SC/F14/J28

An update of the genetic study on stock structure of the
Antarctic minke whale based on JARPAII samples
LUIS A. PASTENE, MUTSUO GOTO AND NAOHISA KANDA
Institute of Cetacean Research, Toyomi-cho 4-5, Chuo-ku, Tokyo 104-0055, Japan
Contact e-mail: pastene@cetacean.jp
ABSTRACTPrevious JARPA analyses on stock structure of the Antarctic minke whale based on mtDNA RFLP,
microsatellite DNA and morphometric suggested significant differences between whales in the mosteastern and western sectors of the research area, and less differentiation in the central sectors. Those
results were consistent with the occurrence of at least two stocks and an area of mixing in the central
sectors (involving mainly sector VW). The areal and temporal pattern of stocks mixing, which is oneof the objectives of JARPAII, is studied in detail in a separated document (SC/F14/J29). In this study
genetic samples obtained during surveys of the JARPAII (2005/06-2010/11) were examined using
mtDNA control region sequencing and microsatellite DNA to test the previous hypothesis on stock
structure derived from JARPA research. A total of 2,278 samples were considered in the mtDNA
analysis (IIIE= 491; IVW= 669; IVE= 50; VW= 271; VE= 418 and VIW= 379), and 2,551 in the
microsatellite analysis (IIIE= 564; IVW= 734; IVE= 74; VW= 297; VE= 478 and VIW= 404). The
hypothesized mixing Area VW was not considered in the analysis. The statistical analysis ofheterogeneity followed a step-wise fashion: first, for each annual survey Areas IIIE, IVW and IVE
were compared in the western sector, and Areas VE and VIW were compared in the eastern sector.
Next the yearly variation in the western and eastern sectors was investigated. Finally a comparison
between western and eastern sectors of the JARPAII research area was conducted. This was done forfemale, males and both sexes combined. For mitochondrial and nuclear markers, significant statisticalgenetic differences were found between whales from the western and eastern sectors, for females,
males and total samples in the case of the mtDNA, and for females and total samples in the case of
microsatellites. Furthermore yearly variation was found for females and total samples, mainly in thewestern sector, in the case of the microsatellites. Therefore genetic results based on JARPAII samples
were consistent with the previous hypothesis of at least two stocks in the research area, one in the mostwestern part and the other in the most eastern part, which have been called previously as I (Eastern
Indian Ocean) and P (Western South Pacific) stocks for convenience. Furthermore microsatellite
analyses suggested substantial yearly variation mainly within the I stock and especially for females.
These yearly differences can be explained by the dynamics of the I and P stocks, which mix with eachother in different proportions in different years in part of the western sector (see also SC/F14/J29), orby the sporadic intrusion of an unknown third stock occurring in the western part of Area III.

KEYWORDS: ANTARCTIC, FEEDING GROUNDS, GENETICS, ANTARCTIC MINKE WHALE,SCIENTIFIC PERMITSINTRODUCTION
Results of mtDNA RFLP, microsatellite DNA and morphometric analyses suggested significant
differences between whales in the most eastern and western sectors of the JARPA research area, andless differentiation in the central sectors, especially Area VW (Pastene, 2006) (Figure 1). Based on
those results, a hypothesis of at least two stocks and an area of mixing in the central sectors (VW) was
proposed (IWC, 2008a). The location of the breeding grounds of these two stocks are assumed to be in
the eastern Indian Ocean (western side of Australia), and western South Pacific (eastern side ofAustralia), respectively, based on sighting distribution patterns (Kasamatsu et al., 1995) (Figure 2).
Based on such assumptions, these stocks are called I (eastern Indian Ocean) and P (western South
Pacific) stocks, respectively. The Australian continent seems to act as a natural barrier preventing thegene flow between these two breeding stocks, similar as in the case of other baleen whale species suchas the humpback whale.

1The longitudinal extent of the mixing area and the yearly pattern of stocks mixing between the I and P
stocks, which is one of the objectives of JARPAII, were studied by Kitakado et al. (2014)(SC/F14/J29).
New genetic samples were collected from Antarctic minke whales during recent JARPAII surveys.
These samples were not used in the analyses that resulted in the stock structure hypothesis mentioned
above. In this study we used JARPAII as an ‘independent’ set of genetic samples to test the pattern of
population genetic structure found during the JARPA research, in particular to test whether the whalesin the most western and eastern sectors are differentiated genetically. For this purpose we used both thematernal inherited mtDNA control region sequences (this genetic marker is used for the first time inthe study of the stock structure of the Antarctic minke whale), and the bi-parental inherited
microsatellite DNA.
All the previous mtDNA analyses to investigate stock structure in the Antarctic minke whale basedon JARPA samples were based on RFLP. Only a limited number of samples from this species was
examined previously using mtDNA control region sequencing in the context of a worldwide
phylogenetic analysis of minke whales (Pastene el al., 2007), however the utility of such a technique
has not been explored in the context of stock structure of this species in the Antarctic based on larger
sample sizes. The mtDNA RFLP approach used was informative of the stock structure pattern of the
Antarctic minke whale in the feeding grounds (Pastene et al., 2006), however the use of direct
sequencing has additional advantages: it has a larger resolution, it allows direct comparability between
studies, and it enables the use of most powerful analytical methods.
Microsatellite data on the other hand have been used previously based on the profiles of six loci. The
number of loci in this study was increased to twelve. Microsatellite DNA data obtained in this study
were also analyzed in the context of the recommendations offered during the JARPA review workshop(IWC, 2008a; IWC, 2008b).
MATERIALS AND METHOD
Samples
Genetic samples obtained during the JARPAII surveys in Areas IIIE, IVW, IVE, VW, VE and VIW
between 2005/06 and 2010/11 were considered in the genetic analysis (Figure 3). A total of 2, 278 and
2,551 samples were considered in the mtDNA and microsatellite DNA analyses, respectively (Table 1).
Samples from the assumed mixing area (Area VW) were not included in the statistical analysis.
Genetic information from this sector was examined in detail by Kitakado et al. (2014) (SC/F14/J29).
Grouping of samples
The statistical analysis followed a step-wise fashion: first, for each annual survey Areas IIIE, IVW andIVE were compared in the western sector, and Areas VE and VIW were compared in the eastern sector.
Next the yearly variation in the western and eastern sectors was investigated. Finally a comparison
between western and eastern sectors of the JARPAII research area was conducted. This was done forfemales, males and both sexes combined.
Laboratory procedures
Genomic DNA was extracted from approximately 0.05g of the outer epidermal layer of the skin tissue
using the protocol of Sambrook et al. (1989). Extracted DNAs were stored in TE buffer (10mM TrisHCl, 1mM EDTA, ph8.0).
MtDNA
Sequencing analyses of a 338bp-segment of the control region of mitochondrial DNA (mtDNA) was
conducted using the primers MT4 (Arnason et al. 1993) and Dlp 5R (5'-CCA TCG AGA TGT CTTATT TAA GGG GAA C-3'). Reactions are carried out in 25 uL volumes containing 10-100ng of DNA,
2.5 pmole of each primer, 0.5 units of Ex Taq DNA polymerase (Takara), 2mM of each dNTP, and 10x
reaction buffer. After an initial denaturation step at 95 C for 5 minutes, a PCR amplification cycle of
30 seconds at 94C, followed by 30 seconds at 50C and 30 seconds at 72C are repeated 30 times.
The amplification is completed with a final extension step of 10 minutes at 72C. Subsequent cycle
sequencing reactions are performed with 100ng of products generated in the above PCR amplifications
using the BigDye® Terminator Cycle Sequencing Kit (Applied Biosystems). The oligo-nucleotides
used to prime the cycle sequencing reaction were the same as employed in the initial PCR amplification
listed above. A total of 25 cycles for 10 seconds at 96C, 20 seconds at 56C and four minutes at 60C

2

are performed. The nucleotide sequence of each cycle sequencing reaction is determined using Applied
Biosystems 3500 Genetic Analyzer (Life Technology) under standard conditions. Both strand samples
are sequenced in their entirety for all samples.
Microsatellite DNA
Genetic variation at microsatellite loci were analyzed using 12 sets of primers: AC045, AC082, AC087,
AC137, CA234, GT129 (Bérubé et al., 2005), DlrFCB14 (Buchanan et al., 1996), EV1, EV104(Valsecchi and Amos, 1996), GT023, GT195, and GT211 (Bérubé et al., 2000). Although primer
sequences followed those of the original authors, an annealing temperature of each of the loci was
optimized for the species. PCR amplifications were performed in 15ml reaction mixtures containing
10-100ng of DNA, 5 pmole of each primer, 0.625 units of Ex Taq DNA polymerase (Takara), and2mM of each dNTP, and 10x reaction buffer containing 20mM MgCl2 (Takara). Amplified products
were run on a 6% polyacrylamide denaturating gel using a BaseStation 100 DNA fragment analyzer(Bio-Rad). Although alleles were visualized using Cartographer software specifically designed for the
BaseStation, allelic sizes were determined manually in relation to the internal size standard (Genescan
400HD, Life technologies) and the Antarctic minke whale’s microsatellites of known size from the'control' individual that is rerun on each gel.
Statistical analysis
mtDNA
Genetic distances among unique sequences (haplotypes) were estimated using the Kimura’s two
parameters method (Kimura, 1980). The degree of mtDNA intra-population diversity was estimatedusing the nucleotide diversity and nucleon diversity (Nei, 1987).
Heterogeneity tests were conducted using the chi-square (Roff and Bentzen, 1989). The level ofstatistical significance was estimated from 10,000 Monte Carlo simulations as the proportion ofsimulations in which a similar or more extreme value of chi-square was observed.
Microsatellite DNA
For data consistency and plausibility, the computer program MICRO-CHECKER (van Oosterhout etal., 2004) was used to check for null alleles and reading/typing errors. The number of alleles per locus,
allelic richness, and expected heterozygosity per locus was calculated using FSTAT 2.9.3 (Goudet,
1995). Statistical tests for the deviations from expected Hardy-Weinberg genotypic proportions wereconducted using GENEPOP 4.0 (Rousset, 2008). Heterogeneity tests among samples were conductedusing conventional hypothesis testing procedure. A probability test (or Fisher’s exact test)
implemented in GENEPOP 4.0 (Rousset, 2008) was used to conduct the heterogeneity tests. Statisticalsignificance was determined using the chi-square value obtained from summing the negative logarithmof p-values over the 12 microsatellite loci (Sokal & Rohlf 1995). FST value was calculated using
FSTAT 2.9.3.
RESULTS
MtDNA
Polymorphic sites and number of haplotypes
The final data set included the first 338 nucleotides of the mtDNA control region. In the total sample
of 2,278 whales a total of 498 haplotypes was found. This number was derived from 103 segregatingsites (90 transitions and 13 transversions).
mtDNA diversity
Table 2 shows the nucleotide and nucleon diversities for whales in both sectors of the research area.
Both indices were high for both groups of whales.
Heterogeneity test
Results of the step wise statistical test are shown in Table 3. No significant differences were foundamong sectors in each of the surveys in the western and eastern sectors. No significant yearly
differences were found in the two sectors. Finally significant differences were found between westernand eastern sectors for females, males and both sexes combined. Differences were stronger for females.
Microsatellites
Diversity

3Genetic diversity indices are shown in Table 4 for whales in the western and eastern sectors of theresearch area. The average number of alleles per loci, average allelic richness, and average expected
heterozygosity, all over the 12 loci, was high for the both sectors, and the levels of these indexes werequite similar between the two sectors. Both sectors showed evidence of deviation from the expected
Hardy-Weinberg genotypic proportions.
Heterogeneity test
Results of the step wise statistical tests are shown in Table 5. No significant differences were foundamong sectors in each of the surveys in the western and eastern sectors. Yearly differences were foundin the two sectors, but mainly in the western sector and for females. Finally significant differences were
found between western and eastern sectors for females and both sexes combined with very low FST
values (less than 0.0001), but not for male animals. In the analysis of yearly variation all possible
pairwise comparisons showed statistically significant differences in the I stock for both cases (female
and both sexes combined), while a significant difference was detected only between 06/07 and 08/09
seasons in the P stock for both sexes combined. Because of quite strong yearly variation especially in
the I stock (Table 5), we conducted heterogeneity tests by separating all of the six season samples assingle samples to see any trends to the mixing patterns irrespective of the prior stock designations.
Although a substantial difference was detected among the samples as expected, no trend was detected(data not shown).
JARPA review recommendations
During the JARPA review workshop some recommendations were made to conduct clustering analyses
based on individual genotypes (IWC, 2008a). In response to these recommendations we used Bayesianclustering approach using STRUCTURE (Pritchard et al. 2000) to determine the number of geneticallydistinct populations present in our samples based on information on the individual genotypes. The
simulation results detected no clustering in our samples probably due to a very low level of genetic
differentiation even between the I and P stocks despite doubling the number of loci used.
DISCUSSION
The main objective of the present study was to test the stock structure hypothesis derived from JARPA
analyses by using a set of new genetic samples obtained by JARPAII, and mtDNA control region
sequencing and microsatellite DNA.
The genetic results from JARPAII confirmed the previous results from JARPA that the most extreme
western and eastern sectors of the research area of JARPA and JARPAII are occupied by different
stocks (I and P stocks, respectively). The microsatellite analysis found significant yearly variation
mainly in the area occupied by the I stock (western), and especially for females. Yearly variation was
not found in the case of the mtDNA despite the fact that some of the tests involved considerable
sample sizes e.g. those involving Areas IIIE and IVW. It is possible that the large number of
haplotypes represented by single individuals (singleton) decreased the power of the statistical analysis
of yearly variation. Despite this the number of samples was large enough for detecting differences
between the western and eastern sectors. Further analyses are required to investigate whether or not theabsence of yearly differences in the mtDNA analysis was due to lack of statistical power.
An interpretation for the yearly differences in the microsatellite analysis is that both the I and P stocks
distribute in part of Area IV in different proportions in different years. This is supported by thesignificant departure from the Hardy-Weinberg equilibrium found. Also this explanation matches wellwith the yearly changes in the distribution and mixing of stocks reported by Kitakado et al (2014)(SC/F14/J29). An alternative explanation is that the yearly differences are due to the temporal intrusion(and mix) of an additional unknown stock from the contiguous west sector of the research area (e.g.
Area IIIW).
The significant yearly differences found by the microsatellite analyses could be due to a Type Error I.
This is possible because the large sample size used and the sensitivity of the method (the number ofloci was increased to twelve) could result in ‘significant’ p values which are not necessarily of
biological significance. Significant microsatellite differences between the western and eastern sectors
are consistent with the results of the mtDNA and therefore the hypothesis of at least two different
stocks separated geographically is better supported.

4

If the explanation that the distribution and mixing of the stocks changes substantially by year iscorrect, then continuous research of the reasons for such changes will be necessary in the future. Stock
distribution can change according to changes in krill distribution which in turn depends on changes inoceanographic conditions in the area.
Same as in the genetic analyses of humpback whales in the feeding grounds, the present genetic study
on Antarctic minke whales showed more temporal and geographical differentiation in females thanmales supporting the view of a stronger fidelity of females to migratory destination than males.
Regarding the work conducted in response to the recommendations from the JARPA review meeting,the number of the microsatellite loci used was doubled from the previous six to current 12. Even withthis substantial increase in the loci numbers, however, the level of genetic differentiation among thesamples was very low. For instance, the FST values were less than 0.0001 between the I and P stocks forfemales and both sexes combined. Because of this low differentiation, the analyses conducted using
STRUCTURE were not informative of the stock structure Antarctic minke whales.
ACKNOWLEDGEMENTS
We would like to thank the researchers and crew members of the first six years of JARPAII survey for
their efforts in collecting the data and samples used in this study. Our acknowledgement also goes to H.
Oikawa and S. Azumi, Institute of Cetacean Research, for their assistance in the laboratory work.
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2006. J. Cetacean Res. Manage. 10 (Suppl.):411-445.
International Whaling Commission. 2008b. Report of the Standing Working Group on Special Permits.
Annex O. J. Cetacean Res. Manage. 10 (Suppl.): 341-350.
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Kitakado, T., Schweder, T., Kanda, N., Pastene, L.A. and Walloe, L. 2014. Dynamic population
segregation by genetics and morphometrics in Antarctic minke whales. Doc. SC/F14/J29 (thismeeting).
Nei, M. 1987. Molecular Evolutionary Genetics. Columbia University Press, New York. x+512pp.
Pastene, L.A., Goto M. and Kanda, N. 2006. Genetic analysis on stock structure in the Antarctic minke
whales from the JARPA research area based on mitochondrial DNA and microsatellites. Paper
SC/D06/J9 presented to the JARPA Review Meeting, December 2006 (unpublished). 22pp.
Pastene, L.A., Goto, M., Kanda, N., Zerbini, A.N., Kerem, D., Watanabe, K., Bessho, Y., Hasegawa,M., Nielsen, R., Larsen, F. and Palsbøll, P.J. 2007. Radiation and speciation of pelagic organisms
during periods of global warming: the case of the common minke whale, Balaenoptera
acutorostrata. Molecular Ecology 16: 1481-1495.
Pritchard, J.K., Stephens, M., and Donnelly, P. 2000. Inference of population structure using multilocus
genotype data. Genetics 155: 945-959.
Roff, D.A. and Bentzen, P. 1989. The statistical analysis of mtDNA polymorphisms: chi-square andthe problem of small samples. Mol. Biol. Evol. 6:539-45.
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Sambrook, J., Fritsch, E.F., and Maniatis, T. 1989. Molecular cloning: A laboratory manual. 2nd Ed.
Cold Spring Harbor Laboratory, New York.
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Ecol. 5: 151-156.
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software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes
4:535-538.

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Table 1. Number of samples used in the genetic analysis: A= mtDNA, B= microsatellite DNA
A
All areas

05/06
06/07
07/08
08/09
09/10
10/11

FM
562
466
535
188
370
157

F
251
319
271
91
207
98

M
311
147
264
97
163
59

FM
377

I stock
IVW
F
166

M
211

149

212

135

96

80

43

FM
34

IIIE
F
17

M
17

224

75

233

137

FM
37

IVE
F
17

M
20

77

13

3

10

37

0

0

0

Mixing area
VW
FM
F
112
49
0
0
86
58
16
3
57
27
0
0

P stock
M
63
0
28
13
30
0

FM
2
287

VE
F
2
205

M
0
82

FM

VIW
F

M

179

114

65

82

42

40

90

46

44

47

20

27

110

78

32

FM

VE
F

M

FM

VIW
F

M

320

232

88

182

117

65

111

58

53

99

50

49

47

20

27

123

88

35

B
All areas

05/06
06/07
07/08
08/09
09/10
10/11

FM
708
502
551
226
396
170

F
311
349
278
112
228
108

M
397
153
273
114
168
62

FM
414

I stock
IVW
F
179

M
235

150

222

138

98

98

59

FM
95

IIIE
F
46

M
49

229

79

240

142

FM
61

IVE
F
29

M
32

84

13

3

10

39

0

0

0

7

Mixing area
VW
FM
F
136
55
0
0
87
58
16
4
58
27
0
0

P stock
M
81
0
29
12
31
0

Table 2. Mitochondrial DNA diversity indices for Antarctic minke whales from two stocks/sectors
Genetic index

Areas IIIE+IV (I stock)

Areas VE+VIW (P stock)

Nucleon diversity
Nucleotide diversity

0.98000.0156

0.97520.0148

Table 3. Results of the spatial and temporal statistical analysis of heterogeneity test (mtDNA
sequencing). Figures shown are p-values.

F+M
Among
sectors/season
Among
seasons/stock
Between I and P
FemaleAmongsectors/season
Among
seasons/stock
Between I and P
Male
Among
sectors/season
Among
seasons/stock
Between I and P

05/06

I stock
07/08

09/10

06/07

P stock
08/09

10/11

0.40970.64880.08980.37770.6960-0.36420.50570.00010.12460.68030.27280.50670.6301

0.0510-0.19940.0001

0.54980.82850.60500.5807

0.78640.8205-0.16860.0085

Table 4. Microsatellite diversity indices for Antarctic minke whales from two stocks/sectors
Genetic index

Areas IIIE+IV (I stock)

Areas VE+VIW (P stock)

No. alleles per locus
Allelic richness
Heterozygosity
Hardy-Weinberg

19.5
18.8
0.854
<0.001

19.0
19.0
0.858
0.00428

Table 5. Results of the spatial and temporal statistical analysis of heterogeneity test (microsatellite
DNA). Statistical results are for all loci combined. ns= not significant.

F+M
Among
sectors/season
Among
seasons/stock
Between I and P
FemaleAmongsectors/season
Among
seasons/stock
Between I and P
Male
Among
sectors/season
Among
seasons/stock
Between I and P

05/06

I stock
07/08

09/10

06/07

P stock
08/09

10/11

0.034

Ns

ns

ns

ns

Ns

0.0000838

0.004
0.00769 (Fst= 0.0002)

ns

Ns

ns

ns

0.000625

ns

Ns

0.047
0.0000559 (Fst= 0.0005)

ns

Ns

ns

ns

Ns

ns

Ns

0.00227
ns

Figure 1. JARPAII research area. Whales in the ‘blue’ and ‘green’ sectors showed genetic andmorphometric differentiation according to the JARPA research. It is assumed that those two sectors areoccupied by different stocks (I and P stocks, respectively). The sector in the middle was proposed as anarea of mixing. The longitudinal extension of the area of mixing and the yearly pattern of mixing ofstocks were studied by Kitakado et al. (2014) (SC/F14/J29). In the present study genetic samples from
JARPAII were used to test the hypothesis of genetic differentiation between whales from the extreme
sectors. This was done using mtDNA sequences and microsatellite DNA.

9

Figure 2: Encounter rates of Antarctic minke whales in 10º squares of latitude and longitude in waters
0º-30ºS in October (from Kasamatsu et al., 1995).

Figure 3. Geographical distribution of the Antarctic minke whales sampled by JARPAII between the
2005/06 and the 2010/11 austral summer season.

10