SC/61/NPM8
Not to be cited without prior consent of the authors

Update of the microsatellite analysis on sub-stock structure of the
J stock common minke whales from the Japanese waters
Naohisa Kanda, Mutsuo Goto, and Luis A. Pastene
The Institute of Cetacean Research, 4-5, Toyomi-cho, Chuo-ku, Tokyo 104-0055, Japan

ABSTRACTThis paper examined the stock structure of common minke whales existing around the Japanese waters. Past
studies indicated that two different stocks of minke whales existed around the Japanese coast: O stock in thewestern North Pacific and the J stock in the Sea of Japan. Kanda et al. (2009, i.e., SC/61/JR5) indicated byutilizing genetic variation observed at 16 microsatellite loci and a Bayesian clustering approach that only the J
and O stocks of common minke whales occupied around the Japanese waters and the proportion of these twostocks varies among the subareas. In this paper, we used the offshore JARPN and JARPNII samples from 1994
to 2007 at the SA7W (140-147°E) and SA11, the coastal JARPNII (7W) samples conducted at Sanriku region inspring of 2003 to 2007, and at Kushiro region in fall of 2002 to 2007, and the bycatch samples from set net
fishery along the Japanese coast from 2001 to 2007 to look for evidence of sub-structuring of the J stock. On
the basis of the results from Kanda et al. (2009), whales in these samples were assigned to either the J stock, theO stock, or unknown origins. We conducted heterogeneity tests with all individuals included (total N = 1,805)
and with only the suspected J stock individuals included (total N = 768). This paper found the seasonal genetic
difference in the SA7 between the minke whales from April to September and from October to March most
likely due to the different proportion of the J and O stocks between the seasons. No evidence of the genetic
differences was detected in the samples from all of the subareas when we used only the J stock individuals forthe tests. This paper found that the SA2 was mainly occupied by the J stock that was not genetically different
from the J stock in the Sea of Japan. Our simulation study indicated that from genetics standpoint the statisticalpower for testing the J sub-stocks (especially in the SA2 and SA6) with our data set was quite high. This study
demonstrated that only the single J stock with no sub-structuring existed around the Japanese waters.
KEYWORDS: COMMON MINKE WHALE, MICROSATELLITE, SCIENTIFIC PERMITS, INCIDENTAL
CATCHESINTRODUCTION
It has been believed that only one population of minke whales exists in the sub-areas (SA) 5 and 6 between
Japan and Korea (Omura and Sakiura, 1956; Ohsumi, 1977; Kato, 1992; Wada and Numachi, 1991; Goto andPastene, 1997). This is J stock that is genetically different from the widely distributed O-stock in western
North Pacific. The J and O stocks differ from each other in body size, conception dates, allozyme allele
frequencies, and mitochondrial DNA (mtDNA) haplotype frequencies, suggesting their reproductive isolation.
Although both stocks migrate to the Okhotsk Sea in spring and stay till the end of summer (Omura and Sakiura,
1956; Hatanaka and Miyashita, 1997), their temporal distribution in the area appears not to overlap completely(Omura and Sakiura, 1956; Goto and Pastene, 1997).
Japanese and Korean scientists have worked together intersessionally to analyze the samples from Japan and
Korea using genetic data (mtDNA and microsatellites) in order to investigate stock structure of common minke
whales in the Japanese and Korean waters. Kanda et al. (2006), Park et al. (2006), and Goto et al. (2007)
detected no genetic difference among the samples from different sampling areas as well as different sampling
years except significant difference between the 1999 Korean sample from SA6 (99KBC-6) and the rest of thesamples from Korea and Japan. Two possibilities were raised by the authors: (i) this could indicate that there
are genetically different stocks in this area, and (ii) this could have resulted from the 99KBC-6 sample not being
representative of the whole J stock given their high genetic diversity (IWC, 2007).
In 2008, the following future work was planned in the Working Group on the in-depth assessment of western
North Pacific common minke whales, with a focus on J stock:(1) Standardize Japanese and Korean microsatellite data,(2) Conduct heterogeneity tests on samples stratified by month and season as well as sex,(3) Include recent data from 2005-2007 to increase sample sizes and power for mtDNA and microsatellite1SC/61/NPM8
Not to be cited without prior consent of the authors

analyses,(4) Investigate whether previously found heterogeneity is due to the 1999 data in general or just a few
individuals in that year,(5) Analyze the 1982 Korean commercial samples (27) together with recent samples, and(6) Include samples from the Pacific side of Japan in work related to the JARPNII review.
Plans were made to conduct analyses to cover these future works for the SC61 meeting using the Japanese andKorean samples. With respect to the microsatellite standardization between the two laboratories, Koreanscientists re-analyzed all of the individuals they possessed with several Japanese samples, as references, for the
16 microsatellite loci we regularly used for making our genetic database, and sent raw output data from the DNA
sequencer (ABI3100) to us in Japan to read genotypes. Although the microsatellite allele standardization has
been done successfully for most of the loci from this way, the Korean data set was still incomplete forproceeding with further analyses. In this paper, therefore, we presented updated, yet preliminary, microsatellite
analysis of common minke whale stock structure using only the Japanese samples. It was thus difficult for us
to cover all of the tasks suggested during the 2008 SC meeting. These tasks will be completed in future when
the Korean data set is finalized.
Kanda et al. (2009, i.e., SC/61/JR5) attempted to distinguish sampled minke whales into genetically distinct
stocks using a combination of microsatellite analysis and a Bayesian clustering approach implemented in thecomputer program STRUCTURE (Pritchard et al., 2000). Samples of 2542 minke whales were collectedduring the offshore component of JARPN and JARPNII from 1994 to 2007, during the coastal component of
JARPNII from 2002 to 2007, and from bycatches in the set net fishery along the Japanese coast from 2001 to
2007, and were analyzed using 16 microsatellite loci. Result of the clustering analysis indicated that oursamples came from two genetically differentiated groups of minke whales. More than 90% of the individuals
were assigned into the either stocks based on their high membership probabilities obtained from the program.
All other individuals with the lower membership probabilities to the either groups were assigned as individualsof unknown origin. Both of the assigned and unassigned individuals were then grouped based on theirsampling origins. Spatial distribution of these individuals clearly indicated that these two stocks were the J andO stocks because individuals from the Sea of Japan and offshore North Pacific tended to be assigned in the
different group (see also Table 2 in this paper). In this paper, we conducted heterogeneity tests among thesesamples grouped based on the assignment results from Kanda et al. (2009) to look for evidence of
sub-structuring of the J stock around the Japanese waters.
MATERIALS AND METHODS
Sample collections
As samples of common minke whales existing around the Japanese waters (Table 1), we used those collectedduring offshore component of JARPN and JARPNII from 1994 to 2007 at SA7W (140-147°E) and SA11, thosecollected during coastal component of JARPNII (SA7W) conducted at Sanriku region in spring of 2003, 2004,
2005, 2006, and 2007, and at Kushiro region in fall of 2002, 2004, 2005, 2006, and 2007, and those bycaught on
set net fishery conducted along the Japanese coast from 2001 to 2007 (bycatch) (Table 1). Details of the recent
JARPNII surveys can be found in Tamura et al. (2009) and Kishiro et al. (2009). As of July 1st 2001, the new
regulation governed by the Japanese Government has allowed the set net fishermen to harvest whales found intheir set net and to sell these on to the market after DNA registration of these for individual identification. The
bycatches used were obtained from the SA2, SA6, SA7, SA10, and SA11 all the year round. Fig. 1 shows the
18 sub-areas set for management purpose of the western North Pacific common minke whale.
Microsatellites analysis
We used genetic data obtained from the 16 microsatellite loci: EV1, EV14, EV21, EV37, EV94 (Valsecchi andAmos, 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). Laboratory procedure used
for this analysis and the level of genetic diversity of these loci in our samples can be found in Kanda et al.
(2009).
Data analysis
In order to detect genetic differences in the samples of minke whales, we performed conventional hypothesis
testing procedure using heterogeneity test in frequencies of the microsatellite alleles among samples. Nullhypothesis to be tested is if the samples came from a genetically same group of common minke whales. If
genetic differences exist, then it could indicate these samples came from genetically different stocks of minke
whales. Markov chain method implemented in the GENEPOP (Rousset, 2008) was used to conduct theheterogeneity tests. Statistical significance was determined using the chi-square value obtained from summing2

SC/61/NPM8
Not to be cited without prior consent of the authors

the negative logarithm of p-values over the 16 microsatellite loci (Sokal and Rohlf, 1995). FST values were also
calculated using ARLEQUIN 2.0 (Schneider et al., 2000) or FSTAT 2.9.3 (Goudet, 1995). The samples withless than 4 individuals when we divided into smaller groups for the tests were excluded from the test procedure.
File conversions were conducted using either computer program CREATE (Coombs et al., 2008) or CONVERT(Glaubitz, 2004).
Assessment of statistical power for the tests of homogeneity
In order to assess statistical power for tests of homogeneity (e.g., Waples and Gaggiotti, 2006), we generated
genotypic data using computer software EASYPOP (Balloux, 2001) and conducted heterogeneity tests with thesegenerated data. We assumed two populations, each of which consists of diploid individuals with a constant sizeand equal sex ratio with random mating. We assumed ratio of effective population size to census population
size to be 1/3 to 1/4 (Roman and Palumbi, 2003). The effective population size of the populations was thus set
as it becomes 1/3 or 1/4 of the census population size. We used census population size of 9000. This
population size was derived from Hakamada et al. (2009), which estimated abundance of the J stock in the area
as over 17,000. At each generation, simulation produces genotype data set for 16 independent nuclear gene loci(microsatellites) for each individual. The number of the loci simulated and maximum number of the allelicstates (18) was set based on the observed data in this study. Bidirectional migration was assumed with an equal
migration rate (m). Migration rates ranged from 0.01 to 0.2, some of which (0.1- 0.2) were quite high forgenetic method to detect. We specified a range of genetic divergence using FST values estimated assuming
island model between the two populations by changing migration rate. Mutation rate of 5x10-4 was chosen to
represent microsatellite loci. For each simulation parameter set, we made 100 replicates. We ran 5,000
generations for each replicate before collecting data. In the final generation of each replicate, a sample of 140
individuals was taken from each population for genetic analysis. The sample size of 140 was come from thenumber of the suspected J stock individuals in the SA2 (Table 2). We conducted homogeneity tests for thegenerated data set using pairwise tests of differentiation option in the FSTAT. In this option, for each pair ofsamples, multiloci genotypes are randomized between the two samples. The overall loci G-statistic is given
and statistical significance was decided with a table wide level of significance at 5%. We conducted thissimulation in order to assess the statistical power to detect the genetic difference between common minke whales
from the SA2 and SA6 for the case that genetically different sub-stocks might exist.
RESULTSOn the basis of the results from the individual assignments to the stocks according to the criteria in Kanda et al.
(2009), we conducted the tests with two different kinds of sample groups: 1) one that included all the analyzed
individuals and 2) one that used only the suspected J stock individuals. Although the proportions of theindividuals of unknown origins were similar to each of the subareas, the proportions of the J and O stockindividuals were quite different among the subareas (Table 2).
Genetic divergence between samples
Yearly and seasonal genetic differences within the subareas. We first looked for evidence of genetic differencesamong the bycatch samples collected from the different years within the SA2, SA6, and SA7W respectively.
No statistically significant genetic differences were detected within each of the subareas for the all individuals
and the only J stock individuals (Table 3).
We then divided the individuals in the samples into those collected from April to September (Early) and from
October to March (Late). We first looked for evidence of genetic difference among the samples collected fromthe different years of the same seasons. No statistically significant genetic differences were detected withineach season for both the all individuals and only J stock individuals, so we combined the samples as Early andLate samples, respectively, and looked for evidence of genetic difference between them (Table 3). For the allindividuals, although no statistically significant difference was detected in the SA2 and SA6, highly significant
heterogeneity was observed in the SA7W. FST between the Early and Late samples was also significantly larger
than 0 in the SA7W. For the only J stock individuals, no statistically significant difference was detected withineach of the subareas.
Genetic difference between bycatch and JARPN/JARPNII samples in the SA7W and SA11. We looked forevidence of genetic difference between the bycatch and JARPN/JARPNII samples in the SA7W as well as SA11
where the samples of both sources were available (Table 4). For the all individuals in the SA7, we comparedtotal, Early, and Late bycatch samples, respectively, to the JARPN/JARPNII sample on the basis of the results
shown above. The bycatch sample from the SA11 was too small to do so. In both the SA7W and SA11, when
all individuals were used for the tests, highly significant difference was observed from the all tests. In contrast,
when only J stock individuals were used, no statistically significant difference was observed within the both3

SC/61/NPM8
Not to be cited without prior consent of the authors

SA7W and SA11.
Genetic difference among the subareas. We finally looked for evidence of genetic difference among thesamples collected from the different subareas (Table 5). For the all individuals, highly statistically significant
difference was observed among them. FST was also significantly larger than 0. We thus conducted pair-wisecomparisons as the next step. Only seven out of the 21 pairs showed no statistical significance. In contrast,
for the only J stock individuals, no statistically significant difference was observed among the samples from thedifferent subareas. No pair-wise comparison was thus conducted for the only J stock group.
Assessment of statistical power for the tests of homogeneity
Table 6 shows the input parameters used and the results of simulation analysis to assess the statistical power forthe homogeneity tests. Our simulation attempted to test the statistical power for very small genetic divergence
between two samples from two genetically different sub-stocks, if they exist, in the SA2 and SA6. With oursample sizes, there was enough power to detect the genetic difference between the two stocks that wereseparated with the migration rate of 0.01 and 0.02.
DISCUSSION
Kanda et al. (2009) found that the J stock appeared to distribute all around the Japanese waters with different
proportion among the area. In some area, such as the SA6, mostly the J stock individuals exist, in the otherareas, such as SA7, mostly the O stock individuals exist. That study also indicated that the SA2 was mainly
occupied by the J stock but not by the O stock, which was different from what we had been thought.
The results of the heterogeneity tests reflected these observations. Among all of the heterogeneity tests we
conducted, significant differences were detected only from the tests that included all individuals. The samples
of all individuals contained not only the J stock individuals but also the O stock individuals and the individualsof unknown origins. The differences in the proportion of the J and O stock individuals among the subareas, but
not the possibility of different stocks in the different subareas, should have been responsible for theheterogeneity we observed. The proportional difference of the two stocks was also seen within the SA7.
Seasonal difference observed in the SA7 was consistent to the observation that the O stock migrate along thecoastline of the SA7 in spring (Hatanaka and Miyashita, 1997). In addition to the difference between the Early
and Late samples, the difference observed between the Early and JARPN/JARPNII in the SA7W was likely due
to the difference in the proportion of the J stock between the samples. The whales in the JARPN/JARPNII
sample were collected further offshore than those in the bycatch sample and the proportion of the J stock waslarger in coastal than in offshore (e.g., Hakamada et al., 2009).
In contrast, no significant difference at all was observed from the tests that used only the J stock individuals. If
the assignment of the whales to the J and O stocks conducted in Kanda et al. (2009) had been unreliable, we
could have detected statistically significant differences between some of the samples even in the only J stockindividuals group. In this study, in order to support the result of the homogeneity we assessed the statisticalpower by generating the genetic data from a simulation. In the final generation of each replicate of thesimulation, a sample of 140 was taken from each population for genetic analysis. The sample size of 140 wascome from the number of the J stock individuals in the SA2. We thus attempted to examine the power to see
whether or not there were two genetically different sub-stocks, one in the SA6 and the other in the SA2. This
simulation study indicated that from genetics standpoint the statistical power for our data set was quite high.
We believe from these results that there is only the single J stock around the Japan.
The Working Group on the in-depth assessment of western North Pacific common minke whales, with a focus
on J stock, agreed the following four stock structure hypotheses (IWC, 2007).
1) One stock in the SA5 and SA6.
2) Two stocks in the SA5 and SA6. The one stock migrates along the Japanese coast, and the other migrates
along the Korean coast.
3) Two stocks. One migrates up to the SA5 and the other migrates further north in the SA6 along the both
Japanese and Korean coast.
4) Two stocks. Both migrate through the SA6 in different time of the year.
Among these hypotheses, only the fourth one suggests that minke whales from two different stocks migrate, but
the different time of the year, along the Japanese coast in the Sea of Japan. This study did not support thishypothesis because we found no seasonal genetic difference in the SA6 samples. We believe that the sample
sizes for this test (Early = 188, Late = 223) was reasonably large to say that. In regard to the stock structure4

SC/61/NPM8
Not to be cited without prior consent of the authors

only within the SA5 and SA6, even this study that used only the Japanese data set can conclude that the fourth
hypothesis is the most unlikely.
Furthermore, all of these four hypotheses assume that the SA2 is occupied by the O stock, which is different
from our observation, because these were raised before the sample from the SA2 were used for the analysis.
With respect to this, our study indicates all four hypotheses are equally unlikely.
Although the working group disagreed to list as standing hypotheses, some members of the group raised two
additional stock structure scenarios that extended covering geographic area to the Pacific east of Japan, i.e., SA2
and SA7 (IWC, 2009). One of them assumes two different stocks in addition to the O stock: one in the Sea ofJapan side (SA6) and the other in the North Pacific side (SA2) of Japan. Again, this study did not support thishypothesis because no evidence of the genetic difference was found between the samples from the SA2 and SA6.
In this paper, we used only the Japanese samples because the new Korean dataset was incomplete for the
analyses. In the new data set from Korea, quite a few individuals lack the data at some of the loci and we also
recognized errors occurred during the recent electrophoresis. Probably due to these factors, substantial genetic
difference observed between the 1999 and other year samples in the previous studies (Kanda et al., 2006; Park etal., 2006; Goto et al., (2007) disappeared when we examined the same individuals using the new genotype
scores at the same sets of microsatellite loci to the previous papers. Unfortunately, we were not able to fix themby the 61SC. We therefore should await complete Korean dataset to conclude stock structure of common
minke whales around the Japanese and Korean waters.
ACKNOWLEDGEMENTS
We thank all those in Japan who were involved in sample collections and those who helped with laboratory
work.
LITERATURE CITED
Balloux, F. 2004. EASYPOP (version 1.7): a computer program for the simulation of population genetics. J.
Hered. 92:301‐302.
Bérubé, M., Jørgensen, H., Mcewing, R., and Palsbøl, P.J. 2000. Polymorphic di-nucleotide microsatellite
loci isolated from the humpback whale, Megaptera novaeanglliae. Mol. Ecol. 9:2181-2183.
Buchanan, F.C., Friesen, M.K., Littlejohn, R.P., and Clayton, J.A. 1996. Microsatellites from beluga whale
Delphinapterus leucas. Mol. Ecol. 5:571-575.
Coombs, J.A., Letcher, B.H., and Nislow, K.H. 2008. CREATE: a software to create input files from diploid
genotype data for 52 genetic software programs. Mol. Ecol. Resources 8:578-580.
Glaubitz, J.C. 2004. CONVERT: A user-friendly program to reformat diploid genotypic data for commonlyused population genetic software packages. Mol. Ecol. Notes 4:309-310.
Goto, M. and Pastene, L.A. 1997. Population structure of the western North Pacific minke whale based on an
RFLP analysis of the mtDNA control region. Rep. int. Whal. Comm. 47:531-537.
Goto M., Park, J.Y., Kanda, N., Sohn, H., Kim, Z.G. and Pastene, L.A. 2007. Progress of genetic analysis ofby-caught J-stock minke whales from Japan and Korea: seasonal pattern of migration. Paper SC/59/NPM6
presented to the IWC Scientific Committee, May 2007, Alaska, USA (unpublished).
Goudet, J. 1995. FSTAT, version 1.2: a computer program to calculate F-statistics. J. Hered. 86:485-486.
Hakamada, T., Miyashita, T., and Hatanaka, H. 2009. Examination of the effects of planned takes byJapanese small-type coastal whaling on ‘O’ and ‘J’ stocks common minke whales in the western North
Pacific. Paper SC/61/O15 presented to the IWC Scientific Committee, May 2009, Madeira, Portugal(unpublished).
Hatanaka, H. and Miyashita, T. 1997. On the feeding migration of the Okhotsk Sea-West Pacific stock ofminke whales, estimates based on length composition data. Rep. int. Whal. Commn. 47: 557-564.
International Whaling Commission. 2007. Report of the working group on the in-depth assessment ofwestern North Pacific common minke whales, with a focus on J-Stock. J. Cetacean Res. Manage. (Supple)
9: 179-187.
International Whaling Commission. 2009. Report of the working group on the in-depth assessment ofwestern North Pacific common minke whales, with a focus on J-Stock. J. Cetacean Res. Manage. (Supple)
11 (in press).
Kanda, N, Park, J.Y, Sohn, H., Kim, Z.G, Goto, M., Pastene, L.A. 2006. Preliminary microsatellite analysis
of bycaught J-stock minke whales from Japan and Korea. Paper SC/58/NPM2 presented to the IWC
Scientific Committee, May 2006, St. Kitts and Nevis (unpublished).
Kanda, N., Goto, M., Kishiro, T., Yoshida, H., Kato, H., and Pastene, L.A. 2009. Update of the analyses on
individual identification and mixing of the J and O stocks around Japanese waters examined by5

SC/61/NPM8
Not to be cited without prior consent of the authors

microsatellite analysis. Paper SC/61/JR5 presented to the IWC Scientific Committee, May 2009, Madeira,Portugal (unpublished).
Kato, H. 1992. Body length, reproduction and stock separation of minke whales off northern Japan. Rep.
int. Whal. Comm. 42:443-453.
Kishiro, T., Yoshida, H., Goto, M., Bando, T., and Kato, H. 2009. Methodology og whale sampling surveys in
the 2002-2007 coastal components under the JARPNII. Paper SC/J09/JR3 presented to the JARPN II
Review Workshop, Tokyo, January 2009 (unpublished). 27pp.
Ohsumi, S. 1977. Minke whales in the coastal waters of Japan. Rep. int. Whal. Comm. 27:164-166.
Omura, H. and Sakiura, H. 1956. Studies on the little piked whale from the coast of Japan. Sci. Rep. Whales
Res. Inst. Tokyo 11:1-37.
Palsbøll, P.J., Bérubé, M., Larsen, A.H., and Jørgensen, H. 1997. Primers for the amplication of tri- andtetramer microsatellite loci in baleen whales. Mol. Ecol. 6:893-895.
Park, J.Y., Goto, M., Kanda, N., Sohn, H., Kim, Z.G. and Pastene, L.A. 2006. Preliminary mitochondrial DNA
control region sequencing analysis of by-caught J-stock minke whale from Korea and Japan. Paper
SC/58/NPM3 presented to the IWC Scientific Committee, May 2006, St. Kitts and Nevis (unpublished).
Pritchard, J.K., Stephens, M., and Donnelly, P. 2000. Inference of population structure using multilocus
genotype data. Genetics 155:945-959.
Roman, J, and Palumbi, S.R. 2003. Whales before whaling in the North Atlantic. Science 301:508-510.
Rousset, F. 2008. Genepop'007: a complete re-implementation of the genepop software for Windows andLinux. Molec. Ecol. Resources 8:103-106.
Schneider, S., Roessli, D., and Excoffier, L. 2000. Arlequin ver 2.000: a software for population genetics data
analysis, Genetics and Biometry Laboratory, Department of Anthropology and Ecology University ofGeneva, Geneva.
Sokal, R.R. and Rohlf, F.J. 1995. Biometry: the Principles of Statistics in Biological Research. Freeman
and Company, New York.
Tamura, T., Matsuoka, K., and Fujise, Y. 2009. General methodology and survey procedure under the
JARPNII, offshore component. Paper SC/J09/JR4 presented to the JARPN II Review Workshop, Tokyo,
January 2009 (unpublished). 16pp.
Valsecchi, E. and Amos, W. 1996. Microsatellite markers for the study of cetacean populations. Mol. Ecol.
5:151-156.
Wada, S. and Numachi, K. 1991. Allozyme analyses of genetic differentiation among the populations andspecies of the Balaenoptera. Rep. int. Whal. Comm. Special issue 13:125-154.
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.

Samples used for this study.
Bycatch

1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Total

JARPN/JARPNII

SA2

SA6

SA7

SA10

SA11

SA7W

SA11

0
0
0
0
0
0
0
10
22
21
23
32
29
46
183

0
0
0
0
0
0
0
26
49
61
66
59
78
72
411

0
0
0
0
0
0
0
12
34
35
26
31
39
35
212

0
0
0
0
0
0
0
4
2
0
0
3
0
0
9

0
0
0
0
0
0
0
2
2
7
0
2
1
2
16

0
0
31
2
25
50
24
43
110
67
73
152
131
186
894

0
0
30
0
0
50
0
0
0
0
0
0
0
0
80

6

SC/61/NPM8
Not to be cited without prior consent of the authors

Table 2. Number of the individuals assigned to either the J stock, O stock, or unknown origins based on genetic
variation at the 16 microsatellite loci and a Bayesian clustering analysis (see Kanda et al., 2009 for details).
BycatchJARPN/JARPNII

SA2

SA6

SA7

SA10

SA11

SA7W* SA11

freq.

144
0.787

383
0.932

93
0.439

7
0.778

15
0.938

103
0.115

23
0.288

freq.

24
0.131

4
0.010

90
0.425

0
0.000

0
0.000

701
0.784

49
0.613

unknown 15
freq. 0.082

24
0.058

29
0.137

2
0.222

1
0.063

90
0.101

8
0.100

J

O

Table 3. Results (p-value) of the tests for genetic difference among the bycatch samples within the same
subarea (SA2, SA6, SA7) collected from different years (Total), collected from different years of the same
season (Early, Late), and from different seasons of the year (E x L). Tests were conducted respectively for all
individuals and for the only J stock individuals. Early: April - September, Late: October - March.
All individuals

Only J

Total

Early Late

ExL

Total

Early

Late

ExL

46
137
0.284 0.265 0.400
0.002 0.001 0.000

144
0.309
0.000

34
110
0.289 0.215 0.871
0.001 0.001 -0.002

Bycatch SA2

N
hypo. test.
FST

183
0.190
0.001

Bycatch SA6

N
hypo. test.
FST

411
188
223
0.502 0.856 0.494 0.717
0.000 -0.002 0.002 0.000

383
178
205
0.556 0.818 0.473 0.700
0.000 -0.001 0.003 0.000

Bycatch SA7

N
hypo. test.
FST

212
0.391
-0.00193
4350
0.888 0.789 0.875 0.530-0.003 -0.003 -0.004 -0.002

143
69
0.209 0.938 p<0.001
0.001 -0.005 0.010** Statistically significant FST.
Table 4. Results (p-value) of the tests for genetic difference between the bycatch and JARPN/JARPNII
samples in the SA7W and SA11. Tests were conducted respectively for all individuals and for only the J stockindividuals. Total indicates that individuals in the bycatch sample were not divided by their collected seasons.
Only the individuals in the bycatch sample were divided into the Early and Late samples by the collected seasonsand each of the samples was compared to the total JARPN/JARPNII sample. Early: April - September, Late:
October - March.
All individuals
Total

Early xOnly J
Late xSA7W

hypo. test.
FST

p<0.001 p<0.001 p<0.001
0.007* 0.002* 0.021*

0.976-0.002

SA11

hypo. test.
FST

p<0.0010.020*

0.632-0.004* Statistically significant FST.

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Table 5. Results (p-value) of the tests for genetic difference among the samples collected from the different
subareas. Tests were conducted for all individuals and for only the J stock individuals. SA2, SA6, SA7, SA10,
and SA11: bycatch sample, NP7 and NP11: JARPN/JARPNII sample.

All individuals
SA2

x
x
x
x
x
x
SA6 x
x
x
x
x
SA7 x
x
x
x
SA10 xx
x
SA11 x
x
NP7 x

SA6
SA7
SA10
SA11
NP7
NP11
SA7
SA10
SA11
NP7
NP11
SA10
SA11
NP7
NP11
SA11
NP7
NP11
NP7
NP11
NP11

Only J

hypo. test.

FST

p<0.001

0.018*

p<0.001p<0.001
0.596
0.302
p<0.001p<0.001p<0.001
0.782
0.311
p<0.001p<0.001
0.388
p<0.01p<0.001
0.076
0.892
p<0.001p<0.05p<0.001p<0.001p<0.010.001*
0.005*-0.0020.001
0.023*
0.010*
0.010*-0.005-0.0010.033*
0.019*
0.004
0.011*
0.007*
0.001-0.0130.028*
0.010*
0.035*
0.020*
0.002*

0.876-0.001* Statistically significant FST.
Table 6. Input parameter sets for generating simulated data set using EASYPOP to assess statistical power for
testing genetic difference between the SA2 and SA6 with our samples and results of the homogeneity tests withthe simulated data using FSTAT. The following were fixed in all sets other than those shown in the table:
diploid, random mating, equal sex ratio, all subpopulations of constant Ne, mutation rate of 0.005, 16 nuclear
gene loci, 18 maximum allelic states, and 100 replicates each with 5000 generations.
Input parameters% rejecting panmixia

n

N

Ne

m

Nem

FST

S

N=3Ne

2
2
2
2
2

9000
9000
9000
9000
9000

3000
3000
3000
3000
3000

0.01
0.02
0.05
0.1
0.2

30
60
150
300
600

0.00830.00410.00170.00080.0004

140
140
140
140
140

100
100
54
27
6

N=4Ne

2
2
2
2
2

9000
9000
9000
9000
9000

2250
2250
2250
2250
2250

0.01 23
0.02 450.05 113
0.1 225
0.2 450

0.01100.00550.00220.00110.0006

140
140
140
140
140

100
100
70
29
8

n = the number of populations, N = census population size, Ne = effective population size, m = migration rate,
Nem = number of the migrants, and S = sample size.

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SC/61/NPM8
Not to be cited without prior consent of the authors

Fig. 1. Eighteen sub-areas for the North Pacific common minke whale management.

9