Population structure and genetic diversity of Ammodytes personatus in the Northwestern Pacific revealed by microsatellites markers

Biochemical Systematics and Ecology 61 (2015) 303e311

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Population structure and genetic diversity of Ammodytes personatus in the Northwestern Pacific revealed by microsatellites markers Gui Jing Ren a, Jing Jie Hu b, Tian Xiang Gao c, *, Zhi Qiang Han c a

Key Laboratory of Marine and Estuarine Fisheries, Ministry of Agriculture, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, PR China b College of Marine Life Science, Ocean University of China, 266003 Qingdao, PR China c Fishery College, Zhejiang Ocean University, 316022, PR China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 26 March 2015 Received in revised form 16 June 2015 Accepted 27 June 2015 Available online 7 July 2015

Genetic diversity and population structure of Ammodytes personatus in the Northwestern Pacific were investigated for 16 collections using eight highly variable microsatellite loci. Microsatellite analyses gave strong support for the presence of two distinct groups of genotypes. Pleistocene glaciations can cause significant geographical differentiation in A. personatus populations. However, microsatellite data cannot confirm completely reproductive isolation between north group and south group. About half of comparison values within the first and second cluster were significant after sequential Bonferroni corrections. Routine oceanic currents associated with strong wind condition may provide an excellent chance for connectivity of among populations within clusters. However, gene flow can be restricted by marine gyres due to complex geographical characteristic. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Ammodytes personatus Microsatellites Population structure Pleistocene glaciations

1. Introduction Ammodytes personatus (Sand lance) is a commercial species which is distributed among the Yellow Sea, East China Sea and Japan (Reay, 1970). Ise Bay, Tohoku area off northeastern Honshu Island and the Seto Inland Sea of western Honshu Island are three major fishing grounds for sand lance in Japan, as described by Tomiyama et al. (2008). The species is regarded as a relatively high-quality forage fish which serves as an important link between zooplankton and marine predatory animal (Hashimoto and Kawasaki, 1981). The species is generally found associated with sandy bottoms, appearing to avoid rocky, muddy, and coarse gravel bottoms (Tomiyama et al., 2008). The distribution of adult sand lance is highly patchy due to the limit of the substrate (Hashimoto and Kawasaki, 1981). The absence of a swim bladder allows this narrow, elongate fish to spend much time burying themselves in intertidal and shallow subtidal substrates, venturing out only to feed or spawn (Robards et al., 1999). There is only limited movement of post-settled sand lance between habitat areas (Hashimoto and Kawasaki, 1981). The length of the pelagic larval stage is about 30 days, surviving for long periods without food after hatching during the larvae stage (Inoue, 1949). The strategy is probably suitable for dealing with the highly volatile environmental conditions. The characteristic enable sand lance an interesting model for investigating differentiation in marine fishes.

* Corresponding author. E-mail address: [email protected] (T.X. Gao). http://dx.doi.org/10.1016/j.bse.2015.06.031 0305-1978/© 2015 Elsevier Ltd. All rights reserved.

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Previous works have studied the genetic structure and morphological variation in natural populations of the A. personatus. Hashimoto and Kawasaki (1981) have demonstrated the differences between subpopulation in Sendai Bay and its neighborhood are distinct across many aspects, i.e, morphology, ecology, and genetics. Subsequently, Okamoto et al. (1988) suggested a distinct genetic differentiation between the north of Iwate Prefecture and the south of Miyagi Prefecture on the basis of allozymes analyses. Kim et al. (2006) found a distinct genetic differentiation between East and West þ South population in Korea inferred from mitochondrial DNA control region sequence, a finding subsequently supported by Kim et al. (2008) used multivariate methods to compare meristic and morphological characters. Han et al. (2012) further detected two lineages across 17 populations using mtDNA data, as the same time significant genetic structure was also detected along within Tsushima Current and between the Kuroshio and Oyashio. Microsatellites are highly polymorphic nuclear loci that have been successfully used to infer fine scale population structure. Compared with mtDNA, the microsatellite mutation rate range from 105 to 103, which is 104e106 times to mtDNA mutation rate. The mtDNA results in the present study reflected historical divergence between populations. Whereas the microsatellite patterns reflected reduced drift effects on preseparation allele frequencies that have yet to approach migrationedrift equilibrium (Shaw et al., 2004). Thus, the addition of microsatellite data will contribute to better understanding of population of A. personatus. The aim of this study was to use microsatellite markers to identify the key evolutionary processes and associated ecological factors shaping genetic population structure of A. personatus. Furthermore, a better knowledge of genetic diversity and structure of the sand lance could provide vital suggestions for sustainable exploitation and management of natural populations. 2. Materials and methods 2.1. Sample collection A total of 372 adult sand lance individuals were collected at 14 locations in coast of Japan and 2 localities in China (Table 1, Fig. 1). Muscle samples were preserved in 95% ethanol or frozen for DNA extraction. 2.2. DNA extraction and PCR amplification Total DNA was extracted from a piece of muscle tissue using a standard phenol-chloroform method (Sambrook et al., 1989). All individuals were genotyped at all 8 loci. Eight microsatellite loci were amplified by polymerase chain reaction (PCR) using primers developed from A. personatus by Ren et al. (2009). PCR amplifications were carried out in 25 ml reaction volumes containing 20 ng template DNA, 1.5 mM MgCl2, 0.2 mM of each dNTPs, 0.5 mm of each primer, 1U Taq polymerase (Takara Co., China). The PCRs were performed under the following conditions: 5 min at 94  C, then 30 cycles of denaturation at 94  C for 1 min, annealing at 59  C (Ape104, Ape308, Ape315), 60  C (Ape302), 54  C (Ape349), 55  C (Ape341, Ape313, Ape327) for 30s, extension at 72  C for 1 min and a final extension at 72  C for 5 min using a Biometra thermal cycler. The size of the alleles was determined according to their migrating distance. The fastest was named allele 1, and those following were named in order. 2.3. Statistical analyses Using the program MICRO-CHECKER (Van Oosterhout et al., 2004), we check for potential technical problems such as null alleles, stuttering and large allele dropout. Null-allele frequencies were estimated according to Chakraborty et al. (1992).

Table 1 Sampling locations, number of individuals typed and collection date of A. personatus populations. ID

Sampling sites

Sample size

Date of collection

Ish Re Sl Ss Qd Ise Hy Fuku Ca Fuka Mu Ot Kas Kag Dl Ha

Ishikari Bay Rebun Island Sendai Bay large Sendai Bay small Qingdao Ise Bay Hyogo Fukuoka Cape Soya Fukaura Mutsu Bay Otsuko Kashima Kagawa Dalian Hachinohe

20 24 24 24 24 24 24 24 24 24 24 18 22 24 24 24

April 2006 June 2006 April 2005 April 2005 April 2005 May 2005 April 2005 April 2005 June 2006 March 2006 March 2006 March 2006 March 2006 April 2005 March 2009 June 2005

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Fig. 1. Sample sites of A. personatus. Different symbols are used for sample sites from different geographical groups (: ¼ North group, C ¼ South group).

Expected (HE), observed (HO) heterozygosity, numbers of alleles per locus and collection (NA) were calculated using the software POPGENE (Yeh et al., 2000). Data were analyzed using Genepop version 3.4 (Raymond and Rousset, 1995) to test conformity to Hardy-Weinberg expectations, checks for linkage disequilibrium between loci. The software FSTAT version 2.9.3.2 (Goudet, 1995) was used for calculating FST (Weir and Cockerham, 1984) and for testing their significance. These results were adjusted for multiple tests using the sequential Bonferroni procedure with a ¼ 0.05 (Rice, 1989). The program was also used to estimate allelic richness (El Mousadik and Petit, 1996). The program STRUCTURE version 2.0 (Pritchard and Wen, 2003) was used to estimate the most likely number of populations represented by the samples. The population structure was considered without prior information of the number of locations at which the individuals were sampled and into which location each individual belongs. With the method of Evanno et al. (2005), Ad hoc statistic DK based on the rate of change in the log probability of data between successive K-values was calculated, since the height of these model values seems to accurately detect a correct estimation of the number of populations. For each data set 20 runs were carried out in order to quantify the SD of the likelihood of each K. We tested a range of K values between 1 and 8. To determine whether any isolated populations carried the molecular signature of a recent bottleneck, Wilcoxon's heterozygosity excess test (Piry et al., 1999) and the allele frequency distribution Mode Shift indicator (Luikart et al., 1998) were performed using BOTTLENECK v1.2.02 (Piry et al., 1999). For Wilcoxon's test, data were examined using both the stepwise mutation (SMM) and two-phase model (TPM) for microsatellite data using 6 microsatellite loci except Ape302, Ape341 (Piry et al., 1999; Cornuet and Luikart, 1996; Luikart and Cornuet, 1998).

3. Results 3.1. Genetic variability The number of alleles per locus within samples varied from 4 at loci Ape341 in Ish population to 25 at locus Ape308 in Kag population. Allele richness per locus and sample varied from 3.49 at locus Ape341 in Ish population to 15.23 at locus Ape308 in Hy population (Table 2). Observed values of heterozygosity (HO) and expected values under the assumption of random mating within samples (HE) and numbers of alleles (NA) for all samples are shown in Table 3. We found only 12 of 128 (9.4%) exact tests deviations from HW proportions (see Table 2) after Bonferroni correction for multiple testing (Rice, 1989). The MICRO-CHECKER detected the presence of null alleles. However, since such potential problems are not substantial and furthermore did not bias the results conducted here, we retained all loci for further analyses.

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Table 2 Levels of genetic variability at eight microsatellite loci in the 16 A. personatus populations, and for all samples pooled: expected heterozygosity (HE) observed heterozygosity (HO), number of alleles (NA), allele richness per locus and sample (Rs) and probability values of concordance with Hardy-Weinberg expectations (P). Population

Re

Ish

Sl

Qd

Fuku

Ise

Ss

Hy

Dl

Ot

Ca

Genetic diversity

HE HO NA P Rs PIC HE HO NA P Rs PIC HE HO NA P Rs PIC HE HO NA P Rs PIC HE HO NA P Rs PIC HE HO NA P Rs PIC HE HO NA P Rs PIC HE HO NA P Rs PIC HE HO NA P Rs PIC HE HO NA P Rs PIC HE HO NA P Rs PIC

Mean across loci

Ape104

Ape327

Ape308

Ape313

Ape315

Ape302

Ape341

Ape349

0.828 0.700 8 0.103 7.133 0.786 0.871 0.889 8 0.532 7.512 0.829 0.846 0.895 8 0.537 7.135 0.802 0.813 0.765 7 0.053 6.254 0.759 0.826 0.850 7 0.618 6.470 0.779 0.850 0.750 9 0.317 7.665 0.808 0.844 0.870 10 0.749 7.964 0.806 0.870 1.0000 8 1.000 7.389 0.825 0.805 0.588 6 0.031 5.773 0.748 0.805 0.800 6 0.194 5.628 0.745 0.822 0.792 7 0.224 6.191 0.777

0.882 0.737 11 0.221 8.781 0.844 0.947 1.000 17 1.000 13.211 0.910 0.959 1.000 17 1.000 13.358 0.928 0.888 0.900 12 0.698 8.993 0.52 0.952 0.667 17 0.284 13.283 0.915 0.937 0.444 14 0.669 11.317 0.903 0.935 0.950 14 0.623 11.185 0.904 0.846 0.467 15 0.467 9.620 0.793 0.700 0.826 6 0.758 4.730 0.632 0.800 0.700 5 0.337 5.000 0.719 0.855 0.917 6 0.855 5.978 0.794

0.938 0.955 21 0.613 13.077 0.911 0.937 0.889 16 0.360 11.935 0.904 0.967 1.000 21 1.000 14.664 0.939 0.945 0.833 19 0.002 12.804 0.921 0.963 1.000 21 1.00 14.104 0.938 0.957 1.000 14 1.000 13.255 0.907 0.935 0.944 14 0.726 11.41 0.902 0.971 1.000 24 1.000 15.230 0.947 0.954 1.000 18 0.025 13.129 0.926 0.946 1.000 15 1.000 12.146 0.910 0.944 0.696 18 0.000 12.390 0.918

0.921 1.000 11 1.000 11.000 0.884 0.926 0.900 15 0.301 11.062 0.895 0.924 1.000 14 1.000 10.775 0.896 0.911 1.000 13 1.000 10.373 0.876 0.939 1.000 15 1.000 11.663 0.911 0.921 0.905 14 0.421 10.528 0.891 0.920 0.957 14 0.874 10.38 0.891 0.914 0.826 14 0.207 10.459 0.885 0.878 0.792 14 0.328 9.635 0.846 0.927 1.000 11 1.000 10.038 0.889 0.889 0.917 10 0.644 8.457 0.857

0.944 0.818 17 0.177 12.155 0.917 0.922 0.824 13 1.000 10.505 0.885 0.934 1.000 16 1.000 11.716 0.907 0.921 0.957 13 0.842 10.344 0.892 0.925 1.000 13 1.000 10.546 0.894 0.913 0.947 14 0.548 10.507 0.880 0.861 0.952 11 0.871 8.19 0.822 0.909 1.000 14 1.000 10.141 0.880 0.899 1.000 12 1.000 10.695 0.866 0.906 0.882 12 0.603 9.822 0.867 0.920 0.913 15 0.106 10.695 0.892

0.910 0.684 16 0.010 11.209 0.877 0.943 0.600 15 0.000 12.236 0.904 0.952 1.000 19 1.000 13.194 0.925 0.938 0.700 17 0.000 12.198 0.909 0.917 0.895 16 0.151 11.537 0.885 0.901 1.000 15 0.166 10.878 0.871 0.937 0.950 17 0.758 12.23 0.908 0.954 0.895 18 0.021 13.286 0.925 0.870 0.913 11 0.718 8.313 0.834 0.863 0.944 9 0.939 7.521 0.820 0.906 0.833 10 0.073 8.990 0.876

0.758 0.333 5 0.001 4.633 0.698 0.591 0.438 4 0.013 3.492 0.489 0.768 0.750 6 0.522 4.816 0.710 0.766 0.696 8 0.045 5.816 0.712 0.803 0.625 7 0.094 6.048 0.756 0.849 0.792 8 0.000 6.984 0.811 0.839 0.526 8 0.000 7.06 0.794 0.847 0.667 9 0.006 7.475 0.808 0.844 0.773 10 0.273 7.615 0.677 0.824 0.765 6 0.225 5.864 0.771 0.735 0.583 7 0.090 5.403 0.677

0.894 0.952 12 0.824 9.010 0.860 0.897 1.000 10 1.000 8.956 0.850 0.889 1.000 10 1.000 8.488 0.849 0.873 0.952 9 0.876 7.921 0.848 0.881 1.000 10 1.000 8.284 0.842 0.895 1.000 12 1.000 9.442 0.859 0.880 0.952 11 0.938 8.32 0.844 0.882 1.000 11 1.000 8.786 0.847 0.897 0.818 12 0.149 9.170 0.817 0.886 0.786 9 0.171 8.286 0.838 0.857 0.905 9 0.847 7.625 0.817

0.884 0.772 12.63 8.21 0.88 0.82 12.25 8.78 0.91 0.96 13.88 10.63 0.88 0.85 12.25 8.37 0.90 0.88 13.25 9.75 0.90 0.84 12.50 8.69 0.89 0.89 12.50 8.55 0.90 0.86 13.13 9.68 0.856 0.820 11.13 8.63 0.870 0.860 9.13 7.0 0.866 0.819 10.25 7.25

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Table 2 (continued ) Population

Mu

Fuka

Ha

Kag

Kas

Genetic diversity

HE HO NA P Rs PIC HE HO NA P Rs PIC HE HO NA P Rs PIC HE HO NA P Rs PIC HE HO NA P Rs PIC

Mean across loci

Ape104

Ape327

Ape308

Ape313

Ape315

Ape302

Ape341

Ape349

0.834 0.647 7 0.047 6.262 0.782 0.760 1.000 6 1.000 5.174 0.695 0.822 0.850 7 0.580 5.935 0.772 0.830 0.833 7 0.593 6.469 0.787 0.796 0.909 6 0.891 5.290 0.743

0.553 0.579 5 0.776 4.091 0.488 0.811 0.824 7 0.592 5.763 0.753 0.772 0.600 7 0.146 5.970 0.711 0.868 0.824 11 0.285 9.324 0.829 0.827 0.688 7 0.071 6.535 0.776

0.962 0.938 18 0.583 13.966 0.927 0.952 0.941 18 0.530 13.209 0.919 0.968 0.786 17 0.000 14.316 0.930 0.966 0.957 25 0.054 14.880 0.942 0.939 0.737 17 0.016 12.202 0.908

0.917 1.000 14 1.000 10.482 0.884 0.926 0.947 14 1.000 11.004 0.894 0.921 1.000 15 1.000 10.770 0.889 0.879 0.826 10 0.443 8.311 0.845 0.897 0.944 11 0.256 9.516 0.860

0.909 0.882 11 0.503 9.579 0.871 0.905 1.000 12 1.000 9.447 0.868 0.882 0.842 12 0.430 9.049 0.844 0.908 0.913 13 0.200 10.234 0.879 0.901 1.000 10 1.000 8.976 0.858

0.889 0.706 10 0.090 8.649 0.849 0.862 0.773 9 0.090 7.196 0.822 0.912 0.947 12 0.795 9.792 0.877 0.860 0.773 9 0.132 7.440 0.822 0.896 0.947 10 0.750 8.634 0.859

0.777 0.773 9 0.378 6.608 0.728 0.902 0.737 10 0.378 9.036 0.728 0.560 0.400 5 0.081 3.756 0.478 0.901 0.792 11 0.115 9.099 0.868 0.876 0.789 9 0.108 7.982 0.837

0.892 0.826 10 0.047 8.787 0.860 0.919 1.000 12 1.000 10.075 0.838 0.889 0.895 10 0.655 8.642 0.851 0.890 0.917 10 0.623 8.419 0.858 0.878 0.789 10 0.361 8.461 0.839

0.842 0.794 10.50 7.92 0.901 0.880 12.25 8.46 0.841 0.790 10.63 7.75 0.888 0.858 12.00 10.05 0.876 0.851 10.00 8.07

3.2. Population genetic differentiation Pairwise FST values ranged from 0.004 to 0.064. Statistics analysis between pairs of populations for each cluster was significant. Pairwise FST between most of populations within cluster are small and non-significant. The populations Fuku and Ise, Re and Sl showed the smallest and nonsignificant genetic divergence (FST ¼ 0.004 P ¼ 0.27). The largest pairwise FST value between Ha and Ise was 0.064 (P ¼ 0.000), which belong to different pooled samples (Table 4). Most of pairwise FST values between first and second cluster were significant after Bonferroni correction except one comparison value between Ot and Mu population. About half of pairwise FST (25 of 45 comparison value) between populations within the first cluster of A. personatus exhibited some statistically significant genetic heterogeneity. A total of 10 of 21 comparison value within the second cluster were significant after sequential Bonferroni corrections. As shown in Table 3, most individuals (above 90%) from nine populations (Dl, Qd, Fuku, Kag, Ise, Hy, Ot, Ss and Fuka) were assigned into the first cluster, about 77.3% of individuals from the population Kas was also assigned into the first cluster. Above Table 3 Proportion of 16 populations in each of the two inferred clusters. Populations

(1) (Dl) (2) (Qd) (3) (Fuku) (4) (Kag) (5) (Ise) (6) (Hy) (7) (Ot) (8) (Kas) (9) (Ss) (10) (Fuka) (11) (Mu) (12) (Sl) (13) (Ha) (14) (Ish) (15) (Re) (16) (Ca)

Inferred cluster

Number of individuals

1 (Admixture model )/LOCPRIOR models)

2 (Admixture model/LOCPRIOR models)

0.020 0.069 0.016 0.013 0.028 0.012 0.013 0.226 0.034 0.057 0.895 0.964 0.973 0.978 0.947 0.966

0.980 0.931 0.984 0.987 0.972 0.988 0.987 0.774 0.966 0.943 0.105 0.036 0.027 0.022 0.053 0.034

24 24 24 24 24 22 24 18 24 24 24 24 24 20 24 24

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Table 4 Pairwise FST estimator (below diagonal) between the 16 populations across 8 microsatellite loci (*, significant at P < 0.05 by the permutation). ID

Dl

Qd

Fuku

Kag

Ise

Hy

Kas

Ot

Ss

Fuka

Mu

Sl

Ha

Ish

Re

Dl Qd Fuku Kag Ise Hy Kas Ot Ss Fuka Mu Sl Ha Ish Re Ca

e 0.038* 0.037* 0.027 0.044 0.046* 0.021* 0.006 0.053* 0.016 0.021* 0.039* 0.065* 0.057* 0.058* 0.037*

0.006 0.041* 0.008* 0.013* 0.051* 0.047* 0.025* 0.050* 0.068* 0.032* 0.051* 0.030* 0.036* 0.041*

0.030* 0.004 0.012 0.036* 0.035* 0.008 0.041* 0.059* 0.022* 0.053* 0.023* 0.048* 0.031*

0.029* 0.019 0.008 0.012 0.026* 0.015 0.047* 0.034* 0.036* 0.051* 0.036* 0.028*

0.014 0.012 0.034 0.011* 0.040* 0.060* 0.025* 0.064* 0.034* 0.029* 0.048*

0.032* 0.023 0.007 0.039* 0.053* 0.024* 0.046* 0.026* 0.052* 0.036*

0.007 0.038* 0.013 0.035* 0.029* 0.041* 0.050* 0.042* 0.022*

0.040* 0.007 0.026 0.029* 0.053* 0.054* 0.039* 0.027*

0.044* 0.058* 0.027* 0.056* 0.029* 0.026* 0.033*

0.033 0.041* 0.043* 0.054* 0.057* 0.028*

0.040 0.059 0.053 0.042 0.030

0.038* 0.005 0.004 0.017

0.031* 0.040* 0.010

0.013 0.022*

0.027*

Ca

89% of individuals from six populations (Mu, Sl, Ha, Ish, Re and Ca) were assigned into the second cluster. The unrooted UPGMA tree based on Da genetic distance clearly showed a high degree of genetic differentiation (Fig. 2). The first cluster is formed by the populations of Qd, Ise, Hy, Fukuoka Fuku, Ss, Da, Kag, Ot, Kas and Fuka. The second cluster is formed by the samples from Ish, Ca, Hachinohe Ha, Mu, Re and Sl. The internal branches were well supported by a bootstrap value of 95. We estimated the maximum probability of genetically distinct clusters to be K ¼ 2, suggesting the sand lance populations can be divided into two clusters (Fig. 3). 4. Discussion 4.1. Genetic variability and bottleneck events Genetic variability is critical for a species to adapt to environmental changes and long-term survival of species. A species with little genetic variability may suffer from reduced fitness in its current environment, without the evolutionary potential Ot Hy Kag Ss Dl Kas Fuku Ise Fuka Qd Ha Ca Mu Re Ish Sl

6

4

2

0

Fig. 2. UPGMA tree (unrooted) for the A. personatus populations. Nei et al.'s (Da) (1983) for topology and dm2 for branch lengths.

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309

Fig. 3. Scatter plot of possible number of clusters (K; horizontal axis) against ad hoc statistic △K (vertical axis) based on rate of change in logarithm probability of data between successive K values across total samples.

necessary for a changing environment (Saccheri et al., 1998; Frankham et al., 2002; Frankham, 2005). The PCR technique provides an excellent chance to describe the levels and distribution of genetic variation among populations at the DNA level (Olsen et al., 1998; Valle-Jimenez et al., 2005; Sui et al., 2009). The species showed high level of genetic variability in neutral microsatellite markers. The gene diversity index (HE ¼ 0.553e0.971) in A. personatus higher than the average value of marine fishes (HE ¼ 0.805) (DeWoody and Avise, 2000). Though there was the record that the Ise Bay population was also prone to overfishing. Poor catch in five consecutive seasons between 1978 and 1982 were attributed to overfishing and to a simultaneous unfavorable natural condition called meandering of the Kuroshio Current (Tomiyama et al., 2008), no effective population size reduction (i.e. bottleneck effect) was found under the IAM SMM and TPM model based on the Wilcoxon signed-rank test and the mode-shift test. One side, a characteristic feature of commercially exploited fish populations is that the stock size often fluctuated greatly from year to year, a fact derived from studies of time series of catch data (Cronin, 1986). Several early life history characteristics of sand lance coupled with their high fecundity and short generation time may contribute to their apparent opportunism. The large fluctuations in abundance every few years are normal (Inoue et al., 1967). Over-harvesting could severely reduce the number of alleles by eliminating rare ones but having no an obvious effect on the genetic diversity (Nei et al., 1975). The other side, the capability of Bottleneck statistical analysis was sometimes powerless to detect bottleneck signatures based on the microsatellite data (Luikart et al., 1998). We found only 12 of 128 (9.4%) exact tests deviations from HW proportions after Bonferroni correction for multiple testing (Rice, 1989). The HW disequilibrium could be attributed to inbreeding, nonrandom mating, Wahlund effect and so on. However, the program Micro-checker detected the presence of null allele in locus or the dropout of large allele in the 12 departure from HW equilibrium populations. Null alleles are common problem with microsatellite loci (Callen et al., 1993). It appears when one allele is unamplified due to mutation in the sequence or technical problems associated with amplification and scoring arise (Hoarau et al., 2002).

4.2. Genetic differentiation between groups and Pleistocene glaciations Microsatellites are widely used to identify hybridization and separation between subspecies. The pattern of large-scale geographical differentiation in microsatellites is generally congruent with that revealed by mtDNA (Englbrecht et al., 2000). Isozymes and mtDNA markers have been applied in earlier studies about the population genetic structure of sand lance (Robards et al., 1999; Kim et al., 2006). Common control region haplotypes were not found among the three populations along Korea coast (Kim et al., 2006). The secondary contact after glaciation isolation of two lineages was supported by isozyme data (Hashimoto and Kawasaki, 1981). However, it is constrained that the ability of mtDNA or isozyme data to disclose recent time scale process due to relatively low mutation rates. The fast mutation rate of microsatellite loci made it sometimes providing a more informative interpretation. Our multilocus microsatellite DNA analysis showed that south group of sand lance was very different from north group populations. Significant allele frequence changes of two groups along the Japan coast and China coast showed high limited gene flow between south and north group. However, like the isozyme data, our results based on microsatellite strongly suggest that there has been little detectable genetic introgression of north group to south group, or vice versa, except Mu and Kas populations. Above 22% alleles in Mu population which owes to north group were attributed to south group. At the same time, above 20% alleles in Kas population which owes to south group were attributed to north group. Therefore, we cannot confirm completely reproductive isolation between north group and south group. Speciation rates in the marine ecosystem are likely reduced by the lack of discrete physical boundaries, coupled with large dispersal potential, resulting in a high degree of gene flow (Hyde et al., 2008). However, some factors such as current, larval retention, the type of reproduction and habitat preference of the species or climatic fluctuations in Pleistocene also affect

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population dynamics. Pleistocene glaciations have been a major influence on the divergences seen in many marine fishes (H€ anfling et al., 2002). The Sea of Japan is a semi-enclosed marginal sea, which is distinguished from the other marginal seas of the Western Pacific by its extremely shallow sills (Kitamura et al., 2001). During periods of peak glaciation, declines in sea levels of 130e140 m have been reported. The Sea of Japan was almost isolated from the Pacific Ocean during glaciation events and formed a refugium for marine species during glaciation events (Kitamura et al., 2001; Liu et al., 2006, 2007). The high levels of population differentiation in the sand lance may also be attributed to their historical isolation in two shelters. 4.3. Larval dispersal and hydrographic environment Different from Han et al. (2012), the result presented a significant genetic divergence in some populations comparisons within cluster. Although A. personatus juveniles and adults do not have high mobility rates, dispersal ability of larvae can be considered the main cause of gene flow. The duration of the planktotrophic phase is a reliable factor in predicting population genetic structure (Silva et al., 2009). Addtionally, the main spawning period of this species is in winter (early Decembereearly January) when the strong northwest wind prevails. A positive correlation between the frequency of strong westerly wind after the main spawning period and the catch of 0-age fish was reported (Hamada, 1966). Routine oceanic currents associated with strong wind condition may provide an excellent chance for connectivity of among populations within clusters. However, gene flow can be restricted by marine gyres due to complex geographical characteristic. Zhan et al. (2009) presented significant genetic differentiation in marine scallops using microsatellite loci, which could be involved with marine currents (gyres). Acknowledgments We are grateful to Dr. J. Cheng and Mr. Q. Liu for checking the contents of the article. The present study could not have been carried out without the willing help of those listed below in collecting sand lance specimens: Prof. Kijima, Dr. Yoshida, Mr. Takahashi, Dr. Yokogawa, Mr. Tomiyama, Mr. Matoba, Mr. Ochi, Mr. Mori, Mr. Nakamura, Mr. Tanaka, Mr. Shimoda, Dr. Sano, Mr. Kono and Mr. Hoshino. This work was supported by Special Fund for Agro-scientific Research in the Public Interest (200903005), Zhejiang Provincial Natural Science Foundation of China (LY14D060001), National Natural Science Foundation of China (31472281) and Open Foundation from marine science in the Most Important Subjects of Zhejiang (20130106). References Callen, D.F., Thompson, A.D., Shen, Y., Phillips, H.A., Richards, R.I., Mulley, J.C., Sutherland, G.R., 1993. Incidence and origin of ‘null’ alleles in the (AC)n microsatellite markers. Am. J. Hum. Genet. 19, 233e257. Chakraborty, R., DeAndrade, M., Daiger, S.P., Budowle, B., 1992. Apparent heterozygote deficiencies observed in DNA typing data and their implications in forensic applications. Ann. Hum. Genet. 56, 45e57. Cornuet, J.M., Luikart, G., 1996. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144, 2001e2014. Cronin, L.E., 1986. Fisheries and resource stress in the 19th century. J. Wash. Acad. Sci. 76, 188e198. DeWoody, J.A., Avise, J.C., 2000. Microsatellite variation in marine, freshwater and anadromous fishes compared with other animals. J. Fish. Biol. 56, 461e473. El Mousadik, A., Petit, R.J., 1996. High level of genetic differentiation for allelic richness among populations of the argan tree (Arganiaspinosa L. Skeels) endemicto Morocco. Theor. Appl. Genet. 92, 832e839. Englbrecht, C.C., Freyhof, J., Nolte, A., Rassmann, K., Schliewen, U., Tautz, D., 2000. Phylogeography of the bullhead Cottus gobio (Pisces: Teleostei: Cottidae) suggests a pre-Pleistocene origin of the major central European populations. Mol. Ecol. 9, 709e722. Evanno, G., Regnaut, S., Goudet, J., 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611e2620. Frankham, R., 2005. Genetics and extinction. Biol. Conserv. 126, 131e140. Frankham, R., Ballou, J.D., Briscoe, D.A., 2002. Introduction to Conservation Genetics. Cambridge University Press, Cambridge, UK. Goudet, J., 1995. FSTAT (vers. 1.2): a computer program to calculate F-statistics. J. Hered. 86, 485e486. Hamada, T., 1966. Studies on the fluctuation in the abundance of larval sand-lance in the Harima-Nada and OsakaBay. III: relationship to weather and sea conditions during the breeding season. Bull. Jpn. Soc. Sci. Fish. 32, 579e584. Han, Z.Q., Yanagimoto, T., Zhang, Y.P., Gao, T.X., 2012. Phylogeography study of Ammodytes personatus in northwestern Pacific: pleistocene isolation, temperature and current conducted secondary contact. PLoS One 7, 1e12. H€ anfling, B., Hellemans, B., Volckaert, A.M., Carvalho, G.R., 2002. Late glacial history of the cold-adapted freshwater fish Cottus gobio, revealed by microsatellites. Mol. Ecol. 11, 1717e1729. Hashimoto, H., Kawasaki, T., 1981. Population studies of sandeel, Ammodytes personatus (Girard), in Sendai Bay and its neighborhood. Tohoku J. Agric. Res. 31, 173e197. Hoarau, G., Rijnsdorp, A.D., Van der Veer, H.W., Stam, W.T., Olsen, J.L., 2002. Population structure of plaice (Pleuronectes platessa L.) in northern Europe: microsatellites revealed large-scale spatial and temporal homogeneity. Mol. Ecol. 11, 1165e1176. Hyde, J.R., Kimbrell, C.A., Budrick, J.E., Lynn, E.A., Vetter, R.D., 2008. Cryptic speciation in the vermilion rockfish (Sebastes miniatus) and the role of bathymetry in the speciation process. Mol. Ecol. 17, 1122e1136. Inoue, A., 1949. An ecological note on sandeel, Ammodytes personatus (Girard). Bull. Jpn. Soc. Sci. Fish. 15, 458e468. Inoue, A., Takamori, S., Kuniyuki, K., 1967. Studies on fishery biology of the sand-launce, Ammodytes personatus Girard. Bull. Nankai Reg. Fish. Res. Lab. 25, 1e347. Kim, J.K., Park, J.H., Kim, Y.S., Kim, Y.H., Hwang, H.J., Hwang, S.J., Lee, S.I., Kim, T.I., 2008. Geographic variations in pacific sand eels Ammodytes personatus (Ammodytidae) from Korea and Japan using multivariate morphometric analysis. J. Ichthyol. 48, 904e910. Kim, J.K., Park, J.Y., Kim, Y.S., 2006. Genetic diversity, relationships and demographic history of three geographic populations of Ammodytes personatus (Ammodytidae) from Korea inferred from mitochondrial DNA control region and 16S rRNA sequence data. Korean J. Genet. 28, 343e351. Kitamura, A., Takano, O., Takata, H., Omote, H., 2001. Late Pliocene-early Pleistocene paleoceanographic evolution of the Sea of Japan. Palaeogeogr. Palaeocol 172, 81e98.

G.J. Ren et al. / Biochemical Systematics and Ecology 61 (2015) 303e311

311

Liu, J.X., Gao, T.X., Wu, S.F., Zhang, Y.P., 2007. Pleistocene isolation in the Northwestern Pacific marginal seas and limited dispersal in a marine fish Chelon haematocheilus (Temminck and Schlegel, 1845). Mol. Ecol. 16, 275e288. Liu, J.X., Gao, T.X., Yokogawa, K., Zhang, Y.P., 2006. Differential population structuring and demographic history of two closely related fish species, Japanese sea bass (Lateolabrax japonicus) and spotted sea bass (Lateolabrax maculatus) in Northwestern Pacific. Mol. Phylogenet. Evol. 39, 799e811. Luikart, G., Allendorf, F.W., Cornuet, J.-M., Sherwin, W.B., 1998. Distribution of allele frequency distributions provide a test for recent population bottlenecks. J. Hered. 89, 238e247. Luikart, G., Cornuet, J.-M., 1998. Empirical evaluation of a test for identifying recently bottlenecked populations from allele frequency data. Conserv. Biol. 12, 228e237. Nei, M., Maruyama, T., Chakraborty, R., 1975. The bottleneck effect and genetic variability in populations. Evolution 29, 1e10. Okamoto, H., Yamazaki, F., Mishima, S., 1988. Genetic divergence among sandlance Ammodytes personatus population in Japan. Nippon Suisan Gakk 54, 1297e1304. Olsen, J.B., Seeb, L.W., Bentzen, P., Seeb, J.E., 1998. Genetic interpretation of broad-scale microsatellite polymorphism in odd-year pink salmon. Trans. Am. Fish. Soc. 127, 535e550. Piry, S., Luikart, G., Cornuet, J.M., 1999. BOTTLENECK: a computer program for detecting recent reductions in the effective population size using allele frequency data. J. Hered. 90, 203. Pritchard, J.K., Wen, W., 2003. Documentation for Structure Software: Version 2. http://pritch.bsd.uchicago.edu/software.html (accessed 23.05.06.). Raymond, M., Rousset, F., 1995. GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J. Hered. 86, 239. Reay, P.J., 1970. Synopsis of Biological Data on North Atlantic Sandeels of the Genus Ammodytes. FAO. Fisheries Synopsis. No. 82. Ren, G.J., Hu, J.J., Bao, Z.M., Jiang, X., Gao, T.X., 2009. Isolation and characterization of eleven polymorphic microsatellite markers of sand lance (Ammodytes personatus). Conserv. Genet. 10, 1837e1839. Rice, W.R., 1989. Analyzing tables of statistical tests. Evolution 43, 223e225. Res. Pap. PNW-RP-521. In: Robards, M.D., Willson, M.F., Armstrong, R.H., Piatt, J.F. (Eds.), 1999. Sand Lance: a Review of Biology and Predator Relations and Annotated Bibliography. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland, OR. Saccheri, I., Kuussaari, M., Kankare, M., Vikman, P., Fortelius, W., Hanski, I., 1998. Inbreeding and extinction in a butterfly metapopulation. Nature 392, 491e494. Sambrook, J., Fritsch, E.F., Maniatis, T., 1989. Molecular Cloning. A Laboratory Manual, second ed. Cold Spring Harbor Laboratory Press, New York. Shaw, P.W., Arkhipkin, A.I., Al-khairulla, H., 2004. Genetic structuring of Patagonian toothfish populations in the Southwest Atlantic Ocean: the effect of the Antarctic Polar Front and deep-water troughs as barriers to genetic exchange. Mol. Ecol. 13, 3293e3303. Silva, I.C., Mesquita, N., Schubart, C.D., Alves, M.J., Paula, J., 2009. Genetic patchiness of the shore crab Pachygrapsus marmoratus along the Portuguese coast. J. Exp. Mar. Biol. Ecol. 378, 50e57. €nXing, B., 2009. Genetic diversity and population structure of the Chinese mitten crab Eriocheir Sui, L.Y., Zhang, F.M., Wang, X.M., Bossier, P., Sorgeloos, P., Ha sinensis in its native range. Mar. Biol. 156, 1573e1583. Tomiyama, M., Komatsu, T., Makino, M., 2008. Sandeel fisheries governance in Ise Bay, Japan. In: Case Studies on Fisheries Self-Governance. FAO Fish Rep, pp. 201e210. Valle-Jimenez, R., Cruz, P., Perez-Enriquez, R., 2005. Population genetic structure of Pacific white shrimp (Litopenaeus vannamei) from Mexico to Panama: microsatellite DNA variation. Mar. Biotechnol. 6, 475e484. Van Oosterhout, C., Hutchinson, W.F., Wills, D.P.M., Shipley, P., 2004. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535e538. Weir, B.S., Cockerham, C.C., 1984. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358e1370. Yeh, F.C., Yang, R., Boyle, T.J., Ye, Z., Xiyan, J.M., 2000. POPGENE 32, Microsoft Windows-based Freeware for Population Genetic Analysis. Molecular Biology and Biotechnology Centre, University of Alberta, Edmonton, Canada. Zhan, A.B., Hu, J.J., Hu, X.L., Zhou, Z.H., Hui, M., Wang, S., Peng, W., Wang, M.L., Bao, Z.M., 2009. Fine-scale population genetic structure of Zhikong scallop (Chlamys farreri): do local Marine currents drive geographical differentiation? Mar. Biotechnol. 11, 223e235.