SC/F16/JR44

Updated genetic analyses based on mtDNA and microsatellite
DNA suggest possible stock differentiation of Bryde’s whales
between management sub-areas 1 and 2 in the 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

ABSTRACT
A total of 1,019 and 1,026 samples of North Pacific Bryde’s whales were examined by microsatellite
DNA (17 loci) and mitochondrial DNA sequencing (299bp), respectively, to examine the plausibility offour stock structure hypotheses used by the IWC SC during the 2007 RMP Implementation. Samples werefrom different sources: JARPNII (catches), Japanese dedicated sighting surveys (biopsy); IWC/POWER
surveys (biopsy) and past commercial whaling (catches). No significant genetic heterogeneity was foundbetween the Western and Eastern Sectors of sub-area 1, a result supported by high statistical power.
However both genetic markers showed significant differences (for males, females and sexes combined)
between sub-areas 1 and 2. Phylogenetic analysis of mtDNA haplotypes revealed no subarea-specificclades. It is proposed that a longitudinal sector around 180 degree could represent a hard boundary or atransition area where the two stocks mix. Based on these results, it is suggested that the plausibility of thestock structure hypotheses for western North Pacific Bryde’s whale used in the 2007 Implementation
whale should be re-examined. The results of this study suggest that the two-stock hypotheses (Hypotheses
2 and 3) could be more plausible than the one-stock hypothesis (Hypothesis 1) and the three-stock
hypothesis (Hypothesis 4).
INTRODUCTION
The RMP Implementation for western North Pacific Bryde’s whale was completed by the IWC SC in2007 (IWC, 2008 pp9). One of the important sources of data and information for the discussion on stock
structure came from JARPNII research.
During the Implementation, two sub-areas (Figure 1; IWC, 2009a pp7) and four stock structure
hypotheses (Figure 2; IWC, 2007a pp8), were used. The IWC SC examined the plausibility of the four
hypotheses based on genetics and non-genetics information available in 2006. That information isreproduced in Table 1 (IWC, 2007b pp95). In the context of the RMP Implementation, the IWC SC
agreed on the plausibility of the four hypotheses as follows: Hypothesis 1: High; Hypothesis 2: High;
Hypothesis 3: High; and Hypothesis 4: Medium. It should be noted that the plausibility rank was agreed
in absence of any DNA data from sub-area 2.
Since the 2007 Implementation a substantial number of additional genetic samples have been collectedfrom sub-area 1 through JARPNII, Japanese dedicated sighting, and IWC POWER surveys. Also samples
became available from sub-area 2 through the IWC/POWER surveys. This new set of samples allowed
new genetic analyses to be conducted for examining further the plausibility of the four stock structure
hypotheses.
There was particular interest to test whether or not the frequencies in alleles and haplotypes in whales
from the western part of sub-area 1 differ significantly from whales in the eastern part, and whether there
are significant differences between sub-areas 1 and 2.

1

The analyses conducted in the present study considered most of the recommendations from the 2009JARPNII review workshop (IWC, 2010) and from subsequent IWC SC Annual meetings (see Annex 5 ofTamura et al., 2016: SC/F16/JR1).
MATERIALS AND METHODS
Samples
JARPNII samples (n=679) of Bryde’s whales obtained from 2002 to 2014 were collected from thewestern North Pacific (Table 2 and Figure 3). Although sampling dates and locations of the surveys
slightly differed year by year depending on the sampling plan of a given year, samples were taken in thenorthwestern North Pacific in the range 35°05’N-49°35’N, and 143°19’E-169°58’E. Skin biopsy samplesof Bryde’s whales were obtained during the IWC/POWER and Japanese dedicated sighting surveys.
POWER survey covered the area south of 40°N from 165°E to 155°W (POWER 2013-14). A total of 82
POWER biopsy samples were used (Table 2 and Figure 3). Japanese dedicated sighting surveys
conducted in 2012 and 2014 covered the areas south of 40°N between 140°E and 170°E. A total of 58
biopsy samples from this source was used. Finally, a total of 200 samples from past commercial whaling(1979 pelagic; 1983/84 coastal) were used (Table 2 and Figure 3).
DNA extraction
The IWC guidelines for DNA data quality (IWC, 2009b) were followed as much as possible (see Kanda
et al., 2014).
Total DNA from each of the whales was extracted from 0.05g of skin tissue in the JARPNII samples, skinbiopsy in the POWER samples and muscle tissue in the commercial samples, using the protocol ofSambrook et al. (1989). Extracted DNA was stored in TE buffer (10 mM Tris-HCl, 1 mM EDTA, pH
8.0).
Microsatellite analysis
Genetic variation at microsatellite DNA was analyzed using 17 loci, none of which was designed
specifically from Bryde’s whales: 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). Primer
sequences and PCR cycling profiles generally followed those of the original authors.
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). Amplified products with internal size standard(GENESCAN400HD, Applied Biosystems Japan) were run on a 6% polyacrylamide denaturating gel(Long Ranger) using BaseStation100 DNA fragment analyzer (Bio-Rad). Although alleles werevisualized using Cartographer software specifically designed for the BaseStation, allelic sizes were
determined manually in relation to the internal size standard and Bryde’s whale’s DNA of known sizethat were rerun on each gel.
Data analysis
Level of polymorphisms
The number of alleles per locus, expected heterozygosity per locus and inbreeding coefficient wascalculated using FSTAT 2.9.3 (Goudet, 1995). Statistical tests for the deviations from expected HardyWeinberg genotypic proportions were conducted using GENEPOP 4.0 (Rousset, 2008).
Homogeneity test
Conventional hypothesis testing procedure was conducted using heterogeneity test in microsatellite allele
frequencies among samples. The null hypothesis to be tested was whether or not the samples came from agenetically same group of Bryde’s whales. If statistically significant allele frequency differences exist, it
could indicate these samples came from genetically different stocks of Bryde’s whales. Probability test
(or Fisher’s exact test) implemented in GENEPOP 4.0 (Rousset, 2008) was used to conduct theheterogeneity tests. Statistical significance was determined using the chi-square value obtained from2

summing the negative logarithm of p-values over the 17 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 value was calculated using FSTAT 2.9.3 (Goudet, 1995).
There were three cases of re-sampling, two involving the same year and one involving more than one
year. In these cases only one individual was used in the statistical analyses.
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 (1W and 1E) 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 16,000 was used. For each generation, the simulation produced genotype datafor 17 independent microsatellite loci for each individual. The number of the loci simulated andmaximum number of the allelic states (18) was set based on the observed data. The bidirectionalmigration 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 was chosen 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
generation of each replicate, sample of 120 individuals were taken from each population for genetic
analysis. The sample size of 120 in this study was approximately equals to the sum of the samples size
from sub-area 1E, which is considered conservative given that larger sample size was actually used for
1W. 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 of samples, multi-loci genotypes
are randomized between the two samples. The overall loci G-statistic is given and statistical significancewas decided with a table wide level of significance at 5%.
Mitochondrial DNA
Sequencing analysis of the 299bp 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 manufacturer. For each sample both strands weresequenced.
Data analysis
Level of polymorphisms
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 males, females and both sexes combined. A p-value smaller than 0.05, was used as acriterion to reject the null hypothesis of panmixia. The FDR approach was used for adjustment of p-value3

in case of multiple comparisons. FST for mtDNA was calculated based on the analysis of molecularvariance (AMOVA) (Excoffier et al., 1992).
Phylogenetic analysis
The genealogy of the mtDNA haplotypes was estimated using the Neighbor-Joining method (Saitou andNei 1987) as implemented in the program PHYLIP (Felsenstein 1993). Genetic distances among
haplotypes were estimated using the program DNADIST of PHYLIP, based on Kimura’s 2-parametermodel (Kimura 1980). A transition-transversion ratio of 5:1was used. The genealogy was rooted using thehomologous sequence from sei and Omura’s whales. To estimate support for each node a total of 1,000
bootstrap simulations were conducted and the majority-rule consensus genealogy estimated.
RESULTS
Microsatellites
Level of polymorphisms
All 17 loci analyzed were polymorphic (Table 3). The total number of alleles per locus ranged from twoat the DlrFCB to 18 at the GATA28 with an average of 8.76. Expected heterozygosity at each of the lociranged from 0.35 at TAA31 to 0.91 at GATA28 with an average of 0.68. No significant departure fromthe expected Hardy-Weinberg genotypic proportions was found. However the FIS for some loci wererelatively large suggesting the possibility of homozygote excess.
Homogeneity test for samples from different sources
No significant genetic heterogeneity was observed in the test in sub-area 1W (Table 4) and sub-area 1E(Table 5) for sample from different sources and period. Consequently all samples in each of these subareas were pooled for the next analyses.
Homogeneity test for sub-areas
No significant genetic heterogeneity was found between sub-areas 1W and 1E (Table 6). Consequently all
samples from sub-area 1 were pooled for the next analysis. In the overall test involving the 17 loci,significant statistical differences were found in the comparison between sub-areas 1 and 2 for males,
females and both sexes combined (Table 7).
Assessment of the statistical power
Table 8 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 1W and 1E were all same. High statisticalpower was detected with m=0.01 and 0.02.
mtDNA
Level of polymorphisms
Levels of mtDNA diversity were relatively high in the North Pacific Bryde’s whale (Table 9).
Homogeneity test for samples from different sources
No significant genetic heterogeneity was observed in the test in sub-area 1W (Table 10A) and sub-area
1E (Table 10B) for sample from different sources and period. Consequently all samples in each of these
sub-areas were pooled for the next analyses.
Homogeneity test for sub-areas
No significant genetic heterogeneity was found between sub-areas 1W and 1E (Table 11A). Consequently
all samples from sub-area 1 were pooled for the next analysis. Significant statistical differences were
found in the comparison between sub-areas 1 and 2 for males and both sexes combined (Table 11B). In
the case of females, the p-value was marginally significant (0.056).

4

Phylogenetic analysis
Figure 4 shows the phylogenetic relationship among mtDNA haplotypes. The figure also shows the
haplotype frequencies in sub-areas 1W, 1E and 2. Several clades were observed in the figure but none
was supported by high bootstrap values. There was no subarea-specific clade.
DISCUSSION
As noted earlier, the main objective of the present genetic analyses was the evaluation of the plausibilityof the four stock structure hypotheses of North Pacific Bryde’s whale used during the 2007 RMP
Implementation. A number of different surveys allowed the collection of additional genetic samples in
sub-area 1, and importantly, the collection of genetic samples from sub-area 2. As noted earlier, theplausibility of the four stock structure hypotheses in 2007 was evaluated in absence of any DNA datafrom sub-area 2. The total available samples were analyzed with two genetic markers, which arecommonly 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 17 loci is a strong tool to investigate genetic differences in weakly differentiated stocks.
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 tothis recommendation and other one from the JARPAII review workshop, a document was prepared andpresented to the IWC SC in 2014 (Kanda et al., 2014). The IWC SC welcomed this document and agreed
that it responded appropriately to the recommendation (IWC, 2015). Another example 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 was used in the present
study.
Other medium and long-term recommendations from the 2009 JARPNII review workshop were also
addressed in this study as well as in previous documents presented to the IWC SC and other documents
presented to this workshop. See more details on the responses to previous recommendations in Annex 5of Tamura et al. (2016: SC/F16/JR1).
Hypothesis testing results and interpretation
Despite the increase in the number of samples used in the analysis from approximately 585 in theprevious study to 973 in the present study, no significant genetic heterogeneity was found in sub-area 1
suggesting that whales in this sub-area belong to a same stock. This result is similar to that foundpreviously by Kanda et al. (2007; 2009). In the present study this result was supported by high statisticalpower.
In contrast both genetic markers showed significant differences between whales in sub-areas 1 and 2, andthis pattern was similar for males and females. In interpreting this result, the large difference in sample
sizes between sub-area 1 (966 and 973 for microsatellite and mtDNA, respectively) and sub-area 2 (53and 53 for microsatellites and mtDNA, respectively) was considered. There was the possibility that thelarge difference in sample sizes could have derived in the significant statistical differences between the
sub-areas. We tested this through a simple simulation exercise based on mtDNA data for male and female
samples combined.
For this simulation exercise, the smaller sample size of sub-area 2 (n=53) was kept fix and randomly resampling of individuals was carried out from sub-area 1 (n=973, with replacement) for n=50, n=100 and
n=150. A total of 100 sets of haplotype frequencies were created for each of these sample sizes in subarea 1, and these were compared statistically to haplotype frequencies in sub-area 2 (using therandomized chi-square test). Results indicated that for n=50, 86% of the 100 tests rejected panmixia. For
n=100 and n=150, 100% of the tests resulted in rejection to panmixia.
The results of the simple simulation above suggested that the difference in sample sizes between subareas 1 and 2 was not the reason for the statistical significant differences found between the two sub-areas
and that such result corresponded to a real biological event. Therefore this result suggested additional
stock structure in the western North Pacific Bryde’s whale, with the possibility that two stocks occur in5this oceanic basin separated by a boundary around longitude 180°. There is the possibility that a sector
around 180°E represent a transition area where the two stock mix. This should be examined further infuture.
Correspondence to patterns of movement of whales
An analysis of Discovery-type marks was carried out by Kishiro (1996). According to the authors a totalof 537 Bryde’s whales were effectively marked by the Japanese marking programme during the year 1972
to 1985, and a total of 52 marks had been recovered by the end of 1987. All of the mark-recaptures
occurred west of 180° (sub-area 1). The main conclusion of the study was that Bryde’s whales summeringin the whaling grounds, winter over a wide latitudinal range (1°S-25°N). The authors did not findevidence of more than one stock of Bryde’s whales in the western North Pacific. As noted in Table 1,
mark-recapture analysis revealed movement of animals within sub-area 1, and a very limited number ofmarks were placed in sub-area 2.
More recently the movement of two Bryde’s whales was recorded in summer using satellite-monitored
radio tags in the western North Pacific (see details in Murase et al., 2016: SC/F16/JR45). These whales
were recorded for 13 and 20 days, respectively. Both whales exhibited a north-south from approximately
40°N to 35°N within sub-area 1.
In summary the available information on movement is not inconsistent with the results of the present
genetic analysis that suggested two stocks separated around longitude 180°.
Conclusions
Based on these results, it is suggested that the plausibility of the stock structure hypotheses for western
North Pacific Bryde’s whale used in the 2007 Implementation whale should be re-examined. The resultsof this study suggest that the two-stock hypotheses (Hypotheses 2 and 3) could be more plausible than the
one-stock hypothesis (Hypothesis 1) and the three-stock hypothesis (Hypothesis 4).
ACKNOWLEDGEMENTS
We gratefully acknowledge the researchers and crew members that participated in JARPN/JARPNII,
IWC/POWER and Japanese dedicated sighting surveys for the collection of genetic samples. We thank H.
Oikawa and S. Azumi (ICR) who collaborated in the process of DNA extraction. We also thank N. Kanda(JANUS-Japan) for preparing the microsatellite data set used in this study, and to T. Hakamada for his
assistance in the simulation on sample sizes.
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Table 1 (reproduced from IWC, 2007b pp95).

8

Table 2. Sample sizes of Bryde’s whales used in the present study in the microsatellite and mtDNA(number in parenthesis) analyses, by sub-area, sex, and source of samples. Only the samples genotyped
for the complete microsatellite loci set were included in the analysis.
Sub-area

1

West
East

2

Sex
Male
FemaleMale
FemaleMale
FemaleTotal

JARPNII
274 (269)
346 (342)
14 (14)45 (45)

Commerc.
105 (116)
66 (73)16 (16)12 (10)679 (670)

1 (1)200 (216)

POWER

10 (10)20 (20)29 (29)23 (23)82 (82)

Jap. dedic.
20 (20)36 (36)
1 (1)
1 (1)

58 (58)Total
399 (405)
448 (451)
41 (41)78 (76)29 (29)24 (24)1019 (1026)

Table 3. The number of alleles (A), expected heterozygosity (He), test results for expected HardyWeinberg genotypic proportion (HW) and inbreeding coefficient (FIS) in North Pacific Bryde’s whale, at
17 microsatellite loci.

Microsatellite loci
GATA98
EV104
GT011
GATA53
GATA41
DlrFCB
DlrFCB
GT23
EV14
GT310
EV1
EV94
GGAA52
EV21
GT575
GATA28TAA31Overall

A

HE
0,80
0,76
0,50
0,63
0,75
0,49
0,88
0,74
0,78
0,62
0,74
0,52
0,77
0,66
0,66
0,91
0,35
0,68

8
7
3
7
10
2
15
9
9
5
13
9
8
7
11
18
8
8,76

9

HW
0,632
0,868
0,303
0,426
0,272
0,279
0,755
0,446
0,851
0,678
0,019
0,357
0,026
0,338
0,457
0,371
0,634
0,305

F IS
0,020
-0,009-0,014
0,027
0,008
0,034
-0,0050,018
0,003-0,0030,033
0,031-0,0010,020-0,014
0,000
0,021
0,009

Table 4. Results of the microsatellite DNA heterogeneity test for different sample sources in sub-area
1W, by locus and sex. Bold indicate significant differences after FDR correction.

JARPNII x Commercial x Sighting in sub-area 1W
Sex combined
Male
FemaleMicrosatellite lociP -value
F ST P -value
F ST P -value
F ST
GATA98
0,154
0,002
0,403
0,000
0,151
0,004
EV104
0,578
0,000
0,814 -0,004
0,759
0,000
GT011
0,559 -0,002
0,904 -0,004
0,217
0,004
GATA530,897 -0,002
0,624 -0,003
0,200 -0,004
GATA417
0,412
0,002
0,510
0,000
0,269
0,002
DlrFCB14
0,464 -0,001
0,659 -0,003
0,593 -0,003
DlrFCB17
0,511
0,000
0,122
0,003
0,351
0,000
GT23
0,571
0,000
0,194
0,002
0,923 -0,004
EV14
0,214
0,000
0,542 -0,002
0,066
0,000
GT310
0,354
0,000
0,799 -0,002
0,393 -0,001
EV1
0,202 -0,001
0,197
0,005
0,299 -0,003
EV94
0,017
0,000
0,126 -0,002
0,561
0,001
GGAA520
0,693 -0,001
0,957 -0,004
0,735 -0,001
EV21
0,458
0,001
0,436
0,002
0,838 -0,003
GT575
0,019
0,001
0,023
0,001
0,210
0,001
GATA280,762 -0,001
0,942 -0,002
0,563
0,000
TAA31
0,011
0,000
0,055 -0,003
0,040
0,005
Overall
0,053
0,000
0,318 -0,001
0,252
0,000

10

Table 5. Results of the microsatellite DNA heterogeneity test for different sample sources in sub-area 1E,
by locus and sex. Bold indicate significant differences after FDR correction.

JARPNII x Commercial x POWER in sub-area 1E
Sex combined
Male
FemaleMicrosatellite lociP -value
F ST P -value
F ST P -value
F ST
GATA980,086
0,000
0,604 -0,011
0,060
0,002
EV104
0,100
0,005
0,484 -0,002
0,256
0,002
GT011
0,352
0,002
0,882 -0,030
0,116
0,035
GATA530,137 -0,002
0,318
0,009
0,362 -0,002
GATA417
0,763 -0,002
0,634 -0,005
0,269
0,011
DlrFCB14
0,361 -0,002
0,424 -0,015
0,895 -0,023
DlrFCB17
0,407 -0,002
0,635
0,006
0,123
0,005
GT23
0,271
0,007
0,526 -0,013
0,078
0,041
EV14
0,784 -0,007
0,786 -0,017
0,699 -0,010
GT310
0,741 -0,007
0,818 -0,027
0,960 -0,018
EV1
0,604 -0,007
0,312 -0,009
0,963 -0,013
EV94
0,713 -0,004
0,079
0,048
0,584 -0,013
GGAA5200,351
0,004
0,428 -0,010
0,697
0,001
EV21
0,322
0,012
0,017
0,079
0,921 -0,017
GT575
0,749
0,002
0,441 -0,009
0,930 -0,010
GATA280,441 -0,001
0,869 -0,008
0,916 -0,010
TAA31
0,675 -0,010
0,959 -0,026
0,589 -0,014
Overall
0,531
0,000
0,666 -0,002
0,612 -0,001

11

Table 6. Results of the microsatellite DNA heterogeneity test for Western and Eastern sectors in sub-area
1, by locus and sex. Bold indicate significant differences after FDR correction.

Subareas 1W x 1E
Sex combined
Male
FemaleMicrosatellite lociP -value
F ST P -value
F ST P -value
F ST
GATA980,614 -0,001
0,540 -0,002
0,598 -0,002
EV104
0,440
0,002
0,843 -0,003
0,577
0,002
GT011
0,115
0,000
0,606 -0,003
0,209 -0,002
GATA530,100
0,002
0,037 -0,005
0,199
0,007
GATA417
0,343 -0,001
0,335
0,000
0,481 -0,002
DlrFCB14
0,549 -0,001
0,298
0,002
0,864 -0,004
DlrFCB17
0,992 -0,001
0,849
0,000
0,481
0,001
GT23
0,570
0,000
0,351 -0,002
0,647 -0,001
EV14
0,368
0,001
0,433
0,000
0,339
0,002
GT310
0,149
0,005
0,061
0,021
0,775 -0,002
EV1
0,149
0,002
0,712
0,003
0,174
0,000
EV94
0,482 -0,001
0,554
0,001
0,306
0,003
GGAA520
0,931 -0,002
0,935 -0,006
0,917 -0,002
EV21
0,160
0,004
0,664
0,002
0,051
0,007
GT575
0,291 -0,001
0,757 -0,004
0,121 -0,001
GATA280,360
0,000
0,771 -0,001
0,167
0,001
TAA31
0,071
0,004
0,090
0,003
0,510
0,002
Overall
0,199
0,000
0,556
0,000
0,357
0,001

12

Table 7. Results of the microsatellite DNA heterogeneity test for sub-areas 1 and 2, by locus and sex.
Bold indicate significant differences after FDR correction.

Subareas 1 x 2
Sex combined
Male
FemaleMicrosatellite lociP -value
F ST P -value
F ST P -value
F ST
GATA98
0,202
0,007
0,372
0,003
0,336
0,011
EV104
0,089
0,000
0,008
0,002
0,532
0,003
GT011
0,199
0,010
0,392
0,000
0,333
0,011
GATA53
0,607
0,001
0,340
0,007
0,637 -0,009
GATA417
0,009
0,005
0,013
0,024
0,155 -0,006
DlrFCB14
0,327
0,000
0,689 -0,007
0,386 -0,002
DlrFCB17
0,037 -0,001
0,124
0,000
0,226 -0,004
GT23
0,174
0,002
0,611
0,001
0,086
0,006
EV14
0,128
0,005
0,110
0,007
0,197
0,016
GT310
0,225
0,003
0,539 -0,005
0,243
0,017
EV1
0,140
0,003
0,342
0,005
0,054
0,011
EV94
0,016
0,009
0,274
0,008
0,084
0,002
GGAA520
0,142
0,002
0,040
0,010
0,739 -0,008
EV21
0,517 -0,003
0,250 -0,005
0,685
0,003
GT575
0,143 -0,001
0,792 -0,006
0,422 -0,004
GATA28
0,002
0,004
0,067
0,005
0,069
0,000
TAA31
0,001
0,031 <0,001
0,038
0,064
0,025
Overall<0,001
0,004 <0,001
0,005
0,031
0,004

13

Table 8. 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 9. Results of mtDNA diversity for North Pacific Bryde’s whale.
Number of haplotypes
Nucleotide diversity (SE)51
0.01086 (0.00036)

Haplotype diversity
0.82797

Table 10. Results of the mtDNA heterogeneity test for different sample source in sub-area 1W (A) and1E (B), by sex. Bold indicate significant differences after FDR correction.
A
1W
Chi-square p-value

Sex combined
0.4886Male
0.9228

Female0.2525

Sex combined-0.00069

FST
Male-0.00590

Female0.00448

Sex combined-0.00547

FST
Male
0.01667

Female0.01068

B
1E
Chi-square p-value

Sex combined
0.2115Male
0.0700Female0.2707

Table 11. Results of the mtDNA heterogeneity test for sectors in sub-area 1(A) and between sub-areas 1
and 2 (B), by sex. Bold indicate significant differences after FDR correction.
A
1W × 1E
Chi-square p-value

Sex combined
0.2223Male
0.5264

Female0.2260Sex combined
0.00218

FST
Male-0.00184

Female0.00062

FST
Male
0.02234

Female0.01424B
1W-1E × 2
Chi-square p-value

Sex combined
0.0001Male
0.0022

Female0.0558Sex combined
0.0222714

Figure 1. Sub-areas used for the management of Bryde’s whale under the RMP (IWC, 2009 pp7).

Figure 2. Hypotheses on stock structure of Bryde’s whale in the western North Pacific used during the
RMP Implementation (IWC, 2007 pp8).

15

125E

135E

145E

155E

165E

175E

175W

165W

155W

145W

45N

45N

35N

35N

25N

25N

15N

15N

5N
125E

135E

145E

155E

165E

175E

175W

165W

155W

145W

5N

Figure 3. Geographical distribution of Bryde’s whales examined in this genetic study. Green: JARPNII
samples; Blue: Japanese dedicated sighting survey samples; Orange: past commercial whaling samples;
Red: IWC/POWER survey samples.

16

Figure 4. Phylogenetic relationship among mtDNA haplotypes of North Pacific Bryde’s whale andhaplotype frequencies in management sub-areas.

17