SC/F16/JR40

Updated genetic analyses based on mitochondrial andmicrosatellite DNA indicated no sub-structure of the ‘O’ stock
common minke whale in the western North Pacific
Luis A. Pastene1, Mutsuo Goto1, Mioko Taguchi2 and Toshihide Kitakado3
1

Institute of Cetacean Research, 4-5 Toyomi-cho, Chuo-ku, Tokyo 104-0055, Japan
Okitsunaka-cho 1347-3-302, Shimizu-ku, Shizuoka-shi, Shizuoka 424-0204, Japan
3
Tokyo University of Marine Science and Technology, 1-5-7 Konan, Minato-ku, Tokyo 108-0075, Japan
2

ABSTRACTThis study examined the genetic population structure of ‘O’ stock common minke whale in the western
North Pacific based on mitochondrial DNA control region sequencing (487bp) and microsatellite DNA(16 loci). Samples used in the tests of homogeneity were obtained during the surveys of the JARPN and
JARPNII in sub-areas of the Pacific side of Japan between 1994 and 2014 (n= 2,071 for microsatellite; n=2,070 for mtDNA). Whales were assigned to the ‘O’ stock by the analysis of STRUCTURE presented in
Pastene et al. (2016: SC/F16/JR38). Tests based on both genetic markers and different grouping of thesamples showed no evidence of sub-structuring in the ‘O’ stock common minke whale in the Pacific sideof Japan. A simulation exercise showed that the statistical power of the homogeneity test was high. In
addition, a Discriminant Analysis of Principal Components (DAPC) based on the total samples used in
Pastene et al. (2016: SC/F16/JR38) showed clear differentiation between J and O stock whales but noevidence of sub-structuring within the O stock samples. Consequently the results of this study suggested alow plausibility for the hypothesis of sub-division of the O stock common minke whale into OW and OE.
INTRODUCTION
Discussions on stock structure of western North Pacific common minke whale have taken place at theInternational Whaling Commission Scientific Committee (IWC SC) since 1993, with the latest
discussions occurring during the RMP Implementation Review (IR) in 2013 (IWC, 2014). Samples anddata obtained by the JARPN and JARPNII have been essential for the discussions on stock structure at the
IWC SC.
A total of 22 sub-areas (Figure 1) and three stock structure hypotheses (Figure 2) were used during thelast IR of western North Pacific common minke whale (IWC, 2013). The plausibility of the three stock
structure hypotheses was discussed at the 2012 IWC SC Annual Meeting. A group of five geneticists
summarized their interpretation of the relative support for and against the five hypothesized stocks
involved in the different hypotheses (JE, JW, OE, OW and Y) (IWC, 2013 pp135). It should be noted that
their evaluation was based on the available genetic information only despite plenty of non-genetic
information was available for the IR. The result of their evaluation is reproduced in Table 1.
Despite this effort by geneticists it was not possible for the IWC SC to agree on the plausibility of thethree stock structure hypotheses. This was in part because the IWC SC has not been able to design an
objective and balanced method to evaluate plausibility of stock structure hypotheses. As a consequence,
all three main stock structure hypotheses were ‘no agreement’ and were therefore treated as if they had
been assigned ‘Medium’ plausibility in the trials (IWC, 2013 pp126).
In this study these three hypotheses are further evaluated by conducting hypothesis testing using the total
genetic samples available from JARPN and JARPNII till 2014 in the relevant sub-areas shown in Figure 1.
This analysis focused only in the investigation of plausibility of O stock sub-structure into OW and OE,
given the fact that the genetic evidence for sub-division of the J stocks into JW and JE is low (Table 1).

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We examined differences in allele and haplotype frequencies among whales in the sub-areas, particularly
we tested whether the frequencies for the whales in the coastal sub-areas (particularly for sub-area 7CN
where the OW has been proposed under one of the three hypotheses), differ significantly from whales inoffshore areas.
This study also analyzed other pieces of evidences that, according to the five geneticists, provide‘Moderate’ support for the hypothesis of the OW (Table 1). For example the results based on PCA(Gaggiotti and Gascuel, 2011) and the suggestion of one or more additional O stocks based on initial one
and two locus Wahlund effects (Waples, 2011).
The analyses conducted considered most of the recommendations from the 2009 JARPNII review
workshop (IWC, 2010) and from subsequent IWC SC Annual meetings (see Annex 5 of Tamura et al.,
2016: SC/F16/JR1).
MATERIALS AND METHODS
Samples
For the hypothesis testing, only samples of the ‘O’ stock common minke whale as assigned by the
STRUCTURE analysis (over 90% probability) (see Pastene et al., 2016: SC/F16/JR38) were used.
Common minke whales samples of the JARPNII offshore component were taken from 2000 to 2014. The
JARPN samples from 1994 to 1999 were also used in this study. Table 2 shows the number of individuals
used in the present microsatellite and mtDNA analyses, by sub-area and sex, and Figure 3 shows thesighting positions of the collected O stock whales.
The Discriminant Analysis of Principal Components (DAPC) was based on the total samples of J and O
stocks (e.g. the same samples used in Pastene et al., 2016: SC/F16/JR38).
DNA extraction
The IWC guidelines for DNA data quality (IWC, 2009) were followed as much as possible (see Kanda etal., 2014). Skin tissues of minke whales taken during the JARPNII were stored in 95% ethanol until DNA
extraction. Genomic DNA was then extracted from 0.05g each of the skin tissues using standard
proteinase K, phenol-chloroform procedure described by Sambrook et al. (1989). Extracted DNA wasstored in the TE buffer (10 mM Tris-HCl, 1 mM EDTA, pH 8.0).
Microsatellite analysis
Microsatellite polymorphisms were analyzed using 16 primers: EV1, EV14, EV21, EV37, EV94,(Valsecchi & Amos 1996), GT23, GT195, GT211, GT310, GT509, GT575 (Bérubé et al., 2000),
GATA28, GATA98, GATA417, TAA31 (Palsbøll et al., 1997), and DlrFCB14 (Buchanan et al., 1996).
EV1, EV14, EV21 were developed from sperm whale, EV37, EV94, GT23, GT310, GT575, GATA28,
GATA98, GATA417, TAA31 were from humpback whale, and DlrFCB14 from beluga whale. All GT,
EV, and DlrFCB primers were dinucleotide repeat, TAA31 trinucleotide repeat, and all GATA primers
tetranucleotide repeat. Most of the primers used here were already tested for amplification on common
minke whales by these authors. Primer sequences and PCR profiles follows those of the original authors
with slight modifications.
PCR amplifications were performed in 15l reaction mixtures containing 10-100ng of DNA, 5 pmole ofeach primer, 0.625 units of Ex Taq DNA polymerase (Takara Shuzo), and 2mM of each dNTP, and 10x
reaction buffer containing 20mM MgCl2 (Takara Shuzo). PCR amplifications followed the
manufacturer’s instructions for the use of Ex Taq DNA polymerase (Takara Shuzo). Amplified productswith internal size standard (GENESCAN400HD, Applied Biosystems Japan) were run on a 6%
polyacrylamide denaturating gel (Long RangerTM) using an BaseStationTM 100 DNA fragment analyzer(Bio-Rad). Although alleles were visualized using CartographerTM software specifically designed for the
BaseStation, allelic sizes were determined manually in relation to the internal size standard and common
minke whale DNA of known size that were rerun on each gel.

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Data analysis
Level of polymorphism
The number of alleles per locus, expected heterozygosity per locus and inbreeding coefficient per locus
were calculated using the software FSTAT 2.9.3 (Goudet, 1995). Statistical tests for deviations from theexpected Hardy-Weinberg genotypic proportions were conducted using the software GENEPOP 4.0(Rousset, 2008).
Homogeneity test
In order to detect genetic differences within the ‘O’ stock sample of common minke whale, conventional
hypothesis testing procedure was conducted using heterogeneity test in frequencies of the microsatellite
alleles among sub-areas. Null hypothesis to be tested was if the samples came from a genetically samegroup of common minke whales. If statistically significant allele frequency differences exist, then it could
indicate these samples came from genetically different stocks of common minke whales. Probability test
(or Fisher’s exact test) implemented in GENEPOP 4.0 (Rousset, 2008) was used to conduct theheterogeneity test. Statistical significance was determined using the chi-square value obtained fromsumming the negative logarithm of p-values over the 16 microsatellite loci (Sokal and Rohlf, 1995). The
False Discovery Rate (FDR) approach (Benjamini and Yekutieli, 2001) was used for adjustment of pvalue in case of multiple comparisons. FST was calculated using FSTAT 2.9.3 (Goudet, 1995).
Assessment of the statistical power
In order to assess the statistical power for the homogeneity test (e.g., Waples and Gaggiotti, 2006),
genotypic data were generated using the computer software EASYPOP (Balloux, 2001), andheterogeneity tests were conducted with these generated data. Two stocks (OW and OE) were assumed
which consists of diploid individuals with constant sizes and equal sex ratio with random mating. Ratiosof effective population size to census population size of 1/3 and 1/4, were assumed (Roman and Palumbi,2003). The effective population sizes were thus set as 1/3 and 1/4 of the census population sizes. A census
population size of 20,000, was used. For each generation, the simulation produces genotype data for 16
independent microsatellite loci for each individual. The number of the loci simulated and maximum
number of the allelic states (27) was set based on the observed data. The bidirectional migration model
was assumed with an equal migration rate (m). Migration rates ranged from 0.01 to 0.5. A range of FST
between the two assumed stocks was obtained, assuming island model. Mutation rate of 5x10-4 waschosen to represent microsatellite loci. A total of 100 replicates were made for each simulation parameter
set. A total of 5,000 generations for each replicate before collecting data, was run. In the final generationof each replicate, sample of 140 individuals were taken from each population for genetic analysis. The
sample size of 140 in this study was approximately equals to the sum of the samples size from SA7 WR
and SA7E where the OE stock was assumed, which is considered conservative given that larger sample
size was actually used for OW. Homogeneity tests were conducted for the generated data set using
pairwise tests of differentiation option in the FSTAT2.9.3 (Goudet, 1995). In this option, for each pair ofsamples, multi-loci genotypes are randomized between the two samples. The overall loci G-statistic isgiven and statistical significance was decided with a table wide level of significance at 5%.
Discriminant Analysis of Principal Component
The Discriminant Analysis of Principal Components (DAPC) was used to identify and describe clustersof genetically related individuals (Jombart, et al., 2010). DAPC relies on data transformation using PCA
as a prior step to DA which ensures that variables submitted to DA are perfectly uncorrelated, and that
their number is less than of analysed individuals. Along with the assignment of individuals to clusters,
DAPC provides a visual assessment of between-population genetic structures, permitting to infer complex
patterns such as hierarchical clustering or clines (Jombart, et. al., 2010). Following these authors, the Kmeans clustering of principal components was used to identify groups of individuals. K-means relies on
the same model as DA to partition genetic variation into a between-group and a within-group component,
and attempts to find groups that minimize the latter (Jombart, et al., 2010). K-means was run withdifferent numbers of clusters, each of which gives rise to a statistical model and an associated likelihood.

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DAPC was used to a two data sets:i)
ii)

Only individuals belonging to the J and O stocks with a probability larger than 90%
according to the criterion used in Pastene et al. (2016: SC/F16/JR38).
Only individuals belonging to the O stock with a probability larger than 90% according to
the criterion used in Pastene et al. (2016: SC/F16/JR38).

Mitochondrial DNA
Sequencing analysis of the 487bp control region of mtDNA was conducted using the primers light-strand
MT4 (Árnason et al., 1993) and heavy-strand P2 (5'-GAAGAGGGATCCCTGCCAAGCGG-3'; Hori etal., unpublished). PCR products were purified by MicroSpin S-400HR columns (Pharmacia Biotech).
Cycle sequencing was performed with the same primers, using BigDye terminator cycle sequence Kit(Applied Biosystems, Inc). The cycle sequencing products were purified by AutoSeq G-50 spin Columns(Pharmacia Biotech). The labeled sequencing fragments were resolved by electrophoresis through a 5%
denaturing polyacrylamide matrix on an ABI 377 or ABI3100 Automated DNA Sequencer (Applied
Biosystems, Inc), following the protocols of the manufacture. For each sample both strands weresequenced.
Data analysis
Level of polymorphism
The number of haplotypes and haplotype diversity were calculated following Nei (1987). The nucleotide
diversity (Nei, 1987: equation 10.5) and its standard error for population sampling and stochastic
processes were calculated from the pair-wise differences between the mtDNA sequences using theKimura’s 2- parameter adjustment (Kimura, 1980).
Homogeneity test
Conventional hypothesis testing procedure was conducted using heterogeneity test in mtDNA haplotype
frequencies among the samples. The randomized chi-square Test of Independence (Roff and Bentzen,
1989) and the conventional FST were used to investigate the temporal/spatial differentiation of mtDNA
variation. In each test a total of 10,000 permutations of the original data were performed. Tests wereconducted for all samples combined as well for males and females separately. A p-value smaller than 0.05
was used as a criterion to reject the null hypothesis of panmixia. The FDR approach was used for
adjustment of p-value in case of multiple comparisons. FST for mtDNA was calculated based on theanalysis of molecular variance (AMOVA) (Excoffier et al., 1992).
RESULTS
Microsatellites
Level of polymorphism
All 16 loci analyzed were polymorphic (Table 3). The total number of alleles per locus ranged from twoat the EV21 to 27 at the EV1 with an average of 11.9. Expected heterozygosity at each of the loci ranged
from 0.33 at EV21 to 0.88 at GT23 and GT211 with an average of 0.70. No significant deviation from theexpected Hardy-Weinberg genotypic proportions was observed. The magnitude of FIS was close or varied
around zero as expected if the samples were collected from the same stock.
Homogeneity test
Table 4 shows the results of the heterogeneity tests for different grouping of the samples. No significant
differences were found for males, females and sex combined in the three stepwise tests conducted. For
males and for a single locus (GATA28) a small p-value was found in the comparison 7CS-7CN / 7WR,
which remained significant after the FDR correction. Given the number of tests carried out and the factthat the test for mtDNA showed no significant differences in this comparison, no biological meaning is
assigned to this result.

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Assessment of statistical power for the tests of homogeneity
Table 5 shows the input parameters used and the results of simulation analysis to assess the statisticalpower for the tests homogeneity. The simulation attempted to test the statistical power for very small
genetic divergence between two samples. For instance, estimated FST values were all smaller than 0.01.
For the homogeneity tests the input parameters for simulating OW and OE were all the same. Highstatistical power was detected with m=0.01 and 0.02.
DAPC
Figures 4 and 5 show the results of PCA and DAPC analyses, respectively, for the case all samples of J
and O stocks were used. Figure 5 was constructed without ‘training data’, which means that no previous
information on J and O stock was used. The approach discriminated clearly between the both stocks (seethe STRUCTURE-like plot in Figure 5). Figures 6 and 7 show the results of PCA and DAPC analyses,
respectively, for the case O stock samples only were used. Samples from different sources andgeographical origin were widely distributed among clusters in Figure 6. When forced to K=2, samples
from different sources and geographical origin were equally distributed in the two groups, and the
STRUCTURE-like figure did not discriminated between these two groups (Figure 7).
mtDNA
Level of polymorphisms
Table 6 shows the values for the mtDNA diversity. Both indices are relatively high.
Homogeneity test
Table 7 shows the results of the tests. For the three stepwise tests conducted no significant heterogeneity
was found for males, females and sexes combined.
DISCUSSION
As noted earlier, the main objective of the present genetic analyses was the evaluation of the plausibilityof additional structure in the O stock common minke whale, e.g. the plausibility of an OW stock incoastal areas of Japan. For doing this, the total samples obtained by JARPN and JARPNII till 2014 wereanalyzed with two genetic markers, which are commonly used in most of the genetic studies on stock
structure presented and discussed at the IWC SC meetings. It is believed that the combined use of
mtDNA control region sequences and microsatellite DNA at 16 loci on large sample sizes is a strong tool
to investigate genetic differences in weakly differentiated stocks.
Firstly the present study addressed all ‘simple issues’ recommended by the 2009 JARPNII review
workshop. For example the workshop recommended description of procedures to ensure data quality. In
response to this recommendation and other one from the JARPAII review workshop, a document was
prepared and presented to the IWC SC in 2014 (Kanda et al., 2014). The IWC SC welcomed thisdocument and agreed that it responded appropriately to the recommendation (IWC, 2015). Anotherexample is the 2009 workshop recommendation to use the False Discovery Rate approach instead of the
Bonferroni correction for the adjustment of p-values in cases of multiple comparisons. This approach wasused in the present study.
More extensive and long-term recommendations from the 2009 JARPNII review workshop were
responded in this document as well as in other stock structure documents presented previously or to thisworkshop. See more details on the responses to previous recommendations in Annex 5 of Tamura et al.
(2016: SC/F16/JR01).
On the genetic information providing ‘moderate’ evidence for the case of OW (Table 1), three wereconsidered the most relevant: hypothesis testing, PCA and one and two locus Wahlund effects.
Hypothesis testing
Table 1 shows that one of the evidence for the case of OW was ‘significant FST differentiation comparing
non-purged samples’. Results of tests based on non-purged samples are very difficult to interpret. This is
the reason of why the IWC SC had recommended genetic analyses separately for J and O stocks, and5specifically recommended the use of alternative methods for exclusion of J stock animals in the analyses
focused on the O stock (IWC, 2003). In fact several genetic studies have conducted analyses separately
for the O stock based on the output of the work of Kanda et al. (2009a; b; 2010), e.g. hypothesis testing(Kanda et al., 2009c) and PCA (Gaggiotti and Gascuel, 2011) based on purged samples.
During IWC SC discussions the circularity for the use of purged samples for hypothesis testing analysesof the O stock was argued, because this process would exclude individuals that are geneticallyintermediate to the mean O and J genotypes (presumably in the unknown samples) (IWC, 2010). It wasalso noted in those discussions that the problem largely would disappear if all or virtually all assignments
can be made with a high degree of certainly (IWC, 2010).
As shown by Pastene et al. (2016: SC/F16/JR38), the ‘unknown’ samples are not whales geneticallyintermediate to the O and J stocks but rather they are the results of the low power due to the number ofmicrosatellite loci. Eventually all or virtually all individuals would be assigned to either J or O stock if alarger number of loci are used. Furthermore the unknown individuals are widely distributed in the western
North Pacific, both temporally and geographically. Therefore this provides a strong rational for the use of
J-purged samples in further analyses on O stock substructure using alternative analytical approaches.
As expected, both genetic markers provided no evidence of significant heterogeneity suggesting that thecommon minke whales used in the analyses belong to a single stock. The FST values were very small andnone was significantly different from zero. Following recommendations from the 2009 JARPNII review
workshop an assessment of the power of the homogeneity test was conducted (Kanda et al., 2009d, thisstudy), and the power was concluded to be high.
PCA and DAPCIn providing ‘moderate’ support for OW, Table 1 stated that ‘PCA results using J-purged O stock sample
provided support for an additional stock in OW compared to OE’. This conclusion in Table 1 was basedon the PCA work by Gaggiotti and Gascuel (2011). These authors concluded that ‘these results suggestthat minke whales in sub-area 7-9 exhibit a hierarchical structure that comprises two main genetic clusters
corresponding to the putative J and O stocks identified by STRUCTURE analyses and further
substructuring within the O-stock. This substructuring, however, is not correlated with body length or
geographic position so for the moment it has no clear biological explanation’. It should be noted that theauthors did not speculate on whether or not such ‘substructuring’ was related to possible additional stock
structure in common minke whales.
It should be also noted that Gaggiotti and Gascuel (2011) noted that there was a good concordance
between results obtained with STRUCTURE and PCA methods and suggested that the substructuring of
the O-stock would be revealed by a STRUCTURE analysis focused only on this stock. Kanda et al.
(2010) conducted STRUCTURE analyses based only on O stock samples but they did not find any
evidence of substructuring.
Based on this brief summary it is concluded that the previous PCA evidences in support of an OW stockcannot be considered ‘moderate’ as indicated in Table 1, but very weak.
The DAPC approach used in this study is a new methodological approach, which retain all assets of DA
without being burdened by its limitations. It relies on data transformation using PCA as a prior step to DA,
which ensure that variables submitted to DA are perfectly uncorrelated, and that their number is less thanthat of analysed individuals (Jombart et al., 2010). This approach used on the large microsatellite DNA
data set of common minke whale provided no evidence of substructuring of O stock in sub-areas 7, 8 and
9.
One and two locus Wahlund effects
In providing ‘moderate’ support for OW, Table 1 noted the ‘suggestion of one or more additional stocks
based on initial one and two locus Wahlund effects’. This suggestion was originally provided by Waples(2011): when only two distinct stocks exist in a given area, the largest departure from equilibrium(departure from FIS) or higher linkage disequilibrium should be seen at the loci or pairs of loci that show
the largest or strong allele frequency differences between the two distinct stocks (describe as θ). Because6the observed relationship of these genetic indices in the samples from SA 7, 2 and 11 were weaker thanthe expected relationship estimates from artificial mixtures of only J and O individuals, Waples (2011)
suggested that samples might have contained individuals from more than two stocks.
It should be noted that Waples (2011) recognized the novel character of the approach used and that ‘more
evaluations are needed to determine how robust the results are’. Apart from this recognition, Kanda andHatanaka (2012) carried out a simulation exercise in response to Waples (2011)’s analyses and concluded
that the weaker observed relationship of the indices could be due to the small sample size rather than the
mixture of individuals from more than two stocks.
Based on this brief summary it is concluded that the Waples (2011)’s evidence in support of an OW stockcannot be considered ‘moderate’ as indicated in Table 1, but very weak.
In conclusion the main evidences listed in Table 1 as moderate support to the existence of a OW stock areconsidered weak. The results of the genetic analyses in the present study (hypothesis testing including anassessment of the statistical power and DAPC) provided strong support for a single O stock in sub-areas 7,
8 and 9.
ACKNOWLEDGEMENTS
We gratefully acknowledge the researchers and crew members that participated in JARPN/JARPNII
surveys for the collection of genetic samples. We thank H. Oikawa and S. Azumi (ICR) who collaborated
in the process of DNA extraction and appreciate Megumi Takahashi (TUMSAT) for her helps on PCA
related works. We also thank N. Kanda (JANUS-Japan) for preparing the microsatellite data set used inthis study.
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be explained by different mixture fractions of the same two core stocks, or is it necessary to postulate
an additional stock(s)? Paper SC/63/RMP7 presented to the IWC Scientific Committee, June 2011
(unpublished). 11pp.
Waples, R.S., and Gaggiotti, O. 2006. What is a population? An empirical evaluation of some genetic
methods for identifying the number of gene pools and their degree of connectivity. Mol. Ecol.
15:1419-1439.

Table 1. Evaluation of the main components of stock structure hypotheses of common minke whale in thewestern North Pacific based on genetic data (IWC, 2013).

Table 2. Sample size of the ‘O’ stock whales used in this study, by sub-area and sex. Whales wereassigned to the ‘O’ stock based on the analysis of STRUCTURE (see SC/F16/JR39).
Sub-area
Microsatellites
mtDNA
Males
Females
Total
Males
Females
Total
11
28
20
48
28
20
48
7CN
534
205
739
534
205
739
7CS
219
220
439
219
220
439
7E
41
4
45
41
4
45
7WR
80
9
89
80
9
89
8
207
17
224
206
17
223
9
439
48
487
439
48
487
Total
1548
523
2071
1547
523
2070

9

Table 3. The number of alleles (A), expected heterozygosity (HE), inbreeding coefficient (FIS), and test
result for the expected Hardy-Weinberg genotypic proportions (HW) at 16 microsatellite loci of ‘O’ stockof western North Pacific minke whale. Bold indicate statistically significant differences after FDR
correction.

HE
0,73
0,83
0,82
0,84
0,82
0,67
0,88
0,85
0,63
0,76
0,88
0,33
0,37
0,57
0,81
0,39
0,70

A
Microsatellite loci
12
EV37
27
EV1
14
GT310
20
GATA28
12
GT575
8
EV94
15
GT23
20
GT509
6
GATA9813
GATA417
16
GT211
2
EV21
5
DlrFB14
6
EV14
12
GT195
3
TAA31
11,94
Overall

10

HW
0,773
0,587
0,899
0,052
0,029
0,791
0,223
0,857
0,490
0,026
0,179
0,577
0,264
0,438
0,040
0,229
0,057

F IS
0,000
0,014
0,001
0,005
-0,011
0,002
0,007
0,005
0,002
0,003
0,000
-0,0130,010-0,001-0,0050,010
0,002

Table 4. Results of the microsatellite DNA heterogeneity test among ‘O’ stock minke whale fromdifferent sub-areas, by sex and total samples. Bold indicate statistically significant differences after FDR
correction.
7CS x 7CN
Microsatellite loci
EV37
EV1
GT310
GATA28
GT575
EV94
GT23
GT509
GATA98GATA417
GT211
EV21
DlrFB14
EV14
GT195
TAA31
Overall

P -values
Sex combined
0,055
0,924
0,288
0,720
0,572
0,583
0,742
0,650
0,530
0,589
0,052
0,118
0,117
0,514
0,077
0,213
0,181

Male
0,025
0,655
0,759
0,473
0,779
0,391
0,816
0,682
0,790
0,690
0,081
0,458
0,202
0,384
0,122
0,486
0,447

Sex combined
0,832
0,263
0,412
0,020
0,625
0,163
0,855
0,808
0,877
0,421
0,923
0,635
0,978
0,436
0,884
0,172
0,741

P -values
Male
0,590
0,490
0,335
0,001
0,828
0,186
0,542
0,776
0,745
0,784
0,901
0,474
0,862
0,379
0,678
0,205
0,438

Sex combined
0,344
0,931
0,216
0,485
0,817
0,568
0,467
0,350
0,349
0,764
0,577
0,456
0,216
0,680
0,308
0,792
0,847

P -values
Male
0,243
0,933
0,373
0,418
0,548
0,313
0,330
0,385
0,243
0,440
0,779
0,525
0,163
0,721
0,354
0,913
0,711

Female
0,869
0,924
0,327
0,707
0,024
0,864
0,363
0,891
0,180
0,330
0,637
0,116
0,520
0,451
0,363
0,684
0,574

Sex combined
0,001
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,001
0,001
0,000
-0,001
0,000
0,001
0,000

F ST
Male
0,002
0,000
-0,0010,000
0,000-0,001-0,0010,000
0,000-0,0010,001-0,001-0,001-0,001
0,000
0,000
0,000

Female-0,001-0,001
0,001
0,000
0,008
-0,0010,000-0,001
0,002
0,000
0,000
0,004
-0,0010,000
0,000-0,002
0,001

F ST
Male
-0,001
0,001
0,000
0,000
-0,0020,005-0,0010,001-0,002-0,003-0,001-0,001-0,003-0,002-0,001-0,003-0,001

Female-0,0060,013-0,018-0,0050,008
0,013-0,0140,005-0,007-0,0150,002-0,008-0,022-0,014-0,010-0,029-0,005

F ST
Male
0,001
-0,0010,001
0,000-0,0010,001
0,001-0,0010,000
0,000-0,0010,000
0,001-0,0010,000-0,0010,000

Female-0,0020,001-0,002-0,005-0,002-0,007-0,0040,000-0,005-0,0060,000-0,004-0,0060,004-0,001
0,013-0,0027CS-7CN x 7WR
Microsatellite loci
EV37
EV1
GT310
GATA28
GT575
EV94
GT23
GT509
GATA98GATA417
GT211
EV21
DlrFB14
EV14
GT195
TAA31
OverallFemale
0,343
0,053
0,972
0,738
0,390
0,401
0,841
0,736
0,267
0,071
0,710
0,554
0,753
0,920
0,562
1,000
0,804

Sex combined-0,0020,000
0,000-0,001-0,001
0,005-0,0020,000-0,002-0,0020,000-0,002-0,003-0,002-0,001-0,003-0,001

7CS-7CN-7WR x 7E x 8 x 9Microsatellite loci
EV37
EV1
GT310
GATA28
GT575
EV94
GT23
GT509
GATA98GATA417
GT211
EV21
DlrFB14
EV14
GT195
TAA31
OverallFemale
0,462
0,541
0,471
0,380
0,892
0,954
0,482
0,347
0,576
0,952
0,459
0,668
0,470
0,790
0,303
0,026
0,791

11

Sex combined
0,001-0,0010,001-0,001
0,000
0,000
0,000
0,000
-0,001
0,000
0,000
0,000
0,000
-0,001
0,000
0,000
0,000

Table 5. Input parameter sets used for generating simulated data set using EASYPOP to assess statisticalpower in our samples and results of the homogeneity tests with the simulated data. The following were
fixed in all sets other than shown in the table: diploid, random mating, equal sex ratio, subpopulations ofconstant Ne, mutation rate of 0.0005, and 100 replicates each with 5000 generations.

N=census population size, Ne=effective population size, m=migration rate, Nem=number of migrants per
generation, S= number of sample size, L = number of loci analyzed, A=possible number of allelic states.

Table 6. Results of mtDNA diversity for western North Pacific common minke whale.
Number of haplotypes
Nucleotide diversity (SE)
Haplotype diversity
102
0.00784 (0.00009)
0.94763

Table 7. Results of the mtDNA heterogeneity test among ‘O’ stock minke whale from different sub-areas,by sex and total samples.
7CS × 7CN
Chi-square p-value

Sex combined
0.223

Male
0.432

Female0.707

Sex combined
0.00046

FST
Male-0.00028

Female-0.00072

FST
Male-0.00144

Female0.02005

FST
Male
0.00035

Female0.00152

7CS-7CN × 7WR
Chi-square p-value

Sex combined
0.238

Male
0.434

Female0.690

Sex combined-0.00097

7CS-7CN-7WR × 7E × 8 × 9
Chi-square p-value

Sex combined
0.066

Male
0.110

Female0.952

Sex combined
0.0003012Figure 1. Sub-areas used for the management of common minke whale under the RMP (IWC, 2013).

13

Figure 2. Stock structure hypotheses used for management under the RMP (IWC, 2013).

14

125E
55N

130E

135E

140E

145E

150E

155E

160E

165E

170E

175E
55N

50N

50N

45N

45N

40N

40N

35N

35N

30N
125E

130E

135E

140E

145E

150E

155E

160E

165E

170E

Figure 3. Geographical distribution of ‘O’ stock common minke whales used in this study.

15

30N
175E

d=1

PCA

PCA of minke whale_res naxes 1-2

O-stock

J-stock

onent

PCA

onent

d = 1axes show
Figure 4. A result of PCA analysis for the common minke whale. The horizontal and verticalthe first and second principal components, respectively. The difference colors mean stocks (J and O)
assigned by STRCTURE, but that sort of information was not used in the analysis itself.

PCA of minke whale_ori naxes 1-2
J-stock

O-stock

16

d=1

Grp= 21
2

J-stock
1,546
62

O-stock
102
2,163

Figure 5. Results of DAPC analyses without any training data. Here training data mean the one withknown origins. Error rates were small enough.

17

Figure 6. A result of PCA analysis for the common minke whales within O-stock (Ow-Oe, K=2)

18

Grp= 2
1
2

Offshore
662
599

Kushiro265
202

Sanriku
198
145

Bycatch114
80

Figure 7. Results of DAPC analysis without any training data. Here training data mean the individuals
with known origins. Error rates were small enough.

19