Technical Reports of the Institute of Cetacean Research (2019)

pp. 32–40

Technical Report (not peer reviewed)

Genetic study of stock structure in North Paciﬁc sei whales
Mioko Taguchi*
Institute of Cetacean Research, 4–5 Toyomi-cho, Chuo-ku, Tokyo 104–0055, Japan*Contact e-mail: taguchi@cetacean.jp

ABSTRACTThe Institute of Cetacean Research has conducted genetic analyses of stock structure in North Paciﬁc sei
whales based on historical genetic samples, and samples collected more recently by the IWC-POWER (biopsy samples) and JARPNII program. Results have contributed to the in-depth assessment of North Paciﬁc sei
whales conducted by the IWC Scientiﬁc Committee. This paper presents details of the research area, samples,
genetic approaches, and main results of the genetic analyses of stock structure of sei whales in the pelagicarea of the North Paciﬁc. Results of the genetic analyses are consistent with the hypothesis of a single stock of
sei whales in the pelagic area of the North Paciﬁc surveyed (140°E–130°W).

INTRODUCTION
Sei whale, Balaenoptera borealis, (Figure 1) is a large
baleen whale species inhabiting all of the major open
oceans (Horwood, 1987; Rice, 1998). Sei whales live up to
60 years of age and their maximum body length is 20 m.
Sei whales migrate from winter breeding grounds in low
latitude to high latitudinal areas in summer, where they
feed on various prey species (e.g. Nemoto, 1959; Nemoto
and Kawamura, 1977).
This species was one of the major targets of commercial whaling worldwide (Horwood, 1987). In the North
Paciﬁc the main period of exploitation was from 1963
to 1973 (Allen, 1980), and they were caught in watersoﬀ California, Canada, Japan, Kamchatka and the Kuril
Islands by pelagic ﬂeets (Horwood, 2018).
Delineation of the stock structure of the species andthe estimation of abundance by stock are key information
required for management and conservation. A pioneering work on stock structure of North Paciﬁc sei whales
was carried out by Wada and Numachi (1991) using
three polymorphic allozyme loci. The study reported no

Figure 1.
32

evidence of temporal and spatial genetic heterogeneity
in samples collected in the area east of 160°E, suggesting
the existence of only a single stock in the area.
The most comprehensive genetic study was performedby Kanda et al. (2009) using 17 microsatellite DNA lociand 487bp of mitochondrial DNA (mtDNA) control region
sequences. The study used a total of 790 specimens collected during the past commercial whaling in 1972–1973
and during the JARPNII surveys in 2002–2007. Results ofthe study showed no evidence of signiﬁcant genetic differences in areas between 140°E and 135°W of the North
Paciﬁc.
Subsequently, Kanda et al. (2013) examined not only
spatial but also temporal genetic diﬀerences of theNorth Paciﬁc sei whales. The study showed no evidenceof temporal (40 years apart between recent and pastsamples) and spatial (the research area divided into western (140°–170°E), central (170°E–150°W) and eastern(150°–130°W) areas) genetic diﬀerences. Results wereconsistent with those found by Kanda et al. (2009). Furthermore, Kanda et al. (2015a) considered that spatial
genetic diﬀerentiation should be tested using samples

North Paciﬁc sei whale (Balaenoptera borealis).

Genetic study of stock structure in North Pacific sei whales

collected in the same year in order to eliminate temporal
negative biases. Consequently, they examined genetic
variations at 16 microsatellite loci using only samples
collected during summer in 2010, 2011 and 2012. Again,
the results of the study failed to ﬁnd evidence of multiple
stocks of sei whales in the North Paciﬁc.
This paper presents details of the research area,samples, genetic approaches, and main results of thegenetic analyses of stock structure of sei whales in the pelagic area of the North Paciﬁc, updated after the work by
Kanda et al. (2015a). This paper also provides information
on some basic concepts of population genetic statistics.

MATERIALS AND METHODS
Research area
The genetic analyses were conducted based on threesampling areas in the pelagic region of the North Paciﬁc:western (140°–150°E), central (150°E–180°) and eastern(180°–130°W) areas (Figure 2). Genetic samples were collected from whales taken during past commercial whaling operations as well during more recent surveys of the
JARPNII research program. Biopsy samples obtained during the IWC-Paciﬁc Ocean Whale and Ecosystem Research(IWC-POWER) were also used (see details below).
Samples and laboratory procedures
Samples and DNA extraction
A total of 1,748 genetic samples of sei whales obtained
during past commercial whaling in 1972–1973 (n=312),
the JARPNII in 2002–2016 (n=1,354) and the IWC-POWER(biopsy samples) in 2010–2013 (n=82) was subjected to
DNA extraction (Table 1).
Total genomic DNA was extracted from 0.05 g of skintissue, preserved in 99% ethanol at room temperatureor stored frozen at −20°C, using the standard phenol-

Figure 2.

chloroform method (Sambrook et al., 1989) or using Gentra Puregene kits (QIAGEN). Extracted DNA was stored in
TE buﬀer (10 mM Tris-HCl, 1 mM EDTA, pH 8.0).
MtDNA sequencing
See an explanation of mtDNA sequencing analyses in Taguchi et al. (2017).
Approximately 500 base pairs of partial control region
were ampliﬁed by the polymerase chain reaction (PCR)
using a set of primers MT4 (Árnason et al., 1993) and Dlp
5R (5ʹ-CCA TCG AGA TGT CTT ATT TAA GGG GAA C-3ʹ).
PCR was performed with an initial denaturation step at
95°C for 5 minutes, followed by 30 cycles of 30 seconds
at 94°C, 30 seconds at 50°C and 30 seconds at 72°C, witha ﬁnal extension step at 72°C for 10 minutes. PCR products were puriﬁed using MicroSpin S-400HR columns(Pharmacia Biotech). Cycle sequencing was performed
using BigDye terminator cycle sequence Kit (Applied Biosystems) and the PCR primers, following the protocols of
the manufacturer. The cycle sequencing products were
puriﬁed using AutoSeq G-50 spin Columns (Pharmacia
Biotech). The labeled sequencing fragments from tissue
samples collected until 2004, during 2005–2010, andTable 1
Number of genetic samples of North Paciﬁc sei whales used inthis study. Sample size for the present data analyses is shown inparenthesis (mtDNA sequences, microsatellite genotypes).
Sample source
Past commercial whaling
JARPNII
IWC-POWER
Total

Sampling area
Western

CentralEastern

181 ( 175, 177) 131 (128, 121)
30 (30, 30) 1324 (1322, 1323)
3(3,3) 79 ( 75, 75)

30 (30, 30) 1508 (1500, 1503) 210 (203, 196)

Sampling position of sei whales used in the genetic analyses. Color indicates sample source: yellow: pastcommercial whaling; red: JARPNII; and blue: IWC-POWER.
33

Technical Reports of the Institute of Cetacean Research (2019)from 2011 were resolved using an ABI PRISM 377, ABI
PRISM 3100 and ABI3500 Genetic Analyzers (Applied Biosystems), respectively.
Microsatellite DNA genotyping
See an explanation of microsatellite DNA analyses in Taguchi et al. (2017).
All individuals sampled in JARPNII and IWC-POWER
surveys were genotyped at 17 nuclear microsatellite loci: EV1, EV14, EV21, EV94, EV104 (Valsecchi
and Amos, 1996), GT011 (Bérubé et al., 1998), GT23,
GT211, GT271, GT310, GT575 (Bérubé et al., 2000),
GATA28, GATA53, GATA98, GATA417, GGAA520 (Palsbøll
et al., 1997), and DlrFCB17 (Buchanan et al., 1996). The
past commercial whaling samples were genotyped at 15
microsatellite loci (EV14 and GATA417 were not used).
Primer sequences and PCR cycling proﬁles generally followed those of the original authors. The multiplex PCR
ampliﬁcations were performed in 15 µl reaction mixturescontaining 10–100 ng of DNA, 5 pmole of each primer,
0.625 units of Ex Taq DNA polymerase (Takara Shuzo),
2mM of each dNTP, and 10x reaction buﬀer containing
20mM MgCl2 (Takara Shuzo), with 94°C for 2 minutes, followed by 30 cycles at 94°C for 20 seconds/54–61°C for 45
seconds/72°C for 1 minute, and a post-cycling extension
at 72°C for 10 minutes.
PCR products from tissue samples collected until 2013
were run on a 6% polyacrylamide denaturating gel (Long
Ranger) with internal size standard (GENESCAN400HD,
Applied Biosystems) using BaseStation100 DNA fragment analyzer (Bio-Rad). Although alleles were visualizedusing Cartographer software speciﬁcally designed for the
BaseStation, allelic sizes were determined manually in relation to the internal size standard and sei whaleʼs DNA of
known size that were rerun on each gel. The PCR productsof samples collected after 2013 were electrophoresed on
an ABI 3500 DNA Analyzer (Applied Biosystems), and allele sizes were determined using a 600 LIZ size standard(Applied Biosystems) and GeneMapper v. 4.0 (Applied
Biosystems). Microsatellite scores from the latter platform were standardized according to those from theformer for each locus.
Data analyses
Three whales re-sampled (two genetic samples obtained
from the same individual whale) were excluded fromthe analysis. Three calves accompanied by their mothers
were also excluded from all subsequent analyses, to ensure an independence of the dataset. In addition to this,nine mtDNA sequences data and thirteen microsatellite
34

genotype data sets were also excluded from the present
data set due to their low data qualities.
FDR correction (Benjamini and Hochberg, 1995) wasused to adjust the signiﬁcance level for all multiple comparisons in this study.
Considering a long time series of sample collection ofover 40 years, annual genetic variations were preliminary
examined in each of the sampling areas, which consistently showed no signiﬁcant genetic diﬀerences. Therefore, the genetic data from several years were combinedin each sampling area in all subsequent analyses. Moredetailed descriptions of the temporal analyses were provided in Appendix 7 in Tamura et al. (2019).
Genetic diversities
For microsatellite DNA, the departure from Hardy–
Weinberg equilibrium (HWE: see Box 1) was tested ineach locus using the R package ʻHWxtestʼ (Engels, 2009).
A global test across loci combining the observed P-values
in each locus by Fisherʼs method was performed using
the R package ʻmetapʼ (Dewey, 2018). The inbreeding coeﬃcient (FIS; Weir and Cockerham, 1984) in eachlocus and across loci was estimated using the R package ʻDemerelateʼ (Kraemer and Gerlach, 2017). The
number of alleles (A) and expected heterozygosity (HE:see Box 2) in each locus and across loci were estimatedusing the program ARLEQUIN v. 3.5.1.2 (Excoﬃer andLischer, 2010).
For mtDNA, haplotype (h) and nucleotide (π) diversities (Nei, 1987) (see Box 2) with sample standard deviations were estimated using the program ARLEQUIN.
Genetic diﬀerentiation and structuring
The FST-like estimates for microsatellites and conventional
pairwise FST for mtDNA (see Box 3) were calculated tomeasure the genetic diﬀerentiation between samplingareas using 10,000 random permutations of the original
dataset in the program ARLEQUIN. A probability test
implemented in the program GENEPOP (Rousset, 2008)
was used to detect the genetic heterogeneity in microsatellite allele frequency among sampling areas. Diﬀerence
in mtDNA haplotype frequency among sampling areas
was also tested using the Monte Carlo simulation-based
chi-square test of independence (Roﬀ and Bentzen, 1989)
in R.
Bayesian clustering analysis was performed using microsatellite data to infer the most likely number of clusters
using STRUCTURE 2.3.4 (Pritchard et al., 2000). The analysis was conducted with ten independent runs for K=2–3.
All runs were performed with 100,000 Markov chain

Genetic study of stock structure in North Pacific sei whales

Box 1. Hardy–Weinberg equilibrium
The genetic composition of a population is usually described in terms of allele frequency, number of alleles and heterozygosity. Allele and genotype frequencies attain an equilibrium, referred to as the Hardy–Weinberg equilibrium (HWE), in the next
generation in inﬁnitely large and random mating populations when there are no mutation, immigration or selection. When anyof the assumptions underlying HWE are violated, then deviations from the equilibrium genotype frequencies will be observed.
Thus, the HWE are widely used to detect if the population has non-random mating, migration or selection.
For a single locus with two alleles, HWE genotype frequencies can be expected according to the relationship:

p 2 + 2pq + q 2 =1
where p and q are the respective allele frequencies; p2 =frequency of one allele homozygotes, q2 =frequency of the other allele
homozygotes, and 2pq=frequency of heterozygotes.
To determine if the observed genotype frequencies deviate from the frequencies expected from the HWE relationship statistically, the diﬀerence in number of genotypes between them is tested using a χ 2 test.
As an example, consider a single microsatellite locus GATA53 with alleles 198 and 202 in ten sei whales (See Table 2.1 inBox 2), hence three genotypes (198/198, 202/198, 202/202) are found. From the observed number of respective genotypes, it
is possible to calculate p and q, and to estimate the HWE genotype frequencies based on the allele frequencies calculated fromthe observed genotype frequencies, as shown in Table 1.1.
Table 1.1
Calculation of allele frequencies based on the observed number of genotypes, and the genotype frequencies expected from the
HWE in a microsatellite locus GATA53 of sei whales.
Observed
Genotypes

Expected

Number ofallelesAllelefrequency

198

198

Number of genotypes (O)Genotype
frequency

198/198

1

1/10
0.1

2×1
2

202/198

4

4/10
0.4

1×4
4

202/202

5

5/10
0.5

Total

10

1.0

202

Number of genotypes (E)p2 =0.3×0.3
0.09

0.09×10
0.9

4/20
0.2

2pq=2×0.3×0.7
0.42

0.42×10
4.2

10/20
0.5

q2 =0.7×0.7
0.49

0.49×10
4.9

q=0.7

1.00

10.0

202

2/20
0.1
1×4
4

4/20
0.2

2×5
10
6

Genotype
frequency

14

Monte Carlo repetitions and 10,000 burn-in length using
the admixture model with correlated allele frequencies.
The web-based program STRUCTURE HARVESTER (Earland vonHoldt, 2012) was used to estimate the mean posterior probability of the data (Also see Goto et al. (2017)
for detailed basic concept of the STRUCTURE analysis).

RESULTS AND DISCUSSION
All 17 microsatellite loci were polymorphic in the entire
dataset of 1,729 sei whales from the North Paciﬁc, whichranged from 22 alleles at EV14 to 3 alleles at GATA53(Table 2). The mtDNA control region sequences of 1,733
sei whales from the North Paciﬁc contained 39 variablenucleotide sites and a single alignment gap deﬁning 84
haplotypes.

p=0.3

Genetic diversities
The HE at each locus and across loci were not largely different among sampling areas (Table 2), and the estimatesacross loci ranged from 0.632 in the western area to0.639 in the eastern area (Table 2). Signiﬁcant deviations
from HWE were not observed at any loci and across loci ineach sampling area after FDR correction (Table 2), whichsuggested that North Paciﬁc sei whales in each of thesampling areas derived from a single breeding population.
Regarding mtDNA, both h and π were similar among
sampling areas, which ranged from 0.908 in the westernarea to 0.927 in the eastern area, and from 0.789% in thewestern area to 0.803% in the central area, respectively(Table 3). These observations were consistent with the35

Technical Reports of the Institute of Cetacean Research (2019)

Box 2. Estimating genetic diversity
Genetic diversity is characterized by allele frequencies at each locus, which can be inﬂuenced by population size, gene ﬂow,
reproductive system, natural selection, genetic mutation or genetic linkage. In natural environment, populations are commonly
fragmented into subpopulations in diﬀerent size partly separated from each other by barriers to migration, and this may change
the genetic diversity among populations.
Observed heterozygosity (HO) is deﬁned as the proportion of individuals heterozygous across a set of loci (hj), or the proportionof loci for which an individual is heterozygous (hi). This estimate can be obtained by direct count of heterozygotes, as shown in
Table 2.1. Overall estimates are averages across the loci used in the study.
Table 2.1
Sub-set of data used in this study involving seventeen microsatellite loci genotyped in each of ten sei whales.
Individual (i)Microsatellite
loci (j)SEI-001

SEI-002

SEI-003

SEI-004

SEI-005

SEI-006

SEI-007

SEI-008

SEI-009

SEI-010EV21
GGAA520GATA98GT211
EV14GATA53
EV1
EV94
GT23
GT575
GATA417GT310
EV104
GATA28
GT271
GT011
DlrFCB17

120124217221102110115115155167202202130146219221116118140146220220108110134140228232096096123127
199203120120221221094098115115159179198198130146217217116126138146224224108108134144228232096096123123203207120120217229098106115117155167
198202144162217219116116146148212240108110134140224232096096123123205221118120225229102102115117157167202202146146213221116116138138216232108108140140216236096096123123
199215120126225229102102115115155167202202148152217225112116138138216224108110140144232232096096123129199207120122217233094102115115161161202202146146217219116116138146212228110110134134212220094096123129199203120120213225098102115115155155198202148158217219116116138148220224108108134142224232094096123123209215120124221229102102115115155159202202148152221223116118146146224224108108140142216232094096123127
183205118122217229094106115119155155198202146148217219116116138148212220108108140144232232096096123123
201203120124221225098102115121155155198202130138217221116120138138216220108114140142212232096096123123203211

0.7
0.9
0.7
0.4
0.6
0.4
0.8
0.9
0.5
0.6
0.7
0.4
0.8
0.8
0.3
0.4
1.0

hi

0.765

0.471

0.765

0.471

0.647

0.588

0.647

0.647

0.647

0.765

0.641

hj

Genotypes are represented by 6-digits codes (each allele is coded by 3-digits), and heterozygotes are indicated with red color.
In this example, 109 of 170 assayed genotypes are heterozygous (overall HO =0.641).
Expected heterozygosity (HE) is deﬁned as the expected proportion of individuals heterozygous from observed allele frequencies, assuming the population is in HWE (see Box 1), as shown in Table 2.2. In the case of two alleles at a locus with their respective frequencies of p and q, the expected heterozygosity HE =2pq. When there are more than two alleles, it is simpler to calculate
HE as follows:

H E =1 −

k

p

2
i

i=1

where pi is the frequency of the i-th allele, and k is the number of alleles. HE is normally used rather than HO since it is less affected by sampling.

comparable genetic diversities among sampling areas
observed in microsatellite DNA.
Genetic diﬀerentiations and structuring
Pairwise FST-like estimates for microsatellites did not
36

show any genetic diﬀerentiations between samplingareas (Table 4), which was consistent with the pairwise
conventional FST estimates for mtDNA (Table 4). Heterogeneity tests also showed no diﬀerences in microsatellite
allele (P=0.679, d.f.=34, χ 2 =29.69) and mtDNA haplotype

Genetic study of stock structure in North Pacific sei whales

Table 2.2
Allele frequencies and three statistics of genetic diversity, i.e. number of alleles (A), observed (HO) and expected (HE)heterozygosity, per microsatellite loci based on the data set in Table 1.1
Alleles (i)MicrosatelliteA
lociallele 1 allele 2 allele 3 allele 4 allele 5 allele 6 allele 7 allele 8 allele 9 allele 10
EV21
GGAA520GATA98GT211
EV14GATA53
EV1
EV94
GT23
GT575
GATA417GT310
EV104
GATA28
GT271
GT011
DlrFCB17

0.10
0.05
0.15
0.80
0.50
0.30
0.15
0.05
0.05
0.50
0.15
0.70
0.30
0.10
0.15
0.80
0.05

0.60
0.20
0.20
0.10
0.05
0.70
0.05
0.40
0.75
0.05
0.15
0.25
0.40
0.10
0.85
0.10
0.20

0.10
0.25
0.50
0.05
0.10

0.15
0.20
0.10
0.05
0.10

0.05
0.25
0.05

0.05

0.20

0.05

0.05
0.25
0.10
0.30
0.25
0.05
0.15
0.05

0.35
0.20
0.05
0.15
0.30

0.20
0.05
0.05

0.10
0.05

0.05

0.05

0.05

0.05

0.15
0.10

0.10

0.50

0.05

0.10
0.05

0.25

0.10

0.10

0.05

0.05

0.05

0.10

0.05

Means

HO

HE

0.7
0.9
0.7
0.4
0.6
0.4
0.8
0.9
0.5
0.6
0.7
0.4
0.8
0.8
0.3
0.4
1.0

0.60
0.79
0.68
0.35
0.69
0.42
0.80
0.73
0.42
0.64
0.80
0.45
0.71
0.71
0.26
0.34
0.86

5.1 0.64

0.60

5
6
5
4
6
2
8
6
5
4
7
3
4
7
2
3
10

The averaged statistics over the seventeen loci were shown at the bottom (overall H E =0.60).
For haplotypic data such as mitochondrial DNA, a simple estimate of diversity, which is equivalent to HE for diploid data,
called as haplotype diversity (H) can be also calculated as H=1−∑ki=1p2i, where k is the number of haplotypes, and pi is the frequency of haplotype i (Nei, 1987). In the case of full sequence comparison, nucleotide diversity (π), which is equivalent to H
at the nucleotide level, is given by π=∑ijxixj π ij, where pij is the proportion of diﬀerent homologous nucleotide sites between
haplotypes i and j, and xi and xj are the frequencies of haplotypes i and j, respectively (Nei, 1987).

Box 3. Measuring genetic diﬀerentiation
F-statistics, which is ﬁrst introduced by Wright (1951), is one of important estimators to describe population genetic structure.
This statistics partitions overall genetic diversities into components within and among populations, and can be calculated using
heterozygosity as the following equations (Nei, 1987):

FIS =(hs − hi ) / hs
FIT = (ht − hi ) / ht
FST = (ht − hs ) / ht
where hi is the HO averaged across all populations, hs is the HE averaged across all populations, and ht is the HE for the total population. Two of which, i.e. FIS and FST, are widely used as estimators to describe genetic structure. The FIS (inbreeding coeﬃcient)can be positive indicating a deﬁciency of heterozygotes when inbreeding or population subdivision occur within populations,while negative values indicating an excess of heterozygotes will be observed when outbreeding occurs within populations. The
FST (ﬁxation index) is a common estimator for genetic diﬀerentiation among populations, and varies from 0 (no diﬀerentiation
between populations) to 1 (ﬁxation of diﬀerent alleles in populations).

37

Technical Reports of the Institute of Cetacean Research (2019)
Table 2
Summary statistics for 17 microsatellite loci in North Paciﬁc sei whale in each sampling area.
Microsatellite
loci
EV21
GGAA520GATA98GT211
EV14GATA53
EV1
EV94
GT23
GT575
GATA417GT310
EV104
GATA28
GT271
GT011
DlrFCB17
OverallWestern
n

A

30 5
30 7
30 5
30 4
30 12
30 3
30 12
30 6
30 6
30 4
30 7
30 3
30 5
30 9
30 3
30 4
30 14
6.41

CentralEastern

HE

HWE

FIS

n

A

HE

HWE

FIS

n

A

HE

HWE

FIS

0.706
0.789
0.686
0.295
0.818
0.413
0.814
0.655
0.554
0.603
0.791
0.513
0.743
0.821
0.242
0.415
0.890
0.632

0.667
0.648
0.441
1.000
0.625
0.291
0.814
0.222
0.335
0.378
0.956
1.000
0.847
0.082
1.000
0.889
0.310
0.939

−0.136−0.014−0.0700.099
0.105−0.0500.017
0.085−0.146−0.108−0.0550.026−0.0780.027−0.105−0.128−0.049−0.034

1501
1499
1485
1503
1326
1500
1502
1502
1503
1503
1326
1502
1500
1503
1503
1503
1502

6
9
7
6
22
3
16
8
14
5
9
5
10
11
4
6
20
9.47

0.644
0.802
0.734
0.308
0.865
0.494
0.836
0.685
0.606
0.593
0.782
0.483
0.726
0.812
0.140
0.449
0.872
0.637

0.867
0.965
0.792
0.019
0.846
0.990
0.583
0.519
0.308
0.657
0.449
0.160
0.878
0.559
0.579
0.899
0.106
0.790

−0.0040.004−0.005
0.016
0.001
0.003
0.010
−0.0230.017
0.024−0.005−0.020
0.003
0.016
0.006
0.006
0.012
0.003

196
193
159
194
74
192
196
196
195
190
75
196
191
196
196
196
188

6
9
6
4
14
3
17
6
11
6
8
4
8
10
3
4
17
8.00

0.651
0.792
0.748
0.316
0.861
0.508
0.835
0.702
0.600
0.577
0.785
0.483
0.703
0.825
0.130
0.458
0.882
0.639

0.295
0.833
0.043
0.124
0.862
0.819
0.407
0.287
0.741
0.816
0.561
0.364
0.678
0.719
1.000
0.751
0.165
0.753

−0.0110.059
0.050−0.078−0.0200.015
0.034−0.032
0.008
0.061
0.049
−0.0770.017−0.015
0.022
0.020
0.023
0.007

Table 3
Summary statistics for mtDNA control region in North Paciﬁc sei whale in each sampling area.
Western
mtDNA

CentralEastern

n

h

S.D.

π (%)

S.D.

n

h

S.D.

π (%)

S.D.

n

h

S.D.

π (%)

S.D.

30

0.908

0.036

0.789

0.454

1500

0.925

0.004

0.803

0.446

203

0.927

0.010

0.797

0.445

Table 4
Pairwise FST between sampling areas. Upper and below diagonals indicate results from microsatellite and mtDNA.
Sampling area

Western

Western
CentralEastern−0.00070.0004

CentralEastern−0.0002−0.0010−0.00140.0002(P=0.699, χ 2 =142.09) frequencies among sampling areas.
The clustering patterns in each K estimated by the
program STRUCTURE also did not infer a distinct genetic
structuring of this species (Figure 3). These results suggested absence of genetic structure among sei whales
distributed in the pelagic area of the North Paciﬁc investigated in this study, which was also supported by thefact that genetic diversities were not diﬀerent among
sampling areas in both markers, and there were no departures from HWE in each of sampling areas.

38

Figure 3.

Bar plot of membership probabilities in STRUCTURE
analyses at K=2–3 for North Paciﬁc sei whales. Eachindividual is characterized by a thin vertical line,
which is divided into K colored segments on the basisof the individualʼs membership fractions in K clusters.

Genetic study of stock structure in North Pacific sei whales

CONCLUSIONSThe present pairwise FST estimates and heterogeneity
tests did not show any signiﬁcant genetic diﬀerentiations
among the three sampling areas for both genetic markers. This was also supported by the STRUCTURE analysis
suggesting a lack of genetic structure of this species.
These ﬁndings implied that the North Paciﬁc sei whale
consists of a single breeding population at least in the pelagic areas surveyed. This inference was consistent withthe comparable genetic diversities, i.e. h, π, HE, among
sampling areas as well as the results from the tests of
HWE and FIS estimates in this study. Kanda et al. (2015b)
suggested that a single stock of sei whale occurs in theentire North Paciﬁc based on genetic and non-genetic
evidence. However, this study did not covered the coastal
areas in both sides of the North Paciﬁc. Additional genetic
samples from the coastal areas are required to conﬁrm
this hypothesis.

ACKNOWLEDGEMENTS
Thanks are due to the researchers and crew membersthat participated in JARPNII and IWC-POWER surveys
for the collection of genetic samples of the sei whales,to Hiroyuki Oikawa and Tomonari Iida (ICR) for theirlaboratory work and to Naohisa Kanda (JANUS-Japan) for
preparing most of the microsatellite data set used in thisstudy.

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