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Genetic analysis of population structure in striped marlin, Tetrapturus audax, in the Pacific Ocean

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Genetic analysis of population structure in striped marlin, Tetrapturus audax, in the Pacific Ocean

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GENETIC ANALYSIS OF POPULATION STRUCTURE IN STRIPED MARLIN,
TETRAPTURUS AUDAX, IN THE PACIFIC OCEAN

by

Catherine Marie Purcell

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOLOGY)

December 2009

Copyright 2009 Catherine Marie Purcell
ii
DEDICATION

This dissertation is dedicated to my family, Ken, Eleanor and Carole Purcell and
Andrew Paul for all of their love and support through this process.

iii
ACKNOWLEDGMENTS

This dissertation could not have been accomplished without the help of a great number of
individuals, and to all of them I owe my gratitude. To Suzanne Edmands, my advisor for
the past 8 years, for all of her guidance throughout this endeavor and for helping me set
up my very first PCR. To my committee members, Dennis Hedgecock, Michael Hinton,
Dale Kiefer, Anthony Michaels and Craig Stanford for their useful advice, support and
mentoring throughout this process. To my lab family, both past and present, Scott
Harrison, Dennis Peterson, Augie Vogel, Annie Hwang, Sara Northrup, Vanessa
Knutson, Lisa Handschumacher, Vicky Pritchard and Tina Weier for technical and
emotional support, and all of the good times. To Don Bingham for all of his help over
many years, including assisting in remotely coordinating my defense. To Linda Bazilian
and Adolfo de la Rosa for all of the guidance and help in navigating the system. To
Pauline Yu, Jason Curole and Tim Lam for technical support, advice and fun-filled
croquet matches. A special thank you to Annie Hwang for all of the help and all of the
fun, and for being such an excellent friend. To Andrew Paul for helping me stay sane
over the last few months, and for loving me even when I wasn’t sane. And of course,
thank you to my family, Ken, Eleanor and Carole Purcell. Their support has meant more
than I can say. They have helped me in every step of this process, from drilling holes in
Styrofoam and labeling tubes, to editing proposals and coordinating late night printing
sessions. I can’t thank them enough.

iv
I also want to thank Paxson Offield, the Offield Family Foundation and the
Environmental Protection Agency for funding this research. Additionally, I need to thank
all of the individuals who have helped in sample collection for this project: Valerie Allain
(SPC), Daniel Curran (NMFS), Luis Daccarett, Michael Domeier (PIER), Ed Everett
(IATTC) , John Graves, Mike Hinton (IATTC), Dave Holts (NMFS), Robert
Humphreys (NMFS), John Hyde (Scripps), Tom Kazama (NMFS), Richard Keller Kopf,
Russ Nelson (Nelson Resources Consulting), Paxson Offield, Sofia Ortega-Garcia
(CICIMAR), Julian Pepperell (Pepperell Consulting), Rosie’s Tournament Staff,
Hirokazu Saito (NRIFSF), Tim Sippel , Kotaro Yokawa (NRIFSF), Zane Grey/Drambuie
Tournament Staff.

v
TABLE OF CONTENTS

Dedication ii

Acknowledgements iii

Table of Contents v

List of Tables vii

List of Figures xi

Abstract xv

Introduction 1

Introduction References 7

Chapter 1 10
Isolation and characterization of 10 polymorphic
microsatellite markers from striped marlin, Tetrapturus
audax

Chapter 1 References 20

Chapter 2 22
Spatial genetic structure of striped marlin, Tetrapturus
audax, in the Pacific Ocean

Chapter 2 References 73

Chapter 3 80
Temporal variability of striped marlin, Tetrapturus audax,
among Pacific collection locations

Chapter 3 References 139

vi

Chapter 4 145
Review of spatial heterogeneity among highly migratory
fish in the Pacific Ocean

Chapter 4 References 192

Conclusion 206

Conclusion References 221

References 225

Appendix 249

vii
LIST OF TABLES

Table 1-1: Characteristics of 10 polymorphic microsatellite loci in
striped marlin (Tetrapturus audax). Size is the number
of base pairs between the primers in the cloned allele.
TA is the PCR annealing temperature. The Beckman
WellRED fluorescent label used for each locus is listed
after the primer sequence (Note: only forward primers
or forward-zip primers were labeled). Primer type
describes if the locus was scored with specifically
labeled fluorescent primers (Specific) or with modified
unlabeled forward primer containing a 25bp zip-code
tag (ZipPrim). Primer concentrations are listed for both
primer types (Note: ZipPrim loci also used .25μM
fluorescently labeled zip-code tags per reaction), along
with MgCl
2
concentrations for each locus. The number
of alleles per locus (N
A
), along with the size ranges of
those alleles is shown (Note: numbers denoted with (*)
include the 25 bp zip-code tag in their size range).
Genbank accession numbers are also listed for each
locus.

Table 1-2: For each locus, the number of individuals (n) genotyped
for each of the 4 locations, expected level of
heterozygosity (H
e
), observed level of heterozygosity
(H
o
), and F
IS
value. Significant deviations from Hardy-
Weinberg equilibrium are denoted by (*) and bold
numbering.

Table 2-1: For samples from all locations, allelic richness and
number of alleles per population.

Table 2-2: Observed (H
o
) and expected (H
e
) levels of
heterozygosity, (H
o
/H
e
) for all samples, listed by
location and locus. Significant p-values are denoted by
(*) for p<.01 and (***) for p<.001.

Table 2-3: F
IS
values used to detect deviation from the Hardy-
Weinberg Equilibrium, significant values are denoted
for p<.05 (*) and p<.01 (**) listed by location and
locus.

17

19

40

41

42

viii
Table 2-4: Weir and Cockerham’s pair-wise F
ST
values above the
diagonal with p<.05 (*), p<.01 (**), and not significant
(
NS
), and Hedrick’s G’
ST
below the diagonal for the null-
corrected data.

Table 2-5: Weir and Cockerham’s pair-wise F
ST
values above the
diagonal with p<.05 (*), p<.01 (**), and not significant
(
NS
), and Hedrick’s G’
ST
below the diagonal for the
original microsatellite data.

Table 2-6: Diversity and population growth or decline statistics for
control region sequences in striped marlin. The number
of sequences (# Seq.), number of polymorphic,
insertion/deletion or missing sites (S), number of
haplotypes (h), haplotype diversity (Hd), average
number of differences between sequences (K), and the
average number of nucleotide differences per site
between two sequences (Pi) is shown for sequences in
each location.

Table 2-7: Measures of genetic differentiation and gene flow for
striped marlin control region sequences.

Table 2-8: The average number of pair-wise nucleotide differences
among sequences in the eight locations (Kxy) above the
diagonal, and the nearest-neighbor statistic (Snn) with
significance values by population pair below the
diagonal. Non-significant values are denoted by (
NS
), p-
values less than 0.05 are denoted by (*), p<.01 (**), and
p<.001 (**).

Table 2-9: The population pair-wise average number of nucleotide
substitutions per site (Dxy).

Table 2-10: Population pair-wise K
ST
s based on the sequence-based
statistics of Hudson et al. (1992) and the corresponding
significance. Non-significant values are denoted by
(
NS
), p-values less than 0.05 are denoted by (*), p<.01
(**), and p<.001 (**).

Table 2-11: Analysis of molecular variance (AMOVA) of spatial
variation in striped marlin control sequences computed
by the distance matrix using pair-wise differences in
Arlequin (10,000 permutations).

44

47

49

51

52

52

53

59

ix
Table 3-1: Observed (H
o
) and expected (H
e
) levels of
heterozygosity, (H
o
/H
e
) for age classes in Japan and
Southern California, listed by age and locus. Significant
deviations from expectations are denoted by (*) for
p<.01 and (***) for p<.001.

Table 3-2: Observed (H
o
) and expected (H
e
) levels of
heterozygosity, (H
o
/H
e
) for age classes in Hawaii, listed
by age and locus. Significant p-values are denoted by
(*) for p<.01 and (***) for p<.001.

Table 3-3: Observed (H
o
) and expected (H
e
) levels of
heterozygosity, (H
o
/H
e
) for age classes in the eastern
Pacific (Mexico, Central America, Ecuador), listed by
age and locus. Significant p-values are denoted by (*)
for p<.01 and (***) for p<.001.

Table 3-4: Observed (H
o
) and expected (H
e
) levels of
heterozygosity, (H
o
/H
e
) for age classes in the
southwestern Pacific (Australia and New Zealand),
listed by age and locus. Significant p-values are denoted
by (*) for p<.01 and (***) for p<.001.

Table 3-5: F
IS
values used to detect deviation from the Hardy-
Weinberg Equilibrium for the age classes in Japan and
Southern California , significant values are denoted for
p<.05 (*) and p<.01 (**).

Table 3-6: F
IS
values used to detect deviation from the Hardy-
Weinberg Equilibrium for age classes in Hawaii,
significant values are denoted for p<.05 (*) and p<.01
(**).

Table 3-7: F
IS
values used to detect deviation from the Hardy-
Weinberg Equilibrium for age classes in the eastern
Pacific (Mexico, Central America, Ecuador), significant
values are denoted for p<.05 (*) and p<.01 (**).

Table 3-8: F
IS
values used to detect deviation from the Hardy-
Weinberg Equilibrium for age classes in the
southwestern Pacific (Australia and New Zealand),
significant values are denoted for p<.05 (*) and p<.01
(**).

94

95

96

97

97

98

99

100

x
Table 3-9: Fs’ values averaged over all loci used to detect temporal
variance between consecutive age-classes in Japan and
Southern California, with upper and lower confidence
intervals.

Table 3-10: Fs’ values averaged over all loci used to detect
temporal variance between consecutive age-classes in
Hawaii, with upper and lower confidence intervals.

Table 3-11: Fs’ values averaged over all loci used to detect
temporal variance between consecutive age-classes in
the eastern Pacific, with upper and lower confidence
intervals.

Table 3-12: Fs’ values averaged over all loci used to detect
temporal variance between consecutive age-classes in
the southwestern Pacific, with upper and lower
confidence intervals.

Table 3-13: Pair-wise Fs’ estimates from comparisons among
individual year-classes with 95% confidence intervals
in Hawaii.

Table 3-14: Pair-wise Fs’ estimates from comparisons among
individual year-classes with 95% confidence intervals
in the E. Pacific.

Table 3-15: Effective population size, N
e
, corrected for overlapping
generations according to life-tables A and B and
corresponding confidence intervals for the four analysis
groups.

Table 4-1: List of genetic studies for the 10 species with reference,
molecular marker type, sample size, study location,
heterozygosity (H) or haplotype diversity (h), measure
of genetic differentiation, and conclusion of the listed
analysis.

102

102

103

103

108

112

117

156

xi
LIST OF FIGURES

Figure 2-1: Sampling locations and number of samples collected in
those locations (yellow box) and the range of striped
marlin in the Pacific (lighter shaded area).

Figure 2-2: Factorial correspondence analyses for the striped marlin
samples, for factor 1 (0.77%) and factor 2 (0.75%) (a),
factor 2 (0.75%) and factor 3 (0.72%) (b), and factor 3
(0.72%) and factor 4 (0.72%) (c).

Figure 2-3: Correlation between geographic distance (km) and
population structure (F
ST
) for striped marlin samples,
with significance determined by a Mantel test.

Figure 2-4: Neighbor-joining haplotype tree of striped marlin
mtDNA control region sequences. Branches
corresponding to separations reproduced less than 50%
of the time are collapsed. The percentages of replicate
trees where individuals clustered together in the
bootstrap test (5000 replicates) are shown next to the
branches for values over 50%. Colored dots represent
individual sequences.

Figure 2-5: Mismatch distributions for control region sequences in
striped marlin, showing the number of expected and
observed pair-wise differences among sequences and
their associated frequency in each population with the
population growth-decline model for Japan, Mature
Hawaii, Immature Hawaii, and Southern California (a),
Mexico, Central America and Ecuador (b), Australia
and New Zealand (c), and all locations (d).

Figure 3-1: Fs’ values for consecutive age-classes in Japan and
Southern California. Error bars show 95% confidence
intervals.

Figure 3-2: Fs’ values for consecutive age-classes in Hawaii. Error
bars show 95% confidence intervals.

Figure 3-3: Fs’ values for consecutive age-classes in the eastern
Pacific. Error bars show 95% confidence intervals.

29

46

49

55

56

104

104

105

xii
Figure 3-4: Fs’ values for consecutive age-classes in the
southwestern Pacific. Error bars show 95% confidence
intervals.

Figure 3-5: Age-classes compared with Fs’ values in Hawaii, for all
year-classes.

Figure 3-6: Average variance (Fs’) in Hawaii year-classes
compared with the number of years between year-
classes.

Figure 3-7: Age-classes compared with Fs’ values in the eastern
Pacific, for all year-classes.

Figure 3-8: Average variance (Fs’) in Eastern Pacific year-classes
compared with the number of years between year-
classes.

Figure 3-9: Graph of average variance, Fs’, compared with the
number of years between year-classes in Hawaii and the
eastern Pacific.

Figure 3-10: Factorial correspondence analyses of age-classes in all
locations, for factors 1 (8.41%) and 2 (4.62%) (a) and
factors 2 (4.62%) and 3 (4.56%) (b).

Figure 3-11: Mandibular length (a) and age (b) distributions for
samples from Japan.

Figure 3-12: Mandibular length (a) and age (b) distributions for
samples from Southern California.

Figure 3-13: Mandibular length (a) and age (b) distributions for
samples from Hawaii.

Figure 3-14: Mandibular length (a) and age (b) distributions of
Hawaiian samples by yearly quarter.

Figure 3-15: Mandibular length (a) and age (b) distributions for
samples from Mexico.

Figure 3-16: Mandibular length (a) and age (b) distributions for
samples from Central America.

105

107

109

111

113

113

115

118

119

122

123

124

125

xiii
Figure 3-17: Lower jaw fork length (a) and age (b) distributions for
samples from Australia.

Figure 3-18: Lower jaw fork length (a) and age (b) distributions for
samples from New Zealand.

Figure 4-1: Pacific distribution of pelagic fish included in the
review: striped marlin (a), black marlin (b), blue
marlin (c), sailfish (d), swordfish (e), albacore tuna (f),
bigeye tuna (g), yellowfin tuna (h), northern bluefin
tuna (i), southern bluefin tuna (j). Dashed lines
indicate the overall distribution of the species, shaded
regions represent areas of high abundance based on
reported catch levels, outlined and darkly shaded areas
represent known spawning locations within the Pacific
(map images from Google Earth).

Figure 5-1: Patterns of spatial variation among sampled striped
marlin locations based on nuclear microsatellite
analyses (a) and mitochondrial sequence analyses (b).

Figure 5-2: Hypothesized movement of striped marlin from Japan
during the late fall/early winter months.

Figure 5-3: Sea surface temperatures in the regions surrounding
Japan in November (a), Hawaii in January (b), and
Japan in January (c); black arrow indicates
hypothesized movement of striped marlin. Sea surface
temperature images from US Navy, NRL global
NCOM.

Figure 5-4: Hypothesized movement of striped marlin from
Hawaii during the late spring/early summer months.

Figure 5-5: Sea surface temperatures in the central and eastern
Pacific region in January (a) and July (b); black arrow
indicates hypothesized movement of striped marlin.
Sea surface temperature images from US Navy, NRL
global NCOM.

Figure 5-6: Hypothesized movement of striped marlin from
Southern California during the mid/late fall months.

127

128

149

207

209

211

212

212

213

xiv
Figure 5-7: Sea surface temperatures in the eastern Pacific region
in October (a) and January (b); black arrow indicates
hypothesized movement of striped marlin. Sea surface
temperature images from US Navy, NRL global
NCOM.

Figure 5-8: Hypothesized movement of striped marlin originating
in the north Pacific from the Mexico region.

Figure 5-9: The mean sea surface temperatures in the Pacific
region; black arrow indicates hypothesized movement
of striped marlin. Mean sea surface temperature image
from US Navy, NRL global NCOM.

Figure 5-10: Sea surface temperatures in the central eastern Pacific
region in January (a) and July (b); black arrow
indicates hypothesized movement of striped marlin.
Sea surface temperature images from US Navy, NRL
global NCOM.

Figure 5-11: Sea surface temperatures in the southwestern Pacific
region in February (a) and September (b); black arrow
indicates hypothesized movement of striped marlin.
Sea surface temperature images from US Navy, NRL
global NCOM.

213

215

215

216

218

xv
ABSTRACT

Striped marlin, Tetrapturus audax, is an Indo-Pacific pelagic fish that is valuable to
commercial and recreational fisheries throughout its range. Populations of striped marlin
are starting to show strain from intensified fishing pressure over the past few decades,
and the management of this fishery is at a critical point for sustaining this resource.
However, the stock structure of this highly migratory species is still in question, and this
has limited the ability to manage this fishery. This research is aimed at resolving patterns
of spatial and temporal variation, and thus the stock structure, of striped marlin
populations in the Pacific using molecular markers. In Chapter 1, the development of 10
microsatellite markers was described. These first striped marlin-specific microsatellites
were developed to increase resolution of genetic variation in subsequent analyses. Using
12 microsatellites and mitochondrial control region sequences, Chapter 2 examined
geographic genetic heterogeneity of striped marlin samples collected from 7 locations
around the Pacific. Microsatellite and sequence results revealed small, but significant
overall spatial subdivision among locations (F
ST
=0.0145 and K
ST
=0.06995,
respectively). Pair-wise microsatellite analysis revealed 4 stocks (1-Japan-Southern
California-Immature Hawaii, 2-Mature Hawaii 3-Mexico-Central America, and 4-New
Zealand-Australia); sequence results were similar but did not detect significant structure
between Mature Hawaii and the Japan-Southern California-Immature Hawaii group.
Temporal variability among year-classes of striped marlin was determined in Chapter 3.
Overall genetic drift did not increase at points separated by longer periods of time, and
xvi
temporal variation, Fs’, shifted widely between/among year-classes. Significant variation
was found in several year-class comparisons, however factorial correspondence analyses
of location/year-classes showed temporal stability in spatial patterns detected in Chapter
2. Effective population sizes, corrected for overlapping generations, were remarkably
small (e.g. 16-76); and this, in addition to the observed shifting Fs’ estimates, strongly
suggests highly variable reproductive success among cohorts. Finally, in Chapter 4, the
heterogeneity detected in striped marlin was compared to other pelagic species in the
Pacific in order to determine if any common patterns could help explain population
subdivision patterns in these fish. Although no single parameter was identified, certainly
the number, size and specificity of spawning locations and the duration of spawning
events play a role in the heterogeneity pattern of a species throughout its distribution.
The results from this dissertation provide a much clearer picture of the stock structure of
striped marlin in the Pacific and of the processes determining that structure.

1
INTRODUCTION

Striped marlin, Tetrapturus audax, are highly migratory pelagic fish that occupy tropical,
subtropical and temperate waters of the Pacific and Indian Ocean (Nakamura 1985).
Within the Pacific, striped marlin are economically valuable both in commercial and
recreational fisheries. In particular, striped marlin are a vital component to the
sportfishing-related economies in areas such as Baja California, Mexico, New Zealand,
Australia, Hawaii and Southern California. Unfortunately there are indications that
fishing pressure is beginning to strain striped marlin populations in the Pacific (Worm et
al. 2005, Bromhead et al. 2004). As fishing pressure intensifies on striped marlin,
accurate stock structure information is important to preserving this resource. There is
debate as to whether this species forms a single Pacific-wide breeding stock (Shomura
1980), or if multiple distinct breeding populations exist (Graves and McDowell 1994).
Currently, the species is managed as a single stock. If this assumption is incorrect,
changes to management strategies will be necessary to protect distinct populations.

Understanding the way a commercial fish species is distributed throughout its range and
how the distribution changes over time is critical to the successful management of a
fishery. However, this type of information is not easily observed or collected for a variety
of marine species (Waples 1998); this is especially true for wide ranging pelagic fish,
such as the striped marlin. In lieu of direct observation, a variety of methods have been
employed to address questions regarding the life history, distribution, and abundance of
2
these species. These methods include catch statistics, larval tows, traditional and satellite
tagging, and otolith ageing and microchemistry. Another key approach is the use of
genetics, a method that has gained widespread use over the past few decades (Avise
1998). In this dissertation, two classes of molecular markers, nuclear microsatellites and
mitochondrial DNA control region sequences, are used to determine the geographic
genetic structure and temporal variability of striped marlin populations in the Pacific.
A previous assessment of population genetic subdivision in striped marlin was conducted
with mitochondrial (mtDNA) restriction fragment length polymorphisms (RFLPs)
(Graves and McDowell 1994). While this was once a commonly used genetic marker for
these types of surveys, other markers were later developed with increased sensitivity to
detection of slight or subtle genetic heterogeneity (Bentzen et al. 1996); one of these
markers are nuclear microsatellites. Five microsatellites have been developed for blue
marlin (Buonaccorsi et al. 2000), and these have shown cross specific amplification in
other billfish, including striped marlin. However, because the level of subdivision is
typically very low in pelagic marine species (Ward et al. 1994) and previously detected
levels in striped marlin have been slight, the use of additional microsatellite loci may
increase the ability to resolve genetic population structure in this species. To increase
sensitivity in subsequent analyses, the first striped marlin-specific microsatellite marker
was developed. The process of this development is described in Chapter 1.
In the next two chapters, Chapters 2 and 3, the spatial heterogeneity among sampling
locations and the temporal stability within and among those locations are assessed.
3
Twelve microsatellites (including the 10 described in Chapter 1) and mitochondrial
control region sequences are used to analyze striped marlin samples collected from seven
Pacific locations representative of the species’ range: Japan, Hawaii, Southern California,
Mexico, Central America, New Zealand, and Australia. The molecular markers chosen
for this research have been widely used in analyses of population subdivision due to
certain characteristics within each marker (Sunnucks 2000). For example, mitochondrial
DNA is haploid and maternally inherited; in most organisms it does not undergo
recombination. As a result, this marker is useful in reconstructing phylogenies and
looking at the evolutionary history of an organism over a longer period of time than is
usually possible with markers like microsatellites. Within the mitochondrial DNA, a
particular area known as the control region shows more variation than the surrounding
genes and is more sensitive for detecting heterogeneity within populations (Finnerty and
Block 1992). The other marker used in this research, nuclear microsatellites, are regions
in the genome containing tandem short repeat units, which are often polymorphic due to
mutations in the number of repeats. Microsatellites are biparentally inherited, and are
considered selectively neutral. These attributes along with the marker’s hypervariability
make it suitable for detecting variation in species with low population subdivision
(Ruzzante et al. 1998).
These markers were used to estimate spatial genetic differentiation among collection
locations of striped marlin samples in Chapter 2. One of the most common estimates of
genetic structure is F
ST
, which measures variation in allele frequencies among groups.
This estimate ranges from 0 to 1, with a value of 0 reflecting homogenized allelic
4
frequencies, and a value of 1 representing fixed allelic differences between groups
(Allendorf and Luikart 2007). In marine fish, F
ST
values are typically much closer to 0
than 1; for example, Ward et al. (1994) found that a median F
ST
value in a survey of 57
marine fish was 0.02. The estimate of heterozygosity is also informative and is reported
to be one of the best measures of genetic variation within a population (Allendorf and
Luikart 2007). Low values of heterozygosity can indicate previous population
bottlenecks, or can indicate inbreeding within a species or drastic declines in population
numbers (Avise 2004). Additionally, sampling across subpopulations (i.e. the Wahlund
effect) can be detected by comparing observed and expected levels of heterozygosity, or
by looking at F
IS
values which detect deviations from Hardy-Weinberg equilibrium, such
that a deficit of heterozygotes results in a positive F
IS
value and a heterozygote excess
results in a negative one (Hedrick 2000). Another common factor responsible for
deviations in heterozygosity is the presence of null alleles, which are particularly
common in microsatellite markers (Kalinowski et al. 2006, Dakin and Avise 2004,
Hedgecock et al. 2004). Null alleles are the result of mutations presumably within the
primer locations that cause the non-amplification of the allele. Individuals that appear
homozygous may actually be heterozygous for a null allele, thereby causing an apparent
heterozygote deficiency. As a result, it is important to address the presence of null alleles
within a data set. Linkage disequilibrium is another estimate that can be informative
when looking at populations. This measure tests whether there is nonrandom association
of alleles across multiple loci (Hedrick 2000), and it has been previously used to
recognize mixed samples.
5
Although variation between collections of samples taken at different points in time have
been previously estimated for striped marlin (McDowell and Graves 2008), Chapter 3
represents the first attempt to estimate temporal variability among year-classes in this
species. The analysis in this chapter uses the temporal moment method, one of the most
common measures of temporal variation (Jorde and Ryman 1995, Waples 1989). This
method, which was developed for populations with discrete generations, measures the
change in allele frequency between cohorts (Waples 1989). When evaluating the overall
structure of a population, assessing temporal variation is extremely important as this type
of variance may alter or lead to misinterpretation of spatial subdivision patterns (Hoffman
et al. 2004, Palm et al. 2003a, Laikre et al. 1998, Waples and Teel 1990). Estimating
effective population size is also considered very important, particularly from a
management perspective (Turner et al 2006, Kalinowski and Waples 2002, Turner et al.
2002). The classic definition of effective population size is that it represents the size of an
ideal population where the genetic drift acts at the same rate as in the population being
studied (Wright 1931). In species with large reproductive potential and high early
mortality (Type III survivorship) (Gaggiotti and Vetter 1999), like striped marlin,
effective population size may be several orders of magnitude smaller than census
population size (Hauser and Carvalho 2008). These temporal estimates can help
determine the extent of reproductive variability in striped marlin, which is important for a
better understanding of recruitment processes for this valuable fishery species.

6
Finally, in Chapter 4, the patterns of heterogeneity detected in Chapters 2 and 3 are
compared to patterns found in other pelagic fish in the Pacific Ocean. In doing stock
assessments, understanding the way a species is spatially distributed throughout its range
is one of the required pieces of information (Ward 2000). However, this knowledge is
often limited and not easily obtained in pelagic fish (Waples 1998). Genetic studies
provide one way of determining spatial structure, but these types of analyses are not
available for all species because of the expense and time required to complete these
studies. In order to identify any common characteristics or parameters that may help to
explain population structure patterns in pelagic fish, the distributions, spawning
characteristics and genetic heterogeneity (when available) were compared in 10 Pacific
pelagic species.

7
Introduction References

ALLENDORF, F. W. & LUIKART, G. (2007) Conservation and the genetics of
populations. Wiley-Blackwell Publishing. 442 pp.

AVISE, J. C. (1998) Conservation genetics in the marine realm. Journal of Heredity, 89,
377-382.

AVISE, J. C. (2004) Molecular markers, natural history, and evolution (Second Edition).
Sinauer, Sunderland, MA. 684 pp.

BENTZEN, P., TAGGART, C. T., RUZZANTE, D. E. & COOK, D. (1996)
Microsatellite polymorphism and the population structure of Atlantic cod (Gadus
morhua) in the northwest Atlantic. Canadian Journal of Fisheries and Aquatic
Sciences, 53, 2706-2721.

BROMHEAD, D., PEPPERELL, J., WISE, B. & FINDLAY, J. (2004) Striped marlin:
Biology and fisheries. Bureau of Rural Sciences, Canberra, pp. 260.

BUONACCORSI, V. & GRAVES, J. (2000) Isolation and characterization of novel
polymorphic tetra-nucleotide microsatellite markers from the blue marlin,
Makaira nigricans. Molecular Ecology, 9, 817-829.

DAKIN, E. E. & AVISE, J. C. (2004) Microsatellite null alleles in parentage analysis.
Heredity, 93, 504-509.

FINNERTY, J. R. & BLOCK B. A. (1992) Direct sequencing of mitochondrial DNA
detects highly divergent haplotypes in blue marlin (Makaira nigricans).
Molecular Marine Biology and Biotechnology, 1, 206-214.

GAGGIOTTI, O. E. & VETTER, R. D. (1999) Effect of life history strategy,
environmental variability, and overexploitation on the genetic diversity of pelagic
fish populations. Canadian Journal of Fisheries and Aquatic Sciences, 56, 1376–
1388.

GRAVES, J. E. & MCDOWELL, J. R. (1994) Genetic analysis of striped marlin
(Tetrapturus audax) population structure in the Pacific. Canadian Journal of
Fisheries and Aquatic Sciences. 51, 1762–1768.

HAUSER, L. & CARVALHO, G. R. (2008) Paradigm shifts in marine fisheries genetics:
ugly hypotheses slain by beautiful facts. Fish and Fisheries, 9, 333-362.

8
HEDGECOCK, D., LI, G., HUBERT, S., BUCKLIN, K. & RIBES, V. (2004)
Widespread null alleles and poor cross-species amplification of microsatellite
DNA loci cloned from the Pacific Oyster, Crassostrea gigas. Journal of Shellfish
Research, 23, 379-385.

HEDRICK, P. W. (2000) Application of population genetics and molecular techniques to
conservation, p. 113-125. In A. Young and G. Clarke (eds.) Genetics,
Demography, and Viability of Fragmented Populations. Cambridge Univ. Press.

HOFFMAN, E. A., SCHUELER, F. W. & BLOUIN, M. S. (2004) Effective population
sizes and temporal stability of genetic structure in Rana pipiens, the northern
leopard frog. Evolution, 58, 2536-2545.

JORDE, P. E. & RYMAN, N. (1995) Temporal allele frequency change and estimation of
effective size in populations with overlapping generations. Genetics, 139, 1077-
1090.

KALINOWSKI, S., WAGNER, A. & TAPER, M. (2006) ML-Relate: a computer
program for maximum likelihood estimation of relatedness and relationship.
Molecular Ecology Notes, 6, 576–579.

KALINOWSKI, S. T. & WAPLES, R. S. (2002) The ratio of effective to census size in
fluctuating populations. Conservation Biology, 16, 129-136.

LAIKRE, L., JORDE, P. E. & RYMAN, N. (1998) Temporal change of mitochondrial
DNA haplotype frequencies and female effective size in a brown trout (Salmo
trutta) population. Evolution, 52, 910-915.

MCDOWELL, J. R. & GRAVES, J. E. (2008) Population structure of striped marlin
(Kajikia audax) in the Pacific Ocean based on analysis of microsatellite and
mitochondrial DNA. Canadian Journal of Fisheries and Aquatic Sciences, 65,
1307-1320.

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annotated and illustrated catalogue of marlins, sailfishes, spearfishes and
swordfishes known to date. FAO Fisheries Synopses, 125, 65 pp.

PALM, S., DANNEWITZ, J., JARVI, T., PETERSSON, E., PRESTEGAARD, T. &
RYMAN, N. (2003a) Lack of molecular genetic divergence between sea-ranched
and wild sea trout (Salmo trutta). Molecular Ecology, 12, 2057-2071.

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9
SHOMURA, R. S. (1980) Summary report of the Billfish Stock Assessment Workshop -
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DOMINIQUE, A. (2006) Life history and environmental variation interact to
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WRIGHT, S. (1931) Evolution in Mendelian populations. Genetics, 16, 97-159.

10
CHAPTER 1
Isolation and characterization of 10 polymorphic microsatellite markers from
striped marlin, Tetrapturus audax

Abstract

Resolution of population genetic structure in species with low genetic subdivision may
be improved by analyses using larger numbers of microsatellite loci. In order to improve
resolution, 10 microsatellite loci were isolated and characterized for striped marlin,
Tetrapturus audax. Thirty individuals from each of 4 locations revealed that all loci were
polymorphic with 2 to 31 alleles per locus. Observed levels of heterozygosity ranged
from 0.3000 to 0.9667. Significant deviations from Hardy-Weinberg equilibrium were
detected in 2 loci, TA105 in Hawaii and New Zealand and TA155 in Hawaii, and null
alleles may be present in loci TA105 and TA155 in those locations, and in locus TA193
in Mexico. No significant linkage disequilibrium was detected in any pairwise-locus
comparison.

Introduction

Striped marlin, Tetrapturus audax, an important recreational and commercial species,
occupies tropical and temperate waters in the Pacific and Indian Oceans (Nakamura
1985). While 5 microsatellite markers were developed for a different marlin species
(Buonaccorsi and Graves 2000), none have been developed specifically for striped
11
marlin. Because striped marlin are known to exhibit subtle genetic differentiation within
the Pacific (McDowell and Graves 2008, Graves and McDowell 1994), 10 microsatellite
markers were developed from a T. audax library in order to further resolve population
genetic structure in this species.

Materials and Methods

Microsatellite Development

A microsatellite-enriched library was generated following the modified protocol of
Hamilton et al. (1999). Total genomic DNA was isolated from frozen striped marlin heart
tissue collected from Catalina Island, California (DeWoody 2002). Twenty-five
micrograms of genomic DNA was digested with Hae III, Rsa I and Nhe I, followed by
incubation with Klenow polymerase to fill in overhangs left by Nhe I. The digest was
dephosphorylated with shrimp alkaline phosphatase, and then double stranded SNX
linkers (Hamilton et al. 1999) were blunt-end ligated to the digest. The product was
amplified with SNX primers using PCR (polymerase chain reaction) and cleaned with a
PCR purification column (Qiagen).

Hybridization with biotin-labeled oligonucleotides, (CATA)
8
, (GATA)
8
, (TCAG)
8
, was
conducted using both the original linker ligation and PCR product. A total of 300
micrograms of streptavidin-coated magnetic beads (Promega) were added to the
12
hybridization and mixed for 30 minutes prior to stringency washing and cleaning with a
PCR purification column, followed by a second round of PCR with the SNX
oligonucleotide primers (Weiner 2000).

Enriched DNA was digested with Nhe I and ligated into pBS II SK(+) Bluescript
plasmids cut with Xba I. These plasmids were used to transform XL2-Blue MRF’
competent Escherichia coli cells, and the transformed cells were grown on LB ampicillin
plates. Colonies containing inserts were amplified with universal M13 primers and
sequenced on a Beckman-Coulter CEQ 8000. Primers (Operon Technologies) were
developed for clones containing repetitive regions using the program Primer 3
(http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi), and the repetitive regions
were then screened for polymorphism.

Amplification conditions varied between microsatellite primer sets. Five microsatellites
were amplified using specific fluorescently labeled forward primers, while the remaining
microsatellites were amplified with a modified unlabeled forward primer containing a
25bp zip-code tag (Chen et al. 2000). A second stage of amplification using fluorescent
primers complimentary to the zip-code tags resulted in labeled fragments 25 bp larger
than the expected amplicon size. Both sets of fluorescent primers were Beckman
WellRED D2, D3, or D4 dyes. PCR was conducted on both a MJ Research PTC-200
DNA Engine and an Applied Biosystems GeneAmp PCR System 9700 with the
following conditions: 15 ng template DNA, 0.3-1 uM primers, 1.5-3.5 mM MgCl
2
(Table
1-1), 0.25 mM dNTPs, 10 mM Tris-HCl, 50 mM KCl, and 0.3 U of Taq DNA
13
polymerase in 12μL total volume. Two cycling conditions were used; specifically
labeled forward primers used the following conditions: initial denaturation at 94°C for 4
min, 35 cycles of denaturation at 94°C for 30 s, primer annealing between 55-65°C
(Table 1-1) for 35 s, followed by extension at 72°C for 30 s. After 35 cycles, a 5 min
final extension at 72° C was used, followed by a hold at 8° C. Primers with zip-code tags
followed these cycling conditions: initial denaturation at 94°C for 4 min, 22 cycles of
92°C for 30 s, 55-65°C (Table 1-1) for 35 s, and 72°C for 30 s. A second stage of cycling
with 15 cycles at 92° C for 30 s, 57° C for 30 s, and 72° C for 30 s, was included to
amplify the zip-code tags, followed by a 5 min extension at 72°C, and a hold at 8°C. The
PCR product was run on a Beckman Coulter CEQ 8000 sequencer following the
manufacturer’s protocol for fragment analysis, and fragments were visually scored.

Sampling Strategy

The samples for this study were provided through both commercial and recreational
fishing efforts. Commercial samples were collected through sponsored observer programs
in collaboration with the National Marine Fisheries Service (NMFS), the Inter-American
Tropical Tuna Commission (IATTC), and the Secretariat for the Pacific Community
(SPC) in New Caledonia. Recreational samples were collected with the help of
independent environmental research firms such as the Pfleger Institute of Environmental
Research (Oceanside, CA), Marine Conservation Science Institute (MCSI) (Fallbrook,
CA), Pepperell Research and Consulting (New South Wales, Australia) and Nelson
14
Resources Consulting (Miami, FL), and scientists working with Interdisciplinario de
Ciencias Marinas (CICIMAR) in Mexico. Recreational fishers also assisted in providing
samples from kill tournaments, and from live fish on catch-and-release trips with biopsy
darts mounted on tagging poles.
Samples consisted of either fin or muscle tissue preserved in ethanol or 20% dimethyl
sulfoxide (DMSO) buffer saturated with sodium chloride (Seutin et al. 1991) for later
genetic analysis. Some samples were not used in genetic analyses due to the presence of a
large amount of oil in the tissue that appeared to inhibit further analyses regardless of the
extraction method used.
Sample Preparation

Several DNA extraction methods were employed depending on the quality of the tissue
sample. For tissue of higher quality, genomic DNA was extracted from small amounts of
tissue using either Chelex (BioRad) or a lysis reaction following a similar protocol to the
one described in Edmands et al. (2005). In the lysis extraction, a few fibers of muscle
tissue or a small amount of skin off of the fin were added to 50 μl of lysis buffer at 65°C
for 1 hour followed by 100°C for 15 minutes. For samples that were more degraded, an
overnight Cetyltrimethylammonium bromide (CTAB)/Proteinase K incubation was
followed by a standard Phenol Chloroform extraction, with an ethanol precipitation, and
if necessary a lithium chloride wash.

15
Microsatellite Analyses

Expected and observed levels of heterozygosity and linkage disequilibrium were
calculated in GENETIX (Belkhir et al. 1996-2004). Exact tests for deviations from
Hardy-Weinberg equilibrium, F
IS
, and linkage disequilibrium were calculated in
GENEPOP (1.2) (Raymond and Rousset 1995) with 10,000 dememorization steps, 1000
batches, and 10,000 iterations. Micro-Checker (van Oosterhout et al. 2004) was used to
check for the presence of null alleles within the microsatellite loci.

Results and Discussion

The 10 microsatellite loci were amplified in a total of 120 samples from 4 populations
(Table 1-2). Microsatellite polymorphism ranged widely, containing from 2 to 31 alleles
(Table 1-1). Significant deviations from Hardy-Weinberg equilibrium were detected in 2
loci, locus TA105 in individuals from Hawaii (F
IS
= 0.235) and New Zealand (F
IS
=
0.217), and locus TA155 in Hawaiian samples (F
IS
= 0.445), which could indicate the
presence of null alleles in these loci. Micro-Checker (van Oosterhout et al. 2004)
revealed possible null alleles in locus TA105 in Hawaii and New Zealand, locus TA155
in Hawaii, and locus TA193 in Mexico. None of the 45 pairwise-locus comparisons
showed significant linkage disequilibrium after sequential Bonferroni correction.
The 10 microsatellite loci contain a sufficient amount of variation to be useful in
evaluating population structure and gene flow in striped marlin, Tetrapturus audax. The
16
addition of 10 microsatellites for analyses of population structure should increase the
power to detect genetic heterogeneity in these fish.

17
Table 1-1: Characteristics of 10 polymorphic microsatellite loci in striped marlin (Tetrapturus audax). Size is the number of base
pairs between the primers in the cloned allele. T
A
is the PCR annealing temperature. The Beckman WellRED fluorescent label used
for each locus is listed after the primer sequence (Note: only forward primers or forward-zip primers were labeled). Primer type
describes if the locus was scored with specifically labeled fluorescent primers (Specific) or with modified unlabeled forward primer
containing a 25bp zip-code tag (ZipPrim). Primer concentrations are listed for both primer types (Note: ZipPrim loci also used .25μM
fluorescently labeled zip-code tags per reaction), along with MgCl
2
concentrations for each locus. The number of alleles per locus
(N
A
), along with the size ranges of those alleles is shown (Note: numbers denoted with (*) include the 25 bp zip-code tag in their size
range). Genbank accession numbers are also listed for each locus.
Locus Repeat
motif
Size
(bp)
Primer Sequence 5’-3’ T
A
Primer
Type
Primer
[ ] μM
MgCl
2

[ ] mM
N
A
Size
Range
(bp)
Accession
Number
TA024 (TGAG)
13
221 F:CTCTGTCTCTGTCCCTCCTTTT
TCA-D3 label
65 Specific 0.3 1.5 16 200-272 FJ644628

R:ACCCTTTTCACCCCCATACGC 1
TA105 (CTAT)
19
174 F:AAGCTAAAATATCCAGCAGT
CACAC-D3 label
60 ZipPrim 0.7 1.5 21 178-290* FJ644620
R:TCTGAGTCCCTAAAACAAGG
TATCTC
1
TA149 (GT)
18
173 F:ATTCTCCCCCTCTCCCTGTAG
CC-D4 label
55 ZipPrim 0.7 1.5 4 199-205* FJ644619
R:ATCCACAACCCTCCTCCCAAT
GT
1
TA155 (GA)
26
201 F:GGTTTCCTATCACATCACCAA
ATGA-D2 label
60 ZipPrim 0.7 3.5 6 205-233* FJ644622
R:AGAAGCACACAGCCAGAACG 1
TA157 (GT)
17
130 F:ACCTGCGGACTTGAGGAGGA
A-D4 label
60 Specific 0.3 1.5 5 128-136 FJ644626
R:CTTCACCCGTCTAACACATCC
AAC
1

18
Table 1-1, Continued:
Locus Repeat motif Size
(bp)
Primer Sequence 5’-3’ T
A
Primer
Type
Primer
[ ] μM
MgCl
2

[ ] mM
N
A
Size
Range
(bp)
Accession
Number
TA162 (GT)
15
216 F:TGACAAGGAAAGTGTTG
ACTGATGG-D3 label
65 Specific 0.3 1.5 17 180-228 FJ644624
R:GGACGAGTGCGATTTGA
GTTTATC
1

TA164 (CA)
4
(CGCA)
7
178 F:GTCAGAAGAGGTGATGTT
GACCAG-D2 label
60 Specific 0.3 1.5 10 163-213 FJ644623
R:GCAGCGTTGTTTTATACC
TAGTTTTC
1
TA193 (CTAT)
22
(AT)
3
(AG)
13

220 F:TGATATGAACTGCTTTAG
CCAGCT-D3/D4 label
60 ZipPrim 0.7 2.5 31 228-304* FJ644621
R:GAAGGAAAATTACAAAA
ACTGCGTTA
1
TA218 (GATA)
9
109 F:TGGGATCTACCTGACCAG
AATC-D2 label
60 ZipPrim 0.7 2.5 2 130-134* FJ644627
R:TGAAGAGGGTAAAAAGG
TTAAAGTG
1
TA235 (CTAT)
4
(CATC)
5
GTT(CTAT)
11

180 F:AAGCCCTTTATTTTTCCTA
AATT-D3 label
55 Specific 0.3 2.5 17 176-226 FJ644625
R:GATGGTATTAGCACTGTG
GAAAG
1

19
Table 1-2: For each locus, the number of individuals (n) genotyped for each of the 4 locations, expected level of heterozygosity (H
e
),
observed level of heterozygosity (H
o
), and F
IS
value. Significant deviations from Hardy-Weinberg equilibrium are denoted by (*) and
bold numbering.
Hawaii (n=30) Mexico (n=30) New Zealand (n=30) Southern California (n=30)
Locus n H
e
H
o
F
IS
n H
e
H
o
F
IS
n H
e
H
o
F
IS
n H
e
H
o
F
IS

TA024 30 0.8622 0.8667 0.012 30 0.8628 0.9667 -0.104 29 0.8888 0.8276 0.086 30 0.8878 0.8000 0.116
TA105 30 0.8967 0.7000 0.235* 30 0.9172 0.8667 0.072 26 0.9112 0.7308 0.217* 25 0.8960 0.9200 -0.006
TA149 30 0.4750 0.4333 0.105 30 0.3750 0.5000 -0.318 26 0.5399 0.5769 -0.049 28 0.4011 0.3214 0.216
TA155 30 0.5272 0.3000 0.445* 30 0.5867 0.6667 -0.120 27 0.4767 0.4815 0.009 26 0.3765 0.3077 0.202
TA157 30 0.5706 0.7667 -0.329 30 0.5194 0.4000 0.246 27 0.5384 0.6667 -0.220 27 0.5343 0.4074 0.255
TA162 30 0.8189 0.7000 0.162 30 0.7883 0.8000 0.002 29 0.8317 0.7931 0.064 29 0.7806 0.7586 0.046
TA164 30 0.5183 0.4667 0.116 30 0.4506 0.4667 -0.019 28 0.7213 0.7143 0.028 30 0.4367 0.4333 0.025
TA193 30 0.9472 0.9333 0.032 30 0.9078 0.8000 0.135 28 0.9082 0.8214 0.113 25 0.9128 0.8800 0.056
TA218 30 0.4994 0.5000 0.016 30 0.4994 0.4333 0.149 30 0.4861 0.7000 -0.426 30 0.5000 0.6000 -0.184
TA235 30 0.8261 0.8333 0.008 30 0.7967 0.8333 -0.029 29 0.8740 0.8621 0.031 30 0.8506 0.9333 -0.081

20
Chapter 1 References

BELKHIR, K., BORSA, P., CHIKHI, L., RAUFASTE, N. & BONHOMME, F. (1996-
2004) GENETIX 4.05, Logici el sous Windows TM pour la génétique des
populations. Laboratoire Génome, Populations, Interactions, CNRS UMR 5171,
Université de Montpellier II, Montpellier (France).

BUONACCORSI, V. P. & GRAVES, J. E. (2000) Isolation and characterization of novel
polymorphic tetra-nucleotide microsatellite markers from the blue marlin,
Makaira nigricans. Molecular Ecology, 9, 820–821.

CHEN, J., IANNONE, M. A., LI, M. S., TAYLOR, J. D., RIVERS, P., NELSEN, A. J.,
SLENTZ-KESLER, K. A., ROSES, A. & WEINER, M. P. (2000) A microsphere-
based assay for multiplexed single nucleotide polymorphism analysis using single
base chain extension. Genome Research, 10, 549-557.

DEWOODY (2002) DeWoody’s microsatellite cloning protocol. (Spring 2002)
Available:
www.agriculture.purdue.edu/fnr/html/faculty/DeWoody/pdfs/msatclngprtcl.pdf.

EDMANDS, S., FEAMAN, H. V., HARRISON, J. S. & TIMMERMAN, C. C. (2005)
Genetic consequences of many generations of hybridization between divergent
copepod populations. Journal of Heredity, 96, 114-123.

GRAVES, J. E. & MCDOWELL, J. R. (1994) Genetic analysis of striped marlin
Tetrapturus audax population structure in the Pacific Ocean. Canadian Journal of
Fisheries and Aquatic Sciences, 51, 1762–1768.

HAMILTON, M. B., PINCUS, E. L., DI FIORE, A. & FLEISCHER, R. C. (1999)
Universal linker and ligation procedures for construction of genomic DNA
libraries enriched for microsatellites. BioTechniques, 27, 500-507.

MCDOWELL J. R. & GRAVES J. E. (2008) Population structure of striped marlin
(Kajikia audax) in the Pacific Ocean based on analysis of microsatellite and
mitochondrial DNA. Canadian Journal of Fisheries and Aquatic Sciences, 65,
1307-1320.

NAKAMURA, I. (1985) FAO Species catalogue, Vol. 5. Billfishes of the world. An
annotated and illustrated catalogue of marlins, sailfishes, spearfishes and
swordfishes known to date. FAO Fisheries Synopses, 125, 65.

RAYMOND, M. & ROUSSET, F. (1995) GENEPOP (version 1.2): Population genetics
software for exact tests and ecumenicism. Journal of Heredity, 86, 248-249.

21
SEUTIN, G., WHITE, B. N. & BOAG, P. T. (1991) Preservation of avian blood and
tissue samples for DNA analysis. Canadian Journal of Zoology, 69, 82-90.

VAN OOSTERHOUT, C., HUTCHINSON, W. F., WILLS, D. P. M. & SHIPLEY, P.
(2004) Micro-Checker: software for identifying and correcting genotyping errors
in microsatellite data. Molecular Ecology Notes, 4, 535-538.

WEINER, M. P. (2000) A microsphere-based assay for multiplexed single nucleotide
polymorphism analysis using single base chain extension. Genome Research, 10,
549-557.

22
CHAPTER 2

Spatial genetic structure of striped marlin, Tetrapturus audax, in the Pacific Ocean

Abstract

This chapter examines spatial genetic structure in the highly migratory striped marlin
(Tetrapturus audax) in the Pacific Ocean. Genetic variation was measured using both
nuclear (microsatellite) and mitochondrial (control region sequencing) DNA markers. A
multi-year concurrent sampling scheme was employed to collect adult tissue from 7
locations representative of the striped marlin’s range in the Pacific: Japan, Hawaii,
Southern California, Mexico, Central America, New Zealand and Australia.
Microsatellite and sequence results revealed small, but significant overall spatial
subdivision among locations (F
ST
=0.0145 and K
ST
=0.06995, respectively). Pair-wise
microsatellite analyses, using null-corrected data, revealed 4 striped marlin groups: 1)
Japan, Immature Hawaii and Southern California 2) Mature Hawaii 3) Mexico and
Central America and 4) Australia and New Zealand. Analyses of the mitochondrial
sequences showed similar results as the microsatellites. Several of the groups were the
same as those determined with microsatellites; however, no significant differentiation
was found between Mature Hawaii and the Japan, Immature Hawaii and Southern
California groups. Resolving the geographic genetic structure in striped marlin is
important for a better understanding of complex migration patterns in this species, and is

23
important for developing effective management strategies for the striped marlin fishery in
the Pacific.

Introduction

Large pelagic species such as tuna and billfish roam the world’s oceans, free of any
obvious physical barriers. It is generally thought that the highly migratory lifestyles of
these marine fishes would lead to genetic homogeneity among conspecific populations.
However, it is being demonstrated that this is not necessarily true for all of these transient
oceanic species.
Several studies illustrate that while some of these free-roaming fish show no genetic
structure, other species exhibit varying levels of genetic heterogeneity. The skipjack tuna,
albacore, yellowfin tuna, and white marlin (Graves 1998, Scoles and Graves 1993) show
little or no subdivision between or within oceans. However, species such as the blue
marlin (Buonaccorsi et al. 2001, Buonaccorsi and Graves 2000, Finnerty and Block 1992)
and sailfish (Graves and McDowell 1995) show genetic differentiation between Atlantic
and Pacific Ocean populations. Genetic subdivision has also been seen within ocean
basins. The bigeye tuna was found to be separated into distinct clades within the Atlantic
Ocean (Sebastian and Fernandez 2002), while the swordfish (Block and Reeb 2000),
Pacific sailfish (Graves and McDowell 1995), and striped marlin (McDowell and Graves
2008, Graves and McDowell 1994) all revealed subdivision within the Pacific Ocean.

24
Why fish with very similar life history characteristics exhibit such different levels of
population structuring is a conundrum. These species occupy the same habitat and
similar ecological niches. They all have the capacity for long-distance migration, similar
reproductive strategies, and early pelagic larval stages. Given these similarities, the
discrepancy in geographic population structure among these species is unexplained.
However, understanding these discrepancies is of the utmost importance as the majority
of those fish are heavily targeted by fishing practices, and many populations have
experienced significant declines likely as a result of these fisheries.
For striped marlin, Tetrapturus audax, additional insight into its spatial structure would
be useful for its management. Although this fish may not be as popular as swordfish or
bluefin tuna, fisheries for striped marlin are important throughout the Pacific. While they
are generally considered a bycatch species, striped marlin are sold commercially in Japan,
Taiwan, Australia, and Hawaii, and smaller targeted fisheries do exist for this species in
several of those locations. Striped marlin are also very important to recreational fisheries
around the Pacific. Large recreational fisheries for this species occur in Hawaii, New
Zealand, Australia, Southern California and Mexico. These recreational fisheries
contribute greatly to the coastal economies in many of these regions through sportfishing
and sportfishing related tourism (Bromhead et al. 2004). Given this species’ economical
importance and the signs of population strain already occurring from fishing activities, it
is important to understand its spatial distribution throughout the Pacific in order to
develop more effective management strategies. This chapter focuses on examining the
spatial genetic structure of striped marlin in the Pacific Ocean.

25
Previous Work

The striped marlin, Tetrapturus audax, occurs in the Pacific and Indian oceans, and is
considered the most abundant and widely distributed of all the billfishes (Nakamura
1985). In the Pacific, its distribution creates a horseshoe-shape pattern across the ocean
basin, occurring in tropical, subtropical and temperate regions. Individual striped marlin
are capable of moving throughout their range as evidenced by tagging studies, however,
the northernmost and southernmost extensions of their range are seasonal, as the waters
become too cool for this species during winter months (Squire 1972).

While it has been very difficult to accurately age striped marlin, they are thought to be
relatively long-lived fish, not reaching sexual maturity for 3-4 years, or between 27 and
40 kilograms (kg.) (Ueyanagi and Wares 1975). It is believed that in some regions of the
Pacific it can take up to 10 years for this species to reach its maximum size of nearly 227
kg. (Hinton and Bayliff 2002, Nakamura 1985). However, most individuals only reach a
maximum weight between 54 and 90 kg. (I. Nakamura, pers. comm.). Unlike other
marlin species, the striped marlin does not show any sexual dimorphism in size or
external morphology (Nakamura, 1986).
Interestingly, striped marlin have relatively strong genetic substructuring within the
Pacific compared to other species (Graves and McDowell 2003, Graves 1998). This is
quite unexpected as the Pacific does not appear to present any clear barriers to gene flow
for this animal. Genetic variation is difficult to detect in migratory species as few

26
migrants would erase subdivision signals but the variation observed in striped marlin
suggests greater differences in life-history traits and/or population demographics between
this species and the other migratory species not exhibiting genetic structure. Trying to
understand what may be leading to these differences is crucial in developing effective
management strategies in order to preserve this fishery and others, long into the future.
Considering the highly migratory behavior of this species, it was reasonable to assume
that only one panmictic population exists within the Pacific (Shomura 1980). However,
alternate stock hypotheses have been submitted based on different supporting evidence.
Morrow (1957) proposed an eastern and western Pacific stock based on morphological
and morphometric characteristics. A year later, Kamimura and Honma (1958) proposed a
northern and southern stock based on other morphological characteristics and a zone of
low hook rate along the equator. These different hypotheses are not necessarily exclusive,
and may instead support a regional stock hypothesis. The spatial and temporal partition of
the striped marlin spawning events bolsters this possibility. The entire ocean basin
separates spawnings that occur concurrently, while those on the same side of the Pacific
are usually isolated by a period of 6 months.
A regional theory may also fit with the results of a study conducted in 1994 by Graves
and McDowell. Using mitochondrial restriction fragment length polymorphisms (RFLPs)
in 4 Pacific locations they found a shallow, but significant level of genetic subdivision
(.01-.06 % nucleotide sequence divergence). Of the 6 possible pair-wise comparisons
between the different sampling locations, 4 produced chi-square values of p<.001. This

27
finding was again supported in a study by McDowell and Graves (2008) using five
microsatellites and sequences of the mitochondrial control region. Their results suggested
four regional stocks: the southwest Pacific (Australia), the north Pacific (Japan, Taiwan,
Hawaii and California), Mexico and the southeast Pacific (Ecuador).
However, initial analyses in this project using twelve microsatellites, two of which were
used in the McDowell and Graves (2008) study, showed patterns differing from Graves
and McDowell (1994) and McDowell and Graves (2008), and therefore warranted further
exploration. Given that genetic variability in striped marlin is high and that sample size
can greatly influence the patterns of genetic subdivision, I wanted to investigate
structuring in striped marlin populations further. Sampling was conducted over 7
locations representative of the striped marlin’s range in the Pacific for several years. In
order to assess better where breeding populations occur, initial analyses kept mature and
immature individuals (in applicable locations) separate to determine if they were
significantly different. If no significant differences were found between mature and
immature fish in a particular location, the samples were combined for subsequent
analyses; otherwise the two groups remained separated.
To increase the power of resolution in this study, two different classes of molecular
markers were used: microsatellites (12 loci) and mitochondrial DNA control region
sequences. A greater number of loci in addition to larger samples sizes may better resolve
population subdivision in a widely distributed and highly migratory species such as
striped marlin. Furthermore, concordance among markers strengthens the results of those

28
markers compared to when used individually. However, differences among markers can
also be informative, and can reduce the potential for genetic signals due to selection or
other forces.
Materials and Methods

Sampling Strategy

Samples were collected from 2000 to 2008 from seven locations around the Pacific:
Japan, Hawaii, Southern California, Mexico, Central America, New Zealand and
Australia (Figure 2-1). The sampling locations in this study were chosen to be
representative of the species’ range in the Pacific. Japan and New Zealand represent the
northern and southern range limits, respectively, in the western Pacific. Southern
California is in the upper range for striped marlin in the eastern Pacific, and is a location
where striped marlin are only present during warmer water months (June-October). The
southern part of the striped marlin’s range in the eastern Pacific was represented by
Central America. Mexico was chosen because of the presence of a large population of
adults in that area and the known spawning location just off the coast of Baja California
(Armas et al. 1999). Hawaii was included in the sampling locations because there is a
considerably large population of striped marlin around the islands. Additionally, one
study suggests that there is a high occurrence of juveniles in that area (Matsumoto and
Kazama 1974) and recently larval striped marlin were found off the leeward side of the
big island of Hawaii (Hyde et al. 2006). Australia was part of the sampling scheme due

29
Figure 2-1: Sampling locations and number of samples collected in those locations
(yellow box) and the range of striped marlin in the Pacific (lighter shaded area).

Hawaii
n=539
Southern
California
n=66
Mexico
n=239
Central
America
n=105
New Zealand
n=86
Australia
n=45
Japan
n=119

30
to a large population surrounding the eastern coast and reported size differences
compared to New Zealand, suggesting a different population or different age-groups
utilizing that region.
The samples for this study were provided through both commercial and recreational
fishing efforts. Commercial samples were collected through sponsored observer programs
in collaboration with the National Marine Fisheries Service (NMFS), the Inter-American
Tropical Tuna Commission (IATTC), the National Research Institute of the Far Seas
Fisheries (NRIFSF) in Japan, and the Secretariat for the Pacific Community (SPC) in
New Caledonia. Recreational samples were collected with the help of independent
environmental research firms such as the Pfleger Institute of Environmental Research
(Oceanside, CA), Marine Conservation Science Institute (MCSI) (Fallbrook, CA),
Pepperell Research and Consulting (New South Wales, Australia) and Nelson Resources
Consulting (Miami, FL), and scientists working with Interdisciplinario de Ciencias
Marinas (CICIMAR) in Mexico. Recreational fishers also assisted in providing samples
from kill tournaments, and from live fish on catch-and-release trips with biopsy darts
mounted on tagging poles.
Samples in most locations were collected over a period of several years, with the
exception of Japan, where this was not possible. Numbers of samples from the locations
varied due to the overall abundance of striped marlin in the area, the type of fishing that
was used to obtain samples, and the participation of contacts in those areas.

31
Samples consisted of either fin or muscle tissue preserved in ethanol or 20% dimethyl
sulfoxide (DMSO) buffer saturated with sodium chloride (Seutin et al. 1991) for later
genetic analysis. Some samples were not used in genetic analyses due to the presence of a
large amount of oil in the tissue that appeared to inhibit further analyses regardless of the
extraction method used.
Sample Preparation
Several DNA extraction methods were employed depending on the quality of the tissue
sample. For tissue of higher quality, genomic DNA was extracted from small amounts of
tissue using either Chelex (BioRad) or a lysis reaction following a similar protocol to the
one described in Edmands et al. (2005). In the lysis extraction, a few fibers of muscle
tissue or a small amount of skin off of the fin were added to 50 μl of lysis buffer at 65°C
for 1 hour followed by 100°C for 15 minutes. For samples that were more degraded, an
overnight Cetyltrimethylammonium bromide (CTAB)/Proteinase K incubation was
followed by a standard Phenol Chloroform extraction, with an ethanol precipitation, and
if necessary a lithium chloride wash.
Determination of Maturity
Samples were divided into reproductively immature and mature individuals. Because
samples came from many different sources, the biological information for each sample
varied, and several methods for determining maturity had to be used. In this study, the
weight at first maturity was 29 kg (64 lbs) as suggested by Hanamoto (1977) based on the

32
fish in the Coral Sea. Eye fork length (EFL) at first maturity was 143 cm, again as
suggested by Hanamoto (1977). However, the EFL of 160 cm, which was suggested by
Bromhead et al. (2004), was also used for comparison. Although a few samples were
excluded with the larger EFL size, this did not change the results, and so the lower size
was used. For conversion of dressed weights into round weights, a conversion factor of
1.2 [dressed weight * 1.2 = round weight] was used based on a published International
Convention for the Conservation of Atlantic Tunas (ICCAT) estimate for billfish (Mejuto
et al. 2002). Lower jaw fork lengths (LJFL) were converted into weights based on Kopf
et al. (2005).
Microsatellite Assays
Twelve microsatellite loci were used in this study. A modified enrichment protocol by
Hamilton et al. (1999) was used to create a DNA library enriched in microsatellites. After
developing and screening microsatellite primers for consistent amplification and
determining if they were polymorphic, a total of 10 microsatellite primers consisting of
both di- and tetra-nucleotide repeats were used in this study (Purcell et al. 2009). Two
additional microsatellites developed by (Buonaccorsi and Graves 2000) were also used in
this project. Polymerase chain reaction (PCR) amplification conditions varied between
microsatellite primer sets. Five of the microsatellites were amplified using specific
fluorescently labeled forward primers, while the remaining 7 microsatellites were
amplified with a modified non-labeled forward primer containing a 25 bp zip-code tag
(Chen et al. 2000). Fluorescent complimentary primers for the zip-code tags were used

33
for amplification of those modified microsatellites, and the resulting fragment sizes were
25 bp larger due to the presence of the zip-code tags. Both sets of fluorescent primers
used Beckman WellRED D2, D3, or D4 dyes. PCR was conducted on both a MJ
Research PTC-200 DNA Engine and an Applied Biosystems GeneAmp PCR System
9700 with the following conditions: 15 ng template DNA, .25-1 μM primers, 1.5-3.5 mM
MgCl
2
, 0.25 mM dNTPs, 10 mM Tris-HCl, 50 mM KCl, and .3 UTaq polymerase in
12μL total volume. Two cycling conditions were used depending on whether the
microsatellites had specifically labeled forward primers or fluorescently labeled zip-code
tags which are described in Purcell et al. (2009). PCR products were analyzed using the
fragment analysis on a Beckman-Coulter CEQ 8000 Capillary Sequencer, and scored
visually. Approximately 7% of samples were re-run for consistency in PCR amplification
and fragment analysis on the sequencer. For consistency in scoring the size of
microsatellite fragments, approximately 20% of samples were re-scored.
Mitochondrial Control Region Assays
The mitochondrial control region was amplified for 451 striped marlin samples from the
locations listed above with a few samples (5) from Ecuador used in two of the analyses.
PCR was conducted using three universal primers: (K: 5’
AGCTCAGCGCCAGAGCGCCGGTCTTGTAAA-3’) (Lee et al. 1995), (L19: 5’-
CCACTAGCTCCCAAAGCTA-3’) (Bernatchez et al. 1992), and (12 SAR-H: 5’-
ATAGTGGGGTATCTAATCCCAGTT-3’) (Palumbi et al. 1991), to amplify
approximately 1000bp of this region. PCR was conducted with the following conditions:

34
25ng template DNA, 1μM forward and reverse primers, 0.25 mM dNTPs, 2.0-2.5 mM
MgCl
2
, 10 mM Tris-HCl, 50 mM KCl, and 1UTaq polymerase in a 34 μL total volume. A
few microliters of each reaction were checked on 1.2% agarose gels for amplification.
Successful amplifications were submitted to High-Throughput Sequencing Solutions
operated by the University of Washington, Department of Genome Sciences for ExoSAP
PCR clean-up and sequencing. The same primers used in the original amplification were
used for sequencing. Sequences were examined and aligned using SEQUENCHER (4.7)
(Gene Codes Corporation).
Microsatellite data analyses
To explore spatial patterns in striped marlin, the 1199 samples were first arranged by
collection location and then further separated into mature and immature fish within each
location (where applicable). For each population, observed (H
o
) and expected (H
e
) levels
of heterozygosity were calculated using ARLEQUIN (Excoffier et al. 2005). Deviations
from Hardy-Weinberg Equilibrium were detected using the F
IS
statistic, and genotypic
disequilibrium was calculated for each pair of loci in each population using GENEPOP
1.2 (Raymond and Rousset 1995) with 10,000 dememorization steps, 1000 batches, and
10,000 iterations. The program ML-NullFreq (Kalinowski et al. 2006) was used to check
for the frequency of null alleles in each locus and population. The original data set was
then corrected for null alleles based on the estimated frequencies found using ML-
NullFreq. Using the Hardy-Weinberg equilibrium equations, the expected number of null
homozygotes and heterozygotes were calculated for all loci within each population. The

35
expected numbers of null homozygotes were added to individuals with missing data using
the null allele “999”. Null heterozygotes were incorporated by randomly adding the null
allele to existing non-null allele homozygotes. The data incorporating the null alleles
were then run through a permutation to mimic a round of sexual reproduction, thereby
randomly mixing the null alleles throughout the population using the program GENETIX
4.04 (Belkir et al. 2000).
Using the null-corrected data, the genetic structure of the mature and immature groups
within locations was determined. If no significant differences were found between these
groups, then the mature and immature samples in that location were combined for the
remaining analyses. However, if groups showed significant structure then they were not
combined, and remained as separate groups for the rest of the analyses.
However, given that the method of null allele correction permutated data within the
locations to shuffle the null-alleles, the original (non-corrected) data from the sample
groups determined above, were used to estimate observed and expected heterozygosity,
F
IS
and genotypic disequilibrium using the programs and parameters mentioned above.
Genetic variability among and within striped marlin populations was measured by allelic
richness (A
R
) and the number of alleles (N
A
) calculated in FSTAT 2.9.3 (Goudet 2001)
was also calculated with the non-null corrected data.
Weir and Cockerham’s overall F
ST
and pair-wise F
ST
s were calculated for all sample
groups using 10,000 permutations in GENETIX 4.04 (Belkir et al. 2000), followed by a

36
straight Bonferroni correction of the pair-wise estimates. For comparison, this was
conducted for both null corrected and non-corrected data. Hedrick’s G’
ST
(2005) was
calculated from estimates of Nei and Chesser’s (1983) G
ST
and H
S
given in GENETIX
(Belkir et al. 2000) for the null-corrected data. A factorial correspondence analysis (FCA)
was also conducted for all groups in GENETIX (Belkir et al. 2000), but redrawn in a 2-D
format within Excel. The correlation between geographic distance and population
structure was tested using average distances among sampling locations in kilometers
(km) using the program ISOLDE within GENEPOP. Four different combinations of
measurements were tested to find the one that best fit the data, F
ST
or F
ST
/(1-F
ST
) versus
distance or Ln (distance). The significance of the correlations between genetic structure
and geographical distance were assessed using the Mantel test in GENEPOP with 1000
permutations.
The model-based Bayesian clustering program STRUCTURE (Pritchard et al. 2000) was
also used to examine population structure. Five replicates were run with a burn-in of
100,000 steps and 500,000 Markov Chain Monte Carlo (MCMC) steps for K from 1 to
11, using the admixture model and the assumption that allele frequencies are correlated
among populations.
Mitochondrial data analyses
The number of haplotypes (h), number of polymorphic, insertions/deletions, missing sites
(S), haplotype diversity (Hd), and average number of differences between sequences (K)

37
were calculated for sequences in each location using the program DNASP (Rozas et al.
2003). The average number of nucleotide differences per site between sequences (Pi) was
calculated in ARLEQUIN (Excoffier et al. 2005). Gamma
ST
, F
ST
, and number of
migrants were calculated in DNASP, along with haplotype-based statistics (H
S
and H
ST
)
and nucleotide sequence-based statistics (K
S
, K
ST
, K
S
*, K
ST
*, Z and Z*). Population
comparisons were conducted for the average number of pair-wise nucleotide differences
(Kxy), for the nearest-neighbor (Snn) statistic, and the average number of nucleotide
substitutions per site (Dxy) in the program DNASP. Also in DNASP, pair-wise genetic
subdivision estimates were calculated using the sequence-based statistic (K
ST
) according
to Hudson et al. (1992). A neighbor joining tree was created using ClustalX 2.0.10
(Larkin et al. 2007) and MEGA version 4 (Tamura et al. 2007). Based on the population
structure estimates (K
ST
), graphs showing the number of pair-wise differences among
sequences and their associated frequency in each population were created for each group
of locations with DNASP using the population growth-decline model. A hierarchical
structuring of the mitochondrial sequences using an analysis of molecular variance
(AMOVA) within ARLEQUIN was used to assess the relative contribution of variance
among groups, within groups and within populations using the distance matrix of pair-
wise differences among sequences.

38
Results
Microsatellite Analyses

During the microsatellite analysis, initial comparisons of observed and expected
heterozygosities, F
IS
values and estimates of null allele frequencies indicated that a null
allele correction should be incorporated into the data set (Appendix Table 2). Null
frequencies ranged from 0% to 100% of the homozygotes for a few locus/location
combinations; however it is important to note that although the null frequencies may have
been 100% in a few locus/location pairs, this may have reflected only one or two
homozygous individuals. Following this correction, the data were used to compare the
mature and immature samples within the locations. Based on F
ST
estimates from this
analysis, immature and mature Mexican samples were grouped together for the remaining
population analyses as were the immature and mature Central American samples.
However, significant structure was detected between immature and mature Hawaiian
samples and so they remained in separate groups.

Genetic Variability (microsatellites)

Because the method of null allele correction permutated the data within locations to
randomly distribute null alleles among samples, the original data (not corrected) was used
in all summary statistic estimates. Genetic variability within striped marlin populations

39
ranged from moderate to high in terms of allelic richness and number of alleles (Table 2-
1), and these estimates varied widely among loci. Allelic richness ranged from a low of
2.4 in locus 164 to a high of 7 for locus Mn08, both found in Japan, but no significant
differences were found among locations in allelic richness (p = 0.99). The number of
alleles varied widely depending on the locus, from 2 alleles in Australia and New
Zealand in locus 218 to 49 alleles in locus Mn08 in Mature Hawaii and Mexico, but again
was not significantly different among locations (p = 0.54).
Observed levels of heterozygosity (H
o
) also varied greatly among microsatellite loci
(Table 2-2); however the average heterozygosity across all loci for each location fell
within a more narrow range. Southern California had the lowest average observed
heterozygosity (0.652) while Australia and New Zealand had the highest averages (0.727
and 0.696, respectively) (Table 2-2), and neither H
o
nor H
e
among locations were
significantly different (p = 0.98 and p = 0.99, respectively). Significant heterozygote
deficits were seen in 7 loci in Mature Hawaii, 5 loci in Immature Hawaii, 4 loci in
Australia, 3 loci in Mexico, 2 loci in New Zealand and in 1 locus in Southern California
and Central America. Japan showed no significant differences.
The measurement of F
IS
was also used to investigate deviations from Hardy-Weinberg
(H-W) (Table 2-3). F
IS
tests were conducted for the 8 population groups, and numbers
ranged from positive values, indicating a deficit of heterozygotes, to negative values,
indicating an excess of heterozygotes. Significant deviations were found in Japan (3

40
Table 2-1: For samples from all locations, allelic richness and number of alleles per population.
Allelic Richness Number of Alleles
Locus JP MT HW IM HW SC MX CA NZ AU JP
MT
HW
IM
HW SC MX CA NZ AU
24
5.5
(n=110)
5.6
(n=282)
5.5
(n=196)
6.1
(n=58)
5.5
(n=218)
5.6
(n=100)
5.7
(n=76)
5.1
(n=42) 19 25 26 19 19 17 17 14
157
3.0
(n=110)
3.1
(n=279)
2.6
(n=197)
2.6
(n=61)
4.6
(n=218)
4.9
(n=99)
2.8
(n=71)
2.5
(n=44) 5 6 6 4 7 6 6 4
162
5.1
(n=108)
5.2
(n=275)
5.3
(n=201)
4.6
(n=59)
2.1
(n=214)
2.3
(n=104)
5.3
(n=78)
5.4
(n=41) 17 21 19 16 16 16 13 13
164
2.4
(n=111)
3.2
(n=279)
3.3
(n=207)
2.3
(n=59)
3.0
(n=224)
3.4
(n=103)
3.7
(n=79)
4.0
(n=45) 8 10 9 7 6 7 7 9
218
2.5
(n=108)
3.0
(n=222)
2.6
(n=172)
2.4
(n=61)
6.3
(n=183)
5.9
(n=93)
2.0
(n=80)
2.0
(n=43) 4 7 5 4 6 3 2 2
235
5.4
(n=108)
5.8
(n=274)
5.4
(n=204)
5.5
(n=63)
3.2
(n=211)
3.9
(n=102)
5.6
(n=79)
4.9
(n=45) 20 27 21 16 22 16 15 9
105
6.2
(n=61)
6.3
(n=100)
6.4
(n=128)
6.1
(n=29)
6.6
(n=84)
6.5
(n=31)
6.4
(n=56)
6.3
(n=42) 19 25 30 17 24 16 23 21
149
2.8
(n=76)
3.1
(n=125)
2.2
(n=144)
2.9
(n=44)
2.8
(n=106)
2.3
(n=60)
3.2
(n=29)
2.1
(n=44) 5 6 4 5 5 7 5 3
155
3.6
(n=68)
3.0
(n=166)
3.5
(n=120)
3.3
(n=37)
5.4
(n=117)
5.2
(n=48)
2.8
(n=67)
3.4
(n=44) 8 6 8 6 7 9 6 6
193
6.3
(n=85)
6.4
(n=180)
6.6
(n=157)
6.6
(n=38)
2.3
(n=188)
2.9
(n=68)
6.3
(n=58)
6.4
(n=44) 26 30 41 22 35 28 23 20
Mn01
5.2
(n=43)
5.3
(n=98)
5.3
(n=76)
5.4
(n=22)
4.5
(n=79)
4.7
(n=37)
5.0
(n=56)
4.5
(n=42) 13 15 15 9 13 9 12 8
Mn08
7.2
(n=50)
7.0
(n=193)
7.1
(n=125)
7.0
(n=37)
7.0
(n=174)
7.0
(n=64)
6.9
(n=67)
6.6
(n=42) 34 49 40 30 49 37 33 22

41
Table 2-2: Observed (H
o
) and expected (H
e
) levels of heterozygosity, (H
o
/H
e
) for all
samples, listed by location and locus. Significant p-values are denoted by (*) for p<.01
and (***) for p<.001.
H
o
/ H
e

Locus
JP
(n=119)
MT HW
(n=312)
IM HW
(n=227)
SC
(n=66)
MX
(n=239)
CA
(n=105)
NZ
(n=86)
AU
(n=45)
24
0.882 /
0.881
0.876 /
0.889
0.857 /
0.880
0.862 /
0.911
0.858 /
0.879
0.890 /
0.884
0.895 /
0.893
0.857 /
0.838
162
0.759 /
0.817
0.763 /
0.832***
0.735 /
0.826***
0.746 /
0.751
0.687 /
0.760
0.680 /
0.783
0.808 /
0.860
0.675 /
0.852*
164
0.396 /
0.383
0.446 /
0.510
0.398 /
0.489
0.356 /
0.353
0.339 /
0.330
0.427 /
0.383
0.582 /
0.653
0.711 /
0.731
157
0.491 /
0.558
0.513 /
0.560***
0.538 /
0.552
0.508 /
0.535
0.564 /
0.581
0.485 /
0.567
0.535 /
0.542
0.636 /
0.556
105
0.902 /
0.923
0.788 /
0.920*
0.754 /
0.927***
0.862 /
0.910
0.845 /
0.921
0.667 /
0.907
0.746 /
0.933*
0.786 /
0.920*
155
0.448 /
0.599
0.333 /
0.423***
0.431 /
0.566***
0.286 /
0.462
0.624 /
0.617
0.604 /
0.674
0.349 /
0.403
0.614 /
0.656
193
0.906 /
0.925
0.817 /
0.927
0.821 /
0.935**
0.868 /
0.934
0.855 /
0.934
0.897 /
0.933
0.772 /
0.920*
0.750 /
0.926***
218
0.481 /
0.510
0.489 /
0.547***
0.444 /
0.504*
0.574 /
0.517
0.404 /
0.509***
0.462 /
0.503
0.538 /
0.499
0.512 /
0.499
235A
0.870 /
0.869
0.813 /
0.888***
0.853 /
0.874
0.857 /
0.875
0.806 /
0.856***
0.824 /
0.847
0.899 /
0.892
0.822 /
0.845
149
0.400 /
0.443
0.333 /
0.403
0.486 /
0.472
0.349 /
0.436
0.500 /
0.465
0.417 /
0.458
0.517 /
0.542
0.545 /
0.484
Mn01
0.744 /
0.821
0.786 /
0.844
0.803 /
0.844
0.773 /
0.855
0.744 /
0.774
0.794 /
0.782
0.804 /
0.827
0.881 /
0.802
Mn08
0.898 /
0.968
0.932 /
0.958***
0.920 /
0.960
0.779 /
0.966*
0.920 /
0.957*
0.794 /
0.963*
0.911 /
0.951
0.929 /
0.944**

42
Table 2-3: F
IS
values used to detect deviation from the Hardy-Weinberg Equilibrium, significant values are denoted for p<.05 (*) and
p<.01 (**) listed by location and locus.
F
IS

Locus
JP
(n=119)
MT HW
(n=312)
IM HW
(n=227)
SC
(n=66)
MX
(n=239)
CA
(n=105)
NZ
(n=86)
AU
(n=45)
24 -0.001 0.015 0.026 0.054 * 0.024 -0.007 -0.002 -0.023
162 0.071 0.084 *** 0.110 *** 0.007 0.096 0.132 0.062 0.210 *
164 -0.036 0.126 0.186 -0.010 -0.027 -0.115 0.109 0.027
157 0.121 0.085 ** 0.026 0.051 0.029 0.145 0.012 -0.146
105 0.023 0.145 *** 0.187 *** 0.053 0.083 ** 0.269 ** 0.203 *** 0.147 *
155 0.254 0.212 *** 0.240 *** 0.385 -0.011 0.105 0.137 0.066
193 0.021** 0.119 *** 0.123 *** 0.071 * 0.085 ** 0.039 * 0.162 0.192 **
218 0.057 ** 0.107 *** 0.118 ** -0.111 0.205 ** 0.081 -0.077 -0.026
235 -0.002 * 0.084 *** 0.024 0.021 * 0.059 *** 0.027 -0.008 0.027
149 0.098 0.173 -0.030 0.202 -0.076 0.091 0.047 -0.129
Mn01 0.094 0.069 *** 0.049 ** 0.098 0.039 -0.015 0.029 -0.100
Mn08 0.073 0.027 * 0.042 0.197 *** 0.040 0.177 *** 0.043 0.017

43
loci), Mature Hawaii (9 loci), Immature Hawaii (4 loci), Southern California (4 loci),
Mexico (4 loci), Central America (3 loci), New Zealand (1 locus), and Australia (3 loci)
(Table 2-3). All significant values were positive except for one value in Japan. Excluding
significant deviations, all locations had predominately positive F
IS
values.
Genotypic disequilibrium for all locus-pairs was calculated for the striped marlin sample
groups (Appendix Table 1). Following Bonferroni correction, significant disequilibrium
was detected in 37 locus-pairs in Australia, and one locus-pair in Mature Hawaii
(Appendix Table 1). In total, 56% of locus-pairs in Australia showed significant
genotypic disequilibrium.
Genetic Structure (microsatellites)
Spatial structure was calculated using Weir and Cockerham’s F
ST
and Hedrick’s
standardized genetic differentiation estimate, G’
ST
. These estimates were conducted for
both the null-corrected data and the original data (without the null correction) in order to
examine the effect of null alleles on this data set. Although the F
ST
values were small,
they were significant for the null-corrected and original data sets, 0.0145 and 0.0123,
respectively. As expected, Hedrick’s G’
ST
estimates for both groups were much larger
than the F
ST
estimates; the G’
ST
value was 0.0802 for the null-corrected data and 0.0559
for the original data.
Pair-wise F
ST
comparisons for the null-corrected data (Table 2-4) detected significant
subdivision in 23 of 28 comparisons. The 5 sample pairs without significant
differentiation were Japan and Immature Hawaii, Japan and Southern California,

44
Table 2-4: Weir and Cockerham’s pair-wise F
ST
values above the diagonal with p<.05 (*), p<.01 (**), and not significant (
NS
), and
Hedrick’s G’
ST
below the diagonal for the null-corrected data.
Population JP (n=119) MT HW (n=312) IM HW (n=227) SC (n=66) MX (n=239) CA (n=105) NZ (n=86) AU (n=45)
JP 0.0074 *** 0.0035
NS
0.0013
NS
0.0099 *** 0.0076 *** 0.0224 *** 0.0284 ***
MT HW 0.0289 0.0066 *** 0.0077 ** 0.0193 *** 0.0176 *** 0.0090 *** 0.0219 ***
IM HW 0.0135 0.0263 0.0054
NS
0.0154 *** 0.0131 *** 0.0121 *** 0.0173 ***
SC 0.0050 0.0302 0.0196 0.0170 *** 0.0144 *** 0.0236 *** 0.0371 ***
MX 0.0337 0.0714 0.0539 0.0567 0.0026
NS
0.0299 *** 0.0374 ***
CA 0.0273 0.0686 0.0483 0.0508 0.0087 0.0298 *** 0.0337 ***
NZ 0.0845 0.0358 0.0457 0.0862 0.1041 0.1103 0.0071
NS

AU 0.1064 0.0879 0.0655 0.1345 0.1298 0.1233 0.0270

45
Immature Hawaii and Southern California, Mexico and Central America, and between
New Zealand and Australia. The values and the range of values in pair-wise comparisons
were larger using G’
ST
rather than F
ST
estimates, with the largest value of G’
ST
, 0.1345,
found between Southern California and Australia, and the smallest, 0.0050, found
between Japan and Southern California (Table 2-4). This is compared to the largest F
ST

estimate, 0.0374, between Mexico and Australia, and the smallest, 0.0013, between
Southern California and Japan (Table 2-4). In Figures 2-2a-c, the factorial
correspondence analysis displays some of the patterns revealed by the pair-wise F
ST

comparisons. In Figure 2-2a Mexico and Central America group together on the right side
of the graph, with the rest of the Pacific grouping together on the left side. Despite the
spatial heterogeneity detected using the estimates above, analyses using STRUCTURE
showed that a single population (K=1) provided the best fit to the data with the largest
log-likelihood estimate of -41364.2.
Pair-wise F
ST
comparisons of the original data revealed similar patterns to the null-
corrected analysis, but with fewer significant relationships (Table 2-5). The key
differences between the original and the null-corrected data were that no significant pair-
wise differences were found between Mature Hawaii and the Immature Hawaii, Southern
California, and New Zealand sample groups. The smallest F
ST
value, 0.0002, was found
between Japan and Immature Hawaii, and the largest, 0.0340, between Central America
and New Zealand. Hedrick’s G’
ST
showed a wider range in estimates, from 0.0004
(Japan-Immature Hawaii) to 0.0771 (Mexico-Australia).

46
Figure 2-2: Factorial correspondence analyses for the striped marlin samples, for factor 1
(0.77%) and factor 2 (0.75%) (a), factor 2 (0.75%) and factor 3 (0.72%) (b), and factor 3
(0.72%) and factor 4 (0.72%) (c).

Figure 2-2a: Factorial correspondence analysis showing factor 1 and factor 2
F actorial Corresp ondence A naly sis, F act or 1 (0.77% ) and F actor 2 (0.75% )
-3
-2
-1
0
1
2
3
4
-2 -1 .5 -1 -0 .5 0 0 .5 1 1 .5
Fact o r 1
Facto r 2
A U
M at H W
Im H W
JP
M X
N Z
SC
CA

Figure 2-2b: Factorial correspondence analysis showing factor 2 and factor 3.
F actorial Corresp ondence A naly sis, F actor 2 (0.75% ) and F actor 3 (0.72% ).
-5
-4
-3
-2
-1
0
1
-3 -2 -1 0 1 2 3 4
Fact o r 2
Facto r 3
A U
M at H W
Im H W
JP
M X
N Z
SC
CA

Figure 2-2c: Factorial correspondence analysis showing factor 3 and factor 4.
F actorial C orresp ondence A naly sis, F actor 3 (0.72% ) and F act or 4 (0.72% )
-4
-2
0
2
4
6
8
-6 -4 -2 0 2
Fact o r 3
Facto r 4
A U
M at H W
Im H W
JP
M X
N Z
SC
CA

47
Table 2-5: Weir and Cockerham’s pair-wise F
ST
values above the diagonal with p<.05 (*), p<.01 (**), and not significant (
NS
), and
Hedrick’s G’
ST
below the diagonal for the original microsatellite data.
Population JP (n=119) MT HW (n=312) IM HW (n=227) SC (n=66) MX (n=239) CA (n=105) NZ (n=86) AU (n=45)
JP 0.0081 *** 0.0002
NS
0.0016
NS
0.0070 *** 0.0077 *** 0.0216 *** 0.0263 ***
MT HW 0.0161 0.0029
NS
0.0030
NS
0.0165 *** 0.0219 *** 0.0047
NS
0.0183 ***
IM HW 0.0004 0.0060 0.0003
NS
0.0120 *** 0.0135 *** 0.0092 *** 0.0155 ***
SC 0.0031 0.0057 0.0008 0.0111 *** 0.0172 *** 0.0151 *** 0.0301 ***
MX 0.0135 0.0320 0.0236 0.0206 0.0004
NS
0.0308 *** 0.0377 ***
CA 0.0149 0.0432 0.0272 0.0332 0.0008 0.0340 *** 0.0335 ***
NZ 0.0440 0.0094 0.0191 0.0300 0.0615 0.0699 0.0083
NS

AU 0.0549 0.0379 0.0327 0.0614 0.0771 0.0702 0.0177

48
The correlation between geographic distance and population structure was tested using
average distances among sampling locations in kilometers (km). Four different
combinations of measurements were tested to find the best fit for the data, F
ST
or F
ST
/(1-
F
ST
) versus distance or Ln (distance) and the combination of F
ST
vs. distance provided the
best fit. There was a significant correlation between distance and population subdivision
for striped marlin samples from different locations (Mantel test, p=0.0087) (Figure 2-3).
The equation for the best fit line is shown in Figure 2-3, and has a R
2
value of 0.3559.
Genetic Variability (mitochondrial sequences)

A total of 451 individuals were sequenced from eight different locations: Japan (n=43),
Hawaii (n=126), Southern California (n=25), Mexico (n=131), Ecuador (n=5), Central
America (n=20), New Zealand (n=53) and Australia (n=48). Out of the 451 control
region sequences, 351 were unique haplotypes. Relative to the number of sequences, the
number of haplotypes in each location was very high, ranging from 18 in Central
America to 91 in Mexico (Table 2-6). The number of polymorphic/indel/missing sites
(S), which spanned from 135 in Southern California to 279 in Hawaii, was also
influenced by the number of individuals sequenced in each location. Not surprisingly,
haplotype diversity was very high among the locations (Table 2-6). Both the average
number of differences between sequences (K) and the average number of nucleotide
differences per site between two sequences (Pi) ranged from a low in Australia (26 and
0.024, respectively) to a high in Central America (42 and 0.040, respectively).

49
Figure 2-3: Correlation between geographic distance (km) and population structure (F
ST
)
for striped marlin samples, with significance determined by a Mantel test.
Isolation by distance
y = 2E-06x + 0.0041
R
2
= 0.3559
p = 0.0087* *
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0 2000 4000 6000 8000 10000 12000 14000
D istance (km)
F ST
Series 1
Lin ear (Series 1)

Table 2-6: Diversity and population growth or decline statistics for control region
sequences in striped marlin. The number of sequences (# Seq.), number of polymorphic,
insertion/deletion or missing sites (S), number of haplotypes (h), haplotype diversity
(Hd), average number of differences between sequences (K), and the average number of
nucleotide differences per site between two sequences (Pi) is shown for sequences in
each location.
Estimate JP MT HW IM HW SC MX CA NZ AU
# Seq. 43 92 34 25 131 20 53 48
S 175 279 148 135 250 136 193 157
h 38 86 33 23 91 18 48 34
Hd 0.993 0.999 0.998 0.990 0.990 0.989 0.996 0.981
K 33 34 29 30 39 42 30 26
Pi 0.032 0.032 0.028 0.029 0.036 0.040 0.029 0.024

50
Genetic Structure (mitochondrial sequences)
The overall genetic differentiation based on the mitochondrial sequences was calculated
by several methods (Table 2-7). Estimates of Gamma
ST
and F
ST
were very close, 0.08473
and 0.08147, respectively. Haplotype-based statistics (H
S
and H
ST
) showed significant
differentiation, as did the nucleotide sequence-based statistics (K
S
, K
ST
, K
S*
, K
ST*
, Z and
Z*). Population pair-wise comparisons were conducted for the average number of pair-
wise nucleotide differences (Kxy) and for the nearest-neighbor (Snn) statistic (Table 2-8).
The lowest estimate of Kxy occurred between Australia and New Zealand (27.9) and the
highest estimate was between Central America and Southern California (44.3). Snn
values were lowest between Immature Hawaii and Japan (0.55) and highest between
Australia and Mexico (0.95). The highest value of Dxy, which is the average number of
nucleotide substitutions per site, was tied between Southern California and Central
America and between New Zealand and Central America with a value of 0.0323.
Another tie occurred for the lowest value between Southern California and Immature
Hawaii and between Immature Hawaii and Mature Hawaii with 0.0213 (Table 2-9).
Pair-wise genetic subdivision patterns were explored using the sequence-based statistic
K
ST
(Table 2-10). Because sequence based methods utilize information not only based on
the frequency of haplotypes, but also on the numbers of differences between haplotypes,
they are powerful in detecting structure in longer sequences or within smaller sample
sizes (Hudson et al. 1992, Hudson 2000). With the K
ST
estimates, no significant
differentiation was detected between any pair-wise combination of the Japan, Mature

51
Table 2-7: Measures of genetic differentiation and gene flow for striped marlin control
region sequences.
Nei 1982 Sequence Data Information
GammaSt 0.08473
Nm 2.7
Hudson, Slatkin and Maddison 1992
F
ST
0.08147
Nm 2.82
Estimates of genetic differentiation
H
S
0.99266
H
ST
0.00493
P-value of H
S
, H
ST
0.0000 ***
K
S
33.98702
K
ST
0.06995
P-value of K
S
, K
ST
0.0000 ***
K
S
* 3.34173
K
ST
* 0.03754

P-value of K
S
*,
K
ST
* 0.0000 ***
Z 46932.463
P-value of Z 0.0000 ***
Z* 10.33616
P-value of Z* 0.0000 ***

52
Table 2-8: The average number of pair-wise nucleotide differences among sequences in the eight locations (Kxy) above the diagonal,
and the nearest-neighbor statistic (Snn) with significance values by population pair below the diagonal. Non-significant values are
denoted by (
NS
), p-values less than 0.05 are denoted by (*), p<.01 (**), and p<.001 (**).
Location JP (n=43) MT HW (n=92) IM HW (n=34) SC (n=25) MX (n=131) CA (n=20) NZ (n=53) AU (n=48)
JP 33.9 31.1 31.3 39.2 43.2 33.2 31.7
MT HW 0.69 ** 31.9 32.4 40.2 45.3 33.5 32.0
IM HW 0.55
NS
0.62
NS
29.5 39.1 43.9 30.6 28.8
SC 0.57
NS
0.69
NS
0.61
NS
39.4 44.3 32.2 30.9
MX 0.92 *** 0.86 *** 0.89 *** 0.81 *** 40.8 39.9 38.9
CA 0.85 *** 0.87 *** 0.83 *** 0.74 ** 0.79
NS
44.1 43.5
NZ 0.83 *** 0.69 *** 0.74 ** 0.83 *** 0.92 *** 0.81 ** 27.9
AU 0.89 *** 0.77 *** 0.79 *** 0.89 *** 0.95 *** 0.94 *** 0.62 *

Table 2-9: The population pair-wise average number of nucleotide substitutions per site (Dxy).
Location JP (n=43) MT HW (n=92) IM HW (n=34) SC (n=25) MX (n=131) CA (n=20) NZ (n=53) AU (n=48)
JP 0.0236 0.0231 0.0238 0.0272 0.0314 0.0260 0.0249
MT HW 0.0213 0.0223 0.0262 0.0306 0.0233 0.0225
IM HW 0.0213 0.0264 0.0310 0.0226 0.0218
SC 0.0271 0.0323 0.0249 0.0241
MX 0.0279 0.0277 0.0272
CA 0.0323 0.0318
NZ 0.0216

53
Table 2-10: Population pair-wise K
ST
s based on the sequence-based statistics of Hudson et al. (1992) and the corresponding
significance. Non-significant values are denoted by (
NS
), p-values less than 0.05 are denoted by (*), p<.01 (**), and p<.001 (**).
Location JP (n=43) MT HW (n=92) IM HW (n=34) SC (n=25) MX (n=131) CA (n=20) NZ (n=53) AU (n=48)
JP 0.0025
NS
-0.0026
NS
-0.0025
NS
0.0320 *** 0.0642 *** 0.0272 ** 0.0462 ***
MT HW 0.0004
NS
0.0048
NS
0.0478 *** 0.0587 *** 0.0183 *** 0.0352 ***
IM HW -0.0006
NS
0.0436 *** 0.1029 *** 0.0139 * 0.0278 **
SC 0.0374 *** 0.1133 *** 0.0392 *** 0.0646 ***
MX 0.0025
NS
0.0587 *** 0.0742 ***
CA 0.0859 *** 0.1172 ***
NZ -0.0010
NS

54
Hawaii, Immature Hawaii or Southern California locations. Also no population genetic
structure was detected between Mexico and Central America or between New Zealand
and Australia (Table 2-10). The K
ST
estimates ranged from -0.0026 between Japan and
Immature Hawaii to 0.1172 between Australia and Central America.
A neighbor-joining tree (Figure 2-4) showed some of the same spatial patterns revealed
by the pair-wise K
ST
values. In particular, the tree showed clusters of individuals from
Mexico, Central America and Ecuador grouped together. However other areas of the tree
showed mixed regions of sequences from different locations, reflecting the overall low
values of genetic differentiation.
Mismatch distributions, which visualize population growth and decline, were graphed for
the groups of locations determined by the pair-wise K
ST
comparisons (Figure 2-5a-d).
These mismatch distributions examine the number of pair-wise differences among
sequences and their associated frequency in each group. Although the population
growth/decline model was a better fit than the constant size model, none of the expected
distributions closely followed what was observed. The group containing Mature Hawaii,
Immature Hawaii, Japan and Southern California had one large narrow peak around 25
pair-wise differences and two smaller bumps around 5 and 40 pair-wise differences
(Figure 2-5a). The group containing Mexico, Central America and Ecuador looked very
different, with three large peaks at 5, 20 and 40 pair-wise differences (Figure 2-5b).
Australia and New Zealand’s mismatch distribution had a broad peak that ranged from
15-30 pair-wise differences, with smaller bumps again at 10 and 40 pair-wise differences

55
Figure 2-4: Neighbor-joining haplotype tree of striped marlin mtDNA control region
sequences. Branches corresponding to separations reproduced less than 50% of the time
are collapsed. The percentages of replicate trees where individuals clustered together in
the bootstrap test (5000 replicates) are shown next to the branches for values over 50%.
Colored dots represent individual sequences

56
Figure 2-5: Mismatch distributions for control region sequences in striped marlin,
showing the number of expected and observed pair-wise differences among sequences
and their associated frequency in each population with the population growth-decline
model.

2-5a: For samples from Japan, MT Hawaii, IM Hawaii, and Southern California.

2-5b: For samples from Mexico, Central America and Ecuador.

2-5c: For samples from Australia and New Zealand.

57
Figure 2-5, Cont.:

2-5d: For samples from all locations

58
(Figure 2-5c). By pooling all the sequences together, the mismatch distribution revealed
a small peak at 5, a slightly larger peak at 40, and finally a much larger narrow peak at 25
pair-wise differences (Figure 2-5d).
Spatial variation was tested using AMOVA for different groupings (Table 2-11). For the
purpose of comparison, several of the previous stock models were tested in this analysis:
northern vs. southern Pacific, western vs. eastern Pacific, and regional (Central America
and Ecuador were grouped together due to the small sample size from Ecuador).
Although, the majority of variation was contained within populations for all comparisons,
those particular groupings did not minimize the amount of variation among populations
within groups. The model that most minimized the within group variance is listed as
‘Best’ in Table 2-11. The best fit model showed that although most of the variation
(approximately 90%) occurred among samples within populations, about 10% (10.55%)
of the variation was contained among the groups, with effectively no variation among the
populations within the groups. This model grouped Mature Hawaii, Immature Hawaii,
Japan and Southern California together, New Zealand and Australia together, and
Mexico, Ecuador and Central America as the final group. Two alternate models were also
provided for comparison. The first alternative moved Mature Hawaii into a separate
group, but left Immature Hawaii, Japan and Southern California together, and kept the
other groups the same as the best fit scenario. This model was explored due to the
significant structure seen in the null-corrected microsatellite data. The AMOVA results
revealed that this grouping is very similar to the best fit model, where effectively no
variance was detected among populations within the groups, however slightly less

59
Table 2-11: Analysis of molecular variance (AMOVA) of spatial variation in striped
marlin control sequences computed by the distance matrix using pair-wise differences in
Arlequin (10,000 permutations).
Structure Tested Variance % Total F Statistics P
1. West-East (Mexico, Ecuador, Central Am., So. California) vs. (Japan, New Zealand, Australia)
vs. (Mature and Immature Hawaii)
Among groups 0.792 Va 4.23 F
CT
=0.0423 0.102
Among populations within
groups 0.951 Vb 5.08 F
SC
=0.0530 0.000
Within populations 16.988 Vc 90.69 F
ST
=0.0931 0.000
2. North-South (Ecuador, Australia, New Zealand) vs. (Japan, So. Cal, Mature Hawaii, Immature
Hawaii Mexico, Central Am)
Among groups 0.084 Va 0.45 F
CT
=0.0045 0.390
Among populations within
groups 1.562 Vb 8.38 F
SC
=0.0842 0.000
Within populations 16.988 Vc 91.16 F
ST
=0.0884 0.000
3. Separate populations (Immature and Mature Hawaii) vs. (Central Am., Ecuador) vs. (Japan) vs.
(Australia) vs. (So. California) vs. (New Zealand) vs. (Mexico)
Among groups 2.338 Va 12.57 F
CT
=0.1257 0.037
Among populations within
groups -0.730 Vb -3.92 F
SC
=-0.0449 0.000
Within populations 16.988 Vc 91.35 F
ST
=0.0865 0.000
4. All populations together (Australia, Mature Hawaii, Immature Hawaii, Japan, Mexico, New
Zealand, So.Cal, Central Am., Ecuador)
Among groups 1.605 Va 8.63 F
CT
=N/A N/A
Among populations within
groups N/A N/A F
SC
=N/A N/A
Within populations 16.988 Vb 91.37 F
ST
=0.0863 0.000
5. Best fit (Mature Hawaii, Immature Hawaii, Japan, So. California) vs. (Australia, New Zealand)
vs. (Mexico, Ecuador, Central Am.)
Among groups 2.001 Va 10.55 F
CT
=0.1055 0.000
Among populations within
groups -0.013 Vb -0.07 F
SC
=-0.0008 0.000
Within populations 16.988 Vc 89.52 F
ST
=0.1048 0.000
6. Alternate group 1 (Immature Hawaii, Japan, So. California) vs. (Mature Hawaii) vs. (Australia,
New Zealand) vs. (Mexico, Ecuador, Central Am.)
Among groups 1.831 Va 9.77 F
CT
=0.0977 0.000
Among populations within
groups -0.085 Vb -0.45 F
SC
=-0.0050 0.000
Within populations 16.989 Vc 90.68 F
ST
=0.0932 0.000
7. Alternate group 2 (Mature Hawaii, Immature Hawaii, Japan) vs. (Mexico, So. California,
Ecuador, Central Am.) vs. (Australia, New Zealand)
Among groups 1.266 Va 6.72 F
CT
=0.0672 0.003
Among populations within
groups 0.576 Vb 3.06 F
SC
=0.0328 0.000
Within populations 16.988 Vc 90.22 F
ST
=0.0979 0.000

60
variance was distributed among groups (9.77%). The next alternative moved Southern
California into the group with the rest of the eastern Pacific. Because of the belief that
striped marlin in California originate from Mexico, it was important to explore this
grouping. However, the results from this comparison revealed that this model is not
nearly as good of a fit as the previous two groupings, and that Southern California does
show spatial variation from the rest of the eastern Pacific.
Discussion
Microsatellite Analyses
Allelic richness and the number of alleles in striped marlin ranged greatly depending on
the locus and location. Levels of heterozygosity were high but similar to what has been
reported for striped marlin in a previous study (McDowell and Graves 2008) and in other
pelagic fish such as yellowfin tuna (Appleyard et al. 2001), Atlantic bluefin tuna
(Carlsson et al. 2004), king mackerel (Broughton et al. 2002), and Atlantic bigeye tuna
(Gonzalez, Beerli and Zardoya 2008).
Null alleles, one of the common causes of discrepancies between observed and expected
heterozygosities, are known to be problematic and prevalent in microsatellite markers
(Kalinowski et al. 2006, Dakin and Avise 2004, Hedgecock et al. 2004). In this study,
null alleles were detected in some of the loci within the different populations. While very
often the presence of null alleles is mentioned as a caveat of population genetic data,
usually no further treatment of the null alleles is attempted (Dakin and Avise 2004).

61
However, given the detection of null alleles and the number of significant deviations in
the heterozygosity and F
IS
estimates, it was important to correct for null alleles in this
data set as it did change the number of significant pair-wise relationships.
The overall F
ST
and G’
ST
values were low using microsatellites, but were significant and
in the range reported for other pelagic marine species (Rooker et al. 2007, Jean et al.
2006, O’Reilly et al. 2004, Hoarau et al. 2002, Shaw et al. 1999, and the reported median
in Ward et al. 1994). The picture that was revealed through analysis of the 8 groups of
null-corrected samples showed that striped marlin form four significantly different
populations.
The first group was located in the southwest Pacific, and contained the Australia and
New Zealand sampling locations. Australia had an unusually high level of genotypic
disequilibrium, with 56% of locus-pairs showing significant disequilibrium. In all other
populations, only one locus-pair in Mature Hawaii showed a significant value, so
Australia was unique in that result. One likely explanation for this was the
misidentification of other billfish species within that region’s collection. For that reason,
all Australian samples were sequenced at the mitochondrial control region; however all
had a striped marlin maternal background.
Mexico and Central America in the eastern Pacific formed the second group; however
these groups were significantly different from Southern California, even though that
location is also in the eastern Pacific. The genetic heterogeneity found between Southern

62
California and Mexico (primarily the Baja California region of Mexico) was unexpected
because of the close physical proximity between those two locations. Additionally,
striped marlin tagged off of Southern California have been found to move south into
Baja, so it was long believed that these fish moved into Southern California to feed and
returned south again to their original location once water temperatures began to cool in
the fall (Domeier 2006). It appears that the Southern California fish do not originate in
Mexico but instead appear to be Japanese fish moving through the area to feed and then
move south through Mexico before returning to the western Pacific to spawn.
Interesting spatial patterns were also found in the northern Pacific using the null-
corrected data. No significant differences were found between Japan and Southern
California, but both were significantly different from Mature Hawaii, which was
unexpected as Hawaii is geographically located between those two locations. Another
intriguing part of this pattern was that the juvenile striped marlin caught in the area
around Hawaii were significantly different from the mature fish in that area, but were not
significantly different from fish in both Japan and Southern California. With no known
spawning ground near Southern California, this implies that striped marlin appearing in
Southern California originated across the Pacific in Japan. It also appears that the juvenile
fish sampled in this study from Hawaiian waters are also from Japan. Thus, according to
the null-corrected data, striped marlin from Japan, California and the immature fish from
Hawaii form the third population, and mature striped marlin in Hawaii constitute the
fourth group.

63
Isolation by distance figures do show that there is a correlation between geographic
distance and population structure, despite the exception between Mexico and Southern
California mentioned above. In contrast to the pair-wise analyses, the program
STRUCTURE did not support more than one population of striped marlin in the Pacific.
However, this program may not be an appropriate way of looking at low levels of genetic
heterogeneity without using very large sample sizes.
When null allele frequencies were not incorporated into the data, the overall F
ST
and G’
ST

estimates were lower but many of the pair-wise relationships remained the same. The
primary difference was that Mature Hawaii showed fewer significant pair-wise
relationships compared with the null-corrected data. When the original data was
analyzed, Mature Hawaii did not show significant differences from Southern California,
the Immature Hawaiian samples or New Zealand. The pair-wise F
ST
values that changed
in significance when analyzed with the original data had some of the smallest significant
values in the null-corrected data set. Thus, in this study, the null-allele correction
appeared to provide additional power to detect significant relationships rather than
change the trends of those relationships. It is interesting to note that the changes in
significant pair-wise groupings all involved Mature Hawaii, which was the population
containing the most deviations in observed levels of heterozygosity and F
IS
estimates
This indicates that the null-allele correction was important for the analysis of spatial
heterogeneity.

64
Mitochondrial DNA Sequence Analyses
Sequences from the mtDNA control region contained a large number of haplotypes, and
the haplotype diversity (Hd) was very high but similar to levels detected in other marine
fish (Wang et al. 2008, Carlsson et al. 2007, Alvarado-Bremer et al. 2005, Carlsson et al.
2004, Hoarau et al. 2004, Hauser et al. 2001).While there were many polymorphic sites
contained within the sequences, the number was largely dependent on the total number of
sequences within an individual location. The average number of differences between
sequences (K), and the average number of nucleotide differences between sequences (Pi)
for all locations, was highest in the eastern Pacific locations, Mexico and Central
America, and lowest in Australia. While it is interesting to see that samples from the
eastern Pacific share this characteristic, it is perplexing that these values are lowest in
Australia. With the unusual genotypic disequilibrium patterns detected in this location, it
was reasonable to think that the disequilibrium could have been caused by subgroups
within Australia. However, with the lowest number of differences between sequences and
lowest average number of nucleotide differences between sequences in this location, this
may not be the case.
The overall genetic structure was significant using Gamma
ST
and K
ST
estimates, and the
pair-wise analyses showed patterns similar to what was seen in the microsatellite
analyses. The group in the eastern Pacific remained the same (Mexico and Central
America) as did the southwest Pacific (Australia and New Zealand); however there was a
change in the north Pacific group. The sequence analyses did not reveal significant

65
structure between Mature Hawaii and Japan, Immature Hawaii or Southern California,
and thus these samples only formed one group in the north Pacific. The nearest neighbor
statistic, Snn, which is a measure of how often the most similar sequences are from the
same location (Hudson 2000), also supported the K
ST
results.
AMOVA comparisons also supported the mitochondrial pair-wise results, with the best
fit resulting in three groupings: 1) Australia and New Zealand, 2) Japan, Immature
Hawaii, Mature Hawaii and Southern California, and 3) Mexico, Central America and
Ecuador. The next grouping nearly matched the best fit and it moved Mature Hawaii into
its own group but kept the other groupings the same. Interestingly, groupings such as the
northern-southern or eastern-western stock models, proved to be a poor fit with the
sequence data in this study. The similarly poor fit of Southern California with the rest of
the eastern Pacific sequences supports the pattern that was also detected in the
microsatellite analysis.
The mismatch distributions also varied by the three groups found in the pair-wise
comparisons. There were clear differences in the patterns of the distributions among the
groups, which would indicate that they have not always shared the same history of
population decline and expansion. In the eastern Pacific, Mexico and Ecuador had a
distribution that was the most distinct from the other groups. This supports the pattern
seen in the neighbor-joining tree, where samples from the eastern Pacific clustered
together more often than samples from any of the other locations.

66
Overall Spatial Structure

After analyzing both the nuclear microsatellites and the mitochondrial control region
sequences, the spatial distribution and the underlying migration patterns of striped marlin
in the Pacific became clearer. In Mature Hawaii, a large number of deviations in
heterozygosity and F
IS
values were at least partially explained by the presence of null
alleles. However, it is also important to consider that a portion of these deviations may
also be indicative of a Wahlund effect, where sampling occurred across cohorts of
juveniles or subgroups (So et al. 2006, Lenfant and Planes 2002, Gaffney et al. 1990,
Johnson and Black 1984), especially as it seems that this location may serve as a stepping
stone or feeding area for non-local striped marlin. While there was disagreement between
the microsatellite and the mitochondrial analyses, it is thought that microsatellites are
more sensitive than sequences from mtDNA, so this discrepancy may reflect very low
levels of gene flow between these locations (Keeny et al. 2005, Feulner et al. 2004). It is
also possible that as juveniles from Japan migrate to (or through) Hawaii that they may
be reflected in samples collected in that location. The mixing of these groups could
impact analyses for both the mature and immature Hawaiian samples, as Japanese
juveniles may stay long enough to grow and be included in analyses of mature Hawaiian
fish while still not reproductively contributing to this location. The reverse may also be
true; a large fraction of the juveniles in this study may originate in Japan, however
juveniles from Hawaii may also be mixed into this sample. Movement of juvenile striped
marlin into Hawaiian waters is supported by previous reports (Squire and Suzuki 1990),

67
where it was believed the fish use the area as a feeding ground before moving on to
spawning locations (Matsumoto and Kazama 1974).
Overall, the north Pacific shows very little spatial subdivision among the Japan,
Immature Hawaii, Mature Hawaii and Southern California locations. Southern California
is a seasonal location for striped marlin and does not have a known spawning location, so
it appears that fish in this area likely originate in Japan (Nishikawa et al. 1978). Striped
marlin from Japan may move eastward with the Kuroshiro current to feeding grounds
near the Hawaiian Islands. If currents are particularly strong in that year or when the
environmental conditions are right, some of these fish may continue eastward until they
reach Southern California. While the seasonal population in Southern California does not
appear to be large, their movement into the region is a regular occurrence. However, from
sampling experience in this area, the timing and number of fish each year does vary.
Then as the water temperatures cool in Southern California, the striped marlin move
south into Mexico. While it is uncertain how long the fish remain in this region, the
striped marlin may eventually utilize the North Equatorial Current to move back across
the Pacific to their spawning grounds in Japan.
Striped marlin in Mexico and Central America appear to form one stock that is
independent from other populations in the Pacific based on the microsatellite and
sequence analyses. While the exclusion of striped marlin in Southern California from the
rest of the eastern Pacific is surprising, the same pattern was reported in McDowell and
Graves (2008). As mentioned before, tagging data indicate that striped marlin caught in

68
Southern California move south into the Baja California region of Mexico (Domeier
2006). Movement of fish south from Southern California is assumed to be associated with
cooling water temperatures off of California and feeding opportunities near Mexico
(Domeier 2006). However, it was thought that if the fish moved south to feed, they
would also appear in representative samples from that region, but it does not look like
they do. This may happen for a couple of reasons. First, their behavior in that region may
reduce their catchability to the recreational fishery, which was the primary source of
samples from this location. This could be plausible as the spawning season for the
Mexican population occurs at roughly the same time in the fall when striped marlin are
moving into Mexico from Southern California. There is evidence that the local striped
marlin shift locations to participate in presumed spawning events (Domeier 2006, Armas
et al. 1999). Catch rates from tournaments indicate that the population of striped marlin is
far larger in Mexico than the population in Southern California. It would seem logical for
recreational and commercial fishers in this region to also shift their effort to follow the
larger, year-round population of Mexican fish to their spawning grounds. As the striped
marlin from Southern California do not appear to be spawning in Mexico, they may
largely escape the fishing pressure by not moving to the spawning locations. When this is
combined with other possible behavioral modifications that may reduce their catchability
and the number of Southern California fish compared to the size of the striped marlin
population in Mexico, it is not surprising that they are not significantly sampled within
this region. Another possibility is that the Southern Californian striped marlin do not
spend a long time in the waters off of Mexico and instead move away from

69
that region quickly enough to avoid being sampled in that area. As mentioned above, the
striped marlin may move through Mexico and then follow currents back across the
Pacific.
In the southwest Pacific, Australia and New Zealand, form another independent stock.
Australia was also a location that displayed unusual genetic variability patterns. Between
the high proportion of locus-pairs in genotypic disequilibrium, and the deviations in
heterozygosity levels, this location may need to be examined in closer detail. It is
possible that gene flow is occurring on a semi-regular basis with striped marlin from the
Indian Ocean given the right environmental conditions (Bromhead et al. 2004). Although
with little genetic work done on striped marlin in the Indian Ocean it is difficult to
determine if mixing is driving some of these differences. If mixing between the Pacific
and Indian Oceans was occurring, higher levels of variation among sequences compared
to the other locations would be expected, and the sequence results do not appear to
support this hypothesis. However, if gene flow from the Indian Ocean was primarily
male-mediated, then this would not greatly impact the mitochondrial sequence variation.
Another explanation is hybridization with other billfish species that share that region. All
of the maternal backgrounds were striped marlin, but a uni-directional hybrid cross could
be possible, though this has not been reported in billfish previously. New Zealand is a
population similar to Southern California, where striped marlin are present only
seasonally, and fish from this location likely originate from Australia, and perhaps from
other areas in the central south Pacific not sampled here (Bromhead et al. 2004).

70
However, movements of striped marlin in this area may be complicated, as these fish are
noted for relatively high dispersal distances that are thought to be correlated with
changing water temperatures, whereas Australian striped marlin make much shorter
distance movements and generally move along the coastline (Domeier 2006, Kopf et al.
2005, Bromhead et al. 2004).
For striped marlin in the Pacific Ocean, although the overall genetic subdivision is
shallow in this species, both markers show that it is significant, and for a pelagic species
capable of long migrations, striped marlin show a considerable amount of spatial
variation among locations.
Management Implications

While it is not easy to subdivide migratory species into distinctly manageable units, there
appear to be some trends from this study that can be applied at a management level.
Based on all samples, the north Pacific (Japan, Mature Hawaii, Immature Hawaii and
Southern California) showed very little genetic differentiation among locations.
However, subtle population structure between mature samples from Hawaii and the other
locations in this group indicates that effective migration (migration with reproductive
contributions) between Hawaii and the other north Pacific locations is probably limited.
And so, it may be more accurate to consider them as separate stocks even though fishing
efforts in Hawaii can impact both the Hawaiian and Japanese striped marlin populations.
The Hawaiian population may be the most interesting or most challenging from a

71
management perspective. Clearly this location is important to several regions within the
north Pacific. Because of Hawaii’s connection to other locations, temporal analyses may
be very informative for the effective management of this population. Next, based on all
data, Mexico and Central America are very distinct from the rest of the Pacific. Despite
the movement of Southern Californian fish through that region, the eastern Pacific should
be managed as a separate stock. Finally, in the southwest Pacific, Australia and New
Zealand form another distinctive stock. Australia, in particular, may be impacted by
migration or hybridization in ways that are unique from other regions in the Pacific.
Overall, there is evidence for four putative striped marlin stocks in the Pacific: Australia-
New Zealand, Japan-Southern California (also including the immature Hawaiian
samples), Mature Hawaii and Mexico-Central America (including Ecuador for mtDNA).
What may be more complicated is the level of independence of the four groups,
particularly with the connections in the north Pacific and possibly the Australian-New
Zealand group and its potential ties to the Indian Ocean.
The results in this study had similarities to the McDowell and Graves (2008) study,
where both found the southwest Pacific to form a distinct stock, and that the California
fish were significantly different from other eastern Pacific populations but not from
Japan. Also in both studies, fish in Hawaii grouped with Japan and Southern California,
but because McDowell and Graves (2008) did not separate immature and mature
Hawaiian fish, they did not see the structure between the two groups that was detected in
this study. Another difference was that Graves and McDowell (2008) found structure

72
between Mexico and Ecuador, while in this study the eastern Pacific (with the exception
of Southern California) comprised a single stock. Despite these differences, overall
values of F
ST
were very similar (using microsatellites) between this study and the
McDowell and Graves 2008 study (0.0145 and 0.0130, respectively).
In summary, the geographic genetic structure analysis, using two different classes of
molecular markers, with over a 1,000 samples from seven locations, revealed four striped
marlin stocks in the Pacific. It is my hope that the results from this research will be
applied to the management of Pacific striped marlin fisheries.

73
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tissue samples for DNA analysis. Canadian Journal of Zoology, 69, 82-90.
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80
CHAPTER 3

Temporal variability of striped marlin, Tetrapturus audax, among Pacific collection

locations

Abstract

While temporal variation is often difficult to assess in wild populations, understanding
the effect of this type of variation on the patterns of heterogeneity is crucial to the
interpretation of population genetic data. Although spatial subdivision has been detected
throughout the distribution of striped marlin, Tetrapturus audax, this information is of
little use for management purposes until it can be determined that these relationships are
temporally stable. To test for temporal stability, striped marlin samples were collected
from 7 locations around the Pacific and separated into 4 groups based on the previously
detected spatial patterns. These samples were then divided into age-classes based on
dorsal spine ageing techniques that were back-calculated to growth curves. Twelve
microsatellites were used to calculate an unbiased estimate of temporal variance, Fs’ and
effective population size, N
e
, which was corrected for overlapping generations. The
magnitude of genetic drift ranged widely between consecutive age-classes, but it did not
alter the spatial patterns previously detected. Effective population sizes were very small
for all groups (e.g. 16-76); however precision in these estimates was low. Even though
this analysis could have been improved by more accurate ageing methods, additional life-
history information, and larger sample sizes; this study represents the first attempt at
applying the temporal method to assess levels of genetic drift among cohorts of striped

81
marlin. Accounting for temporal variation is important when interpreting genetic
information for use in management strategies for striped marlin and other fisheries.

Introduction

When looking at the genetic structure of a population or species, most studies have
examined a set of samples at one particular point in time, a ‘snapshot’ of the spatial
pattern for that organism. While this has been the classical approach in population
genetics, the importance of temporal variation cannot be overlooked when assessing
patterns of spatial heterogeneity, as it can greatly alter the interpretation of genetic
variability (Danneitz et al 2005, Waples and Teel 1990). Temporal instability or genetic
drift may change or even eliminate spatial patterns previously detected (Hoffman et al.
2004). As a result, there is concern for studies that base spatial pattern analyses on a
single sampling event because of the potential for bias within that snapshot (Palm et al.
2003b, Laikre et al. 1998). More specifically the concern is that unrecognized temporal
variation may be interpreted as spatially based patterns (Palm et al. 2003a). When genetic
subdivision in a species is very weak, this problem becomes even more troublesome as it
becomes tougher to discriminate between the real population structure and the noise from
temporal variability (Wirth and Bernatchez 2001).
There are many factors that can impact the degree of temporal variation, both biotic and
abiotic (Laurent and Planes 2007, Bohonak 1999). The amount of time for these factors
to influence genetic signals varies widely, but may happen as quickly as between

82
sampling years (Palm et al. 2003a, Laikre et al. 1998). A few of these factors include
environmental conditions (Laurent and Planes 2007), variable rates of gene flow
(Hoffman et al. 2004, Whitlock 1992), and levels of exploitation (Laurent and Planes
2007, Turner et al. 1999). Another major factor is recruitment variation. The effect of
cohorts in species with overlapping generations can cause significant changes in allele
frequencies between years (Laikre et al. 2008, Hedrick 2005, Palm et al. 2003a, Garant et
al. 2000, Li and Hedgecock 1998). This is a particularly strong consideration in species
with type III survivorship curves, where high levels of mortality in early stages combined
with extremely large reproductive potential can lead to large variance between cohorts
(Gomez-Uchida and Banks 2006, Turner et al. 2002). The life-history of striped marlin fit
into the type III survivorship pattern, and it is reasonable to believe that for this broadcast
spawning species unequal reproductive success or sweepstakes-type events may create
genetic signatures between cohorts that are temporally unstable (Bernal-Ramirez et al.
2003, Planes and Lenfant 2002, Hedgecock 1994).
Although assessing the impact of genetic drift on populations is clearly important (Turner
et al. 2006), it remains very difficult to evaluate (Waples 1989). One way to test for
temporal stability is to sample the same site over time (Balloux and Lugon-Moulin 2002),
and increasingly, population genetic studies are utilizing temporally replicated sampling
to address this source of variability (Palm et al. 2003b , Heath et al. 2002). However, to
truly investigate temporal variance, these samples must span over several generations for
the study organism, and this is not practical for species with longer life-spans such as the
striped marlin (Jorde and Ryman 1995). The other method is to divide samples into

83
cohorts or age-classes (Waples 1989). While this method does not require generational
time periods to pass, it can still be difficult to apply where ageing is not well known or
studied in the species of interest. This is further complicated in organisms with
overlapping generations where additional life-history demographics are necessary to
correct for the overlap (Waples and Yokota 2007, Gomez-Uchida and Banks 2006). For
these reasons, studies looking at temporal change in marine fish (Laurent and Planes
2007, Hutchings and Baum 2005, Bernal-Ramirez et al. 2003), and in other taxa
(Hoffman et al 2004, Hansen et al. 2002) have not been very common despite the
importance for interpreting genetic heterogeneity.
The population structure of striped marlin, Tetrapturus audax, in the Pacific likely
involves some level of temporal variability given the high fecundity and mortality
exhibited in this species; however, the extent of temporal instability in striped marlin
populations has not yet been determined (McDowell and Graves 2008). In this study,
temporal variance was evaluated by examining allele-frequency shifts between year-
classes of striped marlin samples collected from 7 locations throughout the Pacific.
Samples were aged using length-age relationships determined by studies based on growth
patterns in dorsal spines (Kopf et al. 2005, Melo-Barrera et al. 2003). The samples were
grouped according to previously determined spatial patterns (Chapter 2) to increase
sample size among year-classes. Temporal variance and effective population size were
evaluated using the methods described by Jorde and Ryman (2007, 1995), including the
correction for overlapping generations. While life-history demographics have not been
well studied in this species, estimates used to correct for overlapping generations were

84
obtained from a species (bluefin tuna) with similar life-history characteristics (Vaughan
and Saila 1976, Caddy et al. 1974). Additionally, all location-year-classes were analyzed
together in order to determine if the overall spatial patterns remained the same after
accounting for temporal variability.
Understanding the role temporal variation plays in a species’ overall pattern of
subdivision should be a central consideration in its management (Bernal-Ramirez et al.
2003, Heath et al. 2002, Waples and Teel 1990). For the effective regulation of the
striped marlin fishery, it is crucial to resolve whether the spatially based genetic
heterogeneity is temporally stable among populations in the Pacific. Only through
recognition of both types of genetic variation is there hope of developing a model or
management plan that accurately fits that population or species (Cimmaruta et al. 2008,
Fraser et al. 2007).
Materials and Methods

Sampling Strategy

Samples were collected from 2000 to 2008 from 7 locations around the Pacific: Japan,
Hawaii, Southern California, Mexico, Central America, New Zealand and Australia. A
few additional samples that were collected in Ecuador were included in the temporal
analyses. The sampling locations in this study were chosen to be representative of the
species’ range in the Pacific. Japan and New Zealand represent the northern and southern

85
range limits, respectively, in the western Pacific. Southern California is in the upper
range for striped marlin in the eastern Pacific, and is a location where striped marlin are
only present during the warmer water temperature months (June-October). The southern
part of the striped marlin’s range in the eastern Pacific was represented by Central
America. Mexico was chosen because of the presence of a large population of adults in
that area and the known spawning location just off the coast of Baja California (Armas et
al. 1999). Hawaii was included in the sampling locations because there is a large
population of striped marlin around the islands. Additionally, one study suggests that
there is a high occurrence of juveniles in that area (Matsumoto and Kazama 1974) and
recently larval striped marlin were found off the leeward side of the big island of Hawaii
(Hyde et al. 2006). Australia was part of the sampling scheme because of a large
population surrounding the eastern coast and reported size differences compared to New
Zealand, suggesting a different population or different age-groups utilizing that region.
The samples for this study were provided through both commercial and recreational
fishing efforts. Commercial samples were collected through sponsored observer programs
in collaboration with the National Marine Fisheries Service (NMFS), the Inter-American
Tropical Tuna Commission (IATTC), the National Research Institute of the Far Seas
Fisheries (NRIFSF) in Japan, and the Secretariat for the Pacific Community (SPC) in
New Caledonia. Recreational samples were collected with the help of independent
environmental research firms such as the Pfleger Institute of Environmental Research
(Oceanside, CA), Marine Conservation Science Institute (MCSI) (Fallbrook, CA),
Pepperell Research and Consulting (New South Wales, Australia) and Nelson Resources

86
Consulting (Miami, FL), and scientists working with Interdisciplinario de Ciencias
Marinas (CICIMAR) in Mexico. Recreational fishers also assisted in providing samples
from kill tournaments, and from live fish on catch-and-release trips with biopsy darts
mounted on tagging poles.

Samples in most locations were collected over a period of several years, with the
exception of Japan, where this was not possible. Numbers of samples from the locations
varied, owing to the overall abundance of striped marlin in the area, the type of fishing
that was used to obtain samples, and the participation of contacts in those areas.
Samples consisted of either fin or muscle tissue preserved in ethanol or 20% dimethyl
sulfoxide (DMSO) buffer saturated with sodium chloride (Seutin et al. 1991) for later
genetic analysis. Some samples were not used in genetic analyses, because large amounts
of oil in their tissues appeared to inhibit PCR amplification regardless of the extraction
method used.

Sample Preparation

Several DNA extraction methods were employed depending on the quality of the tissue
sample. For tissue of higher quality, genomic DNA was extracted from small amounts of
tissue using either Chelex (BioRad) or a lysis reaction following a similar protocol to the
one described in Edmands et al. (2005). In the lysis extraction, a few fibers of muscle
tissue or a small amount of skin off of the fin were added to 50 μl of lysis buffer at 65°C

87
for 1 hour followed by 100°C for 15 minutes. For samples that were more degraded, an
overnight Cetyltrimethylammonium bromide (CTAB)/Proteinase K incubation was
followed by a standard Phenol Chloroform extraction, with an ethanol precipitation, and
if necessary a lithium chloride wash.

Microsatellite Assays

Twelve microsatellite loci were used in this study. A modified enrichment protocol by
Hamilton et al. (1999) was used to create a DNA library enriched in microsatellites. After
screening these microsatellites for consistent amplification and determining if they were
polymorphic, a total of 15 microsatellite primers consisting of both di- and tetra-
nucleotide repeats were developed, 10 of which were used in this study (Purcell et al.
2009). Two additional microsatellites developed by (Buonaccorsi and Graves 2000) were
also used in this project. Polymerase chain reaction (PCR) amplification conditions
varied between microsatellite primer sets. Five of the microsatellites were amplified
using specific fluorescently labeled forward primers, while the remaining 7
microsatellites were amplified with a modified non-labeled forward primer containing a
25bp zip-code tag (Chen et al. 2000). Fluorescent complimentary primers for the zip-code
tags were used for amplification of those modified microsatellites, and the resulting
fragment sizes were 25 bp larger due to the presence of the zip-code tags. Both sets of
fluorescent primers were Beckman WellRED D2, D3, or D4 dyes. PCR was conducted
on both a MJ Research PTC-200 DNA Engine and an Applied Biosystems GeneAmp

88
PCR System 9700 with the following conditions: 15 ng template DNA, 0.25-1 μM
primers, 1.5-3.5 mM MgCl
2
, 0.25 mM dNTPs, 10 mM Tris-HCl, 50 mM KCl, and 0.3
UTaq polymerase in 12μL total volume. Two cycling conditions were used, as described
in Purcell et al. (2009), depending on whether the microsatellites had specifically labeled
forward primers or fluorescently labeled zip-code tags. PCR products were analyzed
using the fragment analysis on a Beckman-Coulter CEQ 8000 Capillary Sequencer, and
scored visually. Approximately 7% of samples were re-run for consistency in PCR
amplification and fragment analysis on the sequencer. For consistency in scoring the size
of microsatellite fragments, approximately 20% of samples were re-scored.
Age determination

Striped marlin samples from all locations were separated into age classes using age-
length models from two studies. While both studies determined age based on growth
marks in dorsal spines, their models were based on striped marlin from regions of the
Pacific known to have different growth rates (Kopf et al. 2005). The Melo-Barrera et al.
(2003) study, which focused on striped marlin from Mexico, was used to age samples
from Japan, Hawaii, Southern California, Mexico, Central America and Ecuador. Sample
age was determined using a von Bertalanffy model fitted to the back-calculated
mandibular length (ML)/age data according to the equation:

ML
t
= ML
∞
[1-e
-K(t – to)
]

89
where ML∞ (asymptotic length) = 221 cm, K (annual growth rate) =0.23, and t
o
(age in
years at hypothetical length 0) = -1.6 (Melo-Barrera et al. 2003). Weights of striped
marlin were converted into mandibular length (cm) using:

TW (kg) = aML
b

where a = 0.00008 and b = 2.523 (Melo-Barrera et al. 2003). For conversion of dressed
weights into round weights, a conversion factor of 1.2 [dressed weight * 1.2 = round
weight] was used based on a published International Convention for the Conservation of
Atlantic Tunas (ICCAT) estimate for billfish (Mejuto et al. 2002). Finally, the equation:

ML = 23.44 +1.01EFL

was used to convert eye fork length (EFL) (cm) into mandibular length (F. Melo-Barrera
pers comm.).

A study by Kopf et al. (2005), which developed lower jaw fork length (LJFL)/age models
for striped marlin from New Zealand, was used to determine the age of samples from
New Zealand and Australia according to the equation:

L
t
= L [1-e
-K(t-to)
]

90
where L = 3010, K = .22, and t
o
= -0.04. Weights (kg) were converted into LJFL (mm)
using:

W = 2E-08LJFL
2.88
(Kopf et al. 2005).

It is important to note here that these ageing techniques have not been validated. Samples
were then divided into four groups based on spatial genetic structure patterns detected in
Chapter 2: 1) Japan and Southern California, 2) Hawaii, 3) Eastern Pacific- Mexico,
Central America, Ecuador, 4) Southwestern Pacific- Australia, New Zealand.

Microsatellite data analyses

For each year-class in the different groups, observed (H
o
) and expected (H
e
) levels of
heterozygosity were calculated using ARLEQUIN (Excoffier et al. 2005). Deviations
from Hardy-Weinberg Equilibrium were detected using the F
IS
statistic using GENEPOP
1.2 (Raymond and Rousset 1995) with 10,000 dememorization steps, 1000 batches, and
10,000 iterations. Due to the detection of null alleles in Chapter 2, the program ML-
NullFreq (Kalinowski et al. 2006) was again used to check for the frequency of null
alleles in each locus and year-class within the groups. The original data set was then
corrected for null alleles based on the estimated frequencies found using ML-NullFreq.
Using the Hardy-Weinberg equilibrium equations, the expected number of null
homozygotes and heterozygotes were calculated for all loci within each year-class. The

91
expected numbers of null homozygotes were added to individuals with missing data using
the null allele “999”. Null heterozygotes were incorporated by randomly adding the null
allele to existing non-null allele homozygotes. The data incorporating the null alleles
were then run through a permutation to mimic a round of sexual reproduction, thereby
randomly mixing the null alleles throughout the year-class using the program GENETIX
4.04 (Belkir et al. 2000).
Genetic drift was estimated with the program TempoFs (Jorde and Ryman 2007). This
program was used to calculate the unbiased Fs’ estimate (Equation 13 from Jorde and
Ryman 2007) and 95% confidence intervals using sampling plan 2 (Waples 1989).
Additionally, to examine how the variance in genetic drift changes among year-classes,
the data from populations with broad ranges in year-classes, Hawaii and the eastern
Pacific, were reorganized to estimate all year-class pair-wise comparisons. The effective
population size, N
e
, was calculated using the mean Fs’ across all year-classes within each
group with the correction for overlapping generations (Equation 25 from Jorde and
Ryman 1995). For overlapping generation correction, two life-tables were created. The
first was based on survival rates with 0.2 mortality for each year class as estimated for
Atlantic bluefin tuna (Caddy et al. 1974), and fecundity distributed by age-class based on
estimates for bluefin tuna (Vaughan and Saila 1976). However the number of eggs was
scaled to egg estimates from striped marlin (11-28 million) based on Eldridge and Wares
(1974). The second life-table was similar except that survival rates were calculated to
incorporate both natural and fishing mortality, based on the bluefin tuna estimates in
Vaughan and Saila (1976). These estimates were then used to calculate the average

92
generation length, G, according to Equation 10 (Jorde and Ryman 1996) and the
correction factor for overlapping generations, C, by iterating Equations 5-9 (Jorde and
Ryman 1996) in “factor” (provided by P.E. Jorde) (Jorde and Ryman 1995).
A factorial correspondence analysis (FCA) was also conducted for all age-classes within
each of the locations using the program GENETIX (Belkir et al. 2000), and redrawn in a
2-D format within Excel. Unlike the factorial correspondence analysis conducted in
Chapter 2, this analysis examined year/location groupings instead of individuals within
those groups.

Age and size distributions

Finally, age-class and length distributions for individual locations were created in Excel
based on either mandibular lengths or lower jaw fork lengths in centimeters (cm).
Because of suspected movement of juvenile striped marlin through Hawaii during
different times of the year (Matsumoto and Kazama 1974), age-class and length
distributions for this location were also created for each yearly quarter.

93
Results

Summary statistics

Observed levels of heterozygosity ranged widely among loci. For example, in the eastern
Pacific, heterozygosity ranged from monomorphic to completely heterozygous loci (H
o
=
1.000) (Table 3-3). Significant heterozygote deficits were detected in three of the spatial
groups: in four age-classes in Hawaii (Table 3-2), two age-classes in the eastern Pacific
(Table 3-3), and two in each of the 4
th
and 6
th
age-classes and one in the 5
th
age-class in
the southwestern Pacific (Table 3-4). No deviations were detected in the Japan/Southern
California group (Table 3-1). There were no age-classes in any of the groups that showed
heterozygote excess or deficit consistently across all loci. Tests of F
IS
for each year-class
were also used to investigate deviations from Hardy-Weinberg (H-W). Significant F
IS

deviations were found in Hawaii (4 deviations) (Table 3-6), eastern Pacific (2 deviations)
(Table 3-7), and southwestern Pacific (5 deviations) (Table 3-8). Again, no significant F
IS

values were detected in the Japan/Southern California group (Table 3-5). All significant
F
IS
values were positive. Because of the significant heterozygote and F
IS
values
described above, and the previous detection of null alleles in the loci used in this study
(Chapter 2), the null correction was once again applied. Null frequencies for each
location-age-class and locus are listed in the Appendix (Appendix Tables 3 - 6).

94
Table 3-1: Observed (H
o
) and expected (H
e
) levels of heterozygosity, (H
o
/H
e
) for age
classes in Japan and Southern California, listed by age and locus. Significant deviations
from expectations are denoted by (*) for p<.01 and (***) for p<.001.
H
o
/ H
e

Locus
Age 5
(n=22)
Age 6
(n=34)
Age 7
(n=41)
Age 8
(n=19)
Age 9
(n=12)
Age 10
(n=8)
24
0.905 /
0.879
0.862 /
0.897
0.919 /
0.882
0.833 /
0.914
0.833 /
0.891
0.857 /
0.813
162
0.700 /
0.836
0.813 /
0.846
0.853 /
0.825
0.778 /
0.806
0.583 /
0.688
0.800 /
0.867
164
0.429 /
0.362
0.387 /
0.341
0.417 /
0.367
0.211 /
0.198
0.364 /
0.463
0.333 /
0.439
157
0.500 /
0.483
0.594 /
0.594
0.462 /
0.533
0.611 /
0.543
0.455 /
0.654
0.167 /
0.409
105
0.882 /
0.939
0.895 /
0.913
0.900 /
0.931
0.700 /
0.926
0.750 /
0.929
1.000 /
1.000
155
0.550 /
0.622
0.381 /
0.602
0.500 /
0.640
0.455 /
0.385
0.143 /
0.539
0.000 /
0.533
193
0.950 /
0.941
0.917 /
0.942
0.840 /
0.895
0.867 /
0.947
1.000 /
0.958
1.000 /
0.893
218
0.667 /
0.511
0.548 /
0.500
0.425 /
0.504
0.333 /
0.565
0.500 /
0.479
0.429 /
0.539
235
0.905 /
0.847
0.867 /
0.879
0.821 /
0.882
0.889 /
0.871
1.000 /
0.874
1.000 /
0.864
149
0.278 /
0.386
0.476 /
0.418
0.360 /
0.530
0.308 /
0.465
0.429 /
0.539
0.250 /
0.250
Mn01
0.733 /
0.798
0.769 /
0.822
0.333 /
0.758
0.875 /
0.833
1.000 /
0.833
1.000 /
1.000
Mn08
0.923 /
0.954
0.938 /
0.972
0.917 /
0.964
0.818 /
0.965
1.000 /
1.000
1.000 /
0.933

95
Table 3-2: Observed (H
o
) and expected (H
e
) levels of heterozygosity, (H
o
/H
e
) for age classes in Hawaii, listed by age and locus.
Significant p-values are denoted by (*) for p<.01 and (***) for p<.001.
H
o
/ H
e

Locus
Age 2
(n=35)
Age 3
(n=49)
Age 4
(n=27)
Age 5
(n=18)
Age 6
(n=17)
Age 7
(n=27)
Age 8
(n=39)
Age 9
(n=25)
Age 10
(n=22)
Age 11
(n=26)
Age 12
(n=26)
24
0.852 /
0.895
0.811 /
0.874
0.864 /
0.875
0.882 /
0.898
0.813 /
0.855
0.889 /
0.890
0.829 /
0.888
0.958 /
0.899
0.947 /
0.885
0.826 /
0.885
0.917 /
0.899
162
0.719 /
0.797
0.619 /
0.780 *
0.696 /
0.813
0.800 /
0.869
0.786 /
0.820
0.769 /
0.888
0.853 /
0.834
0.696 /
0.841
0.750 /
0.846
0.696 /
0.817
0.750 /
0.829
164
0.242 /
0.321
0.396 /
0.473
0.500 /
0.475
0.500 /
0.585
0.267 /
0.393
0.423 /
0.546
0.500 /
0.644
0.273 /
0.377
0.313 /
0.286
0.500 /
0.482
0.440 /
0.594
157
0.516 /
0.554
0.537 /
0.570
0.616 /
0.542
0.438 /
0.627
0.563 /
0.607
0.640 /
0.509
0.548 /
0.528
0.368 /
0.519
0.579 /
0.573
0.478 /
0.631
0.682 /
0.584
105
0.690 /
0.925
0.795 /
0.910
1.000 /
0.926
0.400 /
0.889
0.571 /
0.934
0.636 /
0.896
0.769 /
0.859
0.889 /
0.941
0.750 /
0.893
0.800 /
0.895
0.714 /
0.894
155
0.556 /
0.603
0.360 /
0.495
0.700 /
0.668
0.571 /
0.528
0.500 /
0.592
0.250 /
0.595 *
0.364 /
0.591
0.429 /
0.476
0.385 /
0.625
0.375 /
0.387
0.500 /
0.502
193
0.931 /
0.944
0.846 /
0.921
0.750 /
0.923
0.769 /
0.905
0.778 /
0.889
0.722 /
0.925
0.850 /
0.962
0.857 /
0.944
0.818 /
0.939
0.778 /
0.910
0.800 /
0.926
218
0.581 /
0.508
0.462 /
0.510
0.421 /
0.539
0.571 /
0.476
0.300 /
0.521
0.333 /
0.508
0.240 /
0.490
0.421 /
0.575 *
0.563 /
0.546
0.350 /
0.501
0.400 /
0.467
235
0.833 /
0.869
0.956 /
0.862
0.920 /
0.876
0.867 /
0.892
0.875 /
0.893
0.741 /
0.839
0.781 /
0.883
0.826 /
0.864
0.850 /
0.874
0.955 /
0.886
0.640 /
0.893 *
149
0.563 /
0.491
0.561 /
0.485
0.526 /
0.531
0.667 /
0.621
0.429 /
0.363
0.313 /
0.417
0.357 /
0.452
0.222 /
0.366
0.300 /
0.279
0.273 /
0.255
0.231 /
0.335
Mn01
0.923 /
0.834
0.737 /
0.845
1.00 /
0.864
0.667 /
0.867
1.000 /
0.908
0.667 /
0.823
0.727 /
0.844
0.800 /
0.795
1.000 /
0.843
0.750 /
0.917
0.875 /
0.842
Mn08
0.933 /
0.966
0.923 /
0.949
1.00 /
0.982
1.000 /
0.978
0.833 /
0.971
0.955 /
0.968
0.842 /
0.973
0.889 /
0.943
0.929 /
0.966
0.938 /
0.936
0.950 /
0.974

96
Table 3-3: Observed (H
o
) and expected (H
e
) levels of heterozygosity, (H
o
/H
e
) for age classes in the eastern Pacific (Mexico, Central
America, Ecuador), listed by age and locus. Significant p-values are denoted by (*) for p<.01 and (***) for p<.001.
H
o
/ H
e

Locus
Age 1
(n=23)
Age 2
(n=12)
Age 3
(n=15)
Age 4
(n=35)
Age 5
(n=51)
Age 6
(n=42)
Age 7
(n=21)
Age 8
(n=23)
Age 9
(n=15)
Age 10
(n=10)
Age 11
(n=7)
Age 12
(n=10)
24
0.870 /
0.892
1.000 /
0.868
0.750 /
0.895
0.771 /
0.865
0.896 /
0.877
0.897 /
0.868
0.737 /
0.858
0.864 /
0.919
1.000 /
0.929
0.900 /
0.911
0.857 /
0.769
1.000 /
0.868
162
0.696 /
0.730
0.818 /
0.853
0.929 /
0.743
0.667 /
0.806
0.612 /
0.719
0.775 /
0.791
0.611 /
0.710
0.579 /
0.799
0.583 /
0.688
0.429 /
0.692
0.833 /
0.849
0.750 /
0.808
164
0.455 /
0.388
0.250 /
0.236
0.500 /
0.426
0.500 /
0.456
0.319 /
0.329
0.310 /
0.366
0.333 /
0.299
0.238 /
0.220
0.400 /
0.356
0.800 /
0.642
0.500 /
0.439
0.200 /
0.195
157
0.429 /
0.553
0.364 /
0.550
0.308 /
0.394
0.600 /
0.648
0.659 /
0.635
0.525 /
0.586
0.684 /
0.603
0.400 /
0.489
0.600 /
0.609
0.400 /
0.568
0.500 /
0.439
0.500 /
0.563
105
0.750 /
0.929
1.000 /
1.000
1.000 /
1.000
0.769 /
0.900
0.667 /
0.733
0.786 /
0.897
0.500 /
0.849
0.778 /
0.941
0.833 /
0.894
0.750 /
0.750
1.000 /
0.833
0.750 /
0.786
155
0.667 /
0.778
0.333 /
0.733
0.667 /
0.600
0.455 /
0.537
0.467 /
0.729
0.471 /
0.642
0.778 /
0.569
0.500 /
0.500
0.500 /
0.668
0.800 /
0.644
1.000 /
0.667
0.714 /
0.758
193
0.923 /
0.929
0.833 /
0.818
0.500 /
0.932 *
0.917 /
0.950
0.857 /
0.920
1.000 /
0.930
0.643 /
0.913
0.947 /
0.936
0.800 /
0.947
0.778 /
0.948
0.857 /
0.945
0.857 /
0.868
218
0.381 /
0.511
0.300 /
0.690
0.250 /
0.489
0.360 /
0.510
0.444 /
0.501
0.515 /
0.478
0.333 /
0.508
0.571 /
0.511
0.091 /
0.507
0.625 /
0.525
0.600 /
0.600
0.333 /
0.546
235A
0.696 /
0.736
0.917 /
0.859
0.800 /
0.883
0.900 /
0.877
0.861 /
0.864
0.800 /
0.837
0.737 /
0.848
0.810 /
0.870
0.929 /
0.865
0.800 /
0.890
0.857 /
0.813
0.778 /
0.922
149
0.429 /
0.405
Mono. /
Mono.
0.600 /
0.467
0.500 /
0.542
0.571 /
0.442
0.480 /
0.451
0.273 /
0.455
0.692 /
0.517
0.857 /
0.582
0.200 /
0.200
0.500 /
0.500
0.333 /
0.600
Mn01
0.600 /
0.844
Mono. /
Mono.
0.333 /
0.867
1.000 /
0.833
0.667 /
0.742
0.833 /
0.864
1.000 /
0.822
0.667 /
0.742
0.625 /
0.808
0.500 /
0.750
1.000 /
1.000
0.833 /
0.849
Mn08
0.769 /
0.966
1.000 /
0.978
0.800 /
0.956
0.909 /
0.954
0.839 /
0.952
0.769 /
0.940 **
1.000 /
0.971
1.000 /
0.960
0.917 /
0.975
0.778 /
0.935
1.000 /
1.000
1.000 /
0.974

97
Table 3-4: Observed (H
o
) and expected (H
e
) levels of heterozygosity, (H
o
/H
e
) for age
classes in the southwestern Pacific (Australia and New Zealand), listed by age and locus.
Significant p-values are denoted by (*) for p<.01 and (***) for p<.001.
H
o
/ H
e

Locus Age 4 (n=11) Age 5 (n=42) Age 6 (n=48) Age 7 (n=23)
24 0.600 / 0.574 0.923 / 0.892 0.975 / 0.898 0.783 / 0.866
162 0.400 / 0.795 0.795 / 0.874 0.786 / 0.871 0.750 / 0.871
164 0.455 / 0.524 0.683 / 0.711 0.674 / 0.717 0.546 / 0.637
157 0.900 / 0.595 0.537 / 0.567 0.658 / 0.561 0.381 / 0.483
105 1.000 / 0.853 0.794 / 0.943 * 0.639 / 0.937 ** 0.813 / 0.873
155 0.700 / 0.758 0.546 / 0.520 0.310 / 0.457 0.550 / 0.537
193 0.636 / 0.926 ** 0.778 / 0.917 0.778 / 0.936 ** 0.714 / 0.902
218 0.400 / 0.526 0.475 / 0.506 0.511 / 0.481 0.714 / 0.508
235 0.818 / 0.779 0.800 / 0.848 0.935 / 0.883 0.900 / 0.914
149 0.100 / 0.521 0.700 / 0.514 0.591 / 0.532 0.333 / 0.451
Mn01 1.000 / 0.870 0.784 / 0.788 0.871 / 0.820 0.765 / 0.822
Mn08 0.818 / 0.844 * 1.000 / 0.948 0.875 / 0.956 0.944 / 0.949

Table 3-5: F
IS
values used to detect deviation from the Hardy-Weinberg Equilibrium for
the age classes in Japan and Southern California , significant values are denoted for p<.05
(*) and p<.01 (**).
F
IS

Locus
Age 5
(n=22)
Age 6
(n=34)
Age 7
(n=41)
Age 8
(n=19)
Age 9
(n=12)
Age 10
(n=8)
24 -0.030 0.039 -0.042 0.091 0.068 -0.059
162 0.166 0.040 -0.034 0.036 0.159 0.086
164 -0.188 -0.137 -0.138 -0.067 0.223 0.259
157 -0.035 0.000 0.136 -0.130 0.315 0.615
105 0.063 0.021 0.034 0.254 0.217 Not Avail.
155 0.118 0.373 0.226 -0.191 0.750 1.000
193 -0.010 0.027 0.062 0.088 -0.047 -0.143
218 -0.315 -0.099 0.158 0.417 -0.047 0.217
235 -0.070 0.014 0.071 -0.021 -0.154 -0.177
149 0.286 -0.143 0.325 0.347 0.217 Not Avail.
Mn01 0.083 0.066 0.583 -0.054 -0.333 Not Avail.
Mn08 0.034 0.036 0.051 0.159 0.000 -0.091

98
Table 3-6: F
IS
values used to detect deviation from the Hardy-Weinberg Equilibrium for age classes in Hawaii, significant values are
denoted for p<.05 (*) and p<.01 (**).
F
IS

Locus
Age 2
(n=35)
Age 3
(n=49)
Age 4
(n=27)
Age 5
(n=18)
Age 6
(n=17)
Age 7
(n=27)
Age 8
(n=39)
Age 9
(n=25)
Age 10
(n=22)
Age 11
(n=26)
Age 12
(n=26)
24 0.049 0.073 0.014 0.018 0.051 0.001 0.068 -0.068 -0.073 0.068 -0.020
162 0.099 0.209 * 0.147 0.082 0.044 0.136 -0.024 0.176 0.116 0.152 0.097
164 0.248 0.166 -0.053 0.149 0.329 0.229 0.226 0.282 -0.095 -0.038 0.264
157 0.070 0.059 -0.138 0.309 0.075 -0.265 -0.039 0.296 -0.010 0.246 -0.173
105 0.258 0.128 -0.083 0.579 0.407 0.300 0.108 0.059 0.182 0.111 0.207
155 0.081 0.276 -0.050 -0.091 0.164 0.588 * 0.390 0.103 0.394 0.032 0.004
193 0.014 0.082 0.193 0.155 0.132 0.225 0.119 0.096 0.135 0.149 0.141
218 -0.147 0.096 0.224 -0.209 0.438 0.349 0.515 0.273 * -0.031 0.307 0.146
235 0.042 -0.110 -0.051 0.029 0.021 0.119 0.117 0.045 0.029 -0.080 0.288 *
149 -0.149 -0.159 0.008 -0.081 -0.200 0.257 0.217 0.407 -0.080 -0.071 0.321
Mn01 -0.112 0.131 -0.177 0.273 -0.109 0.196 0.144 -0.007 -0.200 0.192 -0.043
Mn08 0.035 0.028 -0.019 -0.024 0.147 0.015 0.138 0.059 0.040 -0.002 0.026

99
Table 3-7: F
IS
values used to detect deviation from the Hardy-Weinberg Equilibrium for age classes in the eastern Pacific (Mexico,
Central America, Ecuador), significant values are denoted for p<.05 (*) and p<.01 (**).
F
IS

Locus
Age 1
(n=23)
Age 2
(n=12)
Age 3
(n=15)
Age 4
(n=35)
Age 5
(n=51)
Age 6
(n=42)
Age 7
(n=21)
Age 8
(n=23)
Age 9
(n=15)
Age 10
(n=10)
Age 11
(n=7)
Age 12
(n=10)
24 0.026 -0.161 0.168 0.110 -0.021 -0.035 0.144 0.061 -0.080 0.012 -0.125 -0.167
162 0.049 0.043 -0.261 0.175 0.149 0.020 0.142 0.281 0.159 0.400 0.020 0.077
164 -0.177 -0.065 -0.182 -0.099 0.030 0.155 -0.120 -0.087 -0.128 -0.263 -0.154 -0.029
157 0.229 0.350 0.226 0.075 -0.039 0.105 -0.139 0.185 0.016 0.308 -0.154 0.118
105 0.217 Not Avail. 0.000 0.149 0.111 0.128 0.434 0.183 0.074 0.000 -0.333 0.053
155 0.150 0.600 -0.143 0.160 0.368 0.273 -0.400 0.000 0.262 -0.280 -1.000 0.063
193 0.007 -0.020 0.477 * 0.035 0.069 -0.077 0.304 -0.013 0.163 0.188 0.100 0.014
218 0.259 0.578 0.500 0.299 0.114 -0.079 0.350 -0.122 0.828 -0.207 0.000 0.412
235 0.056 -0.071 0.097 -0.027 0.004 0.045 0.134 0.071 -0.076 0.106 -0.059 0.164
149 -0.061 Not Avail. -0.333 0.081 -0.308 -0.067 0.412 -0.359 -0.532 Not Avail. Not Avail. 0.500
Mn01 0.314 Not Avail. 0.667 -0.225 0.111 0.039 -0.250 0.111 0.239 0.368 0.000 0.020
Mn08 0.211 -0.024 0.180 0.048 0.121 0.184 ** -0.031 -0.043 0.062 0.177 0.000 -0.029

100
Table 3-8: F
IS
values used to detect deviation from the Hardy-Weinberg Equilibrium for
age classes in the southwestern Pacific (Australia and New Zealand), significant values
are denoted for p<.05 (*) and p<.01 (**).
F
IS

Locus
Age 4
(n=11)
Age 5
(n=42)
Age 6
(n=48)
Age 7
(n=23)
24 -0.049 -0.036 -0.087 0.098
162 0.510 0.091 0.099 0.142
164 0.138 0.040 0.061 0.147
157 -0.558 0.054 -0.176 0.216
105 -0.184 0.159 * 0.321 ** 0.071
155 0.080 -0.049 0.326 ** -0.025
193 0.324 ** 0.153 0.171 ** 0.215
218 0.250 0.062 -0.064 -0.422
235 -0.053 0.058 -0.059 0.016
149 0.816 -0.372 -0.114 0.273
Mn01 -0.158 0.005 -0.063 0.071
Mn08 0.032 * -0.056 0.085 0.005

101
Temporal Variance

Jorde and Ryman’s (2007) Fs’ and the associated confidence intervals were calculated for
each pair of consecutive age-class in the four groups. The Fs’ estimates varied within the
groups; from 0.0190 to 0.0500 in Japan/Southern California (Table 3-9), -0.0018 to
0.0741 in Hawaii (Table 3-10), 0.0035 to 0.0759 in the eastern Pacific (Table 3-11), and
0.0055 to 0.0798 in the southwestern Pacific (Table 3-12). Despite the variation in Fs’
estimates, only two year-to-year comparisons, between years 8 to 9 in Hawaii and years 4
to 5 in the southwestern Pacific were found to be significant (Figures 3-2 and 3-4). The
other groups showed no significant temporal variation (Figures 3-1 to 3-4). Only one pair
of year-to-year comparisons, in the southwestern Pacific, was found to be significant
(Figure 3-4). This occurred between age-class comparisons 4 to 5 and 5 to 6 (Figure 3-4).
Despite the range in Fs’ values, the average among the groups fell within a fairly narrow
range (0.0281 and 0.0383). It is important to note that Jorde and Ryman’s Fs’ is intended
to detect allele frequency shifts among generations, not year-classes that are less than a
single generation interval , and that no corrections for overlapping generations were
applied to this estimate. The implications of this violation will be further explored in the
Discussion.

102
Table 3-9: Fs’ values averaged over all loci used to detect temporal variance between
consecutive age-classes in Japan and Southern California, with upper and lower
confidence intervals.
Fs' (Jorde and Ryman 2007)
Group: Japan and Southern California 95% Confidence Intervals
Age-class comparisons Fs' mean Lower (2.50%) Upper (97.50%)
5 to 6 0.0119 -0.0088 0.0325
6 to 7 0.0199 -0.0176 0.0575
7 to 8 0.0480 -0.0119 0.1078
8 to 9 0.0500 -0.0624 0.1624
9 to 10 0.0312 -0.0576 0.1200
Average 0.0322 -0.0316 0.0960

Table 3-10: Fs’ values averaged over all loci used to detect temporal variance between
consecutive age-classes in Hawaii, with upper and lower confidence intervals.
Fs' (Jorde and Ryman 2007)
Group: Hawaii 95% Confidence Intervals
Age-class comparisons Fs' mean Lower (2.50%) Upper (97.50%)
2 to 3 -0.0018 -0.0119 0.0083
3 to 4 0.0117 -0.0031 0.0266
4 to 5 0.0385 -0.0024 0.0793
5 to 6 0.0381 -0.0044 0.0806
6 to 7 0.0217 -0.0014 0.0448
7 to 8 0.0139 -0.0242 0.0520
8 to 9 0.0349 0.0004 0.0694
9 to 10 0.0741 -0.0119 0.1600
10 to 11 0.0325 -0.0319 0.0970
11 to 12 0.0173 -0.0299 0.0644
Average 0.0281 -0.0121 0.0682

103
Table 3-11: Fs’ values averaged over all loci used to detect temporal variance between
consecutive age-classes in the eastern Pacific, with upper and lower confidence intervals.
Fs' (Jorde and Ryman 2007)
Group: Eastern Pacific (MX, CA, EC) 95% Confidence Intervals
Age-class comparisons Fs' mean Lower (2.50%) Upper (97.50%)
1 to 2 0.0708 -0.0329 0.1745
2 to 3 0.0759 -0.0369 0.1886
3 to 4 0.0035 -0.0393 0.0463
4 to 5 0.0393 -0.0130 0.0915
5 to 6 0.0166 -0.0389 0.0722
6 to 7 0.0445 -0.0064 0.0955
7 to 8 0.0266 -0.0093 0.0626
8 to 9 0.0323 -0.0391 0.1038
9 to 10 0.0785 -0.0111 0.1681
10 to 11 0.0038 -0.0682 0.0759
11 to 12 0.0294 -0.0663 0.1252
Average 0.0383 -0.0329 0.1095

Table 3-12: Fs’ values averaged over all loci used to detect temporal variance between
consecutive age-classes in the southwestern Pacific, with upper and lower confidence
intervals.
Fs' (Jorde and Ryman 2007
Group: Southwestern Pacific (AU, NZ) 95% Confidence Intervals
Age-class comparisons Fs' mean Lower (2.50%) Upper (97.50%)
4 to 5 0.0798 0.0263 0.1333
5 to 6 0.0055 -0.0040 0.0151
6 to 7 0.0119 -0.0143 0.0380
Average 0.0324 0.0027 0.0621

104
Figure 3-1: Fs’ values for consecutive age-classes in Japan and Southern California.
Error bars show 95% confidence intervals.
5-6
6-7 7-8
8-9
9-10
-0.075
-0.05
-0.025
0
0.025
0.05
0.075
0.1
0.125
0.15
0.175
Year-to-year groups
Fs'

Figure 3-2: Fs’ values for consecutive age-classes in Hawaii. Error bars show 95%
confidence intervals.
2-3
3-4
4-5
5-6 6-7
7-8
8-9
9-10
10-11 11-12
-0.05
-0.025
0
0.025
0.05
0.075
0.1
0.125
0.15
0.175
Year-to-year groups
Fs'

105
Figure 3-3: Fs’ values for consecutive age-classes in the eastern Pacific. Error bars show
95% confidence intervals.
1-2 2-3 3-4
4-5
5-6
6-7
7-8
8-9
9-10
10-11 11-12
-0.1
-0.075
-0.05
-0.025
0
0.025
0.05
0.075
0.1
0.125
0.15
0.175
0.2
0.225
Year-to-year groups
Fs'

Figure 3-4: Fs’ values for consecutive age-classes in the southwestern Pacific. Error bars
show 95% confidence intervals.
4-5
5-6
6-7
-0.04
-0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
Year-to-year groups
Fs'

106
Patterns in Temporal Variance

To examine how genetic drift and allele frequencies vary over relatively short periods of
time, the two groups with the broadest age-class distributions (Hawaii and the Eastern
Pacific) were compared across all pair-wise year-class combinations. Three types of
comparisons with temporal variance were made: 1) year-class comparisons for all years
2) individual year-class comparisons for all years 3) number of years separating the year-
classes.

In Hawaii, the comparison with all year-classes (Figure 3-5) revealed shifting temporal
variation among the years. Interestingly, most year-classes followed a similar increase in
Fs’ when compared to the 5
th
year, followed by a decrease in the 6
th
year (Figure 3-5).
Overall, year-classes showed independent variation in levels of genetic drift. In the
individual year comparisons significant variation was found between: year-class 2 and
years 7 and 11, year-class 5 and year 10, year-class 8 and years 3, 5, 9 and 11 (Table 3-
13). In comparing variance with the number of years separating year-classes in Hawaii,
shifts in variation were greater for periods separated by up to 6 years, but then appeared
to be reduced for samples separated by 7 to 10 years, although fewer estimates were
available at the later points (Figure 3-6). In general, Fs’ estimates did not increase
between points separated by longer periods of time as would be expected.

107
Figure 3-5: Age-classes compared with Fs’ values in Hawaii, for all year-classes.
-0.05
-0.03
-0.01
0.01
0.03
0.05
0.07
0.09
0.11
1 2 3 4 5 6 7 8 9 10 11 12 13
Age class (years)
Fs'
Year 2
Year 3
Year 4
Year 5
Year 6
Year 7
Year 8
Year 9
Year 10
Year 11
Year 12

108
Table 3-13: Pair-wise Fs’ estimates from comparisons among individual year-classes with 95% confidence intervals in Hawaii.
Fs' and 95% Confidence Intervals (Lower, Upper)
YC 3 YC 4 YC 5 YC 6 YC 7 YC 8 YC 9 YC 10 YC 11 YC 12
Year-class 2
-0.002
(-0.012,
0.008)
0.005
(-0.012,
0.022)
0.032
(-0.001,
0.065)
0.009
(-0.030,
0.049)
0.045
(0.007,
0.083)
0.052
(-0.003,
0.106)
0.052
(-0.005,
0.109)
0.022
(-0.003,
0.047)
0.031
(0.012,
0.050)
0.017
(0.013,
0.047)
Year-class 3
0.012
(-0.003,
0.027)
0.037
(-0.003,
0.077)
0.004
(-0.018,
0.026)
0.029
(-0.001,
0.060)
0.033
(0.002,
0.064)
0.046
(-0.008,
0.100)
0.015
(-0.007,
0.037)
0.017
(-0.005,
0.040)
0.025
(-0.004,
0.054)
Year-class 4
0.039
(-0.002,
0.079)
0.013
(-0.020,
0.047)
0.043
(-0.022,
0.107)
0.036
(-0.005,
0.076)
0.039
(-0.018,
0.095)
0.035
(-0.010,
0.080)
0.023
(-0.019,
0.064)
0.015
(-0.019,
0.049)
Year-class 5
0.038
(-0.004,
0.081)
0.076
(-0.004,
0.157)
0.087
(0.019,
0.155)
0.080
(0.011,
0.150)
0.087
(0.011,
0.163)
0.046
(-0.002,
0.095)
0.044
(-0.005,
0.092)
Year-class 6
0.022
(-0.001,
0.046)
0.017
(-0.022,
0.055)
0.052
(-0.013,
0.117)
0.027
(-0.016,
0.070)
0.019
(-0.011,
0.049)
0.036
(-0.006,
0.078)
Year-class 7
0.015
(-0.023,
0.052)
0.056
(-0.008,
0.120)
0.031
(-0.007,
0.070)
0.072
(-0.027,
0.172)
0.040
(-0.042,
0.122)
Year-class 8
0.036
(0.001,
0.070)
0.062
(-0.011,
0.135)
0.075
(0.000,
0.150)
0.036
(-0.027,
0.100)
Year-class 9
0.075
(-0.011,
0.16)
0.043
(-0.039,
0.126)
-0.001
(-0.019,
0.017)
Year-class 10
0.033
(-0.031,
0.097)
0.055
(-0.014,
0.124)
Year-class 11
0.017
(-0.030,
0.064)

109
Figure 3-6: Average variance (Fs’) in Hawaii year-classes compared with the number of
years between year-classes.
-0.01
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0 1 2 3 4 5 6 7 8 9 10 11
Number of years apart
Average Fs'
HW 2
HW 3
HW 4
HW 5
HW 6
HW 7
HW 8
HW 9
HW 10
HW 11
HW 12

110
In the eastern Pacific, Fs’ estimates in the all year-class comparisons (Figure 3-7) ranged
more broadly than in Hawaii. Most year-classes had an increase in Fs’ estimates when
compared with the 2
nd
year, followed by lower Fs’ values with the 4
th
year, and then
another small increase in Fs’ estimates in the 5
th
year. After that point, the year-classes
did not show any common pattern. Individual year-class estimates did not reveal any
overall variance patterns, although a few significant pair-wise estimates were detected:
year-class 2 and years 7, 10 and 11, and year-class 3 and years 5, 7, 10 and 12 (Table 3-
14). Levels of variance in the eastern Pacific did not show any common trends when
compared to the number of years separating age-classes, although Fs’ estimates were
very large at points 8 and 9 years apart, but not at 10 years (Figure 3-8).

A comparison of average Fs’ with numbers of years separating year-classes for Hawaii
and the eastern Pacific showed that variance fell within a more narrow range
(approximately 0.04) for age-classes separated by 2 to 5 years (Figure 3-9). After 5 years,
average Fs’ values increased in the eastern Pacific (due to the large Fs’ estimates at 8 and
9 years mentioned above) and decreased in Hawaii, followed by a low in the 10 year
point for both groups. Because these estimates are based on average Fs’ values for the
number of years, values at the higher number of years are based on fewer individual Fs’
estimates, and therefore may not be as reliable.

111
Figure 3-7: Age-classes compared with Fs’ values in the eastern Pacific, for all year-
classes.
-0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0 1 2 3 4 5 6 7 8 9 10 11 12 13
Age class (year)
Fs'
Year 1
Year 2
Year 3
Year 4
Year 5
Year 6
Year 7
Year 8
Year 9
Year 10
Year 11
Year 12

112
Table 3-14: Pair-wise Fs’ estimates from comparisons among individual year-classes with 95% confidence intervals in the E Pacific.
Fs' and 95% Confidence Intervals (Lower, Upper)
YC 2 YC 3 YC 4 YC 5 YC 6 YC 7 YC 8 YC 9 YC 10 YC 11 YC 12
Year-class
1
0.071
(-0.033,
0.175)
0.014
(-0.036,
0.064)
0.005
(-0.013,
0.023)
0.053
(-0.034,
0.139)
0.004
(-0.019,
0.026)
0.030
(-0.017,
0.077)
0.022
(-0.025,
0.069)
0.016
(-0.015,
0.047)
0.034
(-0.023,
0.091)
-0.003
(0.065,
0.059)
0.025
(0.027,
0.077)
Year-class
2
0.076
(-0.037,
0.189)
0.074
(-0.015,
0.164)
0.129
(-0.015,
0.273)
0.060
(-0.043,
0.163)
0.059
(0.016,
0.101)
0.102
(-0.008,
0.212)
0.063
(-0.036,
0.162)
0.144
(0.020,
0.269)
0.148
(0.037,
0.260)
0.049
(0.027,
0.124)
Year-class
3
0.004
(-0.039,
0.046)
0.073
(0.020,
0.125)
0.041
(-0.010,
0.092)
0.060
(0.018,
0.101)
0.078
(-0.027,
0.182)
-0.003
(-0.036,
0.030)
0.090
(0.044,
0.135)
0.068
(0.010,
0.146)
0.072
(0.014,
0.136)
Year-class
4
0.039
(-0.013,
0.092)
0.010
(-0.027,
0.047)
0.025
(-0.015,
0.065)
0.038
(-0.040,
0.117)
-0.010
(-0.030,
0.011)
0.049
(-0.002,
0.100)
0.018
(0.030,
0.065)
0.028
(0.008,
0.063)
Year-class
5
0.017
(-0.039,
0.073)
0.082
(-0.013,
0.178)
0.037
(-0.034,
0.108)
0.010
(-0.032,
0.052)
0.060
(0.003,
0.123)
0.082
(0.010,
0.174)
0.080
(0.013,
0.174)
Year-class
6
0.045
(-0.006,
0.096)
0.021
(-0.033,
0.075)
0.025
(-0.036,
0.086)
0.034
(0.003,
0.071)
0.040
(0.020,
0.099)
0.049
(0.010,
0.109)
Year-class
7
0.027
(-0.009,
0.063)
0.028
(-0.024,
0.080)
0.082
(0.015,
0.178)
0.048
(0.044,
0.141)
0.029
(0.057,
0.115)
Year-class
8
0.032
(-0.039,
0.104)
0.044
(0.013,
0.100)
0.010
(0.032,
0.052)
0.020
(0.016,
0.055)
Year-class
9
0.079
(0.011,
0.168)
0.050
(-0.023,
0.122)
-0.004
(0.030,
0.021)
Year-class
10
0.004
(0.068,
0.076)
0.088
(0.043,
0.218)
Year-class
11
0.030
(0.066,
0.125)

113
Figure 3-8: Average variance (Fs’) in Eastern Pacific year-classes compared with the
number of years between year-classes.
-0.05
0
0.05
0.1
0.15
0.2
0 1 2 3 4 5 6 7 8 9 10 11
Number of years apart
A v erag e Fs'
EP 1
EP 2
EP 3
EP 4
EP 5
EP 6
EP 7
EP 8
EP 9
EP 10
EP 11
EP 12

Figure 3-9: Graph of average variance, Fs’, compared with the number of years between
year-classes in Hawaii and the eastern Pacific.
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0 1 2 3 4 5 6 7 8 9 10 11 12
Number of years apart
Average Fs'
HW
EP

114
Factorial Correspondence Analysis

The factorial correspondence analyses for all age-classes, in each location, are displayed
in Figures 3-10a and 3-10b. In Figure 3-10a, the graph of factors 1 (8.41%) and 2
(4.62%) showed that most of the location/year collections separated into the groups that
were used in the above analyses. The different age-classes of Mexico and Central
America formed two clusters, one to the right of the large group of age-classes from the
north Pacific along factor 1 (x-axis) and the other just above the first cluster in the upper
right part of the graph along factor 2 (y-axis). The age-classes from the north Pacific
(Hawaii, Japan and Southern California) were aligned in the middle of the graph, while
most age-classes from Australia and New Zealand were distributed to the left (along
factor 1) of the north Pacific group. The 8 year old age-class from New Zealand grouped
with several samples from Hawaii. In Figure 3-10b, which examines factor 2 (4.62%)
and factor 3 (4.56%), there is more overlap among the location-year-classes, with a group
of overlapping locations and years in the center of the graph. However, the location
patterns are still visible. The north Pacific group is located above most of the eastern and
southwestern age-classes along factor 3 (y-axis), the eastern Pacific has a cluster of year-
classes to the right of the graph along factor 2 (x-axis), and several southwestern age-
classes are to the bottom of the graph along factor 3.

115
Figure 3-10: Factorial correspondence analyses of age-classes in all locations, for factors
1 (8.41%) and 2 (4.62%) (a), and factors 2 (4.62%) and 3 (4.56%) (b).

Figure 3-10a: Factorial correspondence analysis of factors 1 (8.41%) and 2 (4.62%) by
population and age group.
10 HW
6 SC
7 SC
2 MX
3 MX
5 MX
6 CA
5 AU
6 AU
5 JP
6 JP
7 JP
8 JP
9 JP
10 JP
1 HW
2 HW
3 HW
4 HW
5 HW
6 HW
7 HW
8 HW
9 HW
11 HW
12 HW
13+ HW
8 SC
4 MX
6 MX
7 MX
8 MX
9 MX
10 MX
11 MX
12 MX
13+ MX
1 CA
2 CA
4 CA
5 CA
7 CA
8 CA
13+ CA
5 NZ
6 NZ
7 NZ
8 NZ
4 AU
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8
Factor 1 (8.41%)
Factor 2 (4.62% )

Figure 3-10b: Factorial correspondence analysis of factors 2 (4.62%) and 3 (4.56%) by
population and age group.
10 JP
6 SC
8 SC
5 MX
4 CA
13+ CA
6 AU
5 JP
6 JP
7 JP
8 JP
9 JP
1 HW
2 HW
3 HW
4 HW 5 HW
6 HW
7 HW
8 HW
9 HW
10 HW
11 HW
12 HW
13+ HW
7 SC
2 MX
3 MX
4 MX
6 MX
7 MX
8 MX
9 MX
10 MX 11 MX
12 MX
13+ MX
1 CA
2 CA
5 CA
6 CA
7 CA
8 CA
5 NZ
6 NZ
7 NZ
8 NZ
4 AU
5 AU
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
-0.6 -0.4 -0.2 0 0.2 0.4 0.6
Factor 2 (4.62%)
Factor 3 (4.56%)

116
Effective Population Size

Effective population sizes for the different groups were calculated using the overlapping
generation correction according to two different life tables: life-table “A” that used
natural mortality only (Appendix Table 7a) and life-table “B” that accounted for natural
mortality and fishing pressure (Appendix Table 7b). The correction factor, C, for life-
table “A” was 37.93 and the mean generation length, G, was 8.93. The estimates of
effective population size, N
e
, ranged from 56 in the eastern Pacific to 76 in Hawaii (Table
3-15). For life-table “B”, the correction factor, C, was 9.35 and the generation length, G,
was 7.57. Effective population sizes for this table ranged between 16 in the eastern
Pacific and 21 in Hawaii (Table 3-15). With the exception of the southwestern Pacific,
confidence intervals were very large for both tables, with upper limits set at infinite
population sizes. The infinite limits resulted from negative lower confidence intervals for
the Fs’ estimate.

Age and Size Distributions

The Japan and Southern California mandibular length distributions were not significantly
different (t-test, p = 0.108). Both locations had unimodal distributions, and average
lengths of 192 cm and 196 cm, and medians of 193 cm and 196 cm for Japan and
Southern California, respectively (Figures 3-11a and 3-12a). Their age distributions were
also not significantly different (t-test, p = 0.149), with the majority of individuals in

117
Table 3-15: Effective population size, N
e
, corrected for overlapping generations
according to life-tables A and B and corresponding confidence intervals for the four
analysis groups.
Overlapping generation N
e
(Jorde and Ryman 1995)

Life Table A (constant mortality of
M=0.2)
Life Table B (natural and fishing
mortality)
G = 8.93 C = 37.93 G = 7.57 C = 9.35
Location
groups N
e
95% Confidence Intervals N
e
95% Confidence Intervals
(mean Fs')
Lower
(2.50%)
Upper
(97.50%) (mean Fs')
Lower
(2.50%)
Upper
(97.50%)
JP and SC 66 22 Infinity 19 6 Infinity
HW 76 31 Infinity 22 9 Infinity
Eastern
Pacific (MX,
CA, EC) 56 19 Infinity 16 6 Infinity
South-
western
Pacific (AU,
NZ) 66 34 795.02 19 10 231

118
Figure 3-11: Mandibular length (a) and age (b) distributions for samples from Japan.

Figure 3-11a: Length distribution
0
10
20
30
40
50
60
70
80
90
100-120 121-140 141-160 161-180 181-200 201-220 221-240 241-260 261-280 281-300 301-320
Mandibular length (cm)
Number of individuals

Figure 3-11b: Age distribution
0
5
10
15
20
25
30
35
1 2 3 4 5 6 7 8 9 10 11 12 13+
Age groups (years)
Number of individuals

119
Figure 3-12: Mandibular length (a) and age (b) distributions for samples from Southern
California.

Figure 3-12a: Length distribution
0
2
4
6
8
10
12
14
16
18
20
100-
120
121-
140
141-
160
161-
180
181-
200
201-
220
221-
240
241-
260
261-
280
281-
300
301-
320
Mandibular length (cm)
Number of individuals

Figure 3-12b: Age distribution
0
1
2
3
4
5
6
7
8
9
1 2 3 4 5 6 7 8 9 10 11 12 13+
Age groups (years)
Number of individuals

120
Japan falling into the 5 to 8 year old age-classes (Figure 3-11b), and the majority of
Southern California samples in the 6 to 8 year old age-classes (Figure 3-12b).
The mandibular length distribution of Hawaiian samples was unimodal, with most
individuals in 181-260 cm range; there may also be a small mode of individuals in the
120-180 cm range, (Figure 3-13a). The average length of Hawaiian samples was 203 cm
with a median of 208 cm. The distribution of age-classes in Hawaiian samples was
dominated by a large number of individuals in the 13+ age group, which minimized the
scale for the remaining age-classes (Figure 3-13b).
Because of the suspected movement of juveniles through the Hawaiian location during
different times of the year, sizes and ages of the striped marlin from this location were
also examined by yearly quarter (Figures 3-14a-b). Differences in mandibular length
among the quarters were found to be highly significant (ANOVA, p < .001). The 2
nd
and
3
rd
quarters had averages of 222 cm and 206 cm, and medians of 217 cm and 204 cm,
respectively. The majority of 2
nd
quarter samples fell in the range between 181 and 260
cm, and the 3
rd
quarter samples fell in the range of 161 to 240 cm. The size distribution of
1
st
quarter samples was bimodal, with more samples in the mode between 121 to 180 cm,
and a second smaller mode between 181 and 240 cm. This quarter had a large number (n
= 59) of samples in the 141-160 cm range. The average mandibular length for the 1
st

quarter was 176 cm, with a median of 163 cm. The 4
th
quarter also had a distinct
distribution with a large number of samples between 201 to 240 cm (n = 131), with few
fish in the smaller size ranges and almost no fish larger than 240 cm (n = 2). The average

121
length in the 4
th
quarter was 213 cm, and the median was 219 cm (Figure 3-14a). All pair-
wise quarterly mandibular length distributions were significantly different (t-tests, p <
0.05).
As with all of the Hawaiian samples together, the age-distribution by quarter is
dominated by fish over 13 years (Figure 3-14b). Excluding the 13+ group, the age-
distributions among quarters was highly significant (ANOVA, p < .001). The 2
nd
and 3
rd

quarters have a more typical age distribution with larger numbers of middle-aged fish (5
to 9 years). This is in contrast to the 1
st
quarter, where a large number of fish from that
quarter fall between 2 to 4 years. The majority of 4
th
quarter samples occur in the older
years (8+ years).
In the eastern Pacific, Mexico and Central America showed significantly different size
distributions (t-test, p < 0.001) (Figures 3-15a and 3-16a). These differences may be
largely dependent on sample sources, where Central American samples were collected
through commercial fisheries while Mexican samples were primarily collected in
recreational fisheries. In Mexico, the mandibular lengths largely fell between 161 to 260
cm, with an average of 206 cm and a median of 204 cm (Figure 3-15a). In Central
America, there was a group of small fish, from 100 to 140 cm, and another mode from
161 to 220 cm, with an average length of 174 cm and a median of 186 cm (Figure 3-16a).
Similarly, the age distributions for these two locations were also significantly different (t-
test, p = 0.023) (Figures 3-15b and 3-16b). In Mexico, there were a large number of fish
in the 13+ category, with the remaining samples spread between years 2 through 12, with

122
Figure 3-13: Mandibular length (a) and age (b) distributions for samples from Hawaii.

Figure 3-13a: Length distribution
0
20
40
60
80
100
120
140
160
180
100-120 121-140 141-160 161-180 181-200 201-220 221-240 241-260 261-280 281-300 301-320
Mandibular length (cm)
Number of individuals

Figure 3-13b: Age distribution
0
25
50
75
100
125
150
175
200
225
250
1 2 3 4 5 6 7 8 9 10 11 12 13+
Age groups (years)
Number of individuals

123
Figure 3-14: Mandibular length (a) and age (b) distributions of Hawaiian samples by
yearly quarter.

Figure 3-14a: Mandibular length distribution of samples by yearly quarter in Hawaii.
0
10
20
30
40
50
60
70
80
100-120 121-140 141-160 161-180 181-200 201-220 221-240 241-260 261-280 281-300 301-320
Mandibular length (cm)
N umber of individuals
1st Q
2nd Q
3rd Q
4th Q

Figure 3-14b: Age distribution by yearly quarter in Hawaii.
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10 11 12 13+
Age group (years)
N um ber of individuals
1st Q
2nd Q
3rd Q
4th Q

124
Figure 3-15: Mandibular length (a) and age (b) distributions for samples from Mexico.

Figure 3-15a: Length distribution
0
5
10
15
20
25
30
35
40
45
100-120 121-140 141-160 161-180 181-200 201-220 221-240 241-260 261-280 281-300 301-320
Mandibular length (cm)
Number of individuals

Figure 3-15b: Age distribution
0
10
20
30
40
50
60
70
80
1 2 3 4 5 6 7 8 9 10 11 12 13+
Age groups (years)
Number of individuals

125
Figure 3-16: Mandibular length (a) and age (b) distributions for samples from Central
America.

Figure 3-16a: Length distribution
0
5
10
15
20
25
30
35
40
100-120 121-140 141-160 161-180 181-200 201-220 221-240 241-260 261-280 281-300 301-320
Mandibular length (cm)
Number of individuals

Figure 3-16b: Age distribution
0
5
10
15
20
25
1 2 3 4 5 6 7 8 9 10 11 12 13+
Age groups (years)
Number of individuals

126
a small mode from 4 to 6 years (Figure 3-15b). Central America had three age modes; 1
to 2, 5 to 8, and 12 to 13+ year old fish (Figure 3-16b).
Finally, in the southwestern Pacific, the size distributions between Australia and New
Zealand were also significantly different (t-test, p < 0.001). The size distribution, which
in these locations was measured in lower jaw fork length (LJFL), was unimodal in both
locations with average lengths of 213 and 231 cm, and medians of 212 cm and 230 cm in
Australia and New Zealand, respectively (Figures 3-17a and 3-18a). The majority of
Australian samples ranged from 191 to 240 cm, while the New Zealand samples were
slightly larger, with most falling between 211 and 250 cm. Significant variation in age
distributions (t-test, p < 0.001) were found between these two locations; New Zealand
samples were shifted up by one year, with the center of the mode 6 years compared to 5
years in Australia (Figures 3-17b and 3-18b).
It is important to note that age distributions are not comparable across groups, with the
possible exception of Japan/Southern California and Hawaii. Different growth curves
between the regions resulted in age-determination discrepancies, and relative differences
in age distributions should only be compared in locations with similar growth curves (i.e.
analysis groups) (see Discussion for further explanation).

127
Figure 3-17: Lower jaw fork length (a) and age (b) distributions for samples from
Australia.

Figure 3-17a: Length distribution
0
2
4
6
8
10
12
14
16
161-
170
171-
180
181-
190
191-
200
201-
210
211-
220
221-
230
231-
240
241-
250
251-
260
261-
270
271-
280
281-
290
291-
300
LJFL (cm)
Number of individuals

Figure 3-17b: Age distribution
0
5
10
15
20
25
1 2 3 4 5 6 7 8 9 10 11 12 13+
Age groups (years)
Number of individuals

128
Figure 3-18: Lower jaw fork length (a) and age (b) distributions for samples from New
Zealand.

Figure 3-18a: Length distribution
0
5
10
15
20
25
161-
170
171-
180
181-
190
191-
200
201-
210
211-
220
221-
230
231-
240
241-
250
251-
260
261-
270
271-
280
281-
290
291-
300
LJFL (cm)
N umber of individuals

Figure 3-18b: Age distribution
0
5
10
15
20
25
30
35
40
1 2 3 4 5 6 7 8 9 10 11 12 13+
Age groups (years)
Number of individuals

129
Discussion

Summary Statistics

The significant heterozygote deficits found in this study are consistent with the presence
of null alleles (e.g. Dakin and Avise 2004, Hedgecock et al. 2004). Additionally, null
alleles are known to be particularly problematic and prevalent in microsatellite markers
(Kalinowski et al. 2006). For this reason, analyses of temporal variance and effective
population size were conducted with null-corrected data.

When samples were divided into year classes for each of the groups, far fewer significant
differences between observed and expected heterozygosity levels and F
IS
estimates were
found in comparison with the spatial analyses (Chapter 2), in which samples were
grouped only by location. For all of the year-class groups combined, only 11
heterozygote and 11 F
IS
deviations were detected, in contrast to the 23 heterozygote and
31 F
IS
significant values in the spatial analyses. Although some of these differences were
likely due to the presence of null alleles, the difference in the number of deviations may
be indicative that populations do show cohort-type effects.

Interestingly, if strong cohort-effects were acting within these groups, then one would
expect to see reduced levels of observed heterozygosity within certain year classes (Jorde
and Ryman 1996). Levels of heterozygosity within groups ranged widely, but this was
mostly influenced by the locus rather than the age-class. In fact, no age-class showed

130
consistently lower heterozygosity levels across all loci. This may be for a couple of
reasons. First, several age-classes likely contribute to a cohort, and that may reduce the
severity of genetic drift and slow the rate of loss of heterozygosity (Gaggiotti and Vetter
1999). Second, migration even at low levels helps retain heterozygosity within
populations (Jorde and Ryman 1996). For most species, populations are not closed
systems (Hoffman et al. 2004), and although there was significant spatial structure
detected among striped marlin populations, it was low enough (F
ST
= 0.0145) to assume
that migration from other populations likely occurs intermittently.

Temporal Variance

The year-to-year comparisons within the analysis groups showed considerable variability
in temporal variance, Fs’, between consecutive years. However, with large confidence
intervals in most groups, relatively few year-to-year comparisons were significant. Pair-
wise age-class comparisons in Hawaii and in the eastern Pacific also showed rapid and
large variation in Fs’ estimates. For instance, in the eastern Pacific, the Fs’ estimate more
than doubled between consecutive year comparisons. Several pair-wise comparisons for
both Hawaii and the eastern Pacific were significant; interestingly, certain year-classes
showed more significant values than others (e.g. year-class 2 and 8 in Hawaii and year-
class 2 and 3 in the eastern Pacific). Similarly, in the year-class comparisons, certain
years caused all of the year-classes to shift, either by increasing or decreasing Fs’ values.
There may several reasons for these shifts in variance or disproportionate number of

131
significant Fs’ values in certain year-classes. Increases in Fs’ estimates may have been
the result of unusually poor reproductive success in the year contributing to that cohort
(Jorde and Ryman 1995, Hedgecock et al. 1994, Waples and Teel 1990), or could be
caused by influxes of migrants in that age-class, both of which would increase genetic
heterogeneity. Similarly, decreases in Fs’ values may have been influenced by highly
successful recruitment periods or by extremely low levels of immigrants into that
population. Comparisons of temporal variance with the numbers of years between points
were also highly variable. While the pattern in Hawaii appeared to indicate a dampening
of the variation in Fs’ estimates as the number of years between year-classes increased,
the same pattern was not detected in the eastern Pacific. According to Jorde and Ryman
(1996), it was expected that Fs’ values would be low when the number of years
separating cohorts was equal to a generation length. The Fs’ values from Hawaii and the
Eastern Pacific both decreased at 10 years, but the few sampling points at that year and
beyond made it difficult to determine if this pattern was a result of generation length. The
10 year point is also longer than the generation lengths estimated in this study (e.g. 8 and
9 years). In general, it was expected that genetic drift would increase over time, but the
temporal analyses in this study do not support that pattern. Rather the shifts in variance
among years is similar to the phenomenon explained by Jorde and Ryman (1995), where
in populations with overlapping generations, temporal allele frequencies vary widely in
comparison to populations with discrete generations.

132
It should be noted that the analysis of temporal variance here is different from its original
application. The year-classes in this study simply reflect estimated ages of the striped
marlin samples, rather than discrete generations for which the program was intended
(Jorde and Ryman 2007). While correction for overlapping generations is available for
the effective population size, it cannot be applied to the year-to-year comparisons of
variance (Fs’). It is important to discuss the bias that may result from this, along with
other possible biasing factors that may impact the results in this study.

Using analyses intended for discrete generations may create a large amount of bias when
looking at variance over a short time period (Waples 1989b). Specifically, it may greatly
impact the drift/noise ratio (Waples and Yokata 2007). Over shorter time frames, the
signal of genetic drift may be small enough to be lost or misinterpreted by the level of
noise within a sample set. According to Waples and Yokata (2007), one way to reduce
the level of noise is by increasing the number of samples (50 to 100 is considered
appropriate) and improving sample coverage. In this study, most year-class sample sizes
fell below the recommended number. Small samples may show more variation than the
population as a whole, but this type of error may be very difficult to distinguish from real
genetic drift (Waples and Teel 1990). Another possible biasing factor is the pooling of
samples (Turner et al. 1999), however based on the spatial structure analysis, it was
reasonable to divide the samples into the four groups in order to increase sample size. An
additional assumption of the analysis is that there is no migration (Waples 1989). As
mentioned above, this may not be a reasonable assumption between striped marlin

133
populations. The effect of migration is dependent on how differentiated the populations
are, but overall it acts to rapidly change allele frequencies and thereby increase variance
in temporal estimates (Fraser et al. 2007).

It is apparent by the shifts in temporal variance among striped marlin age-classes, that
these populations are not groups of homogenously mixed individuals. Striped marlin have
type III survivorship patterns, which is characterized by large reproductive potential
combined with high rates of early mortality. Species with this type of life history have
unequal and unpredictable larval survival rates, where very few individuals reach
maturity (Laurent and Planes 2007). This results in high levels of genetic heterogeneity
and variance between cohorts (Gomez-Uchida and Banks 2006, Turner et al. 2006,
Turner et al. 1999, Jorde and Ryman 1995). However, the factorial correspondence
analysis of all location-year-class samples revealed that despite the variations among
cohorts, previously detected spatial patterns remained intact. This may be partially due to
the large number of age-classes found in striped marlin populations, where the
contribution of those age-classes reduces the impact of genetic drift for the entire
(Waples and Teel 1990).

The effective population size (N
e
) for the four analysis groups was determined using the
overlapping generation correction. The life-history parameters needed for this type of
analysis have not been conclusively determined in striped marlin, and estimates from a
different species with type III survivorship were used to create the life-tables. While the

134
numbers are likely different for striped marlin, Jorde and Ryman (1995) point out that
precise estimates may not be as important for species where reproduction is distributed
over several year classes. What is clear is that fishing mortality reduced the estimated
effective population size to approximately 1/3 of the size of the population that only
accounted for natural mortality. Similar effects of fishing pressure on N
e
were previously
reported (Hauser et al. 2002, Turner et al. 1999), although in this case, the decrease in N
e
was an artifact of increasing mortality in the life-table and the subsequent effect on the
correction factor, “C’, and generation length “G”. However it is entirely possible that N
e
in striped marlin has already been lowered from historic levels as a result of fishing
pressure.

The effective population sizes for all groups in this study were very small, but it may not
be straightforward to apply these results for a species with overlapping generations
(Waples and Yokata 2007, Jorde and Ryman 1995). It is well known that N
e
can be very
small in populations with planktonic life stages (Waples 1989) or high larval mortality
(Turner et al. 2002, Turner et al. 1999), despite the size of the census population. The
effective population size can be several orders of magnitude smaller than the census
population, ranging at times between 10
-3
and 10
-5
of the census size (Laurent and Planes
2007, Turner et al. 2006, Hedgecock et al. 1994). Turner et al. (2002) estimated that only
about 240 mating pairs of the marine fish, red drum, produced individuals that survived
to maturity, in comparison to the 1.7 million individuals estimated in the census size. It
was also estimated that only approximately 0.7% of offspring produced from those 240

135
pairs would need to successively reproduce to sustain the population (Turner et al. 2002).
Depending on the life-table used, the effective size of striped marlin populations ranged
between 55 and 75, or 16 and 45 individuals. While these numbers are surprisingly
small, they are comparable to those presented by Turner et al. (2002). However, the
precision of these estimates was poor, with all but the southwestern group having upper
confidence limits of infinity. In order to increase the precision of these estimates, more
samples or longer sampling periods are necessary (Waples and Yokota 2007).

Size and age distributions

Because length/age relationships were based on specific geographic samples of striped
marlin, sizes and ages are only comparable within the groups used in the genetic analysis.
Japan and Southern California exhibited similar ranges in both length and age
distributions and were found to not be significantly different. This bolstered the decision
to group them together in this analysis and supported the lack of geographic genetic
structure detected between these locations in Chapter 2.

The distribution of Hawaiian sample lengths was bimodal with a noticeable group of
smaller sized fish. It was previously reported that small striped marlin moved through this
location during different times of the year (Squire and Suzuki 1990). For this reason,
lengths and ages of striped marlin from Hawaii were examined by quarter. Significant
variation was detected for both length and age, and it was evident that small fish were

136
present in large numbers during the 1
st
quarter of the year. Given the genetic
differentiation determined between immature and mature Hawaiian fish in the spatial
analysis (Chapter 2), this may reflect the presumably immature Japanese fish moving
through this region. Matsumoto and Kazama (1974) believed that juvenile striped marlin
moved into Hawaii to feed for several seasons before maturing and moving on to
spawning grounds elsewhere. The small fish were also reflected in the age distribution of
Hawaiian fish by quarter, although the absolute ages of the fish may not be accurate due
to ageing techniques. Unfortunately, the large number of fish in the 13+ age group made
it more difficult to see patterns among the other ages. The improper fit of the ageing
technique for Hawaiian fish is evident by looking at the age distributions. Fish in the 13+
age group could not be used in the temporal analyses; however these individuals
represented a large fraction (~41%) of the total samples.

In the eastern Pacific, size and age distributions were significantly different between
Mexico and Central America. While Mexican samples were unimodally distributed with
few smaller fish, Central America contained a group of smaller samples in addition to
larger individuals. As mentioned above, these discrepancies may be the result of different
sample sources, because Central American samples were collected through commercial
fisheries, while Mexican samples were primarily collected in recreational fisheries. The
recreational fisheries result in more size selective samples than the commercial fisheries
do. As a result, the commercial fishery may be sampling immature Central American
individuals from offshore spawning locations near the Galapagos Islands (Squire and

137
Suzuki 1990, Kume and Joseph 1969). Although, young fish are also likely present in
Mexico given the spawning locations within that region (Armas et al. 1999, Squire 1987),
they are not targeted by the recreational fisheries.

Finally, in the southwestern Pacific, Australian and New Zealand distributions appeared
similar but both length and age were significantly different. It appeared as though the
size and age distributions were slightly larger in New Zealand compared to Australia. In
comparison with the other groups, the ages of fish in this region appear to be relatively
young. The length/age model used for these locations is responsible for this difference
(Kopf et al. 2005), as fish in this region are actually larger than in other areas of the
Pacific.

Conclusion

This study represents the first attempt at using the temporal method to examine genetic
variation among cohorts of striped marlin. Improvements in ageing techniques and life-
history parameters, in addition to larger sample sizes, would greatly benefit this type of
analysis. It has been noted that even moderate differences in life-history parameters can
greatly influence temporal variance and effective population size (Gaggiotti and Vetter
1999). Despite the limitations in this study, it was important to attempt to examine
temporal variance in striped marlin in order to interpret the spatial structure data (Laikre
et al. 1998). In this study, although genetic drift among cohorts was evident, it was not

138
strong enough to alter or eliminate the spatial subdivision detected for this species. After
accounting for temporal variance, far more confidence can be placed in the spatial
patterns resolved for striped marlin and in the appropriateness of using genetic data in
management strategies (Bernal-Ramirez et al. 2003, Waples and Teel 1990).

139
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145
CHAPTER 4

Review of spatial heterogeneity among highly migratory fish in the Pacific Ocean

Abstract

For the management of fisheries, one of the key pieces of information is how a species is
distributed throughout its range and how connected individuals are throughout that
distribution. This is difficult to estimate in pelagic fish, but genetic analysis provides one
way of addressing these questions. Unfortunately, the cost and time needed for these
types of studies limits their use in all species. This review compares the population
structure and life-history characteristics of 10 pelagic fish in the Pacific, in an attempt to
determine if any factors are useful in predicting patterns of population heterogeneity.
While no single parameter was identified; the number, size, specificity and duration of
spawning events likely play an important role in the heterogeneity pattern of a species
throughout its distribution.

Introduction

To successfully manage a commercial or recreational fishery, it is very important to
determine how a species is distributed throughout its range and how individuals
throughout that range interact (Ward 2000). While this information may be difficult to
obtain for pelagic species, genetics provides a way of resolving many questions regarding

146
stock subdivision (Ward 2000). However, determining population differentiation can be
complicated in species with very low levels of subdivision, such as migratory pelagic
fish. Additionally, genetic studies often take a long time to complete due to the necessity
of large sample sizes and often the need to develop molecular markers for use in a
particular organism. This type of research can also be prohibitively expensive to conduct
for all economically important species.

While it can be risky to extrapolate population differentiation from one species to another
(Ward 2000, Graves 1998), the financial and time restraints on stock assessments warrant
at least preliminary comparisons among similar species. This chapter examines the
heterogeneity among a group of pelagic fish in the Pacific Ocean in order to determine if
there are any key factors or patterns that can be used to help predict subdivision patterns
in species where genetic information is unavailable. Finding those factors may obviate
the need for large scale genetic studies, although smaller pilot studies may still be
necessary. Based on the results described in the preceding chapters, another goal in this
review is to determine where striped marlin fit into the spectrum of spatial differentiation
among pelagic fish in the Pacific.

A total of ten species, consisting of both billfish and tunas, were chosen for this
comparison. The species reviewed in this chapter are: striped marlin (Tetrapturus audax),
black marlin (Makaira indica), blue marlin (Makaira mazura), sailfish (Istiophorus
platypterus), swordfish (Xiphias gladius), albacore tuna (Thunnus alalunga), bigeye tuna

147
(Thunnus obesus), yellowfin tuna (Thunnus albacares), northern bluefin tuna (Thunnus
thynnus orientalis) and southern bluefin tuna (Thunnus maccoyii). These species share a
large number of life-history characteristics: they are all pelagic fish with the capacity to
make long distance movements throughout their range in the Pacific; they are broadcast
spawners that begin their lives as planktonic larvae; and they are relatively long-lived
fish, with spawning groups likely consisting of mixed age-classes. Another commonality
among these pelagic species is that fishing pressure, either by commercial and/or
recreational fisheries, is exerted on all of these fish to varying degrees. Importantly, all
of the fish chosen for this comparison have at least part of their distribution in the Pacific
Ocean.

Despite the similarities among these billfish and tunas, studies have shown that they vary
in their levels of population differentiation. This review will examine the distribution,
dispersal capabilities, spawning patterns and stock structure for each of these species
within the Pacific Ocean. Through the comparisons of these fish, characteristics that are
linked to overall patterns of spatial genetic heterogeneity may be identified. Those
characteristics could then be used to predict structure in less well-studied pelagic species.

148
Striped Marlin

Distribution

Striped marlin, Tetrapturus audax, occupies tropical, subtropical and temperate waters in
the Pacific and Indian Oceans. Within the Pacific, this fish ranges between 45° N and 45°
S (Nakamura 1985), and prefers more temperate water temperatures compared to most
other billfish species (Nakamura 1985). Longline data indicate that this species is caught
between 18 and 27 °C (Bromhead et al. 2004), but prefer water between 20- 25 °C
(Domeier and Dewar 2003, Howard and Ueyanagi 1965). For striped marlin, the
distribution of higher abundance, based on catch, creates a horseshoe-shaped pattern
across the Pacific with the base of the horseshoe in the eastern Pacific (Squire 1974).
Areas of high striped marlin abundance are in the eastern and central Pacific and to a
lesser extent the northwest and southwest Pacific (Figure 1a); however there is a low
catch rate along the equator (Ueyanagi and Wares 1975).

Dispersal Capability

Tagging studies indicate striped marlin are capable of moving throughout their range
(Squire 1974), however, the average dispersal distance varies by location (Domeier
2006). Seasonal shifts occur in all of the locations, based on changing environmental
conditions or spawning activity (Bromhead et al. 2004, Squire and Suzuki 1990, Squire

149
Figure 4-1: Pacific distribution of pelagic fish included in the review: striped marlin (a),
black marlin (b), blue marlin (c), sailfish (d), swordfish (e), albacore tuna (f), bigeye tuna
(g), yellowfin tuna (h), northern bluefin tuna (i), southern bluefin tuna (j). Dashed lines
indicate the overall distribution of the species, shaded regions represent areas of high
abundance based on reported catch levels, outlined and darkly shaded areas represent
known spawning locations within the Pacific (map images from Google Earth).

Figure 4-1a: Striped marlin, Tetrapturus audax, distribution in the Pacific Ocean.
Distribution of striped marlin, Tetrapturus audax, in the Pacific Ocean.

Figure 4-1b: Black marlin, Makaira indica, distribution in the Pacific Ocean.
Distribution of black marlin, Makaira indica, in the Pacific Ocean.

150
Figure 4-1, Cont.:
Figure 4-1c: Blue marlin, Makaira mazura, distribution in the Pacific Ocean.
Distribution of blue marlin, Makaira mazara, in the Pacific Ocean.

Figure 4-1d: Sailfish, Istiophorus platypterus, distribution in the Pacific Ocean.
Distribution of sailfish, Istiophorus platypterus, in the Pacific Ocean.

151
Figure 4-1, Cont.:
Figure 4-1e: Swordfish, Xiphias gladius, distribution in the Pacific Ocean.
Distribution of swordfish, Xiphias gladius, in the Pacific Ocean.

Figure 4-1f: Albacore tuna, Thunnus alalunga, distribution in the Pacific Ocean.
Distribution of albacore tuna, Thunnus alalunga, in the Pacific Ocean.

152
Figure 4-1, Cont.:
Figure 4-1g: Bigeye tuna, Thunnus obesus, distribution in the Pacific Ocean.
Distribution of bigeye tuna, Thunnus obesus, in the Pacific Ocean.

Figure 4-1h: Yellowfin tuna, Thunnus albacares, distribution in the Pacific Ocean.
Distribution of yellowfin tuna, Thunnus albacares, in the Pacific Ocean.

153
Figure 4-1, Cont.:
Figure 4-1i: Northern bluefin tuna, Thunnus thynnus orientalis, distribution in the Pacific
Ocean.
Distribution of northern bluefin tuna, Thunnus thynnus orientalis, in the Pacific Ocean.

Figure 4-1j: Southern bluefin tuna, Thunnus maccoyii, distribution in the Pacific Ocean.
Distribution of southern bluefin tuna, Thunnus maccoyii, in the Pacific Ocean.

154
1974). Striped marlin, in the northernmost and southernmost extensions of their range,
are only seasonal, as the waters become too cool for this species during winter months
(Squire 1974). Fish in New Zealand and California, both seasonal locations, have been
shown to move away from those locations when the water temperature cools, with fish
from New Zealand moving a greater distance on average (Domeier 2006, Kopf et al.
2005). In contrast, fish in Mexico show much finer scale movements during the course
of the year (Squire 1987).

Spawning

Spawning occurs in five locations around the Pacific: northwest, southwest, northeast,
southeast and central Pacific. In the northwest Pacific spawning occurs between 10° and
30° N and between 130° and 170° E from May through June (Nishikawa et al. 1978). In
the southwest Pacific spawning occurs in the Coral Sea from 10° to 30° S and 145° to
180° E from November to December (Nakamura 1983). Armas (1999) found that striped
marlin in the northeast Pacific spawn between 18° and 28° N and 104° to 116° W from
June through November. In the southeast Pacific, they spawn from 15° to 20° S and 140°
to 145° W from January to March (Nishikawa et al. 1978). Most recently, striped marlin
larvae were found just off the coast of Hawaii (Hyde et al. 2006). Generally, spawning
occurs only in water temperatures of at least 24 °C (Ueyanagi and Wares 1975).

155
Stock Structure

Although several stock models have been proposed, evidence based on genetic data
supports regional stocks of striped marlin in the Pacific (Dissertation Chapter 2,
McDowell and Graves 2008, Graves and McDowell 1994) (Table 4-1a). A mitochondrial
DNA (mtDNA) restriction fragment length polymorphism (RFLP) study, using samples
collected from four locations, found significant spatial structure in the Pacific with only
moderate sample sizes (Graves and McDowell 1994). A more recent study by McDowell
and Graves (2008) used mtDNA sequences and five microsatellites to examine genetic
subdivision among seven collection locations. Although, mtDNA revealed no significant
pairwise Phi
ST
s, AMOVA and microsatellite analyses indicated four discrete populations:
1. Southwest Pacific (Australia) 2. Ecuador 3. Northern Pacific (Japan, Hawaii, Taiwan,
California) 4. Mexico. In Chapter 2 of this dissertation, a more extensive survey of
spatial and temporal structure of seven locations in the Pacific was reported. An analysis
of over 1000 samples was conducted using approximately 1100 base pairs of mtDNA
sequence and 12 microsatellites. Significant heterogeneity was detected using both
classes of markers, which also indicated discrete populations: 1. Southwest Pacific
(Australia and New Zealand) 2. Eastern Pacific (Mexico, Central America (including
sequences from Ecuador) 3. Japan, immature fish from Hawaii and Southern California,
and 4) mature Hawaiian fish. The North Pacific grouping is complicated; based on
mature individuals in the north Pacific, Japan and Hawaii comprise distinct groups.
However, movement of juveniles from Japan into Hawaii, presumably for feeding
opportunities, links these two locations.

156
Table 4-1: List of genetic studies for the 10 species with reference, molecular marker type, sample size, study location, heterozygosity
(H) or haplotype diversity (h), measure of genetic differentiation, and conclusion of the listed analysis.
Author Markers Sample Size
Study
Location
Heterozygote (H) or
haplotype diversity (h)
Measure of genetic
differentiation Conclusion
Striped marlin (Tetrapturus audax)
Graves and
McDowell 1994 RFLPs
n= 166
( 36 - 47) Pacific (4)
h = 0.820
( 0.690 - 0.840)
significant heterogeneity
among locations in the Pacific
McDowell and
Graves 2008 MSATs
n = 373
(24 - 97) Pacific (7) H = 0.840 - 0.980 F
ST
= 0.013
significant heterogeneity
among locations in the Pacific
mtDNA c.r. seq. n = 108 Pacific (7) h = 0.980 - 1.000 Phi
ST
= -0.010 no significant heterogeneity
Purcell (Chapter
2 of dissertation) MSATs
n = 1199
(45 - 312) Pacific (7) H = 0.650 - 0.730 F
ST
= 0.015
significant heterogeneity
among locations in the Pacific
mtDNA c.r. seq. n = 451 Pacific (7) h = 0.980 - 0.990 K
ST
= 0.07
significant heterogeneity
among locations in the Pacific
Black Marlin (Makaira indica)
Graves and
McDowell 2003 mtDNA c.r. seq. n = 286 Pacific no significant heterogeneity
MSATs n = 300 Pacific no significant heterogeneity

157
Table 4-1 Cont.:
Author Markers Sample Size
Study
Location
Heterozygote (H) or
haplotype diversity (h)
Measure of genetic
differentiation Conclusion
Blue Marlin (Makaira nigricans)
Finnerty and
Block 1992
mtDNA cyt B
seq.
n = 26 (12 -
14)
Pacific/
Atlantic
h = 0.680 [0.600(P) -
0.740(A)]
significant heterogeneity between
Pacific/Atlantic
Graves and
McDowell
1995 RFLPs
n = 114 (56
- 58)
Pacific/
Atlantic
h = 0.690 (P) - 0.960
(A)
significant heterogeneity between
Pacific/Atlantic
Buonaccorsi et
al. 1999 Allozymes n = 107
Pacific (1)/
Atlantic (3) H = 0.300
F
ST
(b/w oceans) =
0.08 ( 0.00 - 0.15)
significant heterogeneity between
Pacific/Atlantic, no significant
heterogeneity within oceans
scnDNA loci n = 457
Pacific (8)/
Atlantic (7) H = 0.370
F
ST
(b/w oceans) =
0.09 (0.00 - 0.12)
significant heterogeneity between
Pacific/Atlantic, no significant
heterogeneity within oceans
RFLPs n = 114
Pacific (2)/
Atlantic (2) h = 0.850
F
ST
(b/w oceans) =
0.39
significant heterogeneity between
Pacific/Atlantic, no significant
heterogeneity within oceans
Buonaccorsi et
al. 2001 MSATs
n = 220(A) -
176(P)
Pacific/
Atlantic
H = 0.935 [0.929(A) -
0.930(P)]
F
ST
(b/w oceans) =
0.03, w/in oceans =
0.002
significant heterogeneity between
Pacific/Atlantic, no significant
heterogeneity within oceans
RFLPs
n = 195(A) -
163(P)
Pacific/
Atlantic
h = 0.087 [0.93(A) -
0.76(P)]
Phi
ST
(b/w oceans)
= 0.22
Allozymes
n = 195(A) -
163(P)
Pacific/
Atlantic
H = 0.024 (A) -
0.028(P)
scnDNA loci
n = 195(A) -
163(P)
Pacific/
Atlantic
H = 0.227(A) -
0.268(P)
F
ST
(w/in oceans)
= -0.006
significant heterogeneity between
Pacific/Atlantic, no significant
heterogeneity within oceans

158
Table 4-1 Cont.:
Author Markers Sample Size
Study
Location
Heterozygote (H) or
haplotype diversity
(h)
Measure of genetic
differentiation Conclusion
Sailfish (Istiophorus platypterus)
Graves
and
McDowell
1995 RFLPs
n = 36(A) -
33(P)
Indo-
Pacific/
Atlantic
h = 0.710(A) -
0.590(P)
significant heterogeneity between
oceans, and within Indo-Pacific,
but not within the Atlantic
Morgan
1992 (as
reported
in Graves
and
McDowell
1995)
Electrophoretic
enzymes
n = 28(A) -
38(P)
Pacific/
Atlantic H = 0.010 F
ST
(b/w oceans) = 0.023
significant heterogeneity between
Pacific/Atlantic

159
Table 4-1 Cont.:
Author Markers
Sample
Size Study Location
Heterozygote
(H) or haplotype
diversity (h)
Measure of genetic
differentiation Conclusion
Swordfish (Xiphias gladius)
Chow and
Takeyama 2000 RFLPs
n = 7 -
101
Pacific/ Indian/
Atlantic/
Mediterranean F
ST
= 0.369
Significant heterogeneity among oceans,
not within oceans
Alvarado-Bremer
et al. 1996
mtDNA
c.r. seq. n = 8 - 39
Pacific/ Atlantic/
Mediterranean h = .980
RFLPs n = 8 - 39
Pacific/ Atlantic/
Mediterranean h = 0.940 - 1.000 Significant heterogeneity among oceans
Rosel and Block
1996
mtDNA
c.r. seq.
n = 159
(20 - 105)
Pacific/ Indian/
Mediterranean h = 0.994
Phi
ST
b/w oceans=
0.062, within
oceans = 0.006
Significant heterogeneity among oceans,
not within oceans
Grijalva-Chon et
al. 1994 RFLPs
n = 42 -
59 Pacific (North) h = 0.637 G
ST
= 0.046
No significant heterogeneity within north
Pacific
Reeb et al. 2000
mtDNA
c.r. seq.
n = 281 (n
= 36 -
100) Pacific h = 0.994
F
ST
= 0.009
(-0.006 - 0.032)
Significant heterogeneity between
northern and southern swordfish in
western Pacific; although not significant
after Bonferroni, U-shaped pattern of
connectivity in Pacific.
Ward et al. 2001
mtDNA
c.r. seq.
n = 38 -
130 Pacific/ Indian h = 1.000 F
ST
= 0.0011 No significant heterogeneity

MSATs
(10)
n = 39 -
114 Pacific/ Indian
H = 0.487 -
0.949 F
ST
= 0.0046
Significant heterogeneity in one locus
between Pacific and Indian Oceans, but
not in 9 other loci.
Lu et al. 2006
mtDNA
c.r. seq.
n = 18 -
21
Pacific (Central and
Western) h = 0.957-0.987 F
ST
= 0.03940
No significant structure between Pacific
samples.

160
Table 4-1 Cont.:
Author Markers
Sample
Size
Study
Location
Heterozygote (H) or
haplotype diversity (h)
Measure of genetic
differentiation Conclusion
Albacore tuna (Thunnus alalunga)
Chow and
Ushiama
1995 RFLPs n = 620
Pacific(10)/
Atlantic(2)
h= 0.590 - 0.690 (P),
0.220 - 0.430 (A)
Significant heterogeneity between Pacific
and Atlantic, but not within oceans
Vinas et al.
2004
mtDNA
c.r. seq.
n = 30 -
54
Pacific/
Atlantic/
Mediterranean h = 0.961 - 1.000 Phi
ST
= 0.041
Significant heterogeneity between
Mediterranean vs. Pacific and Atlantic, but
not Pacific vs. Atlantic.
Takagi et al.
2001 MSATs
n = 32 -
48
Pacific/
Atlantic H = 0.391 - 1.000
F
ST
= 0.009
(0.018 - 0.070)
Significant heterogeneity between Pacific
and Atlantic
Graves and
Dizon 1989
Restriction
enzymes
n = 11 -
12
Pacific/
Atlantic
Could not distinguish between Atlantic and
Pacific samples
Wu et al.
2008
mtDNA
c.r. seq. n = 175
Pacific
(North) h = 0.991 - 1.000
Phi
ST
= -0.001
(-0.013 - 0.004) No significant heterogeneity
Elliott and
Ward 1995 Allozymes ave. n = 7 Pacific H = 0.068

161
Table 4-1 Cont.:
Author Markers
Sample
Size
Study
Location
Heterozygote (H)
or haplotype
diversity (h)
Measure of genetic
differentiation Conclusion
Bigeye tuna (Thunnus obesus)
Grewe and Hampton
1998 MSAT
n = 96 -
105 Pacific H = 0.163 - 0.958
One locus significant (E-W
Pacific), but overall no significant
heterogeneity
Elliott and Ward
1995 Allozymes
ave. n =
10
Pacific
(C/W) H = 0.070
Chiang et al. 2006
mtDNA
c.r. seq.
n = 100
(16 - 50)
Pacific
(W) h = 0.998 - 1.000 F
ST
= 0.002 - 0.007 no significant heterogeneity
Yellowfin tuna (Thunnus albacares)
Scoles and Graves
1993 RFLPs n = 120
Pacific(5)/
Atlantic(1) h = 0.840
F
ST
= -0.015, G
ST
= 0.011 -
0.025 no significant heterogeneity
Ward et al. 1994 Allozymes
n = 34 -
97 Pacific H = 0.360
F
ST
= 0.027, G
ST
= 0.005-
0.013 (ns loci), G
ST
=
0.106( significant locus)
Significant heterogeneity in 1
locus (E vs. C and W Pacific), not
in other loci
RFLPs h = 0.678
F
ST
= 0.012, G
ST
= 0.019
(0.005 - 0.106)
Significant heterogeneity in 1
allozyme locus(E-W Pacific), but
not in others or in RFLPs
Diaz-Jaimes and
Uribe-Alcocer 2003 Allozymes n = 14-51 Pacific (E) H = 0.052 Θ
ST
= 0.048 no significant heterogeneity
RAPDs H = 0.430 Θ
ST
= 0.030
Ely et al. 2005
mtDNA
c.r. seq. n = 41 Pacific (E) h = 0.997
Low differentiation b/w Pacific
and Atlantic, no measurement
w/in Pacific.
Appleyard et al. 2001 MSATs
n = 34 -
560
Pacific
(Western) H = 0.593 F
ST
= 0.002
Significant heterogeneity (driven
by 1 locus), conclusion of very
limited structure.

162
Table 4-1 Cont.:
Author Markers Sample Size Study Location
Heterozygote (H)
or haplotype
diversity (h)
Measure of genetic
differentiation Conclusion
Northern Bluefin Tuna (Thunnus thynnus orientalis)
Ward et al.1995 RFLPs Pacific/ Indian h = 0.902
Allozymes H = 0.046
Elliott and Ward
1997 Allozymes ave. n = 28 Pacific H = 0.049
Southern Bluefin Tuna (Thunnus maccoyii)
Ward et al.1995 RFLPs Pacific/ Indian h = 0.715
Allozymes H = 0.069
Elliott and Ward
1998 Allozymes ave. n = 150 Pacific/ Indian H = 0.065
Grewe et al.
1997 Allozymes n = 90 - 230 Pacific/ Indian H = 0.394 - 0.406
G
ST
= 0.005
(1 locus), 0.001-
0.002 (all other loci)
Aside from 1 locus, no
significant
heterogeneity within
Pacific
RFLPs h = 0.399 - 0.532 G
ST
= 0.006

163
Black Marlin

Distribution

In the Pacific, black marlin inhabit tropical and subtropical areas between 35° N and 45°
S in the western Pacific and between 30° N and 35° S in the eastern Pacific (Nakamura
1985). Compared to other billfish, black marlin are more tropical and less frequently
range into temperate waters (Nakamura 1985); they are thought to have some of the
highest water temperature preferences among billfish in the Pacific (Boyce et al. 2008).
Black marlin have been reported in the eastern Pacific from Mexico to Peru (Talbot and
Wares 1975), and more rarely in the Gulf of California (Howard and Ueyangi 1965),
however their distribution in the eastern Pacific is more restricted than in the western
Pacific (Shomura 1980) (Figure 1b). Black marlin are targeted by recreational fisheries
throughout many of the Pacific Islands, including Hawaii (Whitelaw 2003). Movements
of this marlin into the northwest Pacific are thought to be associated with feeding
opportunities and are not connected with spawning behavior (Shimose et al. 2008).
Compared to many of the other billfish species, black marlin are more closely associated
with landmasses. They even show sized-based spatial structuring near Australia, with
adult black marlin occurring outside the reef (Lowry and Murphy 2003) and juveniles in
nearshore coastal areas (Speare 2003).

164
Dispersal Capability

Black marlin have made some of the longest movements recorded for billfish, with trans-
Pacific movements recorded from individuals tagged in Australia that were later
recaptured near Hawaii and off of South America (Ortiz et al. 2003). However, the
majority of black marlin are recaptured within a shorter distance, and it is generally
thought that this species shows annual site fidelity to the north east coast of Australia
(Ortiz et al. 2003, Pepperell 1990).

This species displays some seasonal shifts in their distribution. Black marlin along the
eastern Australian coast often move north into Micronesia and Indonesia and then south
again as water temperatures warm in the austral summer (Ortiz et al. 2003). The seasonal
migrations are replicated with black marlin in the East China Sea as they move northward
during spring and summer, and south again during autumn and fall. A similar pattern
occurs in the Sea of Japan where in the summer black marlin move with the warm
Tsushima Current and then return south again later in the fall (Nakamura 1985).

Spawning

Spawning occurs in the northwestern part of the Coral Sea between October and
November in water temperatures of at least 27 °C (Gunn et al. 2003, Leis et al. 1987,
Nakamura 1985). Tagging patterns indicate black marlin exhibit spawning site fidelity as
they return to the Coral Sea, which is the only known spawning location for this

165
species (Gunn et al. 2003), although spawning has been suspected in the South China Sea
(Nakamura 1985). Within the Coral Sea, the spawning location may be very specific as
larvae are found in a narrow (.25 nautical mile) band off the reef crest (Leis et al. 1987).

Stock Structure

Although there has not been a great deal of focus on the genetic spatial patterns of black
marlin, it is thought that only one Pacific-wide stock exists. A study by Graves and
McDowell (2003) examined genetic subdivision in this species using seven ascnDNA
loci with 43 restriction enzymes, restriction fragment length polymorphisms (RFLPs) in a
1200 base pair segment of the mitochondrial DNA (mtDNA), and five microsatellites
(Table 4-1a). Across all markers black marlin did not show any significant heterogeneity
among locations or among years in locations, when temporal information was available.
While sample sizes were moderate, other studies with similar numbers of samples (or
fewer in some locations) have detected significant structure (Reeb et al. 2000, Graves and
McDowell 1994). However, it is possible that sample coverage may not have been
adequate to conclusively determine stock subdivisions (Ortiz et al. 2003, Skillman 1990).
Shomura (1980) thought there may be more than one stock in the Pacific, perhaps with a
one in the eastern Pacific and two in the western Pacific (northern and southern),
however no data currently support this theory. Shomura (1980) also thought that mixing
occurs between the western Pacific and Indian Oceans, which could be likely with black
marlin moving north of Australia, and staying within warmer water temperatures.

166
Blue Marlin

Distribution

Blue marlin are the most prevalent marlin caught by commercial fleets in the Pacific, due
primarily to their affinity for the open ocean (Whitelaw 2003). The Indo-Pacific blue
marlin (Makaira mazura) is the most tropical of the billfish species in the Pacific Ocean
(Nakamura 1985), preferring water temperatures of 24 °C or higher (Hinton 2001). The
general range for this species is the tropical and subtropical waters between 45° N and
35° S in the western Pacific and 35° N and 25° S in the eastern Pacific (Nakamura 1985)
(Figure 1c). As mentioned above, this species is more oceanic than black marlin and
sailfish, and usually does not come close to land masses except where shelf drop-offs are
found (Lowry and Murphy 2003, Nakamura 1985). For example, the deep water drop off
near Cabo San Lucas, Mexico allows recreational fishing vessels to target blue marlin in
that region (Cardenas et al. 1999). Catches of blue marlin are also high in areas where
there are strong fronts, or where there is mixing of currents or thermohaline gradients
(Seki et al. 2002). Physical parameters such as sea surface temperature and other oceanic
features play an important role in the distribution of blue marlin (Su et al. 2008), and this
is apparent when looking at how this species’ shifts its distribution during El Nino
conditions (Worm et al. 2005).

167
Dispersal Capability

Blue marlin are capable of making both trans-oceanic and trans-equatorial movements,
and tagging in this species did not reveal regular annual movements or site fidelity (Ortiz
et al. 2003). Despite the capacity for long dispersals, many tag recoveries were made in
the general area of the initial capture (Ortiz et al. 2003). However, as of 2001, Hinton
thought that tagging coverage had been insufficient to accurately describe their
movement patterns within the Pacific. Generally, blue marlin move north and south of
their core distribution along the equator (Nakamura 1985), with shifts in latitude due to
either spawning or feeding opportunities (Shimose et al. 2009). This species also exhibits
mild vertical migrations with the fish moving closer to the surface at night (Holland et al.
1990).

Spawning

Larvae for this species have been found in the western and central Pacific (Nakamura
1985) and summer spawning was reported for this species in Hawaii (Hopper 1990). Near
Australia, larvae have been found from mid-November, January, February, March and
April in areas further offshore than where black marlin spawn (Leis et al. 1987). Larvae
are generally only found in water temperatures of at least 24 °C (Matsumoto and Kazama
1972). Spawning occurs year-round in warmer equatorial waters, but the distribution of
larvae can shift with changes in water temperature at higher latitudes (Matsumoto and

168
Kazama 1972). Spawning has been reported as far north as Yonaguni Island; however
this may represent the northern edge of the spawning range for blue marlin in the western
Pacific (Shimose et al. 2009).

Stock Structure

While most genetic studies in this species have focused on finding subdivision between
Atlantic and Pacific blue marlin (Buonaccorsi et al. 2001, Buonaccorsi et al. 1999,
Graves and McDowell 1995, Finnerty and Block 1992) (Table 4-1b), a couple of these
studies also investigated spatial heterogeneity within the Pacific. Using 44 allozyme loci
and four scnDNA loci, Buonaccorsi et al. (1999) examined spatial heterogeneity among
four locations in the Pacific. Although sample sizes were moderate, no significant
heterogeneity or temporal variation was detected. In 2001, Buonaccorsi et al. re-
examined genetic structure in blue marlin with additional samples and new markers. Five
microsatellites and mtDNA RFLPs were used to examine blue marlin samples collected
from Mexico and Hawaii. Again, no spatial heterogeneity was detected, although samples
were collected from only two locations in the Pacific, and that may not have been
representative of the spatial structure for the whole ocean basin. Interestingly, small but
significant differences among years in locations were detected with mtDNA.
While better sample coverage may be needed for more conclusive spatial subdivision
studies, fishery data indicate that there is a single stock of blue marlin in the Pacific
Ocean (Hinton 2001). Shomura (1980) also supports one equatorially-centered stock,
based on evidence for a single large spawning area in the western Pacific.

169
Sailfish

Distribution

The Pacific distribution of the Indo-Pacific sailfish, Istiophorus platypterus, ranges from
45° N to 35° S in the western Pacific and from 35° N to 35° S in the eastern Pacific
(Figure 1d). Compared to more oceanic billfish such as blue marlin, this species is more
often found close to shore (Nakamura 1985). Generally, this species is strongly
influenced by sea surface temperatures and is less tolerant to colder water temperatures
than other species of billfish (Boyce et al. 2008). In the eastern Pacific, catches of
sailfish are common between Mexico and Ecuador (Talbot and Wares 1975). In the
western Pacific, both small and large sailfish are found near the Sea of Japan (Nakamura
1985). Sailfish are also caught throughout many of the islands in the Pacific (Whitelaw
2003), and near the Australian coast (Ward and Robbins 2001).

Dispersal Capabilities

Based on tagging data, sailfish in the Atlantic show some degree of seasonal movements
(Ortiz et al. 2003), so this pattern may also exist for sailfish in the Pacific. In the western
Pacific, seasonal movements are associated with the Kuroshiro current. In the eastern
Pacific the north-south movements are connected to shifts in the 28 °C isotherm
(Nakamura 1985). Within the eastern Pacific, sailfish shift away from the mouth of the

170
Gulf of California during winter and early spring, but by late spring and early summer
they are very common off the coast of Mazatlan (Squire 1987). Generally, global tagging
efforts for sailfish indicate relatively restricted movements away from the release location
(Ortiz et al. 2003), even after long periods of liberty (Squire 1987). In the Squire (1987)
study, the longest track was only 250 nautical miles away from the release point after 457
days at liberty. Additionally, tagging studies have not found trans-Pacific or trans-
equatorial movements in the Pacific (Ortiz et al. 2003).

Spawning

For Pacific sailfish, spawning occurs close to land masses (Leis et al. 1987) and can
occur more than once a year (Hernandez-Herrera et al. 2000). It takes place throughout
the year in tropical and subtropical areas (Nakamura 1985). Heightened spawning activity
occurs in the respective hemispheric summers (Nakamura 1985). In the eastern Pacific,
between Ecuador and the Gulf of California spawning occurs in the summer and fall in
water temperatures of at least 27-30 °C (Hernandez-Herrera et al. 2000), and in the
southern hemisphere, larvae are found in the Coral Sea between January and March (Leis
et al. 1987). According to Matsumoto and Kazama (1974), sailfish larvae were not found
in a survey of the central Pacific Ocean.

171
Stock Structure

While not much information is available for this species, the possibility of more than one
stock in the Pacific is reasonable given their close association for land masses (Gentner
2007) and the general trend of relatively restricted movements from tagging data (Ortiz et
al. 2003). Shomura (1980) found evidence for two stocks based on the distribution of
catch rates, one each in the eastern and western Pacific. Similar to blue marlin, a great
deal of the genetic work done on sailfish has focused on detection of subdivision between
Atlantic and Pacific populations (Graves and McDowell 2003, 1995) Although only two
locations within the Pacific were sampled, Graves and McDowell (1995) found
significant heterogeneity within the Pacific sailfish despite small sample sizes.
Differences in the distribution of haplotypes between Mexico and Australia were detected
using RFLPs (Graves and McDowell 1995) (Table 4-1c). More recently McDowell
(unpublished), as reported in Graves and McDowell (2003), used 871 bp sequences of
the mtDNA control region and three microsatellites to examine within ocean
heterogeneity. Both classes of markers detected significant spatial subdivision within the
Pacific.

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Swordfish

Distribution

Swordfish have a broad distribution in the Pacific Ocean ranging between 50° N and 45°
S. They are also found throughout a wide range of temperatures, from 5 to 27 °C
(Skillman 1998, Nakamura 1985, Grall and De Sylva 1983), with an optimum range
between 18 and 22 °C (Hinton et al. 2005). General areas of high abundance, indicated by
longline catch rates are in the northwest, southwest and eastern Pacific (Kolody et al.
2008) (Figure 1e). More specifically, this species was noted to closely associate with
frontal zones, with greater densities of swordfish in those areas (Seki et al. 2002,
Sakagawa 1989). In the Pacific, five zones were identified as having particular influence
on swordfish distribution : 1. In the northwest Pacific, where the Kuroshiro meets the
Oyashio current, 2. In southeast Australia, where the East Australian current meets
branches of the Southern West Wind Drift current, 3. Off of northern New Zealand,
where the Southern Equatorial current meets the Southern West Wind Drift current, 4. In
the eastern tropical Pacific, where the Equatorial Counter current collides with the Peru
current, and 5. Along Baja California, Mexico and California, where the California
current mixes with the warm coastal water from the south (Sakagawa 1989).

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Dispersal Capability

While swordfish have the ability for long distance migration they may more often exhibit
regional fidelity or seasonal shifts in their distribution (Takahasi et al. 2003). A study by
Holdsworth et al. (2007) found swordfish that had been tagged 10 and 8 years previously,
had only moved 250 nautical miles (nm) and 113 nm away from their original release
points, respectively. And indeed, seasonal movements have been shown through tagging
data where swordfish move between temperate feeding regions and tropical spawning
grounds (Kolody and Davies 2008, Holdsworth et al. 2007). In the eastern Pacific,
seasonal movements have been recorded with swordfish moving north into Southern
California from Baja in the summer/fall, and back again during the late fall/winter
(Hinton et al. 2005). Despite these patterns, swordfish may exhibit only weak regional
fidelity (Kolody et al. 2008) as gene flow has been demonstrated between swordfish in
the Pacific and Indian Oceans (Lu et al. 2006).

Spawning

Swordfish likely spawn more than once during a season, which is generally linked to
suitable sea surface temperatures (Hinton et al. 2005, Young et al. 2003). For swordfish,
high reproductive activity occurs above 24 °C (Young et al. 2003). Swordfish larvae are
found over a wide area in the Pacific, but are particularly associated with areas of
upwelling (Grall and De Sylva 1983). There are several regions where larvae are found

174
in the north Pacific: 1. Northwest Pacific from 15N-35N and 140E to the eastern Asian
coastline; 2. Central west Pacific from 15S-15N and 115E-140E; 3. Northwest Pacific
15N-35N and 140E-170W; 4. Central Pacific from 5S-15N, 140E-170W; and 5.
Southwest Pacific 25S-5S and 140E-170W (Mejuto and Garcia-Cortes 2003, Grall and
De Sylva 1983). Regions where large numbers of larvae are found include the areas of
the subtropical convergence to the equator in the north Pacific, including Hawaii (Hyde et
al. 2006), and in the Coral Sea and near the Fijian Islands in the south Pacific (Grall and
De Sylva 1983). Abundance of larvae in each hemisphere increased during the respective
early-late summer seasons (Grall and De Sylva 1983).

Stock Structure

Similar to other analyses of genetic structure in inter-oceanic pelagic species, much of the
research has focused on determining levels of gene flow between ocean basins
(Alvarado-Bremer et al. 1996, Rosel and Block 1996, Alvarado-Bremer et al. 1995, and
Kotoulas et al. 1995) (Table 4-1d). However, three collection locations in the Pacific, the
eastern, central and western Pacific were included in the study by Rosel and Block (1996)
and this allowed for intra-ocean heterogeneity to be assessed. No significant variation
was detected within the Pacific, but there did appear to be differences in the frequency of
haplotypes across these locations even with a relatively small sample size (n=105).
Grijalva-Chon et al. (1994) used RFLPs to investigate spatial subdivision within the north
Pacific (n=148) with samples in the western, central and eastern Pacific,

175
but again no significant heterogeneity was detected among those locations. More
recently, Reeb et al. (2000) examined 629 bp sequences of the mtDNA control region in
281 swordfish collected from seven locations across the Pacific. Prior to Bonferroni
correction, a “U”-shaped pattern of subdivision was detected, similar to the abundance
distribution of striped marlin, where the north and south western Pacific showed clear
separation with interconnections along the eastern Pacific. However, following
Bonferroni correction, only significant heterogeneity was seen between Japan and
Australia. While the genetic structure may not be resolved, there does appear to be some
level of reproductive isolation. Multi-stock models for swordfish in the Pacific have been
previously suggested (Hinton et al. 2005, Skillman 1998) with support from discrete
spawning areas (Kotoulas et al. 2006) and CPUE data (Sosa-Nishizaki and Shimizu
1991), with as many as four stocks proposed: 1. off Japan in the northwest and central
Pacific, 2. near Baja California, Mexico, 3. western coast of South America, and 4. off
the eastern coast of Australia and north of New Zealand (Sosa-Nishizaki and Shimizu
1991).

Albacore Tuna

Distribution

Albacore tuna inhabit temperate waters primarily between 45° N and 45° S in the Pacific,
Indian and Atlantic Oceans in waters between 14 and 23 °C (Arrizabalaga et al. 2004,
Sund et al. 1981). Within the Pacific, albacore is divided into roughly symmetrical

176
northern and southern populations that rarely mix (Sund et al. 1981). Their distributions
range from Japan to the western coast of North America and from Australia to South
America in the northern and southern hemispheres, respectively (Figure 1f). As
determined by catch rates, this species is not common along the equator and albacore are
believed to not frequently move across it (Sund et al. 1981). The distribution of albacore
in both hemispheres is greatly influenced by oceanographic phenomena, such as El Nino
or La Nina (Langley 2006, Kimura et al. 1997, Laurs 1983), and to oceanic fronts (Laurs
et al. 1994). Laurs et al. (1994) found that albacore prefer blue oceanic water near
temperature or color (phytoplankton) fronts at the edge of coastal regions, a finding also
supported by Zainuddin et al. (2008 and 2006). In the southern hemisphere, their more
southerly range may enable mixing between ocean basins (Foreman 1980).

Dispersal Capability

In both hemispheres, albacore migrate between tropical spawning grounds and
subtropical or temperate feeding areas. In the south Pacific, albacore make seasonal
movements north and south, between tropical spawning areas and subtropical feeding
regions; however their movements have not been as clearly defined compared to the
north stock (Langley 2004, Sund et al. 1981). The movement of mature albacore in the
north Pacific is more extensive as they make a counter-clockwise migration around the
Pacific. In this cycle, fish move from the central and east Pacific to the northwest Pacific,
then move to the south and central Pacific for spawning and finally back to the central

177
and east Pacific (Kimura et al. 1997). Based on seasonal differences in the eastern
Pacific, it has been reported that there are two different groups of albacore within the
North Pacific. In the eastern Pacific their range extends from Baja California, Mexico to
British Columbia, Canada between the spring and summer (Laurs 1983). Both groups
move to the western coast of North America; however one group is located north of 40°
N and the other group south of 40° N along that coastline (Langley 2006, Laurs 1983,
Laurs and Weatherell 1981). Most individuals of the more northern group migrate from
Japan to the west coast of North America, while most individuals in the other group do
not move to the western Pacific (Kimura et al. 1997).

Age or size specific movement has been found in this species. Examples include regions
used almost exclusively by juveniles to feed, such as the area around New Zealand
(Griggs 2008). Another example is that immature albacore in the north Pacific are
believed to have a migratory route separate from the mature fish. This route varies by
year, primarily as a result of the Kuroshiro current. When the Kuroshiro extends far to the
east, immature albacore are found both in the west and east Pacific, but in years where it
does not, they are found only in the west Pacific (Kimura et al. 1997)

Spawning

There are two spawning areas in the Pacific (Sund et al. 1981), which are both in the
tropical central Pacific; however they are temporally separated by hemispheric summer

178
seasons (Sund et al. 1981). In the southern hemisphere, albacore spawn in the tropical
and subtropical areas between 10° and 25° S and west of 140° W during the warmest
summer months, with peaks from November to February (Ramon and Bailey 1996). In
the northern hemisphere, spawning takes place between 10° and 20° N in a region south
of the Subtropical Convergence (Kimura et al. 1997), although that range extends
northward in warmer months as larvae have been found in Hawaii (Paine et al. 2008).

Stock Structure

Once again, several genetic studies were conducted that investigated inter-ocean
population divergence (Table 4-1e), with three showing significant heterogeneity
between oceans (Vinas et al. 2004, Takagi et al. 2001, and Chow and Ushiama 1995),
while one did not (Graves and Dizon 1989) although it had a very small sample size
(n=23). Wu et al. (2008) looked at genetic subdivision within the northern Pacific
(n=175) using mtDNA sequences of the control region, and did not find any significant
differences. Interestingly, significant heterogeneity was detected using four microsatellite
loci on the same samples used in Chow and Ushiama (1995). Population subdivision was
detected between the northwest and southeast Pacific and between the southwest and
southwest Pacific (Takagi et al. 2001). The north-south differences are easier to
understand as hemispheric stocks between the north and south are commonly accepted
(Arrizabalaga et al. 2004, Murray 1994), and are supported by reproductive isolation
between these two groups (Sund et al. 1981), and very little movement of albacore across

179
the equator (Takagi et al. 2001). There are also two stocks proposed within the northern
Pacific that have distinct distributions in the eastern Pacific, although their distributions
during the rest of the year are not as well documented (see above). The distinction of two
separate north Pacific stocks is supported by differences in growths rates between these
two groups, with the more northern, north Pacific stock having lower growth estimates
(Laurs and Weatherell 1981).

Bigeye Tuna

Distribution

Bigeye tuna, Thunnus obesus, supports some of the largest fisheries in the world
(Hampton and Gunn 1998). Their distribution in the Pacific ranges throughout the
tropical and subtropical regions (Hampton et al. 2004) from 40° N to 40° S (Hanamoto
1987). Highest catch rates of bigyeye occurred in the equatorial region, particularly in the
areas east of Japan, north of Hawaii and east of Australia (Figure 1g). Water
temperatures where bigeye are commonly found vary from 17 to 22 °C (Collette and
Nauen 1983), although Hanamoto reports a lower range between 10 and 18 °C
(Hanamoto 1987). However, bigeye commonly dive to depths where temperatures are
much lower than at the sea surface (Brill et al. 2005) and as a result their distribution may
not be as greatly influenced by water temperature as compared to other tuna and billfish
species. Instead, bigeye distribution patterns are more closely tied to changes in the

180
thermocline (Collette and Nauen 1983). Additionally, bigeye tuna, and especially
juvenile bigeye, show an association with fish aggregating devices (FADs) (Schaefer and
Fuller 2005a, Schaefer and Fuller 2005b, Sibert et al. 2000), and with other
oceanographic features like seamounts (Itano and Holland 2000).

Dispersal Capacity

Similar to yellowfin tuna, tagging of bigeye show most fish remain relatively near their
point of release although a few fish make much longer distance movements (Hampton et
al. 2004, Hampton and Gunn 1998). Bigeye tagged in both the western (Hampton et al.
2004), and eastern Pacific (Schaefer and Fuller 2002) revealed that the majority of tagged
fish made shorter range movements. Overall, the results of these tagging studies suggest
weak regional fidelity, and with relatively few individuals migrating between the western
and eastern Pacific (Hampton et al. 2004, Itano and Holland 2000, Hampton and Gunn
1998). A tagging study by Hampton and Gunn (1998) also showed temporal variation in
levels of tag recapture that could indicate yearly or seasonal movements within locations.

Spawning

Bigeye tuna are serial spawners that spawn when water temperatures reach a minimum of
24 °C (McPherson 1991, Kume 1967). This species spawns throughout the year in
equatorial waters between 10° S and 10° N, and spawn at higher latitudes when water

181
temperatures are warmer during hemispheric summers (McPhearson 1991, Sund et al.
1981). Nishikawa et al. (1978) found that larvae were most abundant in the western and
eastern Pacific, but not as common in the central Pacific. In the eastern Pacific, peak
spawning periods occur between April and September in the northern hemisphere, and
January through March in the southern hemisphere (Collette and Nauen, 1983).

Stock Structure

Using a subset of samples from an inter-ocean comparison, Alvarado-Bremer et al.
(1997) found no significant genetic differences between northern (n=17) and southern
(n=125) Pacific samples using RFLPs. In the same year, Grewe and Hampton (1998)
conducted an extensive survey of genetic spatial patterns of bigeye in the Pacific with
nine collection locations and 750 samples using RFLPs of mtDNA and eight
microsatellites. Small genetic differences were detected between the most distant
collection locations, but these were non-significant after Bonferroni correction (Table 4-
1f). Most recently, Chiang et al. (2006) sequenced the 1
st
hypervariable region (HVR-1)
of the mtDNA control region in 100 samples from the S. China Sea, Philippine Sea and
the western Pacific, but no significant spatial genetic variations were detected. Currently
stock assessments are conducted separately for the western/central and eastern Pacific,
and tagging indicates little mixing between these regions.

182
Yellowfin Tuna

Distribution

Yellowfin tuna, Thunnus albacares, occurs globally in tropical and subtropical waters
(Ely et al. 2005), and most abundantly at latitudes between 30°N and 30° S (Sund et al.
1981), and in water temperatures between 20 and 30 °C (Schaefer et al. 2007). This
species is distributed across the Pacific with high concentrations in the eastern and
western Pacific, and it is considered to be one of the more tropical tunas (Hoyle and
Maunder 2006) (Figure 1h). Yellowfin often move parallel to shorelines, and are
frequently associated with floating fish aggregating devices (FADs) (Brill et al. 1999),
and other features such as seamounts, and island reef ledges (Itano and Holland 2000,
Holland et al. 1999). Itano and Holland (2000) found that 96% of the tagged yellowfin
that were recovered were found near the FADS or the other physical features.

Dispersal Capacity

As evidenced by tagging studies, yellowfin can move more than 1000nm (Hoyle and
Maunder 2006). Notably, a couple of yellowfin had recorded movements from Midway
Island to the coast of Japan and from a seamount near Hawaii to Mexico (Itano and
Holland 2000). Despite the long distance movements of a few individuals, most fish are
recaptured relatively near the point of release, typically within hundreds of nautical miles

183
(Hoyle and Maunder 2006), which indicates some level of regional fidelity (Schaefer et
al. 2007, Hampton and Gunn 1998). It also appears that trans-oceanic movements of
yellowfin between the western and eastern side of the Pacific are not common (Hoyle and
Maunder 2006, Maunder and Watters 2001).

Spawning

Yellowfin tuna are thought to spawn whenever conducive conditions exist (Hoyle and
Maunder 2006), which is typically in water warmer than 24 to 26 °C (Itano et al. 2008,
Hampton et al. 2004). In this species, continuous, year-round spawning has been reported
within 10 degrees latitude of the equator (Itano et al. 2008, Lehodey and Leroy 1999),
however peak areas and times varied depending on the year sampled. In the northwest
Coral Sea, peak spawning times are typically from October through March (Hampton and
Gunn 1998). In Hawaii, peak spawning periods were between June and August, although
larvae could be found from April through October (Itano et al. 2008). In the eastern
Pacific, spawning most often occurred between 26 and 30 °C, but was recorded in water
temperatures as low as 22 °C (Schaefer 1998). Larvae in the eastern Pacific are found
near the Gulf of California and along the eastern Pacific central coast (Sund et al. 1981).
Generally, larvae were found in high densities between 130° and 170° E, 180° and 160°
W and east of 110° W (Lehodey and Leroy 1999).

184
Stock Structure

Compared to most other tuna species in the Pacific Ocean, the genetic spatial structure of
yellowfin tuna has been well studied. Scoles and Graves (1993) found no evidence of
population subdivision among samples from five locations in the Pacific (n=20/location)
using mtDNA RFLPs (Table 4-1f). This was followed by a study by Ward et al. (1994)
that looked at 10 locations (n = 462) using allozymes and mtDNA RFLPs. While no
heterogeneity was detected in four out of five allozymes or in mtDNA haplotypes, one
allozyme locus showed significant heterogeneity (Ward et al. 1994). This result was later
reconfirmed, with the same allozyme locus indicating structure between the
western/central Pacific and the eastern Pacific, while none was detected with the other
allozymes or markers. As reported in Grewe and Hampton (1998), a microsatellite
analysis using four loci detected slight subdivision in one locus between the western and
eastern Pacific and between the Philippines/Solomon Islands and Fiji/Coral Sea using a
different locus (Grewe and Ward unpub.) A more extensive microsatellite analysis, using
1,391 yellowfin samples from eight regions in the Pacific revealed a very small but
significant overall F
ST
that was driven primarily by heterogeneity between two locations
in the western Pacific (Appleyard et al. 2001). Generally there was very little spatial
subdivision detected in yellowfin in the Pacific, as the large allozyme-based pattern was
likely due to selection or another force not directly related to spatial structure, and the
microsatellite pattern possibly due to sampling or scoring errors and not correlated with
geographic distance (Appleyard et al. 2001). One stock, in the central west Pacific, is

185
supported by the broad larval distribution (Itano et al. 2008). However, low rates of
mixing between the western and eastern Pacific (Hoyle and Maunder 2006, Maunder and
Watters 2001) might lead to a two stock model. Within the eastern Pacific, yellowfin
appear to be well mixed (Maunder and Watters 2001), although this is in contrast to
Schaefer et al. (2007).

Northern and Southern Bluefin Tuna

Distribution

Bluefin tuna in the Pacific Ocean are actually comprised of two different species, the
southern bluefin tuna, Thunnus maccoyii, and northern bluefin tuna, Thunnus thynnus
orientalis. Southern bluefin tuna, Thunnus maccoyii, are distributed between 30° S to 50°
S (Caton 1991) and are found throughout the western Atlantic, Indian and western Pacific
Oceans (Farley and Davis 1998) (Figure 1j). In the western Pacific, densities are greatest
around southern Australia and New Zealand; however it is thought that New Zealand is
on the eastern edge of their geographical range (Farley et al. 2007). Northern bluefin
tuna, Thunnus thynnus orientalis, are distributed in temperate waters primarily in the
north Pacific (Itoh 2006), and are common in the western Pacific between Taiwan and
Hokkaido, and in the eastern Pacific between Cabo San Lucas and Point Conception,
California (Bayliff 1991) (Figure 1i). Despite its name, northern bluefin tuna is also
found in the southern hemisphere around Australia and New Zealand, but not in great

186
densities (Smith et al. 2001, Bayliff 1991). Generally, 99.9% of northern bluefin tuna are
caught north of 48.3° S and 99.9% of all southern bluefin tuna south of 31.1° S (Murray
2005).

Dispersal Capacity

In northern bluefin tuna, a small percentage of fish from the western Pacific move
eastward to the North American coast, and tagging data show that these fish eventually
migrate back to the western Pacific (Domeier et al. 2005 and Sund et al. 1981). It is
thought that individuals moving to the eastern Pacific are immature bluefin which return
westward as mature fish (Domeier et al. 2005 and Sund et al. 1981). Movement across
the ocean basin from the west Pacific takes place in late spring and summer; immature
fish then spend two to six years in this region before returning to the western Pacific to
spawn (Bayliff 1980). Tagging studies show juveniles make a direct path across the
Pacific, utilizing the Subarctic Frontal Zone, swimming quickly though this water which
is 14 °C colder than their normal range (Kitagawa 2000). In the eastern Pacific, juveniles
show seasonal shifts with north-south movements between central Baja California,
Mexico in winter/spring to California and Oregon during the summer/fall when water
temperatures are higher (Domeier et al. 2005). Movements for southern bluefin tuna in
the Pacific are not as extensive compared to northern bluefin. They move into the
southwest Pacific from the Indian Ocean to feed in the Tasman Sea, primarily in the
winter, but move back to the Indian Ocean to spawn (Patterson et al. 2008).

187
Spawning

There are no spawning locations for the southern bluefin tuna in the Pacific Ocean; their
only known spawning site is in the Indian Ocean between 7° S and 20° S (Farley et al.
2007, Caton 1991). In this location, spawning occurs from September through April
when water temperatures are higher than 24 °C (Farley et al. 2007, Davis and Farley
2001). For northern bluefin tuna, spawning adults have only been recorded in the western
Pacific (Nishikawa et al. 1985), and not in the eastern Pacific at all despite the presence
of bluefin in this location. Within the western Pacific, spawning occurs in three primary
locations beginning in March near Luzon Island and Taiwan, then shifts to the area near
the Nansei Islands in May and finally moves to the Sea of Japan in July (Itoh 2006,
Kitagawa et al. 2000). The shifting spawning locations and times show structuring by
age/size of the bluefin tuna where smaller/younger individuals spawning earlier in the
season and older/larger ones late in the summer (Itoh 2006).

Stock Structure

Despite the common name of bluefin, these two species are not sister species (Alvarado-
Bremer et al. 1997) and as such their stock structures are quite different. However,
neither the southern or northern bluefin tuna stocks have received very much attention in
regards to intra-ocean genetic stock surveys (Table 4-1g). There have been a few studies
developing genetic diagnostic markers to distinguish these two species, as they are

188
morphologically similar. Ward et al. (1995) and Smith et al. (2001, 1994) found
diagnostic markers for these species using mtDNA haplotypes from RFLPs and
allozymes, respectively. These tools have proven useful in the southwestern Pacific in
areas where the distribution of these species overlaps. Grewe et al. (1997) did examine
the spatial heterogeneity of the southern bluefin tuna, T. maccoyii (n = 758), using six
allozyme loci and mtDNA RFLPs. No spatial heterogeneity was detected in this species,
although this was not surprising given that only one common spawning ground in the
Indian Ocean has been reported for southern bluefin (Grewe et al. 1997). To date, no
genetic analyses of within-Pacific Ocean population subdivision for the northern bluefin,
T. thynnus orientalis, have been conducted. It is thought that there is only one spawning
location in the western Pacific, and while otolith microchemistry can detect differences
among smaller areas within this spawning location (Sea of Japan, East China Sea, Pacific
Ocean) (Rooker et al. 2001), genetic signals would likely not vary significantly over such
a small area. Additionally, otolith microchemistry temporal stability has not been
demonstrated among these spawning regions (Rooker et al. 2001). An analysis of young-
of-the-year bluefin tuna in the Atlantic found genetic heterogeneity between spawning
grounds in the Gulf of Mexico and the Mediterranean Sea (Carlsson et al. 2007), and
while not conclusive, this broadscale and weak level of structuring does lend support to
one Pacific-wide stock of northern bluefin tuna, if only one spawning location in the
western Pacific exists.

189
Discussion

While the pelagic species included in this review share a lot of life-history characteristics,
the extent of their genetic differentiation within the Pacific varies widely. Striped marlin
is on the highly subdivided edge of the continuum, followed by swordfish and sailfish
which both showed some regional genetic heterogeneity. Albacore, next in this
continuum, showed slight subdivision between northern and southern stocks. Bigeye tuna
falls next along the spectrum, showing very mild inconsistent genetic differentiation.
Black marlin and southern bluefin tuna are close to the opposite side of this scale. While
both species show genetic homogeneity throughout their distribution, their overall range
is more limited than many of the other species in this comparison. Finally, on the
opposite side of the genetic differentiation scale, are yellowfin tuna and blue marlin. Both
of these fish have nearly global distributions. Mild subdivision is detected in these
species between ocean basins, but no differentiation was found within the Pacific, despite
their broad distributions. There was not enough information on the genetic structure of
northern bluefin tuna to place the species within the continuum.

Within each species, the results of the genetic analyses varied depending on how many
samples were included in the study and on which markers used. While allozymes and
mtDNA RFLPs were among the most common molecular markers used for all of the
species collectively, it appeared that microsatellites were able to detect low levels of
structure more consistently. Sample size was also important in these analyses; with larger

190
sample sizes being utilized in the majority of the more recently published studies. Based
on comparisons here, Ruzzante’s (1998) general guideline of samples sizes of the
minimum of 50, and preferably, 100 individuals per sampling location would likely have
detected much of the differentiation found in these species. However, when only a few or
single genetic locus is used, there would need to be a compensating increase in sample
size (Ruzzante 1998).

Comparisons of population subdivision among highly migratory pelagic species within
the Pacific did not reveal any particular factor that could be used to predict patterns of
spatial structure. Although no single factor was identified, certainly the number, size and
specificity of spawning locations and the duration of spawning events play a role in the
heterogeneity pattern of a species throughout its distribution. For species with very broad
spawning locations, or protracted spawning seasons (even year round), like blue marlin
and yellowfin tuna, little spatial heterogeneity was detected. However, for species with
much more defined and confined periods of spawning, more heterogeneity was detected,
such as in striped marlin, sailfish and even albacore tuna.

The vagility of the fish species and the connectedness of suitable habitat among locations
also play a role in their genetic structure. For example, the region near the equator
represents continuous suitable habitat for some species and a physical barrier (although
not a complete one) to others. There also appear to be restrictions on east-west
movement across the Pacific, but this may be more important for species that closely

191
associate with land masses or fish aggregating devices, and not a factor for more oceanic
species like blue marlin and swordfish.

While, no one factor was identified, generalizations can be made from certain life-history
characteristics to levels of spatial heterogeneity being exhibited in these species. For
example, in northern bluefin tuna, where little is know about spatial structure, based on
the identified factors, this species would likely consist of a single stock within the Pacific
if only one spawning area in the western Pacific is accurate. This species can clearly
make east-west and north-south movements, and so oceanographical barriers may not be
as important for this species. However, if a spawning location is identified in the
southern Pacific, then it is likely that some reproductive isolation would eventually occur
as migration routes appear to be very specific in this species.

Hopefully by identifying more important associations or potential causes of population
differentiation in pelagic species, reasonable assumptions can be made for species where
little or no genetic information exists.

192
Chapter 4 References

ABITIA-CARDENAS, L. A., GALVAN-MAGANÄA, F., GUTIERREZ-SANCHEZ, F.
J., RODRIGUEZ-ROMERO, J., AGUILAR-PALOMINO, B. & MOEHL-HITZ,
A. (1999) Diet of blue marlin (Makaira mazara) off the coast of Cabo San Lucas,
Baja California Sur, Mexico. Fisheries Research, 44, 95-100.

ALVARADO BREMER, J., BAKER, A. J. & MEJUTO, J. (1995) Mitochondrial DNA
control region sequences indicate extensive mixing of swordfish (Xiphias gladius)
in the Atlantic Ocean. Canadian Journal of Fisheries and Aquatic Sciences, 52,
1720-1732.

ALVARADO BREMER, J., MEJUTO, J., GREIG, T. W. & ELY, B. (1996) Global
population structure of the swordfish (Xiphias gladius) as revealed by analysis of
the mitochondrial DNA control region. Journal of Experimental Marine Biology
and Ecology, 197, 295-310.

ALVARADO BREMER, J., NASERI, I. & ELY, B. (1997) Orthodox and unorthodox
phylogenetic relationships among tunas revealed by the nucleotide sequence
analysis of the mitochondrial DNA control region. Journal of Fish Biology, 50,
540–554.

ALVARADO BREMER, J., STEQUERT, B., ROBERTSON, N. W. & ELY, B. (1998)
Genetic evidence for inter-oceanic subdivision of bigeye tuna (Thunnus obesus)
populations. Marine Biology, 132, 547–557.

APPLEYARD, S. A., GREWE, P. M., INNES, B. H. & WARD, R. D. (2001) Population
structure of yellowfin tuna (Thunnus albacares) in the western Pacific Ocean,
inferred from microsatellite loci. Marine Biology, 139, 383-393.

ARMAS, R., SOSA-NISHIZAKI, O., RODRIQUEZ, R. & PEREZ, V. (1999)
Confirmation of the spawning area of the striped marlin, Tetrapturus audax, in the
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206
CONCLUSION

In this dissertation, patterns of spatial and temporal heterogeneity were determined for
striped marlin in the Pacific. To summarize Chapter 2, significant overall geographic
structure was found using both classes of molecular markers. The pair-wise microsatellite
analyses indicated 4 stocks, one that included fish from Japan, Southern California and
immature fish in Hawaii, the second comprised of mature fish from Hawaii, the third
stock included individuals from Mexico and Central America, and the fourth stock
consisted of fish from New Zealand and Australia (Figure 5-1a). Pair-wise mitochondrial
analyses revealed very similar results, but no significant variation was found between
mature Hawaiian fish and the Japan, Southern California, and immature Hawaiian striped
marlin (Figure 5-1b).

The first analysis of temporal variation among year-classes in striped marlin was
presented in Chapter 3. Shifting variances between years and the extremely small
effective population sizes are strong indicators of highly variable reproductive success in
this species. Although not uncommon in a Type III species (Gaggiotti and Vetter 1999),
such as striped marlin, this shows that relatively few mature fish may overwhelmingly
contribute to the next cohort. This may be challenging from a management perspective
because years of very poor recruitment are likely determined by uncontrollable factors
such as ocean currents (Bailey 1981), water temperatures (Nissling 2004), weather
patterns (Wilderbuer et al. 2002) and food availability for larvae and juveniles (Lasker
1981). However, efforts to more closely link spawning behavior and successful

207
Figure 5-1: Patterns of spatial variation among sampled striped marlin locations based on
nuclear microsatellite analyses (a) and mitochondrial sequence analyses (b).

Figure 5-1a: Pattern of spatial variation among sampled striped marlin locations based
on nuclear microsatellite analyses.

Figure 5-1b: Pattern of spatial variation among sampled striped marlin locations based on
mitochondrial sequence analyses.

208
recruitment periods with environmental parameters may help to predict years or seasons
where successful or poor recruitment is expected (Axenrot and Hansson 2003, Sponaugle
et al. 2002).

While these spatial and temporal analyses have greatly helped form a clearer picture of
stock structure for Pacific striped marlin, these analyses can also be used to gain an
understanding of the processes underlying that structure.

North Pacific

It is reasonable to believe that this species utilizes ocean currents throughout its range, or
that movements of striped marlin, particularly for long-distance, are facilitated by ocean
currents (Steele 1989, Leggett 1977). Similarly, the thermal preference (20-25°C)
(Domeier et al. 2003, Howard and Ueyangi 1965) of this epi-pelagic species also
determines the range and timing of movements throughout its distribution.

Through the analyses in Chapters 2 and 3, we know that juvenile Japanese fish appear in
the area around Hawaii during the first quarter of the year. The movement of young fish
into this region was also reported by Squire and Suzuki (1990), probably for feeding
before moving on to spawning grounds (Matsumoto and Kazama 1974). The young
striped marlin likely move from Japan during the late fall or early winter months utilizing
the southern boundary of the warm Kuroshiro current (Figure 5-2). There is a thermal

209
Figure 5-2: Hypothesized movement of striped marlin from Japan during the late
fall/early winter months.

Kuroshiro
California
Antarctic Circumpolar
Equatorial CC
Peru
East AU
South Equatorial
North Equatorial

210
window in the late fall where suitable water temperatures extend from Japan to Hawaii
(Figure 5-3a and Figure 5-3b), however by the beginning of January that window is
closed as water temperatures near Japan would have pushed the striped marlin much
further southward (Figure 5-3c).

Then during the late spring or early summer, the Japanese fish, and perhaps some
individuals from Hawaii, continue to move eastward (Figure 5-4) as warming water
temperatures along the eastern Pacific provide a corridor into the Southern California
region (Figures 5-5b). During the winter and early spring months, water temperatures are
too cool to allow movement of striped marlin directly eastward (Figure 5-5a). As a result,
striped marlin are only seasonally present in Southern California during the late spring
through mid-fall when water temperatures are warmest. As indicated in tagging studies
(Domeier 2006), in the mid to late fall, the fish are forced southward (Figure 5-6) by
cooling water temperatures along the Southern California coast (Figures 5-7a and 5-7b).

It is uncertain how long the Japan-Southern California-Hawaii fish remain in the Mexico
area, and questions have been raised as to why these fish are not represented in the
Mexican samples. Sampling in this study was opportunistic and samples were primarily
supplied by recreational fisheries in this region. Spawning is known to occur at roughly
the same time when Southern California fish are moving south into the area (Domeier
2006, Armas et al. 1999). It is likely that the recreational fishers track local populations
of striped marlin to their spawning grounds. If the Southern California fish do not move

211
Figure 5-3: Sea surface temperatures in the regions surrounding Japan in November (a),
Hawaii in January (b), and Japan in January (c); black arrow indicates hypothesized
movement of striped marlin. Sea surface temperature images from US Navy, NRL global
NCOM.

Figure 5-3a: Sea surface temperatures in the region surrounding Japan in November;
black arrow indicates hypothesized movement of striped marlin. Sea surface temperature
images from US Navy, NRL global NCOM.

Figure 5-3b: Sea surface temperatures in the region surrounding Hawaii in January; black
arrow indicates hypothesized movement of striped marlin. Sea surface temperature
images from US Navy, NRL global NCOM.

Figure 5-3c: Sea surface temperatures in the region surrounding Japan in January; black
arrow indicates hypothesized movement of striped marlin. Sea surface temperature
images from US Navy, NRL global NCOM.

X

212
Figure 5-4: Hypothesized movement of striped marlin from Hawaii during the late
spring/early summer months.

Figure 5-5: Sea surface temperatures in the central and eastern Pacific region in January
(a) and July (b); black arrow indicates hypothesized movement of striped marlin. Sea
surface temperature images from US Navy, NRL global NCOM.

Figure 5-5a: Figure 5-5b:

California
Kuroshiro
Peru
North Equatorial
Equatorial CC
Antarctic Circumpolar
East
AU
South Equatorial
X

213
Figure 5-6: Hypothesized movement of striped marlin from Southern California during
the mid/late fall months.

Figure 5-7: Sea surface temperatures in the eastern Pacific region in October (a) and
January (b); black arrow indicates hypothesized movement of striped marlin. Sea surface
temperature images from US Navy, NRL global NCOM.

Figure 5-7a: Figure 5-7b:

California
Kuroshiro
North Equatorial
Equatorial CC
South Equatorial
Peru
Antarctic Circumpolar
East
AU

214
to the Mexican spawning grounds, then they may largely escape fishing pressure during
that time. Additionally, these fish may not stay for long in this region, and may instead
move offshore, perhaps further reducing fishing pressure, before eventually using the
northern boundary of the Northern Equatorial current to move back across the Pacific to
spawning grounds (Figure 5-8). The mean sea surface temperature in the equatorial
region shows that this corridor is of suitable water temperature over the course of the year
(Figure 5-9).

East Pacific

The lack of structure between Mexico and Central America is not surprising given that
these locations are connected year-round by water temperatures within the striped
marlin’s thermal range (Figures 5-10a and 5-10b). Although different spawning locations
have been reported for Mexico (Armas et al. 1999, Squire 1987) and for regions near
Central America (Bromhead et al. 2004, Squire and Suzuki 1990, Kume and Joseph
1969), migration between these regions must occur frequently enough to eliminate
significant structure between these areas. The results from McDowell and Graves (2008)
indicate significant structure between Mexico and Ecuador, and it would be interesting to
sample points further southward along the eastern Pacific to see if this pattern changes
using the markers and sequencing techniques in this study. For this research, samples
were collected by observers on commercial fishing vessels operating out of Ecuador;
however the majority of samples were actually caught north of Ecuador (thereby resulting
in the “Central American” label). A few samples were collected just off the coast of

215
Figure 5-8: Hypothesized movement of striped marlin originating in the north Pacific
from the Mexico region.

Figure 5-9: The mean sea surface temperatures in the Pacific region; black arrow
indicates hypothesized movement of striped marlin. Mean sea surface temperature image
from US Navy, NRL global NCOM.

California
Kuroshiro
North Equatorial
Equatorial CC
South Equatorial
Peru
East
AU
Antarctic Circumpolar

216
Figure 5-10: Sea surface temperatures in the central eastern Pacific region in January (a)
and July (b); black arrow indicates hypothesized movement of striped marlin. Sea surface
temperature images from US Navy, NRL global NCOM.

Figure 5-10a: Figure 5-10b:

217
Ecuador, and in sequence analyses they did not show any differentiation from the other
eastern Pacific samples, albeit sample size was very limited. Significant differences were
found in the length and age distribution between Mexico and Central America. As
mentioned in Chapter 3, these discrepancies were likely due to differences in sample
sources; recreational fisheries, the primary source of Mexican samples, are size-selective,
whereas size-selectivity is less pronounced in the commercial fisheries that were the
primary source of Central American samples.

Southwest Pacific

Like Southern California, the New Zealand population of striped marlin is also seasonal,
as water temperatures around New Zealand become too cool for this species during the
austral winter (Figures 5-11a and 5-11b).With no known spawning location in this area,
fish likely move into New Zealand to feed (Kopf et al. 2005). Tagging studies indicate
that striped marlin in New Zealand make longer movements away from this region
compared to movement in other areas in the Pacific (Domeier 2006), with the majority of
individual fish moving toward Australia or into the central Pacific (Langley et al. 2006,
Bromhead et al. 2004). In response to changing water temperatures, striped marlin in
Australia shift their distribution along the eastern Australian coast (Domeier 2006,
Bromhead et al. 2004). Interestingly, Australian striped marlin showed unusual patterns
in linkage disequilibrium that were unique from any other region in the Pacific. Given
that this location is closest to the Indian Ocean; one possible explanation is that Indian

218
Figure 5-11: Sea surface temperatures in the southwestern Pacific region in February (a)
and September (b); black arrow indicates hypothesized movement of striped marlin. Sea
surface temperature images from US Navy, NRL global NCOM.

Figure 5-11a: Figure 5-11b:

219
Ocean striped marlin occasionally breed with the Pacific Australian population when
environmental conditions allow movement between these ocean basins. However, this
mixing is unidirectional as no unusual patterns were detected in the maternally inherited
mitochondrial sequences. If both sexes potentially migrate from the Indian Ocean, then
only males are able to successfully reproduce with the Australian fish.

In Chapter 4, comparisons among highly migratory pelagic species within the Pacific
revealed that the continuum of genetic subdivision ranged from very little or no genetic
structure within the Pacific (Chiang et al. 2006, Grewe and Hampton 1998, Alvarado-
Bremer et al. 1997, Ward et al. 1994, Scoles and Graves 1993), to regional stocks within
this ocean basin (McDowell as reported in Graves and McDowell 2003, Reeb et al. 2000,
Graves and McDowell 1995), with striped marlin falling at the more highly subdivided
end of the extreme (Chapter 2, McDowell and Graves 2008, Graves and McDowell
1994). No one parameter or set of commonalities could fully explain patterns of
population structure in all of these species. Although certainly the number, size and
specificity of spawning locations and the duration of spawning events play a role in the
heterogeneity pattern of a species throughout its distribution. The vagility of the fish
species and the connectedness of suitable habitat among locations also likely play a role
in their genetic structure.

The results of this dissertation have greatly improved our understanding of the spatial and
temporal variation in striped marlin populations in the Pacific, and helped to bring to

220
light some of the behaviors and processes contributing to that variation. The knowledge
gained from genetic studies, such as this one, can profoundly impact management
decisions (Hauser and Carvalho 2008, Ward 2000). Although the integration of genetic
data into fisheries management has not always been a smooth process, genetics can
greatly increase the amount of scientific information available to managers (Waples et al.
2008). It is my hope that these findings will be used by management agencies to improve
stock assessments for this species and strengthen policies regulating striped marlin
fisheries.

221
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249
APPENDIX

Appendix Table 1: Genotypic disequilibrium of the original microsatellite data.
Significant values are in bolded and italicized.
Japan Mature Hawaii Immature Hawaii So. California
Locus Pair Locus Pair Locus Pair Locus Pair
#1 #2 P-Value #1 #2
P-
Value #1 #2
P-
Value #1 #2
P-
Value
24 162 0.9936 24 162 0.3468 24 162 0.3735 24 162 1
24 164 0.2097 24 164 0.2727 24 164 0.3138 24 164 0.0552
162 164 0.4874 162 164 0.3748 162 164 0.5438 162 164 0.0151
24 157 0.7818 24 157 0.8668 24 157 0.8651 24 157 0.6222
162 157 0.8675 162 157 0.9463 162 157 0.3508 162 157 0.197
164 157 0.3975 164 157 0.2833 164 157 0.5478 164 157 0.3225
24 105 0.3207 24 105 0.7354 24 105 1 24 105 1
162 105 1 162 105 0.3959 162 105 0.8408 162 105 1
164 105 0.1793 164 105 0.6977 164 105 0.1877 164 105 0.6684
157 105 0.3674 157 105 0.7883 157 105 0.5836 157 105 1
24 155 0.6194 24 155 0.9993 24 155 0.4703 24 155 0.936
162 155 0.3304 162 155 0.5308 162 155 0.5864 162 155 0.4601
164 155 0.7718 164 155 0.345 164 155 0.2792 164 155 0.9037
157 155 0.3579 157 155 0.3335 157 155 0.5438 157 155 0.2617
105 155 0.1352 105 155 0.3915 105 155 0.6482 105 155 0.5047
24 193 0.5938 24 193 0.7906 24 193 1 24 193 1
162 193 0.7562 162 193 0.0083 162 193 0.9712 162 193 1
164 193 0.7881 164 193 0.8631 164 193 0.3678 164 193 1
157 193 0.3937 157 193 0.885 157 193 0.3107 157 193 1
105 193 1 105 193 1 105 193 0.0865 105 193
Not
Avail.
155 193 0.2434 155 193 0.822 155 193 0.2763 155 193 0.3936
24 218 0.3613 24 218 0.6869 24 218 0.0285 24 218 0.989

250
Appendix Table 1 Cont.:
Japan Mature Hawaii Immature Hawaii So. California
Locus Pair Locus Pair Locus Pair Locus Pair
#1 #2
P-
Value #1 #2
P-
Value #1 #2
P-
Value #1 #2
P-
Value
162 218 0.3653 162 218 0.5785 162 218 0.535 162 218 0.6971
164 218 0.2212 164 218 0.9549 164 218 0.0317 164 218 0.8213
157 218 0.5455 157 218 0.5916 157 218 0.1321 157 218 0.7979
105 218 0.7915 105 218 0.8636 105 218 0.7901 105 218 1
155 218 0.9469 155 218 0.8321 155 218 0.416 155 218 0.6908
193 218 0.3813 193 218 0.2506 193 218 0.6871 193 218 0.4967
24 235A 0.9877 24 235A 0.0002 24 235A 0.0398 24 235A 1
162 235A 0.8561 162 235A 0.5671 162 235A 0.6059 162 235A 0.0793
164 235A 0.4528 164 235A 0.9183 164 235A 0.898 164 235A 0.0739
157 235A 0.0573 157 235A 0.6628 157 235A 0.7828 157 235A 0.028
105 235A 1 105 235A 0.2125 105 235A 0.8742 105 235A 1
155 235A 0.4179 155 235A 0.2729 155 235A 0.7677 155 235A 0.608
193 235A 0.69 193 235A 0.5276 193 235A 0.694 193 235A 1
218 235A 0.6216 218 235A 0.0073 218 235A 0.4415 218 235A 0.829
24 149 0.0295 24 149 0.9445 24 149 0.4567 24 149 0.94
162 149 0.8466 162 149 0.7872 162 149 0.1539 162 149 0.1935
164 149 0.4671 164 149 0.9048 164 149 0.6797 164 149 0.1942
157 149 0.7342 157 149 0.6244 157 149 0.4469 157 149 0.9783
105 149 0.054 105 149 0.265 105 149 0.0144 105 149 0.3748
155 149 0.3768 155 149 0.7899 155 149 0.78 155 149 0.2994
193 149 0.2494 193 149 0.3139 193 149 0.6766 193 149 0.5625
218 149 0.1963 218 149 0.7226 218 149 0.2974 218 149 0.9034

251
Appendix Table 1 Cont.:
Japan Mature Hawaii Immature Hawaii So. California
Locus Pair Locus Pair Locus Pair Locus Pair
#1 #2
P-
Value #1 #2
P-
Value #1 #2 P-Value #1 #2
P-
Value
235A 149 0.4036 235A 149 0.676 235A 149 0.7341 235A 149 0.1806
24 Mn01 0.5131 24 Mn01 0.9858 24 Mn01 0.0603 24 Mn01 0.176
162 Mn01 0.8228 162 Mn01 0.0679 162 Mn01 0.0618 162 Mn01 1
164 Mn01 0.2701 164 Mn01 0.4807 164 Mn01 0.7413 164 Mn01 0.8173
157 Mn01 0.5205 157 Mn01 0.6089 157 Mn01 0.9019 157 Mn01 0.0084
105 Mn01 1 105 Mn01 1 105 Mn01 1 105 Mn01
Not
Avail.
155 Mn01 0.8941 155 Mn01 0.3191 155 Mn01 0.4743 155 Mn01 0.4393
193 Mn01 1 193 Mn01 1 193 Mn01 1 193 Mn01
Not
Avail.
218 Mn01 0.8448 218 Mn01 0.919 218 Mn01 0.8291 218 Mn01 0.7836
235A Mn01 0.6469 235A Mn01 0.4355 235A Mn01 0.4141 235A Mn01 1
149 Mn01 0.4138 149 Mn01 0.0367 149 Mn01 0.1908 149 Mn01 0.9142
24 Mn08 1 24 Mn08 0.708 24 Mn08 0.4083 24 Mn08 1
162 Mn08 1 162 Mn08 0.6726 162 Mn08 0.5289 162 Mn08 1
164 Mn08 1 164 Mn08 0.8371 164 Mn08 0.1977 164 Mn08 1
157 Mn08 0.4875 157 Mn08 0.9832 157 Mn08 0.0289 157 Mn08 1
105 Mn08 1 105 Mn08 1 105 Mn08 1 105 Mn08 1
155 Mn08 0.437 155 Mn08 0.4116 155 Mn08 0.1865 155 Mn08 0.6398
193 Mn08 1 193 Mn08 0.4346 193 Mn08 1 193 Mn08 1
218 Mn08 1 218 Mn08 0.22 218 Mn08 0.3499 218 Mn08 0.6903
235A Mn08 1 235A Mn08 1 235A Mn08 1 235A Mn08 1
149 Mn08 0.623 149 Mn08 0.9519 149 Mn08 0.6636 149 Mn08 1
Mn01 Mn08 1 Mn01 Mn08 1 Mn01 Mn08 0.282 Mn01 Mn08 1

252
Appendix Table 1 Cont.:
Mexico Central Am. New Zealand Australia
Locus Pair Locus Pair Locus Pair Locus Pair
#1 #2
P-
Value #1 #2
P-
Value #1 #2
P-
Value #1 #2
P-
Value
24 162 0.352 24 162 0.1857 24 162 0.718 24 162 0.0002
24 164 0.1242 24 164 0.4513 24 164 0.768 24 164 0.0327
162 164 0.2696 162 164 0.1791 162 164 0.1496 162 164 0.0448
24 157 0.4363 24 157 0.6641 24 157 0.7004 24 157 0.0006
162 157 0.1567 162 157 0.8512 162 157 0.0357 162 157 0.0172
164 157 0.2367 164 157 0.1312 164 157 0.2816 164 157 0.0004
24 105 0.6491 24 105 1 24 105 0.0903 24 105 0
162 105 0.6331 162 105 1 162 105 1 162 105 0
164 105 0.4349 164 105 0.2818 164 105 1 164 105 0
157 105 0.881 157 105 1 157 105 0.8248 157 105 0
24 155 0.8658 24 155 0.9876 24 155 0.9243 24 155 0.1057
162 155 0.2851 162 155 0.9997 162 155 0.2098 162 155 0.3344
164 155 0.456 164 155 0.0603 164 155 0.9221 164 155 0.9019
157 155 0.6963 157 155 0.163 157 155 0.3693 157 155 0.7808
105 155 0.5803 105 155 1 105 155 0.6569 105 155 0.0336
24 193 1 24 193 1 24 193 0.4832 24 193 0
162 193 0.8586 162 193 1 162 193 0.0557 162 193 0
164 193 0.2347 164 193 0.5337 164 193 0.5252 164 193 0
157 193 0.1125 157 193 0.9475 157 193 0.3816 157 193 0
105 193 1 105 193 1 105 193 1 105 193 0
155 193 0.9815 155 193 0.2348 155 193 0.0639 155 193 0.0356
24 218 0.1803 24 218 0.9942 24 218 0.9967 24 218 0
162 218 0.6855 162 218 0.3586 162 218 0.504 162 218 0.0487
164 218 0.9963 164 218 0.1278 164 218 0.1723 164 218 0.2009
157 218 0.419 157 218 0.2226 157 218 0.2622 157 218 0.726
105 218 0.592 105 218 0.5319 105 218 0.9008 105 218 0
155 218 0.2638 155 218 0.5601 155 218 0.2425 155 218 0.4546
193 218 0.6921 193 218 0.1172 193 218 0.011 193 218 0.0037
24 235A 0.0167 24 235A 0.8981 24 235A 1 24 235A 0.0001
162 235A 0.3954 162 235A 0.4266 162 235A 0.91 162 235A 0
164 235A 0.5898 164 235A 0.2622 164 235A 0.8676 164 235A 0.0003
157 235A 0.5019 157 235A 0.5022 157 235A 0.2317 157 235A 0.2325

253
Appendix Table 1 Cont.:
Mexico Central Am. New Zealand Australia
Locus Pair Locus Pair Locus Pair Locus Pair
#1 #2
P-
Value #1 #2 P-Value #1 #2
P-
Value #1 #2
P-
Value
105 235A 0.6653 105 235A 1 105 235A 0.1519 105 235A 0
155 235A 0.377 155 235A 0.8275 155 235A 0.2006 155 235A 0.1837
193 235A 0.2287 193 235A 1 193 235A 0.6203 193 235A 0
218 235A 0.1447 218 235A 0.1379 218 235A 0.4423 218 235A 0.0026
24 149 0.2296 24 149 0.8352 24 149 0.7105 24 149 0.0201
162 149 0.9116 162 149 0.1182 162 149 0.829 162 149 0.3294
164 149 0.8514 164 149 0.0211 164 149 0.693 164 149 0.4895
157 149 0.666 157 149 0.0289 157 149 0.7586 157 149 0.7214
105 149 0.6833 105 149 0.4025 105 149 1 105 149 0.0001
155 149 0.1559 155 149 0.6277 155 149 0.0242 155 149 0.4226
193 149 0.0212 193 149 0.9147 193 149 0.0588 193 149 0
218 149 0.9363 218 149 0.9927 218 149 0.3123 218 149 0.4545
235A 149 0.0335 235A 149 0.6392 235A 149 0.9812 235A 149 0.0059
24 Mn01 0.317 24 Mn01 0.5147 24 Mn01 0.6384 24 Mn01 0
162 Mn01 0.7498 162 Mn01 0.0169 162 Mn01 0.0369 162 Mn01 0.0016
164 Mn01 0.8983 164 Mn01 0.9958 164 Mn01 0.2683 164 Mn01 0.0002
157 Mn01 0.9882 157 Mn01 0.0482 157 Mn01 0.7799 157 Mn01 0.0341
105 Mn01 1 105 Mn01
Not
Avail. 105 Mn01 1 105 Mn01 0
155 Mn01 0.352 155 Mn01 0.6261 155 Mn01 0.0403 155 Mn01 0.072
193 Mn01 1 193 Mn01 1 193 Mn01 0.4006 193 Mn01 0
218 Mn01 0.6606 218 Mn01 0.392 218 Mn01 0.1748 218 Mn01 0.0001
235A Mn01 0.728 235A Mn01 0.8894 235A Mn01 0.7556 235A Mn01 0
149 Mn01 0.19 149 Mn01 0.4101 149 Mn01 0.5719 149 Mn01 0.0002
24 Mn08 0.4017 24 Mn08 0.2358 24 Mn08 1 24 Mn08 0
162 Mn08 0.9939 162 Mn08 0.0284 162 Mn08 0.1882 162 Mn08 0
164 Mn08 0.4052 164 Mn08 0.9937 164 Mn08 0.684 164 Mn08 0
157 Mn08 0.2761 157 Mn08 0.8665 157 Mn08 0.4723 157 Mn08 0
105 Mn08 1 105 Mn08 1 105 Mn08 1 105 Mn08 0
155 Mn08 0.3835 155 Mn08 0.4118 155 Mn08 0.8814 155 Mn08 0.3462
193 Mn08 0.0665 193 Mn08 1 193 Mn08 1 193 Mn08 0
218 Mn08 0.8525 218 Mn08 0.3375 218 Mn08 0.3005 218 Mn08 0.0134
235A Mn08 0.9206 235A Mn08 0.3608 235A Mn08 1 235A Mn08 0
149 Mn08 0.1813 149 Mn08 0.378 149 Mn08 1 149 Mn08 0.0002
Mn01 Mn08 0.394 Mn01 Mn08 1 Mn01 Mn08 1 Mn01 Mn08 0

254
Appendix Table 2: Null allele frequencies by locus and location.
Null Allele Frequencies
Locus
Japan
(n=119)
Mature
Hawaii
(n=312)
Immature
Hawaii
(n=227)
Southern
California
(n=66)
Mature
Mexico
(n=208)
Immature
Mexico
(n=31)
Mature Central
America
(n=75)
Immature
Central
America
(n=30)
New
Zealand
(n=86)
Australia
(n=45)
24 0.000 0.000 0.001 0.090 0.005 0.009 0.015 0.000 0.000 0.013
162 0.093 0.689 0.845 0.002 0.584 0.000 0.468 0.031 0.088 0.585
164 0.039 0.812 1.417 0.000 0.000 0.000 0.000 0.000 0.215 0.052
157 0.347 1.802 0.006 0.000 0.008 0.000 0.244 0.370 0.077 0.000
105 0.000 1.947 2.316 0.021 0.421 0.000 1.944 0.000 1.022 0.260
155 1.800 4.568 4.770 3.051 0.163 0.000 0.180 0.061 0.510 0.058
193 0.107 1.050 0.817 0.095 0.101 1.473 0.033 0.033 0.510 0.459
218 0.400 3.120 1.277 0.068 1.266 3.500 0.043 0.178 0.000 0.000
235 0.043 0.577 0.015 0.076 0.270 0.000 0.017 0.000 0.000 0.000
149 0.669 2.816 0.000 0.673 0.000 0.492 0.347 0.000 0.028 0.000
Mn01 0.347 0.475 0.363 0.106 0.000 0.625 0.000 0.571 0.058 0.000
Mn08 0.122 0.080 0.045 0.660 0.067 0.000 0.568 0.243 0.022 0.000

255
Appendix Table 3: Null allele frequency by year-class and locus for Japan and Southern
California.
Japan and Southern California
Locus Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12
24 0.000 0.004 0.011 0.009 0.008 0.000 0.000 0.811 0.000
162 0.000 0.065 0.004 0.002 0.006 0.021 0.000 0.000 0.000
164 0.000 0.000 0.000 0.000 0.000 0.138 0.000 0.111 0.000
157 0.000 0.000 0.000 0.050 0.000 0.171 0.385 0.315 0.000
105 0.000 0.012 0.000 0.010 0.112 0.000 0.000 0.000 0.001
155 0.000 0.033 0.186 0.068 0.000 0.556 0.800 0.814 0.000
193 0.000 0.000 0.029 0.039 0.031 0.000 0.000 0.000 0.000
218 0.000 0.000 0.000 0.078 0.242 0.000 0.077 0.000 0.000
235A 0.000 0.000 0.018 0.036 0.000 0.000 0.000 0.000 0.000
149 0.000 0.149 0.000 0.193 0.180 0.077 0.000 0.000 0.000
Mn01 0.010 0.052 0.038 0.336 0.000 0.000 0.000 0.000 0.001
Mn08 0.000 0.000 0.000 0.000 0.034 0.000 0.000 0.000 0.000

Appendix Table 4: Null allele frequency by year-class and locus for Hawaii.
Hawaii
Locus Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12
24 0.000 0.029 0.000 0.000 0.014 0.000 0.003 0.000 0.000 0.000 0.000
162 0.076 0.107 0.072 0.047 0.000 0.071 0.000 0.078 0.015 0.067 0.058
164 0.138 0.061 0.000 0.060 0.134 0.071 0.094 0.148 0.000 0.000 0.135
157 0.000 0.044 0.000 0.133 0.000 0.000 0.000 0.185 0.046 0.140 0.000
105 0.136 0.048 0.000 0.336 0.208 0.145 0.000 0.000 0.000 0.040 0.083
155 0.074 0.113 0.000 0.000 0.109 0.400 0.221 0.034 0.244 0.000 0.000
193 0.000 0.035 0.081 0.040 0.039 0.102 0.051 0.041 0.044 0.055 0.046
218 0.000 0.090 0.099 0.000 0.245 0.196 0.333 0.194 0.000 0.165 0.064
235A 0.002 0.000 0.000 0.000 0.000 0.027 0.042 0.000 0.019 0.000 0.157
149 0.000 0.000 0.000 0.000 0.000 0.128 0.100 0.217 0.000 0.000 0.166
Mn01 0.000 0.085 0.000 0.000 0.000 0.093 0.013 0.000 0.000 0.000 0.000
Mn08 0.000 0.000 0.000 0.000 0.049 0.000 0.060 0.000 0.000 0.000 0.000

256
Appendix Table 5: Null allele frequency by year-class and locus for the eastern Pacific.
Eastern Pacific (MX, CA, EC)
Locus Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12
24 0.008 0.000 0.081 0.035 0.000 0.000 0.049 0.029 0.000 0.000 0.000 0.000
162 0.041 0.000 0.000 0.075 0.063 0.000 0.088 0.139 0.050 0.208 0.000 0.000
164 0.000 0.000 0.000 0.000 0.000 0.046 0.000 0.000 0.000 0.000 0.000 0.000
157 0.121 0.127 0.109 0.012 0.000 0.074 0.000 0.063 0.000 0.091 0.000 0.014
105 0.000 0.000 0.000 0.049 0.000 0.042 0.218 0.055 0.000 0.000 0.000 0.000
155 0.000 0.301 0.000 0.021 0.195 0.146 0.000 0.000 0.094 0.000 0.000 0.052
193 0.000 0.000 0.282 0.000 0.023 0.000 0.151 0.000 0.048 0.085 0.000 0.000
218 0.134 0.367 0.304 0.163 0.053 0.000 0.194 0.000 0.540 0.000 0.000 0.200
235A 0.000 0.000 0.018 0.000 0.037 0.000 0.039 0.038 0.000 0.000 0.000 0.067
149 0.000 0.810 0.000 0.000 0.000 0.000 0.228 0.000 0.000 0.000 0.000 0.200
Mn01 0.138 0.931 0.372 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Mn08 0.092 0.000 0.000 0.000 0.061 0.097 0.000 0.000 0.000 0.000 0.000 0.000

Appendix Table 6: Null allele frequency by year-class and locus for the southwestern
Pacific.
Southwestern Pacific (AU, NZ)
Locus Y4 Y5 Y6 Y7
24 0.000 0.013 0.000 0.004
162 0.307 0.024 0.057 0.083
164 0.000 0.037 0.034 0.096
157 0.000 0.000 0.000 0.119
105 0.000 0.087 0.186 0.000
155 0.038 0.000 0.161 0.000
193 0.134 0.067 0.085 0.089
218 0.111 0.025 0.000 0.000
235A 0.000 0.020 0.000 0.000
149 0.381 0.000 0.000 0.120
Mn01 0.000 0.000 0.017 0.000
Mn08 0.000 0.000 0.035 0.000

257
Appendix Table 7: Life tables A (7a) based on natural mortality and B (7b) based on
natural and fishing mortality.

Appendix Table 7a: Life table A based on natural mortality
Life Table A: Based on natural mortality
Age li bi pi
1 1 0 0
2 0.8 0 0
3 0.64 0 0
4 0.512 0.164514149 0.0842312
5 0.4096 0.194111644 0.0795081
6 0.32768 0.28034213 0.0918625
7 0.262144 0.37496668 0.0982953
8 0.2097152 0.48884372 0.102518
9 0.1677722 0.614078931 0.1030253
10 0.1342177 0.759116827 0.1018869
11 0.1073742 0.887911617 0.0953388
12 0.0858993 1.044216989 0.0896976
13 0.0687195 1.199199769 0.0824084
14 0.0549756 1.295627738 0.0712279
15 0 0 0

Appendix Table 7b: Life table B based on natural and fishing mortality
Life Table B: Based on natural and fishing mortality
Age li bi pi
1 1 0 0
2 0.73 0 0
3 0.32777 0 0
4 0.1471687 1.239854559 0.1824678
5 0.0784409 1.462914947 0.1147524
6 0.0525554 2.112787689 0.1110385
7 0.0400998 2.825921973 0.1133189
8 0.0305961 3.684151909 0.1127208
9 0.0222128 4.627982267 0.1028004
10 0.01566 5.721054798 0.0895918
11 0.0108211 6.691711783 0.0724115
12 0.0061788 7.869701214 0.0486256
13 0.0033551 9.037722974 0.0303225
14 0.0022479 9.764448662 0.0219497
15 0 0 0

Abstract (if available)

Abstract Striped marlin, Tetrapturus audax, is an Indo-Pacific pelagic fish that is valuable to commercial and recreational fisheries throughout its range. Populations of striped marlin are starting to show strain from intensified fishing pressure over the past few decades, and the management of this fishery is at a critical point for sustaining this resource. However, the stock structure of this highly migratory species is still in question, and this has limited the ability to manage this fishery. This research is aimed at resolving patterns of spatial and temporal variation, and thus the stock structure, of striped marlin populations in the Pacific using molecular markers. In Chapter 1, the development of 10 microsatellite markers was described. These first striped marlin-specific microsatellites were developed to increase resolution of genetic variation in subsequent analyses. Using 12 microsatellites and mitochondrial control region sequences, Chapter 2 examined geographic genetic heterogeneity of striped marlin samples collected from 7 locations around the Pacific. Microsatellite and sequence results revealed small, but significant overall spatial subdivision among locations (FST =0.0145 and KST =0.06995, respectively). Pair-wise microsatellite analysis revealed 4 stocks (1-Japan-Southern California-Immature Hawaii, 2-Mature Hawaii 3-Mexico-Central America, and 4-New Zealand-Australia)

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Asset Metadata

Creator Purcell, Catherine Marie (author)

Core Title Genetic analysis of population structure in striped marlin, Tetrapturus audax, in the Pacific Ocean

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Biology

Publication Date 05/21/2010

Defense Date 05/08/2009

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag billfish,fishery genetics,genetic structure,marlin,OAI-PMH Harvest,pelagic fish,population structure,striped marlin,temporal variance,Tetrapturus audax

Place Name oceans: Pacific Ocean (geographic subject)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Edmands, Suzanne (committee chair), Hedgecock, Dennis (committee member), Hinton, Michael (committee member), Kiefer, Dale A. (committee member), Michaels, Anthony (committee member), Stanford, Craig (committee member)

Creator Email purcell.catherine@gmail.com,purcellc@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m2748

Unique identifier UC1494335

Identifier etd-Purcell-3374 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-283310 (legacy record id),usctheses-m2748 (legacy record id)

Legacy Identifier etd-Purcell-3374.pdf

Dmrecord 283310

Document Type Dissertation

Rights Purcell, Catherine Marie

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu