Genetic diversity and population structure of black sea bream (Acanthopagrus schlegelii) based on mitochondrial control region sequences: the genetic effect of stock enhancement

Regional Studies in Marine Science 35 (2020) 101188

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Genetic diversity and population structure of black sea bream (Acanthopagrus schlegelii) based on mitochondrial control region sequences: the genetic effect of stock enhancement Binbin Shan a,b,c , Yan Liu a,b,c , Na Song d , Dongping Ji e , Changping Yang a,b,c , Yu Zhao a,b,c , ∗ ∗∗ Tianxiang Gao f , , Dianrong Sun a,b,c , a

Key Laboratory of South China Sea Fishery Resources Exploitation & Utilization, Ministry of Agriculture Rural Affairs, Guangzhou 510300, PR China Guangdong Provincial Key Laboratory of Fishery Ecology and Environment, Guangzhou 510300, PR China c South China Sea Fisheries Research Institute, Chinese Academy of Fisheries Sciences, Guangzhou 510300, PR China d Fishery College, Ocean University of China, Qingdao, Shandong 266003, PR China e Agricultural Machinery Service Center, Fangchengang Agricultural and Rural Bureau, Fangchengang, Guangxi 538001, PR China f Fishery College, Zhejiang Ocean University, Zhoushan, Zhejiang 316022, PR China b

article

info

Article history: Received 10 January 2020 Received in revised form 3 February 2020 Accepted 18 February 2020 Available online 20 February 2020 Keywords: Acanthopagrus schlegelii Stock enhancement Genetic effect Mitochondrial DNA Control region sequences

a b s t r a c t The black sea bream, Acanthopagrus schlegelii (Bleeker, 1854), has a great commercial value and a long history of stock enhancement in most coastal cities in China. The stock enhancement programs made significant achievements and contributions in increasing fishery yield, but few information was reported regarding the genetic effects on the wild population resulting from the releasing. In order to elucidate the potential genetic effects associated with stock enhancement releases, sequence variation based on the mitochondrial control region was investigated to characterize the genetic resources of black sea bream in Pearl River Estuary. The results showed that the broodfish and hatchery-released fish were characterized by lower genetic diversity indices compared with wild populations. In addition, after releasing, the genetic diversity was lower in the samples of Pearl River Estuary than other wild populations. The results of pair-wise FST value and AMOVA revealed low genetic differentiation among hatchery populations, samples in Pearl River Estuary and wide populations nearby the stock areas, which was consistent with the previous study. Hence, the black sea bream stock enhancement may have affected the genetic diversity of wild populations in Pearl River Estuary. In further stock enhancement of A. schlegelii, it is necessary to routinely monitor the genetic effects of stock releasing and improve the genetic management. © 2020 Published by Elsevier B.V.

1. Introduction In recent decades, global fisheries landings have declined due to habitat degradation and over-fishing. Fully fished stocks accounted for 58.1 percent and underfished stocks 10.5 percent based on FAO’s report in 2013 (FAO, 2016). Stock enhancement through the release of hatchery-reared juveniles into natural environments could enhance, conserve or restore the depleted fisheries stock (Waples and Drake, 2004; Lorenzen et al., 2010). Besides increasing the biomass of depleted fishery stocks through a direct contribution of hatchery-reared fish in the wild, stock ∗ Corresponding author. ∗∗ Corresponding author at: Key Laboratory of South China Sea Fishery Resources Exploitation & Utilization, Ministry of Agriculture Rural Affairs, Guangzhou 510300, PR China. E-mail addresses: [email protected] (T. Gao), [email protected] (D. Sun). https://doi.org/10.1016/j.rsma.2020.101188 2352-4855/© 2020 Published by Elsevier B.V.

enhancement can maintain self-sustainable local wild population (Araki and Schmid, 2010). Although stock enhancement programs were successful in many cases, a range of genetic problems have been associated with the releasing (Hamasaki et al., 2010; Escalante et al., 2014). Fish are highly fecund, and it is possible to obtain high numbers of juveniles by only a few breeders. Therefore, there is a potential for genetic deterioration of the offspring if limit breeders are used for juvenile production (Borrell et al., 2011). If the hatchery-reared fish are introduced into wild populations (supportive breeding), it may result in the loss of the genetic variability of the wild population through inbreeding. More importantly, loss of genetic diversity could lead to a reduction in fitness and disease resistance ability or other capabilities to adapt to new environment (Allendorf and Phelps, 1980). It has long been claimed that stock enhancement activities have adverse effects on gene pools of local populations (Gonzalez et al., 2013; Escalante et al., 2014; Létourneau et al., 2018). Genetic monitor is needed to preserve genetic diversity, particularly genetic

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variability, of the recipient populations in the stock enhancement activities. The black sea bream, Acanthopagrus schlegelii, is an important commercial species which is widely distributed throughout coastal areas of China. Hatchery release of A. schlegelii in China began in the early 1980s after the A. schlegelii wild individuals were depleted (Zhong and Ni, 1998). Since then, millions of A. schlegelii juveniles have been released in the majority of coastal city over the country. Evaluations of the effectiveness of A. schlegelii stock enhancement programs were often carried by using external tags or measuring the increment of landing (Lin et al., 2001; Xu et al., 2008). Benefitting from the stock enhancement, the depleted wild population had been recovered (Liang et al., 2010). Nevertheless, after the programs, no effort has been made to monitor the genetic effects of released black sea bream on wild populations in China. Gonzalez monitoring the genetic effects of the A. schlegelii stock enhancement program conducted in Hiroshima Bay, Japan since 1982 (Jeong et al., 2007). The results based on six polymorphic microsatellite loci indicated a small loss of genetic variability and a high level of inbreeding of the broodstock (Gonzalez et al., 2008). In addition, by comparing with wild populations, Jeong et al. (2003) announced a significant reduction in genetic variation in the samples of stock enhancement area after the long-term stock releasing. Conserving genetic diversity is particularly important in stock enhancement. In order to prevent the wild population from undergoing loss of genetic alteration, it is necessary to take special attention on the genetic effect (Bert et al., 2007). Technological advances in molecular genetics continuously provide new tools to deepen our understanding of fisheries and conservation genetics. Mitochondrial DNA (mtDNA), which is haploid, maternally inherited, lacks of recombination and nearly neutrally evolving with higher mutation rate compared to coding regions within the mitogenome, has been widely utilized as a useful genetic marker (Avise et al., 1987; Sugaya et al., 2008; Guo et al., 2012). The control region (CR) has been proved to be particularly useful in detecting genetic diversity and population genetic structure due to its high polymorphism (Avise et al., 1987; Bowen and Grant, 1997). Although, numerous genetic studies of A. schlegelii populations in China have been carried out, few of them focused on that issue (Yang et al., 2004; Gong et al., 2006; Zhao et al., 2010; Zhao, 2015). Previous genetic diversity and populations genetic studies suggested that black sea bream in China comprises two stocks, there is a significant geographic divergence between the southern stock and the northern stock (Zhao et al., 2010; Zhao, 2015). Considering that the large number hatchery-released A. schlegelii annually, it is surprising that no information regarding the genetic interactions between wild and hatchery-released A. schlegelii is available from stock enhancement in China. Previous studies in Japan suggested that, the potential harmful effects on the genetic composition of the wild population make it necessary to routinely monitor the genetic effects of the stock enhancement program (Jeong et al., 2003; Gonzalez et al., 2008). Hence, mitochondrial CR sequences were used to characterize the genetic resources of black sea bream in Pearl River Estuary, where A. schlegelii stock enhancement was carried on. In order to elucidate any potential harmful genetic effects of the A. schlegelii stock enhancement in Pearl River Estuary, we examined the genetic diversity and differentiation of broodstock, hatchery-released juveniles and other A. schlegelii geographic populations. The results are expected to facilitate fishery management for the black sea bream by supplying useful genetic information.

Fig. 1. Map showing the approximate location of sampling localities. Sampling sites of A. schlegelii, ⋆ = releasing sites, = sampling sites for wild populations. FCG, Fangchengang; BH, Beihai; SX, Suixi; LS, Lingshui; YJ, Yangjiang.

2. Materials and methods 2.1. DNA extraction and ethics statement Muscle tissue from 59 broodstock (BR) were sampled after spawning activity. The hatchery-released offspring (OF) were collected prior to releasing activity. The samples of post-stock population in Pearl River Estuary (PRE) were captured after stocking. In addition, wild specimens were collected from Yangjiang (YJ), Lingshui (LS), Suixi (SX), Beihai (BH) and Fangchengang (FCG) (Table 1). Fig. 1 showed the geographical positions of samples in present study. Total genomic DNA was extracted from ethanol preserved muscle tissue using a standard phenol-chloroform procedure (Sambrook et al., 1989). All experiment procedures in present study were conducted in accordance with the Committee on Laboratory Animal Welfare and Ethics of South China Sea Fisheries Research Institute (project identification code: nhdf 2020-01, date of approval: 3 Jan. 2020). 2.2. Mitochondrial DNA control region sequencing and analysis A segment of approximate 420 bp of CR was amplified by the polymerase chain reaction (PCR). The primer sequences were DL-Z 5′ -CCC ACC ACT AAC TCC CAA AGC-3′ (forward primer) and H16254 5′ -CTG GAA AGA ACG CCC GGC ATG-3′ (reverse primer) (Gong et al., 2006; Han et al., 2008). PCR reactions were performed in 25 µL reaction mixture containing 17.5 µL of ultrapure water, 1 µL of primer DL-Z, 1 µL of primer H16254, 2 µL of dNTPs, 2.5 µL of 10 × PCR buffer, 0.15 µL of rTaq and 1 µL of DNA template. PCR amplification was carried out in the Eppendorf thermal cycler under the following conditions: an initial denaturation at 94 ◦ C for 5 min, followed by 35 cycles of denaturing at 94 ◦ C for 45 s, annealing at 50 ◦ C for 45 s, extension at 72 ◦ C for 50 s and a final extension at 72 for 10 min. The amplified products were used as the template DNA for cycle sequencing reactions with Big Dye Terminator Cycle Sequencing Kit, and bi-direction sequencing was conducted on an ABI Prism 3730 automatic sequencer (Applied Biosystems, Foster City, CA). Nucleotide sequences of CR were aligned using Clustal X 2.0 with the default settings, and manually examined (Jeanmougin et al., 1998). Haplotypes were defined based on sequence data without considering sites with gaps using DnaSP ver. 5.00 (Librado and Rozas, 2009). Genetic diversity in each population was quantified as the number of polymorphic sites, number of

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Table 1 Sample information of A. schlegelii including sampling sites, date of collection, sample size and several genetic diversity indices. ID

Sampling time

Sampling size

Number of polymorphic sites

Number of haplotypes

Haplotype richness

Haplotype diversity

Nucleotide diversity

Mean number of pairwise differences

Broodstock (BR) Hatchery-released offspring (OF) Post-stock population (PRE) Yangjiang (YJ) Suixi (SX) Beihai (BH) Fangchengang (FCG) Lingshui (LS)

2015.03 2015.06

59 141

28 21

17 12

0.890 0.796

0.896 ± 0.020 0.796 ± 0.018

0.010 ± 0.006 0.008 ± 0.005

4.394 ± 2.201 3.643 ± 1.856

2015.10 2013.10 2012.05 2010.11 2012.10 2012.05

74 30 35 24 24 30

28 26 27 28 26 30

19 20 21 19 17 17

0.866 0.968 0.946 0.975 0.964 0.920

0.866 0.968 0.946 0.975 0.964 0.920

0.008 0.009 0.011 0.013 0.011 0.008

3.276 3.777 4.744 5.554 4.786 3.267

haplotypes, haplotype diversity, nucleotide diversity and mean number of pairwise differences (Tajima, 1983; Nei, 1987) using Arlequin ver. 3.0 (Excoffier et al., 2005). In addition, a corrected haplotype richness (Hr) was calculated after rarefaction (adjusted to the minimum population sample size) with the software Contrib (Petit et al., 1998). To provide robust estimates, the smallest sample size (n = 24) was used for rarefaction. To measure population differentiation, pairwise F -statistics (F st) (Weir and Cockerham, 1984) was estimated based on mtDNA sequences using the ARLEQUIN v3.0 (Excoffier et al., 2005). Pairwise F -statistics value was calculated using the Kimura-2-parameters model of substitution (Kimura, 1980). Probability associated with the F st values was evaluated through random permutation procedures (at least 10,000 permutations). The significance of P values was adjusted by Bonferroni correction (Rice, 1989). Tests for significant differentiation between samples were conducted using exact P tests with a Markov chain procedure performed in ARLEQUIN. For more detailed and direct display of the differentiation between populations, the heatmap of F st values and exact P tests were showed. In addition, the Mega 5.0 was applied to generate Neighbor-joining (NJ) cluster tree based on the distance between populations. Analysis of molecular variance (AMOVA) based on Kimura 2parameters method was also performed to test the population subdivision and significant population structure. In order to define groups of populations in AMOVA, the program SAMOVA (spatial analysis of molecular variance, Dupanloup et al., 2002) was used to define partitions of populations that are maximally differentiated from each other based on the data combined with geographical location and genetic data (Geo), and based only the genetic data (No-Geo). The broodstock and recaptured population were both captured from the Pearl River Estuary area. In addition, for the similarly genetic context, the broodstock and offspring (hatchery-released) were regarded as one population. The sites of broodstock, hatchery-reared offspring and recaptured populations could be regarded as Pearl River Estuary area. The analysis was based on 100 simulated annealing steps, and a prior definition of the number of groups (G), ranging from 2 to 8. The configuration with the largest associated FCT value is retained as the best grouping (G) of populations. In addition, we evaluated the relationships of haplotypes by minimum spanning trees (MST) created via the MINSPNET algorithm implemented in ARLEQUIN v3.0 (Excoffier et al., 2005). The final networks were drawn by AutoCAD. 3. Results 3.1. Genetic diversity A total of 78 haplotypes were identified by sequencing 420 bp of the mtDNA CR from 417 black sea bream individuals (Table S1). All the haplotype sequences of A. schlegelii in the present study have been submitted to the GenBank (accession numbers:

± ± ± ± ± ±

0.025 0.017 0.021 0.021 0.024 0.033

± ± ± ± ± ±

0.005 0.005 0.006 0.007 0.006 0.005

± ± ± ± ± ±

1.706 1.958 2.377 2.765 2.423 1.730

MH703811-MH703888). There were 68 variable sites consisting of 65 base-substitutions (60 transitions and 5 transversions) with 3 single base pair insertion/deletion. Numbers of polymorphic sites and haplotypes, haplotype diversity, nucleotide diversity and mean number of pairwise difference of each population were summarized in Table 1. Unique haplotypes counted 48, representing 61% of the total, and all hatchery-releasing offspring (OF) samples shared their haplotypes with broodstock (BR) (Table 2). The haplotype frequencies of Hap_8, Hap_9 and Hap_10 in hatchery-releasing offspring and recaptured samples (PRE) were highest, evidencing a reduction in haplotype variability. Hap_3 was shared by seven populations except BH, and there was no haplotype shared by all eight populations. Erosion in haplotype diversity was especially apparent, as values in BR, OF and PRE (0.796–0.896) were significantly lower than that in YJ, SX, BH, FCG and LS (0.920– 0.975). In addition, the haplotype richness (Hr) also showed the wild samples (0.920–0.975) had a significant higher genetic diversity compared with broodstock, hatchery-reared offspring and recaptured samples (0.796–0.890) (Table 1). 3.2. Population genetic structure and differentiation The pairwise F -statistics were estimated based on the mtDNA sequences, the values ranged from −0.014 to 0.215 (Table 2). A higher level of genetic differentiation with statistically significant FST was found between hatchery populations (BR, OF and PRE) and samples in Beibu Gulf (SX, BH and FCG) (FST = 0.109– 0.215, P < 0.01) than those between hatchery populations and samples nearby to Pearl River Estuary (YJ and LS) (FST = 0.062– 0.109, P < 0.05). In addition, the pairwise F -statistics among hatchery populations was low, which revealed a lower genetic differentiation. Similarly, the pairwise F -statistics among SX, BH and FCG was lower than those among other wild populations (FST = −0.014–0.011, P > 0.05). A similar result was found for exact test of population differentiation (exact P test) (Fig. 2). The SAMOVA (Geo and No-Geo) indicated G = 2 as the best grouping option for this data set (FCT = 0.147, Table 3). With the value of G, samples from the SX, BH and FCG were assigned to one group and those from other populations were assigned to the other group. AMOVA was performed under two patterns of gene pools based on the results of the SAMOVA. The results showed that genetic variation between groups was 14.7% (P < 0.05) when we divided the populations into two groups: samples from BR, OF, PRE, YJ, LS; samples from SX, BH, FCG. The highest percentages of variation were observed within populations in both patterns of gene pools (90.6% and 82.5%), which indicated that most of the genetic variations were attributed to the variations within populations (Table 4). Furthermore, the topology of the minimum spanning tree was shallow, there were no significant genealogical branches or clusters of samples corresponding to sample site (Fig. 3).

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B. Shan, Y. Liu, N. Song et al. / Regional Studies in Marine Science 35 (2020) 101188 Table 2 Estimate of pairwise FST (below diagonal) and non-differentiation exact test significance values (above diagonal) among A. schlegelii populations. BR BR OF PRE YJ SX BH FCG LS

0.002 0.019* 0.062** 0.162** 0.176** 0.109** 0.064**

OF

PRE

YJ

SX

0.072 ± 0.005

0.015 ± 0.004 0.000 ± 0.000

0.000 ± 0.000 0.000 ± 0.000 0.000 ± 0.000

0.000 0.000 0.000 0.003

0.013 0.109** 0.212** 0.212** 0.167** 0.090**

0.077** 0.183** 0.215** 0.140** 0.064**

0.047** 0.072 0.002* 0.030*

BH

± ± ± ±

0.000 0.000 0.000 0.002

−0.004 −0.014 0.078**

0.000 0.000 0.000 0.020 0.020

FCG

± ± ± ± ±

0.000 0.000 0.000 0.004 0.005

0.011 0.102**

0.000 0.000 0.000 0.400 0.330 0.088

LS

± ± ± ± ± ±

0.000 0.000 0.000 0.011 0.011 0.006

0.008 0.000 0.000 0.000 0.000 0.000 0.000

± ± ± ± ± ± ±

0.003 0.000 0.000 0.000 0.000 0.000 0.000

0.047**

*P < 0.05. **P < 0.01.

Table 3 Defining groups of 8 populations of A. schlegelii with or without constraint for geographic composition of the groups. Group (G)

Structure (Geo)

FCT (Geo)

Structure (No-Geo)

FCT (No-Geo)

2 3 4 5 6 7

(BR, (BR, (BR, (BR, (BR, (BR,

0.147 0.136 0.130 0.126 0.124 0.104

Consistent with Structure Geo (BR, OF, PRE, YJ, LS) (SX, FCG) (BH) Consistent with Structure Geo (BR, OF, PRE) (SX, FCG) (YJ) (BH)(LS) (BR, OF) (SX, FCG) (PRE) (YJ) (BH) (LS) Consistent with Structure Geo

0.147 0.139 0.130 0.129 0.108 0.104

OF, PRE, YJ, LS) (SX, BH, FCG) OF, PRE, YJ, LS) (SX, BH) (FCG) OF, PRE, LS) (YJ) (SX, BH, FCG) OF, PRE) (SX, BH) (YJ) (FCG) (LS) OF, PRE) (YJ) (BH) (SX) (FCG) (LS) OF) (YJ) (SX) (LS) (PRE) (BH) (FCG)

Table 4 AMOVA of A. schlegelii populations based on mtDNA control region sequences. Source of variation

Variance components

Percentage variation

F -Statistics

P

9.400 90.600

0.094

0.000 ± 0.000

14.700 2.770 82.520

0.147 0.033 0.175

0.021 ± 0.005 0.000 ± 0.000 0.000 ± 0.000

A. One gene pool (BR, OF, PRE, YJ, LS, SX, BH, FCG) Among populations Within populations

0.209 2.016

B. Two gene pools (BR, OF, PRE, YJ, LS) (SX, BH, FCG) Among groups Among populations within groups Within populations

0.358 0.067 2.007

Fig. 2. Neighbor-joining (NJ) cluster tree (left), FST (right, below diagonal) and exact P test (right, above diagonal) of A. schlegelii.

4. Discussion

4.1. Genetic diversity

The black sea bream is an important species in most of coastal city in China. Nevertheless, after releasing, no effort has been made to monitor the genetic effects of released black sea bream on wild populations.

Conserving genetic diversity, particularly genetic variability, is important for stock enhancement, because enough genetic variability in the wild gene pools will enable the species to adapt to changing environments. Gong et al. (2006) analyzed the mtDNA CR of three black sea bream populations (Beihai, Shenzhen and

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Fig. 3. The minimum spanning tree, perpendicular tick marks on the lines joining haplotypes represent the number of nucleotide substitutions, the sizes of circles were proportional to haplotypes frequency.

Qingdao), the haplotype diversity was 0.935–0.968, revealed a high genetic diversity. According to study by Zhao et al. (2010), A. schlegelii populations collected along the coast of China (Yingkou, Laoshan, Dayawan and Dongxing) also showed a high level of genetic diversity of mtDNA CR (haplotype diversity: 0.978–1.000). By comparing the former studies, the wild A. schlegelii populations in the present study (haplotype diversity: 0.920–0.975) also showed a high level of genetic diversity (Gong et al., 2006; Zhao et al., 2010). However, the recaptured samples collected after stocking had a lower haplotype richness and haplotype diversity compared with other wild populations. The same results were obtained in the hatchery populations (broodfish and released offspring). Gonzalez et al. (2008) found the broodfish and wild samples had similar high genetic diversity in a black seabream enhancement program. In contrast, the broodfish in the present study showed a lower genetic diversity. In addition, the genetic diversity in offspring was lower than that in broodfish. The result in the present was consistent with the study of Gonzalez (Gonzalez et al., 2008). Gonzalez et al. (2008) detected the reduction of alleles in the black seabream offspring compared with their broodstock based on microsatellite markers. Generally, stock enhancement was carried out by capturing wild breeding animals during the reproductive season, inducing them to spawn in the hatchery, and releasing the offspring into the natural habitat when they reached the appropriate size (Gonzalez et al., 2010). There is potential for genetic deterioration of the broodstock if small effective number of breeding fish is kept within the hatchery (Hedgecock and Sly, 1990; Smith and Conroy, 1992). In the present study, comparing to broodfish, the numbers of haplotype and polymorphic sites in offspring were lower. It revealed that there was a loss of genetic variation during the reproduction of hatchery-released offspring. Lower gene diversity of recaptured population (PRE) than other natural population was observed in present study revealed by mtDNA. The results of present study were also in agreement with those previously reported result. Jeong et al. (2003) investigated genetic diversity in six wild black sea bream populations, hatchery and post-stock populations from stock enhancement area. The results showed high levels of genetic variation in wild

populations. In contrast, genetic variations in hatchery stock populations were lower than that in wild population. In addition, the post-stock populations were lower than population before releasing and other wild populations. This might be an undoubted result. The post-stock population was a mix population (wild individuals in releasing area with hatchery-released individuals). According to Campton, it was one of the direct ways to affect the wild populations by mixing wild and hatchery individuals (Campton, 1995). For the results of present study, the black sea bream stock enhancement may affect the genetic diversity of the population in Pearl River Estuary. 4.2. Population differentiation In order to lower the genetic impact of the stock enhancement and maintain the gene pool of wild population, it is necessary to decreased the difference between the released offspring and wild populations. Generally, reproduction of releasing individuals for stock enhancement occurs in local aquaculture hatcheries, but releasing individuals could also be transported from other areas and released at enhancement locations (e.g. see Youngson and Verspoor, 1998). The use of non-native individuals as broodstock is another factor causing the significant differentiation between hatchery-released individuals and wild populations. After releasing, reproduction between released individuals and wild individuals could cause genetic introgression (Escalante et al., 2014). The incorporation of alleles from a population into the gene pool of another genetically distinct populations is a threat to the genetic integrity of stocked populations (Létourneau et al., 2018). In the present study, the low FST between broodfish and released individuals demonstrated that there was no genetic differentiation in the hatchery populations. In addition, the FST between released population and PRE were also showed a high genetic similarity. Instead, the value of FST between the samples collected from Beibu Gulf (SX, BH and FCG) and the samples of hatchery populations and PRE were slightly larger, the genetic differentiation of different geographic populations was agreement with previously reported result (Zhao et al., 2010). The

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result of SAMOVA indicated it was best grouping option that samples from the SX, BH and FCG were assigned to one group and those from other populations were assigned to the other group. The result was consistent with the geographical distributions of these samples sites. AMOVA also showed that variations within groups was extremely small. The minimum spanning tree did not show a clustered topology. According to the former studies, the management of the broodstock may contribute to the lower genetic divergence between hatchery populations and natural populations (Doyle et al., 2001; Gonzalez et al., 2010). Several key strategies in stock enhancement were confirmed by researchers. First, the use of numerous native specimens as breeders plays a critical role in the management (Allendorf and Ryman, 1987; Doyle et al., 2001). It is the prerequisite to preserve the gene pool of the natural stocks. In addition, attention should be paid to manage the production of the release individuals. It was reported that the collection of eggs at several times could improve the effective number of breeders, decrease the differences between release and wild populations, conserve the larger genetic variability and genetic resources of the wild populations (Fraser, 2008; Gonzalez et al., 2010). 5. Conclusion In conclusion, the assessment of variability in the mtDNA CR may demonstrated a potential genetic effect of the stock enhancement on wild black sea bream populations. Samples of stock area, as well as broodfish and released individuals, evidenced significant lower genetic variability than any of the wild populations. After releasing, samples of stock area showed a slight genetic difference with other wild populations. The genetic variation of natural populations was the natural gene pools of species. Therefore, in stock enhancement activities, care should be taken to preserve the genetic variation of wild populations. Further massive stocking of A. schlegelii into natural sea areas should improve the genetic management by means of monitoring the genetic variability. Furthermore, a few genetic management principals, when selecting and breeding broodstock and when rearing and releasing hatchery-reared individuals, should be adhered. It could improve the genetic diversity and increase the likelihood that the stock enhancement effort will be successful. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. CRediT authorship contribution statement Binbin Shan: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - original draft, Writing - review & editing. Yan Liu: Validation, Visualization, Writing - original draft. Na Song: Data curation, Formal analysis, Software. Dongping Ji: Investigation, Methodology, Supervision. Changping Yang: Writing review & editing. Yu Zhao: Investigation, Methodology, Supervision. Tianxiang Gao: Conceptualization, Funding acquisition, Writing - review & editing. Dianrong Sun: Conceptualization, Funding acquisition, Project administration, Resources, Writing review & editing.

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