and yellowfin tuna - QUT ePrints

Genetic stock structure and inferred migratory patterns of skipjack tuna (Katsuwonus pelamis) and yellowfin tuna (Thunnus albacares) in Sri Lankan waters

Sudath Terrence Dammannagoda B.Sc (Hons), Ruhuna, Sri Lanka

School of Natural Resource Sciences Queensland University of Technology Gardens Point Campus Brisbane, Australia

This dissertation is submitted as a requirement of the Doctor of Philosophy Degree June 2007

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Statement of Original Authorship This work has not previously been submitted for a degree or diploma at any other educational institution. To the best of my knowledge, this thesis contains no material from any other source, except where due reference is made.

Sudath Terrence Dammannagoda 20th June, 2007

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Dedicated to my beloved parents and to my wife, Shyama

ACKNOWLEDGEMENTS First and foremost, thank you to my supervisors, Peter Mather and David Hurwood (School of Natural Resource Sciences, Queensland University of Technology), and Robert Ward (CSIRO Marine Division, Hobart, Tasmania). Special thanks to Peter for making this project a reality and for his excellent mentoring and support during this project. My mother, I thank her for giving her whole hearted support for our education, while nurturing six kids which indeed was a difficult task. Unforgettable memories of my beloved father always inspired me in my work. Also it makes me very happy to mention here my two sisters and three brothers. Thank you, particularly for the affection and love among ourselves which encouraged me further for my studies. Shyama, my beloved wife, without her significant support during my PhD I would not have completed this study at this time. I thank you for your patience for taking your time for my study. I ever love you! I would like to thank my colleagues all who helped me with extensive sampling around Sri Lanka and the Maldives. I would like to thank fishermen Indika Bandara and Sugathadasa for helping me to collect samples. My bunch of friends in NRS, QUT have made this place very enjoyable and helped me to escape me from cultural shock! I specially thank Vincent Chand, Juanita Wrenwick, Angella Duffy, Natalie Baker, Craig Stratified, Mark de Bruyn and all the friends of the lab for the various help extended for my research. I should thank the Ecology and Genetics Group (EGG) of NRS for suggestions and assistance with my research which helped me to improve my knowledge significantly. I received financial support from the International Postgraduate Research Scholarship (IPRS), Commonwealth Government of Australia and from the Asian Development Bank grant to University of Ruhuna, Sri Lanka, both of which are greatly acknowledged.

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TABLE OF CONTENTS ACKNOWLEDGEMENTS

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TABLE OF CONTENTS

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LIST OF TABLES

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LIST OF FIGURES

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LIST OF PLATES

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A BSTRACT

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CHAPTER 1 GENERAL INTRODUCTION

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1.1 Wild fisheries and the tuna fishery around the world

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1.2 Ecology, biology, life history, migration and taxonomy of tuna

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1.3 The Indian Ocean tuna fishery

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1.4 Management of wild fisheries

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1.5 Fish population genetics

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1.6 Genetic approach to stock assessment

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1.7 Genetic stock structure analysis

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1.8 Population genetic structure of tuna species

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1.9 The tuna fishery in Sri Lanka

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1.10 Specific research questions

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CHAPTER 2 EXPERIMENTAL DESIGN AND METHODOLOGY

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2.1 Sampling design

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2.1.1 Study area

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2.1.2 Study species

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2.1.3 Sample collection

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2.2 Genetic methodologies

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2.2.1 Screening mitochondrial DNA variation

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2.2.2 Temperature Gradient Gel Electrophoresis (TGGE)

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2.3 Screening nuclear DNA variation

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2.3.1 Microsatellite marker development.

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2.3.1.1. Isolation of microsatellites by radio isotopic method

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2.3.1.2 Isolation of microsatellites by magnetic bead method

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2.3.2 Microsatellite screening 2.4 Data analysis Rationale

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2.4.1 Mitochondrial DNA data

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2.4.2 Microsatellite data

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CHAPTER 3 POPULATION STRUCTURE OF YELLOWFIN TUNA

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3.1 Ecology, biology and life history

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3.2 Yellow fin tuna genetic stock structure studies

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3.3 Methodology

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(i)

Mitochondrial DNA variation

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(ii)

Nuclear DNA variation

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3.4 Results

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(i) Mitochondrial DNA variation in YFT

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Genetic variation

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Phylogenetic relationships

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Population structure

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Population history and demographic patterns

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(ii) Microsatellite variation in YFT

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Genetic variability, Hardy-Weinberg and linkage equilibrium 84 Population structure

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Effective population size, population divergence and migration92 3.5 Discussion

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CHAPTER 4 POPULATION STRUCTURE OF SKIPJACK TUNA

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4.1 Ecology, biology and life history of SJT

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4.2 Stock structure studies of SJT

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4.3 Methodology

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(i) Mitochondrial DNA variation

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(ii) Nuclear DNA variation

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4.4 Results

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(i) Mitochondrial DNA variation in SJT

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Genetic variation

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Population history and demographic patterns

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Geographic distribution of clades

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(ii) nDNA variation in SJT

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Genetic variability, Hardy-Weinberg and linkage equilibrium

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Effective population size, population divergence and migration 141 4.5 Discussion

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Demographic history

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CHAPTER 5 GENERAL DISCUSSION

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5.1 Comparison of population genetic structure of YFT and SJT

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5.2 YFT population structure

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5.2.1. Comparison with other tuna studies

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Effect of sampling regime

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Sensitivity of molecular techniques

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Sensitivity and power of analytical techniques

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5.3 SJT population structure 5.3.1. Comparison with other tuna studies Oceanographic factors in the study area

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5.4 Fish stock management

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5.5 Implications for YFT management in Sri Lankan waters

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5.6 Implications for SJT management in Sri Lankan waters

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5.7 Future work

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Appendix 1

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Appendix 2

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Appendix 3

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Appendix 4

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References

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LIST OF TABLES Table 1.1 Tuna species of the Tribe Thunnini and their distribution (Ward, 1995), and the global catch of principal market tunas.

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Table 2.1 Location of YFT and SJT sampling sites.

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Table 3.1 Collection data for YFT

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Table 3.2 Variable nucleotide sites of mtDNA ATPase region of YFT.

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Table 3.3 Haplotype frequency distribution among sampling sites of YFT.

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Table 3.4 Descriptive statistics for YFT samples.

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Table 3.5 Genetic structuring of YFT populations based on mitochondrial ATP region sequence data.

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Table 3.6 MtDNA pair-wise ΦST among sampling sites of YFT for entire collection, after Bonferroni correction.

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Table 3.7 MtDNA pair-wise ΦST among year-wise collections of YFT (after Bonferroni correction.

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Table 3.8 Population structure based on mtDNA differentiation of YFT (in SAMOVA).

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Table 3.9 Statistical tests of neutrality and demographic parameter estimates for YFT. Table 3.10 Descriptive statistics for 3 microsatellite loci among YFT collections.

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Table 3.11 Characteristics of microsatellite loci developed for SJT.

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Table 3.12 Allele frequency distribution of YFT Locus UTD402.

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Table 3.15 Genetic structuring of YFT populations based on microsatellite data.

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Table 3.16 p values of Exact test of differentiation of YFT based on microsatellite

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data Table 3.17 Effective number of gene migrants (M) per generation between pairs of

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sites for YFT based on mtDNA and microsatellite data. Table 3.18 Effective population sizes (N1 and N2) between pairs of sites for YFT

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based on mtDNA and microsatellite data. Table 4.1 collection data for SJT

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Table.4.2 Variable nucleotide sites of SJT mtDNA ATP region

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Table 4.3 Haplotype distribution among sampling sites of SJT.

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Table 4.4 Descriptive statistics for SJT samples. No. of haplotypes.

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Table 4.5 Genetic structuring of skipjack tuna populations based on mitochondrial

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ATP region sequence data. 120

Table 4.6 mtDNA pair-wise ФST among sampling sites of SJT after Bonferroni correction for entire collection.

Table 4.7 mtDNA pair-wise ФST among year-wise collections of SJT after Bonferroni 121 correction for 2001, 2002 and 2003 collections. Table 4.8 mtDNA pair-wise ФST among temporal collections within sites of SJT after 122 Bonferroni correction. Table 4.9 mtDNA pair-wise ФST among different day collections within sites of SJT.

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Table 4.10 mtDNA pair-wise ФST among collections within each clade of SJT after

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Bonferroni correction. 124

Table 4.11 Population structure based on mtDNA differentiation of SJT (in SAMOVA).

Table 4.12 Statistical tests of neutrality and demographic parameter estimates for 125 SJT. Table 4.13 Percentage of ATPase region Clade I and Clade II for each SJT population 130 and year-wise collections around Sri Lanka. Table 4.14 Characteristics of microsatellite loci developed for SJT.

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Table 4.15 Descriptive statistics for 3 microsatellite loci among SJT collections.

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Significant probability values after the Bonferroni correction. 134

Table 4.16 Linkage disequilibrium results. The values in bold type are significant

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probability values of Exact test after the Bonferroni corrections. Table 4.17 Allele frequency distribution of SJT Locus UTD328.

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Table 4.18 Allele frequency distribution of SJT Locus UTD203.

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Table 4.19 Allele frequency distribution of SJT Locus UTD73.

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Table 4.20 Genetic structuring of SJT populations based on microsatellite data.

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Table 4.21 Pair-wise FST among sampling sites of SJT after Bonferroni correction for

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entire collection based on microsatellite data. Table 4.22 Pair-wise FST among sample collections of SJT in different years after

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Bonferroni correction based on microsatellite data. Table 4.23 Admixture analysis of SJT (in STRUCTURE).

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Table 4.24 Effective number of gene migrants (M) per generation between pairs of

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sites for SJT based on mtDNA and microsatellite data.

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Table 4.25 Effective population sizes (N1 and N2) between pairs of sites for SJT based on mtDNA and microsatellite data. Table 5.1 A summary of previous population genetics studies of YFT showing

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heterozygosity estimates and FST values

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LIST OF FIGURES Figure 1.1 Phylogenetic relationships of tunas.

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Figure 1.2 YFT and SJT catch in the Indian Ocean (1950~2005).

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Figure 1.3 Location of Sri Lanka in the Indian Ocean.

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Figure 1.4 Exclusive Economic Zone and major fishing grounds of Sri Lanka.

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Figure 1.5 YFT and SJT catch in Sri Lanka (1950~2005).

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Figure 2.1 A map showing SJT and YFT sampling sites around Sri Lanka, the Maldives and the Laccadive Islands.

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Fig 2.2a Monsoon circulation in the Indian Ocean during Southwest monsoon and Northeast monsoon.

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Figure 2.2.b Main monsoon currents within a year around Sri Lanka.

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Figure 2.4 Heteroduplexed TGGE gels.

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Figure 2.5.a Microsatellite gel images: SJT locus 328

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Figure 2.5.b Microsatellite gel images:YFT locus 402

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Figure 3.1 Sampling sites of YFT in the Indian Ocean.

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Figure 3.2 Unrooted neighbour joining tree of YFT haplotypes based on Tamura and Nei genetic distances.

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Figure 3.3 Parsimony Cladogram of YFT haplotypes showing the evolutionary relationship among haplotypes.

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Figure 3.4 MtDNA haplotype frequency distribution of YFT at sampling sites.

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Figure 3.5 Mismatch distribution of YFT based on mtDNA ATP region data.

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Figure 3.6 Microsatellite allele frequency distributions in YFT.

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Figure 4.1 Sampling sites of SJT.

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Figure 4.2 Unrooted neighbour joining tree of SJT haplotypes based on Tamura and Nei genetic distances.

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Figure 4.3 Parsimony Cladogram of SJT haplotypes showing the evolutionary relationships among haplotypes.

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Figure 4.4 MtDNA haplotype frequency distribution of SJT at sampling sites.

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Figure 4.5 Observed, growth-decline model, and constant population model mismatch distribution for all pairwise combinations of SJT.

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Figure 4.6 Schematic map showing relative proportions of ATPase Clade I and Clade II in each sample site around Sri Lanka.

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Figure 4.7 Microsatellite allele frequency distributions in SJT.

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Figure 5.1 A schematic diagram to show the effect of grographical scale of the

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sampling regime. 170

Figure A2.1 Perpendicular TGGE gels showing the reference sample melting profile.

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LIST OF PLATES

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Plate 3.1 Yellowfin tuna Plate 4.1 Skipjack tuna

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ABSTRACT Tuna are the major marine fishery in Sri Lanka, and yellowfin tuna (YFT) (Thunnus albacares) and skipjack tuna (SJT) (Katsuwonus pelamis) represent 94% of all tuna caught. The tuna catch in Sri Lanka has increased rapidly over recent years and this is true generally for the Indian Ocean. Tuna are a major animal protein source for 20 million people in Sri Lanka, while marine fisheries provide the main income source for most Sri Lankan coastal communities. While the importance of the fishery will require effective stock management practices to be employed, to date no genetic studies have been undertaken to assess wild stock structure in Sri Lankan waters as a basis for developing effective stock management practices for tuna in the future. This thesis undertook such a genetic analysis of Sri Lankan T. albacares and K. pelamis stocks.

Samples of both YFT and SJT were collected over four years (2001 - 2004) from seven fishing grounds around Sri Lanka, and also from the Laccadive and Maldive Islands in the western Indian Ocean. Partial mitochondrial DNA (mtDNA) ATPase 6 and 8 genes and nuclear DNA (nDNA) microsatellite variation were examined for relatively large samples of each species to document genetic diversity within and among sampled sites and hence to infer stock structure and dispersal behaviour.

Data for YFT showed significant genetic differentiation for mtDNA only among specific sites and hence provided some evidence for spatial genetic structure. Spatial Analysis of Molecular Variance (SAMOVA) analysis suggests that three geographically meaningful YFT groups are present. Specifically, one group comprising a single site on the Sri Lankan west coast, a second group comprising a single site on the east coast and a third group of remaining sites around Sri Lanka and the Maldive Islands. Patterns of variation at nDNA loci in contrast, indicate extensive contemporary gene flow among all sites and reflect very large population sizes.

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For SJT, both mtDNA and nDNA data showed high levels of genetic differentiation among all sampling sites and hence evidence for extensive spatial genetic heterogeneity. MtDNA data also indicated temporal variation within sites, among years. As for YFT, three distinct SJT groups were identified with SAMOVA; The Maldive Islands in the western Indian Ocean comprising one site, a second group comprising a single site on the east coast and a third group of remaining sites around Sri Lanka and the Laccadive Islands. The mtDNA ∧

data analyses indicated two divergent ( M = 1.85% ) SJT clades were present among the samples at all sample sites. SJT nDNA results support the inference that multiple ‘sub populations’ co-exist at all sample sites, albeit in different frequencies. It appears that variation in the relative frequencies of each clade per site accounts for much of the observed genetic differentiation among sites while effective populations remain extremely large.

Based on combined data sets for management purposes therefore, there is no strong evidence in these data to indicate that more than a single YFT stock is present in Sri Lankan waters. For SJT however, evidence exists for two divergent clades that are admixed but not apparently interbreeding around Sri Lanka. The identity of spawning grounds of these two clades is currently unknown but is likely to be geographically distant from Sri Lanka. Spawning grounds of the two distinct SJT clades should be identified and conserved.

Key words: Tuna, skipjack tuna, yellowfin tuna, population genetics, population structure, migration, fisheries management, Sri Lanka, Maldives, Indian Ocean, demography.

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General Introduction

CHAPTER 1 GENERAL INTRODUCTION

1.1 Wild fisheries and the tuna fishery around the world Many important wild fisheries around the world are severely depleted or have collapsed in recent times due to overfishing (FAO, 2004; Pauly et al., 1998). Examples of important fish stocks that have declined significantly include Peruvian anchoveta (Engraulis ringens), North Sea herring (Clupea harengus) (Beverton, 1990) and Newfoundland cod (Gadus morhua) (Hutchings and Myers, 1994) largely as a result of overharvesting and poor stock management in the past. According to some fisheries scientists, the species-aggregated biomass of large pelagic fish in the world’s oceans, mainly tunas, has been reduced by up to 80% over the first 15 years of their modern exploitation and is now at 10% of 1950’s preindustrial levels (Myres and Worm, 2003). Very recently, analysis of FAO data on fish and invertebrate catches from 1950 to 2003 within all 64 large marine ecosystems world wide revealed that the rate of fisheries collapses has been accelerating over time globally, with 29% of currently fished species considered collapsed in 2003 (Worm et al., 2006). Furthermore, this research predicts that the trend in ongoing erosion of marine fish diversity will result in a global collapse of all taxa currently fished by the mid-21st century. Therefore many wild fisheries require urgent management to allow for their continued sustainable exploitation and to assist in recovery of depleted stocks.

Tuna have great importance in many nations around the world due to their rich, nourishing and palatable flesh. The history of tuna fisheries extends back to the 6th

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century AD and currently has become a major marine fishery in many parts of the world. Global tuna production has increased continuously from less than 0.6 million tonnes in 1950 to almost 6 million tonnes currently [Fishery Global Information System (FIGIS), 2006. http://www.fao.org/figis]. During the last five decades, tuna accounted for half of total global marine capture fisheries (FAO, 2004). Most tuna species are commercially important and of the tuna species that are fished commercially, southern bluefin tuna (SBFT) (Thunnus maccoyii), Atlantic northern bluefin tuna (ABFT) (Thunnus thynnus)(Collette,1999; Collette et al., 2001), Pacific northern bluefin tuna (PBFT) (T. orientalis) (Collette,1999; Collette et al., 2001), yellowfin tuna (YFT) (Thunnus albacares), bigeye tuna (BET) (Thunnus obesus) and albacore tuna (AT) (Thunnus alalunga) are the most valuable species economically, while skipjack tuna (SJT) (Katsuwonus pelamis), kawakawa (Euthynnus affinis), frigate tuna (Auxiz thazard), mackeral (A. rochii) and bonitos (Sarda orientalis; S. sarda) are important food resources in many developing tropical and subtropical countries. The high economic value of many tuna species, particularly those targeted for the sashimi market, has resulted in rising demand and increased pressure on wild stocks. For example ABFT currently are considered to be severely overfished [International Committee for Conservation of Atlantic Tuna (ICCAT), 2003; National Marine Fisheries Service (NMFS), 1995] and are regarded as the most threatened of all tuna species (Magnuson et al., 1994). Very little also remains of the SBFT fishery in the Indian Ocean today because catches had fallen to 15% by 1992 (Caton, 1994), and by 1995 the spawning stock had fallen to 6%11% of the 1960 size (T. Polachek, pers. comm.). Tuna are also a major protein source for many coastal human populations in tropical developing nations as fish are considered an affordable source of protein by many people around the world.

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Hence global tuna catches have increased rapidly in recent times both for commerce and for food, especially for poor people as human populations have expanded.

Bearing in mind that the status of many wild stocks of tuna species is uncertain, many wild stocks of the principal market tuna species appear to be either heavily or are now considered to be fully exploited (Garcia,1994). Some tuna stocks are certainly overfished and some may be significantly depleted.

1.2 Ecology, biology, life history, migration and taxonomy of tuna Tuna are large marine, pelagic fish widely distributed across the world’s oceans. Most tuna species are distributed in warm tropical and subtropical waters although a few species such as SBFT live in cooler temperate zones. Tuna have a peculiar body shape together with advanced thermal physiology (warm blooded) that make them high energetic, fast swimming and hence potentially long distance dispersers. Tuna are known to make trans-oceanic migrations: Perle et al. (2006) documented Pacific bluefin tuna’s migratory movements from the eastern to the western basin of the Pacific Ocean using electronic tagging. Another characteristic feature of tunas is schooling behaviour. Recent electronic tagging studies have broadened our knowledge, especially about tuna movement patterns, vertical and seasonal migrations, behaviour and general physiology (e.g. Block et al., 2005; Domier, 2006).

With particular relevance to the Indian Ocean tuna a unique aspect of the Indian Ocean is seasonal variation in water circulation associated with the periods of the northeastern and southwestern monsoons. Somali currents that originate around

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Somalia, together with monsoon currents, are believed to have a significant impact on the formation of tuna concentrations in the Indian Ocean. Thermocline and surface variations in water temperature distributions are known to affect tuna aggregations (Brill et al., 1999; Lu et al., 2001). Biological status, species composition of fish aggregations and particularly ‘warm spots’ which stand out against a background of colder waters, influence the formation of tuna concentrations which are important for the purse seine fishery (Nair and Muraleedharan, 1993). Tuna concentrations fished using purse seines are commonly a mixture of small tunas (i.e. SJT, frigate tuna, kawakawa) and juvenile individuals of larger tuna species (i.e. YFT, BET) sometimes mixed with a small number of billfish (Istiophoridae, Xiphidae) and other fishes. Long line catch records show that tuna concentrations commonly inhabit a depth range from 80-380m.

Vertical

migration across and in a parallel direction to water temperature gradient zones has been studied intensively in relation to the tuna long line fishing effort (Gubanov and Paramonov, 1993). While most of the adult free swimming schools consist of a single tuna species, schools associated with floating objects often comprise a mixture of species at different life stages. For example, under floating objects SJT, YFT and BET of different size classes often co-exist. This natural behavioural phenomenon of tuna has been utilized for the tuna fishery and has intensified in recent times by creating artificial fish aggregating devices (FAD) in the Indian and other oceans. These fish aggregations attracted to FADs are targeted for the purse seine fishery. Tuna management strategies are emphasized particularly in the light of evidence indicating fishing technologies in the past 20 years have altered tuna schooling behaviours, and therefore the vulnerabilities of mixtures of juvenile tunas

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mainly YFT and SJT. These actions threaten the sustainability of the fishery as well as the genetic diversity of tuna populations.

Tunas belong to the family Scombridae, sub family Scombroidii and to the tribe Thunnini. There are 13 species worldwide comprising four genera: seven species belong to the genus Thunnus, three species belong to the genus Euthynnus, two belong to Auxis, and one species is recognized in the genus Katsuwonus (Table 1.1).

Table 1.1 Tuna species of the Tribe Thunnini and their distribution (Ward, 1995), and the global catch of principal market tunas. Global catch; in metric tonnes (mt) in 2003 (FIGIS, 2006). P- Pacific Ocean, I- Indian Ocean, A – Atlantic Ocean. Species Scientific name Distribution Global catch (mt) Non-Thunnus species Frigate/Bullet P, I, A Auxiz thazard/ A.rochii Atlantic black skipjack Euthynnus alletteratus A Black skipjack P E .lineatus Kawakawa P, I E .affinis Skipjack P, I, A 3,711,969 Katsuwonus pelamis Thunnus species Northern bluefin Longtail Blackfin Albacore Southern bluefin Yellowfin Bigeye

Thunnus thynnus T. orientalis T. tonggol T. atlanticus T. alalunga T. maccoyii T. albacares T. obesus

A P A A P, I, A P, I, A P, I, A P, I, A

1,589,166 1,560,246 1,558,655 1,572,679 1,558,655 1,972,034

While currently accepted Thunnini taxonomy was established by Gibbs and Collette (1967), some tuna species show high levels of morphometric variability across natural widespread distributions. Taxonomy of the tribe Thunnini has been further investigated using mtDNA sequence data by Takeyama et al. (2001) and Chow et al. (2003). According to a study of rDNA internal transcribed spacer (ITS1)

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variation in the genus Thunnus (Figure 1.1), some revisions were suggested to the previous Thunnus systematic relationships, for example PBFT and ABFT falls well within the range of intra-specific variation (Chow et al., 2006).

Figure 1.1 Phylogenetic relationships of tunas. Neighbour-joining phylogenetic trees constructed using the Tamura-Nei gamma distance method based on rDNA ITS1 data (adapted from Chow et al., 2006)

1.3 The Indian Ocean tuna fishery Tuna fisheries in the Indian Ocean are among the oldest in the world. In the early 14th century a well known explorer, Ibn Battuta, described a massive consumption of tuna by the people of countries along the Indian Ocean coast [Indian Ocean Tuna Tagging Program (IOTTP), 2000]. Until the early 1950’s, small scale artisanal fisheries, such as gill net and pole-and-line fisheries were the dominant method for catching tuna in the Indian Ocean with the catch not exceeding an estimated 50,000 tonnes per annum. Industrial fisheries, such as the long line tuna fishery, developed rapidly in the early 1950’s primarily targeting YFT, BET, AT and SBFT, a development that increased annual catch rates significantly up to the 300,000 tonnes (pa) currently taken, officially. In the early 1980’s, a purse seine fishery that

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concentrates on YFT, SJT and BET, most of which are juvenile individuals, was also developed that targeted free tuna schools and schools associated with floating logs and FADs (IOTTP, 2000).

The Indian Ocean tuna fishery has increased rapidly in recent times, and currently accounts for approximately 25% of the global tuna catch. Eleven tuna species are fished in the Indian Ocean and catches have repeatedly exceeded one million tonnes since 1993. In 2004, of the total tuna catch in the Indian Ocean, SJT and YFT account respectively for 40% and 25% of all tuna taken [Indian Ocean Tuna Commission (IOTC), 2005]. It is apparent from Figure 1.2 that there has been a dramatic increase in both YFT and SJT catches since 1985 that reached a plateau by 1995 that lasted for several years followed by another rapid increase. IOTTP (2000) reported however a plateau observed recently in tuna catch trends for most species YFT

Catch (tonne s)

SJT 600,000 500,000 400,000 300,000 200,000 100,000

20 05

20 00

19 95

19 90

19 85

19 80

19 75

19 70

19 65

19 60

Year 19 55

19 50

0

Figure 1.2 YFT and SJT catch in the Indian Ocean (1950~2005). Data complied from IOTC data base (2006)

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in the Indian Ocean. It was considered as a warning signal that most stocks have already approached or will soon exceed their maximum sustainable yields (MSY). This is the optimum harvest that can be obtained from any fish stock without depleting it while allowing long term sustainability. In addition, according to IOTC (2002), catches of YFT in the Indian Ocean are considered to be close to or possibly above the MSY, yet catches using all main fishing gears have increased in recent years due to both raised fishing pressure and more effective fishing techniques. The same report noted an increase in fishing pressure on juvenile YFT by purse seine fishing on floating objects and commented that this practice is likely to be detrimental to the stock if it continues (IOTC, 2002).

1.4 Management of wild fisheries It is evident that tuna stocks worldwide are probably declining and so management strategies for most tuna species are needed urgently to prevent over exploitation. Several kinds of information are required to develop effective stock management practices to help conserve wild fish populations. Primary objectives of any management are long term resource sustainability and avoidance of stock depletion. These are, however, quite complex objectives to satisfy as fish populations are often naturally highly dynamic both spatially and temporally. According to Avise (1997 pp. 337), “marine organisms often are less accessible for behavioural and natural history observation than are their terrestrial counter parts. Many marine organisms have exceptional dispersal and migratory capabilities. Species ranges can be vast. Life histories may include high fecundities and explosive reproductive potentials”. Understanding the impacts of fishing on dynamics and abundance of fish stocks is always difficult particularly for marine fisheries as the geographical scale is often

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vast, fish population sizes may be very large and widely distributed, and several nations are involved in fishing within an ocean basin. In addition, management decisions based solely on scientific data may cause complex impacts on fishermen that rely on fish resources and also on fish consumers. Because of these reasons, effective fish management strategies need to consider scientific, economic, social and sometimes complex political factors for any specific regulations to be effective.

Important basic scientific information required for any fisheries management strategy include; appropriate stock identification, estimation of stock abundance, bio-mass assessments and an understanding of the stock dynamics of each particular fishery. Specific information is required on; i.

Ecology, biology, life history traits and behaviour of particular species.

ii.

Physical factors of the ocean (bathymetry, ocean current patterns, thermocline and temperature distributions) which influence fish distributions

iii.

Identification of different stocks of particular species, if present

iv.

Population dynamics of each discrete stock

v.

Catch and effort statistics for fishermen targeting particular species

From the above points probably the most important, and critical factor, is appropriate identification of fish stocks (Carvalho and Hauser, 1994a; Ward and Grewe, 1994).

While there have been many studies of the above factors in both the Pacific and Atlantic Ocean tuna fisheries, few extensive studies have been undertaken to date on tuna fisheries in the Indian Ocean. Of specific relevance to the current project is

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the fact that the extent of population structure (i.e. the number of stocks) of important tuna species in the Indian Ocean is currently unknown.

Understanding fish stock structure provides fundamental data for developing effective fish stock management practices (Carvalho and Hauser 1994a; Begg et al. 1999a). Determining stock or population structure of any fish species however, is a complex task as many fish populations vary both spatially and temporally. There are a number of approaches for determining stock structure that include; assessment of growth rates, age composition, morphometrics and micro constituents in calcified structures (e.g. otolith chemistry), assessment of relative parasite load, data from tagging returns, and genetic analyses. The different approaches generally complement each other and help to provide a more complete picture of overall stock structure, but determining what discrete stocks actually exist can still often be very difficult (McQuinn, 1997). For example, while tagging studies can provide a direct approach for stock assessments, the substantial costs associated with successful tagging programmes, and the frequent problem of the low percentage of tag recoveries, often limits the utility of this approach. An example is a recent large scale five year tuna tagging programme with an estimated cost of USD $ 18 million that commenced in 2003 in the Indian Ocean. This project has tagged 15,001 individuals comprising 4952 YFT, 1345 BET and 8708 SJT. By November 2005 however, only 116 tag returns were reported to the Regional Tuna tagging ProjectIndian Ocean (RTTP-IO) under the IOTTP (IOTC, 2005). The weakness of the publicity campaign for the tag recovery scheme was identified as the main reason for very low tag recovery.

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To identify management units for fish species reliably, a single approach will not be adequate or appropriate (Campana et al., 1995; Carlsson et al., 2007). Combining the results of several techniques can provide considerable insight into the stock structure of a species, if it exists (Elliott et al., 1995). Begg et al. (1999b) reviewed different approaches used to identify and classifying stocks and proposed an holistic approach that involves a broad spectrum of complementary techniques including morphometrics, meristics, life history characteristics, otolith microchemistry, tagging, and genetics. They argued that an holistic approach to fish stock identification is highly desirable owing to the limitations and specific conditions associated with any particular method and the requirements of fishery management for separating units based on genotypic or phenotypic differences.

Meristic and morphometric characteristics are influenced by both genetic and environmental factors, in unknown proportions. Phenotypic variation between stocks therefore can provide an indirect basis for identifying stock structure, and although it does not provide direct evidence of genetic isolation between stocks, it can indicate prolonged separation of post-larval fish in different environmental regimes. Life history parameters include characteristics such as growth, survival, age-at-maturation, fecundity, distribution patterns and abundance (Ihssen et al. 1981; Pawson and Jenings, 1996). Differences in life history parameters are often taken as evidence that populations of fish are geographically and/or reproductively isolated, and therefore constitute discrete units for management purposes (Ihssen et al., 1981). Life history characteristics are also phenotypic expressions of the interaction between genotypic and environmental influences (Begg et al., 1999b).

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In reality however, many fish species have complex stock structures rather than consisting of a single or two stocks. Shaklee et al. (1998) described the suitability and power of a genetic approach for mixed stock analysis using case studies for effective fisheries management in Pacific salmon. The suitability and power of genetic-based mixed stock analysis depends upon the magnitude of genetic divergence between the stocks being studied and the relative sensitivity of genetic markers. Ruzzante et al. (1998a) reviewing recent studies that investigated the genetic structure of cod populations in the northwest Atlantic Ocean, and suggested the existence of significant genetic differences between cod populations at different mesoscales. They implied that oceanographic features and known spatio-temporal differences in spawning times may constitute important barriers to gene flow both within and among neighbouring spawning components. Ruzzante et al. demonstrated that use of a combination of genetic, physiological, and ecological, as well as oceanographic information allowed biologically significant differences to be detected between cod populations at a variety of geographic scales. Moreover, they suggested that bathymetric and oceanographic structure represents a rational starting point for developing hypotheses aimed at assessing the genetic structures of marine fish stocks.

For several approaches, the relative influences of environment and genetics are likely to be unknown which hinders interpretation of data in terms of potential management options (Ward, 1995). An important consideration is therefore, that non-genetic methods of stock identification can only infer whether different fish breeding units are present or not. In contrast, genetic methods can directly test this hypothesis. Effective genetic resource conservation is not simply limited to

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preservation of overall levels of diversity, both total allelic variation and associated genotypic variation, but to the diversity that may exist at the intra-specific population level as well. Extinction of locally adapted populations may be irreversible and represent loss of unique sets of co-adapted genotypes (Carvalho and Hauser, 1994b).

Although the maximum sustainable yield is considered an

important numerical approach to fisheries management, it is based on the untested hypothesis that all individuals in a sample belong to the same gene pool. Any rapid and significant reduction in population size, or alteration in genetic structure of a breeding population beyond some critical point, may limit the genetic resources available for numerical recovery particularly if mixed gene pools are involved (Ward, 2000b).

The genetic approach to fish stock assessment can be comparatively very successful, cost-effective and results can be obtained with high accuracy. The genetic approach provides information on levels of genetic diversity in fish populations, degree of genetic differentiation among fish populations and hence genetic population structure, and levels of gene flow among fish populations or effective number of gene migrants that are exchanged among populations. It is therefore important to understand how genetic methods (i.e. population genetics) can measure these genetic parameters.

1.5 Fish population genetics Patterns in gene frequencies allow inferences to be made about relative levels of gene flow among populations. High gene flow results in effective dispersal among populations and hence low population differentiation. Low gene flow produces high

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differentiation among populations and hence implies populations are evolving independently. Whether two fish populations are genetically differentiated can be examined by estimating differences in gene frequencies between two populations. Differences in gene frequencies among fish populations can be measured by calculating inbreeding coefficients (FST; Wright, 1969; Nei, 1987; Bowcock and Cavalli-Sforza, 1991). FST is the proportion of genetic variation that exists among populations (sub population, samples or demes etc.) FST =

HT − H S HT

Where, HT = total heterozygosity, and H S = average sample heterozygosity FST ranges between 0 and 1, where 0 implies no difference among samples to 1 where populations are completely differentiated. When genetic differentiation is measured using haplotype or allele frequencies alone, it is called FST (Wright, 1969; Nei, 1987), while genetic differentiation measured incorporating both haplotype frequencies and sequence data is called ΦST (Excoffier et al., 1992). For mtDNA or nDNA sequence data therefore both FST and ΦST can be calculated while for allozyme, RFLP and microsatellite data we estimate FST. In genetic approaches, while the presence of discrete sets of genotypes limited to specific populations can often be an indication of reproductive isolation, in theory, apparent genetic homogeneity can be maintained even at relatively low levels of gene flow (Ward, 1995) a pattern that can result from back mutation and homoplasy.

Finding population structure for marine fishes can be particularly difficult because there are often few barriers to gene flow. Observed genetic differentiation therefore among samples of marine fish, (mean FST estimated at 0.062) can often be much

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less than that between comparable samples of freshwater fish (mean FST = 0.222) (Ward et al., 1994a). Most stock structure analyses of commercially important marine fishes have reported little significant genetic differentiation among samples (e.g. Ward and Elliott, 2001). Very few intra-specific comparisons of marine fish populations have shown relatively high FST values. A low mean FST among populations of marine fish indicates that in general, the marine environment probably does not impose significant barriers to dispersal for most fish species. This contrasts with the extent of isolation commonly associated with most freshwater systems. Dispersal and gene flow in marine fishes can also be enhanced by the presence of relatively long-lived (>30 days) pelagic larval stages in many species that can allow wide distribution of larvae by currents and/or active dispersal by long-lived migratory juveniles or adults. Because of these factors, intraspecific genetic differentiation among marine fish populations is often low, and where present, can be difficult to identify especially when population sample sizes are also low (Waples, 1998).

Although early studies gave the impression that patterns of genetic population structure were likely to be similar among many marine species with trans-oceanic distribution patterns such as tuna and billfishes, idiosyncratic differences in the patterns observed for individual species have been more evident in recent studies (e.g. ABFT studies by Carlsson et al., 2004, 2006; BET studies by Martinez et al., 2005). This demonstrates the need for developing a sound knowledge of the genetic basis of stock structure for each species independently, to allow appropriate management strategies to be formulated (Graves, 1998). Even though measuring genetic differentiation among marine fish populations is often difficult, the genetic

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approach to assessing population differentiation and hence stock structure is still very important, as this provides information on the real genetic basis of fish populations rather than simply numerical fish stocks.

1.6 Genetic approach to stock assessment Genetic approaches have been used since the 1960’s for defining fish stock structure and for identifying discrete fish stocks where they have existed in the past. Allozymes have been the most widely employed genetic markers used to study genetic variation in fish populations. Disadvantages of this approach include the fact that only a small proportion of DNA sequence variation is examined, and there has been controversy over their presumed neutrality that can restrict utility of the technique. The Restriction Fragment Length Polymorphism (RFLP) approach to examining variation in DNA sequences unlike allozymes, permits direct examination of DNA, but at the same time information is lost because only part of the targeted sequences can be examined. In recent times, sequencing of mtDNA has become the most widely used technique for studies of fish population structure as the molecule is haploid, maternally inherited and evolves rapidly.

MtDNA

sequencing provides a large amount of information on sequence composition and mutations present in a particular mtDNA fragment compared with the RFLP approach which provides limited information on specific mutations only.

As

mtDNA is a haploid marker, and maternally inherited, effective population size is 1/4th of that of nDNA and hence the method is able to detect even relatively small genetic differentiation because genetic drift effects are more pronounced. Today, microsatellites have probably become the most popular nuclear genetic marker for genetic structure studies due to their high rate of polymorphism, and their relative

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abundance across the nuclear genome. These characteristics result potentially in a large number of markers for study. Advances in molecular techniques for examining fish stocks and for identifying individual taxa have been rapid. Recently, Scombrid larval identification has improved from the highly time consuming traditional, morphological identification used in the past, to shipboard, real time, molecular identification of ichthyoplankton samples. A species-specific multiplex PCR assay was developed recently to amplify a single, unique sized fragment of the mitochondrial Cytochrome b gene that can be used to identify eggs and larvae of all six species of Indo-Pacific billfish, both dolphin fish species, and the monospecific Wahoo (Hyde et al., 2005).

1.7 Genetic stock structure analysis In a fisheries management sense, the concept of a “stock” is used instead of ‘population’. The basis for managing fish populations effectively is to define management units or “stocks”. While a number of alternative genetic interpretations of the stocks are provided elsewhere (see for example; Richardson et al., 1986; Allendorf et al., 1997; Shaklee et al., 1990; Utter and Ryman, 1993) Probably the most commonly quoted biological definition of a stock is that a stock is an intraspecific group of randomly mating individuals with temporal and spatial integrity (Ihssen et al., 1981). According to this definition, gene flow is limited among such related stocks and hence different stocks are likely to be genetically differentiated. Effective management requires that each discrete stock be managed independently to ensure ongoing sustainable catch levels. Frequently however, a fishery comprises more than a single stock. Therefore, one of the first issues to determine for any individual species targeted in a fishery is to determine whether a

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single stock or multiple stocks are present. If there are multiple stocks, sustainable catch levels should be estimated independently for each discrete stock unit.

The genetic approach to determining if two samples were taken from a single panmictic population or from multiple independent populations (stocks) is not a simple task. If there are significant genetic differences between two samples, and if it is assumed that these differences are due to restricted gene flow rather than resulting from exposure to different selective pressures, then two stocks can be recognised (Ward, 2000a).

If there are no genetic differences however, two

samples may belong to a single panmictic population or alternatively to discrete stocks that cannot be determined by the analysis (Waples, 1998). Thus, sample homogeneity does not necessarily mean population homogeneity. The null hypothesis of a single panmictic population can not be rejected if the test finds population homogeneity, but an inability to reject that null hypothesis does not necessarily mean that tested populations are truly panmictic. Therefore, recognizing sample heterogeneity provides more powerful resolution of stock structure than finding sample homogeneity. This situation, in which biologically significant differences are present but are not detected statistically, leads to a type II error (Waples, 1998). According to Waples (1991), there is little reason to expect a direct relationship between statistical significance and biological significance. It is therefore risky to decide to manage a fish stock as a single stock based on nonsignificant test results, unless one has first evaluated the power of the test to detect differences between stocks, if they do exist (Taylor and Gerrodette, 1993; Dizon et

al., 1995). The converse can also be true: that is, all statistically significant test results do not necessarily mean stocks/populations are biologically significantly

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different (type I error). Consequences of such outcomes in fisheries management have been described (e.g. Waples, 1998).

Many highly migratory marine teleosts commonly show near cosmopolitan distributions and may occur across large areas of the world’s oceans. The high migratory ability of these fishes, combined with the marine environment’s lack of obvious barriers to gene flow, are in general thought to preclude the development of a strong signal of population genetic structure (Waples, 1988; Smedbol et al., 2002). Analyses of population structure of highly migratory species are further complicated by a need for adequate sampling regimes. If individuals are capable of making extensive migrations, there may be uncertainty regarding the natal origin of all but the youngest life history stages (Graves et al., 1996). These factors make studies of highly migratory species, including species like SJT and YFT, a particular challenge for population geneticists.

These issues are particularly relevant to studies of open ocean species undertaken over very large spatial scales. In recent times however, large scale studies of open oceans that have examined and that document the extent of population structuring in tunas and other pelagic fishes such as swordfishes, marlins and sail fishes, have increased. This development can be supported by use of sensitive molecular techniques associated with powerful statistical approaches. In addition, some studies of marine fish populations have reported very low, but significant population structures when studies have been undertaken at fine spatial scales, even when no obvious physical barriers to gene flow were apparent. One reason for presence of fine scale population differentiation may be that spawning activity is

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restricted to only a limited number of females in a restricted geographical area (Swearer et al., 1999). Hence distinct populations may arise from the limited number of egg clutches produced by only a small number of females. As an example, Knutsen et al. (2003) examined fine–scaled geographical population structuring in the highly mobile marine Atlantic cod (Gadus morhua) within a 300 km region along coastal regions in Norway. They examined ~1800 individuals and screened 10 polymorphic microsatellite loci and detected weak, but consistent differentiation among populations at all 10 loci. While the average FST across loci was only 0.0023, this was still highly significant statistically, demonstrating that genetically differentiated populations can arise and persist in the absence of apparent physical barriers to dispersal or great geographical distances among populations.

An earlier study of Northwest Atlantic cod, by Ruzzante et al. (1998a), that documented variation at five microsatellite DNA loci also provided evidence for genetic structure among 14 cod populations in the northwest Atlantic Ocean. The observed differentiation and population structure were explained by topographically induced gyre-like circulations in localities close to sea mounts that can act as local retention centres for cod larvae. Thus even wide ranging marine species can show population structuring when gross physical barriers to gene flow appear to be absent. Detecting such structure requires populations to be sampled at appropriate spatial scales which may be difficult to establish a priori.

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1.8 Population genetic structure of tuna species While little is known of stock structure in most pelagic fish species, population genetic structure studies of a number of tuna species have revealed little intra- or inter ocean genetic differentiation, although evidence for population structure of tunas has increased in recent times.

Studies of SJT stock structure can be traced back to the 1950’s (Cushing, 1956) and have used a variety of molecular genetic techniques. Fujino’s (1969) allozyme studies of Atlantic and Pacific SJT samples showed only slight frequency differences between samples taken from two oceans.

A lack of genetic

differentiation between Atlantic and Pacific SJT populations was later supported by RFLP analysis of mtDNA variation (Graves et al., 1984) implying that SJT populations in both oceans were derived from a common gene pool and sufficient gene flow was ongoing between the two oceans to essentially homogenise gene frequencies. The relatively small sample sizes used in some of these earlier studies may have limited the power for detecting population differentiation where it was present.

Studies of SJT populations within the Pacific Ocean, in contrast, showed a slight cline at two allozyme loci with substantial divergence in gene frequencies (Argue, 1981; Fujino et al., 1981). Argue (1981) concluded that SJT samples across the Pacific Ocean may not comprise a single panmictic population after reviewing several allozyme analyses of Pacific SJT. Subsequent studies by Richardson (1983) and Elliot and Ward (1995) added further support for this conclusion.

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To date, very few studies of tuna species in the Indian Ocean have employed genetic assessments. According to Fujino et al. (1981) a comparison of genetic data on SJT collected from the Atlantic, Indian and Pacific Oceans, together with results reported earlier, indicate that SJT from the Indian Ocean can be distinguished from those collected in the Atlantic and western Pacific Oceans. They used the observed patterns of variation to suggest SJT probably first evolved in the Indian Ocean and then spread later to other oceans.

Large scale tagging studies carried out in the Pacific Ocean further support the contention that trans-oceanic and intra-oceanic gene flow occurs in SJT (Argue, 1981; Bayliff, 1988; Hilborn, 1991). Some tagging studies in the Indian Ocean have reported a few cases of long distance dispersal by SJT (Yesaki and Waheed, 1992; Bertignac, 1994). Capacity for long distance movement and mixing of SJT from different schools reported in these tagging studies suggest high levels of on-going gene flow and consequently argue that strong population structure in SJT is unlikely.

One of the first genetic stock structure studies of YFT was undertaken by Suzuki (1962). No differences were observed in the frequency of the Tg2 blood group antigen in fish from the equatorial Pacific and Indian Oceans. Several allozyme studies on YFT in the Pacific Ocean (Barret and Tsuyuki, 1967; Fujino and Kang, 1968a) also reported little heterogeneity and hence inferred a lack of strong population structure in Pacific YFT. Later, Sharp (1978) reported differences, for a Glucose Phosphate Isomerase (GPI) locus in YFT collected from the eastern and western Pacific Oceans, and a subsequent study of the same locus by Ward et al.

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(1994a) confirmed this difference in GPI allele frequencies within the Pacific Ocean.

RFLP analysis of mtDNA from YFT samples taken in the Pacific Ocean have not shown evidence for strong YFT population structure (Scoles and Graves, 1993; Ward et al., 1994a). Allozyme and mtDNA studies of YFT samples from the Atlantic , Indian and Pacific Oceans by Ward et al. (1997) suggested the existence of at least four discrete YFT stocks worldwide defined as; Atlantic Ocean, Indian Ocean, west-central Pacific Ocean and east Pacific Ocean. A similar outcome was evident from independent studies of six microsatellite loci (Grave and Ward, unpublished data) and five microsatellite loci (Appleyard et al., 2001) respectively.

Genetic analysis of other large tuna species have, in general, suggested that a lack of strong population structure is common. RFLP analysis of mtDNA in AT showed little genetic divergence between Pacific and Atlantic Ocean samples (Graves and Dizon, 1989). Chow and Ushiyama (1995) showed only a slight difference in mtDNA haplotype frequencies between Pacific and Atlantic AT samples, although they argued that there was no evidence of within ocean population structuring.

Until more recently, there were only a few published microsatellites studies in tuna species. Broughton and Gold (1997) examined population structure in small samples of PBFT and ABFT and found small but significant Atlantic-wide population structure. Grewe and Hampton (1998) examined BET within the Pacific Ocean and revealed lack of Pacific-wide BET structure with some differentiation between Ecuador and Philippines collections at one locus. Takagi et al (1999)

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employed microsatellites on collections of BFT from the eastern and western Atlantic and found lack of Atlantic-wide structure. Examples for some other microsatellite studies for tuna are Carlsson, (2004, 2006) and Durand et al. (2005) which are described later.

Some recent studies, however, have reported significant population structure of pelagic tunas. Very recently, Martinez et al. (2005) identified two distinct clades of BET in the Atlantic Ocean based on mtDNA D-loop sequence data and reported significant genetic differentiation among populations (overall ΦST = 0.22, P