(Sarcocheilichthys Sinensis) Using Microsatellite Markers

Aquaculture and Fisheries 5 (2020) 80–85

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Original research article Genetic diversity and population structure of the Chinese lake gudgeon (Sarcocheilichthys sinensis) using microsatellite markers

∗ Shuting Gua,b, Rongquan Wangb, Chuanwu Lic, Jiale Lia,d,e, Yubang Shena,d,e, a Key Laboratory of Freshwater Aquatic Genetic Resources, Ministry of Agriculture, Shanghai Ocean University, Shanghai, 201306, China b Key Laboratory of Conventional Freshwater Fish Breeding and Health Culture Technology Germplasm Resources, Suzhou Shenhang Eco-technology Development Limited Company, Suzhou, 215225, China c Fisheries Research Institute of Hunan Province, Changsha, 412000, China d Shanghai Engineering Research Center of Aquaculture, Shanghai Ocean University, Shanghai, 201306, China e National Demonstration Center for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai, 201306, China

ARTICLE INFO ABSTRACT

Keywords: The Chinese lake gudgeon is a small benthopelagic freshwater fish. It is presently threatened by human activities Sarcocheilichthys sinensis and environmental factors in China. Understanding the genetic diversity and population structure is funda- Genetic diversity mental for implementation of appropriate conservation measures and a sustainable management program. Population structure However, little is reported about the current genetic diversity and population structure. Here, we used ten Microsatellites microsatellite markers to genotype 175 individuals from six populations. Low levels of genetic diversity were found in all six tested populations. The Xiang river population showed the highest level of genetic diversity. Genetic differentiation was very low but significant among the Changyang, Changqidang and Mayang populations, but the Qiandao lake, Gan river, Xiang river populations all showed significant and strong differentiation from the other three populations. Contemporary gene flow was observed in among Changyang, Changqidang and Mayang populations and between Gan and Xiang river populations, respectively. This is the first genetic study to report the genetic diversity and population structure of S. sinensis and the results will be used to develop management and conservation strategies.

1. Introduction Schmidt, & Finn, 2009). Genetic studies are increasingly used by managers to assess the genetic diversity of wild populations before es- Genetic diversity plays an important role in the ability of a species tablishing management plans. to response to environmental changes and reflects its evolutionary po- The Chinese lake gudgeon, Sarcocheilichthys sinensis, a small ben- tential (Frankham, Briscoe, & Ballou, 2002). In general, populations of thopelagic freshwater fish (Froese & Pauly, 2016), belongs to the family a species with high genetic diversity have higher fitness (Hildner, Soule, Cyprinidae. According to previous literature, this species is historically Min, & Foran, 2003). The level of genetic variation is affected by many widely distributed from the Amur basin to the rivers of Korea and the Xi determinants (Ellegren & Galtier, 2016). It is widely demonstrated that River (Froese & Pauly, 2016). It usually lives in lakes, reservoirs, and demographic history has shaped the current genetic diversity of most streams. In the past decades, the population size of this fish has ex- species (Ellegren & Galtier, 2016). For freshwater wild organisms, hibited significant decline (Zhu et al., 2017a). Furthermore, in China human activities (e.g. overfishing, pollution) (Ma, Cowles, & Carter, conservation and management measures are still not fully im- 2000) and climate changes (Cai & Cowan, 2008) predominantly influ- plemented. The significant decline in this species may be due to over- ence their demographic fluctuations and result in reductions in popu- exploitation, low productivity and water pollution. Several published lation size. However, small populations are at greater risk of extinction studies have focused on captive breeding programs and larval devel- than larger stable populations. Furthermore, organisms living in opment of Chinese lake gudgeon (Song and Ma, 1994, 1996). However, freshwater lakes or rivers compared to marine organisms, become iso- the stocking of hatchery-reared S. sinensis has not been carried out to lated much more easily due to the discontinuous nature of freshwater improve the stock production. Levels of genetic diversity, population systems which restrict the movement of freshwater organisms (Hughes, differentiation and trends in abundance of wild populations remain

∗ Corresponding author. 999 Huchenghuan Road, College of Aquaculture and Life science, Shanghai Ocean University, Shanghai, 201306, China. E-mail address: [email protected] (Y. Shen). https://doi.org/10.1016/j.aaf.2019.06.002 Received 11 November 2018; Received in revised form 13 June 2019; Accepted 13 June 2019 Available online 09 July 2019 2468-550X/ © 2019 Shanghai Ocean University. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/). S. Gu, et al. Aquaculture and Fisheries 5 (2020) 80–85 largely unexplored. Neutral molecular markers frequently used in conservation genetic studies have been developed for S. sinensis, such as the mitochondrial genome (Li et al., 2016) and microsatellites (Shen et al., 2017; Zhu et al., 2017b). Although genotyping by sequencing (GBS) has become the most popular tool for population genetics analysis, microsatellites are still useful (Li et al., 2018; Shen & Yue, 2018). The aim of this study was to use ten microsatellite markers to survey the genetic diversity and population structure of six wild populations of Chinese lake gudgeon. The first goal was to analyze the pattern of genetic diversity to discuss the conservation status. Genetic diversity has a key role in evolution by allowing a species to adapt to a new environ- ment and to fight off parasites. Second, we aimed to decipher population differentiation, which is a direct effect of random genetic drift, mutation and natural selection. This is important information for de- signing conservation and management strategies. This is the first in- vestigation of the genetic structure of S.sinensis, populations and this Fig. 1. Sampling localities of the six populations of Sarcocheilichthys sinensis study will provide useful information for the conservation and man- from the Yangtze River System in China. agement of this species. was used to calculate allele number (A), Observed (Ho) and expected 2. Materials and methods (He) heterozygosity, the inbreeding index (f) and the exact test for linkage and Hardy-Weinberg disequilibrium. Allelic richness (Ar) was 2.1. Sampling and DNA isolation measured using FSTAT v2.9.3.2 (Goudet, 1995), to reduce differences in the number of alleles among populations that was caused by differ- Six wild populations of Chinese lake gudgeon consisting of 175 in- ences in sample size. dividuals were collected between 2014 and 2017. All fish were netted, and after fin tissues were clipped these fish were released. Samples 2.4. Population structure representing the six wild populations were collected from Changyang Lake (CY), Changqidang Lake (CQD), Mayang Lake (MY) and Qiandao The pairwise F for each pair of populations were analyzed with Lake (QD), Xiang River (XJ), Gan River (GJ) (Table 1; Fig. 1). Fin tis- ST Arlequin v3.5 (Excoffier & Lischer, 2010). Statistical significance was sues were sampled and preserved in absolute ethanol at −20 °C. examined using an exact test with 10000 permutations. AMOVA was Genomic DNA was extracted from the fin tissues with the modified used to estimate genetic differentiation using Arlequin v3.5 (Excoffier & salting-out method described by Yue and Orban (Yue & Orban, 2005). Lischer, 2010). Tests for genetic differentiation were performed at three The DNA concentration and purity were checked by running on a 0.8% hierarchical levels of variation: within individuals (F ), among in- agarose gel, then adjusted to 10 ng/μL using the NanoDrop 2000C IT dividuals within populations (F ), among populations within groups spectrophotometer (Thermo scientific, US). IS (FSC) and among groups (FCT). Pairwise FST genetic distance was used to produce an NJ tree with MEGA v7 (Kumar, Stecher, & Tamura, 2016). 2.2. PCR amplification and genotyping Bottleneck hypothesis testing was performed under the infinite allele model (IAM), two-phased model of mutation (TPM) and step-wise Ten pairs of primers previously developed against microsatellites mutation model (SMM) using Bottleneck v1.2.02 (Cornuet & Luikart, (Shen et al., 2017) were used for the present work. The forward primers 1996). These methods are based on heterozygosity excess or deficit to of each pair were fluorescently labelled at the 5’ end. The PCR reaction test for the departure from mutation-drift equilibrium. Genetic re- was as previously described by Shen et al. (2017). Cycling conditions lationships among the populations were evaluated using Structure were as follows: 94 °C for 2 min, followed by 38 cycles at 94 °C for 30 s, v2.3.4 (Pritchard, Stephens, & Donnelly, 2000), then the Bayesian 55 °C for 30 s and 72 °C for 45 s, with a final extension at 72 °C for method was used to assign samples into clusters under an admixture 5 min. Extension products were electrophoresed on a capillary DNA model. We used different values of the length of the burn-in period analyzer (ABI3730xl; Applied Biosystems), and the fragment size of (10,000–100,000) and MCMC repetitions (10,000–100,000) in every alleles were recorded using as a calibrator the size standard LIZ500 and run. The most likely value for K was calculated using the ΔK method. GENEMAPPER v4.0 (Applied Biosystems). The contemporary migration rates among populations were assessed using a Bayesian assignment method implemented in the software 2.3. Genetic diversity BayesAss v3 (Wilson & Rannala, 2003). This method uses Markov Chain Monte Carlo techniques to infer pairwise migration rates over the past Micro-Checker v2.2.3 was used to test the data for presence of null few generations. When background migration rates are relatively low alleles, allelic dropout and allele stuttering (Vanoosterhout, (FST > 0.05), BayesAss v3 can produce accurate estimates of migra- Hutchinson, Wills, & Shipley, 2004). GDA v1.1 (Lewis & Zaykin, 2000) tion. Analyses were run five times for 30,000,000 iterations, of which 10,000,000 were burn-in, and 2000 sampling frequency. Migration Table 1 rates, allele frequencies and inbreeding coefficients were set at 0.10, Sampling Sites, Sample Sizes, Collection Dates, and Abbreviations used for 0.30 and 0.50, respectively, to make the acceptance rates between 20% Sarcocheilichthys sinensis samples in this study. and 60%. Sampling sites Abbreviation N Data sampled

Changyang Lake CY 44 5/2014 3. Results Changqidang Lake CQD 38 5/2015 Mayang Lake MY 17 5/2015 3.1. Microsatellite markers Qiandao Lake QD 40 7/2016 Xiang River XJ 7 7/2017 Gan River GJ 29 10/2016 All 10 tested microsatellite loci were high polymorphic across all tested populations. But Ss232 was not variable in the QD and XJ

81 S. Gu, et al. Aquaculture and Fisheries 5 (2020) 80–85

Table 2 Table 3 Allele number (A), allele richness (Ar), observd (Ho) and expected (He) het- Analysis of the possibility of a recent bottleneck using Sign tests in six popu- erozygosity, inbreeding index (f), and P value for testing HWE (P) in six po- lations of Sarcocheilichthys sinensis. pulations of Sarcocheilichthys sinensis. Population IAM TPM SMM Locus CY CQD MY QD XJ GJ He/Hd a P He/Hd a P He/Hd a P Ss30 A 6449813 Ar 4.0 3.3 4.0 6.6 8.0 5.2 CY 9/1 0.012 9/1 0.021 9/1 0.033 He 0.269 0.283 0.273 0.630 0.923 0.670 CQD 9/1 0.010 8/2 0.071 7/3 0.267 Ho 0.295 0.263 0.294 0.675 0.857 0.552 MY 10/0 0.001 10/0 0.001 10/0 0.002 f −0.099 0.071 −0.081 −0.072 0.077 0.179 QD 8/2 0.049 8/2 0.072 8/2 0.091 P 1.000 0.126 1.000 0.000 0.439 0.113 XJ 8/2 0.085 7/3 0.277 7/3 0.367 Ss52 A 222232 GJ 9/1 0.015 8/2 0.094 5/5 0.476 Ar 2.0 2.0 2.0 2.0 3.0 1.4 a He 0.315 0.251 0.299 0.392 0.275 0.068 He/Hd: ratio of number of individuals with heterozygosity excess to the Ho 0.295 0.237 0.353 0.325 0.286 0.069 number with heterozygosity deficit. P: possibility of heterozygosity excess. f 0.064 0.057 −0.185 0.173 −0.043 −0.018 P > 0.05 indicates that the population has not experienced a recent bottleneck. P 0.630 0.581 1.000 0.411 1.000 1.000 IAM: Infinite Allele Model; TPM: Two-phased model of mutation; SMM: Ss223 A 3424511 Stepwise Mutation Model. Ar 3.0 3.3 2.0 3.4 5.0 6.3 He 0.588 0.557 0.508 0.616 0.824 0.788 Ho 1.000 0.947 0.882 0.950 0.857 0.931 0.069 for Ss52 to 0.957 for Ss223. The average expected heterozygosity f −0.714 −0.718 −0.778 −0.553 −0.043 −0.185 was 0.630, ranging from 0.068 for Ss52 to 0.968 for Ss224. P 0.000 0.000 0.002 0.000 0.756 0.121 Ss224 A 21 15 16 14 10 28 Ar 16.2 12.0 16.0 10.9 10.0 11.4 3.2. Genetic diversity He 0.940 0.888 0.934 0.895 0.956 0.968 Ho 0.800 0.842 1.000 0.775 1.000 1.000 The levels and patterns of genetic diversity for six populations are f 0.151 0.052 −0.073 0.135 −0.050 −0.034 given in Table 2. Allelic richness (Ar) was used to compare genetic P 0.001 0.604 0.920 0.033 1.000 0.853 ff ff Ss226 A 2436911 diversity of di erent populations because the sample size in di erent Ar 2.0 3.6 3.0 5.1 9.0 6.3 populations was different. For the six wild populations, the XJ popu- He 0.444 0.479 0.563 0.641 0.912 0.842 lation showed slightly higher genetic diversity than the others. Allelic Ho 0.465 0.447 0.529 0.675 1.000 0.827 richness ranged from 3.9 in GJ to 5.7 in XJ. Expected heterozygosity f −0.047 0.066 0.062 −0.054 −0.105 0.018 P 1.000 0.933 1.000 0.640 1.000 0.404 was highest in XJ (0.643), followed by GJ (0.609), MY (0.515), CQD Ss228 A 333315 (0.511) and CY (0.496), and the observed heterozygosity followed the Ar 3.0 3.0 3.0 3.0 1.0 3.9 expected heterozygosity pattern and varied from 0.631 in XJ to 0.501 in He 0.608 0.625 0.533 0.654 0.000 0.664 CY. The XJ and GJ populations had positive f suggestive of inbreeding Ho 0.568 0.710 0.529 0.650 0.000 0.724 (0.066 and 0.019, respectively), unlike CY (−0.009), QD (−0.022), f 0.066 −0.140 0.007 0.007 0 −0.093 − P 0.743 0.641 1.000 0.641 1.000 0.199 CQD ( 0.035) and MY (0.064). Most loci showed HWE in all six tested Ss232 A 222112 populations. Ar 2.0 2.0 2.0 1.0 1.0 1.6 Bottleneck analysis was performed for all six wild populations using He 0.221 0.287 0.214 0.000 0.000 0.100 the Bottleneck software under IAM, TPM and SMM. The CY and MY Ho 0.250 0.289 0.235 0.000 0.000 0.103 fi f −0.132 −0.007 −0.103 0.000 0.000 −0.037 populations showed signi cant heterozygosity excess (P < 0.05) P 1.000 1.000 1.000 1.000 1.000 1.000 (Table 3), suggesting that these two populations have experienced a Ss235 A 7 7 6 8 10 14 recent bottleneck. Ar 5.7 6.0 6.0 6.7 10.0 7.9 He 0.484 0.586 0.454 0.769 0.923 0.890 Ho 0.442 0.421 0.412 0.675 0.857 0.621 3.3. Population structure f 0.087 0.284 0.097 0.124 0.077 0.306 P 0.103 0.010 0.209 0.583 0.490 0.003 Pairwise FST values across all six populations are given in Table 4. Ss237 A 17 12 12 19 7 20 F values ranged from 0.007 (CY vs CQD) to 0.279 (CY vs XJ). All F Ar 10.9 10.2 12.0 13.2 7.0 9.9 ST ST fi ff He 0.765 0.843 0.905 0.896 0.890 0.943 values were statistically signi cant at P < 0.05. Genetic di erentiation Ho 0.571 0.842 0.941 0.923 0.428 0.896 between populations from the middle and lower Yangtze River was f 0.255 0.002 −0.041 −0.031 0.538 0.050 high (0.203–0.279). The genetic differentiation between the two po- P 0.003 0.243 0.768 0.915 0.000 0.000 pulations from the middle of the Yangtze River was much lower Ss238 A 433335 fi ff Ar 3.0 2.8 3.0 3.0 3.0 2.9 (0.093). Although statistically signi cant, the genetic di erentiation He 0.327 0.316 0.469 0.427 0.385 0.501 among the populations from Jiangsu province was extremely low Ho 0.318 0.289 0.294 0.400 0.428 0.586 (0.007–0.017). f 0.027 0.085 0.380 0.064 −0.125 −0.172 P 0.061 0.360 0.150 0.284 1.000 0.412 Table 4 Overall A 6.7 5.6 5.3 6.9 5.7 11.1 ff Ar 5.1 4.8 5.3 5.5 5.7 3.9 Pairwise genetic di erentiation (FST) in six populations of Sarcocheilichthys si- He 0.496 0.511 0.515 0.592 0.609 0.643 nensis. Ho 0.501 0.529 0.547 0.605 0.571 0.631 CY CQD MY QD XJ GJ f −0.009 −0.035 −0.064 −0.022 0.066 0.019 CY 0.000 CQD 0.007* 0.000 populations. We detected a total of 164 alleles in the 175 individuals MY 0.015* 0.017* 0.000 from the six populations. No null alleles were detected across the po- QD 0.110* 0.098* 0.100* 0.000 pulations. The allele number of the 10 microsatellite markers ranged XJ 0.279* 0.255* 0.218* 0.208* 0.000 GJ 0.263* 0.249* 0.231* 0.203* 0.093* 0.000 from 2 alleles for Ss232 to 39 alleles for Ss224, with an average of 16.4/ locus. The average observed heterozygosity was 0.560, ranging from *represents significance after Bonferroni correction (p < 0.05).

82 S. Gu, et al. Aquaculture and Fisheries 5 (2020) 80–85

contrast, for the populations of CQD and MY, 29.55 and 25.87% individuals were derived from the population of CY, respectively. For the population of XJ, 18.09% individuals were derived from the population of GJ. Estimated migration rates among the other sampled locations were all low (< 5%).

4. Discussion

This study reports the first description of population genetics in S. sinensis. The Chinese lake gudgeon has a wide distribution and relatively low conservation concerns. The present study provides important information for future application in the conservation and management of this fish. Fig. 2. Phylogenetic relationship among the six analyzed populations of Sarcocheilichthys sinensis constructed using the Neighbor-Joining approach. 4.1. Genetic diversity Bootstrap supports over loci for 1000 times are indicated. Overall, we found low levels of genetic diversity in S.sinensis.In ff ff An unrooted NJ phylogenetic tree was constructed using the pair- order to exclude the e ect of di erences in sample size, we use allelic richness to reduce the difference in the number of alleles, because wise FST genetic distance matrix generated from microsatellite data ff (Fig. 2). The CY, MY and CQD populations from the lower of Yangtze sample size strongly a ects allele number (Leberg, 2002). Based on the River formed one cluster, while the XJ and GJ populations from the microsatellite data analyses, both the average heterozygosity (0.56) and middle of the Yangtze River formed one subcluster, then the QD po- average allelic richness (5.05) (Table 2) were much lower than values pulation from the lower Yangtze River clustered with this subcluster. from microsatellite analysis of other freshwater fishes (0.54 and 9.1, Structure analysis was performed using all six populations in this respectively) (Dewoody & Avise, 2000). Compared to other freshwater study. Similar likelihood values were obtained when different values of fish in China, the genetic diversity of S.sinensis is much lower than that the length of burn-in period (10,000–100,000) and MCMC repetitions of the northern snakehead (Yan et al., 2018) and grass carp (Liu et al., (10,000–100,000) were used to perform structure analysis, so the re- 2009). Furthermore, the levels of genetic diversity of all six tested po- ff fi sults of using a 100,000-long burn-in period and 100,000 MCMC re- pulations did not di er signi cantly. The XJ population was repetitions are only shown. We used 5 values of K (K =2–6). This ana- presented by a very small number of individuals, but this population lysis showed that the most likely value of K with the highest ΔK was 3, showed unexpectedly high amounts of gene diversity and allelic rich- indicating the genetic variation grouped into 3 clusters. Fig. 3 is the ness using microsatellite markers. This can be explained by the fact that clustering assignment of the six populations using K =3.AtK = 3, the this population was collected from the Xiang River which is the second main cluster included CY, MY, and CQD populations. The second cluster tributary of the Yangtze River and covers a bigger area of 94,721 square included GJ and XJ populations and QD population formed its own kilometers. Moreover, the effect of human activities on its population cluster. size is limited. Another reason is that the XJ population from Hunan province did not experience a recent bottleneck. In addition, the in- Pairwise FST values, phylogenetic tree, and structure results indicated that the six populations were partitioned into three major breeding index was very low across all the tested populations. groups. The GJ and XJ populations from the middle of Yangtze River In CY and MY populations, the number of loci with heterozygosity were grouped together. The four populations from the lower Yangtze excess was significantly higher than the number with heterozygosity River were split into two distinct groups, the Jiangsu group (CY, CQD de ficit under the three mutation models, which is indicative of a genetic and MY) and the Zhejiang group (QD). An AMOVA test was used to bottleneck. Compared with the CY population, the effect of sample size evaluate genetic variation among groups. AMOVA (Table 5) results on bottleneck analysis was much larger in MY. However, the two lakes showed that the variation among groups, among populations within which the CY and MY populations originated from are relatively small, groups, among individuals within populations, and within individuals and it is acknowledged that these two populations are overfished. With were 16.32%, 2.29%, −1.16% and 82.54%, respectively. the reduction in population size, the number of alleles in CY and MY Based on estimates from BayesAss the proportion of migrants be- also decreased. This may primarily result in a genetic bottleneck in the tween all pairs of populations was low compared to the estimates of two populations. self-recruitment (Table 6). Three populations (CY, QD and GJ) ex- The genetic diversity assessed in the present study was more precise hibited a strong signal of genetic isolation with high proportions of than previous studies. Since in the present study, the sample size was genetic contribution (94.87–96.45%) from within each location. In much larger than published data for other teleost fishes (Gwak, Lee, & Nakayama, 2015; Nakagawa, Seki, Ishikawa, & Watanabe, 2016; Sasaki, Hammer, Unmack, & Adams, 2016), and capillary electrophoresis was used and gives more precise estimation of individual genotype.

4.2. Population structure

FST analysis, AMOVA structure and population phylogeny revealed that S.sinensis had significant genetic differentiation among the six fi Fig. 3. Genetic assignment of the six populations of Sarcocheilichthys sinensis. populations. All FST values were statistically signi cant, so it is not The most likely K value for both assignment tests in the program Structure is surprising to find such conclusion (Waples, 1989). The P values in our inferred as 3. Each vertical line represents one individual and each population is study indicated low but significant differentiation among CY, CQD and divided by a black line. Each color shows the genetic composition that is as- MY (0.007–0.117) and this may be due to natural or human-mediated signed to a different genetic cluster. (For interpretation of the references to gene flow occurring among these populations (Wright, 1978). The re- color in this figure legend, the reader is referred to the Web version of this sults of BayesAss analyses supported this speculation, with estimates of article.) migration rates indicating that 29.55 and 25.87% individuals of CQD

83 S. Gu, et al. Aquaculture and Fisheries 5 (2020) 80–85

Table 5 Analysis of molecular variation (AMOVA) of microsatellite in six populations of Sarcocheilichthys sinensis.

Source of variation Sum of squares Variance components Percentage of variations Fixation index

Among groups 128.935 0.553 16.32 FCT 0.163**

Among populations within groups 19.498 0.078 2.29 FSC 0.027**

Among individuals within populations 455.896 −0.039 −1.16 FIS -0.014

Within individuals 486.500 2.800 82.54 FIT 0.174** Total 1090.830 3.392 100.00

NS = not signiﬁcant; *P < 0.05; **P < 0.001.

Table 6 management units are genetically isolated, so they are also functionally Estimated migration rates calculated from variation at ten microsatellite loci independent (Moritz, 1994). We suggest that conservation measures from source populations (side) to recipient populations (top) of six populations should ﬁrst focus on the Qiandao Lake that has a genetically more di- of Sarcocheilichthys sinensis. verse population. Our genetic data also suggested that the gene ﬂow Source Destination among the Changyang, Changqidang and Mayang populations needs to be understood. Finally, future genetic studies should be carried out to CY (%) CQD (%) MY (%) QD (%) XJ (%) GJ (%) analyze the population genetics of Chinese lake gudgeon in China.

CY 96.45 29.55 25.87 CQD 67.43 Acknowledgments MY 68.12 QD 96.22 We thank Chen Wenhua and Li Fugui for the sample collections. XJ 70.74 This study was supported by the Jiangsu Key Technology R&D Program GJ 18.09 94.87 (BE2011330). We thank Mr. Ye baoqing of TLL for English editing. Empty cells indicate migration rates < 0.05, values in bold (on the diagonal) represent estimates of self-recruitment. References and MY populations were derived from the CY population. The STRU- Cai, W., & Cowan, T. (2008). Evidence of impacts from rising temperature on inflows to CTURE analysis showed similarity among the CY, CQD and MY popu- the Murray-Darling Basin. Geophysical Research Letters, 35, 154–162. fi fl Cornuet, J. M., & Luikart, G. (1996). Description and power analysis of two tests for lations. This con rmed that high gene ow occurred among these three detecting recent population bottlenecks from allele frequency data. Genetics, 144, populations. 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