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2007; 73: 1104–1112

Stock structure of Japanese flounder inferred from morphological and genetic analyses Yuya SHIGENOBU,1a* Ken-Ichi HAYASHIZAKI,2 Takashi ASAHIDA,2 Hitoshi IDA3 AND Kenji SAITOH1 1

Tohoku National Fisheries Research Institute, Shinhama, Shiogama, Miyagi 985-0001, School of Fisheries Science, Kitasato University, Sanriku, Ofunato, Iwate 022-0101, and 3 PREC Institute Inc., Kouji-machi, Chiyoda-ku, Tokyo 102-0083, Japan 2

ABSTRACT: Stock structure of Japanese flounder Paralichthys olivaceus has been inferred mainly from either morphological or genetic analyses. However, because the results of both analyses did not always agree with each other, an inclusive conclusion has never been obtained. In this study, the stock structure has been inferred from both morphological and genetic analyses using 722 wild Japanese flounder collected from nine locations along the Japanese coast. The dorsal and anal fin ray counts were larger in the southern than in the northern populations. In total, 1041 bp of mitochondrial NADH dehydrogenase subunit-2 (ND2) and 1830 bp of ND5 sequences were aligned. There are 578 variable sites in the concatenated sequence from the two genes, which defined a total of 490 haplotypes. Both results of morphological and genetic analyses indicated that the western Kyushu group, which included the Nagasaki and Kagoshima populations, was divided from the other seven populations. This is the first report to reveal the heterogeneity of the western Kyushu group based on statistical analysis. KEY WORDS: AIC, AMOVA, dorsal and anal fin rays, mitochondrial DNA, Paralichthys olivaceus.

INTRODUCTION Some fish species have several stocks that differ in productivity and resource fluctuation. It is, therefore, necessary to separately manage each stock for sustainable use of fisheries resources.1 In marine fish species, however, stock structure is often obscure because of the gene flow among stocks.2,3 For genetic analysis of marine fish populations with possible gene flow, it is helpful to incorporate other types of datasets. The Japanese flounder Paralichthys olivaceus is widely distributed along the coasts of Japan, Korea, and China.4,5 This species is one of the most important commercial fish in Japan, with approximately 6500 t landed annually. However, the catch has been slightly declining in recent years.6 Stock structure of Japanese flounder has been inferred mainly from either morphological or *Corresponding author: Tel: 81-22-365-9932. Fax: 81-22-367-1250. Email: [email protected] a Present address: SPOC Inc. Reading Venture Plaza 5F 75-1, Ono, Tsurumi, Yokohama, Kanagawa 230-0046, Japan. Received 9 November 2006. Accepted 16 May 2007.

© 2007 Japanese Society of Fisheries Science

from genetic analyses.7–14 Previous morphological studies suggested two different groups in the Sea of Japan; a southern group, which has higher numbers and a northern group, which has lower numbers of both the dorsal and anal fin rays.10,11,13 Alternatively, genetic studies indicated that populations around Japan seem not to be well structured with frequent gene flow.7,8,12,14 However, there is no study which inferred the stock structure from both morphological and genetic analyses. In this study, we collected wild Japanese flounder from nine locations along the Japanese coast and inferred the stock structure from both morphological and genetic analyses.

MATERIALS AND METHODS Materials In total, 722 wild individuals from nine locations (Hokkaido, Akita, Miyagi, Chiba, Fukui, Tottori, Ehime, Nagasaki, and Kagoshima) were used in this study (Table 1, Fig. 1).We excluded hatchery-reared doi:10.1111/j.1444-2906.2007.01442.x

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Table 1 Sampling locality, date, total length and number of individuals used in this study Locality (abbreviation) Ishikari Bay, Hokkaido (HKD) Oga Peninsula, Akita (AKT) Wakasa Bay, Fukui (FKI) Off Tottori, Tottori (TTR) Sendai Bay, Miyagi (MYG) Inubou Point, Chiba (CHB) Sea of Hiuchi, Ehime (EHM) Nishisonogi Peninsula, Nagasaki (NGS) Fukiage Beach, Kagoshima (KGS) Total

Date

Total length (mm)

No. of individuals

June 2003 and June 2005 June 2003 June 2003 June 2003 May–June 2003 June 2003 and April–May 2004 September 2003–April 2004 April 2005 May 2004

395–654 300–690 370–900 40–80 300–818 177–317 151–826 540–795 83–147

55 45 39 74 126 165 112 42 64 722

Japanese flounder, which had abnormal pigmentation.15 Specimens from Hokkaido, Akita, Fukui, Miyagi, and Nagasaki were adults and collected in the spawning season. Therefore, collecting localities of these fish were close to their spawning sites. Some specimens from Ehime were also adults in the spawning season. Other specimens were mostly 1-year-old immatures, but if long distance (over the geographic scale of our present study) migration between foraging and spawning sites were infrequent, they largely represent wider but still localized populations. The method for dorsal and anal fin ray counts followed Nakabo.16 Extractions of genomic DNAs from scales or muscle tissues followed Asahida et al.17

PCR primers referred to the complete mitochondrial sequence of Japanese flounder.20 We first amplified approximately 1.4 kb including the whole ND2 sequence and 2.8 kb including the whole ND5 sequence from genomic DNA with two pairs of PCR primer sets. These first-round PCR products then worked as templates for the secondround PCRs of approximately 1 kb with the secondround PCR primer sets (Table 2, Figs 2 and 3). The first-round PCR were carried out in a 15-mL reaction mixture, which included 5–50 ng of template DNA, 1.2 mL of the dNTP mixture (2.5 mM each, TaKaRa, Shiga, Japan), 1.5 mL of 10 ¥ Ex Taq PCR buffer (TaKaRa), 0.15 mL of Ex Taq DNA polymerase (5 unit/mL, TaKaRa), and 5 pmol of each primer. The first-round PCR cycles were 3 min at 95°C, 33 cycles of 30 s at 94°C, 30 s at 61°C, 3 min at 68°C, and final extension for 10 min at 72°C. The second-round PCRs were carried out in a 15-mL reaction mixture, which included 5–50 ng of template DNA, 1.2 mL of the dNTP mixture, 1.5 mL of 10 ¥ PCR buffer (TaKaRa), 0.15 mL of Taq DNA polymerase (5 unit/mL, TaKaRa), and 5 pmol of each primer. The second-round PCR cycles were 3 min at 95°C, 30 cycles of 30 s at 94°C, 30 s at 61°C, 1 min at 68°C, and final extension for 10 min at 72°C. We used a Model 9600 thermal cycler (Applied Biosystems, Foster City, CA, USA) for the first and secondround PCR amplifications. Sequencing reactions were performed with the same second-round PCR primers and a commercial sequencing kit (Applied Biosystems). Sequencing reaction products were run on an ABI3100 automated DNA sequencer (Applied Biosystems).

PCR amplification and sequencing strategy

Data analysis

We sequenced the mitochondrial NADH dehydrogenase subunit-2 (ND2) and subunit-5 (ND5) genes, using a two-step polymerase chain reaction (PCR) direct sequencing technique.18,19 Design of

To choose the optimal population grouping with the datasets of dorsal and anal fin ray counts, we applied analysis of variance (anova) type model selection based on the Akaike information criterion

Fig. 1 Sampling locations of Japanese flounder used in this study. For abbreviations, see Table 1.

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Table 2 PCR and sequencing primers used in this study L primers†

H primers†

Sequence (5′→3′)

First-round PCR primers ND2 L4366-Gln GAA CCC AAC CTG AAG AGA TCA ND5 L11798-ND4 ACC ATG GTA CTG GCA CGA G Second-round PCR & Sequencing primers ND2 L4450-Met CCA TAC CCC AAC TAT GTA GG ND5 L12321-Leu§ GGT CTT AGG AAC CAA AAA CTC TTG GTG CAA L13099-ND5 CTC AAG CAC GAT AGT TGT CG

Sequence (5′→3′)

39F1‡

AGA TGC TCG CTG GAT TGG

H14597-ND6

TGC TTC GAA CCC ATC ACC TTA

H5544-Trp

ACT CCC GCT TAG GGC TTT GA

H13390-ND5 H14171-ND6

TTC CGG ATG TCT TGT TCG TC GAC TCG TGG GAT GAG TCG

L and H, light and heavy strands, respectively. † Primers designated by their 3′ ends, corresponding to the position of the human mitochondrial genome21 by convention. ‡ Saitoh et al.20 § Miya and Nishida.18

ND1

I Q M

ND2

W A N

ori-L

First-round PCR

L4366-Gln

39F1 Second-round PCR

L4450-Met

ND4

H S L

H5544-Trp

Fig. 2 First and second-round PCR primer annealing sites for ND2 gene sequencing. Transfer RNA genes are designated by single letter amino acid codes.

ND5 First-round PCR

L11798-ND4

ND6 L14597-ND6

Second-round PCR

L12321-Leu

H13390-ND5 L13099-ND5

(AIC) using the statistical package R v2.3.1.22 We refer to this analysis as AIC analysis hereafter. We also examined the heterogeneity of the mean number of dorsal and anal fin rays between putative groups using post hoc Student’s t-tests. We aligned the sequences of ND2 and ND5 genes using DNASIS pro v2.06 (Hitachi Software Engineering, Tokyo, Japan), from which the haplotypes were defined. Initiation and termination codons were excluded from this study. We also excluded overlapping regions of ND5 and ND6 genes from sequence data. Calculations of haplotype and © 2007 Japanese Society of Fisheries Science

H14171-ND6

Fig. 3 First and second-round PCR primer annealing sites for ND5 gene sequencing. Transfer RNA genes are designated by single letter amino acid codes.

nucleotide diversity for each population followed Nei.23 The pairwise heterogeneities of the haplotype frequencies among the nine populations were evaluated using the exact test with 10 000 Monte Carlo simulations.24 We used ARLEQUIN v2.000 software to calculate population pairwise FST values.25 Phylogenetic relationships among the 722 individuals were estimated by the neighbor-joining method26 based on Kimura’s two-parameter distance,27 using PAUP 4.0b10.28 To choose the optimal population grouping with the genetic data, we performed a hierarchical

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analysis of molecular variance (amova)29 using ARLEQUIN v2.000.25 Putative groupings for amova were assessed by geographic relationships, population pairwise FST values, and/or the results of AIC analysis.

RESULTS Variability of mtDNA Lengths of ND2 and ND5 genes were 1046 bp and 1839 bp, respectively. We aligned 1041 bp of ND2 and 1830 bp of ND5 sequences for all individuals. There was no gap in these regions among the material fish examined. We found 578 variable sites in the concatenated 2871 bp from the two genes, which defined a total of 490 haplotypes. Of the 490 haplotypes, 379 occurred in a single individual. Haplotype and nucleotide diversity within the nine populations were 0.9933–0.9990 and 0.0079– 0.0085, respectively (Table 3). No significant heterogeneity of the haplotype frequencies appeared for the entire population pair with sequential Bonferroni corrections30 (P > 0.01). Then we estimated pairwise FST values among the nine populations.

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The FST values indicated that the Nagasaki and Kagoshima populations were genetically heterogeneous compared with the other seven populations (Table 4). In addition, the FST value between the Nagasaki and Kagoshima populations was negative (FST = -0.01430). However, all FST values were not significantly greater than zero with sequential Bonferroni corrections (P > 0.01). We found two major clades (Clade A n = 430 and Clade B n = 281) and one minor clade (Clade C n = 11) from the neighbor-joining tree (Fig. 4). Both individuals of Clade A and Clade B, however, coexisted within all populations (Fig. 5). Variability of fin rays count and AIC analysis Both the dorsal and anal fin ray counts were larger in the southern than in the northern populations (Fig. 6). Previous studies have also reported this tendency.10,11,13 By considering geographic relationships, we assumed that the nine populations could be divided into three groups: (i) Hokkaido, Akita, Miyagi, and Chiba; (ii) Fukui, Tottori, and Ehime; and (iii) Nagasaki and Kagoshima. For AIC analysis, we made three models of putative grouping among the entire range of sampling

Table 3 Haplotype diversity and nucleotide diversity within the nine populations Population† HKD AKT MYG CHB FKI TTR EHM NGS KGS Total

Haplotype diversity (SD)

Nucleotide diversity (SD)

0.9987 (0.0038) 0.9990 (0.0050) 0.9980 (0.0013) 0.9979 (0.0010) 0.9933 (0.0080) 0.9981 (0.0027) 0.9968 (0.0019) 0.9988 (0.0055) 0.9960 (0.0042) 0.9980 (0.0003)

0.0080 (0.0039) 0.0084 (0.0042) 0.0080 (0.0039) 0.0083 (0.0041) 0.0083 (0.0042) 0.0083 (0.0041) 0.0079 (0.0039) 0.0085 (0.0042) 0.0082 (0.0040) 0.0082 (0.0040)

†

Population abbreviations given in Table 1. SD, standard deviation.

Table 4 Pairwise FST values based on concatenated ND2 and ND5 sequences between the nine populations Population† AKT MYG CHB FKI TTR EHM NGS KGS †

HKD

AKT

MYG

CHB

FKI

TTR

EHM

NGS

-0.01111 -0.00751 -0.00486 -0.00838 -0.00591 -0.00682 0.02514 0.03389

-0.00992 -0.00513 -0.00982 -0.00862 -0.00914 0.01798 0.02964

-0.00169 -0.00573 -0.00467 -0.00435 0.02561 0.03622

-0.00856 -0.00665 0.00242 0.01023 0.01692

-0.01256 0.00107 0.00200 0.00906

0.00128 0.00707 0.01463

0.03981 0.05069

-0.01430

Population abbreviations given in Table 1.

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Clade A n = 430

Clade C n = 11

Clade B n = 281

Fig. 4 Neighbor-joining tree for 722 individuals of Japanese flounder derived from the concatenated ND2 and ND5 sequences. Genetic distances among the individuals were estimated by Kimura’s two-parameter method.23 Bar, 0.001 substitutions per site.

Fig. 5 Comparison of genetic composition regarding three phylogenetic clades among the nine populations. Clade A (shaded), Clade B (black), Clade C (gray). Numbers in parentheses indicate number of individuals examined for each population. For abbreviations, see Table 1. © 2007 Japanese Society of Fisheries Science

Fig. 6 Comparison of dorsal (䉬) and anal (䊉) fin ray counts among the nine populations. Vertical bars, standard deviation. Numbers in parentheses indicate number of individuals for each population. Mean counts of dorsal (D) and anal (A) fin rays for each population were: (Hokkaido, D 72.56 ⫾ 2.34 [standard deviation], A 56.25 ⫾ 1.71; Akita, D 72.51 ⫾ 2.80, A 56.29 ⫾ 2.56; Miyagi, D 72.35 ⫾ 2.51, A 55.89 ⫾ 2.08; Chiba, D 72.36 ⫾ 2.54, A 55.57 ⫾ 1.87; Fukui, D 76.31 ⫾ 2.96, A 58.26 ⫾ 2.39; Tottori, D 75.69 ⫾ 3.51, A 58.00 ⫾ 2.57; Ehime, D 75.19 ⫾ 2.75, A 57.60 ⫾ 2.16; Nagasaki, D 77.79 ⫾ 2.63, A 59.45 ⫾ 1.89; Kagoshima, D 78.39 ⫾ 2.47, A 60.08 ⫾ 1.90). For abbreviations, see Table 1.

locations, considering geographic relationships, and population pairwise FST values (Table 4), and/or mean number of fin ray counts (Fig. 6). The putative groups of model 1 were group 1A (Hokkaido, Akita, Miyagi, Chiba, Fukui, Tottori, and Ehime) and group 1B (Nagasaki and Kagoshima). The putative groups of model 2 were group 2A (Hokkaido, Akita, Miyagi, and Chiba), group 2B (Fukui, Tottori, and Ehime) and group 1B. In model 3, we divided all the nine populations into individual groups. Regarding the datasets of dorsal fin ray counts, the AIC of model 2 was the lowest (Table 5). In the case of anal fin ray counts, AIC of model 3 was the lowest, although the difference in the AIC values between models 2 and 3 was small.22 Accordingly, model 2 was a near optimal population grouping with AIC analysis for the datasets of fin ray counts. Both in the AIC analyses, AIC values of model 2 were significantly smaller than of null-models. Therefore, it is worth dividing the nine populations into three groups according to model 2. Additionally, significant differences in the mean number of both the dorsal and anal fin rays between the three groups of model 2 appeared (P < 0.001, Student’s t-test with sequential Bonferroni corrections). Then, we performed another AIC analysis to examine the stock structure in the Sea of Japan

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Table 5 AIC analyses of grouping by fin ray counts for nine populations Group definitions† Dorsal Model 1 (HKD, AKT, MYG, CHB, FKI, TTR and EHM) versus (NGS and KGS) Model 2 (HKD, AKT, MYG and CHB) versus (FKI, TTR and EHM) versus (NGS and KGS) Model 3 (HKD) versus (AKT) versus (MYG) versus (CHB) versus (FKI) versus (TTR) versus (EHM) versus (NGS) versus (KGS) Null-model (HKD, AKT, MYG, CHB, FKI, TTR, EHM, NGS and KGS) Anal Model 1 (HKD, AKT, MYG, CHB, FKI, TTR and EHM) versus (NGS and KGS) Model 2 (HKD, AKT, MYG and CHB) versus (FKI, TTR and EHM) versus (NGS and KGS) Model 3 (HKD) versus (AKT) versus (MYG) versus (CHB) versus (FKI) versus (TTR) versus (EHM) versus (NGS) versus (KGS) Null-model (HKD, AKT, MYG, CHB, FKI, TTR, EHM, NGS and KGS)

AIC

AIC differences

3661.722 3492.289 3497.343

169.433 Best 5.054

3841.546

349.257

3248.473 3132.798 3132.077

116.396 0.721 Best

3409.550

277.473

†

Population abbreviations given in Table 1. AIC, Akaike information criterion.

Table 6 AIC analyses of grouping by fin ray counts for four populations in the Sea of Japan Group definitions† Dorsal Model 4 (HKD and AKT) versus (FKI and TTR) Model 5 (HKD) versus (AKT) versus (FKI) versus (TTR) Null-model (HKD, AKT, FKI and TTR) Anal Model 4 (HKD and AKT) versus (FKI and TTR) Model 5 (HKD) versus (AKT) versus (FKI) versus (TTR) Null-model (HKD, AKT, FKI and TTR)

AIC

AIC differences

1074.412

Best

1077.295

2.883

1131.379

56.967

969.045

Best

972.727

3.682

997.365

28.320

†

Population abbreviations given in Table 1. AIC, Akaike information criterion.

with four populations (Hokkaido, Akita, Fukui, and Tottori). The putative groups of model 4 were northern (Hokkaido and Akita) and southern (Fukui and Tottori) groups. In model 5, we divided all four populations into individual separate groups. The AIC of model 4 was the lowest (Table 6). A significant difference in the mean number of both the dorsal and anal fin rays between the two groups also appeared (P < 0.001, Student’s t-test). We also summed up the dorsal (D) and anal (A) fin ray counts for each of three mitochondrial clades: (i) Clade A, D 74.13 ⫾ 3.36 (standard deviation, SD), A 56.97 ⫾ 2.51; (ii) Clade B, D 74.42 ⫾ 3.61, A 57.23 ⫾ 2.63; and (iii) Clade C, D 73.00 ⫾ 2.86, A 56.18 ⫾ 2.60. There was no signifi-

cant difference for the mean number of both the dorsal and anal fin rays between all pairs of the clades (P > 0.05, Student’s t-test). This was also the case within each population level (P > 0.05, Student’s t-test). AMOVA

We could not infer stock structure from haplotype frequency and pairwise FST values between the nine populations. Previous studies, however, indicated that there was some stock structure around the Japanese coast.7–14 Moreover, the AIC analysis with the datasets of fin ray counts indicated that model 2 was the optimal (dorsal) or near optimal (anal) grouping for the populations. Then we performed hierarchical amova for model 1 and model 2, because the genetic distance between haplotypes is an important factor in amova.29 The fixation index among groups of model 1 was small but significant at the 5% level (FCT = 0.03197, P = 0.02639), but it was not significant among populations within groups (FSC = -0.00523, P = 0.09677) (Table 7). In the case of model 2, the FCT was also significant (FCT = 0.01331, P = 0.01955), but the value was lower than of model 1. To verify whether group 2A and group 2B should be divided, we conducted an amova test between the two putative groups (model 6), and genetic heterogeneity did not appear (FCT = -0.00118, P = 0.93842). Accordingly, we conclude that model 1 is more appropriate than model 2 in amova. To test the genetic heterogeneity between the northern (Hokkaido and Akita) and southern (Fukui and Tottori) groups in the Sea of Japan, we also performed another amova test for model 4, and the result was not significant (FCT = 0.00400, P = 0.33236). © 2007 Japanese Society of Fisheries Science

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Table 7 Hierarchical analyses of molecular variance for Japanese flounder Group definitions†

%

P

Model 1 (HKD, AKT, MYG, CHB, FKI, TTR, and EHM) versus (NGS and KGS) 3.20 0.02639 Among groups (FCT) -0.51 0.09677 Among populations within groups (FSC) 97.31 0.13294 Within populations (FST) Model 2 (HKD, AKT, MYG, and CHB) versus (FKI, TTR, and EHM) versus (NGS and KGS) 1.33 0.01955 Among groups (FCT) -0.48 0.10068 Among populations within groups (FSC) 99.15 0.13196 Within populations (FST) Model 4 (HKD and AKT) versus (FKI and TTR) 0.40 0.33236 Among groups (FCT) -1.18 0.88074 Among populations within groups (FSC) 100.78 0.87488 Within populations (FST) Model 6 (HKD, AKT, MYG, and CHB) versus (FKI, TTR, and EHM) -0.12 0.93841 Among groups (FCT) -0.37 0.76931 Among populations within groups (FSC) 100.48 0.88563 Within populations (FST)

F 0.03197 -0.00523 0.02690 0.01331 -0.00483 0.00854 0.00400 -0.01185 -0.00781 -0.00118 -0.00365 -0.00483

Percent variation (%), probability estimated from permutation (P), and the F-statistics (F) are given at the hierarchical level.29 Population abbreviations given in Table 1.

†

DISCUSSION All of the wild Japanese flounder populations analyzed in this study have a high level of genetic variability (Table 3). Genetic variability held in a population is expected to correlate with the effective population size.31 In addition, maternally inherited mtDNA should be a sensitive indicator of any founder effect, bottle-neck effect, or other population-level processes.32 Accordingly, the high genetic variability of all populations means that this species might have maintained a large effective population size over a long period. The topology of the phylogenetic tree indicates that Japanese flounder had once undergone a strong bottle-neck with fragmentation into at least two geographic populations (Fig. 4). Such fragmentation, however, might have already disappeared because the individuals of both major clades coexisted within all populations (Fig. 5). An interesting finding for the evolutionary history of Japanese flounder will be obtained if the divergence time between the two major clades can be derived by progressive studies. Regarding the stock structure of the Sea of Japan, previous morphological analyses demonstrated the existence of the northern and the southern groups, which were separated around the Noto Peninsula.10,11,13 We also found significant heterogeneity in mean number of both the dorsal and anal fin rays between the northern (Hokkaido and Akita) and southern (Fukui and Tottori) groups. A laboratory experiment revealed that the fin ray counts were positively correlated with water © 2007 Japanese Society of Fisheries Science

temperature in early developmental stages.13 However, water temperature during the spawning season of the southern group is not necessarily higher than that of the northern group. Therefore, Tanaka et al.11 and Kinoshita et al.13 assumed that geographic differences in fin ray counts were not only influenced by water temperature after hatching but also under a genetic control. Nevertheless, significant genetic heterogeneity between the northern and the southern groups in the Sea of Japan has not been observed in previous studies7,8,14 or in the present study. These results indicate frequent gene flow between the groups. Japanese flounder has a pelagic stage of approximately 30–50 days before settling on the bottom.33,34 During the long pelagic stage, Japanese flounder juveniles would be dispersed by currents that flow along the Japanese coast. This mechanism of juvenile dispersion should be a factor in the frequent gene flow among geographically separate populations.9,11,14 Both morphological and genetic analyses indicated that the western Kyushu group (Nagasaki and Kagoshima) was divided from the other populations. This is the first report to reveal the heterogeneity of the western Kyushu group based on statistical analysis. Since the specimens of our study were mostly adults collected around spawning sites, the genetic heterogeneity found in this study represents subdivision of reproductive groups. The western Kyushu group is located at the peripheral area of sampling locations in this study (Fig. 1). In such a peripheral population, the gene flow with other populations is often restricted and

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effective population size would be reduced. Therefore, peripheral populations may experience strong genetic drift. Such an effect may result in lower genetic diversity of peripheral populations and acceleration of population differentiation.35,36 However, the genetic diversity of the Nagasaki and Kagoshima populations is high and comparable to the other populations (Table 3). Trawl surveys revealed that Japanese flounder is widely distributed in the East China Sea and the Yellow Sea.4 We assume that the western Kyushu group may frequently have genetic exchange with populations in the East China Sea and the Yellow Sea. Comparison of the genetic variability among the populations of western Kyusyu, the East China Sea, and the Yellow Sea is necessary to verify this assumption. However, the fit of model 2 of the amova analysis does not necessarily indicate population identity among the other seven localities. The AIC analysis indicates there is heterogeneity between the northern and the southern groups in the Sea of Japan. Additionally, Fujio et al.8 and Sekino and Hara14 reported the genetic heterogeneity of the Hokkaido population, although we were not able to confirm that. The disagreements indicate difficulty to elucidate the stock structure of Japanese flounder with single type of dataset. Therefore, many-sided analysis with other types of datasets is necessary to infer the stock structure in the Honshu and Hokkaido regions.

ACKNOWLEDGMENTS We are grateful to T. Takayama, Shiribeshi–Hokubu Fisheries Extension Office, Hokkaido, S. Okuyama, Akita Prefectural Fisheries Research and Management Center, Y. Kurita and M. Yoneda, Tohoku National Fisheries Research Institute, M. Takagaki, Fukui Prefectural Fishery Farming Center, A. Watanabe, Ehime Prefectural Chuyo Fisheries Experimental Station, T. Ohta, Tottori Prefectural Fisheries Experimental Station, Y. Suda, National Fisheries University, and A. Yamaguchi, Nagasaki University, for sampling. We also thank Y. Inoue, F. Abe, and K. Inomata for assistance in our laboratory work. The DDBJ/GenBank/EMBL accession numbers for mitochondrial ND2 sequences of phylogenetic Clades A, B, and C are AB275877, AB275878, and AB275879, respectively. Each of the accession numbers for ND5 sequences of phylogenetic Clades A, B, and C are AB275880, AB275881, and AB275882, respectively. This paper is a scientific contribution from the Fisheries Research Agency, Japan under contribution number FRATNFRI-B92.

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