genetic stock structure and inferred migratory patterns of ... · lanka and the maldives. i would...

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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

ii

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

iii

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.

iv

TABLE OF CONTENTS

ACKNOWLEDGEMENTS iii

TABLE OF CONTENTS iv

LIST OF TABLES viii

LIST OF FIGURES xi

LIST OF PLATES xii

A BSTRACT xiii

CHAPTER 1

GENERAL INTRODUCTION 1

1.1 Wild fisheries and the tuna fishery around the world 1

1.2 Ecology, biology, life history, migration and taxonomy of tuna 3

1.3 The Indian Ocean tuna fishery 6

1.4 Management of wild fisheries 8

1.5 Fish population genetics 13

1.6 Genetic approach to stock assessment 16

1.7 Genetic stock structure analysis 17

1.8 Population genetic structure of tuna species 21

1.9 The tuna fishery in Sri Lanka 25

1.10 Specific research questions 31

CHAPTER 2

EXPERIMENTAL DESIGN AND METHODOLOGY 32

2.1 Sampling design 32

2.1.1 Study area 32

2.1.2 Study species 35

2.1.3 Sample collection 36

2.2 Genetic methodologies 37

2.2.1 Screening mitochondrial DNA variation 37

2.2.2 Temperature Gradient Gel Electrophoresis (TGGE) 40

2.3 Screening nuclear DNA variation 44

v

2.3.1 Microsatellite marker development. 45

2.3.1.1. Isolation of microsatellites by radio isotopic method 45

2.3.1.2 Isolation of microsatellites by magnetic bead method 46

2.3.2 Microsatellite screening 47

2.4 Data analysis 49

Rationale 49

2.4.1 Mitochondrial DNA data 50

2.4.2 Microsatellite data 57

CHAPTER 3

POPULATION STRUCTURE OF YELLOWFIN TUNA 62

3.1 Ecology, biology and life history 62

3.2 Yellow fin tuna genetic stock structure studies 67

3.3 Methodology 70

(i) Mitochondrial DNA variation 70

(ii) Nuclear DNA variation 71

3.4 Results 71

(i) Mitochondrial DNA variation in YFT 71

Genetic variation 71

Phylogenetic relationships 73

Population structure 74

Population history and demographic patterns 82

(ii) Microsatellite variation in YFT 84

Genetic variability, Hardy-Weinberg and linkage equilibrium 84


Effective population size, population divergence and migration92

3.5 Discussion 94

CHAPTER 4

POPULATION STRUCTURE OF SKIPJACK TUNA 101

4.1 Ecology, biology and life history of SJT 102

vi

4.2 Stock structure studies of SJT 106

4.3 Methodology 109

(i) Mitochondrial DNA variation 109

(ii) Nuclear DNA variation 109

4.4 Results 111

(i) Mitochondrial DNA variation in SJT 111

Genetic variation 111



Population history and demographic patterns 125

Geographic distribution of clades 128

(ii) nDNA variation in SJT 130

Genetic variability, Hardy-Weinberg and linkage equilibrium 130


Effective population size, population divergence and migration 141

4.5 Discussion 143



Demographic history 147

CHAPTER 5

GENERAL DISCUSSION 148

5.1 Comparison of population genetic structure of YFT and SJT 148

5.2 YFT population structure 149

5.2.1. Comparison with other tuna studies 150

Effect of sampling regime 152

Sensitivity of molecular techniques 156

Sensitivity and power of analytical techniques 157

5.3 SJT population structure 157

5.3.1. Comparison with other tuna studies 159

Oceanographic factors in the study area 161

5.4 Fish stock management 161

5.5 Implications for YFT management in Sri Lankan waters 162

vii

5.6 Implications for SJT management in Sri Lankan waters 163

5.7 Future work 164

Appendix 1 167

Appendix 2 169

Appendix 3 173

Appendix 4 188

References 190

viii

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.

Table 2.1 Location of YFT and SJT sampling sites.

Table 3.1 Collection data for YFT

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

Table 3.3 Haplotype frequency distribution among sampling sites of YFT.

Table 3.4 Descriptive statistics for YFT samples.

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

sequence data.

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

after Bonferroni correction.

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

Bonferroni correction.

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

SAMOVA).

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.

Table 3.11 Characteristics of microsatellite loci developed for SJT.

Table 3.12 Allele frequency distribution of YFT Locus UTD402.



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

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

data

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

sites for YFT based on mtDNA and microsatellite data.

Table 3.18 Effective population sizes (N1 and N2) between pairs of sites for YFT

based on mtDNA and microsatellite data.

Table 4.1 collection data for SJT

Table.4.2 Variable nucleotide sites of SJT mtDNA ATP region

Table 4.3 Haplotype distribution among sampling sites of SJT.

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33

70

73

74

75

78

79

80

81

85

87

88

88

89

91

92

93

93

110

112

113

ix

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

Table 4.5 Genetic structuring of skipjack tuna populations based on mitochondrial

ATP region sequence data.

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

correction for 2001, 2002 and 2003 collections.

Table 4.8 mtDNA pair-wise ФST among temporal collections within sites of SJT after


Table 4.9 mtDNA pair-wise ФST among different day collections within sites of SJT.

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


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

SJT.

Table 4.13 Percentage of ATPase region Clade I and Clade II for each SJT population

and year-wise collections around Sri Lanka.

Table 4.14 Characteristics of microsatellite loci developed for SJT.

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

Significant probability values after the Bonferroni correction.

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

probability values of Exact test after the Bonferroni corrections.

Table 4.17 Allele frequency distribution of SJT Locus UTD328.



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

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

entire collection based on microsatellite data.

Table 4.22 Pair-wise FST among sample collections of SJT in different years after

Bonferroni correction based on microsatellite data.

Table 4.23 Admixture analysis of SJT (in STRUCTURE).


114

119

120

121

122

122

123

124

125

130

132

133

134

133

136

136

136

137

138

139

141

x

sites for SJT based on mtDNA and microsatellite data.

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

heterozygosity estimates and FST values

142

142

151

xi

LIST OF FIGURES Figure 1.1 Phylogenetic relationships of tunas.

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

Figure 1.3 Location of Sri Lanka in the Indian Ocean.

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

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

Figure 2.1 A map showing SJT and YFT sampling sites around Sri Lanka, the

Maldives and the Laccadive Islands.

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

Northeast monsoon.

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

Figure 2.4 Heteroduplexed TGGE gels.

Figure 2.5.a Microsatellite gel images: SJT locus 328

Figure 2.5.b Microsatellite gel images:YFT locus 402

Figure 3.1 Sampling sites of YFT in the Indian Ocean.

Figure 3.2 Unrooted neighbour joining tree of YFT haplotypes based on Tamura and

Nei genetic distances.

Figure 3.3 Parsimony Cladogram of YFT haplotypes showing the evolutionary

relationship among haplotypes.

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

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

Figure 3.6 Microsatellite allele frequency distributions in YFT.

Figure 4.1 Sampling sites of SJT.

Figure 4.2 Unrooted neighbour joining tree of SJT haplotypes based on Tamura and

Nei genetic distances.

Figure 4.3 Parsimony Cladogram of SJT haplotypes showing the evolutionary

relationships among haplotypes.

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

Figure 4.5 Observed, growth-decline model, and constant population model

mismatch distribution for all pairwise combinations of SJT.

Figure 4.6 Schematic map showing relative proportions of ATPase Clade I and Clade

II in each sample site around Sri Lanka.

Figure 4.7 Microsatellite allele frequency distributions in SJT.

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7

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27

29

32

34

35

43

48

49

72

75

76

77

83

90

110

115

117

118

126

129

135

xii

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

sampling regime.

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

LIST OF PLATES Plate 3.1 Yellowfin tuna

Plate 4.1 Skipjack tuna

154

170

62

103

xiii

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.

xiv

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 ( %85.1=∧

M ) 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.

General Introduction

1

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 pre-

industrial 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


2

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.


3

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


4

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


5

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 Auxiz thazard/ A.rochii P, I, A Atlantic black skipjack Euthynnus alletteratus A Black skipjack E .lineatus P Kawakawa E .affinis P, I Skipjack Katsuwonus pelamis P, I, A 3,711,969 Thunnus species Northern bluefin Thunnus thynnus A 1,589,166 T. orientalis P 1,560,246 Longtail T. tonggol A Blackfin T. atlanticus A Albacore T. alalunga P, I, A 1,558,655 Southern bluefin T. maccoyii P, I, A 1,572,679 Yellowfin T. albacares P, I, A 1,558,655 Bigeye T. obesus P, I, A 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)


6

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


7

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

0

100,000

200,000

300,000

400,000

500,000

600,000

1950

1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

Year

Catch (tonnes) YFTSJT

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


8

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


9

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


10

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 Project-

Indian 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.


11

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).


12

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


13

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


14

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.)

T

STST H

HHF

−=

Where, HT = total heterozygosity, and SH = 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


15

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


16

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


17

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


18

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 non-

significant 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


19

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


20

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.


21

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.


22

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.


23

(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)


24

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<0.01).

Durand et al. (2005) reported additional support for this finding when they studied

BET mtDNA, nDNA microsatellites and an internal transcriber. Alvarado Bremer et

al. (2005) reported population structuring of swordfish in the Atlantic and the

Mediterranian Sea (ΦST = 0.087, P<0.0039) while Vinas et al. (2004a) identified

some population structuring of Bonito (Sarda orientalis) in the Mediterranean Sea

with two highly divergent clades recognized (ΦST = 0.068, P= 0.00). Carlson et al.

(2004) described weak, but significant genetic differentiation of NBFT populations

in the Mediterranian Sea based on mtDNA D-loop data (ΦST = 0.0233, P <0.000)

and nine microsatellite data (FST = 0.0023, P = 0.038). Carlson et al. (2006)

extended this study to 800 NBFT taken from Icelandic waters and screened six

microsatellite loci that showed slight, but significant genetic divergence among

collections of fish caught early and late in the season respectively, over two years.

As both BET and SBFT are known to have discrete spawning grounds in the

Atlantic and Pacific Oceans respectively, it is likely that this could lead to genetic

divergence within and between oceans as revealed by Chow and Inoue (1993),


25

Bartlett and Davidson (1991), Smith et al. (1994) and Martinez et al. (2005).

Interestingly, a population genetic study using mtDNA markers for BET taken from

the South China Sea, Philippines Sea and the western Pacific Ocean by Hsin-Chieh

Chiang et al. (2005) reported a lack of population genetic structure for BET in this

region. As a general summary therefore, some recent population genetic studies

undertaken on tunas and billfishes have detected intra-specific genetic

differentiation and hence evidence for population structure when more powerful and

sensitive molecular techniques and advanced analytical methodologies were

employed. However, according to a recent study of global population structure in

SJT and YFT by Ely et al. (2005), using mtDNA control region sequence data

population differentiation was not evident for both SJT and YFT between the

Atlantic and Pacific Oceans. This lack of genetic differentiation was argued to be

the result of very large effective population sizes and hence low genetic drift effects

experienced by both species. In the same study, YFT only showed slight genetic

differentiation between the Atlantic and Pacific populations when an RFLP method

was employed for the mtDNA ATP-COIII region.

1.9 The tuna fishery in Sri Lanka

Sri Lanka is situated in the Bay of Bengal, south-east of India and has extensive

marine resources (Figure 1.3). The country has a large Exclusive Economic Zone

(EEZ) proportionate to the area of the island extending to 200 nautical miles from

the coast.


26

Sri Lanka

India

Maldives

Laccadive

Figure 1.3 Location of Sri Lanka in the Indian Ocean. Redrawn from National Geographic web site (www.nationalgeographic.com)

Two major, highly productive fishing grounds are present in the region namely the

Wadge Bank and the Pedro Bank (Figure 1.4). The Wadge Bank situated off the

southern tip of India and to the west of Sri Lanka (outside the Sri Lanka EEZ) is

nourished by the Vaigai River that flows through the southern part of India. The

Pedro Bank situated to the north of Sri Lanka is nourished by the Kaweri River that

flows through the south-eastern part of India. Of the two fishing grounds, the deep

Wadge bank is a well known feeding ground for tuna and billfishes, whereas tunas

and billfishes are rare on the shallow Pedro Bank.

Sri Lanka is a relatively small island with a land surface area of only 65610 km2

with approximately 1000km of coastline. The current population of Sri Lanka is

19.46 million (Dept. of Census and Statistics of Sri Lanka, 2004) making it a


27

densely populated island nation (average population density is 310 persons per

km2).

Figure 1.4 Exclusive Economic Zone and major fishing grounds of Sri Lanka [Marine fishery resources in Sri Lanka, FAO report (1995)]

Approximately 60% of the total population lives in coastal areas, and 60% of the

coastal population is directly employed in the marine fishery. In Sri Lanka, marine

fish production contributes nearly 90% to total fish production, while inland

fisheries and aquaculture account for the remainder (Central Bank of Sri Lanka,

2004).

Tuna have been targeted by fishermen in Sri Lanka for a long time with records

extending back to the 6th Century AD according to a book entitled: “The Great

halla

This figure is not available online. Please consult the hardcopy thesis available from the QUT Library


28

Chronicle” written by the priest, Mahanama. Tuna constitute the major marine

fishery and marine fish are the major animal protein source for most of Sri Lanka’s

coastal population. The importance of the tuna fishery in Sri Lanka, unlike the

commercial large scale industrial tuna fisheries in the Atlantic and Pacific Oceans,

is primarily focused on the local people and provides the main income and

employment source for most coastal fishery communities. If the Sri Lankan tuna

resources were severely depleted, the major animal protein source for Sri Lankan

coastal populations would be at threat, and more importantly, the main income

source for coastal fishery communities would be compromised. So it is clear that

food security (protein) for Sri Lanka depends on maintaining a sustainable tuna

fishery over the long term.

Eleven tuna species occur naturally in the Indian Ocean and also in Sri Lankan

waters. Among them, commercially important species include YFT, SJT, BET

kawakawa, bullet tuna, frigate tuna and longtail tuna. Of these, the main component

species and hence the most economically important species in Sri Lanka are YFT

and SJT. Both single day small fishing vessels and multi-day fishing trawlers are

used in the offshore tuna fishery in Sri Lanka. Gill nets, long lines, surrounding

nets and pole-and-line fishing are the major fishing methods used to catch tuna in

Sri Lankan waters.

The total tuna catch in Sri Lanka almost doubled between 1993 and 2002, and

currently has reached around 100,000 tonnes per annum (Figure1.5). During this

period, the SJT and YFT catches jointly accounted for 89%~95% of the total annual

tuna catch in Sri Lanka.


29

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

1950

1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

Year

Catch (tonnes) YFTSJT

Figure 1.5 YFT and SJT catch in Sri Lanka (1950~2005). Data complied from IOTC data base (2006)

Over the last decade, while a considerable number of studies have been undertaken

on the Sri Lankan tuna fishery, most have been limited in scale and represent only

short-term analyses. Studies include reviews and updates on tuna fisheries (Joseph,

1984; Sivasubramanium, 1985; FAO, 1985; Joseph and Moiyadeen, 1987, 1988;

Dayaratne and De Silva, 1990, 1991; Maldeniya and Suraweera, 1991; Dayaratne,

1994a, 1994b) and studies of tuna biology and various aspects of local fisheries

(Joseph et al., 1985, 1987, 1988; Dayaratne, 1994a; Amarasiri and Joseph, 1987,

1988; Maldeniya and Joseph, 1987,1988; De Silva and Dayaratne, 1990; Maldeniya

and Dayaratne, 1994; De Silva and Boniface, 1990; Maldeniya, 1993). In general,

however, very little data are available on Sri Lankan tuna that could provide the

basis for developing sound stock management practices. Thus any approaches to

management that have been considered in the past, have been based on data


30

collected for the same species elsewhere (e.g. Pacific and Atlantic Ocean studies of

tuna species).

The Indian Ocean has peculiar oceanographic characteristics compared with other

oceans (Fonteneau, 1998). Because of these and the climate, the biological

characteristics of most tunas in the Indian Ocean are considered to be quite unique.

The fishing patterns employed for tunas in the Indian Ocean fisheries are also quite

different to those practiced elsewhere: both the range of species targeted by

fishermen and the deployment of various fishing gears in the regional areas are

largely endemic to the Indian Ocean. Thus, future effective management of tuna

stocks in the region will require the focus to be on both the needs and peculiarities

of the system there, to allow for effective conservation and stock management of

Indian Ocean tuna stocks.

To date, there have been limited attempts to assess wild tuna stock structure in Sri

Lankan waters for management purposes and no studies have used genetic data to

evaluate the extent of population exchange in this region. The current study is the

first to document the wild genetic resources present in the two most important tuna

species in Sri Lankan waters and thereby to develop fundamental data on wild stock

structure for management purposes. The specific research questions of this thesis

therefore, are as follows.


31

1.10 Specific research questions:

1. Are SJT and YFT single or multiple stocks in Sri Lankan waters?

2. If multiple stocks exist for YFT and/or SJT, what are their genetic

population structure and the spatial distributions of homogeneous groups?

3. What are the dispersal patterns of YFT and SJT in Sri Lankan waters?

4. What is the impact of fishing pressure on population size changes over

time?

5. What are the respective phylogenetic relationships and extent of

population divergence of YFT and SJT in Sri Lankan waters?

6. What conservation management strategies can be suggested from the data

for discrete stocks of each species, where they exist?

Experimental Design and Methodology

32

CHAPTER 2

EXPERIMENTAL DESIGN AND METHODOLOGY

2.1 Sampling design

2.1.1 Study area

In this study tuna samples were collected from seven major fishing grounds around

Sri Lanka and also from two major fishing grounds in the Maldive Islands and

Laccadive Islands in the Western Indian Ocean (Figure 2.1 and Table 2.1).

Sampling locations for the current study were basically decided by the presence of

the major tuna fishing grounds in Sri Lankan waters because the main objective of

the study was to determine whether tuna stocks in major fishing grounds are

genetically a single stock or if they constitute multiple stocks.

Figure 2.1 A map showing SJT and YFT sampling sites around Sri Lanka, the Maldives and the Laccadive Islands. Sampling sites; KK- Kandakuliya, NE- Negombo, WE- Weligama, TA- Tangalle, KR- Kirinda, KM- Kalmunei, TR- Trincomalee, MD- Maldives, LC- Laccadive


33

Previous tagging studies have suggested that tuna migrate between Sri Lanka, the

Maldive Islands and Western Indian Ocean (Yesaki and Wheed, 1992). Samples

were therefore also collected from the Maldive Islands and Laccadive Islands to

confirm whether genetic exchange occurs between Sri Lanka, the Maldive Islands

and populations in the Western Indian Ocean.

Table 2.1 Location of YFT and SJT sampling sites

Sampling site Abbreviation Longitude Latitude Kandakuliya KK 79013` 8015` Negombo NE 79018` 6057` Weligama WE 80018` 5034` Tangalle TA 81014` 5042` Kirinda KR 82007` 607` Kalmunei KM 82029` 7008` Trincomalee TR 81051` 8058` Maldives MD 73009` 4020` Laccadive LC 72031` 11017`

Sri Lanka, situated south east of India (N50-100, E 790 - 820) is an island surrounded

by a continental shelf. The Maldive Islands are a group of mainly atolls, close to,

and situated south west of Sri Lanka. The main island of the archipelago is Male, a

coral atoll situated at S00~N70, E720~740. The Laccadive/Lakshadveepa Islands

are a small archipelago of 36 small islands in the Western Indian Ocean near the

western coast of the Indian Peninsula.

A specific characteristic of the Indian Ocean is the seasonal variation in water

circulation connected with the periods of the northeastern and southwestern

monsoons. From November to March, the northeastern monsoon currents activate

and flow in a clock-wise direction around Sri Lanka. During the second half of the

year (May to September) southwestern monsoon currents generate and flow in an


34

anti-clockwise direction around Sri Lanka (Figure 2.2a and 2.2b). Therefore a

complete reversing of the direction of water currents occurs around Sri Lanka each

year with each monsoon (northeastern and south western). Tuna populations around

Sri Lanka are highly influenced by strong monsoonal weather patterns and ocean

current patterns, and this effect could have a major impact on mixing of tuna

cohorts from different spawning grounds. Due to these monsoon current patterns, a

mixing zone can be expected around Sri Lanka, with fish from the western Indian

Ocean and eastern Indian Ocean potentially admixing there.

(i) Summer monsoon (July - August) (ii) Winter monsoon(January - February) Figure 2.2a Monsoon circulation in the Indian Ocean during Southwest monsoon and Northeast monsoon (Schott and McCreary, 2001).

Several tuna spawning grounds have been reported in the Indian Ocean; from

Madagascar to the equator (Conand and Richards, 1982), off the southwest coast of

India in the western Indian Ocean (George, 1990; James et al., 1990), the Andaman

Islands (Boon Ragsa, 1987) and the Gulf of Thailand (Chayakul and Chamcang,

1988) in the eastern Indian Ocean. According to observations of local fishermen,

there are SJT and YFT juveniles in many places around Sri Lanka, likely carried in

by monsoonal currents. Local seasonal ocean currents around Sri Lanka are also

believed to impact on stock structure of relatively small tunas like SJT.

halla



35

As the Maldive islands are mainly atolls, the surrounding sea is deep which makes

ideal habitat for tuna species, producing the second largest annual tuna catch in the

Indian Ocean (IOTC, 2005).

Figure 2.2.b Main monsoon currents within a year around Sri Lanka. (FAO, 1995) 2.1.2 Study species

Seven tuna species are fished in Sri Lanka, with two the most important species that

show large contrasts in their ecology, biology and behaviour selected for this

population genetic and stock structure study. YFT attract the highest price and the

second largest catch (~25%) of the 7 tuna species in Sri Lanka. YFT is a large

(adults ~2m), pelagic and highly dispersive species. SJT is an offshore, relatively

small body sized (~50cm), highly vagile tuna that account for the greatest catch in

halla



36

Sri Lanka (~40%). Ecology, biology, life history and genetics of these two species

are described in detail later.

2.1.3 Sample collection

Relatively large sample sizes were sought for the present study because tuna species

are highly migratory pelagic fish and so natural population size is probably very

large in the open ocean environment. To detect population sub division in such

species if present; a large sample size is required. Therefore approximately 50

samples of both YFT and SJT species were collected from each fishing ground.

Samples were collected across several years from the same fishing grounds where

possible to determine if there are changes in genetic structure over time. Sampling

was conducted over four years from 2001 to 2004. A total of 378 SJT and 338 YFT

samples were collected from seven major fishing grounds around Sri Lanka, the

Maldives and Laccadive islands over the period of the study.

Fresh fish samples were collected directly from fishermen’s boats as fish were

caught or when catches arrived at local landing sites. Tissue samples (white muscle)

were collected from both species and preserved in 95% ethanol before transport to

the Queensland University of Technology’s Genetics Laboratory, Brisbane,

Australia. Sample locality (GPS), body length and sex, and type of fishery data

were recorded for each individual. Most YFT samples were juveniles (except for

KK and KR sites) while for SJT, samples were generally adult individuals.


37

2.2 Genetic methodologies

In this study samples were screened for both mitochondrial DNA (mtDNA) and

nuclear DNA (nDNA) variation to determine both historical and contemporary

levels of gene flow and dispersal patterns. MtDNA variation was examined initially

using the mtDNA Control region (D-loop) but this marker was later replaced by an

ATP region because variation was too high in the Control region. Tri- and tetra-

nucleotide microsatellite markers were developed for both species via DNA cloning

to examine nDNA variation. MtDNA D-loop and ATPase6 and ATPase8 genes

were amplified using PCR, and mtDNA haplotypes were determined by

Temperature Gradient Gel Electrophoresis (TGGE). Each unique haplotype was

sequenced. Both SJT and YFT samples were screened with two species-specific sets

of three microsatellite loci.

Both genomic and mitochondrial DNA were extracted using standard Phenol-

chloroform and modified salt extraction methods (Miller et al., 1988). For detailed

DNA extraction procedures see Appendix 1.

2.2.1 Screening mitochondrial DNA variation

MtDNA commonly exhibits considerable variation among individuals both within

and among populations, and therefore it has proven to be an effective marker for

determining population structure and for assessing patterns of intraspecific

geographical variation (Avise et al., 1987). Evolution of animal mtDNA generally

occurs via single base pair substitutions, making mtDNA a powerful and sensitive

marker for describing population structure. The effective population size of mtDNA

is ¼ that of the nDNA due to uniparental maternal inheritance and haploid mode of


38

inheritance. Moreover, mtDNA markers used in combination with nuclear DNA

markers provide the facility to examine sex-biased dispersal, because of the

maternal mode of mtDNA inheritance and the bi-parental mode of nDNA

inheritance.

Population and phylogenetic studies of marine fish using mtDNA are now common

and studies that have applied mtDNA analysis to the study of tuna species include:

Atlantic BET by Martinez et al. (2005), and Durand et al. (2005); Indian Ocean

BET by Chiang et al. (2005), Atlantic BFT by Carlsson et al. (2004) and (2006);

Mediterranean Bonito by Vinas et al. 2004a, Atlantic swordfish by Alvarado

Bremer et al. (1995; 2005), Chow et al. (1997), Chow and Takeyama (2000);

Pacific and Atlantic YFT by Scoles and Graves (1993); Pacific, Indian and Atlantic

YFT by Ward et al. (1997); Indian Ocean swordfish by Rosel and Block (1996).

In the current study, mtDNA D-loop (Displacement loop/Control region) was

trialled initially to document mtDNA variation in SJT and YFT populations. The

vertebrate mtDNA Control region is non-coding, fast evolving, and highly variable

and hence generates high levels of individual variation rapidly. If relationships

among alleles are to be inferred, sample sizes need to be very large when levels of

variation are high to detect discrete patterns of genetic divergence.

Individual haplotype diversity, in the control region of YFT and SJT was

determined using a heteroduplexed TGGE (Temperature Gradient Gel

Electrophoresis) approach (see section 2.2.2). Preliminary results of TGGE of the

Control region for both species produced very large haplotype diversity with


39

approximately 95% of individuals screened possessing unique alleles that

demanded very large sample size from each site to adequately represent this

variation. Screening such large sample sizes is usually impractical for population

analyses. Extensive analysis of Control region variation indicated a lack of power in

reasonable sample sizes to detect genetic population differentiation. A slower

evolving mtDNA region was therefore required to assess patterns of genetic

variation in the target species. Several other mtDNA regions were then trialed and

sequenced including 12SrRNA, Cytochrome-b and ATPase6 and ATPase8 regions.

According to sequence alignments, the ATP 8.2 L (5’AAA GCR TYR GCC TTT

TAA GC 3’) and COIII.2H (5’ GTT AGT GGT CAK GGG CTT GGR TC 3’)

region (E.Bermingham@http:// nmg.sci.edu/ bermlab.htm) in the ATP-COIII region

was selected for use in this study as a suitable marker, as it showed moderate

intraspecific variation in both species.

Optimization trials for the primers ATP 8.2 L and COIII.2H, resulted in the

following PCR protocol: to each 25µl reaction mixture was added, 16µl ddH2O,

2.5µl Roche 10X buffer, 0.5µl 25mM Fisher MgCl2, 1µl Roche Deoxy nucleotide

tri phosphate (dNTP), 1µl 10 mM primer, 0.2µl Roche Taq DNA polymerase, 1µl

DNA template (~200ng/µl). A master-mix solution was prepared for all PCR

components except DNA template and Taq DNA polymerase that were added

individually immediately before cycling. Temperature cycling was conducted in a

Master Cycler® ep (Eppendorf, Hamburg, Germany) PCR machine using the

following programme: (i) 950 C for 5 minutes, (ii) 940 C for 40 seconds, (iii) 520 C

for 40 seconds, (iv) 720 C for 40 seconds (v) repeat steps (ii)~(iv) 30 times, (vi) 720

C for 8 minutes, (vii) hold at 40 C.


40

As a result a ~975 bp long product was amplified which included the ATP6 and

ATP8 regions. This ~975 bp long product was too large however for electrophoresis

using the TGGE method. TGGE analysis is best undertaken with smaller mtDNA

fragments. Using sequence information gained from initial sequencing of the ~975

bp fragment, the following internal primers were developed yielding a 540 base pair

product which included the ATPase6 and ATPase8 genes to examine levels of

variation appropriate to address the specific aims of the study.

Forward primer: 5’ CCT AGT GCT AAT GGT GCG ATA AA 3’

Reverse primer: 5’ TTC CTC CAA AAG TTA TAG CCC AC 3’

Variation in amplified products of the ATP region was determined by TGGE.

Haplotypes resulting from TGGE were scored, and each unique haplotype was

sequenced. To optimize TGGE for a single product, a melting profile was obtained

for a single PCR product using perpendicular TGGE. TGGE products were then

heteroduplexed with the above PCR procedure and an out group individual (an

individual of the same species, but from a remote place). Optimum running time of

TGGE for heteroduplexed PCR products which provided the best resolution level

was determined by running a time series in TGGE.

2.2.2 Temperature Gradient Gel Electrophoresis (TGGE)

In modern genetic studies, DNA sequence information provides the maximum

information about the genome or a particular region of a gene/s. DNA sequencing is

still a relatively expensive procedure however, particularly for population studies,

and hence this often limits the number of samples that can be examined in any

study. Therefore a direct sequencing approach is not practical for most population


41

genetic studies, especially when large sample sizes are used such as is common for

most marine fish populations.

To overcome this problem in the current study, TGGE was used to screen initial

haplotype diversity in each species. TGGE can provide an efficient option, while

still retaining sufficient resolving power (Lessa and Applebaum, 1993). TGGE is

based on the physical behaviour of DNA during electrophoresis in acrylamide gels,

and can distinguish DNA fragments of identical size that differ by a single bp or

more mutations. The technique is based on separation of double-stranded PCR

products in a gel containing a linear temperature gradient (Po et al. 1987). When a

double-stranded DNA fragment reaches its specific melting point, it will partially

denature and hence the migration rate through the gel will be retarded. Different

sequence blocks within a DNA fragment possess different melting temperatures

because of mutations along the fragment. Hence DNA fragments with different

mutations possess different migration patterns, a characteristic that provides the

basis for distinguishing unique haplotypes (unique DNA alleles).

The resolving power of TGGE can be increased by using an outgroup heteroduplex

method (Campbell et al. 1995). This technique involves formation of a hybrid of

known single DNA sequence with an unknown DNA sequence from the sampled

individuals. Such kinds of heteroduplexes contain two homoduplexes (self-self

hybridization) and two heteroduplexes. Heteroduplexes migrate relatively slowly in

the gel matrix due to incomplete base pairing (Myers et al., 1987). Hence a DNA

fragment with different mutations make different incomplete base pairing

/heteroduplexes causing different heteroduplex banding patterns in the gel.


42

In this study, the DIAGEN horizontal TGGE system (DIAGEN Gmbh, QIAGEN

Inc, 1993) was used to screen SJT and YFT mtDNA sample diversity.

Heteroduplexes were performed prior to electrophoresis to further resolve

differences between alleles. For the D-loop fragment, the heteroduplex outgroup,

YFT outgroup for SJT samples and a SJT out-group for YFT samples, were used

for the ATP fragment to optimize haplotype detection. Haplotypes were scored

using unique banding patterns which represent characteristic DNA sequences.

Amplified, heteroduplexed PCR products were run in a ~3.5% acrylamide gel for 3

hours and 13 minutes across a temperature gradient of 190C - 540C for SJT (Figure

2.4 a), while conditions were 2 hours and 25 minutes duration and a 170C - 520C

temperature gradient for YFT (Figure 2.4 b). After the run, gels were stained using

a Silver Nitrate staining method, and haplotypes were scored by eye and

representatives of each unique band phenotype (haplotype) were sequenced.

Individuals with similar looking banding patterns were re-run next to each other to

confirm whether they constituted unique haplotypes or not. For details of gel

casting, perpendicular and heteroduplexed TGGE, Silver Nitrate staining and

sequencing see Appendix 1.2.


43

Figure 2.4 Heteroduplexed TGGE gels: a) SJT mtDNA ATP region b) YFT

mtDNA ATP region.

PCR products for each unique haplotype were cleaned using Roche High Pure PCR

Product Purification Kit following the manufacturer’s specifications (www.roche-

applied-science.com). Concentration of the purified PCR product was measured by

running 3μl in a 1% agarose gel with a concentration standard. Sequence PCR

mixture consisted of ~600ng of purified PCR products, sequencing buffer, ATP

forward primer, Big Dye terminator and ddH2O. A specific sequence PCR

programme was used for sequencing. Then the sequence PCR product was

precipitated using ethanol precipitation. Air dried sequenced products were sent to

the “Australian Genome Research Facility” (AGRF) (http//www.agrf.org.au) for


44

chromatography on a 3730xl sequencing platform. Full details on sequencing PCR

products are given in Appendix 2.c.

Sequences were checked in CHROMAS (version 2.1.3, http//www.technology.

com.au/chromas.html) and then edited and aligned using the BIO-EDIT (version

5.0.6) sequence alignment editor (Hall, 1999) computer programme.

2.3 Screening nuclear DNA variation

The nuclear DNA genome provides greater flexibility for population genetics

studies compared with the mtDNA genome basically for two reasons:

male and female parents have diploid chromosome sets in the nuclear DNA genome

and hence have two alleles at each nuclear DNA locus. During zygote production,

male and female genotypes recombine and produce four potential nuclear genotypes

in the offspring. The nDNA genome therefore provides genetic recombination via

sexual reproduction and thus the potential for four times different genotypes (than

mtDNA) resulting in more population variation. The effective population size for

nDNA therefore is four times higher than that of mtDNA. One disadvantage of this

characteristic of nDNA is that relatively large sample sizes are required to detect

any population structure, if present. Secondly, the nDNA genome provides a variety

of genetic markers for population genetics studies including microsatellites,

minisatellites, VNTRs (Variable Numbers of Tandem Repeats), EPIC (Exon-

Primed Intron-Crossing PCR) etc. In this study, variation in the nDNA genome was

screened using microsatellite markers to study current gene flow in both target

species.


45

Microsatellites are evident in the nDNA genome as di-nucleotide, tri-nucleotide or

tetra-nucleotide repeats. Di-nucleotide repeats possess only a two base pair

difference between individual alleles. This feature can sometimes make them

technically difficult to optimize and to score alleles due to presence of stutter bands,

especially when two alleles are separated by only a couple of base pairs. In the

current study therefore tri- and tetra-nucleotide repeats were developed to identify

alleles precisely. In this study, microsatellites were first isolated using a mixture of

radio isotopic oligo-nucleotides and a radioactive method. The efficiency of

selectivity for tri- and tetra-nucleotide mirosatellites was low however, using this

method, so later, a more efficient magnetic bead method was followed (Glenn and

Schable, 2002).

2.3.1 Microsatellite marker development.

2.3.1.1. Isolation of microsatellites by radio isotopic method.

Extracted genomic DNA was cut by restriction enzyme digestion using DpnII and

Sau3AI restriction enzymes. 300-700 bp DNA fragments were selected by running

cut DNA in an Agarose gel with a molecular marker IX.

Purified cut DNA was ligated into the Puc18 plasmid vector using ligase enzyme

and then the ligated DNA with vector was transformed into Escheriechia coli

(E.coli) competent cells via heat shock. Competent cells with ligated DNA were

then cultured in culture media LB with Ampicillin, X-Gal and IPTG (Promega), and

incubated. Positive clones with inserted vector were transferred to plates with fresh

culture media and incubated to increase the amount of positive clones. Grown

positive clones were transferred to a marked Hybond+ nylon membrane following a


46

process of denaturation of positive clones and fixation of DNA. Membranes with

fixed positive clones were then hybridized with a radioactively labelled oligo

nucleotide probe mixture. Membranes were then exposed to X-ray films and films

developed. Clones with radioactive oligo-nucleotides (microsatellites) were

identified and the corresponding positive clones with microsatellites were selected.

Positive clones were then grown in a liquid Terrific Broth culture media and DNA

was extracted from them. RNAase treated positive clones were sequenced using

M13 vector primers. Microsatellite loci were identified by checking clone

sequences in Chromas. Subsequently primers were designed to amplify specific

microsatellite loci.

2.3.1.2 Isolation of microsatellites by magnetic bead method

Extracted DNA was digested with TsaI and Bst UI restriction enzymes, and ligated

to a double strand adaptor (S475 and S476). Then adaptor-ligated DNA of 300-700

base pair long was selected in a gel run. Biotinylated microsatellite probes were

hybridized to the adaptor-ligated DNA via PCR. Then the DNA with microsatellites

(enriched DNA) was isolated using Streptavidin Magnesphere paramagnetic

particles and a magnetic particle collecting unit. DNA with microsatellites was

enriched repeatedly through PCR. They were then ligated to the cloning vector and

vector-ligated DNA transformed to E.coli competent cells. Competent cells were

then cultured and colonies were transferred to the marked sterile nylon membrane.

Plasmid DNA in the membrane was then denatured and fixed. Membranes were

again hybridized with biotinylated probes, and biotin labelled hybrids were

identified using a colour detection method. Positive clones from colony lysates were

then sequenced using M13 vector primers. Primers were designed to amplify


47

specific microsatellite loci. Appendix 3 describes the isolation of microsatellites in

full detail.

2.3.2 Microsatellite screening

Microsatellite repeats for YFT and SJT were isolated by either a radio isotopic

method or magnetic bead method. Details of developed and initially trialed

microsatellites, specific primers and PCR conditions for YFT and SJT are

summarised in Tables 3.11 and 4.13 respectively.

Optimum PCR conditions for each locus were identified by running a temperature

gradient PCR (450C - 600 C) in an Eppendorf Thermocycler PCR machine. The

temperature gradient PCR programme was 950C for 4 minutes, then 29 cycles of (i)

950C for 30 seconds, (ii) at relevant annealing temperature for 30 seconds, (iii) 72

0C for 30 seconds; and a final extension step at 720C for 8 minutes, then hold at

40C. The PCR reaction mix consisted of ~50ng/µl DNA 1µl, 1.25µl of 10X PCR

buffer (Roche), 0.25µl of 25mM MgCl2, 0.5µl of 10mM dNTP (Roche), 0.5µl of

each 10mM forward and reverse primers, 0.1µl of Taq (Roche) and ddH2O to a

final volume of 10 µl.

PCR products were mixed with formamide dye at a ratio of 1 PCR product to 4

formamide dye to denature PCR products at 950C and make single strand products.

Denatured PCR products were run out on an automated Gelscan machine (Gel Scan

2000-Corbett Research) with Tamra (T350) marker according to the instruction

manual. In addition to the T350 marker, a reference standard was used in two lanes,

of each gel, for each locus. The reference is a collection of representative alleles


48

present at the respective locus. This reference standard assured consistency of

estimation of allele sizes for allele scoring and hence re-runs of gels were not

required. A digital image (Figure 2.5) was produced and saved for allele scoring.

Details of gel casting and operation of Gelscan runs are presented in Appendix 4.

Figure 2.5.a Microsatellite gel images: SJT locus 328


49

Figure 2.5.b Microsatellite gel images: YFT locus 402.

2.4 Data analysis

Rationale

Both mtDNA and microsatellite data sets were subjected to extensive analyses

using a variety of appropriate genetic statistical analysis programmes as many

previous tuna genetic studies have not been successful at detecting tuna population

structure.


50

The majority of research questions were analysed using more than one analytical

approach. Carrying out different analytical tests directed at the same research

question provides the opportunity to corroborate results among tests potentially

providing greater confidence in overall interpretation of the data. For example,

deviation from neutral (drift/mutation equilibrium) expectation was tested using two

analytical methods Tajima’s D (Tajima, 1989) and Fu’s FS (Fu, 1997). These two

tests are sensitive to different aspects of population processes. E.g. Fu’s FS is more

sensitive to demographic fluctuations while Tajima’s D is sensitive to selection.

Therefore, together these two tests potentially provide a greater overall picture

about deviation from neutrality and their possible causes. Another example of

multiple tests for a same research question is testing population structure. In this

study population structure of both species was tested using Analysis of Molecular

Variance (AMOVA), pair wise ΦST and FST analysis, Spatial Analysis of Molecular

Variance (SAMOVA), and STRUCTURE. While all population structure tests

apply different approaches, together they increase the robustness of inferences

about population structure.

2.4.1 Mitochondrial DNA data

First, mtDNA haplotype sequences were edited and aligned in BioEdit version 7.0.1

(Hall, 1999). Haplotype frequency distribution pie charts were constructed for both

total sample collections and for temporal samples at each site.

The mtDNA sequence data set were first tested for deviation from neutral

expectations implemented in Arlequin version 2.00 (Schneider et al., 2005) and in


51

DnaSP 4.10 (Rozas et al., 2003) to determine whether mutations were neutral or

under influence of other factors, such as selection. Whether a population deviates

from neutral expectations such as mutation/drift equilibrium or gene flow/drift

equilibrium can be tested using Tajima’s D (Tajima, 1989) and Fu’s Fs (Fu, 1997)

tests. If the population does not deviate from neutral expectations, Tajima’s D and

Fu’s Fs tests show non significant values while if the population does deviate from

neutral expectations, Tajima’s D gives a low significant values and Fu’s Fs gives a

significant large negative value. Tajima’s D test detects pair wise differences, and

more sensitive to old mutations while Fu’s Fs more sensitive to recent mutations.

Also a test called the R2 statistic (Ramos-Onsins and Rozas, 2002), implemented in

DnaSP 4.10 (Rozas et al., 2003), was used to test neutrality/population growth

because if a population deviates from neutrality, it is expected that the population

was under expansion, bottleneck effect or selection. According to Ramos-Onsins

and Rozas (2002), the R2 statistic is superior when sample sizes are very small (e.g.

n ~ 10) while Fu’s Fs is appropriate when large sample sizes are used.

The Indian Ocean YFT stocks harvesting rates are considered at MSY or beyond

MSY, while SJT stocks are considered as stable still for a long period of time

(IOTC, 2005). Whether YFT and SJT stocks populations are stable, expanding or

decreasing can be assessed by population demographic history analyses.

Demographic history analyses were therefore carried out for both species. Potential

for population expansion was assessed against constant population size and growth-

decline under sudden population expansion model in two ways; Harpending’s

raggedness index (Hri; Harpending, 1994) implemented in Arlequin, and mismatch

distributions (Rogers and Harpending, 1992; Schneider and Excoffier, 1999; Slatkin


52

and Hudson, 1991) implemented in DnaSP 4.10 (Rozas et al., 2003). Harpending’s

raggedness index and mismatch distribution tests whether the sequence data

deviates significantly from the expectations of a population expansion model. If a

population was expanding, Hri gives very low values, and the probability values are

not significant. Mismatch distribution of pair-wise difference analysis compares the

expected distribution of the frequency of pair-wise differences among all

individuals in the sample with the observed distribution. The pattern of pair-wise

differences among haplotypes usually forms a unimodal wave, in samples from

expanding populations, whereas samples drawn from populations at demographic

equilibrium yield a multimodal pattern of numerous sharp peaks with one mode

corresponding to the number of differences within clades (genetically discrete

groups) and the others to differences between clades (Rogers and Harpending,

1992; Schneider and Excoffier, 1999; Slatkin and Hudson, 1991). Thus mismatch

distribution analyses under the assumption of selective neutrality were also used to

evaluate possible historical events of population growth and decline (Rogers, 1995;

Rogers and Harpending, 1992). Arlequin was also used to calculate past

demographic parameters, including θ; population size (θ0 and θ1) and their

probabilities (Rogers and Harpending, 1992) and τ (Tau) (Li, 1977) taking in to

account the heterogeneity of mutation rates (Schneider and Excofier, 1999). The θ

estimates (θ0 and θ1) are the product of 2μN0 and 2μN1, where μ is equal to the

mutation rate for the entire sequence and N is the effective population size at time 0

and 1. τ (Tau) is a relative measure of time since population expansion, but also can

be used to estimate the actual time (T) since population expansion by T = τ/2μ

(Gaggiotti and Excoffier, 2000).


53

The data were then subjected to Modeltest version 3.6 (Posada and Crandall, 1998)

implemented in PAUP* version 4.0 (Swofford, 1998), to select the evolutionary

model that best fitted the empirical data set. The inferred evolutionary model from

the model test was HKY+G without γ correction. Then Neighbour-joining

phylogeny tree (NJ) analyses were carried out in Mega 2.1 (Kumar et al., 2001)

using the tree building algorithm of Saitou and Nei (1987) to infer the intra-specific

phylogeny of both YFT and SJT. However, since the HKY+G model was not

available in Mega version 2.1, the next best model i.e., TrN93 (Tamura and Nei,

1993) was employed. Robustness of the resulting phylogeny tree was tested with

bootstrapping (Felsenstein, 1985).

A mtDNA parsimony cladogram of haplotypes was constructed (at 95% level

connectivity) using TCS version 1.18 programme (Clement et al., 2000). Haplotype

networks reconstruct the genealogical history of haplotypic variation and illustrate

the evolutionary relationship among unique haplotypes, including the amount of

divergence among haplotypes, showing discrete clades. Under coalescent

principles, internal haplotypes in a haplotype network are assumed to be ancestral,

while the tip haplotypes in the network are considered younger, more recently

derived types (Templeton et al., 1987; Templeton and Sing, 1993; Crandall, 1996).

So a mtDNA parsimony cladogram provides information on the demographic and

geographical history of a population including population expansions, and

bottlenecks. Frequency and site information were incorporated into the network to

illustrate the distribution of haplotypes among locations.


54

Standard diversity indices (Nei, 1987), including the number of polymorphic sites

(S), haplotype diversity (Hd), and molecular diversity indices using the Tamura and

Nei genetic distance method (Tajima, 1993; Nei, 1987; Zouros, 1979; Ewens, 1972)

such as nucleotide diversity (π, Nei, 1987), and the average number of pair-wise

nucleotide differences (k; Tajima, 1983) implemented in Arlequin were determined

for the total sample collection and for each temporal and/or geographic sample. As

population structures for YFT and SJT have been difficult to detect in some

previous studies, a range of analytical techniques were employed.

Population genetic analyses were performed using Arlequin version 2.00 (Schneider

et al., 2005) and DnaSP 4.10 (Rozas et al., 2003) based on mitochondrial ATP 6

and 8 region haplotype and sequence data. An analysis of molecular variance

(AMOVA) and hierarchical AMOVA were used to examine the amount of genetic

variability partitioned within and among populations (Excoffier et al., 1992).

AMOVA measures the genetic variation of sample populations, and incorporates

information on haplotype divergence. Hierarchical AMOVA partitions the total

genetic variation among pre-defined hierarchical groups, yielding three measures of

haplotypic diversity; FST describes variation among sample populations, FSC

describes variation among sample populations within regions, and at a higher level

of the hierarchy FCT describes variation among regions for sample populations

(Excoffier et al., 1992). In this study hierarchical groups were organized in two

ways;

1. Year wise groups: samples from each year (i.e. 2001, 2002, 2003, and 2004) at

each site were grouped as separate groups, irrespective of sampling site. So there


55

were four groups for both species. Genetic variation therefore, was partitioned

among year wise groups (FCT), among sites within years (FSC), and within sites.

Using this hierarchical grouping, the stability of any inferred pattern over time for

both species can be measured: thus we can determine whether the overall genetic

composition of each species at a site is stable over time.

2. Site wise groups: Samples at each site (i.e. for YFT; KK, NE, WE, TA, KR, TR,

and MD) and (for SJT; NE, WE, TA, KM, TR, LC and MD) in different years were

grouped together irrespective of time of collection. So there were seven groups for

both YFT and SJT. Genetic variation therefore was partitioned among sites, among

samples from different years within sites, and within samples. Using this

hierarchical grouping, the following aspects of population structure were measured

for both species: whether there is a significant spatial genetic differentiation among

sites irrespective of time (FCT), whether there is a significant genetic differentiation

among temporal collections within a site (FSC). In addition, for SJT, hierarchical

AMOVA was performed for each clades’ year wise groups and site wise groups.

The significances of variance components for each hierarchical comparison were

tested using 1000 permutations. A permutation test is a type of statistical

significance test in which a reference distribution is obtained by calculating all

possible values of the test statistic under rearrangements of the observed data file. If

the labels are exchangeable under the null hypothesis, then resulting tests yield

exact significance levels (Raymond and Rousset, 1995).


56

Divergence among spatially and temporally differentiated sites was estimated using

the fixation index (ΦST) (Excoffier et al., 1992), which includes information on

mitochondrial haplotype frequency (Weir and Cockerham, 1984), and genetic

distances among haplotypes (Tamura and Nei, 1993). Genetic differentiation was

examined at a further level of resolution by determining the genetic differentiation

between pairs of sites (pair wise ΦST analysis). Significance of pair-wise site

comparisons was tested using 110 permutations. In all instances with multiple tests,

p values were adjusted using the Bonferroni correction (Rice, 1989).

Spatial structure was investigated using Spatial Analysis of Molecular Variance

(SAMOVA) (Dupanloup et al., 2002), which identifies groups of sample sites

which are most similar and geographically meaningful as AMOVA and pairwise

FST do not incorporate geographic data in to genetic differentiation. SAMOVA uses

the statistics derived from an AMOVA, and incorporates geographical information

on sampling sites (i.e. geographic distances among sites) with a simulated

annealing approach to maximise the FCT among groups of populations as well as

identifying possible genetic barriers between them, without pre-defining

populations as is necessary for AMOVA (Dupanloup et al., 2002). So SAMOVA

define groups of samples that are geographically homogeneous and maximally

differentiated from each other (Dupanloup et al., 2002).

To measure extent of population differentiation by testing if sequences with low

divergence are geographically proximate, the nearest-neighbour statistic Snn

(Hudson, 2000) was estimated in DnaSP 4.10 (Rozas et al., 2003). Snn is a measure


57

of how often closely matched sequences are from the same locality in geographical

space. Significance of Snn was tested using 10,000 permutations.

Tests for genetic isolation by geographical distance were done using a Mantel’s

(1967) test in Arlequin version 2.00 (Schneider et al., 2005). Mantel’s test

determines the geographical pattern of gene flow from spatial patterns of

differentiation. This test is based on the observation that a log-log regression of

gene flow on geographic distance should be approximately linear in a population at

equilibrium under restricted dispersal (Slatkin, 1993). The geographical distance

between sites was measured as the coastal distance between (pairs of) sites around

Sri Lanka. The direct geographical distance to the out group MD and LC sites from

Sri Lanka was measured from the WE and NE sites respectively. As the sea in the

northwest area (the border between India and Sri Lanka) is very shallow (<5

fathoms), SJT and YFT probably do not disperse through this very shallow sea belt

as tunas inhabit oceanic waters. Indeed, no tuna fishery exists between the northeast

and northern regions of Sri Lanka due to the shallow sea. The genetic distance

matrix obtained using Tamura and Nei’s (1993) method of substitution (ΦST) and

the above geographic distance matrix were used to produce linear regression of

genetic distance versus geographic distance.

2.4.2 Microsatellite data

Alleles were scored using One D-scan version 2.05 (Scanalytics, Inc., 1998)

software from digital gel images generated using the Gel-Scan-2000 (Corbet

Research). These data were then compiled using MSExcel version (2003) and the


58

entire microsatellite data set checked for the presence of null alleles by searching

for an excess of homozygotes, large allele drop out or error scoring due to stutter

bands, using Micro-checker software version 2.2.3 (Oosterhout et al., 2004). For

some individuals, PCR amplification may not be successful due to mutation/s in the

priming sites of one allele (or sometimes in both alleles). Such PCR products show

only a single band when they are run in a gel and no bands if both allele priming

sites have mutations. In such situations, that single band is falsely scored as a

homozygote, although it may well be a heterozygote in reality. If such null alleles

are frequent in a data set, this will cause an apparent homozygote excess.

“Microchecker” can identify such homozygote excesses via a comparison with

expected and observed number of homozygotes. Sometimes alleles with large

molecular weights do not appear in the gel due to technical problems. Such cases

also can be identified using Microchecker. Another capability of the programme is

the capacity to identify error scoring of non-specific bands. Specially, di-nucleotide

repeats frequently cause non-specific bands/stutter bands. In this study however, as

tri- and tetra-nucleotide repeats were screened, few problems were experienced with

stutter bands.

The entire microsatellite data set was subjected to Hardy-Weinberg equilibrium

tests implemented in Arlequin version 2.00 (Schneider et al., 2005) for deviations

of genotypic distributions (Exact tests; Guo and Thompson,1992), as well as for

calculations of observed (Ho) and expected (HE) heterozygosities, and to test for

heterozygote excess and deficiencies (exact tests). Microsatellite data were also

subjected to linkage disequilibrium tests (Slatkin and Excoffier, 1996) using the EM

algorithm implemented in Arlequin version 2.00 (Schneider et al., 2005) with


59

100000 permutations at a significance level of 0.05 to test whether multiple loci

were in linkage disequilibrium. An Exact test was used to estimate the magnitude

and significance of linkage disequilibrium.

Descriptive statistics including allele diversity were calculated in Arlequin version

2.00. Levels of genetic variation for each sample were assessed primarily in terms

of numbers of alleles per locus using Arlequin version 2.00. An analysis of

molecular variance (AMOVA) and hierarchical AMOVA were used to examine the

amount of genetic variability partitioned within and among populations (Excoffier

et al., 1992). Hierarchical groups were organised for microsatellite data set as for

mtDNA data. Divergence among spatially and temporally differentiated sites was

estimated using the fixation index (FST) (Excoffier et al., 1992), which includes

information on allele frequency (Weir and Cockerham, 1984). Genetic

differentiation was examined at a further level of resolution by determining genetic

differentiation between pairs of sites (pair wise FST analysis). Significance of pair-

wise site comparisons was tested using 110 permutations. In all instances with

multiple tests, p values were adjusted using the Bonferroni correction (Rice, 1989).

Genetic differentiation between pairs of sites was further assessed using a more

sensitive and statistically powerful test; the Exact test of differentiation (Goudet et

al., 1996) for the YFT microsatellite data set as AMOVA for the entire data set did

not show significant genetic differentiation. This test uses a Markov Chain Monte

Carlo (MCMC) statistical approach of 1000 steps in length, and gives the

probability values for differentiation between pairs of sites. While the significance

value used was 0.05, there is no requirement to do Bonferroni correction for

multiple tests as the Exact test of differentiation is a single test.


60

Potential presence of multiple breeding units were tested for the SJT samples using

“STRUCTURE” version 2.0 (Pritchard el al., 2000) for microsatellite data.

STRUCTURE uses a model-based full Bayesian MCMC approach that clusters

individuals to minimize Hardy-Weinberg disequilibrium and gametic phase

disequilibrium between loci within groups. The number of populations represented

in sites was estimated by calculating the probability of the data, assuming that they

originated from 1 to 5 populations (K=1 to 5) in the study area, as described in

Prichard et al., (2000). Each run consisted of a burn-in period of 2 X 104 steps

followed by 105 MCMC iterations. Assignment scores for each individual to the

most likely cluster were then analysed.

Migratory patterns of both YFT and SJT were examined using the programme IM

(Nielsen and Wakeley, 2001) of combined data sets for both mtDNA and

microsatellites. IM performs a coalescent analysis of genealogies from present to

past, and estimates the effective number of migrants per generation (m) moving

between pairs of sample sites, present effective population sizes of the modern

populations (Ne) and the historical population (NA), and time since the two

populations had diverged (t). Coalescent methods of analysis simulate random

genealogies going backwards in time under a neutral evolutionary framework

(Rosenberg and Nordberg, 2002). The IM program uses a MCMC method to

calculate posterior probabilities for each parameter under a Bayesian framework,

resulting in estimates of M (Migration), T (Divergence time) and θ (Population size;

θ = 2Ne μ: where Ne = effective population size and μ = the rate of neutral site

mutations per sequence per generation) (Nielsen and Wakeley, 2001). The model

assumes selective neutrality. A MCMC run consisting of ~2 million steps of

genealogies after discarding the first 100,000 genealogies (burn-in) were carried out


61

for 21 paired combinations of seven sites for each species. To estimate above

parameters, a mutation rate for the mtDNA ATP region of 0.7 X 10-6 years was used

and a generation time in years for YFT = 3.0 and for SJT = 1.5, respectively. IM

analysis was carried out using the high performance computer facility at

Queensland University of Technology, Brisbane, Australia.

Population Structure of Yellowfin Tuna

62

CHAPTER 3

POPULATION STRUCTURE OF YELLOWFIN TUNA

3.1 Ecology, biology and life history

Yellowfin tuna (YFT) belong to the family Scombridae (order Perciformes,

suborder Scombroidei) which comprises the mackerels, bonitos, spanish mackerels

and tunas. Tunas that make up the tribe Thunnini are pelagic, fast swimming,

relatively large predatory fishes. YFT occur in all tropical and sub-tropical oceans

around the world. In the Atlantic Ocean YFT grow rapidly and attain sexual

maturity at the age of three years when they can reach a length of about one metre

[Inter-American Tropical Tuna Commission (IATTC), 1991]. Life span ranges up

to about eight years (IATTC, 1991; Sakagawa et al., 1992) and YFT can grow to

very large body size and weight, so that a mature YFT can reach approximately

300kg in weight and over two metres in length (Plate 3.1).

Plate 3.1 Yellowfin tuna

They are fast swimmers and are capable of undertaking trans-oceanic migrations

(ICCAT, 1993). Like most other tuna species, YFT also show specific schooling

behaviour for feeding, spawning and as free swimming schools. They also undergo


63

daily vertical migrations ~ 400m below the sea, and seasonal migration patterns to

feeding and spawning grounds (Gubanov and Paramonov, 1993; Schaefer and

Fuller, 2006).

As described previously, due to the peculiar oceanographic characteristics and

monsoonal climate of the Indian Ocean, unique biological characteristics are

evident in many fish species there. In fact, schooling behaviour, formation of fish

aggregations, and vertical migration based on changes in depth of thermoclines are

commonly reported phenomena in Indian Ocean pelagic fish species, and this is true

for YFT there as well.

YFT can spawn throughout the year in the tropical zone of the world’s oceans, in

warm waters (Nishikawa et al., 1985), although there may be spawning peaks. In

the Indian Ocean, YFT spawn throughout the year with spawning peaks coinciding

with the two monsoon seasons; southwest monsoon from May to September and

northeast monsoon from November to March. Evidence exists for peak spawning

activity close to islands and archipelagos with elevated primary production (e.g.

around Mexico in the Pacific Ocean, Medina et al., 2006). There are a considerable

number of small scale studies that have been undertaken on scombrid fish spawning

grounds and larvae in the Indian Ocean, indicating that scombrid species spawn in a

number of areas throughout the tropical Indian Ocean. Another study on the

reproductive biology of Indian Ocean YFT by Stequert et al. (1996), reported that

while YFT can spawn successfully throughout the year, the major spawning period

occurs between November and March. George (1990) assessed the distribution and

abundance of scombrid fish eggs and larvae along the southwest coast of India.


64

Highest spawning activity was observed during the southwest monsoon season

(May-August), and in the areas southward of 120N extending from February to

November. Larvae drift southward with prevailing currents (George, 1990) and

concentrations have been observed south of Calicut to the east of Cape Comorin,

and off Ratnagiri along the southwest coast of India. While larvae were present in

all months of the year, they were more abundant during the March-August period.

Juveniles and pre-adults of YFT have also been found to occur in the drift gill net

catches at Cochin, India (James and Jayaprakash, 1990) indicating the presence of

YFT spawning grounds on the south-western coast of India. James and Jayaprakash

(1990) have reported the occurrence of juveniles and pre-adults of YFT in drift-gill

net catches around Cochin (south west coast) and Tutucorin (south coast) and

Madras (east coast) of India. Another study reported the distribution of tuna larvae

(15% of YFT) observed between Madagascar and the equator by 433 plankton tows

(Conand and Richards, 1982), and YFT larvae were abundant in this region during

the summer. Boonragsa (1987) have observed tuna spawning grounds around Thai

waters in the Andaman Sea. Studies of Indian Ocean YFT reproductive biology

have shown, that the minimum length at maturity for female YFT was 52cm

(Timokhina, 1993) and the average batch fecundity was 1.57 million oocytes

(Schaefer, 1996). As a summary, YFT can spawn throughout the year, but

intensively during the two monsoon seasons which occur November to March and

May to September. Spawning grounds have identified around India along southwest

(south of Calicut to the east of Cape Comorin with smaller concentrations detected

near Ratnagiri), south and east coastal areas, and also in the western Indian Ocean

(west of 750E, Mozambique Channel) and eastern Indian Ocean (Pelabuhan Ratu in

western Java).


65

YFT in the major oceans can show a high degree of morphological variation

(Jordan and Evermann, 1926). According to a morphometric study of YFT by

Royce (1964), intra-oceanic is greater than inter-oceanic differentiation, with

morphometric data indicating a single worldwide pantropical species, an

observation supported by Gibbs and Collette (1967).

Several tagging studies have indicated that YFT usually migrate only hundreds

rather than thousands of kilometers (Joseph et al., 1964; Bayliff, 1979; Hunter et

al., 1986; Lewis, 1992). A number of recent electronic tagging studies of YFT in

the Pacific and the Atlantic Oceans have supported this conclusion (e.g. Schaefer et

al., 2006) but some trans-Atlantic YFT tag recoveries have been reported. In the

Indian Ocean, the Japan Marine Fishery Research Center (JAMARC) conducted a

tuna tagging programme from 1980 to 1990 that covered a large geographical area.

Most tag recoveries consisted of short distance movements, with only two cases of

long distance movements by YFT individuals from the central Indian Ocean to the

western region and only three more tag recoveries from the Seychelles to the

Maldives Islands found despite substantial efforts (Yano, 1991). According to

another study, in 1990, 1889 YFT were tagged in the Maldives (Yesaki and

Waheed, 1992), and to the end of February 1992, only 128 YFT were recovered

(6.7%). Of these 128 individuals, 86% were recovered within the Maldives

indicating that YFT stay in their natal waters. Tag recoveries in Sri Lanka and to a

lesser extent in the western Indian Ocean from tuna tagged in the Maldives however

suggests that at least a small percentage of YFT tend to move with the prevailing

ocean currents. It is important to keep in mind however, that tuna tagging returns


66

depend on a number of factors including fishing pressure around the tagging area,

and whether tuna were tagged in a fishing season or not.

In the Indian Ocean, YFT stock delineation studies are very limited, and in general

these studies have been based on YFT morphometry or fishery data. There have

been several local (Kurogane, 1960; BOBP, 1988; Cayre and Ramcharrun, 1990)

and more global studies (Kurogane and Hiyama, 1958; Morita and Koto, 1970;

Yano, 1991; Nishida, 1984) on Indian Ocean YFT that have attempted to delineate

YFT stock structure based on morphological characters and fisheries approaches.

Kurogane and Hiyama (1958) analysed morphometric data of YFT collections from

six different locations in the Indian Ocean and on the basis of these data, recognized

three YFT stocks namely; western Indian and two eastern Indian (Andaman Sea in

central-eastern Indian Ocean and Lesser Sunda area of the far–eastern Indian

Ocean). Morita and Koto (1970) concluded that two Indian YFT stocks exist;

eastern and western separated at the approximate boundary of 1000E longitude

which lies east of the Andaman and Nicobar Islands, after analyzing 1961 to 1965

Japanese long line fishery data.

Nishida (1994) studied YFT stock structure in the Indian Ocean using industrial

long line fishery data. Patterns of time-series trends of catch per unit effort (CPUE)

and body size were compared graphically and statistically to classify homogeneous

sub-area groups. On the basis of this evidence, two major and two minor stocks of

YFT were identified in the Indian Ocean. The two major stocks (“western” and

“eastern”) were limited by 400-900E and 700-1300E respectively. Minor stocks were

also recognized in the far western and the far eastern areas (the latter possibly being


67

part of the Pacific stock) which are located westward of 400E and eastward of

1100E, respectively. According to Nishida (1994) the two major YFT stocks in the

Indian Ocean mix in Sri Lankan waters, this being the boundary area of the two

stocks.

3.2 Yellow fin tuna genetic stock structure studies

As YFT are a major international commodity and comprise the largest tuna fishery

of principal market tunas in the world, a number of genetic studies have been

conducted to establish YFT stock structure. The history of YFT stock delineation

studies extends back to 1962 where Suzuki (1962) examined frequencies of the Tg2

blood group antigen in YFT samples from the equatorial Pacific and Indian Oceans.

No differences were observed however, among samples. Later Fujino (1970)

reviewed allozyme studies of Esterase and Transferrin variation in YFT populations

and concluded that no differences in gene frequencies were apparent among

samples taken from around Hawaii and the Eastern Pacific. Similar results were

reported by Barret and Tsuyuki (1967) and Fujino and Kang (1968a, 1968b).

Sharp (1978) could find no significant differentiation among eastern and western

Pacific Ocean YFT populations for Transferrins, but reported significant variation

for a Phosphoglucose Isomerase polymorphism (GPI-1). According to another

allozyme study that examined four polymorphic loci (ADA*- Adenosine

Deaminase; FH*-Fumarate Hydratase; GPI-1*, GPI-2*) for relatively large YFT

samples taken from the western, central and eastern Pacific Ocean, only GPI-1*

showed spatial heterogeneity allowing recognition of eastern samples from the rest

(Ward et al. 1994a). Elliot, et al. (unpublished data) could find no differentiation


68

among Atlantic and Pacific YFT samples after examining variation at three

allozyme loci (ADA*, FH*, GPI-2*). As a summary therefore, allozyme studies of

YFT have in general, not detected any significant differentiation among inter-

oceanic stocks although some studies have suggested that different YFT stocks may

exist in the eastern and western Pacific Ocean. Allozyme genetic markers may not

be sufficiently sensitive in YFT however, to detect underlying genetic

differentiation that may exist among sites due to low variability and the limited

number of protein coding loci that can be assessed (Ward, 2000b). These sampling

effects might reduce the power of the test hence causing type I and II errors on

conclusions of the study.

A few studies have examined YFT stock structure using mtDNA markers. Scoles

and Graves (1993) described extensive mtDNA haplotype diversity among five

Pacific Ocean samples and one Atlantic Ocean sample, using an RFLP approach.

They employed 12 restriction enzymes although no significant intra or inter-oceanic

differentiation was observed. Ward et al. (1994a) came to a similar conclusion after

screening more than 500 fish for whole mtDNA genome variation from the Pacific

and Atlantic Oceans using an RFLP approach. Later, Ward et al. (1997) proposed

the existence of at least four YFT stocks in the three major oceans; Atlantic Ocean,

Indian Ocean, west-central Pacific Ocean and east Pacific Ocean following a study

that combined allozyme and mtDNA markers. Although the level of mtDNA

differentiation was more limited than allozyme variation among stocks, spatial

heterogeneity was observed for mtDNA haplotypes over the nine regions (p =

0.048) and three oceans (p = 0.009). An RFLP approach may not be sufficiently

sensitive however, to detect variation that may exist in the mtDNA genome when


69

compared with much more sensitive mtDNA sequencing. RFLP’s only provide

information on differences between mtDNA fragments at sites cut by restriction

enzymes while mtDNA sequencing provides information on DNA sequence of the

complete fragment.

While microsatellite studies on YFT are limited, Appleyard et al. (2001) examined

around 1400 YFT from eight regions in the western Pacific Ocean at five

polymorphic microsatellite loci and identified very low, but significant

differentiation (FST = 0.002, p < 0.01) among samples. In previous tuna studies, the

microsatellite markers used were mostly di-nucleotide repeats, which can frequently

cause sub bands or stuttering making scoring difficult and likely to cause errors,

especially if the difference between two alleles is only couple of base pairs. In

addition, tunas usually show high microsatellite allelic diversity as population sizes

are large, so tri- and tetra-nucleotide markers which minimize stuttering and scoring

problems, may provide more reliable markers of neutral nuclear variation. In the

current study therefore tri- and tetra- microsatellite markers were developed and

screened.

All of the above genetic stock structure studies of YFT have been reviewed by

Ward (2000a). All FST values among sites were very low with only a few reaching

significance. The highest FST value (0.012) among samples for mtDNA accounted

for within ocean differentiation while there was no detectable differentiation among

oceans. The general conclusions from the review was that for YFT, substantial gene

flow was evident between the Atlantic and Indo–Pacific Oceans, or the sampling


70

strategy and the genetic markers employed were not able to detect low intra-oceanic

differentiation, if it was present (Ward 2000a).

To date genetic stock structure studies of YFT have largely focused on Pacific

Ocean samples and, to a lesser extent on the Atlantic Ocean. The Indian Ocean

populations are yet to be examined to any significant extent. In this study therefore,

genetic stock structure in YFT populations around Sri Lanka and the Maldives

Islands in the Indian Ocean was assessed to determine if populations constitute a

single panmictic population or if multiple stocks exist. The levels of genetic

diversity and gene flow among YFT populations will also be used to infer dispersal

patterns of YFT around Sri Lanka and the Maldive Islands in the Indian Ocean.

3.3 Methodology

(i) Mitochondrial DNA variation

The ATPase6 and 8 regions of the mtDNA genome was selected for analysis of

YFT samples and the following internal primers were developed yielding a 540

base pair product to examine levels of variation appropriate to address the specific

aims of study.



Haplotype diversity was screened in sample populations using TGGE and

sequencing of all unique haplotypes determined from TGGE (details of PCR

conditions and TGGE in section 2.2.2 and Appendix 2).


71

(ii) Nuclear DNA variation

Twelve tri- and tetra-nucleotide microsatellite markers were developed and

optimised for YFT. Nuclear DNA variation of YFT samples was screened initially

using five microsatellite loci; UTD196, UTD125, UTD402, UTD494 and UTD499

developed for the purpose. Due to amplification problems, and/or null alleles, loci

UTD125 and UTD196 were excluded from the analysis. Details of microsatellites,

primers and PCR conditions are summarised in Table 3.11.

3.4 Results

(i) Mitochondrial DNA variation in YFT

Genetic variation

Genetic analyses were conducted on 285 individuals, from six fishing grounds (KK,

NE, WE, TA, KR and TR) around Sri Lanka and a single site from the Maldive

Islands (MD) (Figure 3.1 and Table 3.1). MtDNA haplotype sequence data

produced alignment of a 498 bp fragment which included partial ATP8 and ATP6

gene regions. A total of 21 nucleotide sites were variable (segregating sites).

Polymorphic sites defined a total of 19 unique haplotypes (Table 3.2).


72

Figure 3.1 Sampling sites of YFT in the Indian Ocean. Redrawn from the National Geographic web site map; www.nationalgeographic.com

Table 3.1 Collection data for YFT

Overall haplotype diversity (Hd) was high (0.613) for this mtDNA region when

compared with reports from other tuna studies. Individual geographic collection

haplotype diversity was also high. Nine haplotypes were singletons, and the most

Population Location Date

Avg. length (cm) n

Total collection 285 Kandakuliya (KK) 79012`,80 20` April-02 138 51 Negombo (NE) 79018`,60 057` June-01 67 6 Aug, Oct-03 28 Weligama (WE) 80018`,50 034` March-01 55 3 Sept-02 15 Nov-03 19 Tangalle (TA) 81014`,50 042` April-02 60 13 Nov-03 17 Kirinda (KR) 820 23`,60 017` June-01 90 52 Trincomalee (TR) 81051`,80 058` Sept-04 70 39 Maldives (MD) 73009`,400 20` Nov-03 60 42

Maldive

KK

NE

WE

MD

TA

KR

TR

Maldive

KK

NE

WE

MD

TA

KR

TR


73

abundant haplotype (Ht2) and the second most abundant haplotype (Ht6) occurred

at all seven sampled sites (Table 3.3). Overall nucleotide diversity, and the average

number of pair-wise nucleotide differences were 0.002 and, 0.839 respectively.

Genetic diversity considerably different among sites at a fine geographic scale.

Population genetics summary statistics are presented in Table 3.4.

Table 3.2 Variable nucleotide sites of mtDNA ATPase region of YFT

Phylogenetic relationships

The NJ phylogeny for YFT is shown in Figure 3.2. All haplotypes are closely

related and essentially formed a single clade with low bootstrap values.

The parsimony cladogram (Figure 3.3) shows that all haplotypes are closely related

to Ht2 which is the most common ancestral haplotype (occurs in centre of network).

11222233 3333334444 4 0239244403 4558990247 8 9757803606 2477092658 3 Ht1 AATTAGCCTT GCGTTCGATG G Ht2 .......... .....T..G. C Ht3 ..C....... .....T..G. C Ht4 .......... .....TA.G. C Ht5 ?......T.. .....T..G. C Ht6 .......... .T...T..G. C Ht7 .......... A..C.T..G. C Ht8 G......... ...C.T..G. C Ht9 ....G..... .....T..G. C Ht10 ....G..... .....T.GG. C Ht11 ....G..... .....T..GA C Ht12 ?...G..... A....T..G. C Ht13 ?G.C.....C .....T..G. C Ht14 ?G........ .....T..G. C Ht15 ?....A.... A..C.T..G. C Ht16 .......... ..A..T..G. C Ht17 ?......... ....CT..G. C Ht18 ........C. .....T..G. C Ht19 ......T... .....?..G. C


74

Pairwise percentage divergence among haplotypes in the parsimony cladogram

ranged from 0 to 2.0%.

Table 3.3 Haplotype frequency distribution among sampling sites of YFT

Population structure

The pattern of YFT haplotype diversity among sites (Table 3.3 and Figure 3.4)

shows that Ht2 was at highest frequency at all sites except for site KK where Ht14

was most frequent (41.17%). This haplotype (Ht14) was present at only KK and the

adjacent site NE. The south-eastern site KR also showed another haplotype (Ht3) at

relatively high frequency (32.69%) and this haplotype was present only at this site,

so constituting a private haplotype.

Site Haplotype KK NE WE TA KR TR MD

Total

Haplotype frequency (%)

Ht1 1 0 0 0 0 0 0 1 0.35 Ht2 16 21 26 26 21 32 29 171 60.00 Ht3 0 0 0 0 17 0 0 17 5.96 Ht4 0 0 0 0 2 0 0 2 0.70 Ht5 1 0 0 0 2 0 1 4 1.40 Ht6 6 5 7 5 7 1 3 34 11.93 Ht7 0 3 0 0 0 0 0 3 1.05 Ht8 0 0 0 0 1 0 0 1 0.35 Ht9 5 0 1 0 1 5 0 12 4.21 Ht10 0 0 0 0 1 0 0 1 0.35 Ht11 0 0 2 0 0 0 0 2 0.70 Ht12 0 0 0 0 0 0 1 1 0.35 Ht13 0 0 1 0 0 0 0 1 0.35 Ht14 21 4 0 0 0 0 0 25 8.77 Ht15 0 0 0 0 0 1 0 1 0.35 Ht16 0 0 0 0 0 0 6 6 2.11 Ht17 0 0 0 0 0 0 1 1 0.35 Ht18 0 0 0 0 0 0 1 1 0.35 Ht19 1 0 0 0 0 0 0 1 0.35 No.of Samples 51 33 37 31 52 39 42 285


75

Table 3.4 Descriptive statistics for YFT samples No. of haplotypes (h), No. of polymorphic sites (S), Gene diversity (Hd), mean pair-wise nucleotide difference (k), Nucleotide diversity (π), Expected heterozygosity per site based on number of segregating sites (θs)

KKNEWETAKRTRMD

Ht10

Ht11

Ht9

Ht12

Ht8

Ht7

Ht15

Ht3

Ht4

Ht17

Ht19

Ht1

Ht5

Ht16

Ht18

Ht6

Ht2

Ht13

Ht14

69

65

48

2948

43

29

13

11

3

12

0.001 Figure 3.2 Unrooted neighbour joining tree of YFT haplotypes based on Tamura and Nei genetic distances. Colours indicate at which sampling sites particular haplotypes were found.

Population n h S Hd k (π) (θs) Total collection 285 19 21 0.613 0.839 0.002 3.211 Kandakuliya (KK) 51 7 8 0.722 1.094 0.002 1.778 Negombo (NE) 6 3 2 0.400 0.804 0.002 0.960 28 4 4 0.595 0.838 0.002 1.028 Weligama (WE) 3 1 - - - - - 15 3 4 0.514 0.819 0.002 1.230 19 4 3 0.521 0.766 0.002 0.858 Tangalle (TA) 13 2 1 0.462 0.463 0.001 0.322 17 2 1 0.117 0.118 0.000 0.296 Kirinda (KR) 52 8 7 0.721 0.989 0.002 1.770 Trincomalee (TR) 39 4 5 0.317 0.434 0.001 1.183 Maldives (MD) 42 7 7 0.507 0.624 0.001 1.627


76

Figure 3.3 Parsimony Cladogram of YFT haplotypes showing the evolutionary

relationship among haplotypes. Each circle represents a unique haplotype in the

sample, and the size of each circle represents the relative frequency of each

haplotype. Colours and their percentage in each circle represent the presence of

each haplotype at different sites and their relative abundance. Cross bars between

circles represent the haplotypes that were not found in the sample.

The presence of a single haplotype (Ht2) at high frequency at the majority of sites

indicates that overall genetic differentiation among YFT populations around Sri

Lanka is likely to be low. Also the pair wise divergence of the parsimony

cladogram was low (0 – 2%).


77

Figure 3.4 MtDNA haplotype frequency distribution of YFT at sampling sites Although examination of the entire data set shows that a single, common haplotype

to be in highest abundance at nearly all sites, because samples were collected over

several years (year classes/cohorts), haplotype frequency distributions at each site

were decomposed into year-wise distributions to test for temporal effects. NE, WE

and TA sites have temporal collections: NE’01, NE’02, NE’03; WE’01, WE’02,

WE’03; TA’01, TA’02, TA’03. Even among years at sites NE, WE and TA the

most common haplotype (Ht2) constant remained in high frequency.


78

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

Structure tested Observed partition Ф statistics

Variance

% Total

1 Total collection (2001,2002,2003,2004) Among sites (Global ФST) 0.06256 12.85 ФST = 0.1285*** Within sites 0.42415 87.15

2 Among years Among year-wise groups (TEMPORAL) 0.02307 4.72 ФCT = 0.0472 Among sites within years (SPATIAL) 0.03686 7.54 ФSC = 0.0791*** Within sites 0.42917 87.75 ФST = 0.1225

3 Among sites Among site-wise groups (SPATIAL) 0.05072 10.43 ФCT = 0.1043* Among years within sites (TEMPORAL) 0.00622 1.28 ФSC = 0.1428

Within sites 0.42917 88.29 ФST = 0.1171 ***p<0.001, ** p<0.01, * p<0.05

Hierarchical analysis of molecular variance using Tamura and Nei corrected

distance (AMOVA) is summarised in Table 3.5. Across the total sample collection,

there was significant genetic variation among sites (Global ΦST = 0.1285, p<0.001)

indicating that a significant genetic differentiation was present among at least two

sites. Hierarchical AMOVA of year-wise groups (i.e. sample collections of different

sites in 2001) constituted a single group. In the same way, 2002, 2003, and 2004

formed separate groups and did not show any significant genetic differentiation

among years (ФCT = 0.0472, p>0.05). This means the overall genetic composition of

YFT populations around Sri Lanka is relatively stable (irrespective of sites) over the

sampling time. There was significant spatial genetic differentiation among sites

however, within years (ФSC = 0.0791, p<0.001). Among sites, hierarchical AMOVA

also showed significant spatial genetic differentiation among sites irrespective of

time period (ФCT = 0.1043, p<0.05) meaning that some spatial structuring of YFT

were evident around Sri Lanka. There was no significant variation among temporal


79

collections within sites (ФSC = 0.1428, p>0.05). As there was no temporal variation

within sites among years, temporal collections per site across years (NE, WE and

TA) were pooled for further analyses.

Spatial structure in YFT was supported by the nearest-neighbour statistic Snn

(Hudson, 2000) conducted in DnaSP, which was significant (Snn = 0.287, P<0.001).

The Snn value indicates that the presence of population structure as a significance

association between ATP region sequence similarity and geographical location.

Table 3.6 MtDNA pair-wise ΦST among sampling sites of YFT for entire collection, after Bonferroni correction. Initial α = 0.05/21 = 0.002

KK WE TA KR TR MD NE KK 0.000 WE 0.129*** 0.000 TA 0.160*** -0. 07 0.000 KR 0.199*** 0.100*** 0.108** 0.000 TR 0.174*** 0.029 0.061 0.117*** 0.000 MD 0.179*** 0.045 0.033 0.119*** 0.044 0.000 NE 0.084 0.028 0.019 0.113*** 0.062 0.055 0.000

(** p<0.01, *** p<0.001)

Pairwise population analyses conducted for the entire mtDNA data set using

Tamura and Nei genetic distances, overall show that genetic differentiation was

limited to differentiation between KK and all other sites and between KR and all

other sites (Table 3.6).


80

Table 3.7 mtDNA pair-wise ΦST among year-wise collections of YFT (after Bonferroni correction α = 0.05/3 = 0.0166 for 2002 collection, ** p<0.01, *** p<0.001, and α = 0.05/6 = 0.008 for 2003 collection, *** p<0.001)

(a) 2002 collection KK WE TA KK 0.000 WE 0.092** 0.000 TA 0.139*** -0.048 0.000 (b) 2003 collection NE WE TA MD NE 0.000 WE 0.059 0.000 TA 0.021 0.046 0.000 MD 0.061*** 0.064 0.000 0.000

Of 21 site pair comparisons, 16 were significantly differentiated (p<0.02). After

Bonferroni correction however, only 10 pairs of sites were significantly

differentiated, with the KK and KR sites highly differentiated from almost all other

sites. No significant genetic differentiation was evident however, between most

population pairs except when comparisons involved the KK and KR sites.

Genetic differentiation for the entire mtDNA data set was tested at a further

resolution level, as for year wise collections (i.e. 2001, 2002, and 2003) while the

2004 collection consisted of one site only. Significant genetic differentiation was

evident with AMOVA for the 2002 and 2003 collections only (data not shown). As

samples from the KR site were collected in 2001, and because the KR site was

differentiated from all other sites, a significant differentiation was expected in the

2001 collection. In the 2001 collection, however, the WE collection consisted of

only a single haplotype (Ht2) (Table 3.4), which probably produced a non-

significant ΦST. Pair-wise comparisons of genetic variation therefore for

significantly different year-wise collections (2002 and 2003) were performed to

determine between which pairs of sites, genetic differentiation was present (Table


81

3.7). In the 2002 collection, the KK site was significantly different from two sites;

WE and TA (after Bonferroni correction). In the 2003 collection, only one pair was

significantly different after Bonferroni correction.

For SAMOVA, temporal collections at each site were pooled as no significant

genetic differentiation was detected among temporal collections within individual

sites. As the best grouping, SAMOVA (Table 3.8) indicated three genetically

different YFT population groups. Specifically sites; KK, and KR and all remaining

sites (NE, WE, TA, TR, MD) (FCT = 0.1458, p<0.05).

Table 3.8 Population structure based on mtDNA differentiation of YFT (in SAMOVA). The row in bold type indicates the details of geographically meaningful groups with maximum genetic differentiation.

No. of Groups Structure

Variation among groups

Variation % FCT p

2 (KK) (NE,WE,TA,KR,TR,MD) 0.0609 13.15 0.1315 0.147 3 (KK) (KR) (NE,WE,TA,TR,MD) 0.0654 14.59 0.1458 0.048 4 (KK) (KR) (TR) (NE,WE,TA,MD) 0.0532 12.24 0.1223 0.0762 5 (KK) (KR) (TR) (MD)(NE,WE,TA) 0.052 12.14 0.1214 0.0342 6 (KK) (KR) (TR) (MD) (NE) (WE,TA) 0.0562 13.2 0.132 0.043

AMOVA analyses of mtDNA, show that there was significant spatial genetic

differentiation among sampled YFT populations in all years around Sri Lanka

except in 2001, and population pair wise analyses show that when significant results

were present, they occurred between specific pairs of population samples only.

Thus in general, there appears to be substantial gene flow among most YFT sample

sites in Sri Lankan waters, but at the same time certain sites (i.e. KK and KR) show

consistent divergence from the main ‘population pool’.


82

The pattern of population genetic differentiation revealed by the pair wise ΦST

analysis for the entire mtDNA data set were then subjected to concordance with

geographical variation. Mantel’s test was undertaken for the seven YFT sample

sites. The pattern of genetic variation in the YFT samples did not show a

significant correlation with geographical location as correlation coefficient was not

significant (0.10878, p = 0.377; regression coefficient = 0.000016). This result

implies that genetic differentiation and hence any apparent stock structure was not

influenced by distance.

Population history and demographic patterns

Descriptive statistics for YFT population samples were presented earlier (Table

3.4). Statistical tests of neutrality and demographic parameter estimates for each

sample population are presented in Table 3.9. Here the Fu’s FS for the entire sample

collection showed a significant large negative value (FS = -15.804, p<0.001)

indicating that the population is under selection or has undergone an expansion.

Table 3.9 Statistical tests of neutrality and demographic parameter estimates for YFT. Figures within parenthesis are p values. Population Tau Hri index Tajima’s D Fu’s FS θ0 θ1 R2 Total collection

0.916 0.108 (0.000)

-1.914 (0.013)

-15.804 (0.000)

0.000 2199.4 0.108 (0.016)

KK 1.201 0.111 (0.080)

-1.065 (0.149)

-1.685 (0.182)

0.000 1990.0 0.123 (0.146)

NE 0.900 0.060 (0.850)

-0.4061 (0.363)

0.026 (0.477)

0.000 8.976 0.138 (0.367)

WE 0.851 0.097 (0.829)

-1.325 (0.092)

-1.2252 (0.208)

0.000 1.810 0.149 (0.160)

TA 0.368 0.2725 (0.340)

0.180 (-0.419)

0.6366 (0.430)

0.000 8.999 0.149 (0.936)

KR 1.174 0.124 (0.030)

-1.1382 (0.131)

-2.835 (0.065)

0.000 1952.5 0.119 (0.137)

TR 3.00 0.246 (0.660)

-1.619 (0.044)

-1.329 (0.181)

0.462 0.463 0.132 (0.474)

MD 0.714 0.113 (0.370)

-1.688 (0.036)

-4.011 (0.002)

0.000 461.72 0.132 (0.078)


83

Harpending’s raggedness index (Hri), for the total sample collection was very low

but significant (Hri = 0.108, p<0.001). The large difference between θ0 and θ1 for

the entire data set shows that the YFT population was under expansion. When

sampling sites were considered separately, in general, Fu’s Fs, Tajima’s D and Hri

did not support population expansion specifically, although according to θ0 and θ1

values, the sample populations KK, KR and MD were under expansion. The R2

statistic was significant for the total sample population (R2 = 0.023, p = 0.016),

strongly supporting a sudden population expansion. Further, the mismatch

distribution for the entire data set was unimodal (Figure 3.5) which also indicates

that the population was expanding. According to the shape of the mismatch

distribution, it is possible that YFT population has undergone a population

expansion, after a population bottleneck.

0

0.1

0.2

0.3

0.4

0.5

0.6

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16No of Differences

Freq

uenc

y

Observed freq. (constant population size)

Expected freq.(constant population size)

Expected freq.(growth decline model)

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

According to the Tau value (τ = 0.916), YFT population expansion happened

relatively recently in the past. The divergence rate for the ATP region in fish is


84

around 1.3% per million years (Bermingham et al., 1997), and YFT mature when

they reach three years of age (IATTC, 1991), hence the estimated time for

population expansion is 0.211X 106 years ago.

This recent sudden population expansion should have a big impact on YFT mtDNA

differentiation contrasting with the genetic differentiation in the nDNA genome,

due to characteristics of both genomes, which will be discussed later.

(ii) Microsatellite variation in YFT

Genetic variability, Hardy-Weinberg and linkage equilibrium

No null alleles, large allele drop outs or error scoring were detected (at 95%

confidence interval) for the YFT microsatellite data set after first excluding loci

UTD125 and UTD196 due to amplification problems, and/or null alleles, at these

two loci. Descriptive statistics for the three remaining microsatellite loci are

summarised in Table 3.10. Some individuals could not be scored due to

amplification problems. The number of individuals amplified for the three loci for

all sites however were generally high (n = 26 to 54) except at WE site (n = 8) for

locus UTD499. Number of microsatellite alleles ranged from 6 (at locus UTD402 at

NE) to 22 (at locus UTD494 at NE).

Sample populations were then tested for Hardy-Weinberg equilibrium. A significant

heterozygote deficiency (p<0.001) was observed at locus UTD402 and locus

UTD494 in NE; and at locus UTD402 in KR and TR sites (p<0.001 and <0.05

respectively). Except for these deviations, all sites conformed to Hardy-Weinberg


85

Table 3.10 Descriptive statistics for 3 microsatellite loci among YFT collections. No. of samples (n), No. of alleles (a), Expected heterozygosity (He), Observed heterozygosity (Ho), Probability values of concordance with Hardy-Weinberg expectations (HW)(p). Values in bold type are significant probability estimates after Bonferroni correction for multiple tests (initial α = 0.05/21 = 0.0023).

Locus Sample UTD402 UTD494 UTD499

Average across loci

KK n 54 54 53 54 a 16 20 17 17.667 He 0.708 0.927 0.918 0.851 Ho 0.722 0.981 0.925 0.876 HW (p) 0.383 0.580 0.433

NE n 26 45 47 39 a 6 22 17 15.000 He 0.666 0.916 0.881 0.821 Ho 0.280 0.773 0.870 0.641 HW (p) 0.000 0.000 0.711

WE n 33 44 8 28 a 10 20 8 12.667 He 0.875 0.934 0.879 0.896 Ho 1.000 1.000 1.000 1.000 HW (p) 0.932 1.000 1.000

TA n 42 40 41 41 a 10 18 19 15.667 He 0.542 0.930 0.912 0.794 Ho 0.571 0.875 0.927 0.791 HW (p) 0.579 0.053 0.192

KR n 44 49 24 39 a 9 20 14 14.333 He 0.749 0.941 0.924 0.871 Ho 0.667 1.000 0.900 0.856 HW (p) 0.000 0.152 0.219

TR n 35 45 25 35 a 7 19 14 13.333 He 0.599 0.915 0.897 0.804 Ho 0.536 0.857 0.960 0.784 HW (p) 0.024 0.549 0.793

MD n 42 42 43 42 a 10 20 14 14.667 He 0.585 0.938 0.911 0.811 Ho 0.548 0.929 0.977 0.818 HW (p) 0.785 0.716 0.700


86

equilibrium expectations. After Bonferroni correction for multiple tests of Hardy-

Weinberg equilibrium, of those sites that deviated significantly from Hardy-

Weinberg equilibrium, only a single locus (i.e. UTD402 at the TR site) was not

significant. Tests of Hardy-Weinberg proportions show that observed genotype

numbers within collections agree very closely with numbers expected of a randomly

mating population.

Linkage disequilibrium was detected for the KK, NE and WE sites only. In KK

2002, NE 2001 and WE 2002, locus UTD402 and UTD494 showed linkage

disequilibrium (p<0.05), while locus UTD 494 and UTD 499 were linked in the NE

2001 and NE 2003 collections (p<0.001). After Bonferroni corrections however,

only UTD494 and UTD499 in the NE 2003 collection showed significant linkage.

Allele frequency distributions for the three loci are presented in Figure 3.6 and

Tables 3.12 to 3.14. Of the three YFT microsatellite loci screened, locus UTD402

possessed a single allele (allele 5) at high frequency in all sites (51.85% to 66.67%).

Allele 5 and a second allele were most frequent at all sites (allele 6; ranges 15.91%

- 28.85%), and together represented 68.52% (KK) to 84.53% (TA) of all alleles

present at this locus (Figure 3.6 and Table 3.12). Number of alleles at this locus

ranged from 6 (at NE site) to 16 (at KK site). UTD494 and UTD499 generally show

high allelic diversity when compared with allelic variation at UTD402. A large

number of alleles, all at relatively low frequencies in all populations, were evident

at these two loci. Genetic diversity differences among populations however, were

much lower for microsatellite loci than for mtDNA.


87

Table 3.11 Characteristics of microsatellite loci developed for YFT. T a 0C Annealing temperature

Locus Repeat motiff primer sequence (5'-3')

Expected product

size Ta 0C

UTD125 (TCC)8 F GGA GGC TTG GCT TGT TTG TTG C 151 56 R CC AGG GAA CGA TAG CTA TGC AG UTD196 (CTAT)17 F CTA GAG GAT CAG ACG GCG ACG C 203 50 R TCA ATA GAT AGA CAG ACA AAT A UTD402 (CTAT)6 F TTT TGG TTG TGA ATT TGA ATG GC 160 51 R GAT CCA AAT ATA CAA CCT GCA CA UTD499 (TATC)7 F GCA GAA TCA CTG TAG CTG GTC A 174 52 R TGT TGA AGG ACA GAC TGC AAA UTD504 (ATCT)11 F CAT GGT GAA TAA TGC CAA CC 153 52 R GGC CTA GCT AGC AGA ATC GTT UTD503 (ATAG)4ACAG(ATAG)2ACAG(ATAG)14 F CGC GTA CGT CTA CTG TGC AT 218 54 R TGA CAG CCT CCC CAT CTA TC UTD497 (TAGA)11TTGA(TAGA)5 F GGC CTA GCT AGC AGA ATC GTT 214 53 R GAT ATG GCG GGT GTG AAT GT UTD502 (GATA)6 F GCT GAA AAT TTG CCT TTT GG 151 49 R GGA ATT CAC GAG CGA TTG TT UTD494 (ATAG)18(ATGG)3ACGG(ATAG)2ACAG(ATAG)2 F ACC CCT GCG TTG TTG TGT A 224 49 R TGA CGA TTT GGG GAT TTT GT UTD501 (ATCT)16 F ATT GTT TGG AAG CCC AAC TG 175 52 R GTT CTC TGA CGT GGG ACA CA UTD507 (CTAT)23 F CCT ACT GAT TAT TAC CAT GCA ACT G 278 53 R TGA GGT GAA AGA ATG GCT AGT G UTD506 (AGAT)14 F GGC CTA GCT AGC AGA ATC GTT 160 53 R ATA ATG CCA ACC CGT AGT GC


88

Table 3.12 Allele frequency distribution of YFT Locus UTD402

Allele KK KR MD NE TA TR WE Total Al-1 - - - 3.85 - - - 0.36 Al-2 - - - 3.85 - - - 0.36 Al-3 0.93 - - - - - - 0.18 Al-4 2.78 11.36 2.38 - 2.38 1.43 1.52 3.44 Al-5 51.85 59.09 63.10 53.85 66.67 64.29 54.55 59.06 Al-6 16.67 15.91 17.86 28.85 17.86 20.00 16.67 18.48 Al-7 7.41 7.95 - 1.92 3.57 1.43 1.52 3.80 Al-8 4.63 1.14 5.95 7.69 2.38 7.14 6.06 4.71 Al-9 3.70 1.14 2.38 - 1.19 - 7.58 2.36 Al-10 1.85 1.14 2.38 - - - - 0.91 Al-11 0.93 0.00 1.19 - - - - 0.36 Al-12 2.78 1.14 2.38 - - - - 1.09 Al-13 0.93 1.14 - - 1.19 2.86 - 0.91 Al-14 1.85 - - - - 2.86 - 0.72 Al-15 - - - - 2.38 - 3.03 0.72 Al-16 0.93 - - - 1.19 - - 0.36 Al-17 - - - - 1.19 - 3.03 0.54 Al-18 0.93 - 1.19 - - - 1.52 0.54 Al-19 0.93 - 1.19 - - - 4.55 0.91 Al-20 0.93 - - - - - - 0.18


Allele KK KR MD NE TA TR WE TOTAL Al-1 - - - 1.06 1.22 - - 0.41 Al-2 - - - 7.45 - 4.00 - 1.87 Al-3 1.89 2.08 - 5.32 1.22 2.00 - 2.07 Al-4 0.94 - - 6.38 3.66 - - 2.07 Al-5 1.89 - 2.33 1.06 1.22 - - 1.24 Al-6 16.98 2.08 5.81 6.38 2.44 4.00 - 7.05 Al-7 2.83 6.25 5.81 20.21 2.44 2.00 25.00 7.68 Al-8 9.43 6.25 9.30 3.19 3.66 6.00 - 6.22 Al-9 6.60 4.17 12.79 3.19 2.44 2.00 6.25 5.60 Al-10 11.32 12.50 9.30 1.06 4.88 14.00 18.75 8.51 Al-11 5.66 10.42 11.63 4.26 19.51 12.00 18.75 10.37 Al-12 9.43 12.50 15.12 15.96 10.98 8.00 12.50 12.24 Al-13 10.38 20.83 12.79 9.57 13.41 24.00 6.25 13.49 Al-14 10.38 6.25 4.65 5.32 7.32 8.00 6.25 7.05 Al-15 0.94 6.25 4.65 4.26 10.98 - - 4.36 Al-16 4.72 2.08 3.49 2.13 6.10 4.00 6.25 3.94 Al-17 3.77 - 1.16 3.19 3.66 8.00 - 3.11 Al-18 1.89 6.25 1.16 - 2.44 - - 1.66 Al-19 - - - - 1.22 2.00 - 0.41 Al-20 0.94 - - - - - - 0.21 Al-21 - - - - 1.22 - - 0.21 Al-22 - 2.08 - - - - - 0.21


89


Allele KK KR MD NE TA TR WE Total Al-1 - - - 1.11 - - - 0.16 Al-2 - - - 5.56 - - - 0.78 Al-3 - - - 2.22 - 2.22 2.27 0.94 Al-4 0.93 - - 6.67 1.25 1.11 1.14 1.57 Al-5 - - - 3.33 - 1.11 - 0.63 Al-6 - - - 2.22 - - 2.27 0.63 Al-7 0.93 - 2.38 4.44 1.25 - 1.14 1.41 Al-8 2.78 - 1.19 4.44 3.75 1.11 2.27 2.19 Al-9 1.85 6.12 3.57 8.89 6.25 1.11 6.82 4.86 Al-10 0.93 6.12 2.38 3.33 2.50 3.33 3.41 3.13 Al-11 6.48 5.10 4.76 3.33 5.00 3.33 2.27 4.39 Al-12 5.56 9.18 8.33 5.56 7.50 2.22 6.82 6.43 Al-13 12.96 7.14 8.33 1.11 3.75 4.44 4.55 6.27 Al-14 6.48 6.12 5.95 - 7.50 12.22 11.36 7.05 Al-15 6.48 7.14 13.10 4.44 7.50 16.67 9.09 9.09 Al-16 12.96 10.20 8.33 8.89 11.25 15.56 7.95 10.82 Al-17 6.48 13.27 7.14 7.78 16.25 6.67 7.95 9.25 Al-18 10.19 11.22 4.76 13.33 6.25 6.67 4.55 8.31 Al-19 9.26 5.10 7.14 5.56 2.50 8.89 9.09 6.90 Al-20 0.93 2.04 10.71 2.22 2.50 5.56 9.09 4.55 Al-21 7.41 1.02 3.57 3.33 8.75 3.33 5.68 4.70 Al-22 3.70 2.04 3.57 1.11 5.00 3.33 - 2.66 Al-23 1.85 3.06 - 1.11 - - - 0.94 Al-24 - 1.02 1.19 - - - - 0.31 Al-25 - - - - 1.25 1.11 - 0.31 Al-26 0.93 - - - - - - 0.16 Al-27 - - 1.19 - - - - 0.16 Al-28 - 1.02 - - - - 1.14 0.31 Al-29 - 1.02 - - - - - 0.16 Al-30 0.93 - - - - - 1.14 0.31 Al-31 - - 1.19 - - - - 0.16 Al-32 - - 1.19 - - - - 0.16 Al-33 - 1.02 - - - - - 0.16 Al-34 - 1.02 - - - - - 0.16


90

Figure 3.6 Microsatellite allele frequency distributions in YFT. Of allele frequency distributions at each site from left to right, Locus UTD402, Locus UTD 494, Locus UTD499.

A characteristic of variation at locus UTD494 in YFT was relatively high allelic

diversity ranging from 18 at site TA to 22 alleles at site NE with all alleles at low

frequencies (Figure 3.6 and Table 3.14). The highest frequency recorded for any

allele at a site was only 16.67% (allele 15 at site TR).

The third locus, UTD499 was also comparatively allele rich with alleles ranging

from 8 (site WE) to 19 (site TA) (Figure 3.6 and Table 3.13). While alleles 11, 12

and 13 were found at relatively high frequencies (10.37%, 12.24%, and 13.49%

respectively) other alleles at this locus were rare.


91


AMOVA analysis for YFT microsatellite data showed no significant genetic

variation for the entire data set (Global FST = −0.0633, p = 0.462) (Table 3.15).

Hierarchical AMOVA among years further showed no significant temporal

heterogeneity among years (FCT = 0.04129, p = 0.085), and no significant spatial

differentiation among sites within years (FSC = − 0.1288, p = 1.000). In this

hierarchical AMOVA, the 2004 collection was excluded as it consisted of only a

single site (TR). Significant genetic differentiation was not evident among temporal

collections for the NE, WE, TA and TR sites (FCT = -0.0445, p = 0.8543) or for

temporal collections within individual sites (FSC = -0.0788, p = 1.0000).

Table 3.15 Genetic structuring of YFT populations based on microsatellite data

Structure tested Observed partition F statistics Variance % Total p 1 Total collection (2001,2002,2003,2004) Among populations −0.0538 −6.34 FST = −0.0633 0.462 Within populations 0.90438 106.34 2 Year-wise collection (2001, 2002, 2003)

Among groups 0.0348 4.13 FCT = 0.04129 0.085 Among populations within groups −0.1043 −12.35 FSC = − 0.1288 1.000

Within populations 0.9142 108.23 FST = −0.0822 3 Temporal collections (NE, WE, TA,TR)

Among groups −0.0317 −4.46 FCT = −0.0445 0.854 Among populations within groups −0.0586 −8.24 FSC = −0.0788 1.000

Within populations 0.803 112.69 FST = −0.1269 To confirm that no significant genetic differentiation was evident among YFT

samples for microsatellite data, an Exact test of differentiation (for all three loci)

was carried out. This showed no significant genetic differentiation between any pair

of sites except between KR and NE. (Table 3.16)


92

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

KK NE WE TA KR TR MD KK - NE 0.1426 - WE 0.4915 0.0682 - TA 1.0000 0.1176 0.5640 - KR 0.2118 0.0261 0.2695 0.2401 - TR 0.5244 0.2030 1.0000 0.4520 0.3812 - MD 1.0000 0.0994 0.6094 1.0000 0.2241 0.4925 -

Taking into consideration the above population differentiation analyses, overall the

YFT microsatellite data do not support population structuring.

Effective population size, population divergence and migration

Effective number of gene migrants between pairs of YFT sites (number of

individuals per generation) resulting from combined mtDNA and nDNA data

analysed in IM are summarised in Table 3.17. Overall however, effective number of

gene migrants and gene flow do not show specific patterns. Estimated divergence

times among population pairs were low indicating a recent population split (not

shown). Historical population sizes (NA) and effective population sizes (N1 and N2)

were very large. NA ranged from 6252 (NE) to 357987 (MD) (Table 3.18).


93


sites for YFT based on mtDNA and microsatellite data. Columns 1 to 7 are

receiving sites while rows are donor sites. Below the diagonal m1 values and above

the diagonal m2 values (e.g. M value from TA to KK (m1) = 0.484 (below the

diagonal), and M value from KK to TA (m2) = 0.046 (above the diagonal). All M

values are within the 95% confidence limit and do not overlap 0.

1 2 3 4 5 6 7 KK NE WE TA KR TR MD 1 KK 0.116 0.030 0.046 0.358 0.040 0.433 2 NE 0.004 0.012 0.285 0.003 0.071 0.003 3 WE 0.007 0.224 0.492 0.011 0.433 2.168 4 TA 0.484 0.042 0.106 0.058 0.382 0.293 5 KR 0.404 0.104 0.009 0.470 0.093 0.392 6 TR 7.276 0.086 2.331 0.529 0.639 4.961 7 MD 1.195 0.052 2.019 0.076 0.050 1.119

Table 3.18 Effective population sizes (N1 and N2) between pairs of sites for YFT

based on mtDNA and microsatellite data. Columns 1-7 represent the estimated

effective population sizes (eps) of the respective column site (e.g. column 1

represents the eps of KK with respect to other six sites in rows 2-7).

1 2 3 4 5 6 7 KK NE WE TA KR TR MD 1 KK 30568 27075 46139 59395 12727 102884 2 NE 99624 53018 22851 73250 24869 73863 3 WE 136936 32456 23598 88793 26564 137306 4 TA 30752 6252 30955 66026 20174 74304 5 KR 97565 30435 75076 48371 23084 142984 6 TR 471528 35908 64401 110692 76164 357987 7 MD 75782 33273 111921 101478 65992 35638

Although the estimated effective population size values for some sites showed

broad distributions they are within the 95% confidence interval.


94

3.5 Discussion

Samples of YFT from seven fishing grounds around Sri Lanka and the Maldives

were examined with respect to variation in mtDNA and three polymorphic

microsatellite loci to test the null hypothesis that YFT populations were panmictic

around Sri Lanka.

MtDNA AMOVA and pair-wise ФST analysis shows that there is a significant

genetic differentiation among YFT sites and SAMOVA analysis suggest three

geographically meaningful YFT groups around Sri Lanka. YFT microsatellite

AMOVA analysis and Exact test of differentiation shows no genetic differentiation

among YFT samples. There is a contradiction therefore between the mtDNA data

and microsatellite data, and it is important to clarify these results and make a

conclusion for YFT management strategy.

Hierarchical AMOVA of mtDNA showed significant genetic differentiation among

some sampled populations. This genetic differentiation however, was not evident

between all pairs of sites as revealed by pair-wise ФST analysis. A careful appraisal

of the variation shows that overall genetic differentiation for mtDNA among YFT

samples mainly resulted from the presence of differences in haplotype distributions

at only two sites, KK and KR. Thus significant genetic differentiation was

essentially limited to between site comparisons that involved the KK or KR sites

with all others. All haplotypes belong to a single monophyletic clade indicating a

common evolutionary history for all sites. While differentiation of sites KK and KR

may be meaningful, it may also have resulted from unrepresentative sampling of

haplotypes as a result of the schooling behaviour of this species. Specifically, the


95

sampled individuals could have largely come from YFT schools that contained non-

random associations of haplotypes.

On the other hand, the genetic differentiation shown here for the mtDNA data may

reflect true divergence of the KK and KR sites from the main population pool. If

true, this pattern may result from a number of different factors; due to discrete

characteristics of the mtDNA and nDNA genomes or perhaps some sex-biased male

mediated gene flow.

There are unique characteristics of the mtDNA genome which can produce

relatively high genetic differentiation in parallel with homogeneity in the nDNA

genome. Differences in levels of genetic differentiation estimated among YFT

populations for mtDNA and nDNA may result from differences in marker modes of

inheritance and the respective mutation rates of the two genomes. The mtDNA

genome has maternal inheritance and haploid and hence no intermolecular

recombination, the effective population size is thus ¼ that of the nDNA genome

(Birky et al., 1989). Hence the mtDNA genome is much more sensitive to genetic

drift and therefore genetic differentiation (if present) is more likely to be detected

by it. This may explain genetic differentiation evident in mtDNA data, while little

or no genetic differentiation evident in nDNA (microsatellite) data. This drift effect

in mtDNA genome can be intensified if the population has undergone a sudden

population expansion in the recent past for example, after a population bottleneck.

When a population has experienced a recent sudden expansion, it results in more

recombination in the nDNA and hence dilutes most of the genetic drift effects

resulting in low or no genetic differentiation in nDNA. While high rates of


96

mutations at microsatellite loci may add to this effect, mtDNA variation will often

retain the historical pattern of past isolation and differentiation. In fact, the YFT

population demographic history here suggests that YFT populations have probably

experienced a recent sudden population expansion. Ward et al. (1997) also showed

a global YFT population expansion. Recently, Ely et al. (2005) have shown that

YFT forms a global single mtDNA clade and this global YFT population has

undergone a sudden expansion. Microsatellite markers are more efficient at

revealing recent isolation (than historical isolation) and independent evolution, as

separation or population differentiation that occurred in the historical past are likely

to be masked by recombination and potential for back mutation of the microsatellite

alleles as time frames increase (Di Rienzo et al., 1994).

Intra-specific differences in patterns of mtDNA and nDNA variation have often

been explained by sex-biased dispersal/philopatry (e.g. Prugnolle et al., 2002;

Fitzimmons et al., 1997; Lyrholm et al., 1999; Pardini et al., 2001). Another

possible explanation for this pattern is that females do not disperse as much as

males and hence female gene flow is more restricted, while dispersal by males is

large hence differentiation is more likely to show with mtDNA. While no evidence

was apparent here that the breeding sex ratio was strongly biased towards female

YFT (e.g. IATTC, 1992), according to a study of 2060 YFT samples collected by

purse seining in the Andaman sea of Thailand (Indian Ocean), ratios of immature:

male: female YFT were 11.6: 1.6: 1 (Weera-Pokapunt and Pattira-Sawasdiworn,

1988). Other studies of Indian Ocean YFT have also reported male dominance in

YFT breeding populations (e.g. Timokhina, 1993). Among tunas, BET shows

apparent site fidelity (Schaefer and Fuller 2002) although it is unclear whether it


97

preferentially affects females rather than males, or alternatively whether long-

distance migrants are more often male. Durand et al. (2005) investigated the

population structure of BET and suggested male-mediated gene flow in BET based

on both mtDNA and nDNA data. Overall however, there is no strong evidence here

for male-mediated dispersal in YFT and hence male-mediated dispersal as the

potential reason for the observed patterns here, is unlikely.

Another potential explanation for the result relates to the fact that mtDNA behaves

as a single locus in terms of recombination. Because the mtDNA genome is

effectively a single genetic locus it may provide a biased view of historical and

modern processes, if for example selection has affected one or more mtDNA genes

it will also drive changes in other linked apparently ‘neutral’ genes. Mutation rate of

a particular mtDNA gene and the potential for selection pressure on mtDNA protein

coding genes can also affect patterns of variation. Tests of neutrality on the YFT

data here however showed no indication of effects of selection.

When all of the above scenarios are considered, it is most likely that sampling

effects have produced the disparity between mtDNA and nDNA patterns here. The

geographical area sampled in the current study was relatively small compared with

the extensive distribution of YFT in the open Indian Ocean environment. The

population and school sizes of YFT are also commonly very large. When sampling

in a relatively limited geographical area there is a possibility that schools may be

sampled in such a way as to lead to non-random associations of haplotypes and

individual rare alleles. To determine whether any stock structure detected is real or

simply a sampling effect, sample sizes and the geographical scale of the area


98

covered should be carefully assessed. The relatively large number of alleles present

for each of the nDNA markers here (i.e. UTD494 and UTD499) requires that a large

number of individuals be surveyed in order to detect differentiation (private alleles)

if it is present. As another issue relevant to the potential sampling effect described

above, is that samples taken from both the KK and KR sites were different age

cohorts to those available at other sites. In general, most YFT individuals collected

for this study varied between 50cm - 70cm and were juveniles and sub adults,

except for the KK and KR sites. YFT taken from the KK and KR sites were fully

grown mature individuals and the average lengths were 138cm and 90cm,

respectively. This large size class difference could have affected the general

‘representativeness’ of these samples, if adult schools are subsets of the range of

available gene pool in younger age classes. These sampling effects potentially

decrease the power of the test and hence the potential for producing type II errors

(Waples, 1998; Ruzzante, 1998). In this study therefore, there is a potential to

accept the null hypothesis incorrectly. To address this issue, it will be necessary to

increase the sample size and the number of loci screened to confirm or refute the

recognition of discrete populations of YFT among the sampled sites.

Even given this issue, the mtDNA analyses still indicate that YFT form a single

clade in this part of the Indian Ocean overall suggesting that population structure if

real, is not strong. A recent study of YFT taken from the Pacific, Atlantic and

Indian Oceans (n = 41, 63 and 44 respectively) and based on mtDNA control region

sequence data and ATP-COIII region RFLP data by Ely et al. (2005), corroborate

the findings here when they reported a single global YFT mtDNA clade. This

single clade for YFT however contrasts with intra-specific phylogenetic patterns in


99

many other tunas. For example, Atlantic BET (Martinez et al., 2005) and Atlantic

Bonito (Vinas et al., 2004) both showed two highly divergent clades within a single

ocean basin, as was the case for swordfish (Alvarado Bremer et al., 1995; Rosel et

al., 1992), blue marlin (Finnerty et al., 1992), and sailfish (Graves et al., 1995;

2003) where Atlantic and Pacific clades were identified.

The mitochondrial DNA and microsatellite results here in combination do not

support a hypothesis for admixture of YFT stocks around Sri Lanka as proposed by

Nishida (1994) based on long line fishery data. If stocks were admixing, mtDNA

should show a mixture of divergent haplotypes at individual sampling sites and a

signature in the form of heterozygote deficiencies and linkage disequilibria across

nuclear loci that differ in allele frequencies. These characteristic effects of admixing

populations were not evident in the Sri Lankan YFT samples examined here.

While in the current study, mtDNA variation showed some genetic differentiation

among sites, it was limited to frequency differences in haplotypes among certain

sites only. Most of this pattern was due to differences at two sites (KK and KR)

rather than the existence of divergent clades. This genetic differentiation, however,

was really quite subtle. In addition, if true divergent stocks/breeding units were

present it should be reflected in divergent clades, but YFT haplotypes here form a

single clade. Hierarchical AMOVA variation at three microsatellite loci in addition

indicate that all YFT sampled populations are essentially homogeneous. An Exact

test of differentiation for all three microsatellite loci showed no significant genetic

differentiation between any pair of populations. Thus, in general, there is no

evidence for strong genetic stock structure in YFT populations in the region. The


100

general uniformity of nDNA variation in Sri Lankan waters observed here is likely

to reflect sufficient ongoing contemporary exchange of individuals or genes so that

the collections sampled are essentially sub samples of an otherwise western Indian

tropical/subtropical population. Differences in gene frequencies among sites may be

attributed to differential reproductive success of particular YFT collections. Overall

therefore, there appears to be little reason to regard the limited differentiation

observed among the sampled populations here as evidence for recognition of

independent evolutionary units. This result concord with previous work on YFT

stock structure in the Pacific and Atlantic Oceans and the limited work completed to

date in the Indian Ocean.

Population Structure of Skipjack Tuna

101

CHAPTER 4

POPULATION STRUCTURE OF SKIPJACK TUNA

Very little attention has been paid to stock structure of small and neritic tunas

including species like; SJT, mackerel and bonito species. This is due largely to the

limited commercial interest in small tunas in the Pacific and Atlantic fish harvesting

communities compared with the commercial importance of large tuna species like

BFT, BET and billfishes. Small tunas constitute however, very important food

resources for coastal indigenous people in many parts of the Indian Ocean and so

their sustainability is an important issue for the region.

The main pelagic catch from artisanal fisheries in the Indian Ocean are small tuna

species mainly SJT. In fact, the highest tuna catches in this region consist of SJT,

and so this species is considered a very important resource and plays a significant

role in the marine fisheries of ‘Indian Ocean nations’. For example in Sri Lanka,

67% of the total tuna catch consists of SJT and this species is the highest single

commodity resulting from the Sri Lankan tuna fishery (IOTC, 2006). The Maldives

tuna catch is also dominated by SJT where this species represents 80% of the total

tuna catch (IOTC, 2006). Collectively, SJT represent 41% of the total tuna catch in

the Indian Ocean.

For the above reasons, SJT are a very important commodity in Sri Lanka and more

widely in the region. Since virtually no data are available on stock structure in this

species it is very important and timely to assess their genetic stock structure. A


102

comprehensive study of genetic stock structure of SJT in the Indian Ocean can

provide the scientific foundation for developing effective stock management for the

species in the future.

Many aspects of the life history, ecology and behaviour of small tunas like SJT

contrast with those of larger pelagic tuna species. As SJT are comparatively more

neritic, narrow niched and are thought to undergo less frequent long distant

movements, this combined with a relatively short lifespan, very high fecundity/

recruitment and very large effective population sizes, suggest that this species could

show greater levels of genetic diversity and population differentiation at smaller

spatial scales than has been detected to date in their larger pelagic relatives.

4.1 Ecology, biology and life history of SJT

Skipjack tuna like YFT are members of the family Scombridae (order Perciformes:

sub order Scombroidei) and have very similar thermo-biological characteristics to

YFT as both belong to the tribe Thunnini which include pelagic, fast swimming,

relatively large predatory fishes. SJT are found in all tropical and sub tropical

oceans around the world and like other tuna species show specific schooling

behaviour as feeding, spawning or free swimming schools. Individuals also undergo

daily vertical migrations and seasonal migration patterns (Gubanov and Paramanov,

1993). Seasonal, regional migrations have a major impact on tuna school structure

and hence the potential for genetic stock structure and thus fisheries in many parts

of the world.


103

Unlike most commercial species of tuna, SJT are relatively small fish with body

lengths that range between 30cm to 75cm (Plate 4.1). Their life span is relatively

short, recorded at a maximum of about six years (Forsbergh, 1980). Relative to

other tunas, they grow very rapidly and reach sexual maturity in their second year

(Cayre and Farrugio, 1986). Generally, SJT inhabit offshore areas, and tagging

studies in the Pacific Ocean suggest that, while they are capable of long distance

movements (Argue, 1981), the majority apparently spend most time within their

natal waters (Yesaki and Waheed, 1992).

Plate 4.1 Skipjack tuna

A considerable number of studies have been conducted on the ecology and biology

of SJT in the Indian Ocean as these factors are considered quite unique to Indian

Ocean tunas due to the peculiar oceanographic characteristics and monsoonal

climate of the Indian Ocean, and this could affect their population structure.

SJT show strong schooling behaviour when free swimming, and are often

associated with floating objects or with marine mammals like whales and dolphins

(Gubanov and Tatarinov, 1993). As tunas are fast swimming fish capable of long


104

distance movements, they need high energy diets. While SJT larvae feed on

zooplankton, the juveniles feed on fish larvae and adults feed mainly on shrimp and

small fishes, like anchovies (Tanabe, 2001).

Several studies have been conducted on the reproductive biology of Indian Ocean

skipjack tuna. Stequert and Ramcharrun (1996, 1999) studied the reproductive

biology of 1656 SJT from the western Indian Ocean and 4387 SJT from Mauritius

Island in the southwest Indian Ocean collected between 1989 and 1994. According

to this study, SJT reach sexual maturity within the first one and half years of life

with body lengths ranging from 41 to 43cm for males and 41to 42cm for females.

70% of females had mature (stage IV) eggs in any month, and this combined with

Gonado-somatic index variation indicated that SJT spawn all year round

interspersed with some periods of more intense sexual activity. Histological

examination of ovaries indicated that mature eggs (post ovulatory follicles) are at

highest frequency in the two monsoon seasons, northeast monsoon (from November

to March) and southwest monsoon (from June to August). This study showed

further that the number of males was significantly higher than females during

intense spawning periods. SJT are highly fecund as their individual batch fecundity

varies from 80,000 to 125,000 eggs per individual female (Stequert and

Ramcharrun, 1996). Pelagic larvae disperse with prevailing monsoon currents and

this is considered a major life history trait that allows populations to persist over

time.

According to several studies that have examined the distribution and abundance of

tuna eggs and larvae in the Indian Ocean, it is evident that SJT spawn in most areas


105

of the tropical Indian Ocean. Scombrid fish eggs and larvae have been observed

along the southwest coast of India; south of Calicut to the east of Cape Comorin

with smaller concentrations detected near Ratnagiri (George 1990). Larvae were

present in all months of the year, with the peak evident during the March-August

period. Some drift of fish larvae to the southern sector off the southwest coast of

India apparently occurs due to a southward surface current during the major part of

the spawning period. SJT with mature ovaries were also found around the waters of

Pelabuhan Ratu in western Java (eastern Indian Ocean) and the frequency

distribution of late maturing eggs (ovary stage III) indicated that SJT are probably

serial spawners (Uktolseja and Purwasasmita, 1990). Even though SJT produce

large number of eggs and eggs/larvae can disperse via ocean currents which would

support panmixia, a low post larva survival rate together with their limitation to

natal waters, suggest that SJT might show genetic differentiation. Another study of

1860 of SJT collected by purse seining in the Andaman Sea near Thailand during

February to May 1988, produced ratios of immature : male : female SJT of 1.8 : 1.4

: 1, respectively (Weera-Pokapunt and Pattira-Sawasdiworn, 1988), indicating male

dominance in SJT spawning aggregations.

A considerable number of tagging studies have been conducted on Indian Ocean

tuna species especially on SJT and YFT (Waheed and Anderson, 1994; Bertignac,

1994; Bertignac et al., 1994; Yesaki and Waheed, 1992). Even though tagging

studies in the Indian Ocean have not generally been highly successful due to low tag

recovery rates, tagging studies in the Maldives inferred that predominantly

southward movement of SJT occurs during the northeast monsoon (Bertignac,

1994). In 1990, 8052 SJT were tagged in the Maldives (Yesaki and Waheed, 1992),


106

and by the end of February 1992, 1407 SJT had been recovered (17.4%). Of these

1407 tagged fish, 98% were recovered within the Maldives region (released area)

suggesting that SJT often remain in natal waters. Additional tag recoveries in Sri

Lanka and to a lesser extent in the western Indian Ocean of individuals tagged in

the Maldives suggest that when SJT disperse they tend to move with prevailing

ocean currents. Taking into consideration the above behavioural factors, life history

traits and oceanographic factors, SJT overall may be structured spatially.

4.2 Stock structure studies of SJT

The first stock delineation study of SJT was undertaken by Cushing (1956) when he

conducted a biochemical genetic study of blood group variation. This work was

extended later by Fujino and co-workers who documented heterogeneity among SJT

samples from the Pacific Ocean, and between the Pacific and Atlantic Oceans

(reviewed by Fujino, 1970).

A considerable number of allozyme studies have been undertaken on SJT in an

attempt to delineate populations, but these have been limited mainly to Atlantic and

Pacific Ocean populations. Transferrins and Esterases were the first polymorphic

proteins to be used in population structure studies on SJT. In early studies, samples

taken from the Pacific, Atlantic and Indian Oceans could not be differentiated

(Fujino, 1970; Fujino et al., 1981; Richardson, 1983). A comparison of genetic data

developed from serum Esterase and Transferrin markers, collected for SJT samples

from the Atlantic, Indian and Pacific Oceans, together with the results reported

above, concluded that SJT from the Indian Ocean can be distinguished from

Atlantic Ocean and western Pacific Ocean SJT, with sub populations evident among


107

the three oceans (Fujino et al., 1981). The study by Fujino (1970) hypothesised that

SJT which now inhabit all of the world’s major oceans, most likely first evolved in

the Indian Ocean following which, individuals dispersed to other oceans, resulting

in genetic diversification associated with geography. A study of EST* 1

heterogeneity in Pacific SJT, suggested that at least five genetically distinct

subpopulations were present in the Pacific Ocean that possessed overlapping

geographical boundaries (Sharp, 1978). Another large scale study of Esterase,

Transferrin and Guanine Deaminase (GDA) variation by Richardson (1983)

confirmed homogeneity for Transferrins but also reported heterogeneity for

Esterase and GDA loci in Pacific SJT. Reviewing earlier allozyme studies of SJT,

the South Pacific Commission (SPC) in 1981 rejected the hypothesis that SJT

populations in the Pacific Ocean were panmictic. Argue (1981) reached the same

conclusion after reviewing several allozyme analyses of SJT from the Pacific

Ocean. The current opinion based on allozyme variation analyses of SJT within the

Pacific Ocean is that slight clines may exist for Esterase and GDA loci across the

Pacific Ocean, but considerable heterogeneity also exists in frequencies among

samples collected from within regions (Argue, 1981; Fujino et al., 1981). It was

suggested that the results could best be explained by either an isolation by distance

model or the presence of distinct breeding cohorts in the central and western Pacific

regions.

RFLP analysis of mtDNA reinforced the recognition that there is a lack of

substantial allozyme divergence between Atlantic and Pacific SJT populations

(Graves et al., 1984). Analysis of nine Pacific and seven Atlantic SJT populations

showed variation within the pooled samples and presence of a single common


108

mtDNA haplotype in both oceans. Menezes et al. (2005) reported genetic

differences between a sample of 20 SJT taken from southeast India and a sample of

44 SJT taken from the eastern coast of Japan, based on a RFLP study of mtDNA

control region sequence. The sensitivity of allozyme markers and the RFLP

approach in general however, may not be sufficient to detect much of the potential

genetic variation present and hence, real population structure that may be present in

SJT regionally. Life history, behaviour, ecology and general biology of SJT reflect

the potential for genetically discrete populations within and among oceans, and

more sensitive genetic markers such as mtDNA sequences and nDNA microsatellite

markers are likely to provide greater sensitivity for detecting discrete SJT

populations within and among oceans, where it exists. As an example, early studies

of BET (Alvarado Bremer et al., 1998) and swordfish (Pujolar et al., 2002) using

mtDNA RFLP and allozyme methods respectively, could not detect significant

genetic differentiation and hence any population structure. The latest, powerful

direct sequencing techniques of the mtDNA genome however, when applied

recently have revealed discrete populations for swordfish at the intra-ocean level,

and for BET at the inter-ocean level (BET; Martinez et al., 2005, and swordfish;

Alvarado Bremer et al., 2005).

To date, genetic stock structure studies of SJT have largely focused on Pacific

Ocean samples and, to a lesser extent the Atlantic Ocean. Having said this, there are

remarkably few mtDNA or nDNA studies of SJT in any major ocean, but this may

relate to the relatively low importance of this species in commercial fisheries

outside the Indian Ocean. Stock structure studies on Indian Ocean SJT at any

significant scale are virtually non-existent and of the few completed, most have not


109

employed genetic assessments. Thus, Indian Ocean populations are yet to be

examined to any significant degree.

In the current study, genetic stock structure of SJT populations around Sri Lanka

and the Maldive Islands in the Indian Ocean was assessed using both mtDNA and

nDNA microsatellite markers to determine if populations constitute a single

panmictic population or if multiple stocks may exist. The levels of genetic diversity

and gene flow among SJT populations were also used to infer dispersal patterns of

SJT around Sri Lanka and the Maldive Islands in the Indian Ocean.

4.3 Methodology

(i) Mitochondrial DNA variation

As described earlier (section 2.2.2), the ATP6 and 8 region of the mtDNA genome

was selected for analysis and the following internal primers were developed,

yielding a 540 base pair product to examine levels of variation appropriate to

address the specific aims of the study.



Frequencies of unique haplotypes were determined using TGGE following

sequencing of all unique haplotypes and were assessed in each population (details

of PCR conditions and TGGE in section 2.2.2 and Appendix 2).

(ii) Nuclear DNA variation

nDNA variation of SJT samples was screened initially using five microsatellite loci;

UTD73, UTD203, UTD328, UTD149, and UTD531. Due to amplification problems


110

and/or null alleles, UTD149 and UTD531 were later excluded. So finally two tri-

and one tetra-nucleotide microsatellite loci; UTD328, UTD203 and UTD73 were

used for the analysis. Details of microsatellites, primers and PCR conditions are

summarised in Table 4.13, and microsatellite screening in Appendix 4.

Figure 4.1 Sampling sites of SJT. Redrawn from the National Geographic web site map

Table 4.1 Collection data for SJT

Population Location Date n Total collection 324

Negombo (NE)

79018`, 60057`

Jan-01 Apr-02 Oct-03

21 14 18

Weligama (WE) 80018`, 50034` Mar-01 52

Tangalle (TA)

81014`, 50042`

Mar-01 Apr-02 Nov-03

7 8

26 Kalmunei (KM) 82029`, 70008` Mar-02 54 Trincomalee (TR)

81051`, 80058`

Apr-02 Sep-04

25 24

Laccadive (LC) 72031`, 11001` Apr-02 48 Maldives (MD) 73009`, 40 20` Nov-03 27 Clade I 281 Clade II 43

Maldive

LC

NE

WE

MD

TA

KM

TR


111

4.4 Results

(i) Mitochondrial DNA variation in SJT

Genetic variation

Genetic analyses were conducted on 324 individuals from five fishing grounds

around Sri Lanka (NE, WE, TA, KM and TR), and single sites from the Maldive

Islands (MD) and Laccadive Islands (LC) (Figure 4.1 and Table 4.2). MtDNA

haplotype sequence data produced alignment of a 488 bp fragment which covered a

portion of the ATPase6 and entire ATPase 8 gene regions. A total of 52 nucleotide

sites were variable (segregating sites) (Table 4.2).


112

Table 4.2 Variable nucleotide sites of SJT mtDNA ATP region

1111 1111111222 2222233333 3333333334 4444444444 44 25580245 5666789255 5667901122 4556788990 0112335477 88 3946967532 3147051714 7035684806 1092739564 7065781369 01 Ht1 TATGACCAAT TTAAACACCC ATTTGCAAGT CCGGCATACA TTCCAGCATT AT Ht2 .......... .......... .........C .T........ CC....T... .. Ht3 ......T... .......... .....T...C .T........ .C....T... .. Ht4 .........C .......... .........C .T........ .C....T... .. Ht5 .......... ......G... ....A..... .T........ .CT....... .. Ht6 .....T.... .......... .........C .T....G... .C....T... .. Ht7 .......... .......... .......... .T....C... .C........ .. Ht8 .......... .......... .........C .T........ AC....T... .. Ht9 .......... .......... .......... TT........ .......... .. Ht10 .......... ........T. .....T...C .T........ .C....T... .. Ht11 .......... ........T. ....AT...C .T........ .C....T... .. Ht12 .......... .......... .C........ .T........ .......... .. Ht13 .G........ .......... .........C .T........ ......T... .C Ht14 .G........ .......... .........C .T........ ......T... .. Ht15 .......... C....T...T ..C....... .T......T. CC....T.A. .. Ht16 .......... C....T...T ..C....... .T....A.T. CC....T... .. Ht17 C........C .......... G......... .TA....... .C........ .. Ht18 .......... .......... .........C .T........ .......... .. Ht19 .......... CC...T...T ..CC...... .T......T. CC....T.A. .. Ht20 .......... .......... .........C .T........ .C....T..C .. Ht21 ....C..... .......... .........C .T........ .C....T... .. Ht22 .......... C....A...T ..C....... .T......T. CC....T.A. .. Ht23 .......... ....G..T.. .......... .T........ .......... .. Ht24 .......... .......... .......... .T........ .C........ .. Ht25 .......... .....T...T ..C....... .T......T. CC....T.A. .. Ht26 .......... ..G....... ......G... .TA....... .C........ .. Ht27 .......... C....T...T ..C.....A. .TA.....T. CC....T.A. .. Ht28 .......... .......... .....T...C .T........ .C....T... .. Ht29 .......... .......... .......... .T........ .......... .. Ht30 .......... ........T. .........C .T........ .C....T... .. Ht31 .......... .......... .........C .T........ .C....T... .. Ht32 .......... C....T...T ..C....... .T......T. CC....T.A. T. Ht33 .......... C....T...T ..C....... .T...G..TG CC....T.A. .. Ht34 .......... .......... G........C .T........ .C....T... .. Ht35 .......... .......... ..C....... .T........ .C........ .. Ht36 ........G. .......... .........C .T.....G.. .C...AT..C .. Ht37 ...C...... C..G.T...T ..C....... .TA.....T. CC.T..T.A. .. Ht38 .......... C....T...T ..C....... .TA....... CC....T.A. .. Ht39 .......... C....T...T ..C....... .TA.....T. CC....T.A. .. Ht40 .......... C....T...T ..C....... .T......T. CC..T.T.A. .. Ht41 ..C....... .......... .......... .T........ .......... .. Ht42 .......... .C........ .......... .T........ .......... .. Ht43 .......... .......... .......... .T........ .......G.. .. Ht44 .......... .......... .......G.. .T........ .......... .. Ht45 .......... .......... .......... .T.....G.. .C........ .. Ht46 .......... .......... .........C .T.A...... .C....T... .. Ht47 .......... .......... .........C .T..T..... .C....T... .. Ht48 .......G.. .......... .........C .T........ .C....T... .. Ht49 ..C....... .......... .........C .T........ .C....T... ..


113

Table 4.3 Haplotype distribution among sampling sites of SJT Site

Haplotype NE WE TA KM TR LC MD

Total

Haplotype Frequency (%)

Ht1 1 0 0 0 0 0 0 1 0.31 Ht2 0 0 0 1 0 0 0 1 0.31 Ht3 0 0 0 0 0 1 0 1 0.31 Ht4 0 0 0 0 0 5 0 5 1.54 Ht5 0 0 2 0 0 0 16 18 5.56 Ht6 3 17 31 3 3 19 0 76 23.46 Ht7 0 15 0 0 0 0 0 15 4.63 Ht8 21 0 0 1 0 0 0 22 6.79 Ht9 0 0 0 0 0 0 1 1 0.31 Ht10 0 1 0 0 0 0 0 1 0.31 Ht11 0 0 0 0 22 0 0 22 6.79 Ht12 20 1 0 2 0 0 0 23 7.10 Ht13 0 0 1 0 0 0 0 1 0.31 Ht14 0 0 1 0 0 0 0 1 0.31 Ht15 0 0 0 2 0 0 0 2 0.62 Ht16 0 0 0 1 0 0 0 1 0.31 Ht17 0 0 0 3 0 0 0 3 0.93 Ht18 3 5 1 1 0 0 0 10 3.09 Ht19 0 5 0 0 0 0 0 5 1.54 Ht20 0 0 1 0 0 15 1 17 5.25 Ht21 0 0 0 0 1 0 0 1 0.31 Ht22 0 1 0 0 0 0 0 1 0.31 Ht23 0 0 1 0 0 0 0 1 0.31 Ht24 1 0 0 0 0 0 0 1 0.31 Ht25 0 1 0 0 0 0 0 1 0.31 Ht26 0 0 0 2 18 0 0 20 6.17 Ht27 1 0 0 0 0 0 0 1 0.31 Ht28 0 1 0 0 0 0 0 1 0.31 Ht29 0 0 0 1 0 0 0 1 0.31 Ht30 0 0 0 0 1 0 0 1 0.31 Ht31 0 0 0 0 0 1 0 1 0.31 Ht32 0 0 0 0 0 0 1 1 0.31 Ht33 0 0 0 0 0 0 1 1 0.31 Ht34 0 0 0 0 0 0 1 1 0.31 Ht35 0 1 0 0 0 0 0 1 0.31 Ht36 0 0 0 0 0 0 2 2 0.62 Ht37 1 3 0 20 2 1 0 27 8.33 Ht38 1 0 0 0 0 0 0 1 0.31 Ht39 0 0 0 1 0 0 0 1 0.31 Ht40 0 0 1 0 0 0 0 1 0.31 Ht41 1 0 0 1 0 0 2 4 1.23 Ht42 0 0 0 0 0 6 0 6 1.85 Ht43 0 0 0 0 0 0 2 2 0.62 Ht44 0 0 0 0 2 0 0 2 0.62 Ht45 0 1 0 0 0 0 0 1 0.31 Ht46 0 0 0 14 0 0 0 14 4.32 Ht47 0 0 1 0 0 0 0 1 0.31 Ht48 0 0 0 1 0 0 0 1 0.31 Ht49 0 0 1 0 0 0 0 1 0.31 No:of Samples 53 52 41 54 49 48 27 324


114

Polymorphic sites defined a total of 49 unique haplotypes (Table 4.2 and 4.3).

Overall haplotype diversity (Hd) was high (0.9105) and individual geographic

population haplotype diversity was also high. Twenty eight haplotypes were

singletons, and the most abundant haplotype (haplotype 6, frequency 23.46%)

occurred in six out of seven sample sites (except MD) (Table 4.3). The second most

abundant haplotype (haplotype 37, frequency 8.33%) occurred in five sites. Overall

nucleotide diversity, and the average number of pair-wise nucleotide differences

were 0.0126 and 6.1334, respectively. Genetic diversity among populations also

varied considerably. Population genetic summary statistics are presented in Table

4.4.

Table 4.4 Descriptive statistics for SJT samples. No. of haplotypes (h), No. of polymorphic sites (S), Gene diversity (Hd), mean pair-wise nucleotide difference (k), Nucleotide diversity (π), Expected heterozygosity per site θs.


All haplotypes were grouped into two distinct, divergent clades (mean divergence =

1.85%).

Population n h S Hd K π θs Total collection 324 49 52 0.9105 3.8559 0.0079 8.1807

Negombo (NE)

21 14 18

3 4 7

3 12 20

0.3381 0.4945 0.6340

0.5175 2.2974 3.5346

0.0011 0.0047 0.0073

0.8338 3.7734 5.8147

Weligama (WE) 52 12 18 0.8009 3.1655 0.0065 3.9833

Tangalle (TA)

7 8

26

2 1 9

1 -

20

0.2857 -

0.5785

0.2862 -

2.1139

0.0006 -

0.0043

0.4082 -

5.2411 Kalmunei (KM) 54 15 20 0.7973 5.4206 0.0111 4.3889 Trincomalee (TR)

25 24

5 2

12 5

0.4767 0.1594

2.3618 0.8073

0.0048 0.0016

3.1780 1.3389

Laccadive (LC) 48 7 17 0.7332 3.5314 0.0073 3.8306 Maldives (MD) 27 9 23 0.6496 5.0965 0.0105 5.9672 Clade I 281 37 38 0.9598 2.6399 0.0054 6.1154 Clade II 43 12 28 0.7730 4.6866 0.0096 6.4714


115

Ht3

Ht28

Ht10

Ht11

Ht30

Ht46

Ht21

Ht47

Ht31

Ht20

Ht36

Ht6

Ht48

Ht4

Ht34

Ht2

Ht8

Ht49

Ht13

Ht14

Ht18

Ht41

Ht29

Ht43

Ht44

Ht9

Ht42

Ht1

Ht12

Ht23

Ht17

Ht26

Ht45

Ht7

Ht5

Ht24

Ht35

Ht25

Ht16

Ht38

Ht22

Ht27

Ht37

Ht39

Ht15

Ht40

Ht32

Ht19

Ht33

4251

3118

33

38

20

18

23

38

98

40

81

54

54

46

39

37

24

10

4

6

14

32

38

29

199

5

8

40

8

11

15

4

2

5

11

2

6

3

0.005

NEWE

TA

KMTRLCMD

Figure 4.2 Unrooted neighbour joining tree of SJT haplotypes based on Tamura and Nei genetic distances. Colours indicate in which sampling sites particular haplotypes were found. Individuals of both clades were found at all sampling sites. Clade I constituted 37

out of a total of 49 haplotypes while only 12 haplotypes were found in Clade II. The

Clade I

Clade II


116

parsimony cladogram (Figure 4.3) shows that the most common Ht6 was ancestral

in Clade I (occurs in the centre of network) while Ht37 was probably ancestral in

Clade II. The level of divergence between the most common two haplotypes in the

two clades (Ht6 in Clade I and Ht37 in Clade II) was 1.85%. Pair-wise divergence

among haplotypes in the parsimony cladogram ranged from 0 to 3.7%. Sample

collections at individual sites represent mixtures of haplotypes from the two clades,

irrespective of time of collection or relative sample size.


The characteristic pattern of SJT mtDNA haplotype diversity among sites (Table

4.2 and Figure 4.4) is that a single haplotype (at TA and MD) or two haplotypes (at

NE, WE, KM, TR, and LC) were at highest frequency at each site. Common

haplotype frequencies varied widely among sites. A large number of singleton

haplotypes were also present at individual sites, 28 out of 49 total haplotypes.

Presence of different haplotypes at high frequencies at each site together with a high

number of singletons produced a relatively high level of mtDNA genetic

differentiation among sites even though the majority of genetic variation was

evident within sites. Ht6 was present at all sites (except MD) and accounted for

23% of all individuals.

The hierarchical analysis of Tamura and Nei genetic distances in AMOVA are

summarised in Table 4.5.


117

Figure 4.3 Parsimony Cladogram of SJT haplotypes showing the evolutionary

relationships among haplotypes. Each circle represents a unique haplotype in the

sample, and the size of each circle represents the relative frequency of each

haplotype. Colours and their percentage in each circle represent the presence of

each haplotype at different sites and their relative abundance at each site. Cross bars

between circles represent the number of base pair difference between individual

haplotypes.

23%

8%

5%

0.3%


118

Figure 4.4 MtDNA haplotype frequency distribution of SJT at sampling sites

Across the total sample collection, there was significant genetic differentiation

among sites (global ΦST = 0.2029, p<0.001) when temporal collections within sites

were pooled. The entire sample collection was then grouped in to year-wise

hierarchical groups to assess the impact of temporal collections. No significant

genetic differentiation was evident among groups for years (among year-wise

collections in 2001, 2002 2003, and 2004) (ΦCT = -0.0002, p = 0.8739). Since no

significant genetic variation was evident among year-wise total collections (among

2001, 2002, 2003 and 2004 collections, irrespective of sampling site), this indicates

that overall genetic composition of sampled SJT populations were temporally stable

during the study. Significant genetic differentiation was evident however, among

populations within groups (within collections for 2001, 2002, 2003 and 2004)


119

indicating that sampled populations were spatially heterogeneous for genetic

variation (ΦST = 0.2030, p < 0.001).

Table 4.5 Genetic structuring of skipjack tuna populations based on mitochondrial ATP region sequence data *** p<0.001

Structure tested Observed partition Ф statistics

Variance

% Total

1 Total collection (2001,2002,2003,2004) – one gene pool Among populations 0.6286 20.29 ФST = 0.2029*** Within populations 2.4689 79.71

2 Among years

Among years (TEMPORAL) 0.0007 0.02 ФCT = 0.0002 Among populations within years (SPATIAL) 0.6280 20.30 ФSC = 0.2030*** Within populations 2.4647 79.68 ФST = 0.2032

3 Among sites

Among sites (SPATIAL) 0.1524 4.92 ФCT = 0.0492 Among years within sites (TEMPORAL) 0.4825 15.57 ФSC = 0.1637***

Within populations 2.4647 79.51 ФST = 0.2049

4 Clade-wise Between two clades 1.9336 56.35 ФCT = 0.5635*** Among samples within clades 0.4457 12.86 ФSC = 0.2945*** Within populations 1.0677 30.80 ФST = 0.6920

a) Clade I-year wise Among years 0.0838 4.52 ФCT = 0.0453 Among sites within years 0.3775 20.39 ФSC = 0.2136*** Within populations 1.3898 75.08 ФST = 0.2492 Clade I-site wise Among sites -0.0883 -4.83 ФCT = - 0.0483 Among years within sites 0.5258 28.77 ФSC = 0.2745*** Within populations 1.3898 76.06 ФST = 0.2394

b) Clade II-year wise Among groups 2.5975 45.36 ФCT = 0.4536 Among populations -0.2878 -5.03 ФSC = -0.0919 Within populations 3.4170 59.67 ФST = 0.4033 Clade II-site wise Among groups 1.8524 35.32 ФCT = 0.3532 Among populations -0.0253 -0.480 ФSC = -0.0075 Within populations 3.4170 65.16 ФST = 0.3484


120

Spatial genetic variation among SJT sampling sites was tested at greater resolution

level using pair wise ΦST analysis. Pair-wise ΦST analysis for the entire collection is

shown in Table 4.6. Highly significant genetic variation was evident between

virtually all pairs of sites as expected because of pooling of temporal collections.

After Bonferroni correction, 16 pairs of sites were significantly differentiated.

Overall pair-wise ΦST analysis showed that gene flow among SJT sites was very

low.

Table 4.6 mtDNA pair-wise ФST among sampling sites of SJT after Bonferroni correction for entire collection. (initial α = 0.05/21 = 0.002) NE WE TA KM TR LC MD NE 0.000 WE 0.116** 0.000 TA 0.081** 0.059 0.000 KM 0.271** 0.186** 0.237** 0.000 TR 0.171** 0.069* 0.117* 0.229** 0.000 LC 0.187** 0.070 0.142** 0.267** 0.111** 0.000 MD 0.142** 0.065 0.071 0.116 0.112** 0.116** 0.000 (*, p< 0.002 **, p<0.0001)

Pair-wise ΦST analyses were carried out for each year-wise collection to determine

the sample collections that contributed most to spatial heterogeneity within years.

Results presented in Table 4.7 are after Bonferroni correction for multiple tests of

pair-wise ΦST analyses. Significant genetic variation was evident between most

pairs of collections in all years, although many of them were not significant after



121

Table 4.7 mtDNA pair-wise ФST among year-wise collections of SJT after Bonferroni correction for 2001, 2002 and 2003 collections; initial α = 0.05/3 = 0.016 for 2001 and 2003 collections; for 2002 collection initial α = 0.05/10 = 0.005

(a) 2001 collection NE WE TA NE 0.000 WE 0.2369** 0.000 TA 0.5822** 0.0187 0.000 (b) 2002 collection NE TA KM TR LC NE 0.000 TA 0.1088 0.000 KM 0.2166 0.1695 0.000 TR 0.3319** 0.3416 0.1662 0.000 LC 0.2115 0.1039 0.2676** 0.1195 0.000 (c) 2003 collection NE TA MD NE 0.000 TA 0.0745 0.000 MD 0.0901 0.0345 0.000

* p<0.005,

AMOVA was also conducted to assess whether significant genetic differentiation

was evident among temporal collections within sites. Significant genetic

differentiation was detected for temporal collections at site NE and site TR but not

for site TA after Bonferroni correction (Table 4.8). For the NE samples, no

significant differentiation was evident between the NE’02 and NE’03 collections

(Table 4.8.a). For the TR collections, there was significant genetic differentiation

between TR’02 and TR’04 (Table 4.8.c). So taken together, these results suggest

that the genetic composition of SJT populations at a given site is not always stable

across years. When temporal collections were compared that had been taken from

the same site over one or a few days genetic differentiation was also evident (Table

4.9). This result contrasts with that observed for YFT at identical sites, a result that

will be discussed in more detail later.


122

Table 4.8 mtDNA pair-wise ФST among temporal collections within sites of SJT after Bonferroni correction (initial α = 0.05/3 = 0.016 for NE and TA collection).

(a) NE collection NE'01 NE'02 NE'03 NE'01 0.000 NE'02 03438** 0.000 NE'03 0.2947** −0.0232 0.000 (b) TA collection TA'01 TA'02 TA'03 TA'01 0.000 TA'02 0.0204 0.000 TA'03 −0.0708 −0.0632 0.000 (c) TR collection TR'02 TR'04 TR'02 0.000 TR'04 0.5289** 0.000

(** p<0.001) Table 4.9 mtDNA pair-wise ФST among different day collections within sites of SJT

Site Pair wise ФST between day 1 and day 2 KM 0.565** LC 0.122*

(* p< 0.05 ** p<0.01) AMOVA analyses, overall, show that while there was significant spatial genetic

differentiation among sampled SJT populations in a given year around Sri Lanka, in

general, the entire SJT collection remained genetically stable over time (i.e. the

same haplotypes remained in similar frequencies across years). Population pair-wise

analyses show that when significant results were present, they were evident between

population pairs or some times between collections within sites. The spatial genetic

differentiation observed here however, could result from temporal instability of

genetic composition within individual sites.

AMOVA and pair-wise ФST analyses therefore were carried out for each clade

separately. Different outcomes were evident however for each clade (Table 4.5).

Significant spatial and temporal genetic differentiation was observed among


123

populations for Clade I individuals (ФSC = 0.2136, p < 0.0001 and ФSC = 0.2745, p <

0.0001 respectively) (Table 4.5), but no significant spatial or temporal genetic

variation was evident for Clade II, and hence the genetic differentiation of SJT

populations results largely from spatial variation in the distribution of Clade I

individuals among sites. The lack of genetic differentiation for Clade II individuals

however, may be due to the relatively low numbers of Clade II (n = 43) individuals

and hence there may have been insufficient power to reject the null hypothesis for

this clade. Pair-wise ФST analyses for samples of the two clades show a striking lack

of gene flow among populations within Clade I, but not for Clade II (Table 4.10).

Table 4.10 mtDNA pair-wise ФST among collections within each clade of SJT after Bonferroni correction (initial α = 0.05/21 = 0.002). Clade I below, and Clade II above diagonal. NE WE TA KM TR LC MD NE 0.1304 -0.3333 0.1024 -0.2000 -1.0000 0.0656 WE 0.1933** 0.3975** 0.4778** 0.3882 0.2254 0.4787 TA 0.1766** 0.0869** 0.2365 1.0000 1.0000 0.3333 KM 0/1942** 0.1311** 0.1375** -0.2565 -0.8414 0.3148 TR 0.2909** 0.1919** 0.2315** 0.2012** 0.0000 0.4000 LC 0.2542** 0.1621** 0.2003** 0.1543** 0.1524** -0.2000 MD 0.2499** 0.1753** 0.1885** 0.1700** 0.2139** 0.1272 (** p<0.0001)

Differences of intra-clade gene flow between two clades may be due to differential

dispersal capabilities associated with the strength of currents acting on eastern and

western sides of Sri Lanka or the relative proximity of different spawning grounds

from each sample site.

As significant genetic differentiation was apparent even among populations for

Clade I individuals, total genetic differentiation among sampled sites not only


124

resulted from admixture of the two clades at individual sites, but also as a result of

different frequencies of Clade I haplotypes at individual sites.

As the best significant grouping, SAMOVA indicated three genetically

differentiated SJT population groups (Table 4.11) specifically; KM, MD and all

remaining sites (NE, WE, TA, TR, LC). SJT population structure was supported by

the nearest-neighbour statistic Snn, (Hudson, 2000) calculated in DnaSP (Snn =

0.6257, p<0.001) indicating the presence of population structure as a significant

association between ATP sequence similarity and geographical location.

Table 4.11 Population structure based on mtDNA differentiation of SJT (in SAMOVA). The row in bold type indicates the details of geographically meaningful groups with maximum genetic differentiation.

No. of Groups

Structure

Variation among groups

Variation %

FCT

p

2 (KM) (NE,WE,TA,TR,LC,MD) 0.328 14.95 0.149 0.135 3 (KM) (MD) (NE,WE,TA,TR,LC) 0.269 12.68 0.126 0.048 4 (KM) (MD) (NE,TA) (WE,TR,LC) 0.234 11.54 0.115 0.003 5 (KM) (MD) (TR) (NE,TA) (WE, LC) 0.230 11.52 0.115 0.006 6 (KM) (MD) (TR) (NE) (LC) (WE,TA) 0.261 13.13 0.131 0.046

The pattern of population genetic differentiation revealed in the above analyses was

then tested for isolation by distance. Mantel’s test in Arlequin was undertaken for

the seven SJT sample sites. The pattern of genetic variation among sites did not

show a significant correlation (correlation coefficient = 0.0147, p = 0.462;

regression coefficient 0.00003) with geographical location of sample sites.

This result implies that genetic differentiation and hence stock structure was not

influenced by isolation by distance (IBD). This interpretation was also supported in


125

the SAMOVA analysis, where one group consisted of several sites (NE, WE, TA,

TR and LC) that were not more geographically proximate.

Population history and demographic patterns

Descriptive statistics for SJT population samples were presented earlier (Table 4.4).

Out of a total number of 324 SJT, 281 individuals were members of Clade I while

only 43 sampled individuals were Clade II types. Genetic differentiation among

populations in Clade I were further assessed using the nearest-neighbour statistic

Snn. The test was highly significant for Clade I individuals (Snn = 0.287, p<0.001).

The Snn value indicates that the probability of occurrence of haplotypes in each

sample was very high.

Table 4.12 Statistical tests of neutrality and demographic parameter estimates for SJT (Figures within parenthesis are p values) Population Tau

(τ) Hri index Tajima’s

D Fu’s FS θ0 θ1 R2

Total collection

0.834 0.0317 (0.2599)

-1.5159 (0.0518)

-25.4287 (0.0000)

3.039 1858.75 0.0581 (0.2690)

NE 2.07 0.1733 (0.070)

-1.567 (0.050)

-1.683 (0.249)

1.837 2.850 0.0905 (0.3660)

WE 3.76 0.061 (0.550)

-0.681 (0.265)

-1.408 (0.308)

2.301 3.862 0.1233 (0.7230)

TA 0.529 0.167 (0.690)

-2.319 (0.002)

-4.122 (0.009)

0.689 0.689 0.0666 (0.0870)

KM 10.084 0.117 (0.070)

0.653 (-0.258)

-0.742 (0.420)

0.006 8.448 0.1512 (0.9440)

TR 4.095 0.254 (0.060)

-0.545 (0.313)

1.213 (0.729)

0.008 2.891 0.1122 (0.5880)

LC 4.912 0.290 (0.000)

-0.297 (0.404)

2.434 (0.852)

0.003 5.369 0.0968 (0.4030)

MD 11.047 0.306 (0.010)

-0.596 (0.296)

0.843 (0.705)

0.019 4.809 0.0928 (0.1840)

Clade1 2.435 0.0435 (0.3100)

-1.6014 (0.0407)

-26.4099 (0.000)

0.000 13.152

0.236 (0.0546)

Clade II 7.125

0.1567 (0.3400)

-0.9329 (0.1866)

-2.8215 (0.1640)

1.672

1.672

0.195 (0.0833)


126

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0 2 4 6 8 10 12 14 16 18 20 22 24 26

No. of differences

Freq

uenc

y

Observed frequency

Growth decline model

Constant growth moel

a. Mismatch distribution of SJT- entire sample collection

0

0.05

0.1

0.15

0.2

0.25

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20No. of differences

Freq

uenc

y

Observed frequency

Constant population model


b. Mismatch distribution of SJT- Clade I

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 2 4 6 8 10 12 14 16 18 20 22 24

Number of differences

Freq

uenc

y

Observed frequency

Constant growth model


c. Mismatch distribution of SJT- Clade II

Figure 4.5 Observed, growth-decline model, and constant population model mismatch distribution for all pairwise combinations of: the entire mtDNA ATPase region data set, 324 individuals; Clade I, 281 individuals and Clade II, 43 individuals.


127

Statistical tests of neutrality and demographic parameter estimates for each sample

population and the two clades are presented in Table 4.12. Here the Fu’s FS for the

entire sample collection showed a significant large negative value (Fu’s FS= -

25.4287, p<0.0001) indicating that the population had undergone an expansion.

When this was recalculated for individual Clades, the Fu’s FS for Clade I showed a

significant high negative value (FS = -26.4099, p<0.0001) indicating that Clade I

was expanding. Fu’s FS for Clade II however, was not significant (FS = -2.8215,

p>0.05).

Population expansion was also tested using Harpending’s raggedness index (Hri)

and θ0 and θ1. Hri for the total sample collection was low and not significant (Hri =

0.0317, p>0.05), supporting a population expansion. Similar results were obtained

for Clade I, Hri = 0.0435, p>0.05; and Clade II, Hri = 0.1567, p>0.05. The

difference between θ0 and θ1 can be used as a measure of population expansion and

if the difference is large this indicates that the population has undergone an

expansion. The difference between θ0 and θ1 values here were very large for the

total population (Table 4.12) supporting a population expansion. In the same way,

the result supports a population expansion for Clade I but not for Clade II. This

agrees with previous observation that no population expansion has occurred in

Clade II.

The mismatch distribution for the entire data set was multimodal (Figure 4.5a), with

one mode corresponding to the number of differences within clades, and the others

to differences between the two clades. While the analysis for Clade I (Figure 4.5b),


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overall shows a population expansion, Clade II (Figure 4.5c) yielded multimodal

distributions showing that Clade II has remained stable for a long period of time.

The R2 statistic was not significant for the total sample population (R2 = 0.0581,

p>0.05), or for Clade I and Clade II (R2 = 0.236, p>0.05 and R2 = 0.195, p>0.05

respectively) a result not strongly supporting a population expansion. According to

Ramos-Onzins and Rozas (2002) the R2 statistic test is superior however, for testing

population growth of small sample sizes (e.g. n = 10), while Fu’s FS is superior for

large sample sizes. Further, according to the same study, Hri and mismatch tests

based on the mismatch distribution and Hri have little power estimating population

growth (Ramos-Onzins and Rozas, 2002). In general therefore, it can be concluded

that Clade I has undergone a recent sudden expansion while Clade II has had a

longer stable history.

Geographic distribution of clades

As the NJT and the parsimony cladogram for SJT showed two distinct clades, the

relative contribution of each clade to each sample site and hence the pattern of

distribution of the two clades over the sampled area was assessed. Frequency

distributions of the two clades at each sampling site (Figure 4.6 and Table 4.13) for

the entire collection show that Clade I individuals were most common at all sites

examined here. When this was analysed at a higher resolution, year-wise collections

and day-wise collections, Clade I was still dominant even in temporal collections,

except at two sites, WE and KM (Table 4.13). Clade I may therefore be the

dominant clade in the Indian Ocean, and Clade II may have colonised from another

ocean. An alternative scenario is that Clade II may be ancestral in the Indian Ocean,


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while Clade I has secondarily invaded/contacted the Indian Ocean or has evolved

later within the Indian Ocean itself. Estimated τ (Tau) values (Clade I τ =2.435, and

τ =7.125 for Clade II) (Table 4.12), together with pairwise mismatch distributions

suggest that Clade II has been stable for a long period of time while Clade I has

expanded recently. The divergence rate for ATP region in fish is estimated at

around 1.3% per million years (Bermingham et al., 1997) and SJT mature when

they reach one and half years (Cayre and Farrugio, 1986; Stequert and Ramcharrun,

1996), hence the estimated times since population growth for Clades I and II are

0.288 X 106 years and 0.844 X 106 years bp, respectively.

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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Table 4.13 Percentage of ATPase region Clade I and Clade II for each SJT population and year-wise collections around Sri Lanka.

Sites Clade NE WE TA KM TR LC MD

Entire collection Clade I 94.33 80.76 97.56 44.44 95.91 97.92 92.60 Clade II 5.66 19.23 2.43 55.56 4.08 2.08 7.40

2001 collection Clade I 100.00 80.76 100.00 - - - - Clade II 0.00 19.23 0.00 - - - -

2002 collection Clade I 92.85 - 100.00 44.44 92.00 97.92 - Clade II 7.15 - 0.00 55.56 8.00 2.08 -

2003 collection Clade I 88.88 - 96.16 - - - 92.60 Clade II 11.12 - 3.84 - - - 7.40

2004 collection Clade I - - - - 100.00 - - Clade II - - - - 0.00 - -

(ii) nDNA variation in SJT

Genetic variability, Hardy-Weinberg and linkage equilibrium

No null alleles, large allele drop out or error scoring were detected (95% confidence

interval) for the three SJT microsatellite loci (UTD73, UTD203, and UTD328,

having excluded loci UTD149 and UTD531), except at locus UTD328. Micro-

checker analyses showed that locus UTD328 results could have been affected by

null-alleles. Subsequent analyses in AMOVA including or excluding locus UTD328

data provided similar outcomes (results in Population Structure section). All three

loci were therefore included in all further analyses. Descriptive statistics for the

three microsatellite loci are summarised in Table 4.15. Some individuals could not

be scored at specific loci due to amplification problems. The number of individuals

amplified for the three loci for all sites however, were generally high (n = 9 to 54,

average 30) except at TA2 site (n = 3 to 5).


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Sample populations were then tested for conformation to Hardy-Weinberg

equilibrium. A significant heterozygote deficiency (p<0.001and p<0.05) was

observed particularly at UTD328 (in 8 collections out of 12) potentially indicating

presence of null alleles. Also in three collections for locus UTD203 and in two

collections for locus UTD73, significant heterozygote deficiencies were observed

(Table 4.15). Site-wise, except at TA, all sites showed heterozygote deficiencies for

at least one locus, and sometimes at two loci. After Bonferroni correction however,

only five collections showed significant heterozygote deficiencies. As deviation

from Hardy-Weinberg equilibrium (HWE) resulting from heterozygote deficiencies

can indicate of stock admixture, only Clade I individuals were then tested for

conformation to Hardy-Weinberg equilibrium with the expectation that deviation

from HWE will be reduced if two clades represent two stocks. Four of the five

heterozygote deficiencies became non-significant (after Bonferroni correction), but

two new populations for locus UTD328 became significant (TR’02 and TR’04)

(data not shown). This result may also provide some indication for admixture of

genetically heterogeneous groups.

In addition to heterozygote deficiencies, SJT microsatellite data also showed

linkage disequlibria (Table 4.16). Heterozygote deficiencies combined with

evidence of linkage disequilibrium may indicate admixture of SJT genetically

heterogeneous groups among the sampled populations. The neighbour joining tree

and haplotype network also show that representatives of the two SJT clades were

widely dispersed across the sampled space, supporting clade admixture.


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Table 4.14 Characteristics of microsatellite loci developed for SJT. T a 0C Annealing temperature

Locus Repeat motiff primer sequence (5'-3') Expected product size Ta 0C.

UTD149 (GGA)11 F ACC GGT GGC TTG AAG ATT GAC AG 262 56 R GTA AAG CTC TCT CTC CTC TCC CT UTD203 (GAA)7CT(GAA)2 F CCC TGT GCT GTC TGT GAA G 157 48 R TTG AAT CAA TGG CAA CTG GA UTD73 (AACT)6 F TGT GTG ATG AAG CTA AAG 135 50 R CAA AAA TAT AGC CTT CGT UTD328 (GCT)8 F GAG AGA GAA GCG GAC AGG ATA GG 143 50 R TGA GTA ATA GAG AGT GGG AAT GG UTD172 (GACT)5 F GTT GTG TAT TTT TGG CTG GAC C 145 55 R CAA CAG CTA ACG GGC AAA TTC C UTD329 (AACT)7 F TAC TGG GTG ATG AAG CTA AAG AC 146 52 R TCG TAA GGG AAT ATA AAA AAG TG UTD 522 (GATA)17 F GATTATGTTCAGTGTTCCAAGCTC 389 58 R CACAGACAGGAAAGCAATCA UTD523 (GATA)18 F TTT GAA TGG GAG ACA TGC AG 247 51 R TGT CCT GCA CTT GTG TTC ACT UTD526 (GATA)28 F GCT CTA AAT TAA ATG GAG CAT CAA A 245 52 R GCA GAA TCC AGT CTA GTG CAA A UTD528 (CTAT)11 F GGC CTA GCT AGC AGA ATC ACT C 150 54.5 R AGT GCC ATT GAA CCC ACC TA UTD529 (GACA)4 GACGA (ATAG)22 F ACCCAGCAATTGACATCTGA 245 58 R ACTAATGAATTCGCGGCC UTD530 (TAGA)14 TATA (TAGA)5 F GTT TAA GGC CTA GCT AGC AGA A 188 52.5 R TCC CCG AGA GTG AAA ATG TC UTD531 (ATCT)16 F GCA GTC CTG TGG GTG ATT AAA 201 55 R GGT AAG TAT CAG AGG CTC TAC CAT C UTD532 (TATC)21 F GGC CTA GCT AGC AGA ATC CA 190 52 R TGC TGC CAT TAT ACC TGC AT UTD533 (CTAT)12 F ACGCGTCAGACTGCACTTC 225 60 R GCACATATTACGGTAAATACACCG UTD535 (AGAT)9 F CAC TGA AGA TAT AGG CAG CCT TG 193 52.5 R TTT CTC CAG CGG CAT TAC AT UTD540 (ATAG)17 F TCA TCC TCT CCA TTG AAC CTC 236 53 R GGC CTA GCT AGC AGA ATC ACA


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

Significant probability values after the Bonferroni correction (initial α = 0.05/36 = 0.0014).

Locus Sample UTD328 UTD203 UTD73

Average across loci

NE'01 n 22 21 21 21 a 10 5 6 7 He 0.850 0.224 0.731 0.602 Ho 0.682 0.238 0.714 0.545 HW (p) 0.000* 1.000 0.500 NE'02 n 16 17 17 17 a 11 6 8 8.33 He 0.919 0.559 0.702 0.727 Ho 0.765 0.294 0.765 0.607 HW (p) 0.011 0.009 0.294 NE'03 n 14 12 14 13 a 10 5 6 7 He 0.873 0.442 0.746 0.687 Ho 0.857 0.167 0.785 0.603 HW (p) 0.736 0.016 0.663 WE'01 n 52 40 52 48 a 12 9 11 10.33 He 0.900 0.448 0.749 0.698 Ho 0.731 0.450 0.577 0.586 HW (p) 0.000* 0.269 0.091 TA'01 n 11 11 9 10 a 6 4 5 5 He 0.839 0.337 0.777 0.651 Ho 0.818 0.273 0.555 0.548 HW (p) 0.111 1.000 0.283 TA'02 n 4 5 3 4 a 7 3 3 4.33 He 0.964 0.377 0.600 0.647 Ho 0.750 0.400 0.333 0.494 HW (p) 0.399 1.000 1.000 TA'03 n 26 26 25 26 a 6 5 7 6.0 He 0.829 0.440 0.493 0.587 Ho 0.692 0.538 0.520 0.583 HW (p) 0.121 0.663 0.848 KM'02 n 52 53 48 51 a 13 5 10 9.33 He 0.857 0.440 0.757 0.684 Ho 0.635 0.547 0.708 0.630 HW (p) 0.000* 0.075 0.000* TR'02 n 23 25 21 23 a 8 5 9 7.33 He 0.843 0.407 0.761 0.670 Ho 0.391 0.240 0.809 0.480 HW (p) 0.003 0.002 0.773 TR'04 n 22 22 17 20 a 9 2 6 5.66 He 0.854 0.210 0.812 0.625


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Ho 0.545 0.181 0.647 0.458 HW (p) 0.009 1.000 0.003 LC'02 n 49 49 48 49 a 13 8 8 9.66 He 0.880 0.432 0.734 0.682 Ho 0.714 0.387 0.771 0.624 HW (p) 0.003 0.598 0.498 MD'03 n 51 53 52 52 a 12 6 10 9.33 He 0.856 0.461 0.679 0.665 Ho 0.667 0.452 0.673 0.597 HW (p) 0.000* 0.264 0.3121

(*, p<0.001)

Table 4.16 Linkage disequilibrium results. The values in bold type are significant probability values of Exact test after the Bonferroni corrections. (initial α = 0.05/36 = 0.0014). Collection Linkage p value of the exact test 1.Site wise NE’01 203 & 73 0.0198 NE’02 None NE’03 None WE’01 328 & 203 0.0033* TA’01 328 & 203 0.0225 TA’02 None TA’03 None KM’02 328 & 73 0.0003* 203 & 73 0.0389 TR’02 328 & 203 0.0102 328 & 73 0.0000* TR’04 328 & 73 0.0271 LC’02 328 & 203 0.0435 MD’03 None 2. Locus-wise WE’01, TR’02, LC’02 328 & 203 NE’01, KM’02 203 & 73 TA’01, KM’02, TR’02, TR’04 328 & 73 (*, p<0.001)

Linkage disequilibrium was detected for NE’01, WE’01, TA’01, KM’02, TR’02, TR’04

and LC’02 (Table 4.18). UTD328 and UTD203 were linked or showed gametic or

genotypic association at the following sites. WE’01, TR’02 and LC’02; locus UTD73 and

UTD 203 were linked at NE’01 and KM’02; Locus UTD 328 and UTD73 were linked at


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TA’01, KM’02, TR’02 and TR’04. After Bonferroni correction (p<0.0042), linkages at

three sites only were significant (WE’01, KM’02 and TR’02).

Of the three SJT microsatellite loci screened, a characteristic of locus UTD203 was that a

single dominant allele (allele 10) was present in relatively high frequencies at all sites

(71.70% to 82.98%) (Table 4.18). Locus UTD328 and locus UTD73 were characterized by

allele 5 and allele 3 at high frequencies, respectively, (Figure 4.7 and Table 4.17 and 4.19).

For all three loci, rare alleles were present in all sites.

Figure 4.7 Microsatellite allele frequency distributions in SJT. Of allele frequency distributions at each site, from left to right Locus UTD328, Locus UTD203, Locus UTD73.


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Table 4.17 Allele frequency distribution of SJT Locus UTD328 Allele NE WE TA KM TR LC MD AL1 3.77 1.92 - 0.96 2.22 - 0.98 AL2 3.77 3.85 - 5.56 6.12 3.92 AL3 7.55 9.62 - 7.69 5.56 12.24 10.78AL4 8.49 13.46 1.22 13.46 11.11 18.37 8.82 AL5 9.43 9.62 9.76 13.46 8.89 7.14 4.90 AL6 16.98 12.50 17.07 7.69 12.22 8.16 12.75AL7 13.21 16.35 26.83 27.88 16.67 16.33 18.63AL8 24.53 16.35 20.73 16.35 31.11 19.39 27.45AL9 8.49 4.81 13.41 4.81 - 6.12 6.86 AL10 0.94 5.77 4.88 1.92 2.22 1.02 1.96 AL11 - 5.77 6.10 2.88 4.44 2.04 - AL12 0.94 - - - - 2.04 2.94 AL13 1.89 - - 1.92 - 1.02 - AL14 - - - 0.96 - - -

Table 4.18 Allele frequency distribution of SJT Locus UTD203 Allele NE WE TA KM TR LC MD AL1 1.00 1.25 - 0.94 - - - AL2 - 0.00 - - - - - AL3 - 1.25 - - - - - AL4 - - - - - - - AL5 - - 3.57 - - - - AL6 - - - - 4.26 - - AL7 4.00 1.25 - 0.94 - 6.12 0.94 AL8 4.00 17.50 5.95 21.70 - 4.08 12.26AL9 79.00 73.75 77.38 71.70 82.98 75.51 72.64AL10 11.00 2.50 10.71 4.72 11.70 10.20 11.32AL11 1.00 1.25 - - - 1.02 1.89 AL12 - 1.25 - - - 1.02 0.94 AL13 - - - - 1.06 - - AL14 - - - - - - - AL15 - - - - - 2.04 - AL16 - - 1.19 - - - - AL17 - - 1.19 - - - -

Table 4.19 Allele frequency distribution of SJT Locus UTD73 Allele NE WE TA KM TR LC MD AL1 - 0.96 - - 5.41 - 1.92 AL2 3.85 7.69 2.70 1.04 10.81 4.17 10.58AL3 38.46 14.42 22.97 17.71 24.32 26.04 19.23AL4 30.77 42.31 58.11 41.67 37.84 40.63 51.92AL5 18.27 22.12 8.11 17.71 16.22 17.71 8.65 AL6 3.85 5.77 6.76 4.17 2.70 8.33 2.88 AL7 0.96 2.88 0.00 11.46 1.35 2.08 1.92 AL8 0.96 1.92 1.35 - 1.35 - - AL9 - 0.96 - 2.08 - 1.04 - AL10 2.88 0.96 - 3.13 - - 1.92 AL11 - - - - - - 0.96 AL12 - - - 1.04 - - -


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Allelic diversity was relatively high at locus UTD328 ranging from 10 (site TR) to 12

alleles per site (sites KM, NE and LC). At locus UTD203 allele diversity ranged from 4

(site TR) to 8 (site WE), while at locus UTD73 alleles were ranged from 6 (site TA) to 10

(site WE) (Figure 4.7, and Table 4.17 to 4.19).


Hierarchical AMOVA analysis results for SJT microsatellite data are summarised in Table

4.20.

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

Observed partition

Structure tested Variance % Total

F statistics

1 Total collection (2001,2002,2003,2004) Among populations 0.009 0.99 FST = 0.10*** Within populations 0.893 99.01 2 Year-wise collection (2001, 2002, 2003, 2004)

Among groups 0.00104 0.11 FCT = 0.0011

Among populations (of the same year) within groups 0.0140 1.55 FSC = 0.0155***

Within populations 0.8902 98.34 FST = 0.0167 3 Site-wise collections

Among groups (different sites) -0.0070 -0.78 FCT = -0.0078

Among populations within groups (same site collections of different years) 0.0216 2.39 FSC = 0.0237

Within populations 0.8902 98.39 FST = 0.0161 4 Clade-wise Between two clades 0.0031 0.34 FCT = 0.0034 Among samples within clades 0.0102 1.12 FSC = 0.0113*** Within populations 0.8920 98.53 a Clade I-site wise Among sites 0.0105 1.16 FST = 0.0116*** Within sites 0.8957 98.84 b Clade II-site wise Among sites 0.0094 1.08 FST = 0.0107 Within sites 0.8606 98.92

(*** p < 0. 001)


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The result for the entire data set showed significant genetic differentiation among sites

namely; NE, WE, TA, KM, TR, LC and MD (FST = 0.10, p<0.001). Pair-wise FST analysis

identified which sample collections were heterogeneous and their relative level of

heterogeneity (Table 4.21). Significant genetic variation was observed between most pairs

of sample sites for nDNA markers.

No significant temporal genetic differentiation was evident among year-wise groups

namely; 2001, 2002, 2003 and 2004 (FCT = 0.0011, p>0.05). Similar results were obtained

when this test were repeated excluding locus UTD328 due to the potential presence of null

alleles at this locus (FCT = -0.00009, p = 0.3900). Significant spatial genetic variation was

detected however, among sites within year (FSC = 0.0155, p<0.001) (Table 4.20), with

similar results obtained for the data set when locus UTD328 (FSC = 0.0226, p<0.001) was

excluded.

Table 4.21 Pair-wise FST among sampling sites of SJT after Bonferroni correction for entire collection based on microsatellite data. (initial α = 0.05/21 = 0.002). NE WE TA KM TR LC MD NE 0.000 WE 0.02 0.000 TA 0.023*** 0.011 0.000 KM 0.026*** -0.008 0.021*** 0.000 TR -0.002 0.005 0.023 0.022*** 0.000 LC 0.004 -0.002 0.02*** 0.011 -0.003 0.000 MD 0.018*** -0.001 0.003 0.013 0.003 0.006 0.000

(*** p < 0.0001)

Pair-wise FST analyses were conducted for each year-wise collection to determine which

sample collections were significantly different and their relative level. Results shown in

Table 4.21 are after Bonferroni correction for multiple tests of pair-wise FST analyses.

Significant genetic variation was evident between most population pairs, in all years, but

only before Bonferroni correction.


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Hierarchical AMOVA was conducted to determine if sites varied temporally in different

years. No genetic differentiation was evident among groups for temporal collections at

individual sites (FCT = -0.0078, p = 0.7185). There was also no significant genetic

differentiation evident among temporal collections at individual sites (FSC = 0.0155, p =

0.1212). Similar results were obtained for the above two tests when locus UTD328 was

excluded (FCT = -0.0217, p = 0.8622) and (FSC = 0.0425, p = 0.5474) respectively.

Table 4.22 Pair-wise FST among sample collections of SJT in different years after Bonferroni correction based on microsatellite data. (initial α = 0.05/3 = 0.016 for 2001 and 2003 collections, and α = 0.05/10 = 0.005 for 2002 collection ). (a) 2001 collection NE WE TA NE 0.000 WE 0.019 0.000 TA -0.011 0.003 0.000

(b) 2002 collection NE TA KM TR LC NE 0.000 TA -0.047 0.000 KM 0.052*** 0.031 0.000 TR 0.043 0.083 0.012 0.000 LC 0.023 0.009 0.011 -0.007 0.000

(c) 2003 collection NE TA MD NE 0.000 TA 0.033 0.000 MD -0.002 0.016 0.000

(***, p<0.001) Significant spatial heterogeneity in each year identified using pair-wise FST analyses were

not significant in most instances after Bonferroni correction.

As SJT mtDNA results reveal two relatively divergent clades, the microsatellite data were

tested for differentiation among the mtDNA clades. No significant differentiation was

observed for three microsatellite loci when individuals were grouped by mtDNA clade


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type, although significant differentiation was evident among populations within clades

(Table 4.20). The lack of consistency between the mtDNA and microsatellite data may

suggest that the mtDNA clades are relics of a vicariant event, or low sample sizes failed to

reveal true genetic differentiation where it was present. The very high within population

variation (>98%) and moderate among populations within clade variation (1%) probably

contributed to the lack of significant differentiation among clades. Microsatellite clade-

wise AMOVA shows that the genetic differentiation was limited to Clade I only (Table

4.20). The low sample size of Clade II individuals at most sites may not have been

sufficient to allow adequate testing of variation at this hierarchical level.

To summarise: population genetic structure analyses of hierarchical AMOVA and pair-

wise FST based on microsatellite data, show that significant spatial heterogeneity in gene

frequencies was evident among sampled sites around Sri Lanka in any given year. MtDNA

data strongly support the same inference.

As heterozygote deficiencies and linkage disequilibrium were observed in the nDNA data,

this suggests the potential for admixture of genetically heterogeneous SJT groups. To test

for admixture, nDNA data were analysed using the programme “STRUCTURE” (Pritchard

et al., 2000) (Table 4.23).

As the SJT sample collections KM’02 and TR’02 both showed large heterozygote

deficiencies and linkage disequilibria, these two collections were subjected to analysis in

“STRUCTURE” with the possible number of groups set at; “K” = 1 to 5. Results of the

analysis (Table 4.23) showed that the TR’04 collection consisted of a single genetic stock

while the KM’02 collection consisted of an admixture of five genetically different


141

populations indicating the potential presence of genetically heterogeneous ‘sub

populations’.

Table 4.23 Admixture analysis of SJT (in STRUCTURE)

KM'02 K lnP(D) Var[ln P(D)] α 1 FST1 FST2 FST3 FST4 FST5 1 -444.7 5.8 0.0019 2 -580.4 336.3 0.7395 0.1942 0.038 3 -460 37.1 2.1478 0.1009 0.0154 0.0387 4 -464.9 131.9 0.0899 0.1674 0.1411 0.0023 0.0558 5 -462.1 111.7 0.1348 0.0004 0.1099 0.0206 0.1216 0.1274

TR'02 K lnP(D) Var[ln P(D)] α 1 FST1 FST2 FST3 FST4 FST5 1 -185.5 1 0.0001 2 -190.1 8.4 2.7287 0.0151 0.0264 3 -194.7 24.5 1.0289 0.0440 0.0691 0.0775 4 -224.4 98.5 0.2557 0.0728 0.0345 0.0233 0.1065 5 -183.3 3.5 1.1951 0.0002 0.0066 0.0055 0.0431 0.042

Effective population size, population divergence and migration

Table 4.24 presents the extent of gene flow (i.e. effective number of gene migrants)

between pairs of sites. While overall results show asymmetry of migrant exchange to each

site from rest of the sites, migration from almost all sites to WE is high. Effective

population sizes however were extremely large (Table 4.25).


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

for SJT based on mtDNA and microsatellite data. Columns 2 to 8 are the receiving sites

while rows are donor sites. (e.g. M value from TA to NE (m1) = 13.659 (below the

diagonal), and M value from NE to TA (m2) = 2.316 (above the diagonal). All M values

are within the 95% confidence limit and do not overlap 0.

NE WE TA KM TR LC MD NE 2.828 2.316 10.983 2.479 0.017 15.550 WE 9.716 0.022 4.546 0.510 1.518 0.045 TA 13.659 16.886 0.321 6.624 8.973 0.066 KM 9.340 135.758 1.332 0.895 0.216 4.043 TR 0.453 11.241 0.562 2.619 0.021 0.043 LC 0.651 10.114 3.720 0.467 1.790 1.254 MD 0.068 0.060 2.416 7.824 2.669 6.396

Table 4.25 Effective population sizes (N1 and N2) between pairs of sites for SJT based on

mtDNA and microsatellite data. Columns 1-7 represent the estimated effective population

sizes (eps) of the respective column site (e.g. column 1 represents the eps of NE with

respect to other six sites in rows 2-7). 1 2 3 4 5 6 7 NE WE TA KM TR LC MD 1 NE 412519 368826 237072 94177 142713 302267 2 WE 277804 208559 145367 148393 209737 384288 3 TA 286744 720949 348896 171841 200336 523268 4 KM 317084 1407684 246969 122932 89122 274230 5 TR 335128 399807 319381 303124 238864 354342 6 LC 240280 891126 261725 282695 138118 495848 7 MD 518944 520216 215013 416153 209889 237439

Although the estimated effective population size values for some sites shows very broad

distributions they are within 95% confidence interval and do not overlap 0. Overall,

effective population sizes are ranged from 1X105 to 5X105, except one value that 140X105

at WE site.


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4.5 Discussion


Samples of SJT from seven fishing grounds around Sri Lanka and the Laccadive and

Maldives islands were examined with respect to variation in mtDNA and three

polymorphic microsatellite loci.

Phylogenetic analyses strongly support the presence of two divergent mtDNA SJT clades

in this part of the Indian Ocean. The level of divergence between the two most common

haplotypes within each of the two clades (haplotype 6 in Clade I and haplotype 37 in Clade

II) and the most common ancestral haplotype (haplotype 6) was 1.85 %. Pair-wise

divergence among haplotypes in the parsimony cladogram in this study (based on ATPase

region) ranged from 0-3.7 %. Studies on a number of other tuna and billfish species have

also described the existence of multiple highly divergent intra-specific mtDNA lineages

within ocean basins or among ocean basins, indicating that in general tuna and billfish

species have been exposed in the past to similar evolutionary processes probably due to

exposure to similar environmental and climatic conditions (e.g. Pleistocene climate

change). Examples for the existence of two highly divergent mtDNA lineages in tuna

species include; Atlantic BET (Martinez et al., 2005), Atlantic Bonito (Vinas et al.,

2004a), and several billfishes (Scombroidei: Xiphidae) including blue marlin (Buonnacorsi

et al., 2001), sailfish (Graves and McDowell, 2003), and swordfish (Alvarado Bremer et

al., 2005; Buonnacorsi et al., 2001; Graves and McDowell, 2003). Atlantic Bonito consist

of two sympatric clades in the Mediterranian Sea (Vinas et al., 2004a) whereas some

billfishes (Alvarado Bremer et al., 2005; Buonnacorsi et al., 2001; Graves and McDowell,

2003) and BET (Alvarado Bremer et al., 1998; 2005) show two sympatric clades in the

Atlantic Ocean. Ely et al. (2005) have also reported two divergent mtDNA lineages of SJT


144

in the Atlantic and Pacific Oceans. The current study is the first report however, of two

divergent mtDNA lineages for SJT within the Indian Ocean basin.

Divergent SJT mitochondrial clades in the Indian Ocean could have originated from a

common vicariant event resulting from a general lowering of the temperature of the water

that produced reduction of tropical marine habitats, and isolation of populations during

recent Pleistocene glacial maxima (Alvarado Bremer et al., 1998, 2005; Graves and

McDowell, 2003; Vinas et al., 2004a) with associated sea level changes during that time

(Rholing et al., 1998). The estimated times since vicariant event for Clades I and II are

0.288 X 106 years and 0.844 X 106 years bp, respectively. Secondary contact during inter-

glacial periods by unidirectional gene flow of formerly allopatric populations could result

in the contemporary asymmetrical distributions of the clades observed here (e.g. Alvarado

Bremer et al., 2005; Peeters et al., 2004).


The two distinct SJT clades (98% bootstrap value) revealed in this study strongly indicates

that there have been at least two genetically distinct SJT evolutionary units in this region of

the Indian Ocean in the past.

Both mtDNA and nDNA microsatellite data show strong evidence for spatial genetic

heterogeneity among SJT populations around Sri Lanka and adjacent areas in the Indian

Ocean. Hierarchical AMOVA and Pair-wise ФST analyses detail the level of heterogeneity

and lack of gene flow among sites. MtDNA data further show temporal genetic

heterogeneity. Less spatial heterogeneity was evident for the nDNA data, but this is

expected given that recombination and differences in mutation rates will tend to mix


145

variation over time, hence there is a four times higher chance of genetic drift effects and

genetic differentiation influencing patterns of variation in mtDNA compared with the

nDNA genome.

The spatial genetic heterogeneity of SJT sample populations revealed by the analyses here,

may however not show ‘true SJT stock structure’. Temporal SJT collections show the

genetic differentiation within sites is not stable over years, and even within months.

Temporal instability of genetic differentiation may be due to unrepresentative samples of

‘real’ populations or inadequate genetic sampling of SJT populations in this region. The

genetic population structure of SJT revealed in the analyses, in this sense, may be due to a

‘genetic noise’. Further, demographic analyses of SJT shows that SJT population sizes are

extremely large. The opportunistic recruitment patterns reported for SJT (Andrade and

Santos, 2004) increase the difficulties for a successful SJT sampling regime to obtain SJT

sample populations that represent true populations. To test whether the sample collections

were representative or not, sampling would need to be done at larger scale within an area

continuously through fishing seasons and at very fine spatial scales at the SJT fish school

level for several years. This in practice however, is very difficult.

The two different mtDNA clades identified here may spawn in distant areas of the Indian

Ocean and move as juveniles towards Sri Lanka as a highly fertile feeding ground. Satellite

images have shown very high primary productivity/chlorophyl ‘a’ concentration around Sri

Lanka during the southwest monsoon period (Wiggert et al., 2006) and in addition there is

a famous fishing ground (Wadge Bank) near to Sri Lanka. In contrast, physical admixture

of the two clades at sites could also result from differential passive transport of larvae of

different clades by monsoonal currents in the Indian Ocean.


146

The pattern of genetic differentiation based on mtDNA data can potentially be explained

by three hypotheses.

1. The two mtDNA SJT clades in this region of the Indian Ocean were allopatric or

may have been isolated in the past due to barrier(s) to dispersal, but have contacted

secondarily when geographical barrier(s) were removed, and since this time

random mating has occurred between the two mtDNA clades.

2. The two mtDNA SJT clades were derived from sympatric SJT population by

random lineage sorting.

3. The two mtDNA SJT clades were isolated historically, and mix currently as adults

in feeding ground, but individuals from the two clades may not interbreed.

The nDNA microsatellite data for the two SJT clades show deviation from Hardy-

Weinberg equilibrium, and linkage disequilibrium which together provide evidence that

individuals from the two clades may not interbreed at random and hence the two clades

may constitute different breeding units. Further, Hardy-Weinberg equilibrium tests for

populations of Clade I individuals only showed reduced deviation from Hardy-Weinberg

equilibrium which provides additional support for the presence of two discrete

heterogeneous SJT units. The, low sample size of Clade II population here limits however,

the power of the analysis. The low sample size of Clade II together with temporal

instability of spatial genetic differentiation evident in the mtDNA data significantly restrict

the power of the analysis and hence may affect the ambiguity of ‘true biological population

structure’. Thus, the genetic heterogeneity of SJT sample populations reported here in

mtDNA and nDNA analyses used to infer apparent SJT ‘population structure’ may be due

to ‘genetic noise’. To conclude unambiguously that there are two or multiple stocks,

further analyses will be required that employ increased temporal sample sizes in general

for all collections and especially for Clade II individuals.


147

The degree and pattern of genetic differentiation observed here in the SJT mtDNA and

nDNA data could have been influenced by many factors, including presence of social

structure, discrete mating systems, differential dispersal capabilities, cohesion of parents

and offspring, and/or historical events. These processes can lead to specific patterns of

gene flow, genetic recombination, natural selection and random drift, which in turn have an

impact on population genetic structure (Avise, 1994). Understanding what has ‘driven’ the

evolution of this pattern in Sri Lankan SJT will require more detailed studies of local

patterns of genetic diversity and fine-scale geographical sampling. Regardless of the casual

factors, this study provides strong evidence that there are at least two distinct SJT

evolutionary units in this region of the Indian Ocean where interbreeding is potentially rare

among the two genetic types. If interbreeding is rare among the two genetic types, this may

reflect the presence of multiple independent spawning grounds.

Demographic history

Effective population sizes are extremely large compared with that evident for YFT in the

same region. The divergence times of SJT also high compared with that of YFT showing

that SJT overall population divergence has occurred a very long time ago while YFT

populations have diverged more recently (data not shown).

General Discussion

148

CHAPTER 5

GENERAL DISCUSSION

5.1 Comparison of population genetic structure of YFT and SJT

The current study has revealed striking contrasts in phylogeny, genetic diversity,

population genetic heterogeneity, gene flow, demographic history and effective

population sizes between YFT and SJT in Sri Lankan waters.

Comparatively, SJT shows high genetic diversity (49 haplotypes were found among

324 individuals) and a deep phylogeny with two divergent clades, while YFT

haplotypes are all closely related to a single common ancestral haplotype that is

present at high frequency in all sampled sites (19 haplotypes were found among 286

individuals).

As YFT shows a single common ancestral haplotype at highest frequency at all

sites, the inferred amount of ongoing gene flow among sites is most likely relatively

high and so populations constitute a single panmictic unit. In SJT, the genetic

differentiation among sites was very high indicating the potential for low gene flow

among sites even though the lack of temporal genetic stability of SJT collections in

the region means that this inference will need further study.

YFT populations have apparently experienced a recent sudden population

expansion probably preceded by a significant population bottleneck. In contrast

SJT populations overall have apparently remained an extremely large, stable

population over a very long time period with a recent expansion of Clade I types.

General Discussion

149

Effective population sizes of YFT were low compared with that of SJT although

YFT populations have undergone an expansion in the recent past. These patterns

imply very different historical and modern demographic histories. Hence the

contrasting genetic diversity, gene flow and effective population sizes of YFT and

SJT essentially require different sampling regimes and strategies for further

investigations to confirm the stock status of the two species.

5.2 YFT population structure

The current study revealed a lack of genetic divergence of YFT indicating a single

panmictic YFT stock in the north-central and west central Indian Ocean. The data

obtained here reflect several potential phenomena (i.e. high level of dispersal, a

characteristic demographic history and/or intense fishing pressure).

YFT are very large, highly energetic fish inhabiting offshore, open ocean

environments, hence a high level of dispersal is expected. As described early,

previous tagging studies have shown high dispersal capability for YFT. Extensive

dispersal provides ample opportunity for interbreeding of distant populations

resulting in high gene flow and hence low levels of genetic differentiation among

YFT populations. The overall results obtained in this study reflect such a situation.

The monsoon ocean currents (which aid long distance dispersal) flow in completely

opposite directions within the two halves of the year, a high degree of mixing would

therefore be expected.

The results obtained here may reflect a characteristic demographic history for YFT.

As demographic history analyses showed, the YFT populations here have

General Discussion

150

undergone a sudden population expansion possibly preceded by a population

bottleneck. Sudden population expansion results in a large effective population size,

and this effective population size will dilute further genetic drift effects.

The lack of deep structure for YFT observed here, may be the result of intense

industrial fishing pressure on the species (e.g. Smith et al., 1990). In fact, as

described previously, commercial fishing pressure on YFT in the Indian Ocean has

been intensifying over a long period of time (IOTC, 2002). Alternatively only a

single stock has been present in Sri Lankan waters for an extended period of time.

Given the above inferences there is a need for further investigation of subtle genetic

differentiation observed here, covering a wider range of geographical area, most

probably in the east-west direction of the Indian Ocean with increased number of

samples and loci.

5.2.1 Comparison with other tuna studies

Due to their commercial importance, a considerable number of population genetic

studies have been undertaken on YFT in recent times, including both within ocean

and among ocean comparisons (Table 5.1). Overall, FST values among populations

have been generally low and most have not been significant. Where significant

differentiation has been identified it has generally been reported among collections

within oceans (e.g. Ward et al., 1997). But, even at this scale, most studies have

reported no significant genetic variation even within ocean collections (e.g. Pacific

and Atlantic YFT study by Scoles and Graves, 1993; Indian Ocean YFT study by

Ward et al., 1997). Thus, the general view has been that there is substantial ongoing

gene flow among YFT populations around the world. While sampled YFT

General Discussion

151

populations in this study showed some significant genetic differentiation at least for

mtDNA data, overall the variation cannot be considered significant and so there is

no strong argument for rejecting the null hypothesis of panmixia.

Why some significant differentiation even at a small geographical area was

identified here most likely relates to several important differences between past

studies and the general approach to sampling regime used here. Differences in

sampling regime, unique ocean current patterns in the study area, and the relative

sensitivity of the molecular techniques used in this study (i.e. mtDNA sequencing in

this study compared with mtDNA RFLP technique in previous studies, and nDNA

microsatellite markers compared with Allozymes in many previous studies), all

have most likely contributed to different data outcomes.

Table 5.1 A summary of previous population genetics studies of YFT showing

heterozygosity estimates and FST values for YFT (Ward, 2000b). P- Pacific Ocean,

A- Atlantic Ocean, I- Indian Ocean. In the column named as ‘marker’ within

parenthesis are numbers of restriction enzymes used for mtDNA, and number of

loci used for micrsosatellites. Het- Heterozygosity . FST - within ocean differences,

FSO - differences among groups within oceans, FOT - differences among oceans.

Oceans Marker Het. No. of

CollectionsFST (Within ocean)

FSO (Among gps- within ocean)

FOT (Among oceans)

Reference

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

mtDNA(12) mtDNA(2) Allozyme(4) mtDNA(12) mtDNA(2) Allozyme(4) Microsatellites(6) mtDNA(2)

0.85 0.68 0.36 0.85 0.68 0.36 0.78 0.71

6 9 8 5 6

6 5-8 2

-0.021 0.010**0.025**-0.015 0.012* 0.027**0.002* 0.009

-0.015 0.012* 0.027**

-0.006 -0.002 -0.002

Scoles & Graves(1993)Ward et al.,(1997) Ward et al.,(1997), Scoles & Graves(1993)Ward et al., (1997), Grewe & Ward (unp) Ward et al., (1997) Grewe & Ward(unp) Ward et al., (1997)

* p<0.05, ** p<0.01

General Discussion

152

Effect of sampling regime

A major difference between results for YFT in the current study and most of the

previous work relates to the scale at which sampling was conducted. The

geographical scale of a sampling in most previous studies has been very large with

individual population’s often constituting individuals sampled across 100’s if not

1000’s of square kilometers of ocean. YFT population sampling in the current study

was conducted at much finer spatial scales and the data indicate that YFT can show

some population differentiation at fine geographical scales. For example, a global

population genetic study of YFT in the Pacific, Atlantic and Indian Oceans showed

only limited spatial heterogeneity among nine collections across three oceans (FST =

0.023, p = 0.048) for mtDNA markers (Ward et al., 1997). Another very recent

study of global population structure in YFT (n = 148) analysed samples from four

regions in three oceans; Atlantic (northwest Atlantic, Ivory Coast), Pacific and

Indian Oceans. MtDNA control region sequence data did not reveal however,

genetic differentiation of samples among oceans or within oceans. The same

samples screened using an RFLP method for the mtDNA ATP-COIII gene region

revealed only slight genetic differentiation between Atlantic and Pacific YFT

populations (Ely et al., 2005). In the current study, some significant spatial

heterogeneity was detected among seven collections within just the north central

region of the Indian Ocean (around Sri Lanka) for mtDNA markers (Global ФST =

0.1285, p<0.0001). This significant spatial heterogeneity however, was largely due

to genetic differences at two sites (KK and KR). Further, a YFT study of six regions

in the western Pacific Ocean (Coral Sea, east Australia, Fiji, Indonesia, Philippines

and Solomon Islands) and two regions in the eastern Pacific Ocean (California and

Mexico) using five polymorphic microsatellite loci by Appleyard et al. (2001)

General Discussion

153

showed no evidence of population differentiation among the eight regions at four

loci, with only a single locus showing small, but significant differentiation (FST =

0.002, p<0.001). Similarly, no overall significant differentiation was detected with

microsatellites at this fine geographical scale in the current study (FST = -0.063,

p>0.05).

When tuna are sampled over a large geographical area, and then compared with

another sample from a similarly large geographical area, it is possible that only very

low genetic differentiation will be evident. Collections/ samples taken over large

geographical areas, can potentially homogenize variation that may exist at smaller,

local spatial scales. If two such collections were compared, genetic differentiation

among them may be very low. If each is partitioned however, fine scale structure

may be apparent. In the current study, population samples represented only a

relatively small geographical area of the Indian Ocean, for example i, ii, or iii in

Figure 5.1. As the haplotype/allele composition within i, ii and iii are different,

when compared they will produce significant estimates of spatial structuring. If

samples were taken at larger geographical scales (i.e. 1) representing the

combination of sub samples from sites i, ii and iii, and if this sample 1 were

compared with similar samples 2 and 3, which also represent collections over large

geographic areas, genetic differentiation among 1, 2 and 3 may be very low, and

hence no structure or only weak structure may be detected. This essentially ignores

real patterns of variation that could have significant management implications. The

geographic scale of ‘management units’ is determined however by a number of

biological and socio-economical factors.

General Discussion

154

Most population genetic studies of tunas and billfishes in the past have been

undertaken with single samples taken to represent very large geographical areas.

Many have also reported either no structure or only limited (weak) population

differentiation. This pattern in general, is true for most large scale studies (inter-

oceanic) of the large commercial tuna species and billfishes; SBT, YFT, BET,

swordfish and marlins (Ward et al.,1994b; Graves et al., 1984; Appleyard et al.,

2001; 2002; Ely et al., 2005; Alvarado Bremer et al., 1995).

A, B,

CA , D, E

A, F,

GA, B,

CA , D, E

A, F,

G

A, B,

C

A , D, E

A, F,

GA, F,

G

A , D, E

A, B,

C

1

23

i

ii

iii

Figure 5.1 A schematic diagram to show the effect of geographical scale of the sampling regime. Map source: Google earth www.earth.google.com Legend: A-G represents different haplotypes/alleles. Small circles represent subsampling areas (i, ii and iii) within a large geographical zone (1, 2 and 3) averaging the distribution and frequencies of haplotypes at large spatial scales can mask underlying population structure at finer spatial scale i.e. at i, ii and iii within zone 1.

General Discussion

155

More recently, when studies of the same species have been carried out at finer

spatial scales representing smaller geographical areas, some of them now report

significant genetic differentiation. One such study examined genetic structure of

Atlantic BFT in the Mediterranean Sea (Carlsson et al., 2004). Samples were taken

from three zones within the Mediterranean Sea (Balearic, Tyrrhenian and Ionian

Seas) and significant spatial genetic heterogeneity was observed among samples for

both microsatellite markers (9 loci) and mitochondrial control region sequences (FST

= 0.0023, p = 0.038 and ΦST = 0.0233, p = 0.000, respectively). A very recent study

of 800 Atlantic BFT taken from a relatively small geographical area south of

Iceland (560N-620N and 120W-300W) and screened with six microsatellite loci,

reported slight, but significant genetic divergence (FST = 0.00223, p = 0.013) among

samples taken south east and southwest of Iceland over two years (Carlsson et al.,

2006). In addition, sequence analysis of the mtDNA control region of Atlantic

bonito in four samples (n = 195) collected along the northern Mediterranian Sea,

revealed significant spatial heterogeneity among regions (ΦST = 0.068, p = 0.000)

(Vinas et al., 2004a). A very good example of this apparent paradox is evident in

Atlantic Cod where fine geographical scale population structure was detected in a

species previously demonstrated to be homogeneous over very large spatial scales

by Knutsen et al. 2003. Around 1800 cod were collected from five sites along a

300km coastal zone in Norway, and were screened using 10 microsatellite loci.

Small, but highly significant genetic differentiation was observed among samples

(FST = 0.0023, p < 0.0001). A similar pattern of local differentiation was evident in

certain Atlantic Cod populations that occur around sea mounts in the central,

northern Atlantic Ocean (Ruzzante et al., 1998a). Sampling at large geographical

scales across these zones would result in the local patterns being ‘hidden’ in gene

General Discussion

156

frequencies homogenized across the complete sampled area. According to Sterner

(2006), recent research shows that population structure, genetics and behaviour of

Atlantic cod may be extremely complicated, and in one limited area such as the

Gulf of Maine or the Kattegatt in the North Atlantic Ocean, there may be numerous

‘sub stocks’ that aggregate and reproduce separately. Each will constitute a separate

‘problem’ for effective fish stock management purposes.

Another aspect of sampling that can mask population structure is sampling time. In

some studies, temporal collections are pooled without consideration made of

potential genetic differentiation among temporal collections. If genetically different

temporal collections are pooled, this can change (increase or decrease) the amount

of genetic differentiation among pooled samples and hence influence overall

interpretation of population structure. As tunas show strong schooling behaviour

and are capable of extensive dispersal, it is important to treat temporal collections

carefully. In this study temporal collections were pooled only if no significant

genetic differentiation was first detected between them, for most of the analyses

employed. A classical example of this problem is evident in some wild Pacific

salmon stocks, where year cohorts of salmon returning to their natal streams to

spawn are out of phase with each other because they must spend at least 2 years at

sea feeding before reaching maturity (MacIsaac and Quinn, 1988).

Sensitivity of molecular techniques

The molecular methods and markers used in the current study (mtDNA haplotypes,

and highly variable microsatellite markers) were very sensitive, and informative and

hence provided high statistical power for detecting structure (if present) compared

General Discussion

157

with those used in some previous studies of YFT. Low detection capacity (RFLP

approach) and general low variability (Allozymes) means that ability to detect real

structure where it is present, may be compromised both by the marker employed

and the methodology used to screen variation.

Sensitivity and power of analytical techniques

Even if the molecular markers and methodologies used in a study are appropriate,

very sensitive analytical techniques may also be required to detect real population

structure, if signals are weak or hidden in ‘noisy’ data. Ryman et al. (2006) have

shown the possibility of getting different results depending on the statistical method

and marker used. In this study, ΦST analyses were employed which includes

information on both haplotype frequency and very sensitive complex information

on genetic sequences to examine patterns of genetic differentiation. In addition,

Bayesian statistical approaches were used in a number of analytical programmes in

this study (e.g. IM and STRUCTURE) which possess greater statistical power

(Nielsen and Wakeley, 2001; Pritchard et al., 2000; Beaumont and Rannala, 2004)

compared with some of the traditional statistical methods used in many earlier

studies.

5.3 SJT population structure

This study revealed a strong genetic stock structure of SJT in the north-central and

west-central region of the Indian Ocean, with divergent clades and possible ‘sub

populations’. SJT data here infer less dispersive, heterogeneous ‘sub populations’,

high genetic divergence, extremely large populations, long term stable clade types

as well as expanding clade types, and temporally dynamic potential ‘sub stocks’.

General Discussion

158

Results of the current SJT study are indicative of fish populations that inhabit

inshore areas with low dispersal and hence are genetically heterogeneous at fine

geographical scales. As described previously, tagging studies support limitation of

SJT to natal waters. SJT may have some behavioural characteristics such as site

fidelity to maintain this inshore nature against persisting monsoonal currents.

The SJT mismatch distribution with multiple modes indicates a large stable

population with two clades possibly arising through random lineage sorting that

contrasts with the inferred YFT’s demographic history. Multimodal mismatch

distribution may be due to two scenarios; random lineage sorting with a large stable

population or the presence of two spawning stocks with mixing of two clades. As

SJT populations have probably diverged a very long time ago, these populations

should have experienced a variety of complex climatic and environmental changes

such as Pleistocene climatic changes and associated sea level changes. For an

example, during the last 500,000 years, sea levels have changed a number of times

in this region (Rohling et al., 1998, Vaz, 2000), and as a result sea level has

dropped by up to -70 meters. Reductions in sea level have caused some shallow

seas to emerge as land masses. One example of such a land mass is the continental

shelf between Sri Lanka and India (Bossuyt et al., 2004). This recurrent land bridge

would have seperated north SJT populations from west and northwest SJT

populations. As the Maldive Islands are coral atolls, post sea level changes could

have affected inshore fish populations such as SJT, probably resulting in ‘sub

populations’.

General Discussion

159

As SJT show extremely large effective population sizes and, overall, these have

maintained stable for long time periods, increased fishing pressure probably has not

had sufficient impact to reduce SJT populations significantly. Indeed, SJT is not a

principal market tuna species and hence until recent times, there has not been

intensive industrial fishing pressure for SJT in the Indian Ocean. On the other hand,

SJT are likely to have a huge recruitment rate to replenish stocks. SJT’s relatively

short lifespan, high fecundity and their opportunistic recruitment patterns (Andrade

and Santos, 2004) facilitate ongoing large population sizes.

Very large population sizes for SJT, however, should not be misinterpreted

resulting in SJT conservation management strategies not being sensitive issues. In

fact, the current study indicates some potential for the presence of two/multiple SJT

heterogeneous groups in this region where one clade is relatively rare at most sites.

If fishing pressure is increased, this relatively small stock (and possibly other

potential ‘sub populations’) can easily be at a threat of over harvesting.

5.3.1 Comparison with other tuna studies

Previous population genetic studies of SJT have been mainly limited to studies on

Pacific and Atlantic Ocean populations. While a number of SJT population genetic

studies have been undertaken in the Pacific Ocean covering large geographical

areas, studies in the Atlantic and Indian Ocean in most cases have been limited to

inter-oceanic comparisons taking samples from very large spatially distant areas. To

date, most of the SJT genetic studies have also been limited to allozyme and

mtDNA RFLP analyses. While most of the previous SJT allozyme studies (inter-

oceanic comparisons) could not detect population structure, some allozyme studies

General Discussion

160

within the Pacific Ocean on SJT have shown heterogeneity among samples from

different regions (e.g. Sharp, 1978). Although the mtDNA RFLP approach can be

more sensitive than allozymes, mtDNA RFLP studies of SJT samples from the

Atlantic and Pacific Oceans did not detect divergence in SJT between the two

oceans (Graves et al., 1984).

Limitations of the above studies again may relate to the geographical scale of the

sampling regimes employed, the sensitivity and resolution power of the genetic

marker/s, and the power of statistical analytical methods used previously. However,

even when highly sensitive molecular markers and molecular techniques were

employed, no significant SJT population structure was detected, potentially

resulting from the geographical scale of the sampling regime employed. As an

example, a very recent study of SJT (n = 115) taken from Atlantic ( northwest

Atlantic, Brazil) and Pacific (east Pacific Ocean, Solamon Islands) with samples

screened using sequencing of the mtDNA D-loop, and an RFLP method for the

ATP–COIII region. Significant population structure could not be detected within

oceans or among oceans for both molecular techniques (Ely et al., 2005), although

at the same time the lack of power of the D-loop to provide discrimination, due to

too much variation, can not be ruled out. The above studies provide strong evidence

for the effects that geographical scale of sampling regime can have, compared with

the results of the current SJT study. If the resolution power of a genetic marker is

relatively low, the potential to detect lack of divergence among samples becomes

very high.

General Discussion

161

Oceanographic factors in the study area

Another important factor relevant to the current study is the peculiar ocean current

patterns in the study area and the potential impact they could have on genetic

composition and population structure of pelagic species like SJT. Unlike other

major oceans, seasonal variation in monsoonal current patterns around Sri Lanka

potentially move SJT in the eastern and western Indian Oceans toward Sri Lanka at

different times of the year. This creates a potential mixing zone around Sri Lanka.

Mixing of SJT populations from different geographical areas as a result of monsoon

currents could result in mixing of differentiated SJT samples around Sri Lanka.

Intensive sampling within a relatively small geographical area and use of genetic

markers with high resolution (mtDNA haplotype sequences and nDNA

microsatellite markers) combined with powerful analytical techniques show clearly

that SJT populations in Sri Lankan waters are genetically heterogeneous. Two

divergent clades are present and these two clades admix around Sri Lanka and in

adjacent regions of the western Indian Ocean. If similar approaches were applied to

SJT populations elsewhere, perhaps more structure would be evident in wild stocks

of this species in other oceans.

5.4 Fish stock management

As described early, the trend in fishery management towards development of

ecologically sustainable fisheries can be enhanced by incorporating data into

decision making processes when identifying fish stocks or when defining

management units. Knowledge of population subdivision is central to developing

sustainable fishery management practices. Uncertainty regarding SJT and YFT

General Discussion

162

stock structure seriously restricts the confidence that scientists and fisheries

managers can place in regional assessments of population diversity in these species

that have been carried out to date. At a national or sub-regional level, fisheries

managers need to have a better idea of the diversity in SJT and YFT from which

fish in their fisheries are drawn.

5.5 Implications for YFT management in Sri Lankan waters

YFT genetic stock structure is not currently well understood even in the Pacific and

Atlantic Oceans. Currently, YFT are managed as western and eastern stocks in the

Pacific Ocean while in the Atlantic Ocean they are considered to be a single stock

based on tagging data (ICCAT, 1995). Currently, no strong management strategies

have been implemented for Indian Ocean tunas. The Indian Ocean Tuna

Commission (IOTC) was established to examine issues regarding tuna catch-effort

statistics and stock status. Recently, a western Indian Ocean tuna organization

convention was established in the Seychelles Islands, and Sri Lanka was invited to

join this convention. So, while much remains to be done to define the stock

structure of important tuna species in the Indian Ocean, and more widely, there is a

strong impetus to move towards effective management of the species. Currently in

Sri Lanka, the only management strategy employed in the tuna fishery is the use of

uniform mesh size regulations for tunas. Prior to the current study, no genetic or

even non-genetic tuna stock assessment programmes have been conducted in Sri

Lankan waters directed at developing appropriate and effective stock management

practices.

General Discussion

163

Results of the present study indicate that further studies are needed to adequately

define stock structure of SJT and YFT in the Indian Ocean. The data however

suggest that YFT can be managed currently as a single stock in Sri Lankan waters

as both mtDNA and microsatellite data do not show sufficient population

divergence to suggest that independent evolutionary or management units are

present. As a management strategy for YFT in Sri Lankan waters therefore,

sustainable biomass can be estimated based on population dynamics of combined

YFT populations. Further, it will be important to estimate the relative natural

contributions/ dispersal from the putative spawning stocks to each country in the

region. The fishing quotas for each country in this region should therefore be

estimated based on this relative natural replacement process.

5.6 Implications for SJT management in Sri Lankan waters

Very few stock studies have been undertaken to elucidate the stock structure of SJT

even in the Pacific Ocean due to the very limited commercial interest in this species

when compared with most other large tuna species. Therefore, currently SJT are

managed as single stocks in each ocean, although genetic studies have shown even

within the western Pacific Ocean, there may be several genetically distinct SJT

stocks present. On the other hand, very few population genetic studies on SJT to

date have used sensitive, powerful genetic markers such as mtDNA sequencing or

microsatellites in any ocean, so much remains to be done to improve our

understanding of genetic diversity in this species to allow stocks to be managed

effectively in the future.

General Discussion

164

In the Indian Ocean, no SJT stock management is practiced currently although the

second largest SJT catch comes from the Indian Ocean, and many local people

depend on SJT stocks as their main protein source. The current study shows clearly

that there are two genetically distinct SJT clades in Sri Lankan waters and they are

apparently physically mixing in this region. Further studies are required to confirm

whether the two clades constitute non-interbreeding stocks. Thus, we need to

recognise that there is a potential for the presence of two management units and that

they require separate consideration if two ‘real’ stocks are present. If the two SJT

clades constitute two ‘real’ stocks but SJT continues to be considered as a single

stock in this region, harvest potential may be over-estimated as for other marine

pelagic fish (Sterner, 2006). This could lead to the stock with the lower population

size being at early risk of over harvesting. Currently, as an interim management

strategy, sustainable biomass can be estimated for both SJT clades in Sri Lankan

waters based on their relative frequencies. In the long term and broadly across the

region however, an effective SJT stock management strategy will require

confirmation of multiple stocks.

5.7 Future work

The current study opens up several important directions for future research for

identifying long term sustainable yields. As the first priority, sample sizes need to

be increased from all sites for both mtDNA and microsatellite loci to avoid potential

type I and type II errors, and to make a firm decision about the stock structure of

YFT in this region. This population study should then be extended to the eastern

and western Indian Oceans to determine whether the high genetic differentiation

observed in the north western (KK) and south eastern (KR) sites are due to

General Discussion

165

sampling effects rather than that these populations may be truly divergent. Further

the number of microsatellite loci examined needs to be increased, especially

because differentiation is expected to be small in the tuna nDNA genome. Thus,

additional loci should be screened for YFT to maximize the probability of detecting

potential population heterogeneity where it exists, even at subtle levels.

As the first priority for SJT, further investigation is required with increased sample

sizes and additional microsatellite loci to confirm whether there is temporal

permanence of the spatial genetic heterogeneity, and the two divergent mtDNA

clades identified here represent independent stocks or not. If the two clades are non-

interbreeding, two stocks may spawn in the same spawning ground (possibly at

different times) or in different locations. A broad genetic study of larval movements

in an east-west direction in the Indian Ocean should be conducted through a time

series analysis to identify potential putative spawning grounds (especially for the

two divergent SJT clades). Discrete spawning grounds of the two stocks if present

and identified should be conserved, and the relative contributions of each stock to

each country in the region should be assessed. The fishing quotas for each country

in this region should therefore be theoretically determined based on this relative

contribution. Until these future studies are carry out, an interim management option

for SJT in Sri Lankan waters would be to estimate the sustainable biomass based on

population dynamics in Sri Lankan waters.

The genetic signal of population differentiation associated with the reproductive

areas of highly migratory species can be obscured by population admixture in

spawning or general feeding areas (Van Wagner and Baker, 1990; Bowen et al.,

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166

1992; Wenink et al., 1994). Implications of admixture for the management of

marine resources are far reaching and the decisions for quota allocation require

mixed stock analysis to be undertaken (Kalinowski, 2004 and references therein).

According to evidence presented here for multiple ‘subpopulation’ of SJT in the

western Indian Ocean, it will be worthwhile to carry out mixed stock analysis for

SJT (e.g. Ruzzante et al., 2000, Nielsen et al., 2003).

The current study of YFT and SJT population structure in the western Indian Ocean

should also open several arenas for future research of the two species in the Atlantic

and Pacific Oceans as well, and more broadly to other tuna species and highly

migratory pelagic fish. The study has highlighted the importance of appropriate fine

geographical scale sampling, temporal sampling, and use of sensitive, powerful

molecular techniques, and analytical tools to detect genetic differentiation that may

be present at fine scales even in highly migratory pelagic fishes.

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APPENDIX

Appendix 1. DNA extraction

a) Phenol-chloroform method

Tissue samples preserved in 95% ethanol were re-hydrated with GTE (Glycine 100mM,

Tris 10mM, EDTA 1mM; final volume to 1L) prior to the digestion of tissue. About 5g of

preserved tissue were kept in 1ml of GTE for 30 minutes. The tissue was then transferred to

another 1.5ml tube with 500µl of extraction buffer (100mM NaCl, 50mM Tris, 10mM

EDTA, final volume to 200ml with 0.5 % SDS 10 ml; pH 8). 10µl of Protinase K (20

mg/ml) were added to the tissue mix immediately prior to the incubation. Tissue samples

were then incubated at 550C overnight until the tissue digested completely.

The following day, 250µl of Chloroform and 250µl Phenol were added to the digested

tissue mix. Tubes were gently inverted for 2 minutes and were centrifuged in an eppendorf

micro centrifuge for 5 minutes at 13000 rpm. After centrifugation the aqueous layer

containing DNA was carefully removed to a new 1.5 ml tube and the chloroform-phenol

step was repeated. Again the upper aqueous layer was transferred to a new 1.5 ml tube and

500µl of chloroform was added. This mix was centrifuged for 5 minutes at 13000rpm. The

supernatant was then transferred to a new tube and twice the volume of supernatant of

100% ethanol and 1/10th of the volume of supernatant of Sodium Acetate were added.

Tubes were inverted gently and kept in the freezer overnight for precipitation of DNA.

The following day, tubes were centrifuged at 13000 rpm for 5 minutes. Supernatant was

removed carefully without disturbing the DNA pellet. The DNA pellet was then washed by

adding 500µl of 70% ethanol and centrifuged at 13000 rpm for 5 minutes. The supernatant

was then discarded and the DNA pellet was dried for 15 minutes at 550C. The DNA pellet

was re-suspended in 50µl of TE buffer (20 ml of 1 M Tris, 4ml of 0.5M EDTA, final

volume to 100ml with ddH2O; pH 8). Tubes with DNA were then labeled and stored in the

freezer until required for genetic analysis.

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b) SALT extraction method

Tissues preserved in 95% ethanol were washed four times in Tris buffer (pH8) by spinning

between washes. Then the tissue was placed in a 1.5ml tube and 500 µl of solution 1

(50mM Tris HCl; pH8, 20mM EDTA; pH8, 2% SDS) was added. Tissue was homogenized

with a sterile plastic tip. 5µl of Protenase K (20mg/ml) was added to the tube and the

sample vortexed. Tissue samples were incubated overnight at 550C.

The next day, digested tissue samples were chilled with ice for 10 minutes. Then 250µl of

solution 2 (6M NaCl) was added. Tubes were inverted gently and were chilled again for 5

minutes. Tubes were centrifuged at 8000 rpm for 15 minutes. 500µl of clear supernatant

was collected to a new 1.5 ml tube and twice the volume of supernatant of 100% ethanol

was added. The tube was frozen overnight at –200C to precipitate DNA. The next day,

tubes were centrifuged at 11000 rpm for 15 minutes. The supernatant was carefully

removed and the DNA pellet was washed with 500 µl of 70% cold ethanol by spinning at

11000 rpm for 5 minutes. The supernatant was carefully removed and the pellet was dried

on a heating block at 550C for 15 minutes. The DNA pellet was then re-suspended in 50µl

of ddH20. Tube with DNA was then labeled and stored in the freezer until required for

genetic analysis.

For mtDNA Polymerase Chain Reaction (PCR) amplification a 200ng/µl template

concentration was used. For microsatellite PCR a 50ng/µl template concentration was used.

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Appendix 2. Temperature gradient gel electrophoresis (TGGE)

A 3.5% Acrylamide gel was selected after trials to obtain the maximum resolving power.

Gels consisted of: 21.6g Urea, 18ml of ddH2O, 900µl of 50XME buffer, 2.25 ml of 40%

glycerol, 3.5ml of 40% acrylamide, 75µl of Temed and 136µl of 10% Ammonium

persulphate (APS). Gels were cast on a gel support plastic film PAGEBOND and left in a

horizontal position undisturbed for 1hr to set. Then the gel on the film was mounted on the

temperature gradient plate using an ultra thin layer of 0.1% Triton for uniform adhesion and

uniform heating of the gel. Each buffer tank was filled with 20ml 50XME and 980ml of

ddH2O and the circuit was completed using wicks. PCR products were then loaded and the

gel was covered with a flexible plastic wrap to prevent gel dehydration.

a) Perpendicular TGGE

A perpendicular temperature gradient gel was run first for a single individual of each

species where the run direction is perpendicular to the temperature gradient, to determine

the melting domain/ profile of the particular mtDNA fragment (Figure A2.1). This melting

profile allows the optimum temperature range and duration of run for the heteroduplex

parallel TGGE gel to be determined.

For perpendicular TGGE, a gel was cast using the above described method. 500ng DNA

from a single individual was combined with 20µl of 50XME + dye and ddH2O to a total

volume of 200µl and electrophoresed for 30 minutes without a temperature gradient. Then

the temperature gradient (200C ~ 60 0C) was stabilized for 15 minutes and DNA was

electrophoresed for further one hour. Melting profiles were determined separately for both

SJT and YFT.

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

gel curve indicates the temperature of transition from double- to single-stranded DNA and

verifies the reversible melting behaviour of the fragment.

DNA was visualized using a Silver Nitrate staining method. Triton in the gel bond film was

carefully removed before staining. First, the gel was incubated in a buffer containing

absolute ethanol and 0.5% acetic acid for 3 minutes. This step was repeated and the excess

was discarded. Then the gel was stained with 1% Silver Nitrate for 10 minutes. Gel was

washed twice with ddH2O to remove excess Silver Nitrate. Following this, the gel was

overlaid with a fresh buffer, containing 1.5% NaOH, 0.01% NaBH4 and 0.015%

formaldehyde (37%) for 10 minutes. Excess was discarded and the gel was fixed with 0.75

% NA2CO3 for 5 minutes.

From the melting profile of the perpendicular gel, the melting temperature of the particular

DNA segment, the optimum temperature range for double stranded DNA separation and the

optimum electrophoretic running time were calculated.

b) Heteroduplexing and optimization of parallel TGGE

For heteroduplexing, a single reference individual was selected for each species using

heteroduplex parallel TGGE trials. Each heteroduplex mix consisted of 0.6µl DNA of

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171

reference individual, 0.6 µl of DNA from each sample individual, 3.5 µl of 8M Urea, 0.6 µl

of 10XME + dye buffer (dye buffer; bromophenol blue and Xylene Cyanol FF dyes,

0.5mg/ml each). This mixture was heteroduplexed using an Eppendorf Thermocycler; and

denatured at 95 0C for 5 minutes, and then reannealed at 50 0C for 15 minutes.

Heteroduplexing was performed immediately prior to the parallel TGGE run (i.e. with the

temperature gradient parallel to the direction of DNA migration).

In each heteroduplex parallel TGGE gel, a reference individual homoduplex (reference

heteroduplexed to reference) was run in the first lane as a control. Heteroduplexed sample

was loaded into individual wells and were run at 300 volts, 20~25 milliamps for the

predetermined optimum time duration. Optimum time duration for heteroduplex parallel

TGGE were determined by a series of time test trials.

DNA was visualized using the silver staining method described above. Each distinct TGGE

banding pattern was assigned distinguishing haplotype number.

c) Scoring and sequencing of haplotypes

50 µl of PCR product combined with 500 µl binding buffer and 50 µl of ddH2O was applied

to the purification kit and then sample centrifuged at 13000 rpm for 1 minute. The filtrate

was discarded and 500µl of washing buffer was added to the purification kit and then

centrifuged at 13000 rpm for 1 minute. This step was repeated using 200µl of washing

buffer. The purification filter was transferred to a new 1.5 ml tube and 50µl of elution

buffer was applied to the filter. This kit was centrifuged at 13000rpm for 1 minute.

3µl of eluted sample (purified PCR product) combined with 3µl of 6X Formamide dye

(Formamide, Xylene Cynol Ff, Bromophenol Blue, EDTA 0.5M) was electrophoresed in a

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2% agarose gel against a 3µl of 20ng/µl pgem (Applied Biosystems, California, USA)

concentration standard, to measure the concentration of purified PCR product.

Sequencing of PCR products was undertaken using 400~600 ng of purified PCR product,

3µl of 5X sequencing buffer (400mM Tris pH8, 10mM MgCl2), 1µl of 3.2 pmol/µl mtDNA

forward primer, 2 µl of ABI Prism® Big DyeTM Terminator Cycle Sequencing Ready

Reaction Mix version 3.1(Applied Biosystems, California, USA) and ddH2O to a total

volume of 12µl. PCR reaction in an Eppendorf Thermocycler. Samples were denatured at

940C for 5 minutes. Then for each cycle de-naturation occurred at 960C for 10 seconds,

annealing at 50 0C for 5 seconds and extension at 60 0C for 4 minutes for 29 cycles. Final

extension took for 10 minutes. Samples were the exposed to 40C for 10 minutes and held at

100C.

Following this 8µl of ddH2O was added to each 12µl PCR product. This mixture was

transferred to a new 1.5 ml tube and 80µl of 75 % cold Isopropanol was added, and

incubated for 15 minutes at room temperature. After this the mixture was centrifuged for 20

minutes at 13000 rpm. The supernatant was then immediately aspirated avoiding the pellet.

Then 250µl of 75% cold Iso-propanol was added to the tube and the sample spun for 5

minutes at 13000 rpm. Samples were then air dried at room temperature and protected from

light.

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Appendix 3. Microsatellite marker development.

3.1. Isolation of microsatellites by radio isotopic method.

i) Restriction Enzyme Digestion

DNA was extracted using a phenol-chloroform method (Appendix 2.1.a). Then 10µg of

DNA was digested with DpnII (5’ GATC 3’) and Sau3A I (3’ CTAG 5’). The reaction

consisted of; 10µg of DNA, 6 µl of DpnII, 10 µl of DpnII Buffer to a total volume of 100µl

with ddH2O. Then the mix was spun briefly and incubated overnight at 37o C for total

digestion. Digested DNA (20µl of loading dye to 100µl of digestion) was run on 1.5 % low

melting point (LMP) TAE agarose gel with the marker IX at the end lanes. The digestion

was run through the gel at 100V for10 minutes then 70V for 3hrs. Following this 300-700

bp DNA was cut out from the gel under a UV light using the marker (ix) as the guide.

Cut DNA gel parts were weighed and DNA was cleaned up using the QIAGEN kit (QIA

quick Gel Extraction kit-Ct.No. 28704) and DNA concentration was measured using an

Eppendorf Biophotometer for the ligation.

ii) Gel Fragment extraction

DNA was extracted from the cut DNA gel fragments according to the QIAquick Gel

extraction kit protocol.

3 volumes of buffer QG were added to 1 volume of gel as 100mg ~100µl. The tube

containing gel pieces was incubated at 500C until all gel had been completely dissolved.

Then 1 gel volume of isopropanol was added to the sample and mixed. A QIAquick spin

column was placed in a 1.5ml tube and the sample was applied to the QIAquick column and

was centrifuged for 1 minute at 13000 rpm. The fluid was discarded and then 0.75 ml of PE

washing buffer was added to the QIAquick column and centrifuged for an additional 1

minute at 13000 rpm. The QIAquick column was placed into a clean 1.5ml micro-

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174

centrifuge tube. Finally 50µl of elusion buffer EB (10mM Tris-HCl, pH 8.5) was added to

the Qiaquick membrane and the sample centrifuged for 1 minute. 2 µl of the elution was

run for 20 minutes at 100V on 2 % TAE gel to check the product.

iii) DNA ligation in to plasmids

According to DNA concentration, vector was mixed with DNA at a ratio of 1:1. The correct

amount of DNA insert was combined with 2µl of T4 DNA Ligase buffer (Roche), 1µl of

Ligase enzyme, 1.5µl of 50ng/µl vector (pUC 18 Bam I /BAP) (Amersham Pharmacia

Biotech) to a total volume of 20µl with ddH2O. At the same time, Controls (reaction with

known DNA insert size and reaction with no insert) were set up. Reactions were incubated

at 16o C overnight.

iv) Transformation of ligated DNA

Heat shock was done for competent cells to transfer the ligated DNA with the vector.

Immediately after the heat shock, 400µl of SOC solution (2g Tryptone, 0.5g Yeast Extract,

0.05g NaCl were added, to a total volume of 100ml with dH2O; and then autoclaved at

1210C for 20 minutes) and SOC salt (0.406g MgCl2, 0.24g MgSO4, 0.72g Glucose; 1ml

SOC Salt: 9ml SOC Solution) into the cuvette and the SOC and cells were carefully

transferred into a 1.5ml tube. This mixture was incubated on a shaker at 37oC for 1 hour.

After cells were incubated, 150µl aliquots were plated out over pre-prepared LB/AMP/X-

Gal/IPTG plates.

Plates were prepared with LB mixture (1L LB; 10g Tryptone, 5g Yeast Extract, 5g NaCl,

15g Agar were made, Make to 1L with dH2O) and autoclaved at 1210C for 20 minutes.

Plates were then placed in a water bath of 550C prior to adding ampicillin, X-gal and IPTG.

For 500ml LB; 0.1M IPTG 2.5ml, 50mg/ml X-gal 400µl 50mg/ml Ampicillin 1ml were

added to screen for positive clones. Plates were inoculated with DNA ligated E. coli

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bacteria and incubated at 37oC overnight. E .coli competent cells naturally produce blue

coloured colonies whereas competent cells with vector insertions produce a white colour

due to X-gal and IPTG hence making it possible to identify colonies with vector insert.

After incubation, white colonies were picked up using sterile toothpicks and were placed on

fresh LB/Ampicillin plates (50mg/ml ampicillin) using a grid underneath the plate.

Transformed plates were incubated at 37oC overnight.

V) Membrane Transfer Hybridization

Hybond + nylon membranes were used for colony transfer. A corner of the membrane was

cut and labelled with a pencil to identify the membrane. The membrane cut sections were

lined up to a mark on the plate and were left on the colony for 30 seconds. After colony

transfer, plates were placed in an incubator for 1-2 hours to help the colonies to recover and

then were kept in 4oC until needed.

The membranes were then transferred from the plate to the denaturation buffer (87.66g

NaCl, 20g NaOH to a total volume of 110ml with ddH2O) wetted blotting paper for 15

minutes. After denaturation, membranes were transferred to the neutralization buffer (pH

7.5; 87.66g NaCl, 60.50g Tris to a total volume of 110ml with dH2O) wetted blotting paper

for 15 minutes. Then the membranes were dried on clean blotting paper and kept in the 2 x

SSC (20 x SSC: 88.23g Tris-Sodium Citrate to a final volume of 1L with dH2O. Then

dilute 20 x SSC 100ml to 1L with dH2O to make 2 x SSC) solution in a tray for 10 minutes

for fixation of DNA. For permanent fixation, membranes were placed in a hybridization

oven at 80o C for 1 hour.

Nylon meshes were placed in between the dried membranes. The mesh and membranes

were rolled up and were then placed in hybridization bottles. Pre-hybridization solution

(10% SDS 1ml, 20xSSC 30ml, 50xDenhard 10ml {50x Denhard’s: 1g Ficoll

(type 400 Pharmacia), 1g PVP (Polyvinylpyrrolidone) (Sigma), 1g BSA (Fraction V)

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(Sigma), final volume to 50ml with dH2O} and 59ml dH2O to a final volume of 100ml)

were warmed at 55oC in a water bath prior to being added to the membranes. 75ml of pre

warmed hybridization solution was added to ~6 membranes and membranes were incubated

at 55oC for 1hour in a hybridization oven. While the membranes were incubating, the

radioactive labeling nucleotide probes were made up in 0.5 ml tube.

Radioactive labelled probes {2 µl of each oligo–probe (10 pmole/µl); 5 µl of P32 ATP (2.5

µM), 4 µl of PNK (Polynucleotide kinase 10 units/µl), 10 µl of 10X PNK Buffer (20 µl of

5X Buffer to a total volume of 100µl with ddH2O)} were incubated at 37oC for 70 minutes

and then at 68oC for 10 minutes in a PCR machine. Then 100µl of incubated oligo-

nucleotide probes were added to the pre-hybridization bottles with the pre-hybridized

membranes and incubated at 55oC overnight.

After incubation overnight, the probe-hybridization solution was poured out and the

membranes were rinsed with 6X SSC, the solution was discarded and more 6 x SSC (dilute

20X SSC 300ml to 1L with dH2O) was added to the hybridization bottle and incubated for

10 minutes at 55oC.

Blotting paper cut to the size of the x-ray film were to be used as backing on which the

membranes lie. Glad wrap were placed over the cut blotting paper and taped on. Then the

membranes were placed on to the back of the prepared sheet. Another layer of glad wrap

was placed over the membranes taped to the back. The backing was pierced using a sharp

pin where the membrane was cut, so that orientation of the membrane was easy. Then an X-

ray film was placed over the membranes and on the reverse side the X-ray film was pierced

through the existing hole. The X-ray film was allowed to develop overnight in the dark.

Positive clones corresponding to the developments on the X-ray film were picked out with

sterile toothpicks/ pipette tips from the plates. Positive clones were placed into 3ml of

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Terrific Broth/ Ampicillin 50mg/ml (TB/Amp) solution (Terrific Broth: 12g Tryptone,

24g yeast extract, 4ml Glycerol, 900ml dH2O was added and autoclaved in batches

of 10 x 90ml bottles). Then 10ml of TB salt (2.31g KH2PO4, 12.54g K2HPO4, final volume

to 100ml with dH2O and was autoclaved) and 200µl of ampicillin (50mg/ml) was added to

90ml of TB. Positive clones in this TB mixture were grown overnight at 37oC.

vi) Miniprep preparation

DNA was extracted from grown out positive clones using miniprep protocol: 5ml of

Terrific broth was inoculated with each clone and grown overnight on a shaker at 370 C. 1.5

ml of culture was poured in to 1.5 ml eppendorf tube and spun for 40 seconds at 13000 rpm

to pellet cells. The pellet was re-suspended in 100µl of TEG buffer (50 mM glucose, 25

mM Tris; pH 8.0, 10mM EDTA; pH 8.0, to a final volume of 100 ml and autoclaved).

Freshly made 1% SDS/0.2M NaOH 200µl were added and mixed by inversion. Then 100

µl of 3M Sodium Acetate (pH4.8) was added and mixed by inversion. 150ml of chloroform

was added and mixed by inversion. This solution was spun for 4 minutes at 13000 rpm. The

supernatant was then decanted to another tube being careful not to get chloroform or white

precipitate. 1ml of 100% ethanol was added to the supernatant and kept for 2 minutes. The

mixture was spun for 4 minutes at 13000 rpm. The DNA pellet was then washed with 70%

ethanol and spun for 4 minutes at 13000 rpm. The supernatant was discarded, the pellet was

air dried, and the dried pellet was re-suspended in 50µl of ddH2O. 1 µl of RNAase

(10mg/ml) was added to each tube and was incubated at 370C for 45 minutes to remove

RNA which may inhibit the PCR.

vii) Sequencing of positive clones

RNAase treated positive clones were sequenced using ~800ng of the clone product, 3µl of

“big dye buffer”, 1µl of 3.2 pmol/µl M13F primer (5’ GTA AAA CGA CGG CCA GT ‘3),

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2 µl of big dye (version 3.1) and ddH2O to a total volume of 12µl. Sequence PCR reaction

was done using an Eppendorf Thermocycler using the following programme. Samples were

denatured at 940C for 5 minutes. Then for each cycle, de-naturation was done at 960C for 10

seconds, annealing at 500C for 5 seconds and extension at 600C for 4 minutes for 29 cycles.

Final extension was done at 600C for 10 minutes. The sample was then held at 100 C.

8µl of ddH2O was added to each 12µl PCR product. This mixture was transferred to a new

1.5 ml tube and 80µl of 75 % cold Isopropanol was added, and incubated for 15 minutes.

The mixture was then centrifuged for 20 minutes at 13000 rpm. All supernatant was

aspirated immediately avoiding the pellet. Then 250µl of 75% cold Isopropanol was added

to the tube and spun for 5 minutes at 13000 rpm. The supernatant was then aspirated

avoiding the pellet. Samples were air dried at room temperature and protected from light.

Labeled PCR products were sequenced at “Australian Genome Research Facility” (AGRF)

(http//www.agrf.org.au).

3.2 Isolation of microsatellites by magnetic bead method

i) Restriction enzyme digestion

DNA was extracted using a phenol-chloroform method and DNA was digested using Rsa I

and Bst UI restriction enzymes in separate tubes as Rsa I recognises GTAC and Bst UI

recognizes CGCG base combinations. Each DNA digestion master mix consisted of 2.5µl

of 10X Ligase buffer (Promega), 0.25µl 100x BSA (Bovine Serum Albumin), 0.25µl 5M

NaCl (50 mM final), 1.00µl Rsa I (NEB catalog # R0167S) or BstU I (NEB catalog #

R0518S), 1.00µl Xmn I (NEB catalog # R0194S) and combined with 20.0µl genomic DNA

(200ng/µl) to a final volume of 25µl with ddH2O.

The above mix was incubated in a water bath at 370C overnight. Immediately after

digestion, samples were prepared for ligation of adaptors to DNA fragments.

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ii) Preparation and ligation of adapters to DNA fragment.

The aim is to ligate a double stranded adaptor on to each end of each DNA fragment.

Primarily, the adaptors provide the primer-binding site for subsequent PCR steps. They also

provide sites for ease of cloning the fragments in to the vectors. The adaptors are therefore

compatible with the restriction sites in the vector’s multiple cloning sites. The two adaptors

used were;

S475: 5’GTTTAAGGCCTAGCTAGCAGAATC

S476: 5’pGATTCTGCTAGCTAGGCCTTAAACAAAA

Double stranded (ds) adaptor mixture consisted of; 3.8μl S475, 8.2μl S476, 4μl 5M

NaCl to a final volume of 200μl with ddH2O. This mixture was heated to 95°C and

cooled slowly to room temperature. This forms the ds adaptor. Then the adaptors were

ligated to DNA using ligase mixture combined with 7.0µl ds adaptor, 1.0µl 10x Ligase

buffer (Promega), 2.0µl DNA T4 ligase (Promega). The mixture was added to DNA and

incubated at 16°C overnight in an eppendorf thermal cycler. While the ligation proceeded,

small aliquots were run on a mini gel to ensure that the DNA had been cut. To make sure

that the adaptor-ligation was successful, 4 µl of ligated product was run on a 1.5% mini gel

using 100bp ladder as a standard.

iii) Size Selection of Adaptor-ligated DNA Fragments

Adaptor ligated DNA can consist of small fragments, which are unlikely to contain long

microsatellites or, very large fragments which are difficult to sequence. To size select the

DNA for microsatellite enrichment the entire ligation product was run on a 1.5 % agarose

gel with a 100bp DNA ladder at the edge of the gel. A 300 to 700bp fragments size range

was selected using the 100bp ladder as a guide. Cut DNA sample were placed into clean

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eppendorf tubes and purified using QIAGEN Gel Spin DNA Purification Kit and

resuspended in 50μl of elution buffer.

iv) Magnetic Bead Enrichment for Microsatellite-containing DNA Fragments

DNA fragments with microsatellite sequences complementary to the microsatellite oligos

were captured and all other DNA fragments were washed away by Magnetic bead

enrichment. First, the hybridization of microsatellite probes to adaptor-ligated DNA was

completed using a special PCR programme. This mixture consisted of 25.0µl 2x Hyb

Solution (12x SSC, 0.2% SDS), 5.0µl Biotinylated microsatellite probe (mix of oligos at 1-

10µM each), 10.0µl size-selected, adaptor-ligated DNA to a final volume of 50.0μl with

ddH2O. This PCR mix was run on the following PCR programme. The program denatures

the DNA and probe mixture at 95°C for 5 minutes. It then quickly ramps to 70°C and steps

down 0.2°C every 5 seconds for 99 cycles (i.e., 70°C for 5 sec., 68.8°C for 5 sec., 68.6°C

for 5 sec., … down to 50.2°C), and stays at 50°C for 10 minutes. It then ramps down 0.5°C

every 5 seconds for 20 cycles (i.e., 50°C for 5 sec., 49.5°C for 5 sec., 49°C for 5

sec.,…down to 40°C), and finally quickly ramps down to 15°C. The idea is to denature

everything, quickly go to a temperature slightly above the annealing temperature of the

oligo mixes, and then slowly go down, allowing the oligos the opportunity to hybridize

with DNA fragments that they most closely match (long perfect repeats) when the solution

is at or near the oligo’s melting temperature.

Then 50µl of Streptavidin MagneSphere Paramagnetic particles were washed, re-suspended

and the beads were washed again with 250µl of TE. Beads were captured using a Magnetic

Particle Collecting (MPC) unit. The supernatant in the tube was carefully removed without

disturbing the beads while the tube was still on the MPC unit. This washing was repeated

with TE, and twice with 1xHyb Solution (6X SSC, 0.1% SDS). Finally the beads were

resuspended in 150µl of 1xHyb Solution. Then the DNA probe mix was pulse-spinned and

all material added to 150µl of washed, resuspended MagneSpheres. This mixture was

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incubated on a shaker at a slow speed for 30 minutes at room temperature. Magnetic beads

bound to DNA and probe mix were captured using the Magnetic Particle Collecting (MPC)

unit. The supernatant was removed by pipetting while solution was still on the MPC unit.

The MagneSpheres were washed two times with 400µl 1xHyb Solution (6X SSC, 0.1%

SDS), each time using the MPC unit to collect the beads after removing the supernatant by

pipetting. Two additional washes were done using 400µl Wash Solution (0.2x SSC, 0.1%

SDS). Finally two more washes were carried out using 400µl Wash Solution (0.2x SSC,

0.1% SDS), and the solution with magnetic beads was heated to 50°C. Then 200µl of TLE

(Tris Low EDTA) were added, vortexed and incubated at 95°C for 5 minutes followed by

bead capture using the MPC unit. Supernatant containing enriched fragments was removed

by pipetting to a new tube. Captured fragments were purified using a QIAGEN PCR

purification Kit. Finally enriched fragments were re-suspended in 25µl of elution buffer.

v) PCR Recovery of Enriched DNA

The aim was to increase the amount of enriched DNA through PCR. 50μl PCR reaction mix

was consist of; 5µl 10x PCR buffer with MgCl2, 8µl S475 (5µM), 1.75µl dNTP (10 mM

each), 0.75 µl Expand Long Template enzyme mix, 29.5µl ddH2O, 5µl captured, purified

DNA. The PCR program was as follows: 94°C for 2 minutes.; then, 25 cycles of 94°C for

30 seconds, 60°C for 30 seconds, 68°C for 1.5 minutes; then 68°C for 7 minutes; finally

25°C for 10 seconds. 4µl of PCR product was run on a 1.5% mini gel to see whether the

DNA had been recovered successfully, using 100bp ladder as a standard. Again the

duplicated PCR products were purified using a QIAGEN PCR purification kit and re-

suspended in 25μl of elution buffer.

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vi) Repeat Enrichment and PCR Recovery of Double Enriched DNA

Enrichment steps were repeated using new purified PCR products. Double Enriched DNA

was run on a 1.5% mini gel, pooled PCR products were purified repeatedly.

vii) Ligating Enriched DNA into Plasmids

The enriched DNA was ligated in to a cloning vector using the Promega TA Cloning Kit

and competent cells. The idea is to place one fragment of DNA into one vector, but to

repeat this for as many fragments as is possible.

viii) Transforming plasmid DNA

Cloning vectors inserted with enriched DNA were incorporated into a bacterial host. The

idea was to place one vector into one bacterial host, and repeat this for as many vectors as

possible. Ampicillin (amp) sensitive bacteria and vectors that carry a gene conferring amp

resistance were used. When a bacterium incorporates the vector, the vector transforms the

phenotype of the bacterium from amp sensitive to amp resistant. Thus, when a mixture of

bacteria is plated on media containing amp, only bacteria with amp resistance (i.e., those

that have incorporated the vector) can grow and form colonies.

ix) Preparation of agar plates

Plates were prepared with LB mixture (1L LB; 10g Tryptone, 5g Yeast Extract, 5g NaCl,

15g Agar were made, Make to 1L with dH2O) and autoclaved at 1210C for 20 minutes.

Then it was placed in a water bath of 550C prior to add ampicillin, X-gal and IPTG. For

500ml LB; 0.1M IPTG 2.5ml, 50mg/ml X-gal 400µl, 50mg/ml Ampicillin 1ml were

added to screen for positive clones. Plates were inoculated with DNA ligated E.coli bacteria

and incubated at 37oC overnight. Colonies were grown overnight in an incubator at 37°C.

Appendices

183

x) Colony Screening

a) Colony Lifting Procedure

White colonies from transformations were spotted on to a fresh ampicillin plate using

sterile toothpicks. At the same time a duplicate plate was made. Both plates were grown

overnight in the 37°C incubator. Then the duplicate plate was stored in the fridge as a

source of uncontaminated colonies. The other plate was cooled in the fridge for 30

minutes.

A sterile nylon membrane was carefully placed onto the surface of the plate of colonies to

be screened. The membrane was left for 1 minute. The orientation of the membrane was

marked with needle pricks in three places in order to be able to identify the positive

colonies after colorimetric detection. The membrane was carefully removed with filter

tweezers and place onto a square of Whatman 3mm paper (colonies-side up). The

membrane (colonies-side up) was placed onto a 3-layer stack of 3mm paper soaked with

Denaturation solution (0.5 M NaOH, 1.5 M NaCl) for 5 minutes to lyse the cells and

denaturing the plasmid DNA. The membrane was removed using two pairs of filter

tweezers being very careful that the membrane stays horizontal at all times to minimize

diffusion of the liberated plasmid DNA and placed on a dry 3mm square. The Denaturation

solution was replenished on the stack of 3mm after removing and discarding the top layer

and the membrane was placed onto the 3mm stack soaked with Denaturation solution for

another 5 minutes. Then the membrane was removed and blotted onto a dry 3mm square.

Next the membrane (colonies-side up) was placed onto a 3 layer stack of 3mm paper

soaked with Neutralization Solution (0.5M Tris-HCl; pH 7.5, 1.5M NaCl ) for 5 minutes

to neutralize the alkaline denaturation solution allowing the DNA to bind to the nylon

membrane. The membrane was removed carefully so that the membrane stayed level and

the colonies do not run into each other and placed on a dry 3mm square. The Neutralization

solution was replenished on the stack of 3mm after removing and discarding the top layer.

Appendices

184

Then the membrane was placed onto the 3mm soaked with Neutralization solution for

another 5 minutes. After that the membrane was removed and blotted onto a dry 3mm

square. The DNA was permanently cross-linked onto the nylon membrane by baking the

membrane for 1 hour at 80oC. Cellular debris was cleaned from membrane by washing

with an SDS solution.

The membrane was treated with Proteinase K treatment as follows:

Membrane was placed onto a clean piece of aluminium foil and 0.5 ml of 2mg/ml

proteinase K (diluted proteinase K, 20mg/ml, 1:10 with 2x SSC) was pipetted onto the

membrane. The solution was distributed evenly, and was incubated for 1 hour at 37oC. The

membrane was placed between two water soaked 3mm papers. Pressure was applied by

rolling a bottle across the 3mm paper. Cellular debris was removed by gently pulling off

the upper filter paper. Cellular debris was removed from the membrane by incubating in a

washing solution at 50°C with gentle shaking for 30 minutes. Solution was changed once

during the incubation time.

b) Hybridization

The hybridization procedure was adapted from the DIG High Prime Labeling and Detection

Starter Kit (Roche; Cat No. 1 745 832) procedure.

Up to 3 membranes were placed into a roller bottle separated by nylon mesh and 20 ml of

Pre-hybridization solution (22.5 ml H2O, 7.5 ml 20x SSC, 3 ml 10X) and blocking solution,

60μl 10% SDS, 0.03g N-lauroylsarcosine) were added which has been pre-warmed to 700C.

Pre-hybridization was done for 1 hour at 50oC. Then the Pre-hybridization solution was

removed from the roller bottles and 5ml of pre-warmed Hybridization solution (22.5ml

H2O, 7.5ml 20x SSC, 3ml 10X blocking solution, 60μl 10% SDS, 0.03g N-

Appendices

185

lauroylsarcosine) was added. After that the biotinylated probe was added to the roller bottle

(250pmoles/ml final concentration). Then the roller bottle was replaced into the

hybridization oven immediately. The membranes were hydridized at 50oC overnight. The

membranes were immediately removed from the bottle and placed in a plastic box

containing 200ml of the post-hybridization fresh RT wash solution {40 ml of 20X SSC,

4ml of 10% SDS with 356ml H2O (final concentration 2X SSC and 0.1% SDS)}. The

membranes were washed for 5 minutes at room temperature with gentle shaking. This step

was repeated with another 200ml of RT wash solution. The RT wash solution was

discarded and 200 ml of the hot wash solution (10ml of 20X SSC, 4ml of 10%SDS, 386ml

dH2O) was added. Then the membranes were washed for 15 minutes at 40°C with gentle

shaking. This step was repeated with another 200ml of hot wash solution. Hot wash

solution was then poured off.

c) Colour Detection of Biotin-labeled Hybrids

The membrane was washed briefly with Maleic acid buffer (0.1M maleic acid, 0.15M

NaCl, adjust pH to 7.5 (20°C) with solid NaOH) for 4 minutes. Then the buffer was

discarded and 100ml of 1X Blocking solution (from DIG High Prime Labeling and

Detection Starter Kit, 1:10 in Maleic acid buffer) was added and incubated at room

temperature for 30 minutes. Streptavidin Alkaline Phosphatase conjugate was diluted in

Blocking Solution (1:5000). The membranes were incubated for 30 minutes at room

temperature in about 20 ml of the diluted conjugate. After that the membranes were washed

two times for 15 minutes with 100ml Washing buffer {Maleic acid buffer with 0.3% Tween

20 (v/v)}. The membranes were equilibrated for 2 minutes in 20ml Detection buffer {0.1M

Tris-HCl, 0.1M NaCl, 50mM MgCl2 pH 9.5 (20°C)}. While the membranes were

equilibrating, the colour-substrate solution (200μl NBT/BCIP stock solution in10ml

Detection buffer) was prepared. The membrane was incubated in 10ml colour-substrate

solution, sealed in a bag, wrapped in foil and kept in the dark without disturbance. After 45

Appendices

186

minutes colour development was checked. Membranes were washed in TE Buffer (10mM

Tris-HCl, 1mM EDTA; pH 8.0). The membranes were preserved by inactivation of alkaline

phosphatase and drying. Then alkaline phosphatase was inactivated by incubation in

100mM EDTA for 15 minutes at 85oC. The membrane was washed twice for 5 minutes in

washing buffer. Finally the membranes were dried in air.

xi) Analysis of positive clones.

Positive clones from colony lysates were sequenced according to the protocol in

(2.3.1.1.vii) using vector primers. Sequence PCR reactions consisted of ~600ng template,

2µl of (3.2 pm/mol) vector primer, 3µl of sequence buffer (version 3.1), 2µl of Big dye

(version 3.1) to a total volume of 12µl with ddH2O. The sequence PCR program consisted

of: at 940C for 5 minutes; then in each cycle de-naturation at 960C for 10 seconds,

annealing at 50 0C for 5 seconds and extension at 60 0C for 4 minutes for 29 cycles. Final

extension was for 10 minutes. Then at 40C for 10 minutes and held at 100C. Sequences

were checked for microsatellite repeats discarding short repeats and duplicated clones.

Primers were designed in the flanking region using primer 3 programme (Steve Rozen and

Helen J. Skaletsky, 2000; http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi)

Appendices

187

Appendix 4. Microsatellite screening

For the temperature gradient PCR normal microsatellite primers were used and hence non-

urea acrylamide gels were used in the Gel scan machine with an Ethidium Bromide

detection method. When the appropriate annealing temperature was found by this method,

hexa labeled primers were used for further optimization and screening of populations using

denaturing urea-acrylamide gels.

a) Gel casting

A 5% non-urea gel mix was prepared with 25ml of 40% ultra pure acrylamide (acrylamide

19:bis acrylamide 1), 12ml of ultra pure 10X TBE to a final volume of 200ml with

millipure water. Gelmix was stored at 4oC.

Denaturing 5% gel mix was prepared with 84g of urea in addition to the above ingredients

making the final volume to 200ml. This gel mix was filtered using a 2µm filter and the gel

mix was stored at 40C.

b) Operation of Gel scan machine

Gel scan machine was run with the voltage set to 1200V and temperature to 400C and pre-

heated. After setting the gel the outsides of the gel, glass plates were cleaned thoroughly for

excess gel mix. Bottom and upper buffer tanks were set in the gel scan machine and filled

with 0.6X TBE buffer. For non-urea gels, ethidium bromide was added to 0.6X TBE buffer

(1µl of 10 mg /ml ethidium bromide to 1l of 0.6 XTBE buffer) in the bottom buffer tank

only. Gel was pre run for 30 minutes at 1200V and 400 C to migrate the buffer through the

gel. After the pre run, the wells of the gel were flushed, and ~1µl of each PCR products

mixed with (1:1 ratio) the formamide loading buffer were loaded into the gel. For

denaturing urea gels; PCR products mixed with loading buffer were denatured at 950C for 3

minutes and quickly placed on ice for 3 minutes to convert the DNA of PCR products to

single strands. T350 Tamra (Applied Bio systems) marker was prepared by mixing

Appendices

188

formamide loading buffer (1µl marker: 2µl formamide buffer). The marker was also

denatured prior to loading the gel. A reference PCR product was used in each gel except for

the marker.

After loading PCR products and the marker, the gel was pulsed for ~12 seconds to push

PCR products in to the gel. Then the wells were flushed again to remove any excess

product in the wells. The gel was run with the above conditions for ~1 hour depending on

the size of the PCR product. A digital gel image was saved in the computer connected to

the Gel scan machine.

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