth-hound, Mustelus canis. Conrath and Musick (2002) also examined the oviducal glands
of dusky smooth-hounds by taking samples of the posterior third of the oviducal gland then embedding
them in paraffin, sectioning them, and staining them with hematoxylin and eosin. The sections were
then viewed with a compound microscope to determine if sperm was present in the oviducal gland.
Oviducal glands contained sperm stored throughout the year with every oviducal gland examined
containing some sperm. Figure 7.11 shows an oviducal gland section and a typical sperm bundle found
within the terminal zone of the oviducal gland.
7.7
ADDITIONAL RESOURCES
One objective of this technical manual was to cite to the greatest degree possible web-based
resources to make it easy to obtain literature on the subjects covered by the text. Unfortunately good
detailed information about elasmobranch biology on the web seems to be quite sparse and difficult to
find. However, there is an abundance of excellent literature about elasmobranch reproductive biology
156
in the primary literature. I have
therefore included a brief
section on the primary literature I have found most useful in
learning about elasmobranch
reproductive biology. As the
literature about elasmobranch
reproductive biology is quite
large, it is undoubtedly incomplete and lacking literature
others would name most useful.
The few good web resources I
located are discussed in the
second section. The last section
of the chapter includes some
guidelines to assist in field data
collection.
7.7.1
Literature
Wourms (1977) and
Compagno (1990) both provide
introductions to elasmobranch
reproductive biology with good
Figure 7.11 a - Cross section of the posterior third of a
M. canis oviducal gland (S = sperm bundle, T = terminal
zone, B = baffle zone), b - sperm bundles found within the
terminal zone of the oviducal gland.
descriptions of the reproductive
modes of elasmobranchs and
the various groups that have
these modes. Descriptive
information on general anatomy can be found in Hamlett (1999) and Hamlett and Koob (1999).
Clasper structure in particular is described in Gilbert and Heath’s (1972) work on siphon sac and
clasper function of piked dogfish and dusky smooth-hounds. Information on defining maturity, reproductive cycles and fecundity are probably best obtained from specific species accounts. Of the many
species accounts some of the early ones include descriptions of the reproductive tract such as Pratt’s
(1979) blue shark paper, Parsons’ (1981) Atlantic sharpnose paper, and Teshima’s (1981) Mustelus
paper. Specific information about staging the testes can be found in either Parsons and Grier (1992) or
Maruska et al. (1996). Sperm storage and oviducal gland structure are discussed in Pratt (1993) and
Hamlett (1999).
157
7.7.2
Web-based resources
Information from the web was current as of February 14, 2003. A general description of
reproductive techniques can be found at the FAO website in the Manual of Fisheries Science (Holden
and Raitt 1974), http://www.fao. org/DOCREP/003/F0752E/F0752E00.htm. The Florida Museum of
Natural History (http://www.flmnh.ufl. edu/) has much information about elasmobranchs on their
website and a link to the IUCN Shark Specialist Group (SSG) can be found here. The SSG publishes
an annual newsletter, Shark News, which can be viewed at http://www.flmnh. ufl.edu/fish/organizations/ssg/ssgdefault.html. Hamlett (1997) has a good review of elasmobranch reproductive modes
published in Shark News 9 (http://www.flmnh.ufl.edu/fish/organizations/ssg/ 9Newsletter/
shark9news1.htm). Henry Mollet has a web page with some information about various species and a
section on oviparous sharks, http://homepage.mac.com/mollet/. Peter Bor also has a website with
photographs of various egg laying elasmobranchs as well as some good introductory material on
oviparous species, http://www.rajidae. tmfweb.nl/.
7.7.3
Field data collection
This section is provided for the purpose of giving some guidance with what data should be
collected in the field in order to accomplish the methods discussed above. A sample data sheet (Figure
7.12) is provided. In addition to standard length and weight measurements, external measurements for
males should include clasper length measurements and a note on the extent of clasper calcification.
Internal measurement data should include a note about whether reproductive tracts were dissected in
the field or preserved for later examination; gonad weight and gonad size (testis weight, maximum ova
diameter, uterus width, etc.); presence or absence of sperm in the seminal vesicle of males; and for
females the presence or absence of mating wounds, the number of developing ova in the ovary,
general observations about maturity (ova size, uterine width, pregnancy, etc.), and uterine content
information (number of eggs or embryos, egg and embryo width, sex and lengths of embryos, etc.).
This field sheet is meant to be a general guide; each study should have previously defined its objectives
and tailored a data sheet to meet those requirements.
158
Figure 7.12 Field data collection sheet (PCL = pre caudal length, FL = fork length, TL = total length,
CL = clasper length, CC = notes on clasper calcification, P/D = preserved or dissected, GW = gonad
weight, GD = gonad size, SP/A = sperm present or absent in the seminal vesicle, #DO = number of
developing ova, Bites = presence/absence of mating wounds, U. content = uterine contents).
159
7.8
REFERENCES
ABDEL-AZIZ, S. H., A. N. KHALIL, AND S. A. ABDEL-MAGUID. 1993. Reproductive cycle of the common
guitarfish, Rhinobatos rhinobatos (Linnaeus, 1758), in Alexandria waters, Mediterranean Sea.
Aust. J. Mar. Freshwat. Res. 44:507-517.
BASS, A. J., J. D. D’AUBREY, AND N. KISTNASAMY. 1973. Sharks of the east coast of southern Africa. I.
The genus Carcharhinus (Carcharhinidae). Oceanographic Research Institute (Durban) Investigational Report No. 33.
CALLARD, G. V. 1991. Spermatogenesis, p. 303-341. In: Vertebrate endocrinology: fundamentals and
biomedical implications, Vol. 4, Part A. P.Pang and M. Schreibman (ed.). Academic Press, San
Diego.
CAPAPE, C. 1993. New data on the reproductive biology of the thorny stingray, Dasyatis centroura
(Pisces: Dasyatidae) from off the Tunisian coasts. Environ. Biol. Fish. 38:73-80.
__________, C. AND J. ZAOUALI. 1995. Reproductive biology of the marbled sting ray, Dasyatis
marmorata (Steindachner,1892) (Pisces: Dasyatidae) in Tunisian waters (Central Mediterranean).
J. Aquaricult. Aquat. Sci. 7:108-119.
__________, C., J. P. QUIGNARD, AND J. MELLINGER. 1990. Reproduction and development of two angel
sharks, Squatina squatina and S. oculata (Pisces: Squatinidae), off Tunisian coasts: semidelayed vitellogenesis, lack of egg capsules, and lecithotrophy. J. Fish. Biol. 37:347-356.
CASTRO, J. I., P. M. UBUCIS, AND N. A. OVERSTROM. 1988. The reproductive biology of the chain
dogfish, Scyliorhinus retifer. Copeia 1988:740-746.
__________. 1989. The biology of the golden hammerhead, Sphyrna tudes, off Trinidad. Environ.
Biol. Fish. 24:3-11.
__________. 1993. The biology of the finetooth shark, Carcharhinus isodon. Environ. Biol. Fish.
36:219-232.
__________. 1996. Biology of the blacktip shark, Carcharhinus limbatus, off the southeastern
United States. Bull. Mar. Sci. 59:508-522.
CHEN, C .-T., T. -C. LEU, AND S. -J. JOUNG. 1988. Notes on reproduction in the scalloped hammerhead,
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164
CHAPTER 8.
MORTALITY ESTIMATION
Colin A. SimpfendorferA , Ramón BonfilB and Robert J. LatourC
A
Center for Shark Research, Mote Marine Laboratory, 1600 Ken Thompson Parkway,
Sarasota, FL 34236 USA
B
International Programs, Wildlife Conservation Society, 2300 Southern Blvd., Bronx,
NY 10460 USA
C
Virginia Institute of Marine Science, College of William and Mary, PO Box 1346, Gloucester Point,
VA 23062 USA
8.1
INTRODUCTION
8.2
INDIRECT METHODS
8.2.1
Age-independent methods
8.2.1.1 Pauly, 1980
8.2.1.2 Gunderson, 1980 and Gunderson and Dygert, 1988
8.2.1.3 Hoenig, 1983
8.2.1.4 Jensen, 1996
8.2.1.5 Brander’s equilibrium mortality estimation
8.2.2
Age-dependent methods
8.2.2.1 Peterson and Wroblewski, 1984
8.2.2.2 Chen and Watanabe, 1989
8.2.3
8.3
Other indirect methods
DIRECT METHODS
8.3.1
Catch curves
8.3.2
Tagging
8.3.3
Telemetry
8.3.4
Others
8.4
CONCLUSIONS AND ADVICE
8.5
REFERENCES
165
166
8.1
INTRODUCTION
Mortality is a key parameter in understanding the dynamics of any population, and sharks are no
exception. Without knowledge of how fast individuals are removed from a population it is impossible to
model the population dynamics or estimate sustainable rates of exploitation or other useful management
parameters. Two separate types of mortality occur in shark (or fish for that matter) populations: firstly,
natural mortality (commonly referred to by the letter M), which is the loss to the population from natural
sources such as predation, disease and old age: and secondly, fishing morality (referred to by the letter F)
which, as the name suggests, is the loss to the population from fishing. Together, fishing and natural
mortality combine to give total mortality (referred to by the letter Z). Values of mortality rates are additive,
such that:
(8.1)
Z=M+F
Mortality values are typically expressed as rates that are either instantaneous or finite. Instantaneous (distinguished here by an upper case letter) and finite rates (lower case letter) are related exponentially. For example:
f = eF
(8.2)
Thus, in one year with a finite fishing mortality rate of 0.4, 40% of the population would be
removed by fishing. However, it is more convenient to work with instantaneous rates in most situations,
and the value of instantaneous fishing mortality that would give a 40% removal if applied over a full year is
0.5 (e.0.5). Ricker (1975) provides a detailed explanation of instantaneous rates and their use in fisheries.
The simple mathematical expressions above mask some of the more complex issues in relation to
mortality rates. For example, it is intuitive that mortality rates are not constant throughout a shark’s life.
While sharks are young their small size makes them more susceptible to predation from larger sharks, and
again as sharks reach their maximum age, they are more likely to die of old age. As a result some researchers have suggested that sharks have a U-shaped natural mortality curve. Similarly, fishing mortality
can vary with age due to the size selectivity of fishing gear or differences in the spatial distribution of fish
of different ages. These complexities should be kept in mind in relation to the techniques described in this
chapter.
Despite the importance of quantifying mortality to understanding the dynamics of shark populations, there have been limited amounts of research directed at this topic. The main reason for this is that
accurately quantifying mortality rates is a difficult task, and one that typically requires substantial amounts
of data. Since population assessment is such an important part of managing fished or endangered populations, indirect methods of estimating mortality have been developed and are commonly used in the population assessment of sharks and other aquatic organisms. These indirect techniques utilize relationships
between life history parameters and mortality (typically natural mortality) from species where research
167
has been undertaken. Typically the relationships utilized for sharks are based on teleost fishes, although
some use data from broader taxonomic groups.
This chapter describes methods for estimating mortality rates in shark populations, starting with
the simple indirect methods and then moving on to the more complex and data intensive direct methods.
We have attempted to use examples from the shark literature throughout. We also attempt to point out the
strengths and weaknesses of each of the methods, and as a conclusion try to provide some guidance on
which techniques to use in different situations. The fisheries literature relevant to both direct and indirect
methods of estimating natural mortality was reviewed by Vetter (1988), and this reference is a valuable
source of information on this topic.
8.2
INDIRECT METHODS
Indirect methods have typically been developed to estimate natural mortality, but in some cases
estimates of total mortality can be made. In cases where a method estimates total mortality (e.g., methods
of Hoenig and Brander, see below) the total mortality value can be assumed to be equal to natural mortality when the population is unfished (i.e., F = 0). If the population is fished, then the value of fishing mortality must be known to determine natural mortality. The majority of these indirect methods assumes that
mortality is independent of age, but two methods that give age-dependent values are also described.
8.2.1
Age-independent methods
8.2.1.1 Pauly, 1980
A commonly used indirect method of estimating natural mortality was described by Pauly (1980).
He related natural mortality to von Bertalanffy growth parameters ( L∞ or W∞ , and K) and mean environmental temperature (T, in degrees Celsius). This method assumes that there is a relationship between size
(measured in either length or weight) and natural mortality. This relationship is quite weak on its own, but
the inclusion of mean environmental temperature increases the fit as an animal living in warmer water will
have higher mortality rates than an equivalent animal living in cooler water (Pauly, 1980). The relationships
developed were based on natural mortality and ambient temperature data for 175 fish stocks, only two of
which were sharks (Cetorhinus maximus and Lamna nasus). The relationship based on length was:
log M = −0.0066 − 0.279 log L∞ + 0.6543 log K + 0.4634 log T
(8.3)
and based on weight was:
log M = −0.2107 − 0.0824 log L∞ + 0.6757 log K + 0.4627 log T
(8.4)
Estimation of natural mortality using these equations is straightforward as long as von Bertalanffy
parameter values are available. Jensen (1996) reanalyzed the data of Pauly and used this to produce a
simpler relationship (see below).
168
8.2.1.2 Gunderson, 1980 and Gunderson and Dygert, 1988
Gunderson (1980) used r-K selection theory to develop a relationship between female
gonadosomatic index (GSI) and natural mortality. This relationship assumes that there is a strong correlation between the amount of energy that a female invests in reproduction and natural mortality.
Gunderson’s original relationship was:
M = 4.64GSI − 0.370
(8.5)
This relationship was based on 10 North Sea teleost species, and uses maximum female GSI. The
calculation of GSI is covered in Chapter 7 of this manual.
This relationship was refined by Gunderson and Dygert (1988) who increased the size of the data
set on which the relationship was based to 20 species, including one shark (Squalus acanthias). The new
relationship was:
M = 0.03 + 1.68GSI
(8.6)
Simpfendorfer (1999a) used these two methods in a study of the Australian sharpnose shark,
Rhizoprionodon taylori. He found that the method of Gunderson (1980) was a poor predictor of natural
mortality, but that the method of Gunderson and Dygert (1988) was one of only two methods that produced reasonable values. Simpfendorfer (1999a), however, pointed out that the results from this method
may be biased since it is assumed that GSI is a proxy for reproductive investment. Since many sharks are
viviparous (such as R. taylori), not all of the reproductive investment is included in the full size ovarian
eggs. Instead, much of the reproductive investment is made later via the placental (or analogous tissues)
connection. Thus, it is more likely that this method will work better with oviparous and ovoviviparous shark
species.
8.2.1.3 Hoenig, 1983
The most widely used indirect method of estimating mortality in shark species is that of Hoenig
(1983) (see Chapter 9). This method uses maximum observed age to predict total mortality, since longer
lived species will die at a slower rate than short-lived species. Hoenig (1983) developed three relationships
that may be of use to shark researchers (a fourth relationship was developed for mollusks). The most
commonly used relationship was for 84 stocks of teleost fishes:
ln Z = 1.46 - 1.01 ln tmax
(8.7)
Hoenig (1983) also developed a relationship for 22 cetacean stocks:
ln Z = 0.941 - 0.873 ln tmax
(8.8)
While this relationship is less useful, it may have some applicability since like cetaceans sharks are
long-lived, slow-growing and have few young. However, cetaceans are also homeothermic, which may
bias the results if applied to sharks.
169
The third relationship developed by Hoenig (1983) was a combination of all of the mollusk, teleost
and cetacean data:
ln Z = 1.44 - 0.982 ln tmax
(8.9)
The values estimated by the relationships of Hoenig (1983) all predict total mortality. As such they
can only be used to predict natural mortality when
. Hoenig (1983) also noted that it is possible to
use a geometric mean regression in developing the predictive relationships, and provided the values for
these parameters. However, it has been standard practice for work with sharks to use the simple teleost
relationship.
8.2.1.4 Jensen, 1996
Jensen (1996) used the Beverton and Holt life history invariants (Charnov, 1993) as a starting
point in determining the relationships between life history parameters and natural mortality. Using optimal
trade-offs between reproduction and survival he showed that:
M = 1.65
(8.10)
xm
where xm is the age at maturity. Similarly, he showed that there was also a simple theoretical relationship
between the von Bertalanffy K value and natural mortality:
M = 1.5K
(8.11)
This relationship is much simpler than that provided by Pauly (1980, see above). Jensen re-analyzed
Pauly’s data and demonstrated that the simple relationship:
M = 1.60K
(8.12)
gives an equivalent fit to the data as the more complex Pauly equation. This simple relationship is very
close to the theoretical value (1.5K), suggesting that these relationships may provide a relatively sound
method of estimating natural mortality.
8.2.1.5 Brander’s equilibrium mortality estimation
Rather than a method to obtain estimates of total, fishing or natural mortality, Brander’s (1981)
method is an easy way to estimate threshold levels of total mortality beyond which stocks will collapse for
organisms like sharks and rays in which the actual number of young produced per year is known. Brander
(1981) proposed a very simple and intuitive relationship to estimate if the total mortality rates of the
juvenile and adult portions of a population are beyond a threshold that would lead to stock collapse. His
method relies on previous biological information and some assumptions as detailed below, and is a simple
and useful way to perform a quick assessment of the status of exploitation of a stock. This method can be
used not only to rapidly estimate if the fishing rate is too high, but also to rank species along a continuum
of resilience to exploitation depending on their life-history traits, along similar lines to the demographic
methods developed by Au and Smith (1997; see Chapter 9). In addition, and borrowing the conventions of
170
demographic analysis, Brander’s method considers only the female part of the population for purpose of
simplicity.
The method calls for three types of information:
•
The age of first sexual maturity of the stock. This is usually taken as the age at which
50% of the population is sexually mature. (See section 7.3.3)
•
The rate of reproduction (how many offspring are produced per year; in the case of
elasmobranchs this would be the number of eggs laid per year for species such as the
skates (Rajidae) and sharks of the Heterodontidae and Scyliorhinidae, or the number of
pups per year for live-bearing sharks and rays).
•
An estimate of the instantaneous total mortality rate of the immature part of the stock.
This method relies on two assumptions:
•
First, that the rate of reproduction is constant and not related to the age or size of individuals. Although in many species there is a known relationship between maternal size and
fecundity, sometimes this is not the case. In other circumstances, an average number of
eggs laid or pups produced can be used as an approximation, or the limits of the range can
be used to place bounds on the uncertainty.
•
Second, the mortality rate of the immature stock from birth to sexual maturity is considered to be constant. Although this is a stronger assumption as newborn survival is often
much lower than for subsequent ages (Manire and Gruber 1993; Heupel and
Simpfendorfer, 2002), an estimate of mortality that is representative of the immature part
of the stock can be used as this is an approximate method.
Brander’s method is based on the fact that for a population to remain at a constant level instead of
decreasing or increasing in size (this is usually referred to as being in equilibrium), the total rate of
mortality of adults or mature fish (Zm) should equal the net rate of recruitment of mature fish to the stock
(Rm):
Zm = Rm
(8.13)
In turn, the recruitment to the mature stock is equal to the number of eggs developing into females
or the number of female pups born (remember that to simplify only females are considered; usually it is
assumed that half of the total eggs laid or embryos in-utero will develop into females, but it is always
advisable to check if this applies to the species being analyzed) multiplied by the survival from birth to
maturity:
Rm = (E 2 )e − Zitm
(8.14)
where E denotes the rate of reproduction (in number of eggs or embryos produced per year), Zi is the total
mortality of the immature part of the stock (as mentioned above, we generally assume that Zi is constant
throughout immature ages) and tm is the number of years from birth to sexual maturity. Thus, for the
population to remain in equilibrium:
171
Z m = (E 2 )e − Zitm
(8.15)
This is Brander’s equation, and by substituting the values of the age at maturity, the rate of
reproduction, and the total mortality of immature fish for the species being analyzed, we obtain the corresponding equilibrium total mortality rate of the adult stock. This is an important reference point for management that indicates the maximum level of total mortality that the adult stock can withstand before the
populations starts to decline.
An additional application of this method involves repeating the above calculations using different
values of Zi to calculate equilibrium curves like those seen in Figure 8.01. In this figure, the mortality
thresholds (equilibrium instantaneous total mortality rates of mature and immature fish) of two hypothetical
species are plotted. Both species have a tm of 11 years but different rates of reproduction (20 and 40
offspring per year). Mortality values to the right and above of each curve will eventually drive the population to collapse. Thus, if we can independently determine the actual values of total mortality for the
immature and mature parts of the stock in question (Zi and Zm), and if the values are to the right of the
corresponding curve, management should attempt to reduce total mortality towards an equilibrium level.
Catch curves (see section 8.3.1) can be used to estimate the level of total mortality for each part of the
stock, but if catch curves can be calculated, then it is usually possible to do a more thorough stock assessment as shown in Chapter 10.
While the two curves in Figure 8.01 illustrate how species with higher fecundity can withstand a
slightly higher level of total mortality, they also show that doubling the fecundity has a relatively small
effect on the equilibrium mortality. The net rate of recruitment is the most important factor and this
depends directly on the cumulative mortality of the immature part of the stock until it reaches maturity.
2
40 offspring/year
1.8
20 offspring/year
1.6
1.4
Zm
1.2
1
0.8
0.6
0.4
0.2
0
0
0.1
0.2
0.3
0.4
0.5
0.6
Zi
172
0.7
0.8
Figure 8.01 Equilibrium mortality curves
for two theoretical shark populations as a
function of total mortality of the mature
(Zm) and immature (Zi) portions of the
stock. In both cases the age of first sexual
maturity is 11 years. Reproductive rate is
40 or 20 offspring per year depending on
the case.
Brander’s method is an easy and simple way to estimate the maximum total mortality of the
mature stock that would guarantee the stability of the population based on age of maturity, rate of reproduction and total mortality of the immature stock. The method was used by Brander to explain why
common rays Dipturus batis (= Raja batis) were virtually extirpated in the Irish Sea and to compare the
“resilience” to exploitation of other ray species. For this, he plotted the highest total mortality that could be
sustained by the five species he was analyzing as a function of fecundity and age of maturity while
assuming that Zm=Zi. The results showed that the least fecund species could withstand the highest
mortality because it had a high net survival to maturity. Brander’s method is very useful for deriving
reference points and making comparative analyses; however, it has never been adopted for the management of a real elasmobranch fishery.
The main limitations of Brander’s approach are: a) it does not provide direct management advice
in the form of an appropriate catch or effort level, b) it is not a dynamic model (considering changes in
time), but offers only a static view, thus processes like density-dependent compensation cannot be taken
into account. Density-dependent compensation is a change in any fundamental process of the population
that is directly related to the abundance level of the stock. In reality, most biological processes are densitydependent, especially mortality and recruitment (which is a consequence of pre-recruit mortality), but
other processes like body growth, population growth and fecundity are often density-dependent too.
8.2.2
Age-dependent methods
8.2.2.1 Peterson and Wroblewski, 1984
To provide an estimate of natural mortality that varied with age, Peterson and Wroblewski (1984)
used dry weight as a scaling factor. Using particle-size theory and data from the pelagic ecosystem
(including fish larvae, adult fish and chaetognaths) they showed that the natural mortality for a given
weight organism (Mw) is:
M w = 1.92 w −0.25
(8.16)
where w is the dry weight of an organism. To make this estimate of natural mortality age-specific, weightat-age data is required. This is normally obtained from a length-weight relationship and length-at-age data
from a von Bertalanffy growth function. Such an approach yields wet weight, and Cortés (2002) suggested that a conversion factor of one fifth be used for sharks to give dry weight. One criticism of this
method has been that it was developed for smaller pelagic organisms. However, McGurck (1986) showed
that it accurately predicted natural mortality rates over 16 orders of magnitude.
8.2.2.2 Chen and Watanabe, 1989
Chen and Watanabe (1989) recognized that natural mortality in fish populations, like most animal
populations, should have a U-shaped curve when plotted against age (they referred to it as a bathtub
curve). To model this curve, they used two functions, one describing the falling mortality rate early in life
and a second describing the increasing mortality towards the end of life. To scale the values of mortality
by age (M(t)) , Chen and Watanabe (1989) used the K and t0 parameters of the von Bertalanffy growth
function.
173
K
k
⎧
, t ≤ tm
−K
k ( t −t 0 )
⎪⎪
1
−
e
M (t ) = ⎨
k
K
,t ≥ tm
⎪
2
⎩⎪ a 0 + a1 (t − t m ) + a 2 (t − t m )
(8.17)
where
⎧
−K
k ( t M −t0 )
⎪ a0 = 1 − e
⎪
ke − kK(t M −t0 )
⎨ a1 = K
⎪
1 2 −Kk ( tM −t0 )
k e
⎪a 2 = − 2 K
⎩
and
(
(8.18)
)
1
t M = − ln 1 − eKkt0 + t 0
k
K
(8.19)
Cortés (1999) used this method to estimate the survivorship of sandbar sharks (Carcharhinus
plumbeus) by age-class. However, he demonstrated no increasing mortality at older age classes due to
senescence. The survivorship values that Cortés (1999) estimated using this method were similar to those
for the Peterson and Wroblewski (1984), Hoenig (1983) and Pauly (1980) methods. Unlike the Peterson
and Wroblewski (1984) method the Chen and Watanabe (1989) method only requires von Bertalanffy
parameters, but the mathematics are more involved. This technique can be simply implemented in a
spreadsheet using the formulae provided (8.17 – 8.19).
8.2.3
Other indirect methods
The indirect methods described above represent the most commonly used approaches in the
elasmobranch literature. However, the fisheries literature contains many other similar techniques, and
researchers may wish to investigate the field further. Other published techniques include Ursin (1967),
Alverson and Carney (1975), Blinov (1977) and Myers and Doyle (1983). In addition, there are a number
of studies that have looked at problems associated with these techniques such as Barlow (1984) and
Pascual and Iribarne (1993).
8.3
DIRECT METHODS
Direct methods provide the researcher with the best estimates of mortality because they are
based on the actual stock in question. However, they are also data intensive and require unbiased data.
Thus, it is important that data are collected so that they are statistically appropriate and that the assumptions and restrictions of each of the methods are understood.
8.3.1
Catch curves
One powerful method of estimating total mortality (natural mortality if F = 0) is the use of catch
curves. Catch curve analysis assumes that the decrease in observed numbers of individuals across the
age-structure of the population is the result of mortality:
174
N t +1 = N t e − Z
(8.20)
Thus, if the numbers of individuals in each age class are known then mortality can be estimated.
This method requires age data for an unbiased sample from a population and involves six steps:
1.
The numbers of animals in each class is determined.
2.
The numbers are log (base e) transformed.
3.
The log-transformed numbers are plotted against age.
4.
A linear regression is fitted to the descending limb (right-hand side) of the catch curve.
5.
The value of total mortality is calculated as the negative slope of the regression.
6.
The error of the estimates is calculated as the error of the slope of the regression.
An example of catch curves from male and female Australian sharpnose sharks from Simpfendorfer
(1999a) is given in Figure 8.02.
One of the most important steps in the application of this method is the selection of the points on
the descending limb of the catch curve. In the perfect situation the catch curve would be a linear set of
points with a negative slope (Figure 8.03a.). However, in reality most catch curves have an ascending limb
at the youngest age classes, due to incomplete recruitment of some age classes to the fishing gear or to
the population and an asymptote at the older age classes (Figure 8.03b). Ricker (1975) suggested using
only the points to the right of the peak ln N
value. It is also possible to exclude points that
are clearly outliers from the line described by
most of the descending limb points. This approach was used by Cortés and Parsons (1996)
for the bonnethead shark, Sphyrna tiburo. In
situations where there are only limited numbers
of age classes including as many points as
Ln (number)
possible will provide the most accurate result
with a lowest error. To do this, Simpfendorfer
(1999a) fitted both a linear and quadratic
function to the points including the peak ln N
value (that Ricker (1975) suggested excluding);
Figure 8.02 Catch curves for (A)
male and (B) female Rhiozprionodon
taylori derived from data from
Simpfendorfer (1993). Data points for
the first age class were not used to
calculate the regression line. From
Simpfendorfer (1999a).
Age class (years)
175
where the quadratic function provided a significant increase in fit, it was assumed that including the
maximum point increased curvature in the data and so the maximum point was excluded.
The use of catch curves requires a number of assumptions to be made about the sampled population. Firstly, the aged animals are representative of the age structure in the population. Secondly, the ages
are accurately determined. Thirdly, the total mortality rate is constant across the age classes to which the
linear function is fitted. Fourthly, that the mortality rate is constant between years (if more than one year
worth of data is used). Fifthly, recruitment is constant between years. And, sixthly, that vulnerability to
fishing gear is equal at all ages and constant over time classes.
Often it is difficult to get a sufficiently large sample of aged animals from a population to get
accurate estimates of mortality. However, there may be sufficient age data to develop an age-length (or
weight) key. This age-length key can be used to assign ages based on length. More details of age-length
keys can be found in Hilborn and Walters (1992). Cortés and Parsons (1996) used an age-based catch
curve and an age-length key derived catch
curve for the bonnethead shark. Both
methods produced very similar results.
Ln (number)
8.3.2
Tagging
Tagging experiments can be separated into two very general categories: 1)
studies where the tagged individuals of
population are killed upon recapture, as in a
commercial fishery, and 2) studies where
tagged individuals are recaptured and released several times. The former are referred to as tag-recovery studies, as evident
Ln (number)
by the fact that fishers recover tags of
individuals that are harvested, while the latter
are referred to as capture-recapture studies,
since it is possible to recapture tagged
individuals on multiple occasions. Moreover,
tag-recovery studies are typically viewed as
fishery-dependent, since the data obtained is
Age (years)
strictly a function of fishing activities, while
Figure 8.03 Hypothetical catch curves from (a) the
“perfect” case based on where Z is constant and the
regression can be fitted to all points, and (b) a more
typical situation where the regression is fitted only to
points to the right of the maximum ln(number) value.
176
for capture-recapture studies, it is best to use
a fishery-independent sampling design to
generate capture histories for tagged individuals. Here we focus on the use of multiyear
tag-recovery studies as a method to derive estimates of mortality, and acknowledge that there is a wealth
of literature on the analysis of capture-recapture data (e.g., see Burnham et al., 1987, Pollock et al., 1990)
The general structure of a multiyear tag-recovery study is to tag Ni individuals at the start of each
year i, for i = 1,…,I years. (Note that the tagging periods do not necessarily have to be yearly intervals;
however, data analysis is easiest if all periods are the same length and all tagging events are conducted at
the beginning of each period.) A total of rij tag-recoveries are then tabulated during year j from the cohort
released in year i, with j = i, i+1, …,J and J ≥ I (here, the term “cohort” refers to a batch of similar (e.g.,
similarly-sized) individuals tagged and released at essentially the same time). The tabulated multiyear tagrecoveries can be displayed in an upper triangular matrix of the following form:
⎡r11 r12
⎢− r
22
r=⎢
⎢M
M
⎢
⎣− −
L r1J ⎤
L r2 J ⎥⎥
O M ⎥
⎥
L rIJ ⎦
(8.21)
Application of multiyear tag-recovery models involves constructing a matrix of expected values
and comparing them to the observed data. The matrix of expected values corresponding to the timespecific parameterization of Brownie et al. (1985), which is referred to as Model 1, takes the form
⎧N I f J
⎪
xJ = ⎨ J-1
⎪ N I ∏ Sk f J
⎩ k =I
if I = J
otherwise
⎡ N1 f 1
⎢ −
Er = ⎢
⎢ M
⎢
⎣ −
N1S1 L S J −1 f J ⎤
L N 2 S 2 L S J −1 f J ⎥⎥
⎥
O
M
⎥
L
xJ
⎦
N1S1 f 2 L
N2 f2
M
−
(8.22)
where fi is the tag-recovery rate in year i, which is the probability a tagged individual alive at the beginning
of year i is caught during year i and its tag is recovered; Si is the annual survival rate for year i, which is
the probability an individual alive at the start of year i survives to the end of the year, and
⎧N I f J
⎪
xJ = ⎨ J-1
⎪ N I ∏ Sk f J
⎩ k =I
if I = J
(8.23)
otherwise
Although Model 1 is not the most general formulation of the Brownie et al. (1985) models, it is the
most commonly applied since it possesses the flexibility to document annual changes in the tag-recovery
and survival rates. In addition to the Brownie et al. (1985) formulation, there are two other types of
models (not described here) that can be used to analyze multiyear tag-recovery data (see Seber, 1970 and
Hoenig et al., 1998a,b).
177
Since the data in each row of the tag-recovery matrix follow a multinomial probability distribution,
the method of maximum likelihood can be used to derive parameter estimates. Also, since all tagged
cohorts are assumed to be independent, an overall likelihood function can be constructed as simply the
product of the individual likelihood functions corresponding to each row of the tag-recovery matrix
(Brownie et al., 1985; Hoenig et al., 1998a). Software packages that numerically maximize product
multinomial likelihood functions have been developed for the use of tag-recovery models. These include
programs SURVIV (White, 1983; http://www.mbr-pwrc.usgs.gov/software) and MARK (White and
Burnham, 1999; http://www.cnr.colostate.edu/~gwhite/mark/mark.htm).
Application of the Brownie et al. (1985) models requires making the following assumptions: 1) the
tagged sample is representative of the target population, 2) there is no tag loss or, if tag loss occurs, a
constant fraction of the tags from each cohort is lost and all tag loss occurs immediately after tagging, 3)
the time of recapture of each tagged individual is reported correctly (i.e., all tags are returned by fishers
during the year in which the individuals were harvested), 4) all tagged individuals within a cohort experience the same annual survival and tag-recovery rates, 5) the decision made by a fisher on whether or not
to return a tag does not depend on when the individual was tagged, 6) survival rates are not affected by
tagging process or, if they are, the effect is restricted to a constant fraction dying immediately after
tagging, and 7) the fate of each tagged individual is independent of the other tagged individuals.
Tag-recovery studies can be plagued by (among others) the following problems:
•
Newly tagged individuals may not have the same spatial distribution as previously tagged
individuals, especially if tagging takes place in only a few locations. (Note that it is best to
tag fewer individuals over a large number of locations rather than many individuals at just
a few locations.) This problem of non-mixing (Hoenig et al., 1998b) constitutes a violation
of assumption 1 and will lead to unreliable parameter estimates. To determine if nonmixing is present, Latour et al. (2001a) developed a test that can be applied prior to data
analysis.
•
Individuals are tagged across a range of ages and/or sizes, and these different age and/or
size groups experience different survival rates due to selectivity of the harvest. This leads
to a violation of assumption 4.
•
Individuals within a particular tagged cohort have a different spatial distribution than the
other individuals within that cohort, perhaps due to age- and/or size-specific migration
patterns (e.g., individuals may leave the estuarine or near coastal nursery grounds once
they become sexually mature). This leads to a violation of assumptions 1 and 4 and can be
accounted for during data analysis by ignoring the data associated with portions of the tagrecovery matrix (for more details, see Latour et al., 2001b).
Although the Brownie et al. (1985) models are simple and robust, they do not yield direct information about year-specific instantaneous rates of mortality (equation 8.1) or even exploitation rates (ui),
178
which are often of interest to fisheries managers. Estimates Si can be converted to Zi via the equation
(Ricker, 1975):
S i = e − Zi
(8.24)
and if information about M is available (say from one of the methods previously described), then estimates
of Fi and can be recovered. Given estimates of the instantaneous rates, it is then possible to recover
estimates of ui if the timing of fishing (i.e., single pulse (Type I fishery) or continuous (Type II fishery)) is
known (Ricke,r 1975):
⎧1 − e − Fi
⎪
ui = ⎨ Fi
( − Fi + M )
)
⎪ F + M (1 − e
⎩ i
for Type I fishery
for Type II fishery
(8.25)
Alternatively, if estimates of the instantaneous rates of mortality are unavailable, it is still possible
to calculate year-specific estimates of exploitation (Pollock et al., 1990; Hoenig et al., 1998a):
ui =
fi
φλ
,
(8.26)
where fi is as previously defined, φ is the short-term probability an individual survives the handling and
tagging process with the tag intact, and λ is the tag-reporting rate (i.e., probability the tag will be reported
given that that individual is harvested). The parameter φ can be estimated by holding newly tagged
individuals in cages or holding pens for a short period of time (e.g., 2-4 days) (Latour et al., 2001b), while
the tag-reporting rate is best estimated by conducting a high reward study (Henny and Burnham, 1976;
Pollock et al., 2001).
Regardless of the goals of a particular tag-recovery study (e.g., estimates of Si, Fi, etc.), it is
advisable to assess the likelihood of assumption violation. This can involve either conducting auxiliary
studies to address specific assumptions (e.g., experiments that allow estimation of the rates of tag-induced
mortality, both short-term and chronic tag shedding, tag reporting, etc.) and/or by using diagnostic tools to
assess model performance (Latour et al., 2001c). Specific to shark tagging studies, a variety of techniques
have been used to assess and adjust for assumption violation. For example, Simpfendorfer (1999b) described a method of correcting dusky shark tag return rates for non-reporting by using compulsory catch
information and the reporting rates of individual fishers, Xiao (1996) described a model for estimating
shedding rates from a double tagging experiment with Australian blacktip sharks (Carcharhinus tilstoni),
and Xiao (1999) described the tag-shedding rates of school (Galeorhinus galeus) and gummy (Mustelus
antarcticus) sharks .
The use of tagging experiments can provide one of the best methods of estimating both fishing and
natural mortality rates in shark populations. There are a wide variety of techniques available for the
analysis of these types of data. The increased computing power available to most scientists and the
179
development of software packages, has opened up increasingly powerful techniques. These techniques,
however, have been rarely used for shark populations. Grant et al. (1979) estimated the fishing and natural
mortality rates of school sharks (Galeorhinus galeus) using animals released in the 1950s, Simpfendorfer
(1999b) estimated fishing mortality rates of juvenile dusky sharks based on tag recaptures in a commercial
gillnet fishery, and Xiao et al. (1999b) estimated fishing and natural mortality rates of the school shark
using a probabilistic model.
8.3.3
Telemetry
Terrestrial biologists often use telemetry methods to estimate mortality rates by regularly monitoring the status of individuals in a population. Despite their popularity in terrestrial biology, these approaches
have rarely been used in aquatic studies. In terrestrial systems radio frequency telemetry methods are
used that can locate individuals over relatively large distances, whereas in aquatic systems acoustic
telemetry methods that have relatively short reception distances must normally be used. This limited
reception distance, and the large ranges of individuals, makes it impractical in most systems to monitor the
status of individuals. Only one study of a shark population has used this technique. Heupel and
Simpfendorfer (2002) used data from an acoustic monitoring system in a nursery area for blacktip sharks
(Carcharhinus limbatus) to estimate both natural and fishing mortality rates. They used analytical
techniques described by Hightower et al. (2001) (Kaplan-Meier and Program SURVIV) to estimate
mortality rates for the 0+ segment of the population through time. This type of approach provides some of
the most detailed understanding of the mortality process in a population (Figure 8.04), but requires a large
amount of data and a high level of effort in the field. The success of the approach used by Heupel and
Simpfendorfer (2002) in estimating mortality rates was due to the use of an array of data-logging acoustic
monitors that continuously recorded the activity of up to 42 sharks per season within the relatively small
and well-confined study site. For more details of this approach, consult Heupel and Simpfendorfer (2002)
or Hightower et al. (2001).
8.3.4
Others
Cohort analysis is a popular method of estimating mortalities in fish populations. This often takes
the form of Virtual Population Analysis (VPA), but also includes a method described by Paloheimo (1980)
that bases mortality estimates on reductions in catches of a single cohort over time. Although commonly
used in studies of teleost fish populations, these techniques have rarely been used in shark populations
studies. Smith and Abramson (1990) used a reverse VPA to estimate the fishing mortality rates of leopard
shark (Triakis semifasciata). Walker (1992) used the technique described by Paloheimo (1980) to estimate the natural mortality of gummy sharks (Mustelus antarcticus), as did Campana et al. (2002) to
estimate total mortality in porbeagle sharks (Lamna nasus). These types of analysis are rarely used in
studies of shark populations as the data requirements, in terms of the catch-at-age and fishing effort
information, are greater than is normally available. However, for populations where good data are avail-
180
Survival from natural mortality
able this type of approach can yield valuable
information on mortality.
8.4 CONCLUSIONS AND ADVICE
The first choice that a researcher
needs to make is whether to use a direct or an
indirect method to estimate mortality. Early in
the assessment of a population indirect methods are used as they can provide quick and
easy results, especially for inclusion in a model.
Survival from fishing mortality
When indirect methods are used for input into
a model then it is prudent to construct multiple
models that use as many of the indirect estimates as possible. This allows the researcher
to include an understanding of the uncertainty
associated with the estimates. Keep in mind
that each method will provide different results,
and in most instances there is no information
Week
that can be used to choose between the differFigure 8.04
Kaplan-Meier estimates of finite rate
of survival from (a) natural mortality and (b) fishing
mortality for juvenile Carcharhinus limbatus. Data
for 1999-2001 summers combined. Dashed lines
indicate 95% confidence intervals. Graphs use the
second week of May as week 1. From Heupel and
Simpfendorfer (2002).
ent values (i.e., they are each as equally likely).
In some cases there is little difference between
methods. For example, Simpfendorfer (1999b)
used five different methods for dusky sharks
and all but one of the results fell within the
range of 0.081 to 0.086. Alternatively, the
estimates of different methods can be very variable. Simpfendorfer (1999a) used seven methods for the
Australian sharpnose shark and found a range of values from 0.56 to 1.65.
One of the first things that becomes obvious in population assessments is that the results are
always very dependent upon the values of mortality used (both F and M). Thus as a researcher tries to
make an assessment more precise and accurate, a direct estimate of mortality will provide a higher level
of certainty about the results. It is at this point that direct methods of estimating mortality are normally
applied. Unlike indirect methods these estimates require a sampling strategy for the specific species to
ensure satisfactory results. Thus they require a much larger amount of field work and data analysis. The
reward for this work can be a much better understanding of mortality in a population and so a more
accurate assessment of its status.
The choice between different direct methods depends on a couple of factors. Tagging studies
probably provide the best data if they can be implemented properly. Of particular importance is the ability
181
to get tag recapture information, tag shedding rates and tag reporting rates. Without these types of data
the estimates of mortality will be biased and may yield results no more accurate than the indirect methods.
In situations where tag recapture data may be more difficult to obtain the catch curve approach may
prove more useful. Catch curves can produce very accurate results, but the data must meet several
assumptions (see section 8.3.1) before the results can be considered accurate. Finally, telemetry methods
are best used in situations where the mortality within a given system is required, and this system can be
adequately sampled acoustically, normally with data-logging monitors. While this telemetry approach may
seem like a dream for some populations, the technological and methodological advances are being made
that will make this more and more available to researchers. As such it is likely to represent the future for
the estimation of mortality in many situations.
8.5
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21: 733-744.
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models with application to Atlantic striped bass (Morone saxatilis). Can. J. Fish. Aquat. Sci. 58:
1716-1726.
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Negaprion brevirostris, p. 65-71. In: Conservation biology of elasmobranchs, S. Branstetter (ed.).
NOAA Technical Report NMFS 115.
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MYERS, R. A., AND R. W. DOYLE. 1983. Predicting natural mortality rates and reproduction-mortality tradeoffs from fish life history data. Can. J. Fish. Aquat. Soc. 40: 612-620.
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__________, J. D. NICHOLS, C. BROWNIE, AND J. E. HINES. 1990. Statistical inference for capture-recapture experiments. Wildl. Monogr. 107.
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returns. Biometrika 57: 313-318.
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from north Queensland, Australia. Environ. Biol. Fish. 36:233-241.
__________. 1999a. Mortality estimates and demographic analysis for the Australian sharpnose shark,
Rhizoprionodon taylori, from northern Australia. Fish. Bull. 97: 978-986.
__________. 1999b. Demographic analysis of the dusky shark fishery in southwestern Australia, p. 149160. In: Life in the slow lane. Ecology and conservation of long-lived marine animals. J. A.
Musick (ed.). American Fisheries Society Symposium 23, Bethesda, Maryland.
SMITH, S. E., AND N. J. ABRAMSON. 1990. Leopard shark Triakis semifasciata distribution, mortality rate,
yield, and stock replenishment estimates based on a tagging study in San Francisco Bay. Fish. Bull.
88: 371-381.
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WALKER, T. I. 1992. Fishery simulation model for sharks applied to the gummy shark, Mustelus
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experiments with exact or grouped times at liberty. Can. J. Fish. Aquat. Sci. 56: 868-874.
185
186
CHAPTER 9.
DEMOGRAPHIC MODELS: LIFE TABLES, MATRIX
MODELS AND REBOUND POTENTIAL
Colin A. Simpfendorfer, Center for Shark Research, Mote Marine Laboratory, 1600 Ken Thompson
Parkway, Sarasota, FL 34236 USA
9.1
INTRODUCTION
9.2
LIFE TABLES
9.2.1
General life table approaches
9.2.1.1 Example – Australian sharpnose shark
9.2.2
9.3
Rebound potential
MATRIX MODELS
9.3.1
Age-structured models (Leslie Matrix)
9.3.1.1 Example – Australian sharpnose shark
9.3.2
Stage-based models
9.3.2.1 Example – sandbar sharks
9.3.3
Elasticities
9.4
CONCLUSIONS AND ADVICE
9.5
REFERENCES
187
188
9.1
INTRODUCTION
Information on the status of shark populations and how they respond to increases in mortality
(e.g., fishing pressure, predation, disease), is critical to making management decisions about fished or
endangered species. It is no surprise, then, that a considerable part of the fish and fisheries literature is
devoted to this type of research. In the ideal situation long time series of information about a population—catches, fishing effort, change in abundance, etc—exist. In this situation dynamic fishery models
can be applied to derive extensive management related information (see Chapter 10). However, in
many situations the data required to support these types of models do not exist. This is often the case
with shark populations, where the collection of these data has been uneconomical or overlooked. In
this situation population models that rely primarily on life history parameters can provide some useful
information for management. The models are normally referred to as demographic models. These
demographic analyses became popular for shark stocks in the 1990s and are the most widely used
form of population model for this group of fishes (Hoenig and Gruber, 1990; Cailliet, 1992; Cortés,
1995, 1998, 1999, 2002; Cortés and Parsons, 1996; Smith et al., 1998; Simpfendorfer, 1999a,b;
Brewster-Geisz and Miller, 2000; Mollet and Cailliet, 2002).
The main parameter estimated by demographic analysis is the intrinsic rate of population
increase (r), which is a measure of potential for growth rate in a population. There are two different
techniques for estimating r – life tables and matrix models. Life tables are based on the Euler-Lotka
equation:
w
∑α l e
− rx
x
m x = 1.0
x=
(9.1)
where lx is the survival to age x, mx is the fecundity at age x (female pups per female), α is the age at
maturity, and w is the maximum reproductive age. The life table is a way of keeping track of the agespecific mortality and reproductive rates, and estimating r.
The second technique uses matrix algebra to estimate the finite rate of population increase (λ)
from reproductive and mortality data. The finite rate of population growth and the intrinsic rate of
population growth are related via the simple relationship:
λ = er
(9.2)
Matrix methods can be applied to age-structured and stage-based data.
It is interesting to note that both life tables and matrix models were introduced to ecologists by
the same person—P. H. Leslie (after whom the age-structured matrix model is named)—in the 1940s
(Caswell, 2001). Life tables immediately became popular and were used extensively. However, matrix
models did not gain favor with ecologists until the 1970s, but have since become extremely popular.
The slow rise in popularity of matrix models was probably the result of the need for an understanding
189
of matrix algebra and the extra computational requirements. The increased availability of computers
enabled researchers to overcome these drawbacks and embrace this powerful technique.
In this chapter I review the use of life table and matrix approaches in modeling shark populations. I restrict this consideration to static assessments of populations. Both life table and matrix
approaches can be used to develop dynamic models of populations, but in the shark literature they
have been largely restricted to static assessments due to the lack of time-series data. For more
general overviews of life table methods several general ecology books provide a more thorough
consideration (Krebs, 1985), and for matrix models the revised “Matrix Population Models” (Caswell,
2001), is the authoritative text. In addition to the simple life table approach, I also describe a method
developed by Au and Smith (1997) that estimates the rebound potential of a population. This method is
based on the life table approach, and is covered as a special case in that section.
9.2
LIFE TABLES
9.2.1 General life table approaches
Life tables were originally developed by life insurance companies as a means of determining
life expectancies of humans. Ecologists, however, have adapted them for use in answering biological
questions. As described in the introduction, the life table approach is based on the Euler-Lotka equation
(9.1). The life table is a simple way of laying out the reproductive and mortality schedule of a population to aid in the solving of this key equation. The classic construction of the life table is shown in
Table 9.01. The columns making up the life table can be simply derived from life history studies. Age
data are essential to the construction of the table, both for maximum age as well as age at first reproduction. Methods of age determination are covered in Chapter 6 of this manual. The proportion of the
population surviving at the beginning of each age class can be derived from estimates of natural and/or
total mortality rates:
l x = l x −1e − Z
(9.3)
Techniques for estimating mortality rates are covered in Chapter 8 of this manual. The initial
value of lx is normally set to a value of one, making it a “per recruit” analysis that examines if the
population will replace that single recruit. The final pieces of data required are the age-specific number
of female pups per reproductive event (litter for viviparous species, total eggs laid for oviparous
species), and the frequency of reproductive events. In studies of shark populations the number of
female pups is used as they are the only group that produces offspring. Thus, in reality this type of life
table is only keeping track of the female portion of the population. Rates of female pup production can
be derived from total litter size by multiplying by the proportion of female embryos and dividing by the
number of years between litters.
The first five columns in the table containing the life history data are then used to calculate the
value of r. The process of calculating r is an iterative one. This process is started by selecting a value
190
of r and calculating the values for the two columns on the right-hand side of the life table. It can be
seen that the summation of the final column is identical to the left side of equation 9.1. Thus when the
final column is summed it will total 1.0 if the correct value of r has been selected. If the value does
not equal 1.0, then a new value of r is picked and the process repeated until the summation of column
7 equals 1.0. This may seem like a time-consuming and arduous task. However, the process is almost
instantaneous with the use of a non-linear optimization routine in a modern spreadsheet. The most
commonly used of these routines is the “Solver” add-in that comes standard with Microsoft Excel. The
life table can be entered into the spreadsheet and a cell containing the starting value of r added. This
cell is then used in the formulae of columns 6 and 7 to represent r. The solver can then be started and
the value of the sum of the final column set to equal 1.0 by changing the value of the cell containing r.
Once the life table has been constructed a number of other statistics can be calculated from
the life table. The net reproductive rate (R0) is the total number of female offspring produced per
individual in a single cohort:
w
R0 = ∑ l x m
(9.4)
x =α
The mean generation length (G) is the mean period between birth of a parent and the birth of their
w
offspring:
∑α l m x
x
G=
x
x=
(9.5)
R0
Krebs (1985) also demonstrated that it is possible to calculate a value related to r – the innate
capacity for increase for the particular environmental conditions (rm). This statistic is calculated as:
rm =
ln (R0 )
G
(9.6)
The value of rm , however, is not equivalent to r and should not be used as a substitute for it. The value
of rm is a useful starting value for the iterative process of estimating r. The population doubling rate
can also be simply calculated:
t×2 =
ln(2)
r
(9.7)
This statistic is handy for very clearly showing differences between populations, or different
mortality or reproductive scenarios within a population. The stable age distribution of the population
(the proportion of individuals in each age class, Cx) can be calculated using the equation:
(e ) l
=
∑ (e ) l
r −x
Cx
x
w
r −x
(9.8)
x
x =0
191
It is an assumption of the life table method that the age structure of the population is stable. In
many situations, especially when a population is exploited, this assumption may be violated causing bias
in the results. The static nature of life tables also means that they may under estimate the growth rate
of a population as they do not include compensatory effects (e.g., decreases in mortality, increases in
reproductive rate, decreases in reproductive age, etc. when population size is decreased). In the next
section (9.2.2) a life table method is described that attempts to overcome this problem of not including
compensation.
Initial use of the life table typically involves using age-specific survival values based only on
natural mortality. However, age-specific values of fishing mortality (F) can easily be included by
basing survival on total mortality. Several studies of shark populations have used this type of approach
to investigate if current (or past) fishing mortality rates were sustainable (Simpfendorfer, 1999b) or at
what level of fishing mortality r = 0 (i.e., the population will start to decline) (Simpfendorfer, 1999a).
This type of information can be useful to resource managers. However, it is often difficult to translate
a value of fishing mortality into a catch level without other information (i.e., catch and abundance
data). Due to the age-structured nature of life tables it is possible to investigate other management
measures. For example, the impact of nursery area closures can be studied by removing fishing
mortality from the 0+ age class, or the impact of size regulations can be studied by applying fishing
mortality to specific age classes.
9.2.1.1
Example – Australian sharpnose shark
Simpfendorfer (1999a) produced life tables for the Australian sharpnose shark
(Rhizoprionodon taylori) in northern Australian waters. One of these life tables (Table 9.01) was
constructed using natural mortality estimated from a catch curve (for females only, M = 0.56 year-1).
Based on these data the value of r was 0.271 year-1, the population doubling time (tx2) was 2.554
years, the generation time 2.304 years, and the net reproductive rate 1.758. Simpfendorfer (1999a)
calculated the fishing mortality at which the population growth rate would be zero (Fc) to be 0.179
year-1. This was achieved by searching for the value of F that produces r = 0. Finally, a contour plot of
r was produced for different values of age at first capture and fishing mortality (Figure 9.01) by
constructing a large number of life tables. The use of spreadsheet software (e.g., Microsoft Excel)
helps to speed the calculation of parameters when multiple life tables are required, with a simple
macro being able to construct multiple life tables in almost no time.
192
Table 9.01 Life table for the Australian sharpnose shark, Rhizoprionodon taylori, from northern
Australia based on data from Simpfendorfer (1999a).
Proportion
surviving(lx)
Female
pups(mx)
Reproductive
rate(lxmx)
lxmxx
e -rx
lxmx.e-rx
0
1
2
3
4
5
6
7
8
9
10
1
0.570638
0.325628
0.185816
0.106034
0.060507
0.034527
0.019703
0.011243
0.006416
0.003661
0
1.982785
2.588733
2.80877
2.888671
2.917685
2.928221
2.932047
2.933437
2.933941
0
0
0.64565
0.481027
0.297824
0.174784
0.10074
0.057694
0.032965
0.01882
0.010741
0
0
0.64565
0.962054
0.893471
0.699137
0.503702
0.346163
0.230757
0.150561
0.096672
0
1
0.762338
0.581159
0.44304
0.337746
0.257477
0.196284
0.149635
0.114073
0.086962
0.066294
0
0.492204
0.279554
0.131948
0.059033
0.025938
0.011324
0.004933
0.002147
0.000934
0
Age at first capture (years)
Age
(x)
Fishing mortality (per year)
Figure 9.01 Contour plot of intrinsic rate of population increase r as a function of fishing mortality
(F) and age at first capture (AAFC) for Rhizoprionodon taylori from northern Australia. Estimates
are based on a life table where natural mortality was calculated by a catch curve. Fishing is sustainable at values of r > 0. From Simpfendorfer (1999a).
193
9.2.2 Rebound potential
Au and Smith (1997) described a modification of the life table approach to estimate what they
termed “rebound potential” (r2M). The rebound potential (or rebound rate) is a measure of how fast a
population will recover after fishing mortality has been removed from a population. The technique will
first be described, and then some potential modifications, assumptions and nuances considered. The
description of the technique will be relatively cursory due to space limitations. Those wishing to find
more detail on this technique should consult Au and Smith (1997) and Smith et al. (1998).
Au and Smith (1997) began by reformulating the Euler-Lotka equation (9.1) by introducing
parameters describing the survival to the mean age at maturity (lα) and average number of female
pups per litter (b). This allows equation 9.1 to be rewritten as:
[
]
e − (Z + r ) = lα be − rα 1 − e − ( Z + r )(w−α +1) = 1.0
(9.9)
The value of Z (total mortality) is substituted for lx (survival to age x) in equation 9.1. This
reformulation allows r to be estimated more simply than the traditional iterative method. However, it
requires several assumptions about the mortality and reproductive schedule (see below). Smith et al.
(1998) noted that a similar formulation was described by Hoenig and Gruber (1990) in terms of the
survival in the first year after birth.
The second step of the technique involves assuming that the maximum sustainable yield
(MSY) is achieved at Z = 2M, and that at this level r = 0. They also assumed that all of the compensation in the population growth rate occurs as a result of increased survival to age at maturity (lα). Thus
by substituting r = 0 and Z = 2M into equation 9.9 the increased value of lα (lα,2M) can be calculated.
Finally, the value of rebound potential (r2M) is calculated by removing the fishing pressure from the
population (i.e., Z = M) but retaining the increased value of lα,2M .
Au and Smith (1997) also considered that in situations where fecundity varied with age the
rebounding population is likely to have a different value of b than the fished population. This would
occur because the average age of mature animals would decrease as more animals recruited after
fishing was stopped. To investigate the impact of these types of changes Au and Smith (1997) and
Smith et al. (1998) used sensitivity tests with 1.0b, 1.25b and 1.5b when solving for r2M. Au and Smith
(1997) showed that for the leopard shark (Triakis semifasciata) that increased values of b resulted in
significant changes in r2M.
When using this method, researchers need to be aware of the assumptions and restrictions on
its use. In reformulating the Euler-Lotka equation much of the ability to include age-specific rates of
reproduction and mortality was lost. The sensitivity of the results to changes in the value of b indicates
194
the limitations of such an approach. The assumption that MSY occurs at Z = 2M also needs to be
considered. Shark populations are known to have limited ability to sustain fishing pressure (Holden,
1977; Musick, 1999) due to their low fecundity and late age at maturity. As such MSY may occur at
lower levels of Z than 2M. In fact, a value of Z = 1.5M may be a more appropriate level for MSY.
This change can easily be included into the technique to estimate r1.5M. As more research is undertaken on shark populations a clearer understanding of the mortality rates that produce MSY will be
gained. As this information becomes available, it may be necessary to address the value of Z used in
this technique.
9.3
MATRIX MODELS
Matrix population models are commonly used by researchers in studying the demography of a
population. They provide a versatile method that can be used in a wide range of situations. It is not
possible here to cover the whole suite of matrix models and how to use them. In this section, two
forms of static matrix models will be considered: age-structured and stage-based. In both cases we
will only consider static formulations of these models that are equivalent to the life tables discussed
above. Matrix models are quickly and easily adapted to produce dynamic population models, but these
fall outside the scope of this chapter. For a thorough coverage of all issues related to matrix population
models consult Caswell (2001) or Caswell (1989). Like life tables, static matrix models only require life
history information. The math involved in producing the estimates of the finite rate of population
growth (λ = er) is more complex and requires an understanding of matrix algebra. However, the need
for such an understanding can be largely overcome by the use of software developed specifically for
use with matrix models. A good example of this type of software is POPTOOLS, a Microsoft Excel
add-in that is available free on the internet (http://www.cse.csiro.au/poptools/).
9.3.1 Age-structured models (Leslie Matrix)
Static age-structured matrix models, also know as Leslie Matrices after the scientist who first
described their use, have been less commonly used in the assessment of shark populations that have
life tables. Hoenig and Gruber (1990) were the first to publish a paper that used a Leslie Matrix to
estimate λ for a shark population (lemon shark, Negaprion brevirostris). The basis for the Leslie
Matrix is:
N t +1 = AN t ,
(9.10)
where N is a vector describing the age composition of the population (either at time t or t+1) and A is
the transition matrix:
⎡m0
⎢ l
⎢ 0
A=⎢ 0
⎢ 0
⎢
⎣⎢ 0
m1
m2
...
0
l1
0
0
0
...
0
0
0
0
0
l w−1
195
mw ⎤
0 ⎥⎥
0⎥
0⎥
⎥
0 ⎦⎥
(9.11)
For consistency with the life table section, the same notation has been used: mx is the number
of female pups per female in age class x and lx is the survival to the end of age x. It is the transition
matrix A that is normally referred to as the Leslie Matrix. The matrix columns represent the age
classes. The value of λ is determined by finding the dominant eigenvalue of A by using matrix algebra.
When the dominant eigenvalue is determined two vectors (the right and left eigenvectors) also can be
calculated. The first of these represents the age-specific reproductive values (v, the left eigenvector),
and the second is the stable age/stage structure (w, the right eigenvector). These sets of values are
functionally equivalent to the lxmx and cx values in life table models.
Like life tables, a Leslie Matrix can be adapted to include information on fishing mortality at
specific ages, or changes in the reproductive schedule. In addition, the static nature of the simple
Leslie Matrix does not include compensatory effects for a population that is being fished.
9.3.1.1
Example – Australian sharpnose shark
As a direct comparison to the example given in the life table section, a Leslie matrix was
constructed from the data provided by Simpfendorfer (1999a) (Table 9.01):
0.00
1.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.98
0.00
0.57
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
2.59
0.00
0.00
0.33
0.00
0.00
0.00
0.00
0.00
0.00
0.00
2.81
0.00
0.00
0.00
0.19
0.00
0.00
0.00
0.00
0.00
0.00
2.89
0.00
0.00
0.00
0.00
0.11
0.00
0.00
0.00
0.00
0.00
2.92
0.00
0.00
0.00
0.00
0.00
0.06
0.00
0.00
0.00
0.00
2.93
0.00
0.00
0.00
0.00
0.00
0.00
0.03
0.00
0.00
0.00
2.93
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.00
0.00
2.93
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
2.93
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.006
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.004
(9.12)
This matrix was analyzed using the Microsoft Excel Add-In POPTOOLS. The dominant
eigenvalue (λ) was 1.257 (r = 0.229 year-1) and the population doubling time was 2.37 years. These
values are similar to those produced by the life table analysis, with the population doubling time different by approximately 0.2 years. The left and right eigenvectors (v and w) are given in Table 9.02.
196
Table 9.02 Age-specific reproductive value (w) and stable age distributions (v) (proportional) of the
Australian sharpnose shark, Rhizoprionodon taylori, estimated using a Leslie Matrix (equation 9.12).
Age
w
v
0
1
2
3
4
5
6
7
8
9
10
62.9%
28.5%
7.4%
1.1%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
4.5%
9.9%
10.9%
11.0%
10.8%
10.7%
10.6%
10.6%
10.5%
10.5%
0.0%
9.3.2 Stage-based models
In some situations the life history of a species can be divided into discrete segments or stages
(e.g., neonate, juvenile, sub-adult, breeding adult, non-breeding adult). In this case a stage-base matrix
model can be applied. This type of model can be useful if there is only limited age information for a
species, or the time spent in stages is variable. In long-lived species, stage-based models can also
simplify the math involved in the calculations. The formulation of the static stage-based transition
matrix is similar to that of the Leslie matrix, but the columns represent stages rather than ages, and the
survival values are divided between the probability of an individual surviving and moving from one
stage to the next (Gi) and the probability of an individual surviving and remaining in the same stage
(Pi). There are several approaches to calculating these parameters. In a study of sandbar sharks
Cortés (1999) applied a method that used the duration of each stage (dj) and the stage-specific
survival probabilities (pi):
p j (1 − pi )
Gi = i
,
d
1 − pi j
(9.13)
⎛ 1 − pi d j −1 ⎞
⎟p
Pi = ⎜⎜
dj ⎟ i .
p
1
−
i
⎝
⎠
(9.14)
d
and
If the stage-specific survival value is not known, it can be estimated as the mean of the agespecific values (survival or mortality, with S = e-Z) in each stage. Other authors (e.g., Brewster-Geisz
and Miller, 2000; Mollet and Cailliet, 2002) have taken a slightly different approach using the probabil197
ity of survival of an individual in a stage (σi) and the fraction of individuals in a stage that move to the
next stage (γi):
Gi = σ i γ i ,
(9.15)
and
Pi = σ i (1 − γ i ) .
(9.16)
This method requires an iterative approach to the estimation of the matrix parameters, and more detail
can be found in Brewster-Geisz and Miller (2001) or Mollet and Cailliet (2002).
The staged-based transition matrix can take many forms depending on how the stages selected for the population are related. The best way to understand the elements of the stage-based
transition matrix is via a life cycle graph. Figure 9.02 shows a life cycle graph for the sandbar shark
(Carcharhinus plumbeus) with five stages (neonates, juveniles, sub-adults, pregnant adults and
resting adults). This life cycle was used by Brewster-Geisz and Miller (2000). The transition matrix for
this stage classification as specified by Brewster-Geisz and Miller (2000) is;
⎡ 0
⎢G
⎢ 1
A= ⎢0
⎢ 0
⎢
⎣⎢ 0
0
0
m4
P2
G2
0
0
P3
G3
0
0
0
0
0
G4
0⎤
0 ⎥⎥
0⎥
G5 ⎥
⎥
0 ⎦⎥
(9.17)
Figure 9.02 Life cycle graph of the sandbar shark, Carcharhinus plumbeus, used to construct the
matrix in equation 9.17. Compartments 1 - 5 represent the different life stages (1 – neonate; 2 –
juveniles; 3 – sub-adults; 4 – pregnant adults; 5 – resting adults). Parameter values shown correspond
to those in equation 9.17. Based on information in Brewster-Geisz and Miller (2000).
198
198
Only stage four animals produce young (hence only m4) on the top line, the transition to all
stages up to pregnant adult are one-way, but animals alternate between pregnant and resting stage
adults on an annual basis (hence the lack of P4 and P5 since they will always move to the other group
if the time step is annual), and finally neonates become juveniles after one year (hence there is no P1
value). Such a transition matrix could be used for many shark populations, but would need to be
modified if reproduction was annual, or if the resting adult stage lasted longer than one year. It is not
possible to specify all possible combinations of matrices here. Caswell (2001) provides a thorough
coverage of how to develop a life cycle graph (which maps out the stages) and the resulting transition
matrix.
Like a Leslie matrix the value of γ of the stage-based model is estimated by determining the
eigenvalue of the matrix. Similarly, the eigenvectors produce information on the reproductive value
and stable age structure, but they are stage-specific rather than stage-specific.
9.3.2.1
Example – sandbar sharks
Brewster-Geisz and Miller (2000) used a stage-based matrix model to examine some management options for the sandbar shark (Carcharhinus plumbeus) in the western North Atlantic. The life
cycle graph for this species is shown in Figure 9.02 and the matrix formulation is shown in equation
9.17. The analysis examined the results of five scenarios with varying amounts of fishing mortality on
the five stages ranging from the current situation (in 1996) to total protection of the neonates and the
pregnant females (including the unrealistic assumption of no natural mortality on neonates). They
estimated that in the current situation r = -0.124 year-1, indicating that the population was over-fished
and declining. The other four scenarios used to explore protection for different stages by eliminating
fishing mortality also returned negative values of r. They examined the effect of fishing mortality on r
(Figure 9.03) and demonstrated that if fishing mortality at all stages was equal, r = 0 occurred at F =
0.071 year-1. This plot also demonstrates that when no fishing occurs the value of r is approximately
Instantaneous rate of increase, r
0.07 year-1.
Instantaneous rate of fishing mortality
199
Figure 9.03 The relation
between the intrinsic rate of
increase (r) and fishing mortality
(F). Fcritical is reached at 0.071. If
F is less than Fcritical , the population will increase. If F is greater
than Fcritical , the population will
decrease. From Brewster-Geisz
and Miller (2001).
9.3.3. Elasticities
One piece of information that can be very useful in interpreting the results of matrix models is
how much influence can changes in vital rates (reproductive and mortality rates) have on the population growth rate. In absolute terms this is known as the sensitivity, but is normally reported as the
elasticity, which is the proportional change. Elasticity is calculated from the elements of the transition
matrix (aij), the population growth rate (˜)
λ and the elements of the right and left eigenvectors (vi, wi):
aij vi wi
λλγ w, v ,
eij =
(9.18)
where (w,v) is the scalar product of the two vectors (i.e, (w,v)=v1w1+v2w2+...+vnwn). Since elasticities
are proportions they sum to give one:
∑∑e
i
i
= 1.
(9.19)
j
For each column of the matrix, which correspond to individual age or stage classes, elasticity
values can also be calculated:
Ei = ∑ ei
(9.20)
j
where Ei is the age or stage elasticity.
Since elasticity will identify the age or stage where the smallest changes in vital rates will
produce the biggest change in population growth rate, the researcher has a powerful tool to find where
management or conservation action might produce the greatest benefits to the population. For example, Cortés (2002) used elasticity values from a wide range of shark species to show that populations of large, slow-growing, long-lived species were most vulnerable to changes in the survival of the
juveniles (as opposed to the adults). Such a result suggests that management arrangements that
protect juveniles (e.g., nursery area closures) would provide greater benefit to the population than
those that protect adults (e.g., maximum size limit). For a much more detailed discussion of the calculation and interpretation of elasticity values for matrix models consult Caswell (2001).
9.4
CONCLUSIONS AND ADVICE
The static modeling approaches outlined in this chapter provide the researcher with methods to
assess the status of a population based solely on life history data. This is particularly useful when there
is little or no fishery information available for a population making more complex dynamic modeling
approaches inappropriate. However, these simple approaches come with limitations and these must be
kept in mind when interpreting the results and applying them to management or conservation. For
200
example, a life table can provide good information on the intrinsic rate of increase for a population, or
the fishing mortality rate at which the population will start to decline. However, it will not provide
information on the abundance of the population, its level of population decline or the appropriate quota
level to achieve a target biomass. These later types of information are more appropriately determined
using the dynamic approaches described in Chapter 10.
The results of the static approaches should also be considered to be conservative in their
estimates of population growth rates. This is because both simple life tables and static matrix models
do not allow for compensatory effects at low population sizes (e.g., increased growth, reproductive or
survival rates). The rebound potential approach of Au and Smith (1997) described in the life table
section is an attempt to overcome this limitations. However, the simple framework in which it is
implemented means that a number of restrictive assumptions need to be made.
The choice between life tables or matrix models is largely a matter of personal preference.
Each of the approaches will provide similar results if used in comparable ways. However, the trend in
the fisheries and ecological literature is towards matrix models. Although the math involved in matrix
models is more complex the development of software to quickly and easily do the analyses means that
these approaches can be easily implemented on a personal computer. In addition the ability to easily
calculate elasticity values, and their usefulness in determining management or conservation strategies,
provides an incentive to take this approach.
Finally, whichever approach is chosen, it is important to remember that there is a degree of
uncertainty and/or variation in the input parameters to any model. For this reason a good demographic
analysis will always include a range of scenarios that consider different sets of life history parameters
that reflect uncertainty or variation. There are two approaches to this. The first is to construct a
number of life tables or matrices that reflect the potential ranges of values. The second approach is to
construct a stochastic analysis such as that used by Cortés (1999) for sandbar sharks. With this
approach probability distributions for the input parameters are constructed and several hundred random
draws from the distributions are made and the life table or matrix solved. The result is probability
distributions of the output parameters (e.g., r). The first approach is best suited to cases where there is
uncertainty in the parameters, and the second is suited to the situation where there is variation in the
parameters.
9.5
REFERENCES
AU, D. W., AND S. E. SMITH. 1997. A demographic method with population density compensation for
estimating productivity and yield per recruit of the leopard shark (Triakis semifasciata). Can. J.
Fish. Aquat. Sci. 54: 415-420.
BREWSTER-GEISZ, K. K., AND T. J. MILLER. 2000. Management of the sandbar shark, Carcharhinus
plumbeus: implications of a stage-based model. Fish. Bull. 98: 236-249.
201
CAILLIET, G. M. 1992. Demography of the central Californian population of the leopard shark (Triakis
semifasciata). Aust. J. Mar. Freshwat. Res. 43: 183-193.
CASWELL, H. 1989. Matrix population models: construction, analysis and interpretation. Sinauer,
Sunderland, Massachusetts.
__________. 2001. Matrix population models. Construction, analysis, and interpretation. 2nd Edition.
Sinauer, Sunderland, Massachusetts.
CORTÉS, E. 1995. Demographic analysis of the Atlantic sharpnose shark, Rhizoprionodon
terraenovae, in the Gulf of Mexico. Fish. Bull. 93: 57-66.
__________. 1998. Demographic analysis as an aid in shark stock assessment and management.
Fish. Res. 39: 199-208.
__________. 1999. A stochastic stage-based population model of the sandbar shark in the Western
North Atlantic, p. 115-136. In: Life in the slow lane. Ecology and conservation of long-lived
marine animals. J. A. Musick (ed.). American Fisheries Society Symposium 23, Bethesda, Maryland.
__________. 2002. Incorporating uncertainty into demographic modeling: application to shark populations and their conservation. Conserv. Biol. 16: 1048-1062.
__________., AND G. R. PARSONS. 1996. Comparative demography of two populations of the
bonnethead shark (Sphyrna tiburo). Can. J. Fish. Aquat. Sci. 53: 709-718.
HOENIG, J. M., AND S. H. GRUBER. 1990. Life history patterns in the elasmobranches: implications for
fisheries management, p. 1 – 16. In: Elasmobranchs as living resources: advances in the biology,
ecology, systematics, and the status of fisheries. H. L. Pratt Jr, S. H. Gruber, and T. Taniuchi
(eds.). NOAA Technical Report NMFS 90.
HOLDEN, M. J. 1977. Elasmobranchs, p.187-215. In: Fish population dynamics. J.A. Gulland (ed.),
John Wiley & Sons, London.
KREBS, C. J. 1985. Ecology: the experimental analysis of distribution and abundance, 3rd ed. Harper
and Row, New York.
MOLLET, H. F., AND G. M. CAILLIET. 2002. Comparative population demography of elasmobranchs using
life history tables, Leslie matrices and stage-based matrix models. Mar. Freshwat. Res. 53: 503516.
MUSICK, J. A. 1999. Ecology and conservation of long-lived marine animals, p. 1-10. In: Life in the
slow lane. Ecology and conservation of long-lived marine animals. J. A. Musick (ed.). American
Fisheries Society Symposium 23, Bethesda, Maryland.
SIMPFENDORFER , C. A. 1999a. Mortality estimates and demographic analysis for the Australian
sharpnose shark, Rhizoprionodon taylori, from northern Australia. Fish. Bull. 97: 978-986.
202
SIMPFENDORFER, C. A. 1999b. Demographic analysis of the dusky shark fishery in southwestern Australia, p. 149-160. In: Life in the slow lane. Ecology and conservation of long-lived marine animals.
J. A. Musick (ed.). American Fisheries Society Symposium 23, Bethesda, Maryland.
SMITH, S. E., D. W. AU, AND C. SHOW. 1998. Intrinsic rebound potential of 26 species of Pacific sharks.
Mar. Freshwat. Res. 48:663-678.
203
204
CHAPTER 10.
FISHERY STOCK ASSESSMENT MODELS AND THEIR
APPLICATION TO SHARKS
Ramón Bonfil, Wildlife Conservation Society, 2300 Southern Blvd, Bronx, NY 10460 USA
10.1
INTRODUCTION
10.2
SURPLUS PRODUCTION MODELS
10.2.1 Logistic growth and the Schaefer model
10.2.1.1 Assumptions
10.2.2 Fox and Pella-Tomlinson models
10.2.3 Data requirements
10.2.4 Advantages and disadvantages of Surplus Production Models
10.2.5 Examples of use of Surplus Production Models in shark stock assessment
10.3
YIELD PER RECRUIT MODEL
10.3.1 Data requirements and assumptions
10.3.2 The method
10.3.3 Advantages and disadvantages
10.3.4 Examples of uses
10.4
DELAY-DIFFERENCE MODEL
10.4.1 Deriso’s simplifying finding
10.4.2 Advantages and disadvantages of the delay-difference model
10.4.3 Examples of the use of delay-difference models in shark stock assessment
10.5
VPA AND CATCH-AT-AGE ANALYSIS
10.5.1 Cohorts as the basis of VPA and CAGEAN
10.5.2 Virtual Population Analysis
10.5.2.1 An illustrative example of the principles of VPA
10.5.2.2
Disadvantages
10.5.2.3
Examples of usage of VPA for shark stock assessment
10.5.3 Catch-at-age analysis
10.6
10.5.3.1
Paloheimo method
10.5.3.2
Doubleday method
10.5.3.3
Other methods
PRINCIPLES OF FITTING MODELS TO DATA
10.6.1 Linear regression
10.6.2 Time-series fitting
10.6.3 Introduction to Bayesian estimation
10.6.4 Data quality
10.6.5 The relationship between CPUE and abundance
10.7
CONCLUSIONS AND RECOMMENDATIONS
10.8
REFERENCES
205
206
10.1
INTRODUCTION
Perhaps the most influential, but not necessarily the best, works on shark stock assessment were
those of Holden in the 1960s and 1970s. Holden (1977) was one of the first scientists to consider the
problem of shark fisheries stock assessment from a general point of view. He correctly pointed out that
sharks were different from bony fishes in terms of their biology, but unfortunately he incorrectly
concluded that classic fisheries models such as stock production models could not be applied to sharks
and rays. Holden dismissed these models and called for new models to be developed. He stated that the
assumptions of surplus production models regarding immediate response in the rate of population growth
to changes in population abundance and independence of the rate of natural increase from the age
composition of the stock do not hold for sharks. These conclusions were based mainly on the time delays
caused by the longer reproductive cycles of sharks and their reproductive mode, which in his view would
cause a linear and direct stock-recruitment relationship.
Because of this very influential paper, surplus-production models have been mostly ignored for
shark stock assessment, and scientists and non-scientists reading Holden’s papers have sought new
methods and models for dealing with shark fisheries stock assessment. For a while, Holden’s thoughts
influenced the works of other scientists who opted for the more detailed approach offered by agestructured models (e.g., Wood et al., 1979; Walker, 1992).
The main problem of surplus production models is not that they are inadequate when applied to
sharks but the way in which they were being applied. A paramount obstacle for the use of classic surplus
production models in the 1960s and part of the 1970s was the equilibrium constraint (see section on
fitting models to data below). Back then, due to the lack of readily available computers to perform
iterative search algorithms, scientists engaged in surplus production model-fitting were forced to assume
that populations were in equilibrium at all exploitation levels (i.e., that every catch observed was
sustainable) to simplify the process of fitting surplus production models to data.
The dangerous consequences of this assumption are well known and explicitly warned against in
fishery text books (Pitcher and Hart, 1982; Hilborn and Walters, 1992). However, the personal computer
revolution has helped to overcome the equilibrium constraint through the availability of non-linear
optimization routines which are accessible to virtually any fishery scientist in the world today. The
diversity of approaches this offers for fitting surplus production models has translated into a new era of
popularity for the utilization of what are presently known as dynamic surplus production models that
have been applied to organisms as slow-growing as whales and sharks (Punt, 1991; Polachek et al., 1993;
Prager et al., 1994; Babcock and Pikitch, 2001). Perhaps the most interesting outcome of all this reappraisal of surplus production models is the view that most of the problems associated with
successfully applying them are due to the quality of the fisheries data (Hilborn, 1979; see also section on
207
data quality below), and the finding that simple surplus production fishery models can sometimes
perform better than the more elaborate and biologically detailed age-structured approaches (Ludwig and
Walters, 1985, 1989; Ludwig et al., 1988; Punt, 1991).
One of the reasons for the difficulty in applying these models to sharks is that the data available
on shark fisheries and our knowledge about shark biological parameters may not be adequate. This is
expressed very clearly in the work of Anderson (1990), Anderson and Teshima (1990) and Bonfil (1996).
In fisheries science, independently of the species in question, the most common problem is the lack of
good and sufficient data and as explained below, the lack of contrast in the data when we have them.
Another big problem often overlooked is that the more ‘realistic’ age-structured models also pose
problems in their application. Age-structured data are much more difficult and expensive to obtain.
Furthermore, the life cycles of most shark species, even in terms of the basic parameters of age, growth
and reproduction, have just started to be unveiled during the last 20 years, and this only in the case of a
handful of stocks (see Pratt and Casey (1990) and Cortés (2000) for reviews). In addition, there are some
relevant areas of elasmobranch population dynamics that are still largely unknown. For example,
empirically derived stock-recruitment relationships have never been documented for any elasmobranch,
although a very strong relationship is suspected due to the reproductive strategies of the group (Holden,
1973; Hoff, 1990); the size, structure and spatial dynamics of most stocks of elasmobranchs are almost
totally unknown. Inadequate knowledge of migration routes, stock delimitation and movement rates
amongst them, can seriously undermine otherwise “solid” assessments and management regimes.
Hoff (1990) favored the use of dynamic surplus-production models for shark stock assessment
for a variety of reasons. Punt (1988; 1991) also reported dynamic surplus production models as the most
reliable for management of slow-growing resources with limited reproductive potential such as baleen
whales, when tested using a simulated fully age-structured population. Similar positive results were
reported with a Schaefer model for a swordfish age-structured simulation model (Prager et al., 1994).
The results of Bonfil (1996) suggest that surplus production models are good enough for shark biomass
assessment but not so much for management parameter estimation. He found that although generally
inferior to the Deriso-Schnute model (see below), surplus production models are capable both of
estimating biomass benchmarks and obtaining good biomass fits for most of the scenarios analyzed.
The best advice that can be given in regard to the model choice for elasmobranch stock
assessment can be found in Chapter 2 (section 2.2.3). Surplus production models can and should be
applied to elasmobranch fisheries as they are one of the easiest to implement, but their results should be
taken as a first and preliminary assessment. A complete and reliable assessment should not end with
surplus production models; delay-difference and fully-age structured models should also be applied.
10.2
SURPLUS PRODUCTION MODELS
These models are among the simplest and most widely used in stock assessment. They are easy
to use because they require only two or three types of data. These models are very flexible and have
208
different variations; the Schaefer, Fox, and Pella-Tomlinson models are some of the best
known.
Surplus production models (SPM) are based on the following principles:
Next biomass = last biomass + recruitment + body growth – catch – natural mortality
If there is no catch
Next biomass = last biomass + production – natural mortality
where production is the sum of recruitment and body growth, and
Surplus production = production – natural mortality
Thus
New biomass = last biomass + surplus production – catch
10.2.1
Logistic growth and the Schaefer model
Population growth has been typified in several ways, but most commonly the logistic model of
population growth has been found to fit a large number of populations both in nature and in captivity.
This model is expressed in the following way (differential equation or continuous model):
dB
B
= rB(1 − )
dt
K
(10.1)
where B = biomass, K = carrying capacity, and r = intrinsic rate of population increase.
The carrying capacity of the system, K (or B∞ ), is the maximum population size that can be
achieved. Mortality, age-structure, reproduction, and tissue growth are all captured by a simple
parameter called intrinsic rate of increase, or intrinsic rate of production, r. In theory, the intrinsic rate of
increase is fully realized at the lowest population level while the finite rate of population growth is
highest at the midpoint of
K. Figure 10.01 illustrates
some of these concepts and
120
Carrying capacity K
shows the trajectories of
POPULATION SIZE
100
population growth for two
different values of r.
80
r = 0.86
r = 0.5
60
40
20
Highest realized intrinsic rate of
increase r is at low population levels
0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Time
209
Figure 10.01 Examples
of population growth
according to the logistic
model. Two different r
values are exemplified.
The Schaefer model is the most commonly used among SPMs (known also as Biomass
Dynamics Models). This model is based precisely on the logistic population growth model. The
continuous logistic model explained above can also be written in discrete form in the following way
(Hilborn and Walters, 1992):
Bt +1 = Bt + rBt (1 −
Bt
)
K
(10.2)
When catch is included in the above equation we obtain the discrete version of the Schaefer (1954)
surplus production model:
Bt +1 = Bt + rBt (1 −
Bt
) − Ct
K
(10.3)
where
Ct = qfBt
(10.4)
and C is catch, q is the catchability coefficient, and f is effort. In the Schaefer model above, the middle
term is known as surplus production. If surplus production is greater than catch, population size
increases; if catch equals surplus production, catch is sustainable and population size remains constant
(Bt+1 = Bt); if catch is greater than surplus production, population size declines.
10.2.1.1
Assumptions
The Schaefer model has the following assumptions:
•
there are no species interactions
•
r is independent of age composition
•
no environmental factors affect the population
•
r responds instantaneously to changes in B (no time delays)
•
q is constant
•
there is a single stock unit
•
fishing and natural mortality take place simultaneously
•
no changes in gear or vessel efficiency have taken place
•
catch and effort statistics are accurate
In practice, many of the above assumptions are not met but this does not mean that the method cannot be
used. As long as it is used critically, the Schaefer model is a very powerful tool for an initial assessment
of a stock.
The management parameters of importance from the Schaefer model are given by:
MSY = r K/4
BMSY = K/2
Optimum effort (fMSY) = r/2q
210
10.2.2
Fox and Pella-Tomlinson models
There are other SPMs that have been proposed to represent fisheries more “realistically”. First is
the Fox model (Fox, 1970), which is based not on the logistic population growth model but on the
Gompertz growth model. The Fox model equation is:
Bt +1 = Bt + rBt (1 −
ln Bt
) − Ct
ln K
(10.5)
The model is supposed to be more “realistic” because it assumes that the population can never be totally
driven to extinction, something that sounds intuitive but is probably wrong in light of the severe
depletion of fishery resources in recent years and the well-documented human-caused terrestrial species
extinctions.
The management parameters of the Fox model are given by:
MSY = rKe-1/lnK
BMSY = Ke-1
fMSY = r/q lnK
Pella and Tomlinson (1969) proposed a generalized model that can take any shape, including that
of the Schaefer (m = 2) and Fox (m = 1) models.
dB
rB m
= rB −
dt
K
(10.6)
However, there is a price to be paid for this “improvement” and that is having to estimate an additional
parameter (m) to fit the model to the data. This model is not much more useful because despite its
“flexibility” the fit will probably be worse than with either the Schaefer or Fox models as there is a
known inverse relationship between the number of parameters to be estimated and the performance of
the models (see Hilborn and Walters, 1992).
10.2.3
Data requirements
In their simplest form, SPMs have only two data requirements:
•
a time series of total catch data (including discards, bycatch, etc.)
•
at least one time series of relative abundance data (usually CPUE from the fishery but
data from fishery-independent surveys are preferable)
The abundance data can be constructed if we have the effort data corresponding to the time series of
catches and if we assume that CPUE is linearly related to abundance. The assessment can greatly benefit
if an estimate of the virgin biomass is also available, but this is not essential for applying the model. The
longer the time series are and the better the quality of these data (see below), the greater chances of
having a good assessment. Modern implementation of SPMs through Bayesian approaches can
211
incorporate additional heterogeneous information such as estimates of the intrinsic rate of increase of the
stock, estimates of historical catches for which no effort or abundance index is available, and others
(McAllister and Pikitch, 1998a; Apostolaki et al., 2002; Cortés et al., 2002).
10.2.4
Advantages and disadvantages of Surplus Production Models
These models offer an excellent cost/benefit ratio. Data requirements are modest compared with
age-structured models, yet SPMs can yield critical information for assessment and management such as
estimates of virgin and current biomass, level of depletion of the population, MSY, optimal effort (fopt).
Most importantly, they can be used to make projections of the population under several scenarios of
management (quotas or efforts) and to evaluate the outcomes of each scenario. This is possible because
SPMs incorporate explicitly the time variable unlike demographic analysis and yield per recruit (Y/R)
models. Thus they are dynamic models that can be used to make predictions.
A further advantage (simplicity) but at the same time criticism (lack of biological reality) of
SPMs is that they do not include age structure. They assume that all the processes occurring in a
population can be captured by the simple processes described above while ignoring the size or age
structure of the population and the dynamics of different parts of the population. Another common
criticism of SPMs, especially in respect to elasmobranchs, is that they do not incorporate time delays
between reproduction and recruitment. While this is true, in practice this seems to be the least of the
problems for the application of SPMs to real shark fisheries. Often the shortage and bad quality of the
data available for the assessment are more pressing problems. Using Monte Carlo simulation, Bonfil
(1996) showed that despite criticisms of these models, SPMs can be useful for certain situations when
applied to elasmobranch fisheries data.
10.2.5
Examples of use of Surplus Production Models in shark stock assessment
Aasen (1964) was the first to apply the Schaefer model to a shark fishery and probably the first
scientist to perform stock assessment of an elasmobranch species. Although there was a dominant view
40 years ago that these models were not adequate for sharks due to incompatibility between the
assumptions of the models and the biology of sharks, they are now widely accepted as applicable
although not necessarily recommended as the best. They have been used in the multispecies shark fishery
of the east coast of the USA (Otto et al., 1977; Anderson, 1980; McAllister and Pikitch, 1998a;
McAllister et al., 2001; Cortés, 2002; Cortés et al., 2002), for the kitefin shark fishery in Portugal (Silva,
1987), the Australian fishery for school and gummy sharks (Xiao, 1995; Walker, 1999) and in the
multispecies skate and ray fishery of the Falkland Islands (Agnew et al., 2000).
10.3
YIELD PER RECRUIT MODEL
This model, first developed by Beverton and Holt (1957), provides a steady-state (static) view of
the population that allows determination of the catch or yield relative to recruitment (catch divided by
recruitment, thus the yield per recruit or Y/R name of the technique) that can be obtained from a stock
according to different levels of fishing mortality F (which is dependent on effort) and age of entry to the
212
fishery. The method is described in detail by Pitcher and Hart (1982), Megrey and Wespestad (1988), and
Quinn and Deriso (1999).
The model describes the population in terms of the biological processes of growth, recruitment
and mortality, and treats the exploited population as the sum of its individual members. It has more
biological detail than surplus production models reviewed above but is not as powerful and detailed as
the fully age-structured models treated below. Also, it is inferior to SPMs in the sense that it is static,
assumes that there is no dependence between stock size and recruitment, and cannot provide estimates of
absolute biomass or be used for making projections of stock size according to different management
strategies. Its main utility is that it indicates if the fishery is catching fish at an age that is too early or too
late to obtain the maximum biomass relative to recruitment, and whether the level of fishing mortality is
adequate.
10.3.1
Data requirements and assumptions
The calculation of yield per recruit requires the following data:
•
at least two mortality rates (Z total mortality; M natural mortality; or F fishing mortality
[F = Z-M])
•
the parameter k of the von Bertalanffy growth function (VBGF),
•
the age of first capture in the fishery
•
the age of recruitment to the stock
•
the maximum age in the stock
The method has the following assumptions:
•
there is a distinct spawning period and all fish recruit at the same time and age (they are
both knife-edge processes)
•
growth parameters do not change over time, stock size or age
•
M is assumed known and constant over all ages and over time and stock size
•
F is constant over all ages
•
recruitment is constant and can be ignored
•
the length-weight relationship has an exponent of 3
•
there is complete mixing within the stock
10.3.2
The method
This model is based on three equations:
1.
Von Bertalanffy Growth Model (in weight):
Wt = W∞ (1 − e − k (t −t 0 ) ) 3
213
(10.7)
2.
Exponential survival model:
N t = R ⋅ e − M ( tc −t r ) ⋅ e -(M + F)(t-t c )
(10.8)
where R is the number of recruits, tc is age at first capture and tr is age of recruitment to the stock.
3.
General yield equation:
t1
Y = ∫ F ⋅ N tWt dt
tc
(10.9)
where Y represents yield (catch).
These three equations can be integrated (not shown here in the interest of space and simplification) to
obtain the yield equation of Beverton and Holt (1957):
n =3
Y = F ⋅ R ⋅ W∞ ⋅ e − M (tc −tr ) ⋅ ∑ F + MΩ n+ nK ⋅ e − nK ( tc −t0 ) ⋅ (1 − e -(M + F + nk)(t1-tc ) )
(10.10)
n =0
where:
•
t0 is the von Bertalanffy parameter that describes age at zero length
•
t1 is maximum age of fish in stock
•
k is the von Bertalanffy growth coefficient
•
and the integration constants Ω0=1, Ω1=-3, Ω2=3, Ω3=-1
Because the level of recruitment is not known, the above equation is usually expressed in relative terms,
as yield per recruit:
n =3
Y
= F ⋅ W∞ ⋅ e − M (tc −tr ) ⋅ ∑ F + MΩ n+ nK ⋅ e − nK ( tc −t0 ) ⋅ (1 − e -(M + F + nK)(t1-tc ) )
R
n =0
(10.11)
The model predicts the level of yield (catch) that can be obtained depending on the age of entry and
maximum age in the stock, and the level of natural and fishing mortality.
This model allows managers to investigate the effects of varying fishing mortality (F) or age of
first entry (tc) on yield. One disadvantage of this model is that the shape of yield is completely
determined by growth and mortality. If the stock has a low rate of growth and high M, the yield curve is
asymptotical (this wrongly suggests yield does not decrease as you fish harder and harder). Conversely,
if the stock has rapid growth rate and low M the yield curve is dome-shaped.
10.3.3
Advantages and disadvantages
The main advantages of the yield per recruit method is that it is relatively simple to implement
and does not require historical data on catch and effort. It is a step forward from demographic methods
because it tells us—within a relatively simple implementation procedure—if we are exploiting fish at the
right age (or size), and also if we are fishing at the right intensity. Using this method we can provide
214
advice on the best age of entry to the fishery and the adequate level of effort, thus offering information
that can potentially translate into direct management recommendations such as changing the mesh size of
the gillnet used to catch sharks, or taking a number of boats out of the fishery to reduce fishing mortality.
The main disadvantages are that the method provides no estimate of the absolute biomass of the
stock and gives only limited advice on management actions. Similarly to life tables, a disadvantage of
this method is that it is not dynamic (there is no time variable) and therefore cannot be used to make
predictions, and does not incorporate density-dependent processes like stock-recruitment relationships.
Other disadvantages are that the model unrealistically assumes constant growth and mortality
rates; it is more expensive to implement than SPMs as age needs to be frequently determined for large
samples of fish; the curve shape is predetermined and inflexible; the model predicts yield even at infinite
effort and this is unrealistic; and yield is not expressed in absolute terms so the real magnitude of the
catch cannot be known.
Using the Y/R method alone can be misleading as pointed by Grant et al. (1979). These authors
suggested that the recommended ten-fold increases in fishing mortality from their Y/R assessment was
bad advice as only a two-fold increase could already be reducing the reproductive stock of school sharks
to less than half of its original abundance. Using a modified demographic method Au and Smith (1997)
showed that the estimates of Y/R obtained by Smith and Abramson (1990) for the leopard shark (Triakis
semifasciata) were considerably lower after adjusting for the effect of reduction in recruitment due to
fishing. Also, Rago et al. (1998) found that the optimum age of entry predicted by the Y/R model would
lead to recruitment failure and stock collapse in spiny dogfish (Squalus acanthias) because of the late
age of maturity in this species. Another problem of the Y/R method is that a poor estimation of growth or
mortality can influence very strongly all the conclusions and lead to decisions that could put the stock in
jeopardy.
10.3.4
Examples of uses
This method has been used for stock assessment of school sharks by Grant et al. (1979), for little
skate by Waring (1984), for leopard shark by Smith and Abramson (1990), for silky sharks by Bonfil
(1990), for sandbar sharks by Cortés (1998) and for porbeagle by Campana et al. (1999; 2001). To the
knowledge of the author this method has not been used as the main basis for the management of any
elasmobranch species.
10.4
DELAY-DIFFERENCE MODEL
The delay-difference model of Deriso (1980) is a clever simplification that allows the inclusion
of biological information of the species to be taken into account in a simple way. This model belongs to
an intermediate class known as partially age-structured models, which represents a step forward from the
rather simple surplus-production models that ignore biological processes like recruitment and individual
growth, while avoiding the demanding data requirements of the more sophisticated fully age-structured
models. It considers age structure implicitly, not explicitly.
215
The biological realism of the delay-difference model includes terms for recruitment, natural and
fishing mortality, and growth. Yet, this model can be simplified to be fitted to data on catch and effort
and an index of abundance, as in the case of surplus production models. Additional requirements are
knowledge of the growth in weight of the species and an estimate of natural mortality. An important
advantage of this model is that it has a smaller number of model parameters to be estimated in
comparison to fully age-structured models. Thus it can be applied to fisheries with limited amounts of
data while still offering a more realistic representation of population dynamics.
10.4.1
Deriso’s simplifying finding
The delay-difference model was first proposed by Deriso (1980) and further generalized by
Schnute (1985). The model incorporates four main types of biological information: body growth,
recruitment, survival, and a measure of age structure. The main formula of the model links present
available biomass (exploitable biomass or that recruited to the gear) to available biomass and population
numbers from the previous year. The advantage of the model lies on several simplifications that allow
the incorporation of important population dynamics processes into a simple equation. However, perhaps
its more important characteristic is that the model allows for time lags in the dynamics of the stock, such
as are found in species with slow growth and late age of entry to the fishery. This ability to take into
account time delay is what gives the model its name of “delay-difference” model. Below is a detailed
derivation of the delay-difference model taken from Hilborn and Walters (1992). This text should be
consulted for further details about this and other models.
The model assumes that body growth of the exploitable stock can be represented by a linear
function (the Brody equation):
wa = α + ρwa +1
(10.12)
where wa is body weight at age a, and alpha and rho are constants. This equation simply states that after
a certain age, the typical von Bertalanffy model of growth in weight shown in Figure 10.02 below can be
alternatively represented by a linear
equation of weight at age a against
1.2
weight at age a+1.
1
In order to find the parameters
weight
0.8
0.6
alpha and rho of the Brody equation, we
0.4
must perform a linear regression as
0.2
shown in Figure 10.03. This figure
shows several possible linear regressions
0
0
2
4
6
8
10
12
14
16
18
20
22
24
differing in how many points are
age
considered for the regression (different
Figure 10.02 Individual growth in weight according
to the von Bertalanffy Growth Model.
216
starting points). Which regression we
Figure 10.03 Ford-Walford plot of weights
at age. Solid diamonds represent the original
data points and each straight line is a linear
regression using a different starting age (0,
2, 4, 6, and 7).
1
0.9
Wa+1
all
0.8
2
4
0.7
6
7
Wa+1
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
Wa
choose (and therefore which alpha and rho parameters we use in the model) depends on the age of entry
to the fishery.
The delay-difference model also assumes that all fish older than age k (in this particular model
age of entry to the fishery) are vulnerable to fishing and have the same natural mortality M.
Another simplification of the model considers that the total survival rate S at time t
St = e − Z
(10.13)
can be decomposed into terms for constant and variable (harvest) survival:
S t = ψ (1 − ht )
(10.14)
where Ø is the natural survival rate and h is the harvest rate in year t. This assumes that harvest (fishing)
takes place in a short time during the beginning or end of the year.
Biomass at age can be represented as numbers at age times average weight at age:
_
B a = N a wa
(10.15)
This can be extended for the whole exploited population plus the recruitment R:
_
⎡ a max
⎤
Bt = ⎢ ∑ N t ,a wa ⎥ + wk R t
⎣ a =k
⎦
(10.16)
remembering that k is the age of recruitment (to the gear or fishery). Population numbers N can be
written as survivors from last year at age a-1, and all the weights at age can be written using the Brody
equation, thus arriving at the following formula:
217
⎡
⎤
a max
_
⎢ a max
⎥
B t = S t −1 ⎢α ∑ N t −1,a −1 + ρ ∑ N t −1,a −1 w a −1 ⎥ + wk R t
a = k +1
⎢ a =k +1
⎥
⎢⎣
⎥⎦
(10.17)
Factoring out terms that do not depend on age results in sums over age k and older for year t-1:
Bt = S t −1αN t −1 + S t −1 ρBt −1 + wk R t
(10.18)
And total numbers in the population are:
N t = St −1N t −1 + R t
(10.19)
But we can write the term áNt-1 of the equation as
αN t −1 = αSt − 2 N t − 2 + αR t −2
(10.20)
And also, the term αSt-2 Nt-2 can be expressed in terms of Bt-1 and Nt-2 using the equation for Bt above, as:
(10.21)
αSt −2 N t −2 = Bt-1 − ρSt −2 Bt − 2 − wk R t −1
Combining the last two equations (substituting) and making some more algebraic manipulations we
arrive at the delay-difference equation (Schnute, 1985):
(10.22)
Bt = (1 + ρ ) S t −1Bt-1 − ρS t −1 S t −2 Bt −2 − ρwk −1 S t-1R t −1 + wk R t
This is the original form of the model and it requires seven parameters to predict biomass dynamics and
to fit the model to catch and CPUE data:
•
ρ and wk, for the Brody growth equation
•
ψ, the natural survival rate (no fishing)
•
a, b or a’, b’, for the stock recruitment relationship
•
B0, the stock size at the beginning of the fishery
•
R0, the recruitment at equilibrium (when mortality equals births)
•
q, the catchability for the catch equation
Recruitment can be expressed using either the Ricker or the Beverton and Holt models,
simplified by assuming that the population was in equilibrium (virgin population) when exploitation
began.
For the Ricker recruitment model the equations are:
218
Rt +1 = S t − k +1e ( a '− b 'S t−k +1 )
a ' = ln
( R0 )
+ B0b'
b'
(10.23)
(10.24)
For the Beverton and Holt recruitment model the equations are:
Rt +1 =
aS t − k +1
b + S t − k +1
a = R0
(b + B0 )
B0
(10.25)
(10.26)
Other parameters needed to fit the delay-difference model can be estimated externally or internally with
some assumptions:
•
ρ and wk estimated directly from growth data
•
ψ using external estimates of natural mortality M
This leaves us with only three parameters to be estimated during model fitting by non-linear methods:
•
b or b’ for the stock recruitment relationship
•
B0, stock size at the beginning of the fishery
•
q, catchability for the catch equation
Thus, the delay-difference model can be simplified by fixing values for the first three parameters listed
above and fitted to the catch and effort data by finding the values of the last three parameters using nonlinear iterative methods such as those included in spreadsheet software. Remember that the parameter a
or a’ of the recruitment model is eliminated by the assumption above.
10.4.2
Advantages and disadvantages of the delay-difference model
The advantages of this model can be summarized as:
•
The model offers more biological realism than SPMs without the demanding data
requirements of fully age-structured models
•
It takes into account the time delays due to growth and recruitment
•
It can be fitted to simple catch-effort time series of data when information on mortality and
growth is available
•
Fitting the model to data requires estimation of a lesser number of parameters than fully agestructured models thus simplifying the estimation process and improving performance
•
Can be used to estimate stock size and for the calculation of management benchmarks
•
Can be used to make predictions of different management scenarios
219
The main disadvantages of this model are (Hilborn and Walters, 1992):
•
They can provide an acceptable fit to the data in terms of goodness-of-fit criteria (see section
on model fitting below) while estimating parameter values that are meaningless from the
biological point of view (extremely high or low virgin stock sizes, virgin recruitment levels)
•
They can sometimes provide very biased estimates of management benchmarks such as
optimum fishing effort
10.4.3
Examples of the use of delay-difference models in shark stock assessment
This smart simplification of age-structured population dynamics was initially welcomed with
excitement but has been seldom used in practice due to the availability of more sophisticated models that
can be easily applied thanks to the powerful computer technology now readily available. The delaydifference model has not been used often for the assessment of shark fisheries, but Monte Carlo
simulations executed by Bonfil (1996) showed that it performed better than surplus production models
for estimating stock size in shark-like fishes. In addition, this model was used as part of the assessment
of the school and gummy shark fisheries of Australia by Walker (1999). Cortés (2002) and Cortés et al.
(2002) used a simplified version of the Deriso (1980) delay-difference model known as lagged
recruitment, survival and growth model as part of the assessment of small and large coastal sharks,
respectively, off the U.S. eastern seaboard.
10.5
VPA AND CATCH-AT-AGE ANALYSIS
This is a family of methods that is based on catch-at-age data. That means that the catch must be
broken down into age-groups. These methods are more sophisticated and detailed, and have a higher
sense of realism than previously reviewed models. Nevertheless, age-structured models are also
extremely data demanding and require a lot of detailed information that is often expensive to obtain.
Age-structured models can be classified into two groups (Hilborn and Walters, 1992): Virtual
Population Analysis or VPA, and statistical catch-at-age analysis or CAGEAN. These methods are
recursive algorithms that calculate stock size based on catches broken down by each age class. Using
these methods it is possible to estimate the magnitude of fishing mortality, levels or recruitment and the
numbers at age in the stock for each past year using only catch-at-age and an estimate of natural
mortality M.
VPA does the calculations without having a specific statistical underlying assumption. In
contrast, the more sophisticated CAGEAN methods depend on formal statistical models and have been
developed to the degree that various types of data can be integrated in a statistical framework to be used
for the assessment. Thus, data on stock-recruitment (S/R) relationships, CPUE time series, biomass time
series, and others can be integrated into a very powerful analysis. The stock synthesis method of Methot
(1989) is one of the best examples of a sophisticated CAGEAN model.
220
10.5.1
Cohorts as the basis of VPA and CAGEAN
A fundamental part of age-structured models is the concept of cohort. A cohort comprises all the
individuals (fish in this case) that were born in the same year. An example of a human cohort is all the
persons that were born in 1960. The cohort of 1960 can be followed through time year after year by
looking at individuals that are age 1 in 1961, age 2 in 1962, and so on. The size of the 1960 cohort in the
year 2003 consists of all the individuals that were born in 1960 and have survived up to that year. The
cohort concept is illustrated in Figure 10.04.
Birth
Age 1 Age 2 Age 3 Age 4
… Age 40 Age 41 Age 42 Age 43
1960 N1960,0
1961
1962
1963
N1960,1
N1960,2
N1960,3
N1960,4
1964
…
2000
…
N1960,40
N1960,41
2001
N1960,42
2002
N1960,43
2003
Figure 10.04 Diagrammatic representation of the 1960 cohort of humans (all individuals born in
1960). N represents the numbers of age A alive each year for cohort 1960.
VPA and CAGEAN are recursive recipes (algorithms) that track the history of each cohort in the
exploited population back in time from the present to the time each cohort was born or more commonly
to the time it recruited to the fishery. In other words, they calculate the number of fish alive in each
cohort for each past year, following each cohort through time. Their aim is to reconstruct the entire
exploited population in order to estimate fishing mortality and numbers at age for each age class each
year.
10.5.2
Virtual Population Analysis
VPA is also known as cohort analysis because each cohort is treated separately. The method is
based on the following equation:
N alive at
(N alive at
beginning of =
beginning of
–
(catch this year) – (natural mortality
next year
this year)
this year)
In this particular case recruitment is not considered because we are analyzing only a single cohort.
We can change the above equation to:
N alive at
(N alive at
beginning of =
beginning of
this year
next year)
+
221
(catch this year)
+
(natural mortality
this year)
Assuming that natural mortality M is known and that at some age x there are no more fish alive (that is,
that all fish in the cohort die after age x) we can iteratively calculate the number of fish alive each year,
starting from the oldest age and moving backwards to the youngest.
The basis of the method is the assumption that if we know that this year we have zero fish of the
oldest age left alive, and we know how many of them we caught last year (in theory those were the last
fish of that age left in the sea after those which died of natural causes) and if we know the instantaneous
natural mortality rate, then, for fisheries where the fishing period is short, it can be assumed that there is
no natural mortality during the short fishing period so that:
Nt = Nt+1 + Ct +Dt
(10.27)
Dt = Nt (1-S)
(10.28)
Nt - Dt = Nt+1 + Ct
(10.29)
Nt - Nt (1-S) = Nt+1 + Ct
(10.30)
Nt - Nt + Nt S = Nt+1 + Ct
(10.31)
Nt S = Nt+1 + Ct
(10.32)
Nt = (Nt+1 + Ct) / S
(10.33)
where
so that
where N is number of fish, C is catch, D is deaths (numbers dying), t is time (year) and S is the finite
survival rate.
The last equation above is the key equation for VPA or cohort analysis, when fishing takes place
in a single short period of time during which we can consider M to be negligible. This equation allows
the calculation of the numbers last year from the numbers this year, the catch-at-age and natural
mortality, but because we assume there were no more fish left of the oldest age this year (we fished them
all or they died) we can calculate the numbers last year with only catch and mortality.
10.5.2.1
An illustrative example of the principles of VPA
We will illustrate VPA with a hypothetical example. Consider a shark species that lives only to
10 years (such as Rhizoprionodon terraenovae) when we assume that all the individuals die. Consider a
situation where this species recruits to a fishery at age three. Furthermore consider that this fishery takes
place in only a couple of weeks each year when the fish come to a mating aggregation. The information
we need for the cohort analysis is an estimate of M, which for this stock we will consider to be 0.5 (finite
rate), and the total catch of fish in each age class for each year. A table with such hypothetical catch data
is given in column 3 of Table 10.01 and represents the total numbers in the catch for the cohort of
Rhizoprionodon terraenovae born in 1980. Using these data and the following equations we can obtain
estimates of:
222
•
the population at the end of the fishery each year
•
the population just before the fishery each year
•
the harvest rate, and
•
the instantaneous fishing mortality rate
Year
Age
Catch
Cohort size at
start of year
1990
1989
1988
1987
1986
1985
1984
1983
10
9
8
7
6
5
4
3
0
900
2,480
6,032
13,985
8,183
7,653
2,045
0
1,800
8,560
29,184
86,338
189,042
393,390
790,870
Cohort size
before fishery
Harvest
rate
Instanteous
fishing
mortality rate
900
4,280
14,592
43,169
94,521
196,695
395,435
1.00
0.58
0.41
0.32
0.09
0.04
0.01
Infinite
0.87
0.53
0.39
0.09
0.04
0.01
Table 10.01
Hypothetical example of data (bold font) required and the results of a cohort analysis
for a short-lived elasmobranch. Loosely based on the life history of Rhizoprionodon terraenovae. See
text for explanation on methods to calculate each column.
For this we will need the equation for numbers at the start of the year:
Nt = (Nt+1 + Ct) / s
(10.34)
And the following equations:
For numbers alive at the beginning of the fishery:
Nt’ = Nt S
(10.35)
For the harvest rate:
ht= Ct/Nt’
(10.36)
And for the instantaneous fishing mortality rate:
Ft = - ln (1-ht)
(10.37)
Table 10.01 shows the results of the calculations for the cohort born in 1980; but other cohorts
can be treated in the same way for a full VPA. For the last cohort in the last year of data we assume there
are no fish left, they all die after age 10 in 1990. The table is constructed for this cohort using equation
(10.34) to calculate cohort size at the beginning of each year (note that fish age 10 in 1990 were age 9 in
1989, etc.). The equations for VPA when fishing takes place during the whole year (continuous fishing)
are more complicated and can be found on Hilborn and Walters (1992) and Quinn and Deriso (1999),
while Sparre and Venema (1992) introduce length-based VPA.
223
The above example of cohort analysis includes only one cohort. For a complete VPA, the same
method should be applied for all cohorts that have completely ceased to exist, which is all cohorts that
are no longer present in the fishery. One remaining problem after doing this is that we still would not
have information to do the analysis for incomplete cohorts (those still present in the fishery) and these
are usually the most important for managers.
One way to solve the problem of incomplete cohorts is to estimate the fishing mortality rate of
cohorts currently being fished and use this to estimate the sizes of the incomplete cohorts. Two ways to
estimate the size of current cohorts are to obtain population size estimates from surveys or markrecapture methods, or most commonly, to assume a value for the current F and estimate previous values
from there.
This last case is known as the terminal F assumption and comes from the following equation:
Nt =
Ct ⎛ Ft + M ⎞
⎜
⎟
(1 − e − zt ) ⎜⎝ Ft ⎟⎠
(10.38)
There are two ways to estimate the F here, one is from tag-recapture methods, or we can estimate it from
effort (f) data while assuming that q is known using F = fq.
The catchability coefficients q for each age can be obtained from the complete cohorts, and
assuming q is constant over time we can use that together with effort data to calculate F for each age.
Another variation of this approach is known as the “tuned” VPA which first uses the q’s from complete
cohorts and this is used to derive a new set of q’s for the incomplete cohorts.
10.5.2.2
Disadvantages
The problems of VPA are that using the wrong M estimate can lead to severely overestimated or
underestimated cohort sizes. More worryingly, when catchability increases as the stock declines in size,
using the assumption that the terminal F has not changed has been found to introduce great errors,
overestimating the stock size and probably recommending larger catches than can be sustained, which
can lead to overfishing of the stock.
Another problem is that to obtain the necessary catch-at-age data it is essential to perform
routine ageing of large samples of fish from the catch (which is costly) and if the ages are wrongly
estimated this will introduce systematic biases to the assessment.
10.5.2.3
Examples of usage of VPA for shark stock assessment
Smith and Abramson (1990) used backward VPA in combination with Y/R to estimate
replacement rates of leopard sharks off California
10.5.3
Catch-at-age analysis
CAGEAN, or statistical catch-at-age analysis, is very similar to VPA with the difference that, in
order to deal with the incomplete cohorts, it uses formal statistical methods to estimate the current
224
abundance of these cohorts. CAGEAN methods also provide a means to estimate natural mortality rate
provided that the data have clearly contrasting levels of fishing effort and total mortality rate.
CAGEAN starts by using the catch curve concept (see Chapter 8), to calculate the instantaneous
total mortality rate for each age class from the catch at age data. Just in the same way we can build catch
curves for the catches at age of one single year, we can apply the same concept to the catches of all
cohorts between subsequent years. The equation used for normal catch curves (one single year of data) is
a linear regression of the numbers-at-age in the catch (Ca) against age (a), where the slope of the line is
the estimate of Z, and the intercept of the Y axis represents the logarithm of the recruitment (R) times the
vulnerability to the gear (v):
ln(Ca ) = ln( Rv) − Za
(10.39)
In order to use the catch equation to estimate mortality within a single cohort we use a modified version
of the catch curve with the following equation:
ln(C aj ) = ln( R j v ) − Za
(10.40)
where j is used to denote a specific cohort. This allows the estimation of the total mortality and the
relative recruitment “strength” of each cohort. This method assumes that fishing and natural mortality
are constant, and that vulnerability to the fishing gear is constant above a given age. One problem is that
these catch curves do not allow us to estimate natural mortality rate or vulnerability, so their usefulness
is limited. CAGEAN is a modification of these techniques. A simple introduction to the CAGEAN
methods explained below is provided by Hilborn and Walters (1992) and is recommended for beginners;
Quinn and Deriso (1999) offer an updated and mathematically and statistically more rigorous treatment
of the same topics.
10.5.3.1
Paloheimo method
There are several versions of the CAGEAN method. That of Paloheimo (1980) is the simplest
one and the one analyzed here with some detail. The Paloheimo method uses the following equations and
some algebra to arrive at its key equation.
We begin with the catch equation, which in this version assumes that fishing mortality acts
separately from natural mortality and is responsible for a fraction (F/Z) of the total mortality
C=N
[
F
1 − e −( F + M )
F+M
225
]
(10.41)
Secondly, the equation below relates numbers at age a to recruitment times cumulative fishing and
natural mortality for each previous age.
N a = Re − ΣF −ΣM
(10.42)
We also use the following equation which assumes linearity between effort and fishing mortality:
F = fq
(10.43)
where f is effort and q is catchability.
All the above equations combined together and manipulated through algebra give us:
Ca
1 − e− z
= Re −qΣf −ΣM q ⋅
f
Z
(10.44)
This equation relates CPUE at age to recruit numbers, catchability, total and natural mortality, and effort.
Applying algebra it can be shown that this becomes:
⎛ 1 − e− z ⎞
⎛C ⎞
⎟⎟
ln⎜⎜ a ⎟⎟ = ln(Rq) − q ∑ f − ∑ M + ln⎜⎜
Z
⎝ f ⎠
⎝
⎠
(10.45)
The Paloheimo method assumes that M is constant over years and uses a well-known approximation for
the last term (which is valid for values of Z that are no larger than 0.7):
⎛ 1 − e−z ⎞
Z
⎟⎟ ≈ −
ln⎜⎜
2
⎝ Z ⎠
(10.46)
Then after some algebra we obtain the final Paloheimo equation:
⎛ Caj ⎞
⎛ j −i
f ⎞
⎟ = ln(R j-a q ) − q⎜ ∑ f k + j ⎟ − M (a − 12 )
ln⎜
⎜
⎜ f ⎟
2 ⎟⎠
⎝ k = j −a
⎝ j ⎠
(10.47)
where j = year, a = age, and k = the number of years that the cohort has been fished.
The above equation is only a linear multiple regression of the form:
Y= b0 + b1X1 + b2X2
(10.48)
Given the needed data (usually catch by age for several ages, and the corresponding effort that produced
the catches), this equation can be easily solved with standard multiple regression packages to obtain
estimates of Rq, q, and M.
The following simple example (taken from Hilborn and Walters, 1992) shows the application of
the Paloheimo method. Table 10.02 presents the required data on catches at age and corresponding
efforts for Lake Erie perch.
226
Age
2
3
4
5
Catch
103
59
11
3
Effort
15.9
15.4
13.5
12.6
Table 10.02 Data on catch at age and corresponding effort for the 1971 cohort of Lake Erie perch
(taken from Hilborn and Walters, 1992).
The estimates of the parameters after applying the Paloheimo methods are as follows:
Ln (Rq) = 2.37
q = - 0.22
M = 4.34
The correlations between the parameters are shown in table 10.03.
Parameter correlations
Rq
q
M
Rq
1
-0.71
-0.69
q
M
1
-1
1
Table 10.03 Parameter correlations for the CAGEAN analysis based on the Paloheimo method for
the data of table 10.02 (taken from Hilborn and Walters, 1992).
These results are suspicious and suffer from strong parameter correlation. This occurs because of
poor data contrast (see section 10.6.4 below); q is negative, which is impossible, while M is extremely
high. Notice that to be able to perform this catch-at-age analysis we needed not only the catches at age
for each year for this cohort, but also the efforts that were applied to fish them. These efforts are all of
the same magnitude and almost constant (very poor contrast in effort), and this is why there is a strong
parameter negative correlation between q and M.
If instead we were to simultaneously analyze data for three cohorts of Lake Erie perch using this
method (see example in Hilborn and Walters (1992) for further details), we would have to resort to using
dummy variables or what is known as an experimental design table, to perform the multiple linear
regression. In this case, the equation becomes:
Y = b1X1 + b2X2 + b3X3 + b4X4 + b5X5
(10.49)
where the first three b’s represent the recruitment level of each cohort. The dummy variables X1-3 take
the values 1 or 0 depending on which cohort we are analyzing, so that the corresponding b (recruitment)
is taken into account or not. The last two terms are the same as before; they are the efforts and the
number of years of accumulated natural mortality. If we were to perform the analysis the results would
still not be satisfactory because there is still poor data contrast in the effort for this set of data despite the
227
fact that there are data for three different cohorts and four different years of fishing. It is still impossible
to differentiate between the effects of natural and fishing mortality from these data. However, it is
possible to obtain good estimates of the recruitment levels because there is good contrast in the relative
abundance data (CPUE).
10.5.3.2
Doubleday method
Another and more general approach to the catch-at-age method was put forward by Doubleday
(1976). This method does not assume a linear relationship between the variables and is thus more
difficult to calculate, requiring non-linear estimation methods. Its advantages are that fishing mortality F
is not assumed proportional to effort, so the method can be applied in the absence of effort data.
However, this method is not free from the general problem that a good contrast is needed between
fishing mortalities for good parameter estimation. The main Doubleday equation is presented below,
more details about this method can be found in Hilborn and Walters (1992) and Quinn and Deriso
(1999).
a −1
a −1
⎛
ln (Caj ) = ln(R j-a ) − ∑ Fa − k , j − k − ∑ M a − k , j − k + ln⎜ − Faj 1 − e −(Faj + M aj )
⎜F +M
k =1
k =1
aj
⎝ aj
[
10.5.3.3
]⎞⎟⎟
⎠
(10.50)
Other methods
An even more sophisticated and more powerful method is that developed by Fournier and
Archibald (1982). Paloheimo and Doubleday derived their models assuming an underlying deterministic
process but in nature everything is measured with error and occurs also with natural variability, which
can be interpreted as noise. The method of Fornier and Archibald is very flexible and accounts for
explicit estimation of errors in:
•
C, the catch measurement,
•
F, the fishing mortality,
•
S/R, the stock recruitment relationship
Their method explicitly accounts for a stock recruitment relationship. This method is very
sophisticated both mathematically and statistically so it is not analyzed here, but has the advantage that it
can include several types of external information that can help in the estimation of parameters, such as
estimates of recruitment levels, fishing mortalities from other studies, and effort data. A further
sophistication of this type of analysis was developed by Methot (1989) and is even able to use CPUE,
gear selectivity and independent survey biomass data in the estimation of parameters.
10.6
PRINCIPLES OF FITTING MODELS TO DATA
Some of the models used in fisheries stock assessment are very simple but the estimation of their
parameters, which implies fitting the models to the data, is not always a simple task. In the case of the
surplus production models treated above, there are three main approaches that are commonly employed
for the estimation of their parameters.
228
First, we might assume equilibrium conditions, that is, that all the catches observed so far in the
fishery are sustainable. This is absolutely wrong and must always be avoided. Equilibrium methods
were used decades ago to simplify the computations because of difficulties in calculating parameter
values analytically. However, modern computers allow anyone to use any of the other methods
mentioned below or even more sophisticated ones and there is no longer any excuse to assume
equilibrium. Never use equilibrium methods.
10.6.1
Linear regression
A better option than assuming equilibrium is to use linear regression. Using the case of the
Schaefer model as an example, it is shown below that this model can be expressed as a linear equation to
which we can then apply standard regression methods to find the values of the parameters and fit the
model to our data.
Given the Schaefer model equation for biomass dynamics in a fishery:
Bt +1 = Bt + rBt (1 −
Bt
) − qf t Bt
K
(10.51)
we have that
Ut =
Ct
= qBt
ft
(10.52)
Ut
q
(10.53)
and
Bt =
thus, substituting the last equation in the first, we arrive at:
U t +1 U t
U
U
=
+ r t (1 − t ) − f tU t
q
q
q
qK
(10.54)
Rearranging, dividing by Ut and multiplying by q we obtain:
r
U t +1
−1 = r −
U t − qf t
Ut
Kq
(10.55)
The above equation is in reality a linear equation of the general form:
Y = b0 + b1 X 1 + b2 X 2
(10.56)
which can be easily solved using the multiple regression facilities available in most spreadsheet software
programs.
Although regression methods are easily applied to solve fisheries models, it has been
demonstrated that they can give very biased answers (Uhler, 1979). They can also produce obviously
229
wrong answers, such as negative values of r or q, which are biologically impossible. The general
corollary is that illogical answers only mean bad data!
10.6.2
Time-series fitting
The most recommended method to fit fisheries models to data is time-series fitting. According to
Hilborn and Walters (1992), this method was first proposed by Pella and Tomlinson (1969) and implies
taking an initial estimate of the stock size at the beginning of the time series of data (catch and CPUE)
and using the Schaefer model to predict each point in the entire time series of data. Initial parameters
values (guesses) are iteratively adjusted to minimize the difference (åt) between the observed CPUE and
the CPUE predicted by the Schaefer model:
∧
ε t = (U t − U t ) 2
(10.57)
Where U (CPUE) is:
(10.58)
∧
∧
U t = q Bt
This means that we have to estimate r, q, K, and the initial biomass size B0. Usually, the problem of
finding the best parameter values (while minimizing the above difference) is solved by using nonlinear
estimation procedures (such as those available in spreadsheets).
10.6.3
Introduction to Bayesian estimation
Bayesian estimation is the state-of-the-art and most powerful method for fitting fisheries models
to data. It is a very useful method because it allows the incorporation of previous knowledge we might
have about the system in question into the estimation process, effectively helping to find solutions that
make more sense. The types of additional information that can be incorporated into Bayesian estimation
are extremely varied and include items like fishery CPUE, independent survey CPUE, catches, estimates
of intrinsic rate of population growth from life-table analyses, biological limits, knowledge from similar
stocks, mark-recapture information, and others.
Another advantage is that Bayesian estimation is extremely useful because it tells us a lot about
the uncertainty of the parameter estimates. The estimation is based mainly on using previous knowledge
to assume a probability distribution for the parameters that will be estimated. This distribution is known
as the prior probability distribution or just “the prior”. Although relatively new in fisheries stock
assessments, Bayesian estimation has rapidly become the most powerful and accepted method to fit
models to data in recent years.
Bayes theorem is based on the conditional probability, and states that the probability of a
parameter or group of parameters given certain data is equal to the product of a) the probability of the
data given the parameters and b) the probability of the parameters themselves, all of this divided by the
sum over all possible parameter values of the product of a) and b):
230
Pr{parameters | data } =
Pr{data | parameters }∗ Pr{parameters }
(10.59)
∑ Pr{data | parameters}∗ Pr{parameters}
parameters
The left term of the equation is the posterior probability distribution or “posterior”. The right-most terms
on the upper and lower part of the equation imply that we have previous knowledge about the shape of
the distribution of the parameters. And this is the strength of the method as this is what allows us to
include additional “external” information into the estimation process, such as biological or fisheries
information we might have at hand.
Depending on the type of external information that we want to incorporate, there are different
possible prior distributions we can use for the parameters such as the binomial, normal, uniform,
Poisson, multinomial and others. For more details about the types of distributions for different types of
data users should consult a statistical text book.
A rudimentary but simple way to implement Bayesian statistics is to calculate the “kernel” which
is based on the sum of squares:
L( parameters) = SS
− t −21
(10.60)
where L is the likelihood of the parameters and SS, the sum of squared differences between the real data
and the estimated data points derived from a given set of model parameter values, and t-1 is the degrees
of freedom.
Pr( parameters | data) =
SS
− t −21
∑ SS
− t −21
(10.61)
parameters
Bayesian approaches have been recently applied to elasmobranch fisheries by McAllister and
Pikitch (1998a,b), Punt and Walker (1998), Babcock and Pikitch (2001), McAllister et al. (2001), and
Apostolaki et al. (2001, 2002) among others. Berger (1985), Gelman et al. (1995), and Congdon (2001)
provide a comprehensive treatment of Bayesian analysis. The reader is also referred to Hilborn and
Walters (1992), Quinn and Deriso (1999) and Haddon (2001) for a more in-depth treatment of parameter
estimation issues.
10.6.4
Data quality
An extremely important principle of practical fisheries science identified by Hilborn and Walters
(1992) and one often overlooked is that we cannot understand how a fish stock will respond to
exploitation until the stock has been exploited. A good stock assessment depends as much on having an
adequate model to describe the system dynamics as on the quality of the data that the model is fitted to.
Data quality does not only mean whether there are biases or errors, but also on how much information is
embedded in the data. Historical variation in stock size and fishing pressure is needed in the data in
231
order to estimate the parameters of the model with any degree of reliability. Otherwise the assessment
can produce a meaningless set of numbers that do not represent the stock dynamics well.
The most important quality of fisheries data is the degree of contrast imbedded in the data. In
order to obtain good parameter estimates data must have high contrast. Following with the SPM
example, ideally we should have a data point at low stock sizes with low fishing effort (for information
about r), a data point at high stock sizes with low fishing effort (to estimate qK) and a data point at high
fishing effort to estimate q. This is very difficult to find in a real fishery because of the way most
fisheries develop. Typically, low effort at large stock sizes is gradually increased to very high levels that
usually lead to low stock sizes. Thus we usually miss having a point of low fishing effort at low stock
sizes. This common way in which fisheries develop leads to the most uninformative type of data and a
typical case known as the “one way trip”, in which the data show an increase in effort with time that is
accompanied by a declining CPUE (see Figure 10.05). This lack of contrast in the data makes for very
uncertain parameter estimates. In general, the standard deviation of such parameters is as large as, or
larger than, the actual parameter values, clearly signaling very unreliable results. Under such
circumstances management will be severely handicapped.
CPUE
A typical
'one-way
trip'
A typical
“one-way
trip”
Effort
Figure 10.05 Hypothetical example of a “one-way trip” type of data (modified from Hilborn and
Walters, 1992).
Data with better contrast can be obtained when a fishery shows a period of increased effort
followed by a period when effort was reduced gradually such that the stock was allowed to rebuild after
heavy exploitation. This case has been termed by Hilborn and Walters (1992) as “moving up and down
the isocline”. Note from Figure 10.06 how there is a better scatter in the data points instead of all falling
along one single line as before. These data have inherently more variation and contrast than the
preceding example (the solid diamonds in the figure represent the start and finish points of the time
series). Typically, in these cases the model parameters are much more precisely estimated than in a “oneway trip” case, but the slow pace of change in effort in these data still does not generally provide enough
contrast for good precision. In cases like the one pictured in the figure, the standard deviation of the
232
parameters is usually about half or less than the actual parameter estimates and although not good
enough it is better than in the previous example.
CPUE
Data with better contrast
Effort
Figure 10.06 Hypothetical example of data with better contrast (modified from Hilborn and
Walters, 1992).
Data sets with high contrast have strong variations in the data, with relatively rapid changes
back and forth between high and low effort. In these cases, parameters can be much more precisely
estimated although other factors such as the total number of points in the time series of data and the
intrinsic variability of the data also have an influence on the final precision of model parameter
estimates.
In summary, when fitting models to fisheries data it is imperative to look at the uncertainty in the
parameter estimates, not only at a single “goodness-of-fit” measure such as the sum of squares. It is
always advisable to apply different models to the same data set and compare the results between models,
trying to validate results or to ask questions about why results might be different and what the
implications of this are. In addition, it is important to learn how to use uncertain (“bad”) results to try to
improve the contrast in the data through carefully thought and well planned management regulations
aimed at improving the quality of the data (such as large variations in effort over short periods of time).
10.6.5
The relationship between CPUE and abundance
At the core of most fisheries models that make use of fisheries-dependent CPUE information
(and most of them do) there is an important assumption: the abundance of the fish stock (or other
aquatic animal) has a direct relationship with CPUE. In other words, these models assume that CPUE is
an index of abundance. This can be expressed mathematically for fisheries where the fishing season
occurs as a single pulse or over a relatively short part of the year as:
Ct = qf t Bt
U t = qBt
where Ut is CPUE at year t. According to the last expression, CPUE is directly linked to biomass
(abundance) by a constant factor q, known as catchability factor. The above model assumes that there is
proportionality (a linear relationship) between CPUE and the abundance of the stock. This is a very
233
dangerous but necessary assumption of most fisheries models, but one that should be questioned and
checked for. According to Hilborn and Walters (1992) the relationship between CPUE and abundance
can have at least two other forms apart from the linear form. Hyperdepletion occurs when the stock
abundance decreases at a much slower rate than the CPUE. Thus the CPUE signal tells us that the stock
abundance is low when it is still high. If we do not detect that this is a case of hyperdepletion, we would
believe that we are overexploiting the stock when in fact the stock might be in a good state.
Hyperstability happens when the stock abundance falls more rapidly than the CPUE index, thus giving
us the opposite impression, that the stock abundance is still high when in fact we might be already
dangerously overexploiting the resource.
Hyperdepletion can occur when the species is being exploited only over a relatively small part of
its range, as when there are natural refuge areas (such as deeper waters or rougher grounds where the
gear cannot fish). In such cases the exploited part of the stock will decrease rapidly but the overall
abundance of the entire stock might not. Given that the abundance index (CPUE) is based only on the
fishing grounds, it will show a faster decrease than if it was based on fishing over the entire geographical
range of the stock.
Hyperstability is a well-known phenomenon in fisheries for highly gregarious or schooling
species such as herrings, sardines, anchovies, and tunas. In these fisheries, searching for fish schools is
highly efficient and fishing an entire school once located is also relatively quick and efficient, while the
remaining schools remain concentrated as the overall abundance of fish goes down.
Possible ways to detect a lack of proportionality between CPUE and effort include mapping and
stratification of CPUE and effort data to analyze spatial patterns, and depletion experiments to gain
additional information. Overall, hyperstability is far more common in the real world apart from being
more dangerous, as it leads to stock collapses. However, a more straightforward, if not easier, way
around this is to obtain fishery-independent indices of stock abundance (see Chapter 12), either through
research cruises or by coordinating efforts with fishermen to perform orchestrated experiments to fish in
other areas or other ways than they would usually do, such as following a systematic sampling design.
Quinn and Deriso (1999) summarize different ways to model non-linear relationships between CPUE
and abundance.
Finally, it should be mentioned that generalized linear models (GLMs) are becoming common
practice to standardize fishery-dependent CPUE data. These methods take account of the effect of
various factors (such as environmental variables or fishery operational variables) on catch rates.
10.7
CONCLUSIONS AND RECOMMENDATIONS
Fisheries stock assessment in not really a problem of the species or group under analysis but
rather a problem of the approach used for the analysis. There are several methods available to perform
stock assessment and some of them have been presented here in detail. However, the important message
234
to take home and the one to keep always in mind is that there are three main rules for good stock
assessment:
1. The data drive the analysis, and although we should always try to do the best we can with
whatever data we have, only complete and good quality data will provide us with reliable
assessments in the long run. Having limited or incorrect data will always provide only limited
and uncertain advice no matter which models are used. The main focus and problem for
elasmobranch stock assessment is not the model used, but the data that are available. For this
reason, fisheries managers should strive to build the necessary systems to collect the appropriate
information needed for stock assessment.
2. There is no single “best” model that should be used for fisheries stock assessment. The best
assessment is one that uses ALL the models that can be applied depending on available data, and
compares the results of all models to detect inconsistencies, coincidences, and patterns. A
complete picture of the situation can only be obtained when we question the conclusions from
one analysis with those of a different analysis and critically use the different results to gauge our
conclusions, improve the data and therefore have the capacity for better assessments in the
future.
3. Stock assessment is a neverending and dynamic process. It is one in which we use the models
not only to decide how many fish we should take next year or how many fishermen we should
allow to fish, but also, and perhaps more importantly, to set goals about the ways in which we
obtain our fisheries data, the type of data we are lacking (including biological and ecological
information) and that must be obtained from this point onwards in order to improve the quality
of our assessments. Fisheries stock assessment must be a feed-back system in order to be
successful.
Table 10.04 presents a few examples of real elasmobranch fisheries with a list of their
characteristics, the methods used in each case for stock assessment, the status of the fishery and major
references. These examples can be reviewed more closely by those interested in more detailed analyses
of real elasmobranch fisheries and the practice of their stock assessment and management.
235
Fishery
Species
Management
System
Catch level
Stock Assessment
Methods
Southern
Australian shark
fishery
Galeorhinus galeus,
Mustelus antarcticus
and other spp
2,800 t/y
Controls on
amount of gear
(licenses)
Canadian
Porbeagle shark
fishery
Lamna nasus
850 t/y
TAC (250 t), Fishing Catch curves, catch
licenses plus fishing rate trends, agerestrictions
structured model
New Zealand
shark fisheries
Galeorhinus galeus,
Squalus acanthias,
Callorhinchus milii,
17,000 t/y
Mustelus lenticulatus,
Raja spp. Hydrolagus spp.
and other 15 spp
Status
Main
References
Surplus Production,
Overexploited, under
Delay-difference and
recovering
Walker 1999
Age-structured models regulations
Overexploited, under
Campana et al.
severe recovering
1999, 2001
regulations
ITQs and TACs
None, quotas
established through ad Recovered after
overxploitation or
hoc methods
unknown
(proportion of past
catches)
Bayesian Surplus
Production Models
Francis and
Shallard 1999
Overexploited, under MacAllister and
recovering
Pikitch. 1998a, b;
regulations
Branstetter 1999
East coast of US 39 species mostly
shark fishery
Carcharhinus
3,500 t/y
TAC
Gulf of Mexico
shark fisheries
35 species mostly
Carcharhinus
12,000 t/y
5 prohibited
species and other
simple regulations
None
Unknown, likely
Bonfil 1997,
heavily overexploited Castillo et al. 1998
Argentinean
shark fisheries
Mustelus schmitii,
Galeorhinus galeus,
30,000 t/y
Carcharhinus brachyurus
and other 10 spp
None
None
Unknown, likely
Chiaramonte 1998
heavily overexploited
Table 10.04 A referenced selection of real shark fisheries, summarizing their main characteristics,
the assessment methods in use and the state of management and the resource.
10.8
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DERISO, R. B. 1980. Harvesting strategies and parameter estimation for an age-structured model. Can. J.
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GRANT, C. J., R. L. SANDLORD, AND A. M. OLSEN. 1979. Estimation of growth, mortality and yield per
recruit of the Australian school shark, Galeorhinus australis (Macleay), from tag recoveries.
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__________, AND C. J. WALTERS. 1992. Quantitative fisheries stock assessment: choice, dynamics and
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__________. 1977. Chapter 9: Elasmobranchs, p. 187-215. In: Fish Population Dynamics. J. Gulland
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__________, C. J. WALTERS, AND J. COOK. 1988. Comparison of two models and two estimation methods
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240
CHAPTER 11.
FISHERY-DEPENDENT SAMPLING: TOTAL CATCH,
EFFORT AND CATCH COMPOSITION
Alexia C. Morgan and George H. Burgess, Florida Museum of Natural History, University of Florida,
Gainesville, FL 32611 USA
11.1
11.2
11.3
11.4
11.5
11.6
11.7
11.8
11.9
11.10
11.11
11.12
INTRODUCTION
CATCH ESTIMATES
11.2.1 Why and how to collect catch estimate data
11.2.2 Catch disposition
11.2.3 Bycatch
CATCH PER UNIT EFFORT (CPUE)
11.3.1 Definition of CPUE
11.3.2 How to collect CPUE data
11.3.2.1 Gillnet fishing gear
11.3.2.2 Longline fishing gear
11.3.2.3 Trawl fishing gear
11.3.2.4 Purse seine fishing gear
LANDINGS
11.4.1 Landing reports
11.4.2 Problems associated with species identification
FISHING MORTALITY
11.5.1 Alive vs. dead at time of capture
FISHING AREA
11.6.1 Recording fishing location
11.6.1.1 Different methods for recording location
11.6.2 Catch time series
SPECIES IDENTIFICATION
11.7.1 Importance of accurate species identification
11.7.2 Problems with species identification
11.7.3 Materials used for species identification
11.7.4 How to collect species-specific data
SIZE
11.8.1 Importance of size structure in shark fisheries
11.8.2 Fisheries targeting size classes
11.8.3 Weight and morphological measurements on land and at sea
SEX
11.9.1 Segregation
11.9.2 Identification of males and females
11.9.3 Reproductive data collection
AT-SEA VS. SHORESIDE SAMPLING
11.10.1 Fisheries observers
11.10.2 Shoreside sampling
11.10.3 Logbooks
11.10.4 Telephone and dockside sampling
CONCLUSION
REFERENCES
241
242
11.1
INTRODUCTION
Fishery-dependent data collection is one of the most valuable tools available to fishery manag-
ers. The management plans put into effect based on this type of sampling will only be as good as the
data collected. It is critical that managers determine what is the most important data to be collected
and implement some system of data recording before signs of overfishing occur. One of the biggest
mistakes fishery managers make is waiting until the populations are in peril before initiating some type
of management plan. This chapter provides a wealth of information on what type of data should be
collected in a shark fishery, why they should be collected and what methods can be used for collection.
11.2
CATCH ESTIMATES
11.2.1 Why and how to collect catch estimate data
Fisheries resource managers must rely on several key factors in determining the status of a
fishery. Among these factors are the catch estimates for both target species and any bycatch involved
in the fishery, or of all species in a multi-species fishery. Each individual fishery should maintain a
continuous database that includes all reported catch, estimates of discard, and estimates of nonreported catch. Catch estimates can be obtained in a variety of ways including fishery observers,
logbooks, dockside and shoreside monitoring. Each of these monitoring systems will be discussed in
more detail later in this chapter.
Catch estimates are used to illustrate the species composition of individual fisheries and
utilization rates, monitor quotas, estimate fishing mortality, and to calculate Catch Per Unit Effort
(CPUE). These estimates include not only what is sold at port, but also that which is discarded or
utilized as bait at sea and that which is retained for personal consumption or transferral by the vessel’s
crew. In other words, all fishes retained or discarded should be documented. This type of information
becomes extremely important in fisheries where quotas are used as a management tool. Catch
estimates allow managers to determine the current status of a fishery and whether the quotas have
been met, are being underutilized or have been exceeded. The data produced from catch estimates
can also be used to show historical trends in the fishery commonly used to build quota systems, and
estimate the population abundance. These numbers can also be integrated into models to predict the
outcome of future management plans or what effect current management will have on the stock.
Catch estimates are critical and can be a very contentious shark fishery management issue in
countries with well-developed fisheries and fishery management regimes. Catch numbers data often
come to fishery managers from vessel captains/owners or shoreside marketers who, understanding
that high catch figures might lead to management resulting in reduced future catches, are prone to
underreport the actual catches. However, in areas with government-run fisheries, the opposite may be
true as fishers and marketers are inclined to demonstrate higher productivity to their superiors. In
Individual Transferable Quota fisheries, fishers may overreport their catch in order to ensure a large
243
individual quota. Since managers who determine the status of a fishery use these data, underreporting
or overreporting can result in inappropriate or unfair management measures, such as unreasonably
high quotas, and can lead to overfishing, which ultimately negatively affects all stakeholders. It is
imperative that every effort be made to monitor the accuracy of all catch estimates.
11.2.2 Catch disposition
In areas where not all the catch is marketed, at-sea monitoring provides the most robust catch
data. At sea, fishery observers should accurately record the number of individuals by species, note
whether the shark is alive or dead when landed, and record the final disposition of each shark brought
aboard a vessel. Disposition is the final fate of the shark, (e.g., saved for market, used for bait, discarded live, discarded dead, discarded after removing fins, etc.). On field data sheets, codes should be
made for each possible disposition that are both easy to use and to remember; commonly, initials or
letters are used that correspond to each type of disposition.
Disposition estimates for individual species allow fishery managers to better understand what
is actually happening in the fishery. For example, in the U.S. Atlantic shark fishery, several hammerhead species are commonly caught but not landed (because their flesh is not marketable). Therefore,
the catches of these species do not appear in market or dockside data sets. Disposition data taken by
at-sea observers allow fishery managers to acknowledge the cryptic mortality incurred by all species
caught and can help detect declines in abundance. At-sea catch estimates often give a very different
view of what is actually happening in a fishery than landings (marketed catch) data. However, in areas
where the entire catch is brought back to port, landings data accurately depict the scope of total fishing
mortality (but not the gear-induced fishing mortality).
11.2.3 Bycatch
Bycatch is a common side effect of directed fisheries, its level depending upon the type of
gear employed and amount of effort expended. Sharks commonly are caught as bycatch in a number
of directed fisheries such as the oceanic tuna and swordfish longline fisheries; inshore and offshore
gillnet fisheries targeting mackerels (Scombridae), herrings (Clupeidae), and other species; and shrimp
trawl fisheries. The catch numbers, mortality, and disposition for all of these sharks must be recorded
in the same manner as in directed and multi-species fisheries.
11.3
CATCH PER UNIT EFFORT (CPUE)
11.3.1 Definition of CPUE
Catch per unit effort (CPUE) is a ratio commonly used to eliminate temporal and regional
trends in fish stock abundance. The “catch” portion of the measure may be expressed as the number
or weight of the entire catch, a selected subset of the catch, or a particular species in the catch. The
“unit effort” portion of the rate usually refers to the time a uniformly designed and employed piece of
fishing gear is deployed in the water. In the absence of uniform gear use, CPUE can be applied on a
244
coarser scale utilizing whatever effort data is available. Units of effort are dependent on the type of
fishing gear used and can use (in increasing levels of finescale reliability) such measures as the
numbers of vessels, vessel-days, gillnet or longline sets or number of hook hours, and trawl or gillnet
hours. Many aspects of the fishery can be monitored utilizing CPUE analysis, including trends in
overall fishery catch rates, catch rates of target vs. bycatch species, catch rates in specific depth
strata, seasons or subregions, catch rates of size classes and sexes, and catch rates of specific vessels
or types of vessels.
CPUE is a much more powerful tool than catch data alone. A decline in CPUE over a time
period is usually a good indication that stocks are declining. However, advancements in fishing gear,
improvements in fishing abilities of captains and crews, and changes in fishing grounds, current patterns or weather can influence CPUE trends. Interpretation of CPUE data, therefore, must be undertaken with knowledge of such potentially contributing factors. See Chapter 10 (section 10.6.5) for
further discussion of CPUE.
11.3.2
How to collect CPUE data
11.3.2.1 Gillnet fishing gear
The important characteristics of gillnet gear
include total net length; mesh size; number of panels;
panel length and depth; water depth at deployment;
deployed depth in the water column (bottom, midwater
or surface set); orientation of the set (parallel or
perpendicular to shore or current); and soak time (time
the gear is in the water) (Fig. 11.01). The type of
information fisheries managers are seeking from
CPUE data dictates the catch and unit effort measures
used to calculate CPUE. The following are examples
of possible CPUE calculations:
CATCH RATE OF FEMALE SHARKS CAUGHT PER PANEL
HOUR.
For this calculation, we must know the total
hours the gear was in the water during the entire
fishing period, how many panels were in the water
during that time period, and how many female sharks
were caught during the time period. Consider a situation in which the total fishing hours was 300, the total
panels fished was 5, and total number of female sharks
caught was 10. Unit effort is calculated by multiplying
245
Figure 11.01 Three variations in the
placement and design of gillnet fishing
gear. The net floats and anchors are all
visible (courtesy of NOAA).
the total hours (300) by the total number of panels (5), resulting in 1500 panel-hours of effort. The
female catch (10 sharks) then is divided by the panel-hours (1500), resulting in a CPUE of 0.0067
females per panel-hour. If a CPUE measure is a small number, as in this case, the CPUE’s numerator
and denominator often are multiplied by an exponent of 10 (e.g., 10, 100, 1000) to produce a larger and
more easily expressed CPUE numerator. For example, if our CPUE of 0.0067 female sharks caught
per panel-hour is multiplied by 1000, the result is a more readily understood catch rate of 6.7 sharks
caught per 1000 panel-hours.
CATCH RATE OF SHARKS VS. OTHER SPECIES IN 100 MM MESH PANELS. Here we need to know the
total hours the 100 mm panel gear was in the water during the entire fishing period, the catch of sharks
in these panels during that period, and the catch of other species in these panels during the same time
period. If 2000 kilogram (kg) of sharks and 4000 kg of bycatch species were captured during 1000
hours of fishing, the calculated CPUE of sharks would be 2.0 kg per hour (2000/1000) of fishing of
100 mm panels and the CPUE of other species would be 4.0 kg per hour of fishing of 100 mm panels
(4000/1000).
11.3.2.2 Longline fishing gear
Longline gear characteristics include mainline
length; gangion length, number, size and type of hooks;
water depth at deployment; where deployed in the
water column (bottom, midwater depth or surface set);
orientation of the set (parallel or perpendicular to shore
or current); and soak time (Fig. 11.02). As with gill
nets, the types of catch and unit effort measures used
by fisheries managers to calculate CPUE are based on
the specific information they are seeking. The following are examples of possible CPUE calculations:
CATCH RATE OF SHARKS TAKEN IN DEPTHS OF 2550 M. To calculate the catch rate of sharks per hookhour, one must know the total number of sharks
captured while fishing in depths of 25-50 m, the total
number of hooks used while fishing in this depth range,
and the total time the gear was in the water in this
depth range. Assume 12 sharks were caught on 100
Figure 11.02 Diagram of pelagic longline gear
(courtesy of National Marine Fisheries Service).
246
hooks fishing for 12 hours. The fishing effort, then, is 1200 hook-hours (100 hooks x 12 hours) and the
CPUE is 0.01 sharks per hook-hour (12/1200), which also can be expressed (after multiplying by 100/
100) as 1.0 shark per 100 hook-hours fishing at depths of 25-50 m. CPUE expressed as catch per
hook-hour or as expressed by number of hooks are the preferred measures of expressing longline
CPUE; the alternative, catch per set, is less useful because the number of hooks and set time varies
from vessel to vessel and from set to set.
CATCH RATE OF SHARKS TAKEN BY AN ARTISANAL FISHING VILLAGE DURING THE MONTH OF JANUARY.
Sometimes minimal data is all that is available for calculating CPUE. For instance, consider a situation
where the only measure of catch is an artisanal fishing village’s monthly sales of fins to a fin dealer.
Having obtained that number, a crude CPUE can be calculated even if minimal effort data is available.
An estimate of the number of fishing vessels can be derived from vessel counts at the port. Thus,
CPUE based on 700 kg of fins originating from 7 vessels fishing in January would yield a CPUE of
100 kg of fins per vessel per month. Effort data might be refined if interviews of fishers revealed that
those vessels fished only five days per week throughout the month. That information would produce a
new effort of 140 vessel-days (7 vessels x 5 days x 4 weeks) and a more meaningful CPUE of 0.5 kg
of fins per vessel-day (700/140). If a count of the fins sold also is available, then an estimate of the
number of sharks caught can be made after interviewing fishers to learn the number of fins that are
harvested from an individual shark. If four fins are routinely taken from a shark, and the 700 kg of fins
represented 1120 fins, then the January catch was 280 sharks (1120/4) and a much refined CPUE of
2.0 sharks per vessel-day (280/140) is generated.
11.3.2.3 Trawl fishing gear
Trawl CPUE is usually determined as
the catch per hour of bottom trawling time.
Variables that affect trawl CPUE include
mesh size; length and width of net; distance
between trawl doors; lengths of bridles and
foot rope; length and depth of float line; time
of trawling; presence of a Turtle Excluder
Device (TED), Bycatch Reduction Device
(BRD), beam, “tickler chain,” or rollers; and
cod end mesh size and configuration (Fig.
11.03). Standardization of gear type employed,
trawling speed, and time of trawling greatly
Figure 11.03 A shrimp boat rigged with otter
trawl gear. Floats, nets, and doors are all
visible (courtesy of NOAA).
increases the reliability of generated CPUE’s.
247
Non-standardized trawl gear and methodologies can result in considerable variation in CPUE’s, making
data suspect. (See Chapter 12.)
Figure 11.04 Fishing vessel pulling
in a purse seine net. The floats, net,
and circular enclosure are all visible
(courtesy of NOAA).
11.3.2.4 Purse seine fishing gear
Purse seine nets vary in circumferential length and depth, mesh size, and ability of the fishing
crew (Fig. 11.04). CPUE is usually calculated as the number of sharks caught per set, but, as in
trawling gear, standardization in gear type greatly facilitates comparisons of CPUE’s.
11.4
LANDINGS
11.4.1 Landings reports
Landings reports are one part of the process of estimating total catch and are also used to
show how many of each species of shark are brought to port for distribution or sale. There is often
quite a difference in the number of sharks caught and the number of sharks actually landed. Historical
landings data can be used to correlate increases and decreases in certain species landings to changes
in the local market and export demand. Management plans that use quota systems often use only the
reported landings against the quota. This is a biased assessment of the actual catch; because many
sharks may be discarded at sea, there is commonly underreporting (and occasionally overreporting) of
the landed sharks, and sharks are difficult species to identify. A well-designed management plan will
utilize both catch and landings data.
11.4.2 Problems associated with species identification
A major shortcoming in using landings data is the common lack of species identification. In
many shark fisheries, the sharks are dressed at sea in order to ensure high quality of the flesh. Properly dressing a shark involves removing the head, fins and entrails as soon as possible after being
caught (often after bleeding the shark by removing the caudal fin at the caudal peduncle, see Chapter
14) (Fig. 11.05). This makes it nearly impossible to accurately identify sharks to species at landing. If
248
proper identification is not made at sea, then the landings reports will only reveal the total number of
sharks caught and cannot be used to show trends in species abundance. There are a few exceptions to
this, including the landings of sharks with telltale external coloration or morphological features such as
tiger, leopard, whale, blue, white and mako sharks. Some regional guides to carcasses (called “logs”)
or to fins may be available.
Carcassed landings also eliminate the ability to
record the total size or weight of a shark. Measurements
of sharks at the dock after they have been dressed are not
accurate. Fishermen use different dressing techniques and
so measurements of a carcass will not be a true indication
of the size of the shark, but rather the style or ability of the
fishermen. However this can be solved by developing length
relationships by species and relating interdorsal distance to
total length (see Moutopoulos and Stergiou, 2002 for examples of length-length relationships in teleosts). The weight
of landed sharks is more easily measured on shore than at
sea, but trying to convert from dressed to whole weight can
be tricky because conversion factors may vary between
Figure 11.05 Dressed carcasses
ready for sale in the U.S. (courtesy
Florida Museum of Natural History).
fishers and over time. In addition, sex and reproductive
maturity cannot be determined after the shark has been
dressed. Quantification of bycatch is also lost using landings
data, as is information on cryptic mortality (e.g., freshly-caught sharks used as bait at sea) and vitality
(alive or dead) of captured sharks.
Landings data are easy to obtain because it is done on land, the sharks are dead, and there is
usually more space and equipment available. However, because of the limitations noted above, landings records offer a restricted amount of pertinent information and should be used with discretion.
11.5
FISHING MORTALITY
Fishing mortality is a very important but sometimes underreported aspect of fishery-dependent
monitoring. The more than 400 species of modern sharks have evolved from multiple phyletic lines,
occupy a wide range of habitats, and engage in a variety of life-styles concordant with their morphological and physiological attributes. Individual species react differently to being hooked or ensnared in a
net. The respiratory mode of a species—in particular, whether a species utilizes ram-jet ventilation
(and thus must constantly be in motion to respire) or can actively pump water over their gills—is the
largest single factor affecting survival time after initial capture in fishing gear, but preexisting physiological stress, ontogenetic stage (size), and soak time are factors as well.
249
11.5.1 Alive vs. dead at time of capture
The condition, alive or dead, of every shark that is caught, whether targeted or taken as
bycatch, should be recorded. This condition does not refer to the final fate of the shark, rather to its
status—alive or dead—when initially removed from the fishing gear. There are a number of shark
species, notably the tiger (Galeocerdo cuvier), blue (Prionace glauca), sand tiger (Carcharias
taurus), and many orectolobiform species, including the nurse shark (Ginglymostoma cirratum), that
typically survive longer than other sharks when taken on a longline hook or in a gillnet. In some regions
these species are considered of low market value and often are returned alive to the sea. By contrast,
species like the dusky (Carcharhinus obscurus) and hammerhead (Sphyrna spp.) sharks have
decidedly short survival times when captured in fishing gear. Managers that solely rely on landing data
and/or catch estimates without considering the at-vessel fishing mortality of all species may be inclined
to overlook the need for management of a significant segment of the fishery. Knowledge of the high
fishing mortality these species endure may affect a fishery regulator’s choice of management measures. For example, at the time of writing, the dusky shark was prohibited from being landed in the
northwest Atlantic waters of the United States. This regulatory measure, which might appear to be a
well-considered tool enacted to eliminate fishing mortality, actually is largely ineffective because about
70% of longline-caught dusky sharks are dead by the time the fishing gear is retrieved. This type of
management measure, therefore, has a limited effect on conserving the dusky shark population and an
alternative strategy aimed at keeping fishers away from dusky concentrations should be considered.
See Chapter 13 for further discussion of management measures.
11.6
FISHING AREA
Development of preferred fishing areas is dependent upon vessel size and cruising range, the
availability of targeted species and size classes, weather, currents, and bottom configuration. Recording accurate fishing locations associated with catch data allows fishery managers to distinguish geographical variability in catch rates, denote changes in the activities of the fishing fleet, and determine
sub-population differences in life history parameters of target and bycatch species. Significant declines
in regional catch rates should be examined carefully because such trends often are indicative of
localized overfishing.
11.6.1 Recording fishing location
The most specific and preferred way to report fishing location is by recording the latitude and
longitude of every set. Usually those coordinates are recorded as gear first enters the water, at the
point all gear is deployed and effective fishing has begun, as retrieval of gear begins ending effective
fishing, and at the time all gear is returned to the vessel. Total water depth, fishing depth, and time of
day also should be recorded at each of these four events, the latter of critical importance in calculating
250
accurate fishing effort—see section 11.3. Recording the locations and depths at times of release and
retrieval is important even when anchored gear such as some longlines and gillnets are employed
because these gears often are moved by currents and waves, or they may be picked up intentionally or
mistakenly by other vessel operators and dropped off at a different location. Similarly, a single bottom
trawl may cover a range of water depths and varying seafloor topography. This information is entered
into a database and can be plotted to show all the locations and depths where sets are being made.
11.6.1.1 Different methods for recording location
Most commercial fishing vessels from developed nations have GPS or LORAN systems on
board. For those that do not, a hand-held GPS can be used to determine location. Biologists working
aboard vessels in regions where such gear is routinely absent must determine the best way to record
an equivalent form of this information. For nearshore fisheries, distance from shore and landmarks
such as shore structures, islands, rock formations, inlets, or channels can be used to develop an
approximate location. In the Maldives, fishers locate their local fishing grounds by counting the number
of oar strokes from port (Anderson, 1993). When monitoring fisheries from shore, interviews with
fishers may reveal which fishing grounds, reefs or banks were visited on a given trip. If only fishing
range of a vessel or fleet is known, a semi-circle originating from the home port can be constructed
using that range as the radius.
11.6.2 Catch time series
Catch time series from frequented areas are monitored to determine changes. Prime fishing
grounds such as the banks and reefs are exceptionally vulnerable to overfishing because they are easy
to find, may host a variety of harvestable species, and may support multiple target fisheries. Fishers
exploit such areas until catch rates drop so low that they are forced to move to new locations. Nursery
areas, critical regions where young sharks congregate, also are prime fishing sites because of shark
abundance and relative ease of capture. These fishing areas are extremely susceptible to fishing
pressure and regionally-specific management is required to prevent localized overfishing. Management
measures used to alleviate these problems may include area closures, seasonal closures, and regional
fishing area quotas (Shotton, 1999).
A detailed analysis of fishing locations utilized by a specific fleet using catch or CPUE time
series often shows clear trends. These changes may simply reflect natural temporal variation in shark
populations or may be directly attributable to the effects of fishing mortality. They also may be the
result of changes in fishing practices, such as moving fishing effort to new target species or different
size classes, altering fishing gear, and increases in the fishing ability of fishers. If major changes are
observed, further analysis must be undertaken to determine which factors are important. Catch
estimates, changes in fishing practices landing reports, market values, and export data are useful clues
used in determining the influences of change.
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11.7
SPECIES IDENTIFICATION
11.7.1 Importance of accurate species identification
Accurate identification of individual shark species is one of the most important and difficult
aspects of fishery-dependent sampling and is integral to good fishery management. Many directed
fisheries target a large suite of species and bycatch of sharks in other fisheries often involves multiple
species. Many groups, especially the requiem sharks of the genus Carcharhinus (Carcharhinidae),
some triakids (particularly Mustelus spp.), catsharks of the family Scyliorhinidae, and squaloid sharks
(spiny dogfishes and their kin) often look very similar to the untrained eye and even experts may have
difficulty in identifying some species. The skates and rays (Batoidea) are also difficult to identify and
there are many species still awaiting formal scientific description (see Chapter 3). In many areas these
difficulties in species identification have lead to aggregated data simply recorded as “shark” (for all
chondrichthyans), as “shark” or “ray”, or as only slightly narrower categories such as “large shark”
and “small shark” (e.g., as “tiburon” and “cazón” in Mexican shark fisheries; Bonfil, 1997). Vernacular
names of sharks frequently vary between geographic regions and should not be the only form of
identification utilized in data taking. Use of the Latin binomial (“scientific name”)—genus and species—eliminates any confusion between regional vernacular names. Every effort should be made to
make sure that the total catches of all sharks—be they targeted or bycatch—are correctly identified to
species level. In the Maldives, the vernacular name of sharks varies from island to island (Anderson,
1993).
Shark catches need to be reported at the species level to facilitate better fishery management.
Lack of species-specific data has forced many countries to report national catches and/or manage
their sharks using designated multi-species groups. Japan reports its shark catch in three broad groups,
“pelagic”, “benthic” and “coastal.” These groups are reflective of the fisheries targeting them, namely
the tuna longline, trawl, and “other” fisheries, respectively (Nakano, 1999), rather than biological
similarity. The United States places 39 species into three groups, “large coastal”, “small coastal” and
“pelagic.” Individual shark species, primarily taken in the pelagic longline, bottom longline and drift
gillnet fisheries, are placed into these management groups based on their broad habitat preference and
similarity of appearance. As fishing effort increased, it became evident that certain species could not
withstand the same fishing pressure as others within a management group, resulting in sharp declines
of certain species and revealing the inherent difficulty associated with managing a multi-species
fishery. Recording of species-level data and associated advances in understanding of biological attributes of the affected species now allows fishery biologists to fine-tune the management process.
11.7.2 Problems with species identification
Lack of species-specific data collection forces fishery managers to use aggregated shark data
in their analyses. This can lead to mismanagement because of the large variation in life history patterns
252
exhibited by individual shark species. The lack of species-specific reporting is a global epidemic in
shark fishery management. According to the United Nations Food and Agriculture Organization (FAO)
records (Shotton, 1999), in 1966 15.8% of reported world shark and ray landings were identified to
species level, 30.9% to genus and 53.7% to order. Thirty years later, only 8.8% of the world catch was
reported to species level, 18.4% to genus and 55.3% to order. Six entire FAO reporting regions, the
Atlantic West Central, Eastern Indian Ocean, North Eastern Pacific, Eastern Central Pacific, Western
Central Pacific and Southwestern Pacific, did not report any catches to species level in 1996, and the
six countries that lead the world in reported chondrichthyan landings (Indonesia, India, U.S.A., Pakistan, Mexico, and Taiwan) did not report any catch at the species level. Of those, only the U.S.A.
reported at the genus level.
11.7.3 Materials used for species identification
Prior to the start of a shark fishery or as soon as possible after its start some type of species
identification reference guide should be made available to fishers, observers, fish marketers, and any
others who will be responsible for recording catch or landing data. Identification guides vary in complexity based on the diversity of species present or captured in a region, the difficulty in distinguishing
similar species, the level of education or training of the intended audience, and the resources available
to the author producing the guide. See Chapter 3 for a list of some regional identification guides.
These guides are readily usable by trained fishery observers or other working biologists, but
consideration should be given to developing a simpler layout for use by fishers and marketers (Fig.
11.06). Most useful for field use are abbreviated, identification-only publications printed in a small
format or as laminated cards such as Casey (1964), Schwartz and Burgess (1975), Castro-Aguirre and
Perez (1996) and Castro (2000 a,b). Books or large guides are too bulky and too complicated for most
fishers and marketers, who are not prone to devote much time to leafing through large volumes in
order to identify a species. Lack of literacy is a problem in many regions, as well. An alternative
means of increasing the quality of identification is provision of an appropriate-sized poster outlining the
key differences among species. Such a poster can be posted on the wall of a cabin or wheelhouse
aboard a vessel or in a fish market. If taking a guide or poster to sea is not practical because of limited
vessel size, fisher illiteracy, or fiscal restraint, data takers should receive introductory identification
training to the shark fauna they will be encountering.
11.7.4 How to collect species-specific data
To facilitate data taking, a unique species code should be assigned to each shark taken in the
fishery. Simple combinations of the first letters of the genus and species or the universally accepted
vernacular name are easy to remember and to record quickly (Fig. 11.07). Requiring data recorders to
write an entire shark name on a data sheet is too time consuming and will result in missing or faulty
253
Figure 11.06 Example of a simple layout for species identification (courtesy of Florida
Museum of Natural History).
data. As noted above, the use of vernacular names is discouraged unless the name is uniformly used
throughout the recording area.
11.8
SIZE
11.8.1 Importance of size structure in shark fisheries
The sizes of all sharks in the catch should be consistently and accurately taken. This can be
an arduous task and may be unrealistic for some fisheries. Such data is critical because many species
of sharks show dramatic population declines when certain size/age classes are targeted. During the
1940’s in Australia intense fishing for adult school sharks lead to severe reductions in abundance and a
subsequent change in the fishery. Fishers were forced to move further offshore and further from home
in order to catch sub-adult sharks to make up for the loss of the adult population (Walker, 1999).
Temporal shifts in the size of the catch can signal overfishing, but this may also be the result of changing fishing practices. The dusky shark in the western North Atlantic, before becoming a prohibited
species, was a target of both recreational and commercial fishers. Specimens from all size classes
were heavily targeted, which consequently lead to one of the most dramatic population declines in
recent history.
254
11.8.2 Fisheries
targeting size classes
Many fishers target
specific size classes of sharks,
while others are forced to do so
because of enacted management
regulations such as time/area
closures or size limits. Size limits
are an efficient way to protect
selected age classes from overfishing, but the size of the sharks
being taken in the fishery must
be known in order to determine
the potential effect of the
measure. The regional market
demand for sharks often is size
specific. In Mexico, for example,
sharks are sold as either “cazon”
(>150 cm) or “tiburon” (<150
cm) and receive different prices
per kilogram (Bonfil, 1997).
Prevailing weather patterns also
can force fishers to set their
gear repetitively on certain
fishing grounds, which may lead
to an increase in the catch of
certain size classes.
11.8.3 Weight and
morphological measurements on land and at sea
Figure11.07 Species codes used in a commercial shark
fishery observer program (courtesy Florida Museum of
Natural History).
Recorded weights of
landed sharks are also used to show trends and shifts in the fishery. Most fisheries measure the
quantity of landed sharks as dressed weight metric tons (dw mt). Landing tonnages often are used as
surrogate indicators of catch increases and decreases. This can be very misleading if the sizes and
255
numbers of sharks being caught are not reported as well. In the absence of numerical data, potential
shifts in the size composition of the catch will be missed.
A variety of measurements are taken on sharks, including fork length, total length, precaudal
length, first dorsal rear insertion to precaudal pit, eye to eye (for hammerhead species) and other
miscellaneous measurements (see Chapter 3). The three most frequently used measurements are fork,
total and precaudal length. When only a single measurement can be taken, fork length is the choice of
most shark biologists because it provides a consistent measure of body length (see below). All data
takers should employ consistent modes and units of measurement; the metric system is preferred
internationally. It is not unusual to find the tip of the upper lobe of the tail damaged or missing owing to
a previous injury, or as the result of shark-on-shark scavenging prior to retrieval of fishing gear, or the
upper lobe cut off by fishers immediately upon being brought onboard. In these cases alternative
measurements should be taken. Measuring from the tip of the snout to the precaudal pit, the distinct
notch located just anterior to the caudal fin, is a good alternative when the caudal fin is damaged or
missing. A measurement from the rear base of the first dorsal to the precaudal pit also is useful,
especially if only butchered carcasses (heads, tail, and fins removed) are available. If tail amputation
removes the caudal pit, a measurement from the rear margin of the first dorsal fin to the anterior
insertion of the second dorsal fin is a good substitute. For each species taken in the fishery, one should
take several of the measurements noted above on each of at least 30 individuals in order to develop
statistically significant correlations between those measurements. These relationships allow fishery
biologists to convert an alternative measurement into a missing desired primary measurement, for
example fork, total or precaudal length.
If sharks are landed at market whole, measurements can be made at that time. However, in
many fisheries sharks are processed at sea and measurements must be made prior to finning and
gutting. In some circumstances, sharks come aboard a vessel or are unloaded too quickly to measure
each shark and thus only an estimated length can be made. Estimating lengths should be done only as
a last resort, but is sometimes the only option. For example, observers monitoring the U.S. Atlantic
directed shark drift gillnet fishery estimate shark lengths (to within 30 cm) while they are still suspended in the net (Carlson and Lee, 2000). A meter stick or other measuring device can be placed on
the gunnel where the sharks come aboard the boat as a means of reference.
Obtaining the weight of a whole shark at sea is difficult, time consuming, and often impossible
due to logistic considerations. A hanging scale can be used on board for smaller shark species, but this
is usually not a viable option for larger sharks. For this reason, most biologists weigh the whole shark
or carcass at the dock or simply estimate the weight. A major problem in dockside weighing is that any
sharks used for bait or discarded at sea are not weighed. In addition, since only butchered carcasses
are landed in many fisheries, whole body weight data is unobtainable. Generating statistically signifi256
cant length-weight curves for major species early in the monitoring process is important because these
relationships allow one to convert subsequent length data into biomass estimates. An alternative is to
develop relationships between different lengths and between length and weight from fishery independent surveys where all the catch can be accurately measured and weighed. (See Kohler et al. (1995)
for length/weight regressions for several species of common sharks.)
11.9
SEX
11.9.1 Segregation
Sexual segregation of sharks based on depth, season, area and sexual maturity is common in
some species. The Atlantic sharpnose shark (Rhizoprionodon terraenovae) and spiny dogfish
(Squalus acanthias), common species in the northwest Atlantic, aggregate by sex. Pregnant
sharpnose sharks move offshore as a group during gestation and return to the shallows to give birth
(Castro, 1983). Catches of spiny dogfish off New England, in which large adults are sought, result in
catches composed primarily of females. In the western Australian fishery, gummy sharks also are
found in single sex groups. Many fisheries operate at only certain times of the year or in selected
locations and thus may have a propensity to target, intentionally or unintentionally, a certain sex or
maturity stage. The Mexican artisanal shark fishery, for example, catches a large proportion of neonate and juvenile sharks in its inshore sets (Castillo-Geniz, 1998). Other fisheries target sharks in the
same location at different times of the year, resulting in catches of seasonally different sexual maturity
groups. The northwest Atlantic bottom longline fishery catches sexually mature sandbar sharks
(Carcharhinus plumbeus) in the summer and immature sharks in the winter in North Carolina waters
(Burgess and Johns, 1999). Sharks generally have a long gestation period, produce few young and
reproduce on yearly, biannual, or even triannual basis. Large fishing mortality on one sex or on a
particular state of maturity can adversely affect the dynamics of a population. For that reason, it is
imperative that representative samples of the sex and maturity composition of the catches are obtained
regularly.
11.9.2 Identification of males and females
The sex of a shark is easily identifiable by the presence of claspers in males and their absence
in females. In addition, the following information should be recorded whenever possible: for males,
clasper size and maturity; and, for females, uterine condition, average ovum diameter, and the sizes
and sexes of embryos. These observations should be taken according to the protocols described in
Chapter 7.
11.9.3 Reproductive data collection
Reproductive data collection on female sharks is much more labor and time intensive. The
ability to collect this data is dependent on the training of data collectors and time considerations. See
Chapter 7 for a full discussion of determining maturity stages in female sharks.
257
If detailed reproductive data is being taken, those involved need to be properly trained. This
will be discussed in more detail in the At-Sea vs. Shoreside Sampling section.
11.10 AT-SEA VS. SHORESIDE SAMPLING
Several methods are utilized in the collection of fisheries and biological data. These include
fisheries observers, shore- and dockside sampling, logbooks and surveys. Each have positive and
negative aspects, and the decision to use one over the other usually depends upon the size of the
vessels in the fishery and the length of fishing trips, which data are desired, and the funding available
to support data gathering. The data that are collected will only be as good as the method and people
used to collect them. Usually a combination of two or more methods is required for adequate data
gathering.
11.10.1
Fisheries observers
Fisheries observer programs are used worldwide to collect fisheries data including biological
data, species composition, discards, etc. This is the preferred means of gaining accurate and in-depth
data, but it is more costly than other data gathering methods. Observers should be trained in biology
and be able to obtain better quality data than fishers. Observers receive training in collection and
sampling techniques from fishery professionals involved with and often employed by the fishery
organization that manages the fishery. Observer programs tend to be enacted after a fishery has
demonstrated a decline, but their use is a wise monitoring strategy in healthy or developing fisheries as
well. The amount of data observers collect is dependent on the goals of the management organization.
Observers can collect a variety of information, including fishing location and depth; time of sets and
haul back; oceanographic data (e.g., water temperature and salinity); type and amount of gear used;
species identification; catch vitality; sexes, lengths and weights, and maturity; and biological samples
(Fig. 11.08). Observers are extremely beneficial to management programs because of the amount and
accuracy of the information they collect. However, observer programs can be expensive, time consuming, and impractical if the boats in the fishery are too small.
11.10.2
Shoreside sampling
Shoreside and dockside sampling is very useful in fisheries where sharks are landed whole,
such as recreational and some artisinal fisheries. Unfortunately sharks often are dressed at sea and
landed headed and gutted, which can pose significant problems for land-based sampling since species
identification, sex, fork and total length, reproductive sampling, and at-vessel vitality cannot be determined. If sharks are landed intact, then a shore-based data collector can produce many of the same
data as an at-sea observer. Elicited cooperation with fishing captains can lead to additional data
gathering, such as fishing location and depth, type and amount of gear utilized, lengths of sets, etc. If
the exact location is known, fishing charts can be used to determine the depth, and water temperatures
258
Figure 11.08 Examples of data sheets used by fisheries observers (courtesy of National Marine
Fisheries Service/FLMNH).
259
can be estimated in some circumstances using existing oceanographic data. There are several ways to
conduct shore- or dockside sampling. An example of a data gathering format is shown in Figure 11.09.
1.
Samplers can be contacted by boats coming in and meet them ashore as they unload.
2.
Samplers can patrol docks/shore every day awaiting boats.
3.
In single day fisheries, samplers can be waiting at the dock/shore when all the boats
come in at the end of the day.
The number of boats sampled is dependent on what percentage of the fishery each management organization is interested in observing. That percentage most often is determined by the fiscal
constraints.
11.10.3
Logbooks
Logbooks are used in many fisheries but data gathered in such a manner is highly variable and
can be suspect. Despite this, logbooks are commonly used in stock assessments and as the major data
collection source in numerous fisheries. Fishers are required to fill out logbooks while at sea. The
following data can be recorded in logbooks: species identification, number caught, sex, size, disposition,
gear and amount used, gear modifications, location, time of set and haul back, depth and water temperature (Fig. 11.10). It is widely recognized that fishers do not always record accurate data, underreport their catches, and frequently identify species incorrectly. Fishers busy bringing in and working
up their catch are not likely to record accurate data at the expense of fishing productivity. Many
fishers do not fill in their data at the time of fishing and recreate data from memory at later dates.
Fisher illiteracy is a problem in some regions. Correct species identification is a major issue because
most fishermen are not scientifically trained in proper identification techniques. In addition, many
fishers dislike any type of management plan and are unwilling to go out of their way to collect data.
Finally, there is no quality control in logbook data gathering, with no on-board monitoring of logbook
entry. However, this type of data collection is inexpensive and is often the only method available if
funding is lacking or if vessels are too small to take observers. Some estimates of the accuracy of
logbook data may be available when limited observer data are also available from the same fishery.
This can be done by comparing the observers record of various parameters to those in the logbook
records.
11.10.4
Telephone and dockside sampling
Telephone or dockside surveys often used to monitor recreational fishers involve either calling
or going to the docks and interviewing fishermen about their trips as they come back in. Surveyors
usually ask questions about the species targeted and catch composition, type and amount of gear
employed, gear modifications and lengths, and size of the vessel. This is a very basic type of data
collection and there are real problems associated with this type of sampling. Interviews are often done
several days after a trip, which results in fisher memory lapses and poor data quality. As in logbook
260
Figure 11.09 Examples of a shoreside survey data collection sheet (courtesy of National Marine
Fisheries Service).
261
Figure 11.10 Example of a logbook used to collect data by fishers while at sea
(courtesy of National Marine Fisheries Service/FLMNH).
data, this type of data gathering is relatively inexpensive and provides an alternative to more costly
methodologies.
11.11 CONCLUSION
The use of fishery-dependent data is a vital component of the fishery management process.
This chapter has provided the tools necessary for managers from different areas to determine what
type of data should be collected, and how to collect it. The methods used will vary depending on
locality, experience and the types of management plans utilized. In all cases the collection of even the
simplest data set will help eliminate the threat of overfishing and subsequent population collapses.
11.12 REFERENCES
ANDERSON, R.C. 1993. The shark fisheries of the Maldives. Ministry of Fisheries and Agriculture,
Republic of Maldives and FAO, Madras, India.
BONFIL, R. 1997. Status of shark resources in the southern Gulf of Mexico and Caribbean: implications
for management. Fish. Res. 29:101-117.
BURGESS, G. AND K. JOHNS. 1999. Commercial shark fishery observer program: analysis of the large
coastal shark fishery-July and August 1998 season in the southeastern United States, with a
review of the 1998 commercial shark fishery in the region. Final Report to Highly Migratory
Species Division, National Marine Fisheries Service, Silver Spring, Maryland.
262
CASEY, J.G. 1964. Angler’s guide to sharks of the northwestern United States: Maine to Chesapeake
Bay. U.S. Fish and Wildlife Service, Bureau Sport Fisheries and Wildlife 179; Circ.
CARLSON, J AND D. LEE. 2000. The directed shark drift gillnet fishery: catch and bycatch 1998-1999.
Report to Sustainable Fisheries Division, National Marine Fisheries Serivce, Silver Spring, Maryland.
CASTRO, J. 1983. The sharks of North American waters. Texas A&M University Press, College
Station, TX.
CASTRO, J. 2000A. Guía para la identificación de las especies de tiburones de importancia comercial
del Océano Pacífico. Dirección General de Administración de Pesquerías, México.
CASTRO, J. 2000B. Guía para la indentificación de las especies de tiburones de importancia comercial
del Golfo de México. Dirección General de Administración de Pesquerías, México.
CASTRO-AGUIRRE, J.L. AND H.E. PEREZ. 1996. Listados faunisticos de Mexico VII. Catálogo
Sistemático de las rayas y especies afines de México (Chondrichthyes: Elasmobranchii:
Rajiformes: Batoideiomorpha). Instituto de Biologia, México.
CASTILLO-GENIZ, J.L., J.F. MARQUEZ-FARIAS, M.C. RODRIGUEZ DE LA CRUZ, E. CORTÉS, AND A. CID DEL
PRADO. 1998. The Mexican artisanal shark fishery in the Gulf of México: towards a regulated
fishery. Mar. Freshwat. Res. 49:611-620.
KOHLER, N. E., J. G. CASEY, AND P. A. TURNER. 1995. Length-weight relationships for 13 species of
sharks from the western North Atlantic. Fish. Bull. 92:412-418.
MOUTOPOULOS, D.K. AND K.I. STERGIOU. 2002. Length-weight and length-length relationships of fish
species from Aegean Sea (Greece). J. Appl. Ichthyol. 18(3):200-203.
NAKANO, H. 1999. Fishery management of sharks in Japan, p. 552-579. In: Case studies of the management of elasmobranch fisheries. R. Shotton (ed.). FAO Fisheries Technical Paper 378. FAO,
Rome.
SCHWARTZ, F.J. AND G.H. BURGESS. 1975. Sharks of North Carolina and adjacent waters. Information
Series, North Carolina Department of Natural and Economic Resources, Division of Marine
Fisheries, Morehead City, North Carolina.
SHOTTON, R. 1999. Species identification practices of countries reported landings of chondrichthyan
fishes in the FAO nominal catches and landings data base, p. 904-920. In: Case studies of the
management of elasmobranch fisheries. R. Shotton (ed.). FAO Fisheries Technical Paper 378.
FAO, Rome.
WALKER, T.I. 1999. Southern Australian shark fishery management, p. 480-514. In: Case studies of the
management of elasmobranch fisheries. R. Shotton (ed.). FAO Fisheries Technical Paper 378.
FAO, Rome.
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CHAPTER 12.
FISHERY-INDEPENDENT SAMPLING: SURVEY TECHNIQUES AND
DATA ANALYSES
Paul J. Rago, Northeast Fisheries Science Center, NOAA Fisheries, Woods Hole, MA 02543 USA
12.1
INTRODUCTION
12.1.1 Utility of surveys
12.2
BASIC THEORY
12.2.1 Survey design
12.3
IMPLEMENTATION
12.3.1 Gear types
12.4
STATISTICAL ESTIMATION AND PRECISION
12.4.1 Smoothing procedures
12.4.2 Influence of covariates
12.4.3 Data structures
12.5
CONCLUSIONS
12.6
ACKNOWLEDGMENTS
12.7
REFERENCES
265
266
12.1
INTRODUCTION
Fishery-independent estimates of abundance form the cornerstone of many stock assessments for
teleost and shellfish species. The advantages of such abundance indices are well described in Hilborn and
Walters (1992) and in greater detail by Gunderson (1993), Doubleday and Rivard (1981), and Smith
(1990). Fishery-independent surveys provide valuable measures of relative abundance, rates of population
change, and size and sex composition for a wide range of species. As these measures are obtained from
scientific sampling or within an experimental design, they are less subject to the unknown and often
confounding factors that complicate the interpretation of fishery-dependent indices of stock status. Such
generalizations, however, do not apply entirely to elasmobranchs.
For a variety of reasons, fishery-independent surveys for elasmobranchs are more difficult to
interpret (Simpfendorfer et al., 2002) than surveys for teleosts and shellfish. Perhaps the most important
reasons are the average size of individuals and the need to use passive sampling gear (also called “fixed”
or “static”). Large elasmobranchs often have swimming speeds that exceed the towing speed of active
fishing gear and therefore, have low probabilities of capture in mobile gear (perhaps with the exception of
purse seines). In the context of survey design, indices of abundance based on passive gear are challenging because the “zone of influence” depends on both environmental factors and the behavior of the animal.
To be captured, fish must swim to the gear and become entangled or hooked. In the latter case, fish must
also be hungry and encounter a baited hook. This chapter will explore these issues, suggest possible
methods for design and analysis, and highlight the benefits of fishery-independent surveys. Emphasis is
placed on gears that have actually been used in such surveys (trawls, longlines, and gillnets) rather than
potential, but as yet unproven, gear (purse seines, traps) or other technologies (such as acoustics, optical
systems, or automated underwater vehicles).
12.1.1 Utility of surveys
Whether one is initiating a new survey or using an existing survey, the first order question must be
“For what area, species and populations can I make valid inferences?” The scope of inference can be
defined rigorously, but initially it has more value as a conceptual tool for evaluating survey utility. Surveys
are circumscribed by physical boundaries and if one is fortunate, the species of interest will reside within
those boundaries. Otherwise the survey will only allow valid inference about the fraction of the population
that is present in the sampling area during the time of the survey (e.g., Carlson and Brusher, 1999). If the
fraction of the population in the sampling area is unknown, then other methods, such as population models
or empirical smoothing methods, may allow inferences to be made about the entire population. Thus
fishery-independent surveys strike a balance between the ability to make inferences about one or more
populations versus the usage of a specific area by one or more species. An overview of the different uses
of fishery-independent surveys for elasmobranchs is provided in Table 12.1.
There are two primary uses of fishery-independent surveys. The first use is to generate an
estimate of population abundance. For such an estimate to be valid, the survey index (I) must be strictly
267
Use
Estimate Relative Density
Comment
Can be used to infer trends over time and calibrate
numerical population models but the target
population and area must be defined. Otherwise
inferences are restricted to population available to
area sampled.
Define mating, spawning and nursery areas
Useful for monitoring trends in important localized
habitats.
Biological attributes of population
Inferences regarding size composition, growth rates,
sex ratio, maturity status, fecundity, etc., can be
extended to whole population if samples are
representative.
Seasonal presence/absence
Same restrictions as for relative density, but in this
case the information is qualitative.
Relative selectivity of commercial gear
Establish sampling properties of commercial gear
and facilitate interpretation of relative biases.
Evaluate alternative fishing methods
Assist in development of fishery management
measures.
Tag release programs
Essential information for defining stock structure,
possible migration patterns and rates, validating
growth rates, and so forth.
Table 12.1 Uses of fishery-independent surveys for elasmobranchs.
proportional to stock abundance P or expressible as a monotonically increasing function of true stock size,
e.g., I =aP or I=aPb. The second use of fishery-independent surveys is to examine attributes of the
sampled population (such as size frequency, maturity, sex ratios, age). These attributes have value in
understanding the basic species biology and in developing life history models. If the attributes of the
sampled population are representative of the population as a whole, then the survey results can be used to
infer the expected effects of exploitation. In turn, the size and sex composition of the sample may be
sufficient to estimate the likely magnitude of harvest rates on the population (Rago et al., 1998).
Derived indices of abundance are used to calibrate various population models for teleosts but have
had less applicability for elasmobranchs for a number of reasons. Many of their life history characteristics
confound the interpretation of such data. Elasmobranchs are often long-lived and difficult to age. Many
shark species approach their maximum size at relatively young age and live many years near their asymptotic size. For these species, body size provides little information about age, making it difficult to distinguish
268
cohorts. Larger elasmobranch species can be highly migratory, aggregating on prey species or in response
to environmental factors that are difficult to detect a priori. Under these circumstances, fishery-independent surveys that encounter such clusters may produce widely varying indices over time (see Warren,
1997; McAllister, 1998). When variations in estimates of relative abundance are inconsistent with the
biology of the species, variations in survey catchability may be responsible. In many instances, indices
derived from surveys must be processed with other statistical techniques (see sections 12.4.1 and 12.4.2)
to take account of unplanned sources of variation.
Survey requirements for elasmobranchs fall in between those for most fish species and those for
marine mammals. Owing to their larger size, fast swimming speeds, pelagic behavior, and in many cases,
scarcity, many shark species are infrequently captured by trawl survey gear. At this time, the only feasible
alternatives to trawls are various types of fixed gear. Surveys for marine mammals rely primarily on the
visual sighting of surfacing animals, or in the case of pinnipeds (Ver Hoef and Frost, 2003), visual or
photographic surveys of seasonal aggregations on known terrestrial habitats (e.g., haul-out sites). Line
transect methodologies may be used effectively for these species (Burnham et al., 1980; Palka and
Hammond, 2001). In the long run, it may be possible to develop methodologies that combine acoustics,
pattern recognition, and line transect techniques to assess large-sized elasmobranch species. Even if
possible from a technological standpoint, these approaches will require significant advances in sampling
theory to define the scope of inference. In the meantime, the principles outlined in this chapter can be used
to develop useful estimates of stock abundance and biological attributes for elasmobranch species.
12.2
BASIC THEORY
Relevant theory for the design and analysis of fishery-independent surveys draws heavily from
traditional sampling theory (Cochran, 1963; Thompson, 2002). As Morrison et al. (2001) note, estimating
the abundance of animal populations is an atypical problem. The most important distinction between
sampling theory designed for human populations and animal populations is the loose definition of the
sampling frame. Far from being a statistical nuance, the sampling frame is a critical issue in fisheryindependent surveys.
The sampling frame is defined as a list, or total set, of sampling units (Mendenhall et al., 1971).
The total number of sampling units is the total area of the population domain divided by the average size of
the sampling unit. For human populations the sampling frame might consist of a list of residents, a list of
households, or list of firms. In turn, each resident, household or firm would constitute a sampling unit. In
the case of fisheries surveys the sampling unit is the site for deployment of the gear. For active fishing
gear, the area of the site is defined as the footprint of the gear. Depending upon the species and its response to the advancing gear, the footprint is defined as the product of the length of the tow times the
effective width of the gear. For species that respond to visual cues, the width of the gear may be as large
at the distance between the trawl doors; for others, it may only be the distance between the wings of the
net. These considerations alone can induce the sampling frame to vary by a factor of two. The sampling
269
frame is modified further by areas that cannot be sampled within the population domain. Rocky bottom,
shallow water, interference with passive gear (e.g., lobster traps, gill nets) and wrecks all act to reduce the
total area that can actually be sampled. The sampling frame may also be reduced if the fish that frequent
such areas are never available to capture elsewhere.
The ambiguity of defining the number of sampling units for active gear is small relative to the
problems of defining the effective area of passive gear. For gill nets and hook gear, the footprint must be
defined in terms of the zone of influence. Conceptually this is the area over which there is a reasonable
expectation that an animal could encounter the gear. This will be a function of the environmental conditions, the average swimming speed of the species, and the duration of the set. All things being equal, the
encounter rate with the gear should increase with the average swimming speed of the animals and the
duration of the set. For hook gear, the encounter expectation is also conditioned on the presence of a
baited hook and a feeding response.
Design-based surveys assume that each sampling unit has an equal probability of being included in
the sample (Smith, 1996). A random sample is obtained by selecting one or more sampling units from the
sampling frame. Let yi denote a response variable that is measured from sampling unit i, (e.g., the number
of sharks per longline
set). The mean is estimated as the sum of the observations divided by the number of
n
samples, y =
∑y
i
n . The variance is estimated as the sum of the squared differences between each
n
2
observation and the mean divided by the sample size minus one, s = ∑ ( y i − y ) (n − 1) . In most
i =1
2
i =1
fisheries surveys, the variance tends to be unacceptably large relative to the mean (Pennington, 1983),
highlighting the need to reduce the variance in some way.
Figure 12.1 Example strata definition for bottom trawl surveys conducted by the Northeast Fisheries
Science Center, NOAA Fisheries, Woods Hole, MA.
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12.2.1 Survey design
One of the primary methods of reducing the variance of the estimate is to stratify the sampling
frame into sets of sampling units with more homogeneous properties (Fig. 12.1). The overall variance is
estimated as a weighted average of the within-stratum variances. If the strata have been defined appropriately, the stratified estimate of the variance will be smaller than that obtained from a simple random
sample. Combining the notation in Cochran (1963) with the fisheries orientation in Gunderson (1993) we
can illustrate the general principles of design-based estimation and extend it to model-based estimation.
The details of these derivations become very complicated and the reader should consult standard statistical
texts for further details. In what follows, the objectives are to provide an intuitive understanding of the
underlying concepts, identify relevant literature, and illustrate the need to carefully weigh the utility of
fishery-independent surveys.
We begin with some basic definitions. Let L denote the total number of stratum and let h denote
the stratum index, such that h =1, 2,…, L. The total number of samples taken over all strata (n) can be
written as n = Snh, where nh is the number of samples taken in each stratum h. The sampling frame is
defined as N =SAh /ah, where Ah is the area for stratum h and ah is the size of the sampling unit in stratum
h. The maximum number of sampling units in stratum h is defined as Nh = Ah /ah. A weighting factor,
defined as the fraction of sampling units in stratum h, is denoted as Wh = Nh/N. In most fisheries-independent surveys the fraction of the sampling frame that is sampled is very small and can generally be ignored.
Under these conditions the stratified mean y st and variance Var ( y st ) are estimated as
L
y st = ∑Wh y h
(12.1)
h =1
and
L
Var ( y st ) = ∑ Wh2
h
where
s h2
nh
where
nh
s h2 = ∑
i =1
(y
h ,i
− yh )
(12.2)
2
nh − 1
The above formulation allows the size of the sampling unit to vary with stratum. If the sampling
units vary with each sample, then the sampling frame would be approximated as the total area of the
stratum divided by the average area of the sampling unit within the stratum, N h ~ Ah a h . Such a situation
could occur in a trawl survey if the footprint of the trawl varied significantly
with each tow in the stratum.
n
The stratum-specific area swept per tow would then be defined as a h =
∑a
h
h ,i
nh .
i =1
Equation 12.2 illustrates two ways in which the overall variance of the mean can be reduced.
Since the within stratum variances are weighted inversely by the number of samples per strata (nh),
allocation of additional samples to strata with the highest variances can be an important strategy. Since the
271
variance increases with the mean in almost every fishery example, the allocation of additional sampling
units to high density strata makes intuitive sense. The second way that variances can be reduced is
through choice of stratum boundaries. The strong association between the mean and variance suggests
that defining stratum boundaries according to density zones will act to reduce the variance within strata to
minimal values. Selection of strata and allocation of samples is not simply a mathematical problem.
Biological information on habitat and oceanographic features must be considered before an algorithmic
approach. Moreover, most fishery-independent surveys are designed to estimate relative abundance for
more than one species. In such cases, it is unlikely that a single stratification or allocation scheme is
optimal for every species.
Overall cost represents one of the most important factors in the design of a survey. Samples are
expensive to collect and it is generally desirable to minimize the variance of a survey subject to a total cost
constraint. Strategies, such as Neyman allocation (Cochran, 1963, p. 97; Mendenhall et al., 1971, pp. 6473) can be used to define appropriate sample allocations wherein the optimal number of samples is proportional to the product of total sampling units within stratum h and the standard error, and inversely proportional to the square root of sampling cost in stratum h. The costs of collecting additional information on the
biological attributes of the species are generally small relative to the costs of deploying vessels and crew.
Hence it is desirable to collect as much information as feasible on individual specimens. “Feasibility” to
collect such information is generally controlled by the average time between adjacent stations.
A significant fraction of the total sampling effort should be allocated to continuous experimentation
and quality control measures. The former measures are important during the early years of a survey when
the penalty for altering a design is small relative to the gains in precision. As the number of survey years
increases, the gains in precision must be progressively greater because the costs of discarding historical
information increase. Quality assurance/control (QA/QC) measures include ongoing verification of gear
performance, evaluation of fixed stations, and comparisons with other vessels and gears. Some of these
measures, such as fixed stations, can be incorporated in the overall survey design via partial replacement
designs (Warren, 1994). Rather than viewed as a burden, QA/QC measures should be evaluated with
respect to the question, “Can I afford to redo this study?”
Stratified random designs are one of many designs that might be implemented for evaluation of
elasmobranch species. Systematic surveys are worthy competitors and have desirable properties inasmuch
as they may provide better support for kriging methods. The important caveat for such studies is that
strictly speaking, a design-based variance is not estimable (Cochran, 1963; Gunderson, 1993). Other
authors cautiously advocate systematic designs, noting that their desirable properties can outweigh the
problems of variance estimation (Levy and Lemeshow, 1991; Hilborn and Walters, 1992; Morrison et al.,
2001).
272
Gear
Trawl
Advantages
Design assumptions easier to
satisify
Multispecies perspective
Disadvantages
—Size selectivity
—Species selectivity
—Requires large vessel and high costs
—Limited utility for pelagic habitats
—Limited utility in rocky, coral reef, and
complex bottom areas
—Vessel effects may reduce catchability
Gill Net
—Relatively inexpensive
—Can use smaller vessels
—Wider range of bottom types
—Benthic and pelagic zones
—Size limitations based on mesh selectivity
—Domain of influence difficult to specify
—Day vs night and turbidity differences
—Movement required
Hook Gear
—Effective
—Relatively inexpensive
—Can use smaller vessels
—Wide range of habitats
—Benthic and pelagic zones
—Movement required
—Must detect bait, encounter and consume
baited hook
—Competition with other fish for hooks
—Once hooked, potential predation by larger
fish
—Loss of bait reduces effective sampling time
Table 12.2 Gear-specific design considerations for primary gear used in fishery-independent surveys of
elasmobranchs.
12.3
IMPLEMENTATION
Although many types of gear can catch or detect elasmobranchs, three basic gear types define
the range of applicable fishery independent methods (Table 12.2). Trawls (Rago et al., 1998; Graham et
al., 2001), hook gear (Musick et al., 1993), and gill nets (Nakano and Nagasawa, 1996) have all been used
to define abundance metrics. Other gear, such as traps and purse seines, may be useful for specialized
surveys, but no examples of routine surveys are known. Similarly, routine use of acoustic surveys is
hampered by the lack of a swim bladder in elasmobranchs, difficulties in identifying species, fast swimming ability relative to the survey vessel, and the relatively low density of individuals. Surveys based on
explosives or poisons, are both ecologically unacceptable and unlikely to be effective in ocean environments.
12.3.1 Gear types
The ability to implement a valid survey design ultimately depends on the performance of the
sampling gear and the ability to satisfy the assumptions of the survey design. All sampling gears are biased
and no study can fully meet all of the assumptions of a design-based survey. The relevant question however, is the magnitude of these violations and their influence on the bias and precision of the survey. Table
12.3 summarizes some of the critical assumptions and potential tests for fishery independent surveys.
Many of these issues can be addressed through simulation studies wherein the validity of conclusions is
conditioned on the realism of the simulation (Punt et al., 2002).
273
Problem
Gear saturation
Scaling of catch rates
•
•
•
•
•
•
Fixed vs. random stations
Gear avoidance
Vessel effects
•
•
•
Size selectivity
•
•
•
Zone of influence
Simple random vs. stratified vs. systematic
sampling designs
•
Recommendation
Evaluate set duration or tow duration
Alternative hook spacing
Record environmental variables
Video monitoring
Field experiments
Evaluate catch rates for various lengths of
nets or longlines, and tow duration for trawl
nets
Partial replacement design
Compare day vs. night differences
Use acoustics to evaluate potential dispersal
of fish during deployment
Multiple mesh size panels in gillnets
Alternative hook and bait sizes, and types
Compute design effects for reduction in
variance
Consider all species unless survey specifically
targets a single species
Table 12.3 Recommended measures for survey programs to reduce bias and improve precision.
The following list is indicative, but not exhaustive.
In general, no single type of gear is equally effective for all life stages of a single species, much
less so for multiple species. As a simple example, catch rates of selected shark species from Northeast
Fisheries Science Center autumn and spring research vessel bottom trawl surveys during 1967-2003 are
summarized in Table 12.4. Except for smooth dogfish, the average percentage of positive tows was less
than 5%. Larger sharks were caught less frequently and average numbers per positive tow were low.
Maximum observed sizes were less than 2 m and no trends are immediately apparent for the larger
species. If trawl data were the only source of information for these species, it would be difficult to draw
any conclusions regarding stock status.
As a consequence, it is helpful to examine several types of gear and gear configurations when
designing a survey program. In a survey based on longlines, it would be useful to test for differences
between sizes of hooks, types and sizes of bait, spacing of hooks, duration of sets and so forth, even after
a standard protocol had been established. Reserving some of the sampling effort for ongoing experimentation can be an effective strategy for improving fishery-independent surveys.
Gear bias is just one violation of an assumption in a survey design. Most fishery scientists have a
good intuitive concept of what constitutes a valid random sample. In practice, they could agree on the
inclusion or exclusion of a particular sample within a survey design. However, as the number of samples
and complexity of the design increases, the basis for agreement on the validity of an overall survey is likely
to diminish. As a simple example, suppose that a study demonstrates an optimal soak time of six hours
and that a 24-hour soak time in an area of high shark abundance is generally too long, resulting in either
gear saturation or loss of bait. Should a 10-hour set be rejected as unrepresentative? Real world con274
Survey
Statistic
Fall
19672002
(13100
stations
sampled
over 36
years)
Total
number of
positive
tows
Number of
years with
positive
tows
Average
number per
positive tow
Maximum
size (cm)
captured
Total
number of
positive
tows
Number
years with
positive
tows
Average
number
per
positive
tow
Maximum
size (cm)
captured
Spring,
19682003
(12209
stations
sampled
over 36
years)
Angel
Black- Dusky Sandbar Chain
Bonnet- Smooth
nose
Dogfish head
Dogfish
262
2
25
123
125
2
2172
36
2
5
30
33
1
35
1.51
1.00
1.24
1.31
2.38
1.00
7.46
126
102
211
186
47
99
150
190
1
39
53
493
2
706
35
1
16
20
36
2
36
2.39
1.00
3.44
2.09
2.59
1.00
24.45
123
105
212
168
50
88
140
Table 12.4 Summary of fishery-independent catch statistics for selected shark species caught during fall
(1967-2002) and spring (1968-2003) bottom trawl surveys from Cape Hatteras to the Gulf of Maine
conducted by the Northeast Fisheries Science Center, NMFS, 1967-2003.
straints on deployment, retrieval and processing of samples rarely allow for resetting gear, and rejection of
samples will typically reduce the utility of already sparse sampling designs.
As another example, consider the interactions between the duration of a survey and the movement patterns of the resource. Fishery-independent surveys typically attempt to provide snapshots of the
population at a particular time. However, if the normal movements of a species are large relative to the
spatial distribution of the survey, it may be difficult to distinguish changes in abundance from variations in
seasonal migration or foraging patterns. For example, finfish bottom trawl surveys conducted during the
spring and autumn by the Northeast Fisheries Science Center typically occur over an eight-week period
275
and proceed from Cape Hatteras to the Gulf of Maine. For less mobile species, the duration of the survey
is a negligible source of bias, but for highly migratory species the bias effect could be significant.
Walsh (1997) provides an excellent review of the performance characteristics of active and static gear.
This thorough review gives a clear exposition of the major factors influencing the capture rates and
relevant technology that can be used to better understand gear performance. A useful companion article
by Millar and Fryer (1999) describes modern techniques for comparing the relative fishing power of
various gear types. The general statistical methodology of Millar and Fryer conceivably could be used to
generate adjustment factors for size composition estimates from survey data. Together these articles
provide a useful foundation for selecting, deploying, and analyzing the basic survey gear types used in
fishery-independent surveys for elasmobranchs.
12.4
STATISTICAL ESTIMATION AND PRECISION
Assuming that a reasonable sampling design can be developed and implemented, the next step is
to analyze the results. Realized means and variances can be estimated using equations 12.1 and 12.2, but
studies rarely go exactly as planned. Gear and vessels fail, storms curtail sampling and may alter fish
distributions, non-target species may fill nets or longlines, and emergencies may prevent execution of a full
design. Such restrictions on randomization will generally necessitate consideration of alternative analytical
approaches, imputation methods for missing strata, or post-stratification of the original design. The statistical literature is not unanimous on how such issues should be addressed, except to acknowledge that
restrictions on randomization reduce the scope for inference by uncertain magnitudes.
One of the ongoing problems in fishery-independent surveys is the overdispersion of population
variances. Excessively high catches in a single realization of a survey design can bias means upward and
imply low precision (Kappenman, 1999). Alternatively, low number of sample units within a stratum may
underestimate the true variance. Model-based estimation methods have been proposed by a number of
authors (e.g., Pennington, 1983). Others have noted that model-based estimators are preferable when the
assumed model is true but undesirable when the model is not true (Myers and Pepin, 1990; Syrjala, 2000).
As an alternative to model-based confidence intervals and design-based estimators that rely on asymptotic
variance estimators, Smith (1996a, b) was the first to demonstrate the properties of bootstrap estimation in
fisheries surveys. His results suggested that percentile confidence limits could be developed from complex
surveys.
Model-based estimators encompass a broad range of methodologies. In general they are characterized by an assumption that the catches are derived from a particular distribution (e.g., Poisson, log
normal, negative binomial—Taylor, 1953; Power and Moser, 1999). Compound distributions such as the
delta distribution can be useful for evaluating abundance measures. Pennington’s (1983) work on this
distribution has been influential in stimulating methodological research (Myers and Pepin, 1990; Syrjala,
2000). Cortés (2002) recently assessed four coastal shark populations, utilizing nine fishery-independent
time series (trawls, gill nets, longlines). He modeled these time series with a Generalized Linear Model
276
(GLM) as a two-stage process in which presence/absence is considered a binomial process, and positive
catches are treated as a Poisson process. Similar methods have been applied to migratory bird populations
(Link and Sauer, 1998).
12.4.1 Smoothing procedures
Model-based estimation can be viewed as a general class of smoothing procedures in which the
results of a survey design are interpreted as realizations of a complex, but continuous underlying function.
Moving averages constitute perhaps the simplest such procedure, wherein time series of observations are
expressed as simple averages of adjacent observations. The moving average process can be extended to
include locally weighted regression methods with robust treatment of residuals. This approach goes by the
acronym, LOWESS or “locally weighted regression scatter plot smoothing” (Chambers et al., 1983).
Figure 12.2 depicts the use of LOWESS in illustrating temporal changes in the abundance of mature
female spiny dogfish off the northeast U.S. coast. The LOWESS smoothing separates the major signal
from the noise of year to year sampling variability.
Auto-regressive, integrated moving average (ARIMA) models (Pennington, 1985) employ a more
formal approach to smoothing by explicitly accounting for the correlated error structure. These techniques
have been applied rather infrequently in fisheries, perhaps due to their rather demanding and hard-to-test
assumptions. When the number of years in a data set is small, it may be difficult to test the assumption of
stationarity and estimate the autocorrelation precisely.
Spatial correlation between observations underlies the geostatistical modeling approaches that
have been applied to some fish stocks (see Petitgas, 2002). One of the useful features of these approaches is the ability to approximate the precision of non-random surveys. Generalized linear models
Swept Area Biomass: All>=80 cm
Biomass (000 mt)
500
400
300
200
100
0
6
19
5
6
19
9
19
73
77 981 985 989 993 997 001 005
19
1
1
1
1
1
2
2
YEAR
Figure 12.2 Swept area biomass estimates of spiny dogfish biomass (000 mt) in spring research vessel
bottom trawl surveys (1968-2003) for dogfish greater than 80 cm, both sexes combined. Line represents
LOWESS smooth with tension factor = 0.5.
277
(GLM) and Generalized Additive Models (GAM) are distinct from geostatistical models but share a
common objective of describing the underlying structure of the data in terms of one or more explanatory
variables. Swartzman et al. (1995) successfully applied GAM models to walleye pollock populations.
Variation in abundance indices are a function of the true variation in the temporal and spatial
distribution of the resource, sampling error from the statistical design, and measurement error associated
with gear performance. Changes across years provide a measure of the true change in abundance and
variations in the catchability of the resource. It was noted earlier that when variations in estimates of
relative abundance are inconsistent with the biology of the species, variations in survey catchability may be
responsible. There are several ways in which such variations can be addressed but none of them are
entirely satisfactory.
The ratio of changes in average catch rates across years can be used as an aggregate measure of
population change, if the factors that influence the average catch rate remain the same over time. Otherwise the rate of population change is confounded with changes in the fraction of the stock in the sampling
frame, variations in the zone of influence of the gear, and other factors. Ways of considering these other
factors are outlined below.
12.4.2 Influence of covariates
One of the major advantages of model-based estimation is that the model parameters can be
expressed as functions of explanatory variables. The importance of various explanatory variables can then
be evaluated in the context of their reduction of the variance in the observations. Cortés (2002) demonstrates the utility of this approach wherein model parameters were expressed as functions of a set of
variables (time of day, depth, temperature and so forth.). Baum et al. (2003) successfully applied a zerotruncated negative binomial GLM in analyzing shark landings recorded in commercial fisheries logbooks
from the U.S. pelagic longline fleet. By adjusting for the effects of various covariates, Baum et al. (2003)
were able to identify population trends apart from variations in extraneous variables. Ver Hoef and Frost
(2003) demonstrated the utility of hierarchical Bayesian models for the assessment of trends in seal
populations; application of such models to elasmobranch longline surveys should be a productive area of
future research.
Perry and Smith (1994) developed a nonparametric method to examine the degree of association
between catch rates and environmental factors. One of the most useful features of their approach is the
explicit incorporation of the sampling design into the measure of association. Shepherd et al. (2002) applied
this approach to the analysis of spiny dogfish catch rates in Canadian trawl surveys.
12.4.3 Data structures
Efforts to collect fishery-independent data are wasted unless similar efforts are made to develop
and maintain databases. All of the statistical methods described above rely on a proper data structure.
Here our emphasis is on the development of modern relational databases. Such databases are required not
only to support complex statistical models, but also to support questions that have not yet been asked and
278
techniques that have not yet been developed. Properly structured relational databases have the potential to
meet these challenges; poorly structured and maintained databases have much lower chances.
Guidance on data collection and handling procedures may be found in a number of sources. The
general principle is to collect and check as much data as possible while at sea, subject of course, to safety
considerations and the overall constraints of the mission. Recent advances in at-sea data collection
technologies utilizing electronic measuring boards, scales, sample custody processing, and integration of
environmental data, can greatly improve the speed and quality of survey data. The Fisheries Scientific
Computing System at the Northeast Fisheries Science Center is one example of an integrated data acquisition system. (See http://www.nefsc.noaa.gov/nefsc/publications/crd/crd0117/symposium/benigni-mchughshields-stepka_presentation/index_files/v3_document.htm for additional details.)
Data should be summarized in a relational database such as Oracle, Access or other commercially
available products. At least four table types should be considered. For economy, denote these tables as the
STATION, CATCH, LENGTH, and BIOLOGY tables, respectively. The STATION table should summarize the attributes of the sampling station. This table should include the design attributes (strata, station
number), date, time, location, gear performance measures, and environmental data. The STATION table
should also include information on the vessels used, gear deployed, type of station (random vs. fixed).
Using one or more key fields from this table, other tables can be developed to summarize the total number
and weight of all species (CATCH table), the length, weight, maturity status, and sex of each measured
fish (LENGTH) and finally one or more BIOLOGY tables to identify attributes of individual fish. Table
fields may include age, stomach volume and/or contents, tag release number, special treatments (e.g.,
tetracycline injection) and so on. Collectively, the relational database provides a compact way of summarizing the voluminous data from individual surveys. More importantly, it provides a standardized, long-term
system for archiving data for elasmobranch populations.
The costs of collecting additional environmental information while at sea are minor compared to
the overall survey costs. Post-hoc analyses of capture rates are likely to be necessary for all but the most
abundant species. Collection of environmental data may be especially important for these types of analyses. Collection of various environmental variables at the sampling site is standard in most surveys. However, the relevant conditions that affect capture rates may occur on larger spatial scales (e.g., proximity to
frontal zones) or longer temporal scales (e.g., rate of warming of shelf waters). This type of information is
more difficult to incorporate as a standard data table, but the efforts may allow a much more coherent
interpretation of the data.
A related issue is the acquisition of data on gear performance. For mobile gear, various forms of
electronic data acquisition allow scientists to evaluate gear configuration and contact time, each of which
is critical for judging the quality of a sample. For passive gear, the requirements are less rigorous but
equally useful. The recent analyses by Baum et al. (2003) of commercial longline data for shark populations relied heavily on detailed, set-specific data records.
279
The archival importance of fishery-independent survey data cannot be overemphasized. In some
cases, the value of long-term data has only recently been summarized (Jackson et al., 2001; Hoey et al.,
2002; Baum et al., 2003). Some surveys may be difficult to interpret when viewed in isolation but clear
when compared with one or more other surveys. Thus it is important for governments, universities, and
other groups that collect fishery-independent survey data to recognize the enduring responsibility of
maintaining these data for the world’s managers, scientists, and harvesters alike. While these arguments
are true for all surveys, they are particularly relevant for large-bodied species whose abundances have
declined on a worldwide basis (Myers and Worm, 2003).
Data should be routinely audited with specialized software that attempts to identify infeasible
codes and improbable biological attributes such as excessive maximum sizes or unlikely combinations of
lengths and weights. (As an example, additional details on the procedures used at the Northeast Fisheries
Science Center may be obtained by contacting scientists via the following web site: http://
www.nefsc.noaa.gov/esb/survey.htm.) Standardized summarization programs are useful for ensuring that
appropriate statistical methods are used and for verifying the selection criteria employed. Most fishery
research institutions have developed standardized summarization programs for fishery-independent data.
Examples include the SURVAN program used at the Northeast Fisheries Science Center (Kramer, 2001)
and SPlus software for bootstrapping surveys developed by Stephen Smith (DFO, Bedford Institute of
Oceanography, Nova Scotia). Information on a sophisticated, commercially available survey analysis
system, known as SUDAAN, may be found at http://www.rti.org/sudaan/home.cfm. The “Fideas” computer program by Swartzman et al. (2002) combines features of database management systems, geographic information systems, higher level programming languages, and web browsers into a general
software tool for analyzing fishery-independent survey data. The Environmental Analysis System (EASy;
Tsontos and Kiefer, 2003) is an advanced, PC-based Geographical Information System designed for the
storage, dissemination, integration, analysis and dynamic display of spatially referenced series of diverse
oceanographic and biogeographic data. It can be found at http://www.runeasy.com.
12.5
CONCLUSIONS
Fishery-independent surveys fulfill a wide range of objectives and are a valuable component of
any stock assessment program. They fulfill an important role in the monitoring and assessment of the
world’s elasmobranch species. Long-term data sets have undeniable value for detecting changes in
abundance. As the length of time series increases, the ability to confirm the validity of historical data is
diminished. Through rigid adherence to standard protocols and appropriate documentation, fisheryindependent surveys help ensure that such data can be evaluated at a later time. Properly designed
surveys have enduring value. For example, Graham et al. (2001) compared trawl survey data collected in
1996-97 with data from earlier surveys in 1976-77. Despite the gap between survey periods, Graham
et al. were able to demonstrate significant declines in abundance of multiple elasmobranch species after
20 years of intensive fishing.
280
This chapter provides only an introduction to the many challenges for designing multispecies
fishery-independent surveys for elasmobranchs. Additional details on the statistical methods may be found
in statistics text books (Cochran, 1963; Mendenhall et al., 1973; Levy and Lemeshow, 1991; Thompson,
2002) and in various applied fields. Given the biology of elasmobranches, useful approaches may be found
not only in fisheries (Gunderson, 1993) but also in wildlife (Seber, 1973; Morrison et al., 2001), forestry
(Schreuder et al., 1993) and even sociology (Kish, 1987). While it is yet unclear that sustainable fisheries
can be developed for large elasmobranchs (Walker, 1998), fishery-independent surveys provide essential
information necessary for sound management.
12.6
ACKNOWLEDGMENTS
I wish to thank F. Almeida, N. Kohler, and L. Natanson for helpful discussions during the planning
of this chapter. I especially thank J. Musick, R. Bonfil, and F. Serchuk for their constructive and timely
reviews.
12.7
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284
CHAPTER 13.
MANAGEMENT MEASURES
Terence I. Walker, Marine and Freshwater Systems, Primary Industries Research Victoria,
PO Box 114, Queenscliff, Victoria 3225, Australia
13.1
INTRODUCTION
13.2
FISHERIES, BIOLOGY AND ASSESSMENT OF CHONDRICHTHYAN SPECIES
13.2.1 Fisheries impacting chondrichthyan species
13.2.2 Biological characterization of chondrichthyan species
13.2.3 Fishing mortality
13.2.4 Rapid assessment for evaluation of risk
13.3
FRAMEWORKS FOR FISHERIES MANAGEMENT
13.3.1 International developments
13.3.2 Jurisdictional and institutional frameworks
13.4
USE RIGHTS
13.4.1 Territorial use rights
13.4.2 Limited entry
13.4.3 Quantitative input rights (effort rights)
13.4.4 Quantitative output rights (catch quotas)
13.5
TECHNICAL MEASURES
13.5.1 Regulation of fishing gear
13.5.2 Area and time restrictions
13.5.2.1
Marine Protected Areas
13.5.2.2 Fishing area closure
13.5.2.3 Regional fisheries management
13.5.3 Product form
13.5.4 Size limits
13.6
SPECIAL PROTECTION OF THREATENED SPECIES
13.7
PRODUCT CERTIFICATION AND ECOLABELLING
13.8
REFERENCES
285
286
13.1
INTRODUCTION
Fisheries management can be viewed as an assemblage of restrictions on fishing or, alterna-
tively, viewed with positive connotations as bestowing use rights for harvesting fish to an individual,
company, group or community. With use rights go the obligation to apply those rights in a responsible
manner.
In allocating fishing rights, clear objectives need to be set for a fishery. These objectives will
relate to sustainable use of the resource, provision of food and other products, economic return to the
community, welfare of fishing communities, biodiversity conservation, and maintenance of the structure
and function of ecosystems. The mix of objectives for any fishery will inevitably change with community attitudes and with stage of development depending on whether the fishery is evolving from a
traditional to an artisan fishery or from an artisan to an industrial fishery. Compromises are inevitable
to address competing social, political, legal, economic and biological objectives.
Fisheries impacting populations of chondrichthyan animals (sharks, rays and chimaeras)
require careful management. Where excess fishing capacity occurs, mechanisms need to be established to reduce capacity to levels commensurate with the biological productivities of the species taken
to ensure sustainable and rational use of the resources. Similarly, where bycatch species are depleted
or threatened, then steps need to be taken to manage and, if necessary, provide special protection to
those species for biodiversity conservation. Critical habitats need to be protected and, where affected
by fishing or other human activities, restored. At a broader level, trophic interactions and the effects of
fishing need to be understood and, if necessary, managed to ensure that the resilience of ecosystems
are not impaired.
The present chapter briefly characterizes fisheries impacting populations of chondrichthyan
species and identifies those features of their biology that can cause their populations to be sensitive to
the effects of fishing. It outlines the elements of fishing mortality and how these need to be understood
when considering gear restrictions or constructing more environmentally benign gear for conservation
and management of this group of animals. The chapter develops a method for rapid assessment of risk
for identifying species most in need of precautionary management. It also describes the outcomes of
complex political processes culminating in the International Plan of Action for the Conservation and
Management of Sharks and describes the jurisdictional and institutional frameworks required for
administration, consultation, monitoring, research, assessment, and surveillance in fisheries. The tools
of fisheries management are presented here in the framework of use rights and restrictions imposed
through technical measures. For chondrichthyan animals, special attention is required to protect
newborn and young juveniles and maternal animals for species that have nursery, pupping and mating
grounds, or migration lanes. The advantages of prescribing in law the form in which these animals can
be landed are also discussed.
287
The terminology adopted mostly follows the Code of Conduct for Responsible Fisheries
developed under the auspices of the Food and Agriculture Organisation of the United Nations (Anonymous, 1995). The term “catch susceptibility” is adapted from the scientific literature (Stobutzki et al.,
2001; Stobutzki et al., 2002) for the purpose of the present chapter. In addition, a distinction is made
between the terms “fishing area closure” and “marine protected area.” This distinction is made to
distinguish area closures designed to meet fishery-management objectives of ensuring sustainable use
of a resource, biodiversity conservation, amelioration of ecological impacts of fishing, and reduction of
interference with other human activities (e.g., shipping and recreation) from area closures designed to
meet other community objectives. The concept of fishing area closure, which is an essential management tool for managing animals of low productivity such as chondrichthyans, is extended to promote
to the concept of “regional fisheries management.”
13.2
FISHERIES, BIOLOGY AND ASSESSMENT OF CHONDRICHTHYAN
SPECIES
13.2.1 Fisheries impacting chondrichthyan species
The harvest of animals for products from shark and other chondrichthyan species pre-dates
recorded history. Every part of these animals has been used for some purpose. Depending on region
of the world, shark meat is important food consumed fresh, dried, salted or smoked. The demand for
fins of sharks has grown rapidly in recent years such that they are now among the world’s most
expensive fishery products. Similarly, the demand is rising for shark cartilage and other products for
medicinal purposes. In some fisheries, only the meat is retained, while the rest of the animal is discarded. In other fisheries, only the fins, or liver or skin is retained; few fisheries utilize all parts of the
animals.
The number of shark species targeted is small compared with the number of species of
teleosts and many of the invertebrate phyla harvested. This has resulted in a lack of studies of sharks
and inappropriate stock assessment techniques being applied to these animals. Most of the shark catch
is taken by fishers targeting teleost species, which results in most of the catch reported as unidentified
shark or mixed fish or not reported at all. In addition, sharks can be difficult to identify down to species
level, particularly given the need to behead and eviscerate sharks at sea to reduce spoilage rates of the
meat and the fishers’ preference to remove fins at sea. Taxonomic problems need to be resolved,
particularly for batoids, before effective monitoring, research and management can be achieved. This
lack of species identification for catches and lack of information on fishing effort means basic data for
fishery stock assessment are currently available for only a few species (Walker, 1998).
Although the overall number of species harvested is relatively small, sharks are captured with
a wide variety of types of fishing gear and vessels. Sharks are mostly taken by gillnet, hook or trawl in
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industrial and artisanal fisheries. Small amounts are taken in traditional and recreational fisheries
(including game fishers and divers) and bather protection programs by beach gillnet and drumline
fishing. There are several fisheries directed at one or a small number of species of sharks, but most
sharks are taken in multi-species fisheries where the fishers tend to target more highly valued teleosts.
In some fisheries, part or the entire shark catch is discarded. Shark fisheries can be classified as
“coastal hook and gillnet fisheries”, “demersal trawl bycatch fisheries”, “deepwater bycatch fisheries”,
“pelagic bycatch fisheries” (primarily bycatch in tuna longline and purse seine fisheries), and “freshwater fisheries” (Anonymous, 2000).
Coastal hook and gillnet fisheries operate in regions of the continental shelf. Construction of
the fishing gear depends on topography of the fishing grounds and on the available species mix of
shark, chimaerid and teleost species. Much of the artisanal catch is taken by bottom-set longlines and
by bottom-set gillnets, mostly constructed of monofilament webbing with some constructed of multifilament webbing. These gears take a variety of shark species and teleost species. In regions of
narrow continental shelves, where deep waters off the continental shelf are readily accessible, or, in
regions of broader continental shelves, the artisanal fleet uses surface-set longlines and driftnets to
target pelagic sharks (Anonymous, 2000).
In demersal trawl bycatch fisheries, demersal trawl fisheries are impacting stocks of dogfishes
(Squaliformes), angel sharks (Squatiniformes), rays (batoids), and chimaeras (holocephalans). As in
the high seas fisheries, much of the trawl bycatch of sharks and rays is discarded dead and often not
reported. Fishery-independent surveys in several parts of the world show that many species of these
groups have exhibited marked declines in abundance.
In deepwater bycatch fisheries, like many of the teleost species studied from the deeper and
colder waters of the continental slopes, the deepwater dogfishes (notably genera Centrophorus,
Centroscymnus, Etmopterus, Dalatias, and Deania) have particularly low productivity. The continental slopes are usually steep and the total area of associated seabed is small compared with the areas
on top of the continental shelves and on the abyssal plains of the oceans. As some species of dogfish
are confined to particular depth ranges on these slopes, the total area occupied by some of these
species is small. Expansion of demersal trawl fisheries into progressively deeper water to target
dogfish and high valued teleosts on the continental slopes in some regions of the world is placing
several species at high risk of severe depletion. Already demersal trawling occurs on the continental
slopes at depths exceeding 1000 m. Part of the catch is targeted or is bycatch taken by gillnets and
hooks (Walker, 1998).
In pelagic shark bycatch fisheries, longline, purse seine and driftnet fisheries targeting tunas
and tuna-like species on the high seas and in the Exclusive Economic Zones through bilateral access
289
agreements take significant bycatch of sharks. Blue shark (Prionace glauca) is the main species
caught and other species caught widely in lower quantities include Isurus oxyrinchus, Alopias
supercilious, Carcharhinus falciformis, Carcharhinus longimanus, and Lamna nasus (Bonfil,
1994; Anonymous, 2000).
Shark species occurring in freshwater habitats are among some of the most threatened
species. There are several reasons why these species are more vulnerable than those inhabiting
marine waters. The amount of freshwater in rivers and lakes is small compared with the amount of
seawater on Earth. The tropical rivers and lakes where freshwater species occur are mostly in
developing countries with large and expanding human populations. These areas are more accessible to
exploitation than marine waters. Freshwater habitats are also less stable than marine habitats in terms
of water temperature, dissolved oxygen, clarity and water flow, and these factors are gradually being
changed through deforestation. Contamination of the water with toxicants from mining and agriculture,
physical modifications to the waterways through dam construction and irrigation, and inevitable
changes to the flora and fauna in freshwater habitats are likely to alter them beyond the tolerance of
some shark species. Several species of sharks and rays have declined such that they are now extremely rare (Compagno, 1984; Compagno and Cook, 1995).
13.2.2 Biological characterization of chondrichthyan species
Populations of shark and other chondrichthyan species tend to have lower reproductive rates
and lower natural-mortality rates than populations of teleost and invertebrate species. Consequently,
for many chondrichthyan species, only a small proportion of the population can be removed annually if
the catches and populations are to remain sustainable. Such populations are said to have low biological
productivity.
Harvested populations of these animals therefore require careful management and monitoring.
Managers need to take a somewhat more precautionary approach to the management of fisheries
taking sharks than they might to the management of fisheries based on teleost or invertebrate species.
Late maturity, low fecundity, and parturition cycles often exceeding one year provide for close stockrecruitment relationships, with relatively little interannual variability in response to environmental
variation, and for long stock recovery periods in response to overfishing.
There are directed fisheries for sharks in various parts of the world, but most species of shark
are captured in multi-species fisheries directed at more productive and usually more highly valued
teleost species. Harvest strategies designed to optimize economic and social benefits from these multispecies fisheries inevitably deplete the less productive shark and other chondrichthyan species unless
strategies for reducing the catch of the less productive species can be developed and implemented. As
fishing effort increases, characteristic and predictable changes occur in the fish assemblages. The
290
number of large animals decline or disappear from the assemblage and are replaced by smaller
animals. This results in a gradual drift towards shorter-lived, faster-growing species. This is accompanied by an initial increase and later a decrease in the number of species in the exploitable population
although the number of fish actually appearing in the catch can increase to a maximum level.
In multi-species fisheries where the main target species are teleosts, sharks landed as nontarget species (byproduct) or caught and discarded (bycatch) might require special management to
prevent severe depletion. Some species of shark are apex predators and naturally have comparatively
small population sizes. Whereas some species have very wide geographic distributions, others have
very restricted ranges falling within the full range of a fishery or the range of other anthropogenic
influences. Some species have complex spatial stock structures, with critical habitats such as nursery,
parturition and mating areas, and migration lanes, which might need special protection (Walker, 1998).
The magnitude of change in many of the world’s fisheries has not been well appreciated
because most of the change occurred during the early developmental stages of the fisheries before
surveys began, and subsequent fisheries management has only been effective at stabilising fish stocks
at low levels. Recent meta-analysis of large survey data sets from throughout the world indicates
industrialized fisheries typically reduce community biomass by 80% during the first 15 years of exploitation, which inevitably leads to marked changes in coastal ecosystem structure and function. The
analyses suggest that the global ocean has lost more than 90% of large predatory fish (Myers and
Worm, 2003). This paints a bleak picture for the world’s fish fauna and marine ecosystems in general,
but given the biological characteristics of the chondrichthyan fauna, it can be expected that this group
of animals is among the most severely affected. This is exacerbated in the open ocean for large
predatory sharks, which, along with tunas, billfishes and sea turtles, tend to aggregate at distinct
diversity hotspots associated with coral reefs, shelf breaks and sea mounts. These animals appear to
be particularly vulnerable to targeting in latitudes 20-30° N and S where tropical and temperate
species overlap (Worm et al., 2003).
The failure to manage for sustainability at rational levels is primarily due to socio-political
pressure for short-term gain in harvests and due to intrinsic uncertainty in predicting the harvest that
can cause stock collapse. There is, nevertheless, a growing awareness of the need for a more holistic
approach by considering multispecies interactions and influences of the physical environment to
achieve sustainability through adaptive management (Botsford et al., 1997).
This requires an ecosystem approach to fisheries management, which integrates information
from a wide range of disciplines and applies mathematical models to synthesize multiple processes at a
wide range of spatial and temporal scales. Greater use of fishing area closures and moratoria can
reduce risk to sustainability through application of the precautionary principle. Harvest refuges effec-
291
tively protect a proportion of the exploited population and reduce uncertain assumptions about relationships between fishing effort, catch and biomass (Botsford et al., 1997). Exposing an entire population
to exploitation without a sound understanding of the dynamics of the fishery can risk depletion,
whereas fishing area closures can serve as a hedge against inevitable uncertainty (Lauck et al., 1998).
However, such areas are insufficient protection alone because they are not isolated from all critical
impacts; scales of fundamental processes, such as population replenishment, are much larger than the
areas can encompass. Fishing area closures need to be complemented by other management and
conservation measures outside the closures (Allison et al., 1998).
An ecosystem approach to fisheries management requires management over broad regions
and across fisheries, and away from single-species and single-fishery management that characterizes
past and present practices. The approach involves monitoring all species impacted by the effects of
fishing and requires better understanding of the dynamics of fish movement and species interactions
through food chains. Whereas complex models and comprehensive long-term monitoring data sets are
required to reduce uncertainty, it is essential in the short-term to develop rapid assessment methods
based on simpler data sets and judgement that can provide for interim management of species and
ecosystems at risk.
13.2.3 Fishing mortality
In fishery models, fishing mortality rate for a harvested population is usually expressed as the
product of the two quantities: fishing effort and catchability. Fishing effort can be quantified as the
number of fishing vessels in a fleet, a measure of the amount of fishing gear deployed, amount of
fishing time, or some other variable that is a mix of these variables. Catchability is the proportion of the
exploited population taken by one unit of fishing effort and has a value in the range 0–1 for any age or
size of fish. It is the product of three parameters, each of which also has a value 0–1. The three
parameters comprising catchability are availability, encounterability, and selectivity; i.e.,
catchability = availability x encounterability x selectivity.
Availability is the proportion of the habitat area of a population fished by the fleet. A population
with a habitat area extending well beyond the range of the fishing fleet has a low availability value.
Conversely, a population with a habitat area that falls entirely inside the range of the fishery has a high
availability value of one, unless parts of the habitat area are inaccessible to the fishing fleet.
Encounterability is the proportion of that part of the population available to fishery encountered
by one unit of fishing effort. For any species, encounterability depends on construction of the fishing
gear and on the biological characteristics of that species. Pelagic and semipelagic species that actively
swim in the water column are more likely than less active species to encounter passive gears such as
gillnets or longlines with baited hooks. These actively swimming species therefore have a higher
292
encounterability to these gears than the less active species. For active gears such as demersal trawl,
bottom-dwelling, sluggish species, such as angel sharks (Squatiniformes) and batoids have a higher
probability of capture and therefore higher encounterability than the more powerful swimming species,
such as the whaler and hammerhead sharks (Carcharhiniformes) and mackerel sharks
(Lamniformes). Sixgill and sevengill sharks (Hexanchiformes), sawsharks (Pristiophoriformes),
dogfishes (Squaliformes), catsharks, wobbegong and carpet sharks (Orectolobiformes) and horn
sharks (Heterodontiformes) probably exhibit intermediate trawl encounterability.
Selectivity is the proportion of the animals encountering the fishing gear that is captured by the
fishing gear. For any fishing gear, selectivity gives rise to a range of complex dynamics that relate
features of the fishing gear to size of animals captured. Selectivity by trawl nets for size of chondrichthyan animals is not well understood, and hook-size selectivity for size of animal is weak. For gillnets,
however, sharks of different sizes are not equally vulnerable to capture. Small animals swim through
gillnets but become progressively more vulnerable to capture as they grow. After reaching the length
of maximum vulnerability they then become progressively less vulnerable with further growth as they
deflect from the meshes of the nets (Kirkwood and Walker, 1986). These size-selectivity effects are
stronger for fusiform-shaped sharks than for more dorsoventrally-flattened species or for species with
protruding structures such the heads of hammerhead sharks, the rostral teeth of pristiophorid
sawsharks and pristid sawfishes, and the dorsal spines of squalid and heterodontid sharks and
chimaerids. When captured by gillnet or hook, fast swimming species, dependent on ram-jet ventilation
of their gills for respiration tend to die more quickly than bottom-dwelling species when caught.
Bottom-dwelling species with spiracles to aid gill ventilation are better able to pass water over their
gills after capture by gillnets and can struggle vigorously to either escape or become more tightly
enmeshed in the gear. Species that can struggle vigorously after capture in gillnets tend to have
narrower selectivity ranges than species that struggle less. Hence, for some species, careful regulation
of mesh-size can be used to ensure that the sharks captured are large enough to avoid growth overfishing and small enough to facilitate escapement of large breeding animals to avoid recruitment
overfishing (Walker, 1998).
The concept of catchability is usually applied to target and byproduct species where most of
the animals captured are retained. So as to broaden the concept to include bycatch, the term “catch
susceptibility” (Stobutzki et al., 2002) and the term “post-capture mortality” are adopted here to allow
for survival of part of the catch released. The parameters catch susceptibility and post-capture mortality both have values in the range 0–1 and are related to each other and to catchability by the equation
catch susceptibility = catchability x post-capture mortality,
which can hence be further expanded to provide the equation
catch susceptibility = availability x encounterability x selectivity x post-capture mortality.
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Post-capture mortality is the proportion of the animals that die as a result of being caught in or
of encountering the fishing gear. Animals of target and byproduct species that are mostly retained have
a post-capture mortality value approaching one. This can be less if some are discarded because of
their size or breeding condition. Post-capture mortality for discarded species can vary markedly. In
addition to handling by fishers, the fishing gear and biological characteristics can contribute to various
kinds of mortality referred to as unaccounted fishing mortality or as collateral mortality. Dead sharks
not tightly enmeshed can drop out of gillnets and contribute to unaccounted fishing mortality through
drop-out mortality. Sharks eaten by other fish or mammals after capture in the gear contributes to
unaccounted fishing mortality through predation mortality. Dead sharks either partly or totally decomposed or eaten by invertebrates and vertebrates when fishing gear is left in the water for extended
periods also contribute to unaccounted fishing mortality. Also, lost gillnets contribute to unaccounted
fishing mortality through ghost fishing mortality until they are rolled into a ball by tidal flow. Postcapture mortality from normal handling by fishers is low for heterodontid and orectolobid sharks but
high for carcharhinids.
13.2.4 Rapid assessment for evaluation of risk
Stock assessment of chondrichthyan species that incorporates time series of catch and indices
of relative abundance, includes biological parameters, and accounts for fishing gear selectivity has
been undertaken for only a few species, such as Mustelus antarcticus (Walker, 1994; Walker, 1998)
and Galeorhinus galeus harvested off southern Australia. The assessments of G. galeus also incorporate spatiality (Punt et al., 2000) and evaluation of risk in a Bayesian framework (Punt and Walker,
1998; Punt et al., 2000). Such assessments require large data sets from long-term fishery monitoring
and from extensive biological and gear selectivity studies (see Chapter 10). Because of their comparatively low biological productivity and, for many species, because of their high catch susceptibility, most
chondrichthyan species require management action long before sufficient data are available to undertake full stock assessment. It is therefore necessary to apply rapid assessment techniques for evaluation of risk from the effects of fishing.
A rapid assessment approach for evaluating risk to chondrichthyan species was applied to
species caught as bycatch in a tropical prawn fishery in northern Australia (Stobutzki et al., 2002). This
method ranks the relative sustainability of each species on the basis of its “susceptibility” and “recovery” (Stobutzki et al., 2001; Stobutzki et al., 2002), which are assessed on the basis of the biological
attributes of the species. A similar approach is proposed here for sharks and other chondrichthyans,
but the approach alters the terminology and the method of quantification of the various parameters
used to be more compatible with more comprehensive fishery assessment methods.
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The alternative method proposed here provides a framework for considering a species’
ecological risk, risk of depletion, or risk of extinction. For this method, the terms catch susceptibility
and biological productivity are used in place of susceptibility and recovery to represent parameters
associated with fishing mortality and population growth, respectively.
Species of high biological productivity can be viewed as having rapid population turnover,
whereas species of low biological productivity can be viewed as having slow population turnover. For
an unexploited population to remain in equilibrium, there has to be a balance between the natural
mortality rate reducing numbers and the reproductive rate increasing numbers. Otherwise, over time, if
the reproductive rate exceeded the natural mortality rate, the population would grow to infinity; conversely, if the natural mortality rate exceeded the reproductive rate, the population would go extinct.
Low reproductive rate and low natural mortality rate are associated with low biological productivity,
whereas high reproductive rate and high natural mortality rate are associated with high biological
productivity. It follows, therefore, that either reproductive rate or natural mortality rate can serve as a
proxy for biological productivity for rapid assessment.
Other expressions of biological productivity include the “intrinsic rate of population growth”
parameter formulated variously in biomass dynamics models (Schaefer, 1957; Schnute, 1985), demographic models (Lotka, 1922), and various adaptations of these models for sharks (Au and Smith,
1997; Xiao and Walker, 2000). Using a particular formulation of a demographic model to allow for
density-dependent change in natural mortality (Au and Smith, 1997), one study classed 26 Pacific
shark species on the basis of the “intrinsic rate of population growth” (referred to by the authors as
“rebound potential”) (Smith et al., 1998). In addition, intrinsic rate of population growth is related to
inter-generation period and reproductive output per generation (Heron, 1972). Application of biomass
dynamics models requires time series of catch and relative abundance data, and demographic analysis
combines available parameter estimates for natural mortality rate and reproduction. Required information for this purpose on chondrichthyan reproduction for a population includes the maternity ogive
(proportion of the female population contributing to annual recruitment expressed as a function of
length or age), fecundity expressed as a function of maternal length or age, and sex ratio of progeny. If
the maternity ogive and fecundity are expressed as a function of length, then the relationship between
length and age is also required for the application of demographic models.
Using natural mortality rate as a proxy for biological productivity requires some caution, as the
natural mortality rate is likely to be density-dependent and age-dependent. Also, fishing is likely to
remove the oldest animals from the population and reduce the maximum age detected in a sample of
animals collected for ageing purposes. Notwithstanding these potential biases, rough estimates of
natural mortality or maximum age can be used for broad categorization of risk. The instantaneous
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mortality rate, Z, can be approximately related to maximum age, tmax, by the equation ln(0.01) = –Z tmax,
where 0.01 represents survival of 1% of the animals reaching maximum age (Hoenig, 1983). Because
natural mortality rate is much higher for the young age classes than the older age classes, as demonstrated from modelling shark populations (Walker, 1994; Punt and Walker, 1998), this equation is
reformulated here for application to chondrichthyans by considering only that part of the population of
age greater than 2 years. Assuming that mortality is constant for all age classes, calculations of
instantaneous total mortality rate for 1% of 2-year-old animals to survive to ages 8, 16 and 24 years
are 0.77, 0.33 and 0.21, respectively. If total mortality is divided evenly between natural mortality and
fishing mortality, a condition sometimes assumed for a population in equilibrium producing the maximum sustainable yield (Thompson, 1992; Au and Smith, 1997), natural mortality rates for 2-year-old
animals surviving to these ages approximate to 0.38, 0.16 and 0.10, respectively. These values are
used as a basis for arbitrary categorization of chondrichthyan species for risk (Table 13.1). For example, based on published instantaneous natural mortality rates, Galeorhinus galeus (Punt and
Walker, 1998; Smith et al., 1998), Carcharodon carcharias, Carcharias taurus, Carcharhinus
plumbeus and C. obscurus (Smith et al., 1998) can be classed at high risk. Similarly Mustelus
antarcticus (Walker, 1994), M. californicus, M. henlei and Sphyrna tiburo (Smith et al., 1998) can
be classed at medium risk, and Rhizoprionodon terraenovae can be classed at low risk (Smith et al.,
1998).
Parameter
Values for three arbitrary categories of risk
Low (L)
Medium (M)
High (H)
Total mortality (y–1)
>0.76
0.32–0.76
0.00–0.31
Natural mortality (y–1)
>0.38
0.16–0.38
0.00–0.15
0–8
9–16
>16
Availability
0.00–0.33
0.34–0.66
0.67–1.00
Encounterability
0.00–0.33
0.34–0.66
0.67–1.00
Selectivity
0.00–0.33
0.34–0.66
0.67–1.00
Post-capture mortality
0.00–0.33
0.34–0.66
0.67–1.00
Catch susceptibility
0.00–0.33
0.34–0.66
0.67–1.00
Maximum age (y)
Table 13.1 Values of various parameters for three arbitrary categories of risk
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Catch susceptibility and each of its four components (availability, encounterability, selectivity,
and post-capture mortality) can also be arbitrarily divided into three categories of risk. This is achieved
here by evenly dividing the possible value range of 0.00–1.00 into the three ranges 0.00–0.33, 0.34–
0.66 and 0.67–1.00 and designated low (L), medium (M) and high (H), respectively. For each fishing
method adopted in the fisheries of south-eastern Australia, for example, it is possible to categorize
encounterability, selectivity and post-capture mortality into one of the three categories on the basis of
chondrichthyan taxonomic order (Table 13.2) by considering the animals’ biological characteristics.
This means that the only parameter to be determined for any particular species is “availability,” which
for rapid assessment can be estimated as the ratio of the area fished within the spatial range of that
species divided by the entire area inhabited by the species. By adopting the upper limit values for the
three ranges of 0.33, 0.66 and 1.00 for low, medium and high risk, respectively, then catch susceptibility can also be categorized as low, medium or high risk. For example for a fishing method where
mortality is high, then catch susceptibility is low. This is calculated as catch susceptibility = 0.33 x 1.00
x 1.00 = 0.33 (i.e.., catch susceptibility = LHHH=L)
Taxonomic order
Common name
Envounterability
trawl/
seine Gillnet
Selectivity
Hook
trap/
pot
Discard post-harvest mortality
trawl/ Gillnet
Trap Trawl Gillnet
Trap/
seine 6-6½ in Hook pot
seine 6-6½ in Hook pot
Pelagic and semipelagic species
Carcharhiniformes
Whaler and hammerhead shark
L
L
L
L
H
M
H
L
H
H
M
H
Lamniformes
Mackerel and thresher sharks
L
L
M
L
H
M
H
L
H
H
M
H
Demersal species
Carcharhiniformes
Whaler and hammerhead shards
L
H
H
L
H
M
H
L
H
M
L
H
Squatiniformes
Angel sharks
H
L
L
L
H
L
H
M
M
L
L
L
Pristiophoriformes
Sawsharks
M
H
M
L
H
H
H
M
H
H
L
M
Squaliformes
Dogfishes
M
H
H
L
H
L
H
H
M
M
L
L
Hexanchiformes
Sixgill and sevengill sharks
L
H
H
L
H
M
H
H
H
H
M
H
Orectolobiformes
Catsharks, wobbegongs, carpet sharks
M
H
H
M
H
M
H
H
M
L
L
L
Heterondontiformes
Horn sharks
M
H
M
L
H
M
H
H
M
L
L
L
Pristiformes
Sawfishes
H
L
L
L
H
H
H
H
H
L
L
L
Rhinobatiformes
Shovelnose and guitar rays
H
L
L
L
H
L
H
H
H
L
L
L
Torpediniformes
Electric rays
H
L
L
L
H
L
H
H
H
L
L
L
Rakofpr,es
Skates
H
L
L
L
H
L
H
H
H
L
L
L
Myliobatiformes
Eagle and devil rays and stingrays
H
L
L
L
H
L
H
H
H
L
L
L
Holocephaliformes
Chimaeras
M
L
L
L
H
M
H
H
H
H
M
L
__________________________________________________________________________________________________________________________________________
Footnote: The values presented in this table are based on species found in southeastern Australia, but they should be applicable to most regions of the world, except “
selectivity” of gillnets, which is presented here for 6-6½ inch mesh and is likely to vary regionally depending on size of animals for each species in the region.
Table 13.2 Catch susceptibility of chondrichthyan animals to demersal fishing gear. Catch
susceptibility is defined as “availability” x “encounterability” x “selelectivity” x “post-capture
mortality”; “availability” is the ratio of area of range of specimens divided by the area of the
range of the fishery; “catch susceptibility”, “availability”, “vulnerability”, “selectivity”, and
“discard post-harvest mortality” all have values ranging 0-1, for risk assessment these are
categorized as L (low, 0.00-0.33), M (medium 0.34-0.66), and H (high, 0.67-1.00).
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13.3
FRAMEWORKS FOR FISHERIES MANAGEMENT
13.3.1 International developments
Growing widespread concern during the past decade about expanding fisheries for sharks and
for the potential impacts of fishing on their populations and those of rays and chimaeras led to initiatives to implement better management of these animals. During the mid-1990s, submissions were
presented to the Convention for International Trade in Endangered Species of Wild Fauna and Flora
(CITES) seeking restrictions on the trade of products from sharks as a means of controlling the
harvest of these animals. In response to requests from CITES, the Food and Agricultural Organization
of the United Nations (FAO) subsequently initiated a worldwide process that led to development of the
International Plan of Action for the Conservation and Management of Sharks (IPOA-Sharks). The
IPOA-Sharks was endorsed by the FAO Committee of Fisheries and its 80 or so member nations
during 15–19 February 1999. The IPOA-Sharks provides guidelines to member nations for development of National Plans of Action for the Conservation and Management of Sharks (NPOA-Sharks)
and for coordination of shark management at global, regional, and sub-regional levels under the auspices of FAO. The IPOA-Sharks forms part of the Code of Conduct for Responsible Fisheries and
defines “sharks” to include sharks, rays and chimaeras.
Through the IPOA-Sharks and other international developments, the scope of fisheries
management for these animals is expanding beyond the focus of sustainable use of the resource to
take account of the need for biodiversity conservation and maintenance of ecosystem structure and
function. There is also growing emphasis on bycatch reduction and on ethical issues associated with
full utilization of dead sharks and the handling and processing of these animals (Anonymous, 2000).
13.3.2 Jurisdictional and institutional frameworks
Fisheries management presupposes a minimum set of institutional arrangements and recurrent
activities at local, sub-national, national, sub-regional, regional and global levels. Entities engaged in
fisheries management require appropriate policy, and legal and institutional frameworks to adopt
measures for the long-term conservation and sustainable use of shark fishery resources. Conservation
and management measures need to be based on the best scientific evidence available. Effective
coordination of implementation of fisheries management at a national level through development of
shark plans and ongoing shark assessments requires a structure, a definition of roles, agreed processes, and mobilization of resources. All relevant fishing sectors, fishing communities, non-government organizations, and other interested parties should be consulted as part of the decision-making
process. Creation of public awareness of the need for the management of shark resources and
participation in the management process by those affected should be promoted.
To be effective, management of fisheries has to be concerned with whole stock units over the
entire area of distribution of the species harvested. The best scientific evidence available should be
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used to determine the area of distribution of the resource and the area through which a fish in the
stock migrates during its life cycle. Where a stock falls entirely within the Exclusive Economic Zone
(EEZ) of a single nation then that resource can be managed under the single jurisdiction of that nation.
On the other hand, where a stock is distributed in the EEZs of more than one nation or in the high
seas, complex jurisdictional arrangements are required. Shared or transboundary straddling stocks
need to be managed through bilateral and multilateral arrangements or Regional Fisheries Management
Organizations (RFMOs) (Anonymous, 2000).
All nations are free to harvest fish in the high seas and regulation is beyond the control of any
individual country. Straddling and highly migratory fish stocks are managed cooperatively under a
United Nations treaty. This treaty is the Agreement for the Implementation of the United Nations
Convention on the Law of the Sea of 10 December 1982 Relating to the Convention and Management
of Straddling Fish Stocks and Highly Migratory Fish Stocks. It is more briefly termed the UN Fish
Stocks Agreement. Ratification of the Agreement by nations provides rights and obligations to those
nations and prescribes fisheries management principles for the long-term conservation and sustainable
use of straddling and highly migratory fish stocks. The Agreement provides a framework for cooperation between fishing nations, including through RFMOs. Also, it provides rights to member nations of
RFMOs to board and inspect fishing vessels on the high seas to check compliance with regionally
agreed conservation and management measures. Nations signing the Agreement accept the principles
of the Agreement. The UN Fish Stocks Agreement depends on “Flag State responsibility,” which is a
principle of international law. The national law applying to a vessel on the high seas is the law of the
country whose flag the vessel is entitled to carry. If there is any infringement of rules, the Flag State of
the vessel concerned is responsible for undertaking investigation and appropriate enforcement action.
13.4
USE RIGHTS
Granting use rights bestows property rights whereby an individual, company, or defined group
or community can own fish after the fish have been captured. Once captured the fish become private
property. Before they are captured, the fish are private property only if the water body holding the fish
is private property. Within a country’s EEZ, fish in the water are usually deemed the property of the
citizens of that country and said to be state property. Nevertheless, a state can legislate to privatize
fish in the water and thereby grant ownership to an individual, company, or defined group or community. Where the fish in the water are owned in common by a defined group or community, the fish are
said to be common property. For example, where a government legislates to bestow ownership of fish
in a specific body of water to people traditionally using that fish resource, the fish in the water become
the common property of those people. However, the fish of an entire nation are often referred to as
common property. Here the group or community is defined as including all citizens of a nation; the
term common property is equated to state property (Charles, 2002).
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Through fisheries management, use rights can be implemented under any of private property,
state property or common property. In addition, the United Nations Fish Stocks Agreement (Article 10)
provides facility to prescribe use rights in waters outside the EEZs of nations on the high seas where
the fish in the water are deemed “non-property” (Charles, 2002). Fishery managers need to ensure
that no vessel is allowed to capture sharks or take sharks as bycatch unless authorized in a manner
consistent with international law for the high seas or in conformity with national or sub-national legislation within areas of national or sub-national jurisdiction.
The FAO World Fisheries Conference in Rome during 1983 recognized that open access to
non-managed fisheries resulted in competition for limited resources, overcapitalization, and depletion of
stocks. It was considered that fishers should have clearly defined fishing rights and that catches should
not exceed the productivity of the resource. One approach to allocate rights for the capacity to fish is
through input controls such as license allocation. Another approach is to allocate rights for specified
shares of the resource through output controls in the form of catch quotas (King, 1995).
13.4.1 Territorial use rights
Rights can be assigned to individuals or communities to fish in certain locations based on longstanding tradition of use (customary usage). This approach is variously termed Territorial Use Rights in
Fishing (TURFs) and Customary Marine Tenure (CMT). A feature of these systems is the local
solution of usage issues. Many fishing communities informally regulate their fishing effort, based on
their observations of fish abundance and their interpretation of their indicators of abundance over time
(Charles, 2002).
Territorial management is highly effective where it is supervised by the fishing community
itself or by its elected leaders. Many TURF and CMT systems have declined as traditional fisheries
commercialize. Nevertheless, several countries of Oceania, such as Solomon Islands, Fiji and Samoa,
have moved to re-establish these systems. Customary fishing ground boundaries based on oral claims
are being formalized in legislation (Charles, 2002). Japan, for example, has integrated ancient local
systems of management into fisheries planning at all levels of local, regional and national government
(Pinkerton, 2002). A challenge for countries is to support traditional approaches to management and to
integrate them into regional and national management systems through co-management agreements.
There is evidence of customary usage of sharks and rays in Canada, northern Australia (Last and
Stevens, 1994), Solomon Islands (Sant and Hayes, 1996), and New Zealand (Francis, 1998). However,
there are no examples where territorial use rights have been formally granted specifically for the
harvest of chondrichthyan animals in recognition of traditional usage.
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13.4.2 Limited entry
Limited entry is a common management tool whereby the management agency issues a
limited number of licenses to take fish. This creates a use right to participate in a particular fishery.
License limitation is the restriction of fishing rights to those fishers, fishing units, or fishing vessels
licensed in a fishery.
Several types of fishing licenses are used for fisheries management throughout the world. A
“personal license” authorizes a particular fisher to deploy fishing gear for catching fish, but requires
the licensed fisher to be present at the site of fishing operations. A “vessel license” authorizes a
particular vessel to deploy fishing gear for catching fish, but requires operations to be made from the
licensed vessel. A “fishery access license” authorizes the holder, or a person nominated by the holder,
to deploy fishing gear for catching fish from any nominated vessel. A “gear license” authorizes the use
of a particular item of fishing gear for catching fish by the holder, or a person nominated by the holder,
from any nominated vessel. Special conditions or endorsements on such licenses can be used to
nominate one or more fisheries, species, gears, catch levels, or effort levels authorized.
Licenses are either non-transferable or transferable. Non-transferable licenses are auctioned
or issued at the discretion of the licensing authority through a Minister of State. Development of merit
criteria as guidelines for issuing non-transferable licences by licensing authorities are usually criticized
as discriminatory. Allocation of non-transferable licenses according to merit inevitably leads to dissatisfaction and pressure from holders to make the licenses transferable. Transferable licenses are exchanged by mutual financial agreement between the seller and buyer (usually within guidelines prescribed by the licensing authority). Once transferable, licenses acquire a value related to earnings that
might be acquired from possessing the license. Debts associated with the purchase of transferable
licenses create an incentive to increase the catch to service the loans, which create a need to reduce
the number of licenses or entitlements associated with each license. If there is the need to reduce the
number of licenses in a fishery, the licensing authority can withhold non-transferable licenses, but has
to buy back transferable licenses from license holders at market price.
Annual license fees collected by the licensing authority can be used to recoup management,
surveillance, research and fishery monitoring costs, and collect a resource rent on behalf of the
community. Personal licenses can be effective in artisanal and recreational fisheries, but fishery
access or vessel licences are favored in industrial fisheries where costly assets are required and there
is a need to exchange fishing masters or skippers on a vessel to ensure its economic viability. All four
types of license have been variously applied in fisheries either targeting sharks or taking sharks as
byproduct or bycatch.
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Limited entry caps the number of operators in a fishery, but is rarely sufficient to manage a
fishery. Once license limitation is implemented, improved skill of the operators and technological
innovation inevitably increase the fishing power of the vessels in the fleet. Limited entry is best implemented during the early phase of the development of a fishery, before the catching power of the fleet
is excessive. It is difficult to reduce the number of licenses once there is overcapacity in the fleet.
Whereas limited entry is a reasonable mechanism for assigning use rights, it must be implemented as
part of a management portfolio.
13.4.3 Quantitative input rights (effort rights)
Input controls designed to limit or reduce fishing mortality requires some form of restrictive
licensing, which limits the number of fishing vessels engaged in a particular fishery, and some measure
for limiting the fishing effort of the licensed vessels. Where overfishing occurs and the fleet is too
large, there is a need to reduce the number of licensed vessels or reduce the fishing efficiency of the
vessels. Furthermore, where license limitation is established, incremental technological advances in
vessel and fishing gear design and improvements in fish-finding equipment and navigation aids are
likely to cause the effective fishing capacity of a fleet to increase with time. In addition, if the licenses
are transferable and acquire progressively higher value, economic forces will cause inactive vessels
with their associated latent effort to become activated and increase the total effort applied by the
entire fleet. Hence, with any input control system, increasing efficiency and increasing effort create an
ongoing need to reduce the number of vessels or efficiency of each vessel. Overcapacity of a fleet
can be reduced in several ways: removing vessels, reducing fishing time of the vessels, limiting the
amount or size of gear that a vessel can carry, or reducing efficiency of fishing effort.
Removing vessels from the fleet requires rescinding licenses. This involves removing the rights
from some vessels to operate in a fleet. Just systems applied for this purpose are referred to as BuyBack Schemes or Decommissioning Schemes where funds are made available by government, the
industry itself or some other stakeholder group to purchase licenses as a means to removing vessels
from the fishery. A feature with these schemes is that the least efficient operators have the highest
economic incentive to sell their licenses. Whereas this improves the overall economic efficiency of the
fleet, it can result in a large number of vessels being removed with very little change in overall fishing
mortality.
Reducing vessels’ fishing time can be implemented by imposing limits on the number of days
or times of the day vessels can operate. Extended closed seasons, closed days of the week, or closed
times of day are unpopular with fishers as it reduces flexibility and creates incentives to operate under
adverse weather conditions. Closed days of the week are seen as inequitable as it impacts greatest on
larger vessels that undertake extended periods at sea. Closed periods disrupt market supply of fish and
employment patterns.
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Fishing capacity of a fleet can be restricted by limiting the size of vessel and engine power and
thereby restrict the ability of vessels to tow fishing gear such as demersal trawls. For most other
fishing methods, however, the relationship between the size of the gear and the size of the vessel or
power of the engine is not so clear. Nevertheless, fishing capacity of a fleet can be restricted by
limiting the size of vessels and thereby restricting the number of fishing days by weather conditions.
This can cause problems of safety for fishers if there are strong economic incentives to operate under
hazardous conditions.
Fishing gear can be limited in type, size and number. Gillnets can be restricted by controlling
the length and height of the nets, the mesh-size of the webbing, and hanging ratio for the construction
of the nets. Longlines can be restricted by controlling the length (or volume) of mainline and the
number of hooks that can be used during each operation. Restrictions might also be placed on hooksize and presence or absence of a wire trace between the hook and the snood, and on the use of
automatic baiting and setting machines. Trawl nets can be limited to a maximum length of headline.
Gear regulations tend to restrict the efficiency and cost of catching fish for each operator.
Gear restrictions are often implemented where there are the social objectives of providing employment
and food to a large number of traditional and artisan fishers. Hence, gear restrictions are minimized
where there is the economic management goal for reducing the number of operators and improving
economic efficiency, but can be adopted as a means to maintenance of fishing communities and equity
of incomes among participants (Pope, 2002).
Some of the benefits of limits on the quantity of fixed gear used, such as gillnets, can be offset
by the gear being in the water for extended periods. Legislating for vessels not to leave the gear
unattended discourages the practice of returning to port while the gear remains set at sea. This
practice leads to cryptic fishing mortality from predation mortality and ghost fishing mortality if the nets
are lost.
Meeting the biological objective of reducing fishing mortality by reducing vessel efficiency is
incompatible with the economic objective of improving economic efficiency of the fleet. Similarly,
meeting the biological objective by reducing vessel numbers is incompatible with the social objective of
providing employment for fishing communities.
There are many general vessel and fishing regulations that apply across fisheries, but few
have been implemented specifically for chondrichthyan species. Within the European Union, every
country has agreed to a maximum gross tonnage of vessels and maximum engine power. The limits
are set for each fishery, fishery sector, and, in some cases, fish stock (Pawson and Vince, 1999).
During the late 1980s and the 1990s, a complex system of quantitative rights was adopted for
the shark fishery of southern Australia. Depending on historic catches by a vessel, vessel licenses
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were endorsed to use various length of gillnets. These gear holdings were not transferable except for a
short period when a small proportion of the licenses could be amalgamated to allow for an increase in
gear holdings. After amalgamation, the maximum gear holding was 6000 meters long, but this was
subsequently reduced to 4200 meters (Walker, 1999). This type of effort rights was taken a step
further in the shark fishery of Western Australia. Here time-gear units were allocated where a timegear unit authorized the use of a particular length of gillnet for one month of the year (Simpfendorfer,
1999).
13.4.4 Quantitative output rights (catch quotas)
Also referred to as output control, limitation of catch can take the form of a global catch
quota, individual quotas as non-transferable individual quotas or individual transferable quotas (ITQs)
with a total allowable catch (TAC), bag limits or trip limits.
A global catch quota, alternatively referred to as a competitive TAC, is the maximum catch
allowed from a resource by the entire fleet for a year or season. Under this system, individual fishers
compete for catch until the fleet reaches the overall limit and the fishery is then closed. Such a system
requires rapid collation of catch statistics to be effective. Individual fishers feel compelled to operate
under hazardous weather conditions and to capitalize in vessels and gear to attain a competitive edge.
This can result in progressively shorter seasons, which disrupt employment patterns and market
supplies.
Bag limits are a simpler form of catch limit where the number of animals a person or vessel
can retain. Bag limits are usually applied on a daily basis for recreational fishers where an individual is
permitted to land up to a specified catch weight or a prescribed number of carcasses. Limiting the
number of carcasses can create an incentive to retain the largest animals and discard small animals,
which might be dead and hence contributing to cryptic fishing mortality.
Trip limits may be applied on a trip or daily basis for fishers who do not hold a license to
operate in the fishery. Trip limits may be designed to avoid wastage by allowing non-licensed operators
to land byproduct catch. However, the trip limit needs to be sufficiently low so as not to encourage
targeting by non-licensed operators. Trip limits may be applied also in a fishery to discourage “derby
fishing” and to spread the take of a quota over a long period of time.
Individual non-transferable quotas are where each operator has a prescribed catch, which is
usually fixed as a specified proportion of the TAC. This avoids the competitive element, but does not
allow the operator the opportunity to increase catch by personal choice.
Individual Transferable Quotas (ITQs) are where each operator has a prescribed catch of one
or more units of catch. The ITQs can be traded freely, or traded between specified operators. Operators can hold one or more ITQs, depending on the number they choose to buy. By prescribing an ITQ
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or non-transferable quota as a proportion of the TAC, the catch allowed under each ITQ varies
depending on the TAC, which can be set annually or some other period. The facility to trade ITQs
allows less efficient operators to sell all or part of their quota to more efficient operators at the market
price of the quota. A substantial enforcement effort is required to ensure that individual quotas are not
exceeded. Individual catch quotas create an incentive to under-report catches and a temptation to sell
to black market buyers. In addition, management by individual quotas can encourage operators to
discard that part of the catch that potentially receives a low price (maybe damaged, small or large
animals) and replace them with animals that would receive a higher price. This practice is referred to
as high grading.
TACs for some species of fish are expressed as the number of fish, but they are usually
expressed as weight. Although they should ideally relate to the catch, for administrative convenience
they are limits on landings. Components of TACs are often used as a basis for resource allocation
between different user groups, such as between recreational users and commercial users or between
sectors or regions of the commercial users. This also occurs in internationally shared fisheries where
allocations are negotiated between countries.
Various types of TACs are administered for shark resources. For management of the United
States Atlantic Shark Fishery, 39 species of sharks are categorized into four groups: “large coastal”,
“small coastal”, “pelagic”, and “prohibited” for the commercial sectors of the fishery. Apart from the
prohibited group, each group has a separate TAC, reviewed periodically. In the absence of limited
entry in the fishery, the commercial catches have regularly exceeded the TACs. In addition, there is a
commercial trip limit of 4000 pounds weight for the large coastal group and a recreational fishing bag
limit of two sharks per boat per day plus two Atlantic sharpnose sharks (Rhizoprionodon
terraenovae) per person per day or trip (Branstetter, 1999). New Zealand and Australia have set
TACs for key individual species of shark and have ITQs (Francis, 1998; Walker, 1999).
13.5
TECHNICAL MEASURES
13.5.1 Regulation of fishing gear
Ideal fishing gear achieves many things simultaneously. It is efficient at capturing target
species while avoiding small animals to minimize growth overfishing and avoiding large breeding
animals to minimize recruitment overfishing of the species. It has negligible direct or indirect impact on
bycatch species, habitats, and substrates, and it causes minimal damage to animals captured and in no
way diminishes the food quality of the animals caught.
Regulation of fishing gear can be used for control of fishing mortality, of impacts on habitats
and ecosystems, and of the food quality of fish retained. Regulation of fishing gear should not be used
as a way of controlling the fishing effort component of fishing mortality, but rather as a way of control-
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ling the catch susceptibility component of fishing mortality. This can be achieved by variously controlling one or more of the four components of catch susceptibility—availability, encounterability, selectivity, and post-capture mortality. Availability can be controlled through the fishing area closure to the use
of specific gears, whereas encounterability, selectivity and post-capture mortality can be controlled or
influenced through regulation of the construction of the gear or the way it is used.
Fishing gears are classified as passive or active. This classification is based on the behaviour
of the target species in relation to the gear. Passive gears include gillnets, trammel nets, longlines,
handlines, jigs, droplines, troll lines, pots and fish traps. Active gears include spears, harpoons, dredges,
demersal trawls, mid-water trawls, Danish seine nets, Scottish seine nets, beach seines, and purse
seines. Table 13.3 provides an evaluation of different fishing gears for selectivity and ecosystem
effects of fishing. The values presented are from evaluation across many fisheries, but specific values
for a particular fishery, particularly as it might relate to chondrichthyan species, can be altered depending on regulation of the fishing gear (Bjordal, 2002).
Fishing gear
Size
Species
selection selection
Bycatch
mortality
Ghost Habitat
fishing effects
Energy
efficiency
Catch
quality
Ecosystem
effect index
Gillnets
8
4
5
1
7
8
5
5.4
Trammel nets
2
3
5
3
7
8
5
4.7
Handlining
4
4
6
10
9
9
9
7.3
Longlining
6
5
6
9
8
8
8
7.1
Pots
7
7
9
3
8
8
9
7.3
Traps
5
5
8
8
9
9
9
7.6
Spear, harpoon
8
9
5
10
10
8
9
8.4
Pelagic trawl
4
7
3
9
9
4
8
6.3
Demersal trawl 4
4
6
9
2
2
6
4.7
Beam trawl
4
4
6
9
2
1
6
4.6
Shrimp trawl
1
1
7
9
4
2
6
4.3
Seine net
5
5
6
9
4
5
8
6.0
Purse seine
-
7
5
9
9
8
8
7.7
Beach seine
2
2
5
9
6
9
9
6.1
Table 13.3 Estimates of ecosystem effects of fishing for different fishing gears. Ranking is a scale
from 1 (non-favorable) to 10 (highly favorable) for different ecosystem-related factors; ecosystem
effect index is the mean of the other seven factors (reproduced from Bjordal, 2002).
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Fishery managers need to ensure that fishing methods and practices in a fishery are consistent
with the code of conduct for responsible fishing. Those methods that are not should be phased out and
replaced with acceptable methods and practices (Anonymous, 1995; Anonymous, 2000).
The type of fishing gear used and the species of shark taken as bycatch determines which
techniques and equipment are appropriate for minimizing bycatch. For trawl nets, there is evidence
that catches of sharks have been reduced when fitted with turtle exclusion devices, suggesting there
might be advantages investigating alternative devices designed specifically to exclude sharks. Also,
there is scope to reduce bycatch of sharks in gillnets by regulating mesh size and possibly the breaking
strain of the webbing filaments. Many species of sharks remain alive on hooks for extended periods
and can be released alive. There might be scope to improve survival of sharks by prohibiting the use of
wire traces used to attach hooks to the snoods on a longline and by regulating for reduced breaking
strains of the snoods. Wire traces reduce the probability of hooks being bitten off the snoods by
sharks. Regulation of hook size may provide a means of eliminating or reducing the catch of smaller,
younger individuals in a shark populations (Dowd, 2003). Minimum mesh sizes or square mesh panels
in codends of trawl nets are applied widely, but are not selected specifically for chondrichthyan
species. Selection of appropriate trawl codend mesh size and shape might have some benefit in
allowing neonate and small juvenile sharks to escape.
Regulation of mesh size is a highly effective measure for shark management. Careful selection of mid-sized mesh allows small animals to pass through the meshes and large animals, notably
breeding and other mature animals, to escape (Kirkwood and Walker, 1986). Adoption of a predominantly 6-inch mesh size during 1975 has been the key to success in sustainable use of the gummy
shark (Mustelus antarcticus) stocks in Bass Strait. In this fishery, not only does the gear selectivity
allow escapement of small and large animals, but the fishers operate in areas inhabited by mid-sized
animals, which tend to be away from the inshore areas inhabited by pre-recruits and breeding females
(Walker, 1998). In Western Australia, mesh sizes, number of meshes deep, and length for the construction of shark gillnets are also controlled. These vary between different zones (Simpfendorfer, 1999).
13.5.2 Area and time restrictions
Closures involve restricting all or particular methods of fishing in selected areas, and the
closures can be permanent, temporary, seasonal, daily or part of the day. Spatial and temporal closures
are frequently applied to meet specific fishery-management objectives, but they are also used to meet
other community objectives. Other objectives for closures include protecting marine, estuarine, and
freshwater biota, items of special cultural value, or geologic interest. In addition, areas might be set
aside for specific purposes such as navigation, aquaculture, or mining. The various purposes of clo-
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sures have produced confusion and debate over terminology. So for the purpose of this chapter, a
distinction is made between closures designed to meet fisheries management objectives and closures
designed to meet other community objectives. The two terms adopted are “fishing area closure” and
“marine protected area”.
These terms are not meant to be mutually exclusive, but rather to provide a means for distinguishing between addressing fisheries management objectives as they relate to sustainable use,
biodiversity conservation, and protection of ecosystem structure and function from the effects of
fishing and addressing other community objectives. The fishery manager needs the flexibility of
prescribing management boundaries and varying rules between zones. At the simplest level, this might
be prohibiting angling from a jetty to avoid injury to bathers. At a more complex level, this might be
zoning a broad region of thousands of square kilometers to meet a range of fishery and ecological
objectives through a complex system of licensing use rights, gear restrictions, and area closures across
several fisheries. For example, gear restrictions across a complex of zones might be designed to
provide high sustainable yields from target species of high biological productivity, while simultaneously
minimizing impacts on bycatch species of low biological productivity. Where marine protected areas
are proposed within broad fishing areas, the astute fishery manager will endeavor to influence the
positioning of the boundaries that are compatible with fisheries objectives or at least gain some benefit
for a fishery. Examples of how marine protected areas and fishing area closures can benefit sustainable use of target species and biodiversity conservation of chondrichthyan animals are presented in the
following section.
13.5.2.1 Marine Protected Areas
A Marine Protected Area (MPA) is defined by the World Conservation Union (IUCN) as
“any area of intertidal or subtidal terrain, together with its overlying water and associated flora, fauna,
historical and cultural features, which has been reserved by law or other effective means to protect
part or all of the enclosed environment” (Anonymous, 1988). An MPA can be a large or small area
and the overall objectives for an MPA can be specific or broad. Large MPAs with broad objectives
are often divided into geographically smaller zones and designated for multiple use. Depending upon
the objectives, an MPA, or a zone within an MPA, might be designated for one or more uses. MPAs
are usually declared from judgement using qualitative information, as quantitative evaluation is costly
and long time series of environmental or community-monitoring data are rarely available. Selected
areas are usually judged as being unique or having high conservation value. An example of a unique
area declared an MPA is the stromatolite assemblage of Shark Bay, Western Australia. Corner Inlet in
Victoria, Australia, on the other hand, was declared an MPA in 1983 because it was judged to have
several high conservation values. These values include the presence of international migratory birds,
soft substrate biotic communities, mangrove stands, and Posidonia sea grass meadows (Plummer et
308
al., 2003). In Australia and South Africa, for example, networks of MPAs are presently being established to protect representative areas of a range of habitat types.
MPAs with single or multiple zones have been declared throughout the world for providing
various levels of protection and for a variety of uses. A preservation zone or wilderness zone usually
provides the highest level of protection through very limited access. A cultural zone is designed to
provide protection to special items of cultural value and sites of historic, cultural or religious significance. Items of cultural value include shipwrecks, archaeological relics, submerged aboriginal middens,
and fossils. Zones, which allow for access, but for minimal disturbance, include education, science,
experimental, and recreation zones. An education zone is usually a relatively safe diving or intertidal
area that can be visited for training and educational purposes. A scientific zone is an area where
authorized researchers can undertake the study of particular species or ecology of marine communities. Other types of zones, such as recreational zones or traditional fishing zones, allow for exploitative
activities. A recreational zone might allow for diving and photography but no fishing, or might allow for
recreational fishing activities. A traditional fishing zone recognizes traditional fishing rights of a community or group of individuals and allows for ongoing subsistence fishing.
MPAs are highly suitable for management of chondrichthyan species known to aggregate,
where they are vulnerable to capture or disturbance by human activities (Bonfil, 1999). There are
several examples from various parts of the world where these have been applied for sharks and rays.
In New South Wales, Australia, the grey nurse shark (Carcharias taurus) is fully protected, but, to
avoid unintentional kill in the coastal waters from longline fishing, a system of 10 sanctuary areas was
established during December 2002. Each sanctuary extends 200 meters out from an island or a section
of coast with buffer zones extending a further 800 meters. Fishing is prohibited, and new controls on
scuba diving include bans on night diving, feeding, touching, harassing or chasing sharks, and on use of
electronic shark repelling devices and electric scooters in these areas. In the Florida Keys National
Marine Sanctuary, nurse shark (Ginglymostoma cirratum) mating aggregations at the Dry Tortuga
Island group were recently given added protection by implementing a seasonal closure to boat traffic
(Bonfil, 1999; Stevens 2002). The Ningaloo Reef Marine Park in northern Western Australia on the
edge of the Indian Ocean provides protection to whale shark (Rhincodon typus) when these animals
aggregate in this region from late March to early May. The number of divers and hours that divers and
boats can approach these animals is restricted. Touching the animals or use of camera-flash lights is
prohibited (Tricas et al., 1997). The Kinabatangan wildlife sanctuary in Sabah, East Malaysia, includes
about 27,000 hectares of tropical forest and the lower reaches of the Kinabatangan River and provides
some protection (although some artisanal fishers operate there) to several rare freshwater elasmobranch species. These include the river speartooth shark (Glyphis sp.), giant freshwater stingray
(Himantura chaophraya), and greattooth sawfish (Pristis microdon) (Payne and Andau, 2002).
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13.5.2.2 Fishing area closure
Fishing area closure is defined here as closing an area to all or selected fishing gears for
continuous or selected time periods to limit fishing mortality on all or particular length or age classes of
one or more fish species, or to reduce gear impacts on habitats or other uses. Fishing area closures
can be applied to target, byproduct, or bycatch species. MPAs can also limit fishing mortality, but areas
closed to meet fisheries management objectives are not normally referred to as MPAs, marine parks,
reserves or sanctuaries. In MPAs, more than fishing mortality and impact of fishing gear are controlled.
Fishing area closure as a fisheries management tool is applied to meet specific fisheries
objectives. One important objective is to protect aggregations of small (pre-recruit) animals to allow
these animals to grow and thereby improve yield per recruit and avoid growth overfishing. Another
important objective is to protect aggregations of breeding or mature animals to enhance survival of the
largest animals, which produce the highest number of offspring, and thereby avoid recruitment overfishing.
Fishing area closure will be used much more extensively in the future and there are several
reasons why it has been applied conservatively in the past. The first is that fisheries managers have
tended to focus attention on abundant species with high biological productivity, whereas closures are a
more essential management tool for managing less abundant species with low biological productivity. A
second reason is that setting boundaries for closures requires extensive data sets to provide detailed
information on distribution and biological condition of fish and often these data sets have not been
available. A third reason is that fishery managers have been reluctant to prescribe in law complex
demarcation boundaries because they have been difficult to enforce and fishers have been often
uncertain of their navigational position at sea in relation to demarcation boundaries.
There have been several developments in recent years to facilitate greater application of
fishing area closures in the future. One of these developments is the growing awareness in the community that chondrichthyan species are among the least biologically productive animals and need
special conservation and management attention. In addition, three important technological developments in recent years make fishing area closures a more practicable fisheries management tool. The
first development is that of Geoglobal Positioning Systems (GPS), which enables the navigational
position of a vessel to be known continuous with high precision. The second and third developments
are linked to GPS. The second development is that of Geographic Information Systems, which allow
for better management, analysis, and visual display of spatial data. This innovation is providing facility
to better understand the spatial and temporal distributions of species and habitats and to better evaluate
the significance of various areas. The third development is that of Vessel Monitoring Systems (VMS),
which overcomes the need for deployment of high-cost vessels at sea for surveillance purposes. VMS
310
allows the navigational positions of vessels at sea to be electronically monitored using satellite communication systems. As costs of VMS decline so too will the surveillance cost for effective enforcement
of fishing area closures.
Two types of fishing area closures have been implemented in the shark fishery of southern
Australia since the 1950s. Closure to shark longline fishing in nursery areas of school shark
(Galeorhinus galeus) in the inshore waters of northern and south-eastern Tasmania were first
adopted during 1954 and extended during the 1960s. In 1990, fishing gear regulation was extended to
included gillnets used for targeting sharks (>150 mm mesh-size) and gillnets for recreational and
commercial fishing to target other species (60–70 mm mesh-size) in some of these areas. These
closures were designed to prevent targeting pregnant females entering shallow waters for parturition,
as well as to reduce the incidental kill of neonate and small juvenile animals (Williams and Schaap,
1992). In addition, closed seasons during October or November (months immediately prior to parturition) were adopted across the entire fishery during 1953–67. During 1994, the use of gillnets were
prohibited during the period from 8 October to 22 November for the area west of the South Australia–
Victoria border and during the period from 11 November to 25 December for the area east of the
border. These rolling closures were designed to protect pregnant animals as they migrated from the
western region of the fishery to the nursery areas in the eastern region for parturition (Walker, 1999).
Other examples of fishing area closures for sharks, include large areas being closed to gillnet
and longline fishing for sharks in Western Australia to protect breeding animals of Carcharhinus
obscurus and C. plumbeus (Simpfendorfer, 1999). Also, although not specifically designed for chondrichthyan species, many nations designate coastal waters for artisanal fisheries and those further
offshore for industrial fleets. This is designed for social reasons, but it does provide some limitation on
fishing mortality in coastal waters.
The most promising approach to fisheries management is to take a more regional approach to
fisheries management and adopt greater use of fishing area closures. There are numerous examples
where fishing area closures have been applied in the past, but they have tended to be small in inshore
areas.
13.5.2.3
Regional fisheries management
Regional fisheries management is defined here as integrated management of a broad region of
waters across species and fisheries. Management is through allocation of use rights and application of
fishing area closures and other technical measures. It is designed to efficiently harvest resources in
specified areas and to meet the triple goals of sustainable use with high yields, biodiversity conservation, and maintenance of ecosystem resilience. Open and closed areas are selected to minimize
impacts on pre-recruit and breeding and other mature animals of target species, on species of low
biological productivity, and on habitats, particularly critical habitats.
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A regional approach to fisheries management through the judicial use of fishing area closures
is required to avoid depletion of the populations of species with low biological productivity impacted by
the fishing gear used to target species of high biological productivity. Maximum benefits from fishing
area closures can be attained by aligning refuge areas for species of high catch susceptibility and low
biological productivity (low reproductive rates and low natural mortality rates) with areas containing
critical habitats, and pre-recruit and breeding animals of the target species. Whilst some trade-offs are
inevitable, where practicable, the fishing area closures should not be so large that there are insufficient
fishing grounds open to efficiently harvest high-valued target species to ensure high sustainable yields.
Regional fisheries management requires an exhaustive information base. Extensive data sets
on monitoring distribution, abundance and fishing mortality, and on critical habitats and population
biology are not only required for intensive management of target species, but for all byproduct and
bycatch species. The positions of the boundaries of the fishing area closures need to be flexible so
they can be updated as improved information is acquired. Through improved information and an
adaptive management approach, the goal is to optimize yields across species, biodiversity conservation
and ecosystem maintenance.
The low biological productivity of many chondrichthyan species is likely to have a major
influence on the selection of the boundaries of area closures and will provide an impetus to adopt the
regional fisheries management approach. Also, species found in temperate regions tend to have lower
productivity than those found in tropical regions, and those found in cold deepwater on the continental
slope tend to have lower biological productivity than those found in the warmer waters on the continental shelf in temperate regions. The recent depletion of the deepwater squalids and chimaerids on
the continental slopes of the Earth’s temperate regions, such as southern Australia (Graham et al.,
2001), has created a need to establish substantial refuge areas for these species.
Multispecies modelling tools for evaluation of alternative spatial policy options are emerging.
Such models need to account for trophic interactions with important top-down impacts of predators on
prey and dispersal responses of harvested species and redistribution of fishing effort in response to
trophic cascades. Determination of appropriate sizes and effectiveness of closed areas are highly
dependent on predator-prey relationships and movement rates of harvested species. In general, a few
large closed fishing areas are likely to be more effective than a large number of small ones. Local
protection can be negated by fishing effort concentrated at the boundaries of closed fishing areas or at
nearby sites where the presence of prey species can rapidly attract highly mobile predator species out
of the closed areas (Walters et al.,1999). Importantly, the perimeter-to-area ratio decreases as size of
closed area increases. Closed area boundaries can be minimized by having large closed fishing areas,
and by placing the closed fishing areas adjacent to land or in bays and inlets (Walters, 2000).
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Closure of fishing areas can have unintended consequences caused by the redistribution of
effort from those areas, particularly if the fisheries are principally managed by TACs and ITQs.
Whereas TACs and ITQs control the catch of quota species, which are usually target or byproduct
species, they do not control bycatch species. Hence, redistributed effort from closed areas might have
undesirable effects on bycatch species. An alternative potential management alternative to TACs with
ITQs is to adopt a total allowable effort with ITQs specified as “transferable effort quotas” (Walters
and Bonfil, 1999). These quotas could be allocated according to a carefully prescribed distributional
pattern, but would be dependent on VMS for surveillance.
13.5.3 Product form
Products from sharks and other chondrichthyans when landed by fishers, transported, sold, or
exported occur in many forms. These forms include whole animal, carcass, tissue or processed
product. The carcass form can be beheaded and eviscerated carcass with skin on and fins on, beheaded and eviscerated carcass with skin on and fins off, or beheaded and eviscerated carcass with
skin off and fins off. Tissues, body parts, or product can be in the form of filleted meat only, heads
only, jaws only, head cartilage, vertebral column, powdered cartilage, skin only, fins only, whole livers
only, or liver oil.
This wide range of product forms creates difficulties identifying the species or measuring
these animals when they are brought ashore. This creates ambiguity in the official catch statistics.
Monitoring sex composition of the catch is not possible if the pelvic fines and claspers of males are
removed. Monitoring length-frequency composition and enforcing size limits usually involves measuring
partial length, which can be uncertain if all fins and the tail are removed.
Fishers should not be forced to land sharks whole, because sharks need to be gutted and gilled
as soon as practicable after capture to avoid degrading the quality of the meat and other products.
Species, sex and partial length of a shark can be determined ashore if sharks are beheaded and
eviscerated at sea, and landed in the product form as carcasses with fins, skin, claspers and, where
applicable, dorsal spines attached. Leaving the head attached, with the gills removed, is an option
where species identification from the carcass with fins attached is uncertain. If there is a requirement
for species identification for marketing or trade purposes, field guides based on fins and other body
parts will need to be prepared. There may also be advantages in establishing regulations to ensure that
shark products (carcasses, meat, fins, skins, heads, vertebral columns, livers, liver oil and jaws) are
clearly labelled with species name. If sharks are not required by law to be landed in a standard product
form, statistics forms may require provision for reporting the product form of the sharks, in addition to
reporting weight of catch. This also applies to data from landing sites, processing plants and markets,
and applies to trade data. All trade products should be specified by species and as frozen or dried.
Without these provisions catch weights will be ambiguous.
313
If more than one product form occurs it is necessary to have appropriate weight conversion
factors to produce a single set of standard statistics. Similarly, if it is necessary to adopt more than one
standard length measurement, the data should be converted to a single standardized length, ideally total
length or fork length.
To standardize the statistics for chondrichthyan species, Australia has adopted the following
wording in its National Plan of Action for the Conservation and Management of Sharks (Anonymous,
2002).
•
Fishers should be required to report shark weights for the form in which they are
landed and, where practical, all sharks be landed in the carcass form where a carcass
is defined as a beheaded and gutted shark with all fins and, for males, the claspers
attached. Leaving the claspers intact enables monitoring the sex of sharks after
landing ashore.
•
Fishers should be required to report chimaera weights for the form in which they are
landed. Where practical, all chimaeras should be landed in the carcass form where a
carcass is defined as a beheaded and gutted chimaera with all fins and, for males, the
claspers attached, except for the pectoral fins and belly flaps which are removed.
•
The issue of standard reporting of rays needs to be addressed. There is a growing
practice of retaining the outer margins of the discs (pectoral fins) of the animal and
discarding the rest of the animal for several large-sized species. This involves
removing a relatively small proportion of the animal and might be regarded as
wasteful and analogous to finning.
•
Official statistics of catch weights should be published as standard carcass weights
and, where reported by fishers in a different form, the weights are converted to the
standard carcass form.
13.5.4 Size limits
Size limits can be legal minimum sizes or legal maximum sizes. They can be an effective
management measure where the animals are landed from the fishing gear live and in condition where
the survival rate of released animals is high. Conversely, size limits are ineffective measures where the
animals are landed dead or in poor condition and the survival rate of released animals is low. Hence,
they are effective for many species that survive release from hooks, seine nets, and fish traps, but are
not effective for many species released after capture by gillnets and trawls where survival rates are
low.
Legal minimum sizes can be used to avoid growth overfishing. Growth overfishing occurs
where the yield from a fishery is sub-optimal; many of the animals are caught when they are small and
314
at an age such that the yield from the fishery is lower than the potential yield had the animals been
given time to grow and increase their mass.
Legal maximum sizes can be used to avoid recruitment overfishing. This is potentially useful
for those many species of sharks where the proportion of the females in breeding condition each year
increases with size and fecundity increases with maternal size. Where reproductive rates increase
with size, the contribution to recruitment is likely to be much higher for large animals than for small
animals. Hence, there can be stock benefits in releasing large animals live. A legal maximum size is
likely to be of higher value for females than for males.
In addition, there is usually a strong correlation between mercury concentrations in shark meat
and size of shark (Forrester et al., 1972; Walker, 1976). Where the concentrations in large animals
exceed food standards, legal maximum sizes have occasionally been used as a means of reducing the
number of sharks with high mercury concentrations from reaching the consumer (Walker, 1980).
Fishers recognize the benefit of releasing undersized animals and usually endorse legal minimum sizes. They are prepared to release undersized animals on the understanding that they can be
recaptured at a later time and benefit from a mass gain. On the other hand, they are less likely to
support legal maximum sizes. Large animals have a higher market value and fishers are aware of the
uncertainty of survival of large animals released. It is therefore preferable to apply alternative management measures to protect large animals.
Legal minimum sizes and legal maximum sizes are usually expressed as legal minimum lengths
and legal maximum lengths, respectively. Because most sharks are beheaded when the animals are
landed, length needs to be prescribed as a partial length rather than a total length. The longest reliable
partial length that can be taken from a beheaded and eviscerated carcass is from the last gill slit to the
distal end of the caudal fin. The last gill slit closely coincides with the anterior edge of the pectoral fin,
or, where the fins are removed, the cartilage from the pectoral girdle is usually intact. Where the
caudal fin is removed, then the base of the caudal fin should be adopted.
In Australia, a legal maximum length was applied for school shark (Galeorhinus galeus) in
Victoria, during 1972–85 as a way of reducing the average mercury concentration in shark meat
reaching the consumer (Walker, 1999). Similarly, a maximum weight of 18 kg for trimmed carcass
applies to all sharks in Western Australia (Simpfendorfer, 1999). Legal minimum lengths for sharks
have been applied in southeastern Australia for school shark (Galeorhinus galeus) and gummy shark
(Mustelus antarcticus) since 1949 (Walker, 1999).
13.6
SPECIAL PROTECTION OF THREATENED SPECIES
Naturally rare species and species with poor conservation status may require special protec-
tion or management through such measures as a prohibition on catch, injury and interference. Where
315
such species are inevitably killed, injured or disturbed accidentally, consideration should be given to
establishing sanctuaries through fishing area closures or MPAs.
There are no internationally agreed definitions of “threatened” or “endangered with extinction”, but some countries have adopted classifications such as “endangered”, “threatened” and “depleted”, which have legal status in their jurisdictions. The most widely accepted classification for the
conservation status of chondrichthyan species is the IUCN Red List, which classifies species as
“critically endangered”, “endangered”, “vulnerable”, “lower risk”, and “data deficient”. The first three
of these are grouped as “threatened” species. Criteria for classifying species include rate of population
depletion (percentage decline over three generations), overall population size, and geographic area and
extent of fragmentation within the distributional range of the species (Anonymous, 1994; Hilton–Taylor,
2000). Chondrichthyan species first appeared on the IUCN Red List in 1996 (Hudson and Mace,
1996). Later, during 2000, when the list was last updated by the IUCN Shark Specialist Group, 40
chondrichthyan species were listed as threatened worldwide and an additional five species were listed
within isolated local populations. More recently, 31 chondrichthyan species have been identified as
becoming extinct at particular localities and one regionally extinct (Dulvy et al., 2003).
Some species are classed as threatened on the basis of extreme rarity. These include all the
river sharks (Glyphis spp.), all freshwater sawfish (Pristis spp.) and several other freshwater batoids.
Others species are classified as threatened because their populations have been depleted by the
effects of fishing. These include several species of angel shark (Squatina spp.) and batoid species
severely impacted by trawl fisheries. Species that have naturally small populations and have been
depleted, include the whale shark (Rhincodon typus), basking shark (Cetorhinus maximus), grey
nurse shark (Carcharias taurus), and white shark (Carcharodon carcharias) (Anonymous, in press;
Camhi et al., 1998).
Various initiatives to protect endangered species have been taken in various parts of the world.
Fishing for whale sharks is banned in the Maldives. The number of divers and hours that divers and
boats can approach these animals is restricted in Ningaloo Reef Marine Park to minimize disturbance.
White shark is now protected in South Africa, Namibia, Australia, USA, Maldives and Malta. In
addition to declaring full protection for this species, Australia has developed species recovery plans for
the white shark and grey nurse shark. Several additional steps have been taken to reduce the accidental kill, injury or disturbance of these animals. Ten grey nurse shark sanctuaries were recently declared
in New South Wales waters, and there is a total ban on the use of shark fishing gear and the use of
mammal blood or oils for attracting sharks in all Victorian waters. There are legislative requirements to
report all interactions with white sharks and codes of practice are being developed for ecotourist
activities.
316
13.7
PRODUCT CERTIFICATION AND ECOLABELLING
Product certification and ecolabelling can be applied in support of fisheries management.
Product certification is a measure mandated by governments to ensure that only legally harvested and
reported fish landings can be traded and sold on domestic and international markets. Product certification is an extension to normal fisheries management activities. Where there are problems regulating
access, such as on the high seas, product certification schemes provide a means of reducing illegal,
unreported, and unregulated fishing. Ecolabelling programs can create market-based incentives for
better management of fisheries by creating consumer demand for seafood products from well-managed stocks by tapping the growing public demand for environmentally preferable products. Criteria
used for the accreditation process are a compromise between the demands of consumers and the
capabilities and willingness of the producers to meet those demands (Wessells et al., 2001).
13.8
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322
CHAPTER 14.
SHARK UTILIZATION
John A. Musick, Virginia Institute of Marine Science, College of William and Mary, P.O. Box 1346,
Gloucester Point, VA 23062 USA
14. 1
INTRODUCTION
14. 2
CONSUMPTIVE UTILIZATION OF ELASMOBRANCHS
14.2.1 Meat
14.2.2 Fins
14.2.3 Skin
14.2.3.1 Skin as food
14.2.3.2 Shark skin leather
14.2.4 Cartilage
14.2.4.1 Shark cartilage as food
14.2.4.2 Dried cartilage pills
14.2.4.3 Shark cartilage extracts
14.2.5 Liver
14.2.5.1 Liver as food
14.2.5.2 Liver extracts
14.2.5.2.1 Vitamin A
14.2.5.2.2 Squalene
14.2.5.2.3 Squalamine
14.2.5.2.4 Other liver extracts
14.2.6 Miscellaneous Products
14.3
NON-CONSUMPTIVE UTILIZATION OF ELASMOBRANCHS
14.3.1 Recreational diving
14.3.2 Recreational catch and release fishing
14.4
REFERENCES
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324
14.1
INTRODUCTION
Sharks and their relatives may provide a multitude of usable products including but not re-
stricted to: meat, fins, liver, skin, cartilage, and jaws and teeth. Unfortunately, tens of millions of
sharks taken in fisheries each year have their fins removed and their carcasses discarded overboard
(Fowler and Musick, 2002). This practice, called finning, represents a considerable waste as the fins
on average make up only about 5% of the total weight of a shark (Vannuccini, 1999). Such waste is
contrary to the United Nations Food and Agricultural Organization (FAO) Code of Conduct for
Responsible Fisheries (Article 7.2.2 (g)) which stresses the importance of avoiding waste and discards in fisheries. In addition, the FAO International Plan of Action for the Conservation and Management of Sharks (IPOA- Sharks) encourages full use of dead sharks and retention of sharks from
which fins have been removed (paragraph 22). Therefore, this chapter will briefly review the wide
spectrum of uses that may be afforded by elasmobranchs in order to encourage their more complete
and effective use. For a more comprehensive review see Vannuccini (1999) wherein an entire
volume (470 pages) is devoted to the subject. A strong word of caution is necessary here: full utilization of shark carcasses should not be used as a pretext to fish unsustainably (Camhi, 2002). The goal
of this manual is to provide information necessary to lead to sustainable elasmobranch fisheries.
14.2
CONSUMPTIVE UTILIZATION OF ELASMOBRANCHS
14.2.1 Meat
Shark meat has been used as food in coastal regions for over 5,000 years (Vannuccini, 1999).
Most historical use of shark meat was local because the meat does not travel well without refrigeration. Sharks retain urea in their blood and tissues as part of their osmoregulatory physiology (Musick
and McMillan, 2002). After a shark dies the urea breaks down into ammonia which imparts a strong
smell and odor to the meat and which may be toxic in high concentrations. This problem may be
avoided easily by rapid bleeding of the freshly caught animal, and thorough washing of the carcass
with seawater. Usually the head, fins, gills and viscera are removed from larger sharks at sea, or in
some artisanal fisheries immediately upon landing. Subsequent soaking of the meat in a weak acid
solution (citrus juice or vinegar) may remove up to 90% of the urea (Gordievskaya, 1973). Various
species have different concentrations of urea, spiny dogfish (Squalus acanthias) having the lowest
and hammerheads (Sphyrnidae) having the highest concentrations of several species measured
(Gordievskaya, 1973). In addition, elasmobranchs captured in brackish estuaries should have lower
urea concentrations than those taken in full seawater (Evans et al., 2004).
After bleeding and soaking, carcasses should be iced or frozen to prevent enzymatic and
bacterial breakdown. Small species with naturally low urea content like spiny dogfish (which also
occurs in cold water (<12°C)) may be landed whole to be processed onshore (Kreuzer and Ahmed,
325
1978). Small sharks are preferred for meat in many markets because they usually have lower concentrations of urea and mercury, which is naturally absorbed from sea water and through dietary uptake
may reach high concentrations in larger, older sharks (Forrester et al., 1972, Walker, 1980). However,
in some markets such as Hong Kong larger sharks are preferred (Parry-Jones, 1996).
Shark fillets also may be salted and diced or smoked. In Germany, the belly flaps of spiny
dogfish are smoked as Schillerlocken, an expensive gourmet item. Meat from blue sharks (Prionace
glauca) which is not used directly for food in most places may be processed into surimi and subsequently used in a variety of seafood recipes (Nakano, 1999), shark paste or happen, (Kiyono, 1996).
Batoid meat is also used widely throughout the world. In many areas batoid landings may
approach those of sharks (Shotton, 1999) and in some places there are directed batoid fisheries
(Agnew et al., 1999; Kulka and Mowbray, 1999; Pawson and Vince, 1999).
Some batoids such as the guitarfishes (Rhinobatidae) and sawfishes (Pristidae) are very
shark-like in their morphology and their meat is processed similarly to that of sharks. However, in
more typical batoids such as the skates (Rajidae), stingrays (Dasyatidae) and eagle rays
(Myliobatidae) the body is dominated by the wing-like pectoral fins which, unlike those of sharks, are
thick and muscular. These “wings” are cut from the body, then the dorsal and ventral meat is filleted
away from the cartilage framework and usually skinned. Depending on the taxonomic group, the meat
may vary from very delicate and white (Rajidae) to thick and dark (Myliobatidae). Batoids should be
bled upon capture and the meat soaked as in sharks. Batoid wings do not contain the “needles” so
valuable in shark fins (see below), but sawfish, guitarfish, and wedgefish dorsal fins contain needles
and are some of the preferred fins in the market.
14.2.2 Fins
Shark fins are used to make a traditional shark fin soup in the Chinese culture, and are among
the most valuable fish products in the world (Camhi et al., 1998). Only the fine collagenous fibers
called “needles” which support the fin margin are used in the soup. In most sharks the first dorsal,
pectorals and lower lobe of the caudal fin are the most valuable and these are usually sold as a set
from each shark. The lower lobe of the caudal is used because it contains the collagenous needles
whereas the upper lobe is supported by the vertebral column and has no needles. The smaller second
dorsal and pelvic fins (“chips”), also are taken but are of much lower value and lots are mixed from
several sharks. Because the base of the fin contains large cartilaginous supporting elements called
radials and muscle not used in soup, the fin is removed with a semi-circular cut (Fig 14.1) to eliminate
some of these materials at the base of the fin (Trachet et al., 1990). Any meat left adhering to the
base of the fin will spoil during drying thus lowering the quality or even destroying the value of the fin.
The greater care taken in removing fins the greater their value (Vannuccini, 1999).
326
Figure 14.1 Method for cutting shark fins (after Trachet et al., 1990).
Fins are traded virtually during all stages of processing. These include:
1)
Wet fins; fresh, iced or frozen
2)
Dried “raw” fins; with skin (including denticles) and some radial elements intact. Fins salted
before drying are usually of lower value because they retain more moisture. Fins are sundried, and turned frequently to facilitate drying to prevent curling. Fins should be kept out of
the rain and dew and away from insects. Drying may take 7-14 days to produce an acceptable product (18% moisture content (Vannucinni, 1999)). Dried fin sets are usually packed
in 25 kg sacks and dried “chips” in 50 kg sacks.
3)
Semi-processed or “cooked” fins; with the denticles and radials removed, but needle fan
intact. In this presentation fins are soaked in water for 8-10 hours (wet fins) or 16-24 hours
(dry fins), then further soaked in water pre-heated to 80-90°C until the scales and skin be
come loose. Then softened fins are placed into chilled water and scales and skin removed
with a wire brush. After washing again, any remaining meat and the cartilaginous radials are
removed. The pre-processed fins are then dried on bamboo mats for 4-6 days.
4)
Fully processed; with the needle fans separated into individual strands. Semi-processed fins
may be further processed to separate the needle bundles by soaking in water for up to 12
hours then boiling for 5-10 minutes. The needles may then be easily separated from the
surrounding membrane in cold water. Fin needles may be traded as wet fin needles or pro
cessed into fin nets.
5)
Fin nets; usually from smaller fins, the fin needles have been boiled, separated, re-dried and
packaged in loose clumps.
327
6)
Ready to eat products; canned or instant shark fin soup.
Most fins are traded as dried fins and imported for further processing in Hong Kong, Singapore or
Taiwan for domestic use or subsequent re-export.
14.2.3 Skin
14.2.3.1 Skin as Food
Shark skin may be consumed as food in several countries including the Maldives, Japan,
Taiwan, and the Solomon Islands (Vannuccini, 1999). Preparation involves drying, removing the
denticles, bleaching, then drying again (Chen et al., 1996). Skin from dusky, thresher and whale sharks
as well as skin from the giant guitarfish (Rhynchobatus djiddensis) is eaten in Taiwan. Shark skin is
processed into the gelatinous food nikigori in Japan (Kiyono, 1996). In Singapore and Malaysia, after
processing, cooked shark skin is marketed as “shark lips” or “fish lips.” In the Solomon Islands shark
skin is salted and then sun dried or smoked after which it is boiled and the denticles are removed. The
resulting product is then made into soup with coconut milk (Matthew, 1996).
14.2.3.2 Shark skin leather
Untanned shark skin, with the rough denticles attached is called shagreen and has been used
as sandpaper in woodworking and other industries for centuries. It has also been used to cover sword
hilts (providing a slip-free grip) and as a striking surface for matches (Kuang, 1999). The greatest use
for shark skin has been for leather. Shark skin is tanned much in the same way as are the skins of
other animals (Tanikawa, 1985). Shark leather may be used to make a variety of products including
furniture, bookbinding, shoes and handbags. Historically, the major markets for shark leather products
have been in the USA, Germany, France and Japan with tanneries located in several countries. Today,
because of environmental restrictions on the tanning industry and problems with a steady supply of
raw skin, most tanned leather is produced in Mexico (Kuang, 1999). Top quality skins usually come
from larger sharks which must be carefully skinned soon after capture. Skin from shark carcasses
used for meat and frozen or stored on ice are usually damaged to the point that they are useless for
making leather.
Most shark leather products have had the denticles removed. However, some products such
as the expensive Boroso leather made from small Moroccan shark hides retain their denticles which
are polished to a high gloss (Kuang, 1999). Recently stingray skin has been used in luxury leather
products in the USA (Boncompagni, 2003).
Shark skin is thick and tough and may be difficult for a novice to remove properly. However,
with practice, experienced shark skinners can efficiently remove a shark’s hide in a matter of minutes.
A diagram of the skinning process is provided in Fig. 14.2 (after Kreuzer and Ahmed, 1978).
328
Figure 14.2 Method for skinning sharks for leather (after Kreuzer and Ahmed, 1978).
14.2.4 Cartilage
14.2.4.1 Shark cartilage as food
Shark cartilage is used as food in China and Japan where it is boiled, cleaned of meat, and sun
dried for later cooking. Cartilage utilized includes fin radials (left over from fin processing), pieces of
jaw and chondrocranium, and most importantly the vertebral column. The latter is usually marketed
dry as a cyclindrical rod about one meter long with the vertebral processes removed (Vannuccini,
1999).
14.2.4.2 Dried cartilage pills
Shark cartilage has been dried and pulverized into a powder that can be delivered in pills or
capsules. The market for shark cartilage pills expanded dramatically after the publication of a book
(Lane and Comac, 1992) that purported to show that sharks do not get cancer (an assertion shown to
be incorrect, Musick and McMillan, 2002), and that claimed that shark cartilage pills could cure human
cancers. The use of shark cartilage pills ingested orally subsequently has been found to be worthless
329
in the treatment of cancer in humans (Horsman et al., 1998; Leitner et al., 1998; Miller et al., 1998).
These results were not surprising as the digestive system would breakdown any biologically active
proteins in cartilage into constituent amino acids before absorption through the gut lining (Kava, 1995).
However, cartilage in general is a good source of chonrdroitin and glucosomine sulfate, and shark
cartilage is no exception. These compounds have been found to be useful in treating various forms of
arthritis, and to that end, shark cartilage capsules are marketed today.
14.2.4.3 Shark cartilage extracts
It has been known for many years that tumors require the development of blood vessels
(angiogenesis) in order to grow, and that some substances in cartilage could inhibit angiogenesis and
retard tumor growth. Recently, Aeterna Laboratories, a pharmaceutical company based in Toronto,
Canada, http://www.aeterna.com/, has developed a unique proprietary process to extract biologically
active molecules contained in cartilage. Aeterna uses shark cartilage as raw material because
cartilage makes up to 6% of a shark’s body weight and shark cartilage has been a readily available byproduct of shark fisheries for which fins and/or meat are the principal targets. The resulting product
from Aeterna’s process, called Neovastat, has been shown to have multiple mechanisms of
antiangiogenesis action, and to be effective in treating cancers of many types as well as other diseases
where angiogenesis is a mitigating factor. Neovastat is in the final stages of clinical trials at this
writing, but should be available for use shortly.
14.2.5 Liver
14.2.5.1 Liver as food
Shark liver has been eaten as food in China and the Solomon Islands and elsewhere
(Vannuccinni, 1999). The liver may be cooked fresh or salted for later preparation.
14.2.5.2 Liver extracts
Shark liver is rich in various hydrocarbons, and oils extracted from livers have been used in the
farming and textile industries, as lubricants, in cosmetics, as lamp fuel, as a wood preservative on boat
hulls, and in the pharmaceutical industry (Kuang, 1999). The pharmaceutical use of shark oil products
holds most present interest and future promise.
14.2.5.2.1 Vitamin A
Shark liver is high in vitamin A and target fisheries for shark livers developed in the 1940s.
These fisheries were short-lived because of the development of synthetic vitamin A (Kreuzer and
Ahmed, 1978). Even so, the short but intense fishery for the soupfin shark (Galeorhinus galeus) off
the west coast of the United States led to rapid stock collapse (Ripley, 1946) that has lasted for several
decades (Camhi et al., 1998).
330
14.2.5.2.2 Squalene
Squalene is a highly unsaturated aliphatic hydrocarbon found primarily in the livers of deep-sea
dogfishes (Squaliformes). This low density (0.86 s.d.) compound provides buoyancy to the sharks
(Thorson, 1990). Squalene has been used as a fine lubricant because it is stable over a wide temperature range (-75ºC to 330ºC) (Kuang, 1999). Its most widespread use appears to be in skin creams to
soften skin, and as a moisturizer, to speed up wound healing, and as a bactericide. It is often hydrogenated to the more stable form Squalane before use (Anonymous, 1996; Kuang, 1999). The problem in
developing further markets for squalene is that the squaloid sharks from which it comes are among the
slowest growing, latest maturing sharks known. Thus, these species may be very quickly overfished if
harvesting is not controlled at some low level (Musick et al., 2000).
14.2.5.2.3 Squalamine
Squalamine is one of several aminosterols (steroids) found in shark liver (oore et al., 1993;
Rao et al., 2000). This steroid has been found to be a broad spectrum antibiotic which exhibits potent
bactericidal activity against both gram-negative and gram-positive bacteria. Also, squalamine induces
osmotic lysis of protozoa and is fungicidal (Moore et al., 1993). In addition, squalamine has recently
been shown to be an effective inhibitor of angiogenesis and directly blocks blood vessel cell activation,
migration and proliferation by many growth factors (Sills et al., 1998). Genaera corporation (http://
www.genaera.com/antiangiogenesis.htm) has recently synthesized squalamine and although its pharmaceutical potential is vast, the future demand for the compound directly from shark livers is probably
minimal at best (as with vitamin A).
14.2.5.2.4 Other liver extracts
Shark liver contains many biologically active compounds some of which may remain to be
discovered. Among known compounds alkoglycerols have been shown to have some benefit in the
regression of tumor growth (Hallgren and Larsson, 1962; Brohult et al., 1986).
14.2.6 Miscellaneous Products
Rose (1996) has reviewed the use of peripheral shark products from various regions
around the world. These include
1)
Jaws and teeth as curios
2)
Sawfish rostra as curios
3)
Whole preserved small sharks as curios
4)
Bait in pot or long-line fisheries
5)
Fishmeal and fertilizer
6)
Dogfish as dissection specimens in schools
7)
Exhibition in public aquaria
8)
Small specimens in private aquaria
331
14.3
NON-CONSUMPTIVE UTILIZATION OF ELASMOBRANCHS
14.3.1 Recreational diving
Recreational diving has been one of the fastest growing recreational activities worldwide for
several years (Anderson, 2002). Estimates of the number of active recreational divers run to several
million. Sharks and rays are always the major diving attraction wherever they occur (Anderson,
1999). Diving magazines regularly carry articles and advertisements concerning dive destinations
where “shark watching” is offered (Murphy, 1993; Saunders, 1995). Shark diving destinations are
widespread throughout the developing and developed world and include (among others) South Africa,
Egypt and Sudan, the Maldives, Myanmar, throughout Southeast Asia, Australia, Palau, French
Polynesia, California, and the Bahamas (Anderson, 2002). The shark diving industry generates
hundreds of millions of dollars for local economies worldwide. Divers may pay from $75 to $200 for a
single dive with sharks and rays, and recreational diving expeditions to cage dive with sharks may cost
several thousand dollars (Anderson, 2002). Because many species of sharks may be residential at
particular dive sites, and because individual sharks may live for at least a decade, an individual shark
may be observed by a multitude of divers over time. Therefore, considering the cumulative input to
the economy by shark divers, Anderson and Ahmed (1993) estimated that in 1992 a single gray reef
shark (Carcharhinus amblyrhynchus) was worth US $33,500 per year at the most popular shark
watching site in the Maldives. In contrast, a dead gray reef shark was calculated to have a one time
value of US $32 to local fishermen (Anderson and Ahmed, 1993). Likewise, in the Bahamas where
shark watching contributed about six million dollars a year in the early 1990s (Hall, 1994), a single
Caribbean reef shark (Carcharhinus perezi) was calculated to be worth between $13,300-$40,000
annually (Amsler, 1997; Anderson, 2002), yet a dead Caribbean reef shark was estimated to have a
one time value of US $50-60. Therefore in those areas where recreational diving may be a viable
industry non-consumptive use of sharks may contribute several orders of magnitude more to the local
economy than consumptive uses.
14.3.2 Recreational catch and release fishing
Recreational shark fishing has been popular in many areas at least since the mid-1970s when
the motion picture film “Jaws” was released (King and Cailliet, 1992; Pepperell, 1992). In recent
years an increasing number of recreational shark fishers have been choosing to release their catches
often after tagging (Casey and Kohler, 1992; Hueter, 1996). The value of recreational fishing to local
communities may be huge considering the costs to fishers of food, accommodation, bait, tackle, boat
charter, etc.
Therefore, the value of an individual shark in a recreational fishery even where harvest is
practiced is several fold greater than its value in a commercial fishery. Catch and release provides
even greater value because individuals may be caught multiple times by several anglers, and even with
332
some post-release mortality (Heuter, 1996; Skomal and Chase, 1996) catch and release fishing clearly
contributes to the sustainability of the shark stocks. Post-release survivorship may be increased
through the use of circle hooks, and care in handling the animals when landing and releasing.
This paper is a contribution from the National Shark Research Consortium and is also VIMS
contribution #2564.
14.4
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336
INDEX
Note: In this index, the most detailed entries are under the scientific name (Latin binomial);
vernacular names list page numbers only.
Abundance
CPUE and, 232–233
relative, 72
Active vs. passive (fixed) fishing gear, 267, 270, 306
Adelphophagy, 137
Aeterna Laboratories, 330
Age
absolute vs. periodicity of ring formation, 116
defined, 99
at first reproduction, 145
at maturity, 143–145
Age bias plot, 113
Age-dependent mortality estimation methods, 173–174
Age determination, 105–115
ageing protocols, 106–107
back-calculation methods, 113–115
background and principles, 99–100
deviations and, 106–107
field sampling and storage, 101–102
reader agreement, precision and bias, 111–113
sampling coverage, 122–123
specimen collection and preparation, 100–105
staining protocols, 108–111
verification, 115–119
Web-based resources, 123
Age distribution, 191–192
Age-independent mortality estimation, 168–173
Age-structured matrix model (Leslie matrix), 195–197
Akaike’s Information Criterion, 66
Allozymes, 83–84
Alopias pelagicus (pelagic thresher shark), 136
337
Alopias superciliosus (bigeye thresher shark), 136
Alopiidae (thresher sharks), 48
Amplified Fragment Length Polymorphism (AFLP), 88–89
Anacanthobatus (leg skates), 51
Anal fin, family key, 38, 41
Analysis of molecular variation (AMOVA), 81–82
Angel sharks (Squatiniformes), 46–47, 289
fecundity, 154
Pacific (Squatina californica), 84, 99, 141–142
Annuli (rings), 105, 107
Aplacental viviparity, 136
Aplacental yolk sac, 138
Aptychotrema rostrata (Eastern Australian shovelnose ray), 152
Arches
haemal (transverse processes), 102
neural, 102, 110–111
Archival tags, 74–75
pop-up, 74–75
Area restrictions, 307–308
ARIMA (auto-regressive integrated moving average) models, 277
Arlequin software, 82
Artisanal fishing, 247
Assumption violation, 179
Atlantic sharpnose shark (Rhizoprionodon terraenovae), 85, 87, 151, 257
Atlantic stingray (Dasyatis sabina), 137
Atlantic yellowfin tuna (Thunus albacares), 12
At-sea vs. shoreside sampling, 258–262
Australian Cooperative Game-Fish Tagging Program, 63
Australian sharpnose shark (Rhizoprionodon taylori), 146–147, 153, 169, 175
Auto-regressive integrated moving average (ARIMA) models, 277
Availability, 292
Average percent error (APE) technique, 111–112
Back-calculation
in age determination, 113–115
Dahl-Lea direct proportions method, 114
338
linear-modified Dahl-Lea method, 114
quadratic-modified Dahl-Lea method, 114–115
size-at-birth-modified Fraser-Lea equation, 115
Bag limits, 304
Banding, centrum, 99–100
Barbeled houndsharks (Leptochariidae), 49
Basking shark (Cetorhinus maximus), 1, 48
Bathyraja brachyurops (broadnose skate), 101
Batoids, 23, 46–47, 289
consumptive uses, 326
reproductive strategy, 135
vs. sawsharks, 50
Bayesian estimation, 230–231
Beverton and Holt recruitment model, 219
Bias, gear, 273–274
Bigeye thresher shark (Alopias superciliosus), 136
Biological objectives, 15–16
Biological productivity, 290–292, 295–296
Biological reference points (BRPs), 13
Biomass dynamics models, 295
Birthmark ring, 107
Blacktip sawtail catshark (Galeus sauteri), 145
Blacktip shark (Carcharhinus limbatus), 85, 90, 142, 153, 180
Blacktip shark (Carcharhinus tilstoni), 179
Blind sharks (Brachaeluridae), 47–48
Bluefin tuna, southern (Thunnus maccoyii), 74
Blue shark (Prionace glauca), 66, 87, 141, 151, 249, 326
Bluntnose stingray (Dasyatis say), 153
Body measurements, 255–257
family key, 42
Bomb carbon dating, 116, 118
Bonnethead shark (Sphyrna tiburo), 169, 175
Brachaeluridae (blind sharks), 47–48
Bramble sharks (echinorhinids), 46
Brander’s equilibrium mortality estimation, 170–173
Broadnose skate (Bathyraja brachyurops), 101
339
Brody equation, 216–217
Bullhead sharks (Heterodontiformes), 47
Butterfly rays (Gymnuridae), 51–52
Buy-back schemes, 302
Bycatch, 244, 307
Bycatch fisheries, 289–290
Bycatch Reduction Devices (BRDs), 247
CAGEAN model, 220, 224–228
Doubleday method, 228
Fournier and Archibald method, 228
Paloheimo method, 225–228
Calcein validation, 118
Calcification, 99–100
of neural arches, 110–111
Callorhinchidae (elephant fishes), 52
Capture-recapture studies, 176, see also Tag-recovery studies
Carcharhias taurus (sand tiger shark), 2
Carcharhinidae (requiem sharks), 49–50
Carcharhiniformes (ground sharks), 49–50, 135, 136
Carcharhinus isodon (fine-tooth shark)
fecundity, 154
reproductive cycle, 151
staining methods, 109, 110
Carcharhinus limbatus (blacktip shark)
male maturity, 142
mortality estimation, 180
reproductive strategy, 153
Carcharhinus obscurus (dusky shark), 1, 2, 249
Carcharhinus plumbeus (sandbar shark), 65, 85, 142
matrix modeling, 198–199
mortality estimation, 174
sexual segregation, 257
Carcharhinus signatus (night shark), 2
Carcharhinus sorrah (spot-tail shark), staining method, 111
Carcharhinus tilstoni (blacktip shark), 85, 90, 179
340
Carcharias taurus (grey nurse shark), sanctuaries, 309
Carcharias taurus (sand tiger shark)
oxytetracycline validation, 118
reproductive interval, 152
reproductive strategy, 137
survival time, 249
Carcharodon carcharias (white shark), 87, 90
Carpet sharks (Orectolobiformes), 47–48
Cartilage extracts, 330
Cartilage pills, 329–330
Cataloguing, 31
Catchability, 292, 293
Catch-and-release fishing, 332–333
Catch-at-age analysis, see CAGEAN model
Catch curves, in mortality estimation, 174–176
Catch estimates, 243–244, see also CPUE
bycatch, 244
disposition estimates, 244
Catch Per Unit Effort, see CPUE
Catch quotas, 304–305
Catch rate
by artisanal fishing, 247
in depths of 25–50 M, 246–247
sharks vs. other species, 246
Catch time series, 251
Catshark, 47–48, 49
blacktip sawtail (Galeus sauteri), 145
chain (Scyliorhinus retifer), 154
false (Pseudotriakis microdon), 49, 137
finback (Proscylliidae), 49
small-spotted (Scyliorhinus canicula), 145, 154
Caudal fin, family key, 34, 36–37, 39, 40, 41, 42
Caudal thorns, in age and growth studies, 101
Centre for Environment, Fisheries and Aquaculture Science (CEFAS), 74
Centrophorids (gulper sharks), 24, 46
Centroscyllium fabricii (deep sea black dogfish), 145
341
Centrum banding, 99–100, 105
Centrum edge analysis, 116–117
Cetorhinus maximus (basking shark), 1, 48, 168–173
Chain catshark (Scyliorhinus retifer), 154
Chemical tagging, 116
Chen and Watanabe mortality estimation method, 173–174
Chimaeriformes (chimaeras), 23, 25, 52, 289
Chirrhigaleus, 46
Chondrichthyans, 23–24
Claspers, 138–139, 157
length at maturity, 143–144
maturity and, 141
Closed periods, 302
Codes for species identification, 255
Coffin rays (Hypnidae), 50
Cohort analysis
CAGEAN, 220, 224–228
Virtual Population Analysis, 180, 220–224
Common skate (Dipturus batis), 63
Conceptual models, 13–15
Conservation objectives, 15–16
Contrast, in data, 232–233
Convention for International Trade in Endangered Species (CITES), 3, 298
Cownose rays (Rhinopteridae), 51–52
CPUE, 211
abundance and, 232–233
in CAGEAN, 226
data collection, 245–248, see also Fishing gear
definition, 244–245
in delay-difference model, 218
in surplus-production model, 211–212
Crocodile sharks (Pseudocarchariidae), 48
Crystal violet protocol, 109–110
Customary Marine Tenure (CMT), 300–301
342
Dahl-Lea direct proportions method, 114
Dalatias, 46
Dart tag, 63
Dasyatidae (stingrays), 51–52
Dasyatis centroura, 152
Dasyatis marmorata, 152
Dasyatis sabina (Atlantic stingray), 137
Dasyatis say (bluntnose stingray), 153
Data, contrast in, 232–233
Data auditing software, 280
Database creation, 278–280
Database software, 279
Data collection, 63–73, see also Tag recovery studies
for species-specific sampling, 253–254
Data quality, 231–233
Data storage (archival) tags, 74–75
Decommissioning schemes, 302
Deep sea black dogfish (Centroscyllium fabricii), 145
Delay-difference (Deriso) model, 215–220
advantages and disadvantages, 219–220
examples, 220
Demographic models, 187–203
background and principles, 189–190
conclusions and advice, 200–201
life tables, 190–195, see also Life tables
matrix models, 195–200, see also Matrix models
Devil rays (Mobulidae), 51–52
Dipturus batis (common skate), 63
Disc, family key, 33, 36
Disposition estimates, 244
Dissection, 32
Diversity
haplotype, 86
nucleotide sequence, 86
Diving, recreational, 332
DNA
343
mitochondrial, 83, 84–86
nuclear, 83
DNA microsatellites, 86–88
Dockside sampling, 260–262
Dogfish (Squaliformes), 46
deepsea black (Centroscyllium fabricii), 145
piked/spiny (Squalus acanthias), 106, 139, 140, 142, 148, 153, 155, 169, 257
smooth (Mustelus canis), 108, 109
Dorsal fin, family key, 33, 38, 39, 44
Dorsal fin spines
in age and growth studies, 101
preparation, 104–105
Doubleday method, 228
Dusky shark (Carcharhinus obscurus), 1, 2, 249
Dusky smooth-hound (Mustelus canis), 138, 139, 145–146, 148, 151
Dynamics, fishery, 11
Eagle rays (Myliobatidae), 51–52
Eastern Australian shovelnose ray (Aptychotrema rostrata), 152
Echinorhinids (bramble sharks), 46
Ecolabeling, 317
Economic objectives, 16
Ecosystem approach, 291–292
Effort (quantitative) use rights, 302–304
Elasmobranchs, 23
Elasticity, 200
Electric rays (Torpidiniformes), 50
Elephant fishes (Callorhinchidae), 52
Enclosure, fishing area, 310–311
Encounterability, 292–293
Endangered species, 315–316
Environmental Analysis System (EASy), 280
Equilibrium constraint, 207
Equilibrium mortality estimation methods, 170–173
Etmopteras granulosus (southern lanternshark), 46, 154
Exclusive Economic Zones, 299
344
Exploitation rates of mortality, 178–179
Exponential survival model, 214
Extinction, 2–3
Eyelid
family key, 44
nictating, 2y
Eyes, family key, 43–44
Fabens equation, 121
False catshark (Pseudotriakis microdon), 49, 137
Family key, 32–45
anal fin, 38, 41
body, 42
caudal fin, 34, 36–37, 39, 40, 41, 42
disc, 33, 36
dorsal fin, 33, 38, 39, 44
eyelid, 44
eyes, 43–44
flap, 37
gill openings, 33, 43
gill slits, 33, 36
head, 39
mouth, 38, 40, 43
nostril, 41
pelvic fin, 35, 39, 44
precaudal pit, 44
snout, 33, 35, 38, 39–40, 40, 45
spiracles, 44–45
tail, 42
thorns or fine denticles, 35–36
trunk, 39
Fecundity, 1, 153–155, 194, see also Reproductive biology
Field data collection, reproductive biology, 158–159
Field identification, 28–30
body measurements, 29
color, 28–29
345
fin placement, 29
teeth counts, 30
vertebral counts, 29–30
Finback catsharks (Proscylliidae), 49
Finetooth shark (Carcharhinus isodon), 109, 110, 151, 154
Fins, consumptive uses, 326–328
First-growth rings, 107
First-year survival, 194
Fisheries
defined, 9
Individual Transferable Quota, 243–244
stock assessment examples of real, 236
Fisheries management, see Management
Fisheries observer programs, 258, 259
Fisheries science, see also Management; Stock assessment
defined, 9
objective, 9–10
Fishery-dependent sampling, 241–263, see also under CPUE; Sampling
Fishery dynamics, 11
Fishery-independent sampling, 265–274, see also under Sampling
Fishing area closure, 308, 310–311
Fishing gear, 288–289
gear bias, 273–274
gillnet, 245–246, 289
hook, 289
longline, 246–247
passive (fixed) vs. active, 267, 270, 306
purse seine, 248
regulation, 305–307
regulation of, 303, 305–307
survey design considerations, 273–276
trawl, 247–248, 289
Fishing gear performance databases, 279
Fishing licenses, 301, 302
Fishing location, 250–251
catch time series, 251
346
recording methods, 250–251
Fishing mortality, 13, 192, 225–226, 249–250, 292–294
Flap, family key, 37
Fluorescein validation, 118
Fournier and Archibald method, 228
Fraser-Lea equation, size-at-birth-modified, 115
Freshwater species, 290
Frilled sharks, 45
FST statistic, 81
Furgaleus macki (whiskery shark)
reproductive interval, 152
sperm storage, 156
Galeocerdo cuvier (tiger shark), 66, 249
male maturity, 142
Galeomorphi, 23
Galeorhinus galeus (school shark), 179, 180, 311, 315
Galeorhinus galeus (soupfin shark), 1
Galeorhinus galeus (tope shark), 66
fecundity, 154, 155
reproductive strategy, 153
sperm storage, 156
Galeus sauteri (blacktip sawtail catshark), 145
GAMs (Generalized Additive Models), 278
Gear bias, 273–274
Gear performance databases, 279
Gear selectivity, 69–70
Genepop software, 82
Generalized Additive Models (GAMs), 278
Generalized linear models (GLMs), 277–278
Genetics, 79–96, see also Molecular markers; Stock structure
Giant guitarfish (Rhynchobatus dijidensis), 328
Gillnet fishing gear, 245–246, 273, 289
Gill opening, family key, 33
Gill slits, family key, 33, 36
Ginglymostoma cirratum (nurse shark), 249
347
Ginglymostomatidae (nurse sharks), 47, 87
Glossaries, see Terminology
Goblin sharks (Mitsukurinidae), 48
Gollum attenuatus (slender smooth-hounds), 137
Gompertz growth function, 119, 121–122
Gonadosomatic index (GSI), 146–147
GPS systems, 251
Ground sharks (Carcharhiniformes), 49–50
Growth
in captive animals, 118
vertebral, 99
Growth models, 119–122
background and principles, 99–100
field sampling and storage, 101–102
Gompertz growth function, 119, 121–122
sample size and, 119–120
sampling coverage, 122–123
verification, 115–119
von Bertalanffy growth function, 119, 120–121
Web-based resources, 123
Growth rates, 66–69
Guitarfish
common (Rhinobatos rhinobatos), 151, 154
giant (Rhynchobatus dijidensis), 328
sharkfin (Rhyncobatiformes), 51
Gulper sharks (centrophorids), 46
Gummy shark (Mustelus antarcticus), 1, 84, 89–90, 109, 137, 179, 180, 307
Gunderson–Dygert mortality estimation method, 169
Gunderson mortality estimation method, 169
Gymnuridae (butterfly rays), 51–52
Habitat utilization, 65
Haemal arches (transverse processes), in age and growth studies, 102
Hammerhead, 49, 87, 249
scalloped (Sphyrna lewini), 155
smalleye (Sphyrna tudes), 141–142, 153
348
Haplotype diversity, 86
Hardy-Weinberg equilibrium, 81–82
Head, family key, 39
Hemicyllidae (longtail carpet sharks), 47–48
Hemigaleidae (weasel sharks), 49
Hemiscyllids, 48
Heterodontiformes (bullhead or horn sharks), 47, 135
Heterodontus portusjacksoni (Port Jackson shark), 148
Hexanchiformes, 45
reproductive strategy, 136
Hexanchus griseus (sixgill sharks), 111
Hexatrygonoidea (sixgill stingrays), 51–52
Himantura, 24
Histological examination, of reproductive tract, 147–150
Hoenig mortality estimation method, 169–170
Hook fishing gear, 273, 289
Hook size, 307
Houndshark, 49
barbeled (Leptochariidae), 49
Hyperdepletion, 233
Hyperstability, 233
Hypnidae (coffin rays), 50
Identification
field, 28–30
laboratory, 30
Illumination, of sectioned centra, 105–106
Individual Transferable Quotas (ITQs), 304–305
Instantaneous rates of mortality, 178–179
Institutional frameworks, 298–299
Internal anchor tag, 61–62, 63
body cavity type, 61–62
button type, 62
International developments, management measures and, 298
International Plan of Action for the Conservation and Management of Sharks (IPOA-Sharks), 3, 298
Intrinsic rate of population growth, 209, 295
349
Island model of migration, 82–83
Isurus oxyrinchus (shortfin mako shark), 70
bomb carbon dating, 118
reproductive strategy, 136
IUCN Red List, 316
Japan, reporting classes, 252
Jensen mortality estimation method, 170
Jumbo Rototag, 62–63
Jurisdictional frameworks, 298–299
Kimura method, 120
Laboratory identification, 30
Lake Erie perch, Paloheimo method example, 226–228
Lamna ditropis (salmon shark), 87, 104
Lamna nasus (porbeagle shark), 1, 66, 87
bomb carbon dating, 118
mortality, 168–173
mortality estimation, 180
reproductive interval, 152
reproductive strategy, 136
Lamniformes (mackerel sharks), 48–49, 139
Landings, carcasses, 249
Landings sampling, 248–249
Lanternshark, southern (Etmopterus granulosus), 154
Large ray (Rhina ancylostoma), 50
Latin binomial vs. vernacular names, 252
Leather, shark skin, 328–329
Leg skates (Anacanthobatus), 51
Lemon shark (Negaprion brevirostris), 65, 88, 105, 109, 195
Length/weight relationship, 66
Leopard shark (Triakis semifasciata), 111, 180, 194
Leptochariidae (barbeled houndsharks), 49
Leslie Matrix, 195–197
Leucoraja erinacea (little skate), 99
350
Licenses, 301
rescinding of, 302
Life tables, 190–195
Australian sharpnnose shark (Rhizoprionodon taylori) example, 192–193
general approaches, 190–193
rebound potential, 194–195
Light, transmitted vs. reflected, 105–106
Lighting, of sectioned centra, 105–106
Likelihood ratio tests, 120
Limited entry use rights, 301–302
Linear models, generalized (GLMs), 233
Linear-modified Dahl-Lea method, 114
Linear regression, 229–230
Little skate (Leucoraja erinacea), 99
Liver, consumptive utilization, 330–331
Lobed stingaree (Urolophus lobatus), 156
Locally weighted regression scatter plot smoothing (LOWESS), 277
Logbooks, 260, 261
Longline fishing gear, 246–247
Longnose chimeras (Rhinochimaeridae), 52
Longtail carpet sharks (Hemiscylliidae), 47
LORAN systems, 251
LOWESS method, 277
Mackerel sharks (Lamnidae), 48–49
Magnuson-Stevens Fishery Conservation and Management Act (1996), 16
Mako, shortfin (Isurus oxyrhinchus), 70, 85, 90–91, 118, 136
Management, 285–321
background and principles, 287–288
as balancing act, 17–18
basic concepts, 9–10
biological aspects, 288–298
fisheries impact, 288–290
fishing mortality, 292–294, see also Fishing mortality
risk evaluation, 294–297
species biology, 290–292
351
ecosystem approach, 291–292
frameworks for, 298–299
objectives of, 15–18, see also Management objectives
product certification and ecolabeling, 317
technical, 305–315
area and time restrictions, 307–308
fishing area enclosure, 310–311
fishing gear regulation, 305–307
Marine Protected Areas (MPAs), 308–309
product form, 313–314
regional fisheries management, 311–313
size limits, 314–315
use rights, 289–305
limited entry, 301–302
quantitative input (effort), 302–304
quantitative output (catch quotas), 304–305
territorial, 300–301
Management objectives, 15–18
balance among, 17–18
biological and conservation, 15–16
economic, 16
in opposition, 15
recreational, 17
social, 17
Management principles, 1–6
Marginal Increment Ratio (MIR), 117
Marine Protected Areas (MPAs), 308–309
MARK software, 178
Masked stingaree (Trygonoptera personata), 153
Mating, 146, see also Reproductive cycle
Matrix models
age-structured (Leslie matrix), 195–197
elasticities, 200
stage-based, 197–199
Maturity, 140–145
age at, 143–145
352
assessing, 140–141
female, 142–143
male, 141–142
Maximum economic rent (MER), 16
Maximum sustainable yield (MSY), 11–13, 194, 195
Mean generation length, 191
Megachasmidae (megamouth sharks), 48
Mercury content, 326
Mesh size, 307
Microsatellites, 86–88
Migration, island model of, 82–83
Mitochondrial DNA, 83, 84–86
Mitsukurinidae (goblin sharks), 48
Mobulidae (devil rays), 51–52
Model complexity, 13–15
Molecular markers, 83–91
allozymes, 83–84
Amplified Fragment Length Polymorphism (AFLP), 88–89
case studies, 89–91
blacktip shark (C. limbatus), 90
gummy shark (Mustelus antarcticus), 89–90
shortfin mako, 90–91
white shark (Carcharodon carcharias), 90
microsatellites, 86–88
mitochondrial DNA, 83, 84–86
Random Amplified Polymorphic DNA (RAPD), 88
tissue collection, 89
Mortality, 13, 72
exploitation rates of, 178–179
fishing, 192, 225–226, 249–250, 292–294
instantaneous rates of, 178–179
natural, 192, 295–296
post-capture, 292, 293
respiratory mode and, 249
types of, 167
Mortality estimation, 165–185
353
background and principles, 167–168
conclusions and advice, 181–182
direct methods, 174–181
indirect methods, 168–174
age-dependent, 173–174
age-independent, 168–173
catch curves, 174–176
cohort analysis (Virtual Population Analysis), 180–181
tag recovery studies, 176–180
telemetry, 180
Mortality rates
finite, 167
instantaneous, 167
Mouth, family key, 38, 40, 43
Movement, data collection, 70–72
Multispecies modeling, 312
Mustelus antarcticus (gummy shark), 1, 84, 89–90
age determination, 109
mesh size regulation and, 307
mortality estimation, 180
reproductive strategy, 137
tag-recovery studies, 179
Mustelus canis (dusky smooth-hound)
age at first reproduction, 145
fecundity, 155
reproductive anatomy, 139
reproductive cycle, 145–146, 148, 151
reproductive strategy, 138
sperm storage, 156
Mustelus canis (smooth dogfish), age determination, 108, 109
Mustelus griseus (spotless smooth-hound), 138, 148
Mustelus lenticulatus (spotted estuary smooth-hound), 70, 137, 151
Mustelus manazo, 148
Mustelus spp., 24, 50
reproductive strategy, 137–138
Myliobatidae (eagle rays), 51–52
354
Myliobatiformes (stingrays), 51–52
Myloiobatoidei (stingrays), 51–52
Narcinidae (numbfishes), 50
Narkidae (sleeper rays), 50
National Marine Fisheries Service, see NMFS entries
National Plans of Action, 3, 298, 314
National Shark Assessment Reports, 3
Natural mortality, 192, 295–296
Negaprion brevirostris (lemon shark), 65, 88, 195
age determination, 109
banding patterns, 105
Neovastat, 330
Net reproductive rate, 191
Neural arches
in age and growth studies, 102
calcification, 110–111
Nictating eyelid, 27
Night shark (Carcharhinus signatus), 2
NMFS
Candidate List for Threatened and Endangered Species, 2
Cooperative Shark Tagging Program, 63
Fishery Management Plan, 2
Nostril, family key, 41
Nucleotide sequence diversity, 86
Numbfishes (Narcinidae), 50
Nursery areas, delineation, 65
Nurse shark (Carcharias taurus), 309
Nurse shark (Ginglymostoma cirratum), 249
Nurse sharks (Ginglymostomatidae), 47, 87
Odontaspidae (sand tiger sharks), 48
Oophagy, 136–137
Orectolobidae (wobbegongs), 47–48
Orectolobiformes (carpet sharks), 47–48
reproductive strategy, 135, 136
355
survival time, 249
ORI tag, 62–63
Osteichthyes, 23
Output control, 304–305
Oviparity, 135–136
Ovulation, 150
Oxynotids (prickly dogfishes), 46
Oxytetracycline validation method, 117–118
Pacific angel shark (Squatina californica), 84, 99, 141–142
Paloheimo method, 225–228
Parascylliidae (collared carpet sharks), 47–48
Parascylliids, 48
Passive (fixed) vs. active fishing gear, 267, 270, 306
Pauly mortality estimation method, 168
Pelagic thresher shark (Alopias pelagicus), 136
Pelvic fin, family key, 39, 44
Percent reader agreement, 112
Petersen disc tag, 63
Peterson and Wroblewski mortality estimation method, 173
Photography, 30–31
Piked/spiny dogfish (Squalus acanthias), 1, 106, 139, 140, 142, 148, 153, 155, 257
Placental viviparity, 137–138
Platyrhinidae (thornback rays), 52
Platyrhinoidei (stingrays), 51–52
Population growth
finite rate of, 209
intrinsic rate of, 295
Pop-up archival tags, 74–75
Porbeagle shark (Lamna nasus), 1, 66, 87, 118, 136, 152, 180
Port Jackson shark (Heterodontus portusjacksoni), 148
Post-capture mortality, 292, 293
Pre-birth rings, 107
Precaudal pit, family key, 44
Predator role, 2–3
Prickly dogfishes, 46
356
Prionace glauca (blue shark), 66, 87, 249, 326
reproductive cycle, 151
reproductive maturity, 141
Prior probability, 230
Pristiformes (sawfishes), 46, 50, 136
Pristiophoridae (sawsharks), 46, 50, 136
Probability
conditional, 230
prior, 230
Product certification, 317
Product form, 313–314
Profit maximization, 16
Proscylliidae (finback catsharks), 49
Pseudocarchariidae (crocodile sharks), 48
Pseudotriakidae (false catsharks), 49
Pseudotriakis microdon (false catshark), 137
Purse seine fishing gear, 248
Quadratic-modified Dahl-Lea method, 114–115
Quality assurance/quality control, in surveys, 272
Quantitative input (effort) use rights, 302–304
Quantitative output (catch quotas) use rights, 304–305
Quantitative prediction, 13
Quotas, catch, 304–305
Raja clavata (thornback ray), 74, 136
fecundity, 154
Rajidae, reproductive strategy, 135
Rajiformes (skates), 51
Rajimorphii, 23, see also Batoids
Random Amplified Polymorphic DNA (RAPD), 88
Rays, see also individual species
Eastern Australian shovelnose (Aptychotrema rostrata), 152
electric (Torpidiniformes), 50
taxonomic terminology, 25
thornback (Raja clavata), 136, 154
357
Reader agreement, in age determination, 111–112
Rebound potential, 194–195
Recreational objectives, 17
Recreational utilization, 332–333
Recruitment
Beverton and Holt model, 219
Ricker model, 218–219
Regional Fisheries Management Organizations (RFMOs), 299, 311–313
Relative abundance, 72
Relative marginal increment analysis (RMI), 117
Reproductive anatomy, 138–140
Reproductive biology, 133–164, see also Subtopics
anatomy, 138–140
female, 139–140
male, 138–139
fecundity, 153–155
field data collection, 158–159
maturity, 140–145
female, 142–143
male, 140–142
reproductive cycle timing, 145–153
reproductive strategies, 135–138
resources, 156–158
sperm storage, 155–156
Reproductive cycle, 145–153
female, 150–153
examples, 152–153
gestation cycle and time of birth, 150–151
ovulation, 150
reproductive interval, 152
histological examination, 147–150
male, 146–147
mating, 146
Reproductive data collection, 257–258
Reproductive strategies
aplacental viviparity, 136
358
adelphophagy, 137
aplacental yolk sac, 138
oophagy, 136–137
placental analogues, 137
oviparity, 135–136
placental viviparity, 137–138
Requiem sharks (Carcharhinidae), 49–50
Rhina ancylostoma (large ray), 50
Rhincodontidae (whale sharks), 47
Rhincodon typus (whale shark), 309
Rhinidae, 50
Rhiniformes (sharkrays), 50
Rhinobatiformes, 136
Rhinobatos rhinobatos (common guitarfish)
fecundity, 154
reproductive cycle, 151
Rhinochimaeridae (longnose chimeras), 52
Rhinopteridae (cownose rays), 51–52
Rhizoprionodon taylori (Australian sharpnose shark)
Leslie matrix example, 196–197
life table example, 192–193
mortality estimation, 169, 175
reproductive cycle, 146–147
reproductive strategy, 153
Rhizoprionodon terraenovae (Atlantic sharpnose shark), 85, 87
reproductive cycle, 151
sexual segregation, 257
as VPA example, 222–224
Rhynchobatidae, 50
Rhynchobatus, 50
Rhynchobatus dijidensis (giant guitarfish), 328
Rhyncobatiformes (wedgefishes, sharkfin guitarfishes), 51
Ricker recruitment model, 218–219
Rig (Mustelus lenticulatus), 70
Rings (annuli), 105
birthmark, 107
359
first-growth, 107
pre-birth, 107
Risk assessment, 294–297
Rototags, 62–63
Salmon shark (Lamna ditropis), 87, 104
Sample size, von Bertalanffy growth function and, 121
Sampling
for age and growth studies, 101–105
at-sea vs. shoreside, 258–262
cleaning, cutting, mounting vertebrae, 102–105
fishery-dependent, 241–263
catch estimates, 243–244
CPUE, 244–248
fishery-independent, 265–274, see also Surveys
basic theory, 269–273
fishing gear, 273–276
implementation, 273–276
statistical methods, 276–280
survey design, 271–273
utility of surveys, 267–269
fishing area, 250–251
fishing mortality, 249–250
landings, 248–249
Latin binomial vs. vernacular names, 252
sex, 257–258
size, 254–257
species identification, 252–254
Sampling coverage, for age/growth studies, 122–123
Sampling design, 64
Sampling frame, 269–270
Sandbar shark (Carcharhinus plumbeus), 65, 85, 142, 174, 198–199, 257
Sand tiger shark (Carcharias taurus), 2, 48, 118, 137, 152, 249
Sawfishes (Pristiformes), 50
Sawsharks (Pristophoriformes), 46, 50
Scalloped hammerhead (Sphyrna lewini), 155
360
Schaefer surplus production model, 208–210
School shark (Galeorhinus galeus), 179, 180, 311, 315
Scyliorhinidae (catsharks), 47–48, 49
Scyliorhinus canicula (small-spotted catshark), 145, 154
Scyliorhinus retifer (chain catshark), 154
Sectioning, vertebral, 102–103
Selectivity, 293
Sevengill sharks, 45
Sex identification, 257
Sex segregation, 257
Sharkfin guitarfishes (Rhyncobatiformes), 51
Sharkrays (Rhiniformes), 50
Sharks, taxonomic terminology, 24
Shark skin leather, 328–329
Sharpnose shark
Atlantic (Rhizoprionodon terraenovae), 85, 87, 257
Australian (Rhizoprionodon taylori), 169, 175, 192–193, 196
Shortfin mako shark (Isurus oxyrinchus), 70, 85, 90–91, 136
Shortnose chimaeras (Chimaeridae), 52
Shortspine sourdog (Squalus mitsukurii), 154
Silver nitrate protocol, 110–111
Sixgill shark (Hexanchus griseus), 45, 111
Size, 254–257
fisheries targeting size classes, 255
importance in sampling records, 254
weights and morphological measurements, 255–257
Size-at-birth-modified Fraser-Lea equation, 115
Size limits, 255, 314–315
Skates (Rajiformes), 51
broadnose (Bathyraja brachyurops), 101
consumptive uses, 326
little (Leucoraja erinacea), 99
Skinning, 328–329
Sleeper rays (Narkidae), 50
Sleeper sharks (somniosids), 46
361
Slender smooth-hound (Gollum attenuatus), 137
Smalleye hammerhead shark (Sphyrna tudes), 153
Small-spotted catshark (Scyliorhinus canicula), 145, 154
Smooth dogfish (Mustelus canis), 108, 109
Smooth-hound
dusky (Mustelus canis), 138, 139, 145–146, 148, 151, 155, 156
reproductive cycle, 148
slender (Gollum attenuatus), 137
spotless (Mustelus griseus), 138
spotted estuary (Mustelus lenticulatus), 137, 151
Snout, family key, 33, 35, 38, 39–40, 40, 45
Social objectives, 17
Software
data auditing, 280
database, 279
Excel POPTOOLS, 196
molecular biology, 82
for mortality estimation, 178
Somniosids (sleeper sharks), 46
Soupfin shark (Galeorhinus galeus), 1
Southern bluefin tuna (Thunnus maccoyii), 74
Southern lanternshark (Etmopterus granulosus), 154
Spatial and temporal distribution, 72
Species identification
codes for, 255
materials used for, 253
problems in landings data, 248–249
problems with, 252–253
in sampling, 252–254
Specimen collection, 31
Specimen preservation, 31
Spermatocyst, 147
Sperm stages, of testis, 149
Sperm storage, 155–156
Sphyrna lewini (scalloped hammerhead), 155
Sphyrna spp. (hammerhead), 45, 87, 249
362
Sphyrna tiburo (bonnethead shark), 175
Sphyrna tudes (smalleye hammerhead)
male maturity, 141–142
reproductive strategy, 153
Spiny dogfish, see Piked/spiny dogfish; Squalus acanthias
Spiracles, family key, 44–45
SPlus program, 280
Spotless smooth-hound (Mustelus griseus), 138
Spot-tail shark (Carcharhinus sorrah), 111
Spotted estuary smooth-hound (Mustelus lenticulatus), 151
Squalamine, 331
Squalene, 331
Squaliformes (dogfishes), 46, 289
reproductive strategy, 136
Squalomorphi, 23
Squalus acanthias (piked/spiny dogfish), 1
age determination, 106
fecundity, 155
female maturity, 142
mortality estimation, 169
reproductive anatomy, 139, 140
reproductive cycle, 148, 153
sexual segregation, 257
Squalus mitsukurii (shortspine sourdog), 154
Squalus spp., 24, 46
Squatina californica (Pacific angel shark), 84
growth and age, 99
male maturity, 141–142
Squatina oculata (angel shark), 154
Squatina squatina (angel shark), 154
fecundity, 154
Squatiniformes (angel sharks), 46–47, 289
reproductive strategy, 136
Stable age distribution, 191–192
Stage-based matrix models, 197–199
Staining methods, 108–111
363
crystal violet protocol, 109–110
silver nitrate protocol, 110–111
Statistical estimation, 276–280
database creation, 278–280
influence of covariates, 278
smoothing procedures, 277–278
Stegostomatidae (zebra sharks), 47, 48
Stingaree
lobed (Urolophus lobatus), 137, 156
masked (Trygonoptera personata), 153
Stingarees (Urolophidae), 51–52
Stingray
Atlantic (Dasyatis sabina), 137
bluntnose (Dasyatis say), 153
Stingrays (Myliobatiformes), 51–52
Stock assessment, 10–15, 205–240
age-structured models, 208
background and principles, 207–208
Bayesian approaches, 15
cohort models, Virtual Population Analysis, 220–224
conclusions and recommendations, 234–236
cross-comparison, 13–15
decision analysis approaches, 15
defined, 9, 10
delay-difference model (Deriso), 215–220
examples, 236
fishery dynamics, 11
maximum sustainable yield (MSY), 11–13
model complexity, 13–15
model fitting, 228–234
Bayesian estimation, 230–231
data quality, 231–233
linear regression, 229–230
time-series, 230
quantitative prediction, 10–11
surplus production (biomass dynamics) models, 208–212, see also Surplus production models
364
uncertainty in, 11
yield per recruit model, 212–215
Stock collapse, 1–2
bycatch and, 2
mixed-species fishery mortality, 2
Stock identification, 65
Stock structure, molecular genetics, 79–96, see also Molecular markers
Stratum boundaries, 272
Stratum index, 271
SUDAAN program, 280
Surplus production models, 208–212
advantages and disadvantages, 212
data requirements, 211–212
dynamic, 207, 208
Fox model, 211
logistic growth/Schaefer model, 208–210
Pella –Tomlinson model, 211
principles of, 209
problems with, 207
SURVAN program, 280
Survey design, 271–273
Survey index, 267–268
Surveys
duration, 275–276
overall cost, 272
stratified random vs. systematic designs, 272
utility of, 267–269
Survival, 72, see also Mortality
first-year, 194
to mean age at maturity, 194
respiratory mode and, 249
species comparisons, 249
stage-specific probabilities, 197–198
SURVIV software, 178
365
Tagging, chemical, 116
Tag recovery studies
in age/growth validation, 116
approaches, 65
assumptions, 73
assumption violation in, 179
background and principles, 59–60
data collection and analysis, 63–73
gear selectivity, 69–70
growth rates, 66–69
length/weight relationship, 66
in mortality estimation, 176–180
movement, 70–72
problems of, 178
relative abundance, 72
sampling design, 64
spatial and temporal distribution, 72
species composition, 72–73
survival/mortality, 72
tag type and placement, 60–63, see also Tags
Tags
archival, 74–75
pop-up, 74–75
dart, 63
internal anchor tag, 61–62, 63
body cavity type, 61–62
button type, 62
Petersen disc tag, 61, 63
Rototags, 62–63
Jumbo, 62–63
ORI, 62–63
Tail, family key, 42
Taxonomy
classification, 23–24
dissection, 32
family key, 32–45
366
field identification, 28–30
glossary/terminology, 24–28
laboratory identification, 30
orders and families, 45–53
photography, 30
specimen collection, preservation, and cataloguing, 31
Telemetry, 65, 180
Telephone sampling, 260–262
Terminal F assumption, 224
Terminology, taxonomic, 24–28
Territorial Use Rights in Fishing (TURFs), 300–301
Testis, sperm stages, 149
Tetracycline validation method, 117–118
Thornback ray (Raja clavata), 74, 136, 154
Thornback rays (Platyrhinidae), 52
Thorns, caudal, in age and growth studies, 101
Thorns or fine denticles, family key, 35–36
Threatened species, 315–316
Thresher shark
bigeye (Alopias supercilops), 136
pelagic (Alopius pelagicus), 136
Thresher sharks (Alopiidae), 48
Thunnus maccoyii (southern bluefin tuna), 74
Tiger shark (Galeocerdo cuvier), 66, 142, 249
Time restrictions, 307–308
Time-series model fitting, 230
Tope shark (Galeorhinus galeus), 66, 153, 154, 155, 156
Torpedinidae (torpedo rays), 50
Torpediniformes (electric rays), 50
reproductive strategy, 136
Torpedo rays (Torpedinidae), 50
Total allowable catch (TAC), 304
Transmitted vs. reflected light, 105–106
Transverse vs. sagittal sectioning, 100–101
Trawl fishing gear, 247–248, 273, 289
Triaenodon obesus (whitetip reef shark), 146
367
Triakidae (houndsharks), 49, 50
Triakis semifasciata (leopard shark), 180
rebound potential, 194
staining methods, 111
Trip limits, 304
Trophonemata, reproductive strategy, 137
Trunk, family key, 39
Trygonoptera personata (masked stingaree)reproductive strategy, 153
Turtle Excluder Devices (TEDs), 247
Uncertainty, in stock assessment, 11
UN Fish Stocks Agreement, 299
United Nations Food and Agricultural Organization (FAO), 3, 298, 300
United States, reporting classes, 252
Urea content, 326
Urolophidae (stingarees), 51–52
Urolophus lobatus (lobed stingaree), 137, 153, 156
U.S. National Marine Fisheries Service, see NMFS entries
Use rights, 289–305
limited entry, 301–302
quantitative input (effort), 302–304
quantitative output (catch quotas), 304–305
territorial, 300–301
Utilization, 323–336
consumptive, 325–331
cartilage, 329–330
fins, 326–328
liver, 330–331
meat, 325–326
peripheral products, 331
skin, 328
non-consumptive, 332–333
catch-and-release fishing, 332–333
recreational diving, 332
368
Validation
absolute age vs. periodicity of ring formation, 116
of age/growth findings, 115–119
determinate methods (validation), 117–119
indeterminate methods (verification), 116–117
Vernacular names vs. Latin binomial, 252
Vertebrae, transverse vs. sagittal sectioning, 100–101
Vertebral counts, 29–30
Vertebral growth, 99
Virtual Population Analysis, 180, 220–224
example, 222–224
Vitamin A, liver as source, 330
Viviparity
aplacental, 136
placental, 137–138
Von Bertalanffy growth function, 66–67, 119, 120–121, 168, 213–214
sample size and, 121
Weasel sharks (Hemigaleidae), 49
Web resources
for age/growth studies, 123
reproductive biology, 158
Wedgefishes (Rhyncobatiformes), 51
Weight recording, 255–257
Weir & Cockerhan q estimator, 82
Whale shark (Rhincodon typus), 47, 309
Whiskery shark (Furgaleus macki), 152, 156
White shark (Carcharodon carcharias), 87, 90
Whitetip reef shark (Triaenodon obesus), 146
Wobbegongs (Orectolobidae), 47–48
World Conservation Union Shark Specialist Group, 2
Wright’s FST statistic, 79
Yield per recruit model, 212–215
advantages and disadvantages, 214–215
data requirements and assumptions, 213
369
examples, 214
method, 213–214
Yolk sac, aplacental, 138
Young-of-the-year (YOY) tagging, 65
Zanobatoidei (stingrays), 51–52
Zebra sharks (Stegostomatidae), 47, 48
370