i
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
Population structure 91
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
Phylogenetic relationships 114
Population structure 116
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
Population structure 137
Effective population size, population divergence and migration 141
4.5 Discussion 143
Phylogenetic relationships 143
Population structure 144
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 3.13 Allele frequency distribution of YFT Locus UTD499.
Table 3.14 Allele frequency distribution of YFT Locus UTD494.
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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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
Bonferroni correction.
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
Bonferroni correction.
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 4.18 Allele frequency distribution of SJT Locus UTD203.
Table 4.19 Allele frequency distribution of SJT Locus UTD73.
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).
Table 4.24 Effective number of gene migrants (M) per generation between pairs of
114
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130
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136
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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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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
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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
General Introduction
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.
General Introduction
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
General Introduction
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
General Introduction
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)
General Introduction
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
General Introduction
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
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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)
General Introduction
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
General Introduction
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
General Introduction
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.
General Introduction
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).
General Introduction
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
General Introduction
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
General Introduction
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
General Introduction
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
General Introduction
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
General Introduction
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
General Introduction
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
General Introduction
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
General Introduction
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.
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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.
General Introduction
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.
General Introduction
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)
General Introduction
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),
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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.
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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
General Introduction
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
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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.
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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
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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.
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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
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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
Experimental Design and Methodology
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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
Experimental Design and Methodology
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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.
Experimental Design and Methodology
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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
Experimental Design and Methodology
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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.
Experimental Design and Methodology
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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
Experimental Design and Methodology
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
Experimental Design and Methodology
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.
Experimental Design and Methodology
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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
Experimental Design and Methodology
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.
Experimental Design and Methodology
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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.
Experimental Design and Methodology
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
Experimental Design and Methodology
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.
Experimental Design and Methodology
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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
Experimental Design and Methodology
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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
Experimental Design and Methodology
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
Experimental Design and Methodology
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
Experimental Design and Methodology
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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.
Experimental Design and Methodology
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
Experimental Design and Methodology
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
Experimental Design and Methodology
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).
Experimental Design and Methodology
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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.
Experimental Design and Methodology
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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
Experimental Design and Methodology
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).
Experimental Design and Methodology
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
Experimental Design and Methodology
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
Experimental Design and Methodology
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
Experimental Design and Methodology
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.
Experimental Design and Methodology
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
Experimental Design and Methodology
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
Population Structure of Yellowfin Tuna
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.
Population Structure of Yellowfin Tuna
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).
Population Structure of Yellowfin Tuna
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
Population Structure of Yellowfin Tuna
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
Population Structure of Yellowfin Tuna
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
Population Structure of Yellowfin Tuna
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
Population Structure of Yellowfin Tuna
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
Population Structure of Yellowfin Tuna
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.
Forward primer: 5’ CCT AGT GCT AAT GGT GCG ATA AA 3’
Reverse primer: 5’ TTC CTC CAA AAG TTA TAG CCC AC 3’
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).
Population Structure of Yellowfin Tuna
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).
Population Structure of Yellowfin Tuna
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
Population Structure of Yellowfin Tuna
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
Population Structure of Yellowfin Tuna
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
Population Structure of Yellowfin Tuna
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
Population Structure of Yellowfin Tuna
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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%).
Population Structure of Yellowfin Tuna
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.
Population Structure of Yellowfin Tuna
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
Population Structure of Yellowfin Tuna
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).
Population Structure of Yellowfin Tuna
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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
Population Structure of Yellowfin Tuna
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’.
Population Structure of Yellowfin Tuna
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)
Population Structure of Yellowfin Tuna
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
Population Structure of Yellowfin Tuna
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
Population Structure of Yellowfin Tuna
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
Population Structure of Yellowfin Tuna
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.
Population Structure of Yellowfin Tuna
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
Population Structure of Yellowfin Tuna
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
Table 3.13 Allele frequency distribution of YFT Locus UTD499
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
Population Structure of Yellowfin Tuna
89
Table 3.14 Allele frequency distribution of YFT Locus UTD494
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
Population Structure of Yellowfin Tuna
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.
Population Structure of Yellowfin Tuna
91
Population structure
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)
Population Structure of Yellowfin Tuna
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).
Population Structure of Yellowfin Tuna
93
Table 3.17 Effective number of gene migrants (M) per generation between pairs of
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.
Population Structure of Yellowfin Tuna
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
Population Structure of Yellowfin Tuna
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
Population Structure of Yellowfin Tuna
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
Population Structure of Yellowfin Tuna
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
Population Structure of Yellowfin Tuna
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
Population Structure of Yellowfin Tuna
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
Population Structure of Yellowfin Tuna
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
Population Structure of Skipjack Tuna
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.
Population Structure of Skipjack Tuna
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
Population Structure of Skipjack Tuna
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
Population Structure of Skipjack Tuna
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),
Population Structure of Skipjack Tuna
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
Population Structure of Skipjack Tuna
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
Population Structure of Skipjack Tuna
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
Population Structure of Skipjack Tuna
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.
Forward primer: 5’ CCT AGT GCT AAT GGT GCG ATA AA 3’
Reverse primer: 5’ TTC CTC CAA AAG TTA TAG CCC AC 3’
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
Population Structure of Skipjack Tuna
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
Population Structure of Skipjack Tuna
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).
Population Structure of Skipjack Tuna
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... ..
Population Structure of Skipjack Tuna
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
Population Structure of Skipjack Tuna
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.
Phylogenetic relationships
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
Population Structure of Skipjack Tuna
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
Population Structure of Skipjack Tuna
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.
Population structure
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.
Population Structure of Skipjack Tuna
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%
Population Structure of Skipjack Tuna
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)
Population Structure of Skipjack Tuna
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
Population Structure of Skipjack Tuna
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
Bonferroni correction.
Population Structure of Skipjack Tuna
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.
Population Structure of Skipjack Tuna
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
Population Structure of Skipjack Tuna
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
Population Structure of Skipjack Tuna
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
Population Structure of Skipjack Tuna
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)
Population Structure of Skipjack Tuna
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
Growth decline 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
Growth decline 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.
Population Structure of Skipjack Tuna
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),
Population Structure of Skipjack Tuna
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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,
Population Structure of Skipjack Tuna
129
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.
Population Structure of Skipjack Tuna
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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).
Population Structure of Skipjack Tuna
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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.
Population Structure of Skipjack Tuna
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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
Population Structure of Skipjack Tuna
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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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134
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
Population Structure of Skipjack Tuna
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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.
Population Structure of Skipjack Tuna
136
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).
Population structure
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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138
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.
Population Structure of Skipjack Tuna
139
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
Population Structure of Skipjack Tuna
140
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
Population Structure of Skipjack Tuna
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.
Population Structure of Skipjack Tuna
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4.5 Discussion
Phylogenetic relationships
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
Population Structure of Skipjack Tuna
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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).
Population structure
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
Population Structure of Skipjack Tuna
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.
Population Structure of Skipjack Tuna
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.
Population Structure of Skipjack Tuna
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.,
General Discussion
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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167
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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172
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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176
(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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180
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.
Appendices
182
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.
References
190
REFERENCES Allendorf FW, Bayles D, Bottom DL et al. (1997). Prioritizing Pacific salmon stocks for conservation. Conservation Biology. 11: 140-52. Alvarado Bremer JR, Mejuto J, Baker AJ (1995). Mitochondrial DNA control region sequences indicate extensive mixing of swordfish (Xiphias gladius) populations in the Atlantic Ocean. Canadian Journal of Fisheries and Aquatic Science. 52:1720-1732. Alvarado Bremer JR, Stequert B, Robertson NW, Ely B (1998). Genetic evidence for inter-oceanic subdivision of bigeye tuna (Thunnus obesus) populations. Marine Biology. 132: 547-557. Alvarado Bremer JR, Vinas J, Mejuto J, Ely B, Pla C (2005). Comparative phylogeography of Atlantic bluefin tuna and swordfish: the combined effects of vicariance, secondary contact, introgression, and population expansion on the regional phylogenies of two highly migratory pelagic fishes. Molecular Phylogenetics and Evolution. 36: 169-187. Amarasiri C, Joseph L (1987). Skipjack tuna (Katsuwonus pelamis); aspects on the biology and fishery from the western and southern coastal waters of Sri Lanka. Stock assessment of tunas in the Indian Ocean. FAO-UNDP. Colombo. 1. Amarasiri C, Joseph L (1988). Skipjack Tuna (Katsuwonus pelamis); aspects of the biology and fishery from the western and southern coastal waters of Sri Lanka. Stock assessment of tunas in the Indian Ocean. FAO-UNDP.Colombo. 94-107. Andrade HA, Santos JAT (2004). Seasonal trends in the recruitment of skipjack tuna (Katsuwonus pelamis) to the fishing ground in the southwest Atlantic. Fisheries Research. 66: 185-194. Anon., (2000). Indian Ocean tuna fisheries data summary, 1989-1998, IOTC data summary no. 20. Anon., (2005). Indian Ocean tuna fisheries data summary, 1993-2002, IOTC data summary No. 24. Appleyard SA, Grewe PM, Innes BH, Ward RD (2001). Population structure of yellowfin tuna (Thunnus albacares) in the western Pacific Ocean inferred from microsatellite loci. Marine Biology. 139: 383-393. Appleyard SA, Ward RD, Grewe PM (2002). Genetic stock structure of bigeye tuna in the Indian Ocean using mitochondrial DNA and microsatellites. Journal of Fish Biology. 60: 767-770. Argue AW (ed) (1981). Report of the second skipjack survey and assessment program workshop to review results from genetic analysis of skipjack blood samples. South Pacific Commission, Skipjack Survey and Assessment Programme, Technical report No 6. pp 39.
References
191
Avise JC, (1994). Molecular markers, Natural History and Evolution. Chapman and Hall Inc., New York. Avise JC, (1997). Conservation genetics in the marine realm. The journal of Heredity. 89: 377-382. Avise JC, Arnold J, Ball RM, et al. (1987) Intraspecific phylogeography: The mitochondrial DNA bridge between population genetics and systematics. Annual Review of Ecology and Systematics.18: 489-522. Barret I, Tsuyuki H (1967). Serum transferrin polymorphism in some scombroid fishes. Copeia: 551-557. Bartlett SE, Davidson WS (1991). Identification of Thunnus tuna species by the polymerase chain reaction and direct sequence anlysis of their mitochondrial cytochrome b genes. Canadian Journal of Fisheries and Aquatic Science. 48: 309-317. Bayliff WH (1979). Migration of yellowfin tuna in the eastern Pacific Ocean as determined from tagging experiments initiated during 1968-1974. Inter-American Tropical Tuna Commission Bulletin. 17: 445-506. Bayliff WH (1988). Integrity of schools of skipjack tuna, Katsuwonus pelamis, in the eastern Pacific Ocean as determined from tagging data. Fishery Bulletin. 86: 631-643. Beaumont MA, Rannala B (2004). The bayesian revolution in genetics. Genetics. 5: 251-261. Begg GA, Friedland KD, Pearce, JB (1999a). Stock identification and its role in stock assessment and fisheries management: an overview. Fisheries Research. 43: 1-8. Begg GA, Waldman JR (1999b). An holistic approach to fish stock identification. Fisheries Research. 43: 35-44. Bermingham E, McCafferty SS, Martin AP (1997). Fish biogeography and molecular clocks: perspectives from the Panamanian Isthmus. In Molecular Systematics of Fishes (eds. Kocher TD, Stepien CA).113-128. Academic Press, San Diego. Bertignac M (1994). Analysis of skipjack (Katsuwonus pelamis) tagging data in the Maldives Island using a spatial tag attrition model. FAO-UNDP Indo Pacific Tuna Development and Management Programme, Colombo, Sri Lanka. 8: 231-238. Bertignac M, Kleiber P, Waheed A (1994). Analysis of Maldives Islands tuna tagging data with a spatially aggregated attrition model. FAO-UNDP Indo Pacific Tuna Development and Management Programme, Colombo, Sri Lanka. 8: 226-31.
References
192
Beverton RJH (1990). Small Marine pelagic fish and the threat of fishing: Are they endangered? Journal of Fish Biology. 37(Suppl. A): 5-16. Birky CW, Fuerst P, Maruyama T (1989). Organelle gene diversity under migration, mutation and drift: equilibrium expectations, approach to equilibrium, effects of heteroplasmic cells, and comparison to nuclear genes. Genetics.121: 613-627. Block BA, Dewar H, Blackwell SB, Williams TD, Prince ED, Farwell CJ, Boustany A, Teo SLH, Seitz A, Walli A, Fudge D (2001). Migratory movements, depth preferences, and thermal biology of Atlantic bluefin tuna. Science. 293: 1310-1314. Block BA, Teo SLH, Walli A, Boustany A, Stokesbury MJW, Farwell CJ, Weng KC, Dewar H, Williams TD (2005). Electronic tagging and population structure of Atlantic bluefin tuna. Nature. 434: 1121-1127. BOBP/Bay of Bengal Programme (1988). Studies of the tuna resources in the EEZs of Maldives and Sri Lanka. FAO/BOBP/REP. 41: 143. Bohonak AJ (1999). Dispersal, gene flow and population structure. Quarterly Review of Biology. 74: 21-45. Boonragsa V (1987). Tuna resources in Thai waters, Andaman Sea. FAO-UNDP Indo-Pacific Tuna Development Programme, Colombo, Sri Lanka. 267-280. Bossuyt F, Meegaskumbura M, Beenaerts N, Gower DJ, Pethiyagoda R, Roelants K, Mannaert A, Wilkinson M, Bahir MM, Manamendra-Arachchi K, Ng PKL, Schneider CJ, Oommen OV, Milinkovitch MC (2004). Local endemism within the western Ghats-Sri Lanka biodiversity hotspot. Science. 306: 479-481. Bowcock AM, Cavalli-Sforza L (1991). The study of variation in the human genome. Genomics. 11: 491-498. Bowen BW, Meylan AB, Ross JP, Limpus CJ, Balazs GH, Avise JC (1992). Global population structure and natural history of the green turtle (Chelonia mydas) in terms of matriarchal phylogeny. Evolution. 46: 865 – 881. Brill RW, Block BA, Boggs CH, Bigelow KA, Freund EV, Marcinek DJ (1999). Horizontal movements and depth distribution of large adult yellowfin tuna (Thunnus albacares) near the Hawaiian Islands, recorded using ultrasonic telemetry: implications for the physiological ecology of pelagic fishes. Marine Biology. 133: 395-403. Broughton RE, Gold JR (1997). Microsatellite development and survey of variation in northern bluefin tuna (Thunnus thynnus). Molecular Marine Biology and Biotechnology. 6:308-314. Buonaccorsi VP, McDowell JR, Graves JE (2001). Reconciling patterns of inter-ocean molecular variance from four classes of molecular markers in blue marlin (Makaira nigricans). Molecular Ecology. 10: 1179-1196.
References
193
Campana SE, Anand MC, McMillan JI (1995). Graphical and statistical methods for determining the consistency of age determinations. Transactions of the American Fisheries Society. 124: 131-138. Campbell NJH, Harris FC, Elphinstone MS, Baverstock PR (1995) Outgroup heteroduplex analysis using temperature gradient gel electrophoresis: high resolution, large scale, screening of DNA variation in the mitochondrial control region. Molecular Ecology. 4, 407-418. Carlsson J, McDowell JR, Carlsson JEL, Olafsdottir D, Graves JE (2006). Genetic heterogeneity of Atlantic bluefin tuna caught in the eastern North Atlantic Ocean south of Iceland. Journal of Marine Sciences. 63: 1111-1117. Carlsson J, McDowell JR, Diaz-James P, Carlsson JEL, Boles SB, Gold JR, Graves JE (2004). Microsatellite and mitochondrial DNA analyses of Atlantic bluefin tuna (Thunnus thynnus thynnus) population structure in the Mediterrenian Sea. Molecular Ecology. 10: 1179-1196. Carvalho GR, Hauser L (1994a). Molecular genetics and the stock concept in fisheries. Reviews in Fish Biology and Fisheries. 4: 326-350. Carvalho GR, Nigmatullin CH (1994b). Stock structure analysis and species identity in the genus, Illex. In Illex Recruitment Dynamics (eds) Rodhouse PG, O’Dor R. Rome, FAO. Caton AE (1994). Review of aspects of southern bluefin tuna biology, population, and fisheries. In Interactions of Pacific Tuna Fisheries Vol. 2. Papers on Biology and Fisheries (Shomura RS, Majkowski J, and Langi S eds), 296-343. FAO Fisheries Technical Paper 336/2. Rome: FAO.
Cayre P, Ramcharrun B (1990). Results of the tagging operations conducted within the regional tuna project (Indian Ocean Commission) in 1988 and 1989. FAO/IPTP/TRW/90.61: 10. Central Bank of Sri Lanka (2004). Annual report. Chayakul R, Chamchang C (1988). Description and identification of longtail tuna larvae (Thunnus tonggol) in the Gulf of Thailand. FAO-UNDP Indo-Pacific Tuna Development Programme. Colombo, Sri Lanka. 71-79. Chiang HC, Hsu CC, Lin HD, Ma GC, Yang HY (2005). Population structure of bigeye tuna (Thunnus obesus) in the South China Sea, Philippine Sea and western Pacific Ocean inferred from mitochondrial DNA. Fisheries Research. 79: 219-225. Chow S, Inoue S (1993). Intra- and interspecific restriction fragment length polymorphism in mitochondrial genes of Thunnus tuna species. Bulletin of the National Research Institute of Far Seas Fisheries. 30: 207-225.
References
194
Chow S, Nakagawa T, Suzuki N, Takeyama H, Matsunaga T (2006). Phylogenetic relationships among Thunnus species inferred from rDNA ITS1 sequence. Journal of Fish Biology. 68 (Supplement A): 24-35. Chow S, Nohara K, Tanabe T, Itoh T, Tsujii S, Nishikawa Y, Ueyanagi S, Uchikawa K, (2003). Genetic and morphological identification of larval and small juvenile tunas (Pisces, Scombridae) caught by a mid-water trawl in the western Pacific. Bulletin of Fisheries Research Agency. 8: 1-14. Chow S, Okamoto H, Uozumi Y, Takeuchi Y, Takeyama H, (1997). Genetic stock structure of the swordfish (Xiphias gladius) inferred by PCR-RFLP analysis of the mitochondrial DNA control region. Marine Biology. 127: 359-367. Chow S, Takeyama H, (2000). Nuclear and mitochondrial DNA analyses reveal four genetically separated breeding units of the swordfish. Journal of Fish Biology. 56: 1087-1098. Chow S, Ushiyama S (1995). Global population structure of albacore (Thunnus alalunga) inferred by RFLP analysis of the mitochondrial ATPase gene. Marine Biology. 123:39-45. Clement M, Posada D, Crandall KA (2000). TCS: a computer programme to estimate gene genealogies. Molecular Ecology. 9: 1657-1660. Collette (1999). Mackerels, molecules and morphology. Proceedings of 5th Indo-Pacific Fish Conference, Noumea 1997. (eds) Seret B, Sire JY. Paris: Societe Francaise d’Ichtyologie. 149-164. Collette, Bruce B, Carol Reeb, Barbara A Block (2001). Systematics of the tunas and mackerels (scombridae). In Tuna: Physiology, Ecology and Evolution. (eds) Barbara A Block and Stevens ED, Academic Press, San Diego, 1-33. Conand F, Richards WJ (1982). Distribution of tuna larvae between Madagascar and the equator, Indian Ocean. Biological Oceanography. 1: 321-336. Crandall KA (1996). Multiple inter species transmissions of human and simion T-cell leukemia/ Lymphoma virus type I sequences. Molecular Biology and Evolution. 13: 115-131. Cushing JE (1956). Observations on serology of tuna. U.S. Fish and Wildlife Service: 183 : pp 14. Dayaratne P (1994a). An assessment of frigate tuna (Auxis thazzard) stocks in the southern waters of Sri Lanka. 5th expert consultation on Indian Ocean Tunas. Mahe, Seychelles, IPTP – Indo-Pacific Tuna Development and Management Programme. Dayaratne P (1994b). Present status of the tuna fisheries in Sri Lanka. 1st Annual Scientific Session of National Aquatic Resource Agency (NARA). Colombo, Sri Lanka.
References
195
Dayaratne P, De Siva J (1990). Tuna fisheries in Sri Lanka-an update. Expert consultation of stock assessment of tunas in the Iindian Ocean. Bangkok, Thailand. 341-358. Dayaratne P, De Siva J (1991). Recent trends in fisheries for small tunas in Sri Lanka. Expert consultation of stock assessment of tunas in the Indian Ocean. Bangkok, Thailand. 174-181. De Silva J, Boniface B (1990). The study of the handline fishery on the west coast of Sri Lanka with special reference to the use of dolphin for locating yellowfin tuna (Thunnus albacares). Expert consultation of stock assessment of Tunas in the Indian Ocean. Bangkok, Thailand. 314-324. De Silva J, Dayaratne P (1990). Observation on the recently developed offshore fisheries for skipjack and yellowfin tunas in Sri Lanka. Expert consultation of stock assessment of tunas in the Indian Ocean. Bangkok, Thailand. 304-313. Department of Census and statistics of Sri Lanka (2004). Statistical Abstract. Di Rienzo A, Peterson AC, Garza JC, Valdes AM, Slatkin M, Freimer NB. (1994). Mutational processes of simple sequence repeat loci in human populations. Proceedings of the National Academy of Sciences. United States of America. 91: 3166-3170. Dizon AE, Taylor BL, O’Corry-Crowe GM (1995). Why statistical power is necessary to link analyses of molecular variation to decisions about population structure? American Fisheries Society Symposium. 17: 288-294. Domier M (2006). Preliminary testing of a prototype popup satellite archival tag. Proceedings of the 57th Tuna conference. Lake Arrowhead, California, USA. pp34. Dupanloup I, Schinder S, Excoffier L (2002). A simulated annealing approach to define the genetic structure of populations. Molecular Ecology. 11: 2571-2581. Durand JD, Collet A, Chow S, Guinand B, Borsa P (2005). Nuclear and mitochondrial DNA markers indicate unidirectional gene flow of Indo-Pacific to Atlantic bigeye tuna (Thunnus obesus) populations, and their admixture off southern Africa. Marine Biology. 147: 313-322. Elliott NG, Ward RD (1995). Genetic relationships of eight species of Pacific tunas (Teleostei: Scombridae) inferred from allozyme analysis. Marine and Freshwater Research. 46. 1021-1032. Ely B, Vinas J, Alvarado Bremer J, Black B, Lucas L, Covello K, Labrie AV, Thelen E (2005). Consequences of the historical demography in the global population structure of two highly migratory cosmopolitan marine fishes: the yellowfin tuna (Thunnus albacares) and skipjack tuna (Katsuwonus pelamis). BMC Evolutionary Biology. 5: 19.
References
196
Ewens (1972). The sampling theory of selectively neutral alleles. Theoretical population biology.3: 87 -112. Excoffier L, Smouse PE, Quattro JM (1992). Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics.131: 479- 491. FAO (1985). Tuna fishery in the EEZs of India, Maldives and Sri Lanka. Colombo, FAO, UNDP: 91. FAO (1995). Food and Agricultural Organisation hand book. Marine Fishery Resources in Sri Lanka. Rome. FAO. FAO, Fisheries Department. (2004). The status of world fisheries and aquaculture (SOFIA), Rome: www.fao.org/fi/statist/statist.asp. Ferguson A (1989). Genetic differences among brown trout, Salmo trutta, stocks and their importance for the conservation and management of the species. Freshwater Biology. 21: 35-46. Finnerty JR, Block BA (1992). Direct sequencing of mitochondrial DNA detects highly divergent haplotypes in blue marlin (Makaira nigricans). Molecular Marine Biology and Biotechnology. 1: 1-9 Fisheries Global Information System (FIGIS) (2005). FAO. (http://www.fao.org/figis) FitzSimmons NN, Moritz C, Limpus CJ, Pope L, Prince R (1997). Geographic structure of mitochondrial and nuclear gene polymorphisms in Australian green turtle populations and male biased gene flow. Genetics. 147. 1843-1854. Fonteneau A (1998). Overview of the tuna resources and exploitation in the Indian Ocean. International Tuna Conference 1998; Tuna prospects and strategies for Indian Ocean. Mauritius, Orstom. Forsbergh ED (1980). Synopsis of biological data on the skipjack tuna, Katsuwonus pelamis (Linneaus, 1758) in the Pacific Ocean. In Synopsis of biological data on eight species of Scombrids. (ed. Bayliff WH). 295-360. IATTC Special Report No.2. La jolla, CA. Fu YX (1997). Statistical tests for neutrality of mutations against population growth, hitchhiking and background selection. Genetics.147: 915-925. Fujino K (1969). Atlantic skipjack tuna genetically distinct from Pacific specimens. Copeia. 626-629. Fujino K (1970). Immunological and biochemical genetics of tuna. Transactions of the American Fisheries Society. 99: 152-178. Fujino K, Kang T (1968a). Transferrin groups of tunas. Genetics. 59: 79-91.
References
197
Fujino K, Kang T (1968b). Serum esterase groups of Pacific and Atlantic tunas. Copeia. 56-63. Fujino K, Sassaki K, Okumura S (1981). Genetic diversity of skipjack tuna in the Atlantic, Indian and Pacific Oceans. Bulletin of the Japanese Society of Scientific Fisheries. 47: 215-222. Gaggiotti OE, Excoffier L (2000). A simple method of removing the effect of a bottleneck and unequal population sizes on pairwise genetic distances. Proceedings of the Royal Society of London Series B. 267: 81-87. Garcia SM (1994). World Review of Highly Migratory Species and Straddling Stocks. FAO Fisheries Technical Paper No. 337. Rome: FAO. George KC (1990). Studies on the distribution and abundance of fish eggs and larvae off the south-west coast of India with special reference to scombrids. IPTP – Indo-Pacific Tuna Development and Management Programme, Colombo, Sri Lanka.40. Gibbs RH Jr, Collette BB, (1967). Comparative anatomy and systematics of the tunas, genus Thunnus. U.S. Fish and Wildlife Series Fisheries Bulletin. 66: 65-130. Glenn CT, Schable M (2002). Microsatellite isolation with Dynabeads, in www.uga.edu/srel. Goudet J, Raymond M, de Meeus T, Rousset F (1996). Testing differentiation in Diploid populations. Genetics. 144: 1933-1940. Graves JE (1998). Molecular insights in to the population structures of cosmopolitan marine fishes. Heredity. 89: 427-437. Graves JE, Dizon EE (1989). Mitocondrial DNA sequence similarity of Atlantic and Pacific albacore tuna. Canadian Journal of Fisheries and Aquatic Science. 46:870-873. Graves JE, Ferris SD, Dizon, AE (1984). High genetic similarity of Atlantic and pacific skipjack tuna demonstrated with restriction endonuclease analysis of mitochondrial DNA. Marine Biology. 79: 315-319. Graves JE, Gold JR, Ely B, et al. (1996). Population genetic structure of bluefin tuna in the north Atlantic Ocean: Identification of variable genetic markers. International commission for the conservation of Atlantic tunas, Collective Volume of Scientific Papers. 45: 155-157. Graves JE, McDowell JR (1995). Inter-ocean genetic divergence of Istiophorid billfishes. Marine Biology. 122: 198-203. Graves JE, McDowell JR (2003). Stock structure of the worlds Istio-phorid billfishes; a genetic perspective. Marine Freshwater Research. 54: 287-298.
References
198
Grewe PM, Hampton J (1998). An assessment of bigeye (Thunnus obesus) population structure in the Pacific Ocean based on mitochondrial DNA and DNA microsatellite analysis. Report for the Forum Fisheries Agency and Pelagic fisheries Research Programme. CSIRO Marine Research, Australia. 1-29. Gubanov EP, Paramonov VV (1993). Oceanographical prerequisites for formation of concentrations of large pelagic predators in the Indian Ocean. In Resources of tunas and related species in the world Ocean and problems of their rational utilization. 69-71. Guo S, Thompson E (1992). Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics. 48:361-372. Hall TA (1999). BioEdit: A user friendly biological sequence alignment editor and analysis programme for Windows 95/98/NT. Nucleic Acids Symposium. 41: 95-98. Harpending HC (1994). Signature of ancient population growth in a low-resolution mitochondrial DNA mismatch distribution. Human Biology. 66: 591-600. Hilborn R (1991). Modelling the stability of fish schools: Excahnage of Individual fish between schools of skipjack tuna (Katsuwonis pelamis). Fishery Bulletin. 48: 1081-1091. Hudson RR (2000). A new statistic of detecting genetic differentiation. Genetics. 155: 2001-2014. Hunter JR, Argue AW, Bayliff WH, Dizon AE, Fontaneu A, Goodman D, Seckel GR (1986). The dynamics of tuna movements and evaluation of past and future research. FAO Fisheries Technical Paper No. 277. Rome: FAO. Hutchings JA, Myers RA (1994). What can be learned from the collapse of a renewable resource: Atlantic cod, Gadus morhua, of Newfoundland and Labrador. Canadian Journal of Fisheries and Aquatic Sciences. 51: 2126-2146. Hyde JR, Lynn E, Humphreys R Jr, Musyl M, West AP, Vetter R (2005). Shipboard identification of fish eggs and larvae by multiplex PCR, and description of fertilized eggs of blue marlin, shortbill spearfish, and wahoo. Marine Ecology Progress Series. 286: 269-277. IATTC (1991). 1990 Annual Report of the Inter-American Tropical Tuna Commission. La Jolla. CA. IATTC (1992). 1991 Annual Report of the Inter-American Tropical Tuna Commission. La Jolla. CA. IATTC (1993). 1992 Annual Report of the Inter-American Tropical Tuna Commission. La Jolla, CA. ICCAT (1995). Report for biennial period, 1994-95 part I (1994). 1: Madrid, Spain.
References
199
ICCAT. International Committee for Conservation of Atlantic Tuna (2003). Report of the standing committee on research and statistics 2002-2003. ICCAT. Madrid. Ihssen PE, Booke HE, Casselman JM, McGlade JM, Payne NR, Utter FM (1981). Stock identification: materials and methods. Canadian Journal of Fisheries and Aquatic Sciences. 38: 1838-1855. IOTC (2002). Collections of resolutions and decisions by the Indian Ocean Tuna Commission. Resolution 02/08. Seventh session of the IOTC. IOTC (2005). Report of the Eighth session of the Scientific Committee, Victoria, Seychelles. p 19. IOTC data base (2006). Nominal catch data base 1950-2005. http://www.iotc.org/English/data/databases.php. IOTTP, Indian Ocean Tuna Tagging Program (2000). Detailed report; Indian Ocean tunas tagging programme. Working document prepared by the IOTC tagging working group. James PSBR, Jayaprakash AA (1990). On the occurrence of yellowfin tuna (Thunnus albacares) in the drift gill net catch at cochin. Workshop on stock assessment of yellowfin tuna in the Indian Ocean. Indo-Pacific Tuna development and Management Programme. 170-177. Jordan DS, Evermann BW (1926). Review of the giant mackeral- like fishes, tunnies, spearfishes and swordfishes. California Academy of Science Occasional Papers. 12: 1-113. Joseph J, Alverson FG, Fink BD, Davidoff EB (1964). A review of the population structure of yellowfin tuna, Thunnus albacares, in the eastern Pacific Ocean. Inter-American Tropical Tuna Commission Bulletin. 9: 53-112. Joseph L (1984). Review of tuna fishery in Sri Lanka. Colombo, FAO, UNDP: 29. Joseph L, Amarasiri C, Maldeniya R (1985). Driftnet fishery for tuna in the western coastal waters of Sri Lanka. Colombo, FAO, UNDP. Project for Marine Fisheries Resource Management. Bay of Bengal Programme. 72-88. Joseph L, Maldeniya R, Knapp M Van der (1987). Fishery and age growth of kawakawa (Euthynnus affinis) and frigate tuna (Auxis thazard). Expert consultation on stock assessment of tunas in the Indian Ocean. Colombo, FAO, UNDP-IPTP. Joseph L, Maldeniya R, Knapp M Van der (1988). Fishery of kawakawa and frigate tuna, their age and growth. Studies of the Tuna resource in the EEZs of Sri Lanka and Maldives. 22-29. Joseph L, Moiyadeen NM (1987). Tuna fisheries-an update for Sri Lanka. Collective volume of working documents presented at the expert consultation on stock assessment of tunas in the Indian Ocean. Colombo, UNDP, IPTP: 299-309.
References
200
Joseph L, Moiyadeen NM (1988). Studies of the tuna resource in the EEZs of Sri Lanka and Maldives. Colombo. 79-93. Kalinowski ST (2004). Genetic polymorphism and mixed stock fisheries analysis. Canadian Journal of Fisheries and Aquatic Science. 61: 1075 – 1082. Knutsen H, Jorde PE, Andre C, Stenseth CHR (2003). Fine-scaled geographical population structuring in a highly mobile marine species: the Atlantic cod. Molecular Ecology. 12: 385-394. Kumar S, Tamura K, Jakobsen IB, Nei M (2001). MEGA2: Molecular evolutionary genetics analysis software. Bioinformatics. 17: 1244-1245. Kurogane K (1960). Morphometric comparisons of yellowfin tuna from the Banda Sea and its adjacent waters. Records of Oceanography works in Japan. 5: 105-119.
Kurogane K, Hiyama Y (1958). Morphometric comparisons of the yellowfin tuna from the six grounds in the Indian Ocean. Bulletin of Japanese Society for Scientific Fisheries. 24: 487-494. Lessa EP, Applebaum G (1993). Screening techniques for detecting allelic variation in DNA sequences. Molecular Ecology. 2: 119-129. Lewis AD (1992). Stock structure of Pacific yellowfin-a review. South Pacific Commission. Noumia, New Calidonia. 14. Lu HJ, Lee KT, Lin HL, Liao CH (2001). Spatio-temporal distribution of yellowfin tuna Thunnus albacares and bigeye tuna Thunnus obesus in the tropical Pacific Ocean in relation to large-scale temperature fluctuation during ENSO episodes. Fisheries Science. 67: 1046-1052. Lyrholm T, Leimar O, Johannesson B, Gyllensten U (1999). Sex-biased dispersal in sperm whales: contrasting mitochondrial and nuclear genetic structure of global populations. Proceedings of the Royal Society of London B. 266: 347-354. MacIsaac DO, Quinn TP (1998). Evidence for a hereditary component in homing behaviour of Chinook salmon (Onchorynchus tshawytscha). Canadian Journal of Fisheries and Aquatic Sciences. 45: 2201-2205. Magnuson JJ, Block BA, Deriso RB, et al. (1994). An assessment of Atlantic bluefin tuna. National Research Council. National Academy Press, Washington, DC. Maldeniya R (1993). Food composition of yellowfin tuna (Thunnus albacares) in Sri Lankan waters. NARA annual scientific session. Colombo. Maldeniya R, Dayaratne P (1994). Changes in catch rates and size composition of skipjack (Katsuwonus pelamis) and yellowfin tuna (Thunnus albacares) in Sri
References
201
Lankan waters. 5th expert consultation on Indian Ocean Tunas. Mahe, Seychelles, IPTP – Indo-Pacific Tuna Development and Management Programme. Maldeniya R, Joseph L (1987). On the distribution and biology of yellowfin tuna (Thunnus albacares) from the western and southern coastal waters of Sri Lanka. Indo-Pacific Tuna Development and Management Programme (IPTP) of United Nations Development Programme(UNDP)- FAO. Maldeniya R, Joseph L (1988). Recruitment and migratory behaviour of yellowfin tuna (Thunnus albacares) from the western and southern coasts of Sri Lanka. Indo-Pacific Tuna Development and Management Programme (IPTP),Colombo, Sri Lanka. pp16. Maldeniya R, Suraweera SL (1991). Exploratory fishing for large pelagic species in Sri Lanka. BOBP – Bay of Bengal Programme. Madras. Martinez P, Gonzalz EG, Castilho R, Zardoya R (2005). Genetic diversity and historical demography of Atlantic bigeye tuna (Thunnus obesus). Molecular Phylogenetics and Evolution. 39: 404-416. McQuinn, IH (1997). Metapopulations and the Atlantic herring. Reviews in Fish Biology and Fisheries. 7: 297-329. Medina DL, Garcia SO, (2006). Spatial-temporal variability of yellowfin tuna catches in adjacent waters to the Islas Marias, Mexico. Proceedings of 57th Tuna conference. Lake Arrowhead, California, USA. 21. Menezes MR, Ikeda M, Taniguchi N (2005). Genetic variation in skipjack tuna Katsuwonus pelamis (L.) using PCR-RFLP analysis of the mtDNA D-loop region. Journal of Fish Biology. 68: Supplement A, 156-161. Miller SA, Dykes DD, Polesky HF (1988). A simple salting out procedure for extracting DNA from nucleated cells. Nucleic Acids Research. 16: 1215. Morita Y, Koto T (1970). Some consideration on the population structure of yellowfin tuna in the Indian Ocean based on the longline fishery data. Bulletin of Far Seas Fisheries Research Lab. 4: 125-140. Myers RA, Worm B, (2003). Rapid worldwide depletion of predatory fish communities. Nature. 423: 280-283.
Myers RM, Maniatis T, Lerman LS (1987). Detection and location of single base changes by denaturing gradient gel electrophoresis. Methods Enzymology.155: 501-527. Nair KNV, Muraleedharan PM (1993). Pattern of spatial and seasonal fluctuations in temperature profile in Indian EEZ and its influence on tuna fishing. In Tuna research in India. (eds) Sudarshan D, John ME. Bombay, India fish survey. 167-180.
References
202
Nei, M. (1987). Molecular Evolutionary Genetics. New York, Columbia University Press. Nielsen EE, Hansen MM, Meldrup D, Gronkjaer P (2003). A hybrid zone in Atlantic cod (Gadus morhua) in the Baltic and the Danish Belt Sea revealed by individual admixture analysis. Molecular Ecology. 12: 1497-1508. Nilesen R, Wakeley J (2001). Distinguishing migration from isolation: A markov chain monte carlo approach. Genetics. 158: 885- 896. Nishida T (1992). Considerations of stock structure of yellowfin tuna (Thunnus albacares) in the Indian Ocean based on fishery data. Fisheries Oceanography.1: 143-152. Nishikawa Y (1985). Identification for larvae of three species of genus Thunnus by melanophore patterns. Bulletin of the Far Seas Fisheris Research Laboratory. 22: 199-129. NMFS (National Marine Fisheries Service) 1995. Supplimental draft environmental impact. Statement for a regulatory amendment for the western Atlantic bluefin tuna Fishery. NMFS-NOAA, Silver Spring, MD. Oosterhout C, Hutchinson WF, Wills PMD, Shipley P (2004). Microchecker: software for identifying and correcting genotyping errors in microsatellite data. Molecular Ecology Notes. 4: 535-538. Pardini AT, Jones CS, Noble LR, Kreiser B, Malcolm H, Bruce BD, Stevens, JD, Cliff G, Scholl MC, Francis M, Duffy CAJ, Martin AP (2001). Sex-biased dispersal of great white sharks. Nature. 412: 139-140. Pauly D, Christensen Dalsgaard, Froese R, Torres F Jr. (1998). Fishing down marine food webs. Science. 279: 860-863. Pawson MG, Jenings S (1996). A critique of methods for stock identification in marine capture fisheries. Fisheries Research. 25: 203-217. Peeters FJC, Acheson R, Brummer GJA, de Ruijter, WPM, Schneider RR, Ganssen, GM, Ufkes E, Kroon D (2004). Vigorous exchange between the Indian and Atlantic Oceans at the end of the past five glacial periods. Nature. 430: 661-665. Perle C, Matteson R, Castleton M, Block B, and Farwell C (2006). Trans-Pacific migrations of Pacific bluefin tuna. Proceedings of the 57th Tuna conference, Lake Arrowhead, California, USA. pp33. Po T, Steger G, Rosenbaum V, Kaper J, Reisner D (1987). Double-stranded cucumovirus associated RNA 5: experimental analysis of necrogenic and non-necrogenic variants by Temperature-Gradient Gel Electrophoresis. Nucleic Acid Research.15: 5069-5083.
References
203
Posada D, Crandall KA (1998). Modeltest; testing the model of DNA substitution. Bioinformatics. 14: 817-818. Pritchard JK, Stephens M, Donnelly P (2000). Inference of population structure using multi locus genotype data. Genetics. 155: 945-959. Prugnolle F, de Meeus T (2002). Inferring sex-biased dispersal from population genetic tools: a review. Heredity. 88: 161-165. Pujolar JM, Roldan MI, Pla C (2002). A genetic assessment of the population structure of swordfish (Xiphias gladius) in the Mediterranian Sea. Journal of Experimental marine Biology and Ecology. 276: 19-29. Ramos-Onsins SE, Rozas J (2002). Statistical properties of new neutrality tests against population growth. Molecular Biology and Evolution. 19: 2092-2100. Raymond M, Rousset F (1995). An Exact test for population differentiation, Evolution. 49: 1280-1283. Rice WJ (1989). Analysing tables of statistical tests. Evolution. 43: 223-225. Richardson BJ (1983). Distribution of protein variation in skipjack tuna (Katsuwonus pelamis) from the central and south-western Pacific. Australian Journal of Marine and Freshwater Research. 34: 231-251. Richardson BJ, Baverstock PR, Adams, M (1986). Allozyme electrophoresis: A Handbook for Animal Systematics and Population studies. Sydney, Academic Press. Rogers AR (1995). Genetic evidence for a pliestocene explosion. Evolution. 49: 608-615. Rogers AR, Harpending H, (1992). Population growth makes waves in the distribution of pairwise genetic differences. Molecular Biology and Evolution. 9: 552-569. Rohling EJ, Fenton M, Jorissen FJ, Bertrand P, Ganssen G, Caulet JP (1998). Magnitudes of sea level lowstands of the past 500000 years. Nature. 394: 162-165. Rosel PE, Block BA (1996). Mitochondrial control region variability and global population structure in the sword fish, Xiphias gladius. Marine Biology. 125: 11-22. Rosenberg NA, Nordberg M (2002). Geneological trees, coalescent theory and the analysis of genetic polymorphism. Genetics. 3: 380-390. Royce WF, (1964). Morphometric study of yellowfin tuna Thunnus albacares (Bon-naterre). Fishery Bulletin of the US. 63:395-443. Rozas J, Sanchez-DelBarro JC, Messeguer X, Rozas R (2003). DnaSp, DNA polymorphism analyses by the coalescent and other methods. Bioinformatics. 19: 2496-2497.
References
204
Ruzzante DE (1998b). A comparison of several measures of genetic distance and population structure with microsatellite data: bias and sampling variance. Canadian Journal of Fisheries and Aquatic Sciences. 55: 1-14. Ruzzante DE, Taggart CT, Cook D (1998a). A nuclear DNA basis for shelf- and bank-scale population structure in northwest Atlantic cod (Gadus morhua): Labrador to Georges bank. Molecular Ecology. 7: 1633-1680. Ruzzante DE, Taggart CT, Cook D, Lang S (2000). Mixed-stock analysis of Atlantic cod near the Gulf of St. Lawrence based on microsatellite DNA. Ecological Applications. 10: 1090-1109. Ryman N, Palm S, Andre C, Carvalho GR, Dahlgren TG, Jorde PE, Laikre L, Larsson LC, Palme A, Ruzzante DE (2006). Power for detecting genetic divergence; difference between statistical method and marker loci. Molecular Ecology. 15: 2031-2045. Saitou N, Nei M (1987). The neighbour-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution. 4: 406-425. Sakagawa GT, Kleiber PM (1992). Fisheries and stocks of yellowfin tuna in the Pacific and Indian Oceans. Status and review of assessment methods. Collective volume of scientific Papers of ICCAT. 38: 203-217. Schaefer HM, Fuller DW (2002). Movements, behaviour and habitat selection of bigeye tuna (Thunnus obesus) in the eastern equatorial Pacific, ascertained through archival tags. Fish Bulletin (Washington DC). 100: 765-768. Schaefer K, Fuller D (2006). Movements of bigeye and yellowfin tunas in the eastern Pacific ocean, ascertained from archival tags. Proceedings of 57th Tuna conference, Lake Arrowhead, California, USA. pp 31. Scheafer KM (1996). Spawning time, frequency and batch fecundity of yellowfin tuna (Thunnus albacares) near Clipperton atoll in the eastern Pacific Ocean. Fishery bulletin. 94: 98-112. Schneider S, Excoffier L (1999). Estimation of past demographic parameters from the distribution of pairwise differences when the mutation rates vary among sites; application to human mitochondrial DNA. Genetics. 152:1079-1089. Schneider S, Roesslli D, Excoffier L (2005). Arlerquin version 2.00: a software for population genetics data analysis. Schott AF, McCreary JP Jr. (2001). The monsoon circulation of the Indian Ocean. Progress in Oceanography. 51: 1-123. Scoles DR, Graves JE (1993). Genetic analysis of the population structure of yellowfin tuna (Thunnus albacares), from the Pacific Ocean. Fishery Bulletin of the US. 63: 690-698.
References
205
Shaklee JB, Phelps SR, Salini J (1990). Analysis of fish stock structure and mixed stock fisheries by electrophoretic characterization of allelic isozymes.In Electrophoretic and Isoelectric Focusing Techniques in Fisheries Management. (eds) Whitmore DH, Raton B. Florida, CRC press: 173-196. Shaklee JB, Bentzen P, Coleman F, Travis J (1998). Genetic identification of stocks of marine fish and shellfish. Bulletin of Marine Science, 62: 589-621. Sharp GD (1978). Behavioural and physiological properties of tunas and their effects on vulnerability to fishing gear. In The Physiological Ecology of Tunas (eds) Sharp GD, Dizon AE. 397-449. New York, Academic Press. Sivasubramanium K (1985). The tuna fishery in the EEZ of India, Maldives and Sri Lanka. Appendix 1. Colombo, FAO-UNDP: 19-47. Slatkin M (1993). Isolation by distance in equilibrium and non-equilibrium populations. Evolution. 47: 264-279. Slatkin M, Excoffier L (1996). Testing for linkage disequilibrium in genotypic data using the expectation-maximization algorithm. Heredity. 76: 377-383. Slatkin M, Hudson RR (1991). Pairwise comparisons of mitochondrial DNA sequences in stable and exponentially growing populations. Genetics. 129: 555-562. Smedbol RK, McPerson A, Hansen MM, Kenchington E (2002). Myths and moderation in marine ‘metapopulations’? Fish and Fisheries. 3: 20-35. Smith PJ, Conroy AM, Tailor PR (1994). Biochemical genetic identification of northern blue fin tuna (Thunnus thynnus) in the New Zealand fishery. New Zealand Journal of Marine and Freshwater Research. 28: 113-118. Smith PJ, Francis RICC, McVeagh M (1990). Loss of genetic diversity due to fishing pressure. Fisheries Research. 10: 309-316. Stequert B, Panfili J, Dean JM (1996). Age and growth of yellowfin tuna (Thunnus albacares) from the western Indian Ocean, based on otolith microstructure. Fish Bulletin. 94: 124-134. Stequert B, Ramcharrun B (1995). The fecundity of skipjack tuna (Katsuwonus pelamis) from the western Indian Ocean. Aquatic Living Resources. 8: 79-89. Stequert, B, Ramcharrun, B. (1996). Reproduction of skipjack tuna (Katsuwonus pelamis) from the western Indian Ocean. Aquatic Living Resources 9: 235-247. Sterner T (2006). Unobserved diversity, depletion and irreversibility. The importance of subpopulations for management of cod stocks. Ecological Economics. (in press).
References
206
Stevens TA, Withler RE, Goh SH, Beacham TD (1993). A new multilocus probe for DNA fingerprinting in chinook salmon (Oncorhynchus tshawytscha), and comparisons with a single-locus probe. Canadian Journal of Fisheries and Aquatic Science. 50: 1559-1567. Suzuki A (1962). On the blood types of yellowfin and bigeye tuna. American Naturalist. 96: 239-246. Swearer SE, Caselle JE, Lea DW, Warner RR (1999). Larval retention and recruitment in an island population of a coral-reef fish. Nature. 402: 799-802. Swofford DL (1998). PAUP*. Phylogenetic Analysis Using Parsimony (* and other methods), Version 4. Sinauer Associates, Sunderland, MA. Tajima F (1983). Evolutionary relationship of DNA sequences to finite populations. Genetics. 105: 437-460. Tajima F (1989). Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics. 123: 585-595. Tajima F (1993). Simple methods for testing molecular clock hypothesis. Genetics. 135: 599-607. Takagi M, Okamura T, Chow S, Taniguchi N (1999). PCR primers for microsatellite loci in tuna species of the genus Thunnus and its application for population genetic study. Fisheries Science. 65: 571-576. Takeyama H, Chow S, Tsuzuki H, Matsunaga T (2001). Mitochondrial DNA sequence variation within and between Thunnus tuna species and its application to species identification. Journal of Fish Biology. 58: 1646-1657. Tamura K, Nei M (1993). Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Molecular Biology and Evolution. 10: 512-526. Tanabe T (2001). Feeding habits of skipjack tuna (Katsuwonus pelamis) and other tuna Thunnus spp. Juveniles in the tropical western Pacific. Fisheries Science. 67: 563-570. Tautz D (1989). Hypervariability of simple sequences as a general source for polymorphic DNA markers. Nucleic Acids Research. 17: 6463-6471. Taylor BL and Gerrodette, T (1993). The uses of statistical power in conservation biology: the vaquita and northern spotted owl. Conservation Biology. 7: 489-500. Templeton AR, Boerwinkle E, Sing CF (1987). A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping-I. Basic theory and an analysis of alcohol dehydrogenase activity in Drosophila. Genetics. 117: 343-351.
References
207
Templeton AR, Sing CF (1993). A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping-IV. Nested analyses with cladogram uncertainty and recombination. Genetics. 134:659-669. Timokhina OI (1993). Some aspects of reproductive biology of tunas in the Indian Ocean. In Resources of tunas and related species in the world Ocean and problems of their rational utilization. (eds) Kerch-Ukraine Yugniro. pp103. Uktolseja JCB, Purawasamita R (1990). Preliminary study on the fecundity of skipjack tuna from the waters adjacent to Pelabuhan Ratu. Expert consultation of stock assessment of tunas in the Indian Ocean. Bangkok, Thailand. Utter F, Aebersold P, Winans G (1987). Interpreting genetic variation detected by electrophoresis. In Population Genetics and Fishery Management. (eds) Ryman N, Utter F. Seattle and London, University of Washington Press: 21-46. Utter F, Ryman N (1993). Genetic markers and mixed stock fisheries. Fisheries. 18: 11-21. Utter FM (1991). Biochemical genetics and fishery management: an historical perspective. Journal of Fish Biology. 39(Suppl. A): 1-20. Van Wagner C, Baker AJ (1990). Association between mitochondrial DNA and morphological evolution in canada geese. Journal of Molecular Evolution. 31: 373 -382. Vaz GG (2000). Age of relict coral reef from the continental shelf off Karaikal, Bay of Bengal: evidence of last glacial maximum.Current Science. 79: 228-230. Vinas J, Alvarado Bremer J, Pla C (2004a). Phylogeography of the Atlantic bonito (Sarda sarda) in the northern mediterranian: the combined effects of historical vicariance, population expansion, secondary invasion and isolation by distance. Molecular Phylogenetics and Evolution. 33: 32-42. Vinas J, Alvarado Bremer J, Pla C (2004b). Inter-oceanic genetic differentiation among albacore (Thunnus alalunga) populations. Marine Biology. 145: 225-232. Waheed A, Anderson RC (1994). The Maldivian tuna tagging programmes. 5th expert consultation on Indian Ocean tunas. Mahe, Seychelles, FAO-UNDP. Indo Pacific Tuna Development Programme. Waples RS (1991). Pacific salmon, Oncorhynchus spp., and the definition of “species” under the Endangered Species Act. Marine Fisheries Review. 53: 11-22. Waples RS (1998). Separating the wheat from the chaff: patterns of genetic differentiation in high gene flow species. Journal of Heredity. 89: 435-450. Ward RD (1995). Population genetics of tunas. Journal of Fish Biology. 47(Supp. A): 259-280.
References
208
Ward RD (2000a). Genetics of fish populations. In Handbook of Fish Biology and Fishries Management. (eds) Hart PJB, Reynolds JD. 1: 200-224. Blackwell publishing. United Kingdom. Ward RD (2000b). Genetics and fisheries management. Hydrobiologia. 420: 191-200. Ward RD, Elliot NG, Innes BH, Smolenski, AJ, Grewe, PM (1997). Global population structure of yellowfin tuna (Thunnus albacares) inferred from allozyme and mitochondrial DNA variation. Fisheries Bulletin. 95: 566-575. Ward RD, Elliotte NG (2001). Genetic population structure of species in the south east fishery of Australia. Marine and Freshwater Research. 52: 563-573. Ward RD, Elliotte NG, Grewe PM, Smolenski AJ (1994b). Allozyme and mitochondrial DNA variation in yellowfin tuna (Thunnus albacares) from the Pacific Ocean. Marine Biology. 118: 531-539. Ward RD, Grewe PM (1994). Appraisal of molecular genetic techniques in fisheries. Reviews in Fish Biology and Fisheries. 4: 300-325. Ward RD, Woodwark M, Skibinski DOF (1994a). A comparison of genetic diversity levels in marine, freshwater and anadromous fish. Journal of Fish Biology. 44: 213-232. Weera-Pokapunt, Pattira-Sawasdiworn (1988). Observation of skipjack tuna (Katsuwonus pelamis) and yellowfin tuna (Thunnus albacares) in the Andaman Sea of Thailand. FAO-UNDP Indo-Pacific Tuna Development and Management programme. Colombo, Sri Lanka. 211-220. Weir BS, Cockerham CC (1984). Estimating F-statistics for the analysis of population structure. Evolution. 38: 1358-1370. Wenink PW, Baker AJ, Tilanus MGJ (1994). Hypervariable control-region sequences reveal global population structuring in a long-distance migrant shorebird, the Dunlin (Calidris alpina). Proceedings of Natlural Academy of Science. U.S.A. 90: 94 -98. Wiggert JD, Murtugudde RG, Christian JR (2006). Annual ecosystem variability in the tropical Indian Ocean: Results of a coupled bio-physical ocean general circulation model. Deep-Sea Research II. 53: 644-676. Worm B, Barbier EB, Beaumont N, Duffy JE, Folke C, Halpern BS, Jackson JB, Lotze HK, Micheli F, Palumbi SR, Sala E, Selkoe KA, Stachowicz JJ, Watson R (2006). Impacts of biodiversity loss on ocean ecosystem services. Science.314: 787-790. Wright S (1969). Evolution and the Genetics of Populations. The Theory of Gene Frequencies. 2: Chicago, University of Chicago Press.
References
209
Yano K (1991). An interim analysis of the data on tuna tagging collected by R/V Nippon Maru in the Indian Ocean, 1980-1990. FAO/IPTP/SEAC/90/17: 107-124. Yesaki M, Waheed A (1992). Results of the tuna tagging programme conducted in the Maldives during 1990. IPTP – Indo-Pacific tuna development and management programme. Colombo. Zouros E (1979). Mutaton rates, population sizes and amounts of electrophoretic variation of enzyme loci in natural populations. Genetics. 92: 623-646.