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Article

Maintenance of Genetic Diversity of Black Sea Bream despite Unmonitored and Large-Scale Hatchery Releases

by
Te-Hua Hsu
1,2,
Hung-Tai Lee
3,
Hsueh-Jung Lu
3,
Cheng-Hsin Liao
3,
Hong-Yi Gong
1,2 and
Chang-Wen Huang
1,2,*
1
Department of Aquaculture, National Taiwan Ocean University, Keelung 20224, Taiwan
2
Center of Excellence for the Oceans, National Taiwan Ocean University, Keelung 20224, Taiwan
3
Department of Environmental Biology and Fisheries Science, National Taiwan Ocean University, Keelung 20224, Taiwan
*
Author to whom correspondence should be addressed.
Biology 2022, 11(4), 554; https://doi.org/10.3390/biology11040554
Submission received: 21 February 2022 / Revised: 12 March 2022 / Accepted: 30 March 2022 / Published: 2 April 2022
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Abstract

:

Simple Summary

Stock enhancement aggressively replenishes depleted wild finfish populations. However, stock enhancement of black sea bream in Taiwan with complex genetic sources, especially when successful, maintains genetic diversity but dramatically changes the genetic structure within and among wild populations.

Abstract

Stock enhancement, used for replenishing depleted wild finfish populations, is an aggressive approach. Stock enhancement projects in Taiwan involve black sea bream (Acanthopagrus schlegelii), a major commercial species. During 2004–2015, even management agencies conducted stock enhancement projects, leading to numerous private releases that have not been recorded. Stock enhancement by a private hatchery without accurate genetic records may lead to a genetic structure change in wild populations. Using allele frequencies at nine microsatellite loci, we studied the genetic effects of stock enhancement in 19 samples collected from populations in the hatcheries and the wild. In 458 individuals from nine hatchery samples, most populations showed weak but significant genetic differences and complex clusters in structure analysis, indicating dramatic stock change within and among hatcheries. The 10 wild populations (n = 773) also had a complex genetic composition and were genetically different among sampling sites and times. However, a simple and clear cluster in structure analysis was found for only one sampling site, which had no release history. Thus, stock enhancement with complex genetic sources helps maintain genetic diversity but dramatically changes the genetic structure within and among wild populations, especially when stock enhancement is successful.

1. Introduction

Advances in fishing technology have led to fish stocks, which are renewable fishery resources, being exhausted. Approximately half of all fish stocks have been deemed “fully exploited” or “overexploited” [1,2]. Pollution and habitat destruction due to human activities have drastically reduced the abundance and distribution of marine fish and invertebrate populations [2,3,4] and depleted fishery stocks in more than 100 species to date [5,6,7].
Aiming to improve fishery resources, Taiwan’s government promotes a massive stock enhancement program in its coastal waters every year [8,9]. Under this program, numerous artificial breeding fry are released, with a small number of fish labeled to enable survival rate evaluation [8,9,10]. Moreover, the government annually allocates a considerable amount of money to this program. However, the role of genetic factors in stock enhancement has been neglected [6,11,12]. Conventional markers (biological, physical, and chemical) cannot estimate the reproduction rate of released fish [5,6,13]. Stock enhancement programs should incorporate genetic information such as genetic stock structure and diversity. The official fishery organization (the Taiwan Fisheries Sustainable Development Association; TFSDA) in Taiwan procures fish fry for stock enhancement from one or several private hatcheries. According to several genetic studies, negative effects on natural populations were noted after successful stock enhancements [7,14]. These enhancements involved the release of hatchery populations or the escape of numerous relatively unfit [14,15,16,17] and not genetically diverse individuals, such as the Adriatic sturgeon [18], Korean starry flounder [19], and black sea bream from Japan [20]. The genetic effects of these hatchery fish in the wild have received considerable attention in developed countries. For example, Japan, the United States, and several countries in Europe have created official agencies for managing stock enhancement [7,21,22]. These agencies not only verify the reliability of external markers, but also evaluate the genetic composition of offspring and their reproductive rate through mark–recapture studies [5,7,22]. However, identifying fish sources is difficult when release records are complex and unclear, such as in Taiwan [8,9,10,11,12].
Black sea bream (Acanthopagrus schlegelii), an economically vital species for both fisheries and aquaculture, is widely distributed along West Pacific coasts from Japan and Korea to the East China Sea and Taiwan. In southern China, black sea bream males become sexually mature within 1 year, and 50% of them change sex by two years old. In this fish, reproduction occurs at 1–2 years of age in coastal waters and at river mouths [23]. The species is abundant off the west coast of Taiwan, where it is a popular sport fish. Notably, concern over the rapid decline in black sea bream stocks is growing because the related catch production declined from 718 tons in 2000 to 212 tons in 2015, according to the Fisheries Statistical Yearbook of Taiwan [24]. Black sea bream aquaculture began in the 1980s, and Taiwanese hatcheries are mainly located in the Kaohsiung and Ping-tung areas. Broodstocks are from main fishery areas, which are off the west coast of Taiwan (Chiayi–Yunlin–Changhua). Using reliable mass production techniques [8,9], massive hatchery juveniles are released into the wild to replenish the insufficient natural supply [8,9,10]. Black sea bream has thus become a dominant species for stock enhancement in Taiwan. During 2004–2015, more than 12 million hatchery black sea bream fry were released into the coastal water off Taiwan by TFSDA [8,9,10,11,12].
Although no genetic or hatchery information is available for stocks and fry in Taiwan during 2004–2015, investigating the genetic structure of hatchery and wild populations and assessing the effectiveness of stock enhancement are critical [8,9,10,11,12]. The analysis data allow study of the genetic diversity of fish fry and tracing of fish origins in the absence of hatchery information [11,12]. In this study, we used microsatellite DNA markers to distinguish cultured black sea bream populations from wild ones and to understand the genetic effects of stock enhancement on these wild populations.

2. Materials and Methods

2.1. Sample Preparation

In total, 1231 black sea bream specimens (458 from the hatcheries and 773 from the wild) were obtained from 2015 to 2017 (Table 1). Species were identified by following the method of Hsu et al. [25]. Fresh specimens—at least 20 individuals for each batch, including broodstock, juveniles, and subadults/adults—were sampled from three types of hatchery source: (1) a private hatchery for the TFSDA release project and without genetic information (KS_C1, KS_C2, and KS_C3); (2) an unknown hatchery for private (religious) release (PR_C1 and PR_C2); (3) aquaculture farms from offshore islands of Taiwan (KM_C and MT_C) and southern China (XM_C); and (4) an aquaculture farm from northern China (QD_C). Ten batches from eight field locations: (1) Miaoli County, site of the TFSDA release project during 2013–2015 (ML_W1, ML_W2, and ML_W3); (2) Yunlin County, Penghu County, Tainan City, and Taipei City, with a hatchery fish release history during 2004–2015 (YL_W, PH_W, TN_W, TP_W, and KM_W); (3) Chiayi County, with no hatchery fish release history during 2004–2015 (CY_W); and (4) Nagasaki Prefecture, Japan, used for comparison (JP_W; Table 1; Figure 1). The geographical locations of these populations, sampling locations with abbreviated population names, and sample size for each population are presented in Figure 1 and Figure 2 and Table 1. Small muscle tissue pieces (approximately 3–5 mm) were prepared from fresh (2% alcohol used for anesthesia) or frozen fish specimens, transported to our laboratory for molecular study, and preserved in 95% ethanol. The standard proteinase K/phenol method modified from an animal DNA extraction protocol was used. Moreover, DNA template quality was assessed through 0.8% agarose gel electrophoresis.

2.2. SSR Markers

In accordance with Hsu et al. [26], nine microsatellite markers for black sea bream were selected and validated through multiplex polymerase chain reaction (PCR) to determine those ideal for genetic analysis (Table 2). PCR amplification was performed in 20 µL reaction volumes containing 5–10 ng of template DNA, 1× PCR buffer (10 mM Tris and 50 mM KCl, pH 9.0), 200 μM of each dNTP, 1.5 mM MgCl2, 0.5 U of Taq polymerase (Promega, Madison, WI, USA), and 4 pmol of each primer. Thereafter, PCR cycling was performed in an Autorisierter Thermocycler (Eppendorf, Hamburg, Germany) with initial denaturation at 95 °C for 2 min, which was followed by 30 cycles of denaturation at 95 °C for 30 s, annealing at locus-specific temperatures for 30 s, extension at 72 °C for 30 s, and a final extension at 72 °C for 10 min. The fragments in PCR products were analyzed using an ABI 3130 Genetic Analyzer (Applied Biosystems, Foster City, CA, USA), with the output analyzed using GeneMapper software (version 4.0, Applied Biosystems, Foster City, CA, USA).

2.3. Population Genetic Analysis

The observed genetic diversity (Ho), expected genetic diversity (He), and FIS were calculated using Genalex 6.41 [32]. The chi-square Hardy–Weinberg equilibrium (HWE) test results, pairwise FST values, and associated p values were also determined using Genalex 6.41 [32]. To determine FST significance levels, multilocus genotypes were randomized between sample pairs (9999 permutations), and the significance after Bonferroni correction was calculated [33,34]. Genotyping errors and null alleles were estimated using Microchecker 2.2.3 [35]. To elucidate a population’s genetic structure on the basis of multilocus genotypes, an admixture model with correlated allele frequencies was developed using Structure version 2.3.4 [36,37]. Five independent runs were performed for the entire data set for K values (numbers of groups) ranging from 1 to 9. All runs were based on 1,000,000 iterations of burn-in, followed by an additional 5,000,000 iterations. The best estimation of the K value was obtained using Structure Harvester [38]. Summation and graphical representation of the structure analysis results were performed using Clumpak [39] and Structure Plot version 2.0 [40], respectively. The population structure estimation was based on the highest-probability structure obtained at K = 8. Hierarchical analysis of molecular variance (hierarchical AMOVA) was performed to partition the total genetic variance within and between regions, as described by Excoffier et al. [41]. Arlequin was used to perform AMOVA. The most-supported grouping can be automatically detected using SAMOVA (based on Arlequin 3.5) [42,43]. We calculated pairwise relatedness (unrelated, half-sibling, full-sibling, or parent–offspring) between each pair of individuals by using RELATED [44] and Wang’s [45] estimator. The expected relatedness values (r) were 0.5 between full-sibling pairs and parent–offspring pairs, 0.25 between half-sibling pairs, and 0 between unrelated individuals [44]. We prepared a kinship network by using a pairwise relatedness (r) of >0.4, which indicates related individuals [46]. Parentage assignment between the 94 broodstock (KS_C1) and 192 wild (recaptured) samples (ML_W3) was estimated using Cervus 3.0.7 [47]. For accuracy, parentage was assigned by employing a minimum of nine loci, 0.9; proportion of loci typed, 0.01; proportion of loci mistyped, 0.01; error rate in likelihood calculations, 0.01; and simulation of 100,000 offspring at an 80% confidence interval applying the LOD confidence determined.

3. Results

3.1. Genetic Diversity within Populations

Across the nine microsatellite markers, all the populations were successfully genotyped. No monomorphic loci were found among the 19 populations. In total, 170 alleles were detected from the samples (n = 1231). In all individuals, the marker AS324 exhibited the highest number of alleles per locus (31 alleles), whereas the marker CL011 exhibited the lowest number (12 alleles; Table 3). Fewer alleles were found in the hatchery populations (n = 388; 139 alleles) than in the wild populations (n = 739; 148 alleles); however, the allele frequency pattern was similar (Figure 3).
The marker AS324 in PH_W exhibited the highest number of alleles per locus and highest allele richness (21 alleles and 13.921, respectively), whereas the marker SaI10 in KM_C exhibited the lowest (4 alleles and 1.387, respectively; Table 4 and Table 5). The average heterozygosity (Ho) over all loci was between 0.565 and 0.725 among the 19 populations. CY_W exhibited the highest expected heterozygosity for all the loci, whereas XM_C exhibited the lowest (Table 4 and Table 5). The mean estimates of expected heterozygosity (He) over all loci were between 0.683 and 0.742 among the 19 populations. PR_C2 exhibited the highest expected heterozygosity for all the loci and XM_C and KS_C3 the lowest (Table 4 and Table 5). Of the 171 population–locus combinations, 67 displayed deviations from the HWE significant at the p < 0.001 level (Table 4 and Table 5), with no strong trends of deviation observed for specific loci (between 5 and 10). No possible genotyping errors were noted in any loci, and null alleles were found to be possible for only one locus (AC229) in KS_C1 (Table 4 and Table 5). The average FIS was between −0.051 and 0.164 among the 19 populations. The lowest FIS was found in TP_W and the highest in XM_C (Table 4 and Table 5).

3.2. Genetic Differentiation among Populations

Pairwise comparisons between sampling locations were performed. The pairwise FST values ranged from 0 (KS_C1–ML_W1) to 0.056 (XM_C–JP_W), and most of them were significant (p = 0), suggesting small but significant genetic differentiation (Table 6). Most comparisons among the 19 populations indicated low genetic differentiation (below 0.04), but the results of comparisons of other locations with QD_C and JP_W were high (QD_C–others: 0.012–0.052; JP_W–others: 0.018–0.056) (Table 6). Pairwise comparisons among Taiwan hatchery populations obtained values ranging from 0.005 to 0.037 (average 0.023); among Taiwan wild populations, 0.005 to 0.034 (average 0.017); and among Taiwan hatchery–wild populations, 0 to 0.039 (average 0.020; Table 6).

3.3. Genetic Structure of Populations

An initial AMOVA indicated low differentiation (FST = 0.022) with 2.17% genetic variation distributed among the 19 populations (Table 7). However, the overall FST differentiation was significant among the populations, based on 999 permutations (p = 0 < 0.001; Table 7). Even if JP_W and QD_C were removed, the overall FST was still significant among the remaining populations (p = 0 < 0.001; Table 7). Lower genetic differentiation and higher population connectivity were noted across the wild sampling sites (average FST = 0.015; gene flow (Nm) = 15.929), whereas higher genetic differentiation and lower population connectivity were noted across the hatchery sampling sites (average FST = 0.022; Nm = 10.895; Table 7).
A hierarchical AMOVA (SAMOVA) with the highest ΦCT was performed, and all groupings were significantly supported by permutations (0.05 ≥ p ≥ 0.01; Table 8). In the 19 populations analyzed, JP_W, QD_C, and others were separated (K = 3, ΦCT = 0.021, p = 0.009, variance 2.09). In the 17 populations (i.e., those other than JP_W and QD_C), the PH_W–TN_W and KS_C2–YL_W pairs were grouped (K = 15, ΦCT = 0.015, p = 0.001, variance 1.47). In the nine wild populations, when K = 6, the following was determined to be the optimal grouping by using the SAMOVA program [(ML_W2; ML_W3; KM_W); (PH_W; TN_W); (TP_W); (CY_W); and (ML_W1); (YL_W)]; these groups exhibited the highest intergroup variance (1.20%; Table 8). In the eight hatchery populations, when K = 7, [(KS_C); (PR_C1; PR_C2); (MT_C); (KS_C2); (XM_C); (KS_C3); and (KM_C)] was determined to have the highest ΦCT and variance (0.017 and 1.65%, respectively; Table 8).
In the STRUCTURE analysis, the best estimation of the K value (number of groups) was eight, and this corresponded to a stable representation (data not shown; Figure 2). No distinct clade pattern was noted across all populations, but the patterns for CY_W (blue), QD_C (green), and JP_W (light blue) were more clear. Despite KS (KS_C1, KS_C2, and KS_C3) and ML (ML_W1, ML_W2, and ML_W3) being from the same sampling location with three consecutive years (2015–2017), no distinct clade pattern was observed. There is a clear genetic structure change among KS and ML populations (Figure 2).
In the kinship network analysis based on pairwise relatedness within the 19 populations, some individuals displayed close kinship in the hatchery (KM_C, KS_C1, and KS_C3) and wild populations (CY_W, ML_W2, and ML_W3; Figure 4). However, the kinship network had a relaxed structure in seven wild populations and a concentrated structure in nine hatchery populations, indicating more inbreeding in the hatcheries (Figure 5). Parentage analysis showed that 12–49 individuals (3%–13%; 95%–80% confidence) in ML_W3 were possibly related to individuals in KS_C1 (at least a single parent; Table 9).

4. Discussion

4.1. Genetic Difference among/between Hatchery and Wild Populations

Black sea bream, A. schlegelii, is a crucial aquaculture species in East Asia, from Taiwan, China, and Korea to Japan. Black sea bream aquaculture began in the 1980s, and Taiwanese hatcheries are located in areas near Kaohsiung. Broodstocks are obtained from the main fishery areas, which are off the Penghu Islands and the west coast of Taiwan (Yunlin–Chiayi–Tainan–Kaohsiung). Black sea bream has been cultured for more than 30 years in Taiwan. This is the first study analyzing the genetic diversity of cultured and wild black sea bream populations in Taiwan coastal waters. Nine hatchery populations were collected, including one in northern China (as an outgroup population), QD_C (Qingdao City); one in southern China, XM_C (Xiamen city): two from Taiwan’s offshore islands, KM_C (Kinmen) and MT_C (Matsu); and five from southern Taiwan, PR_C1, PR_C2, KS_C1, KS_C2, and KS_C3 (Kaohsiung City). According to the allele number (Na), observed heterozygosity (Ho), and expected heterozygosity (He) of nine microsatellite loci, the genetic diversity of the cultured populations was slightly lower than that of the wild populations, except for some such as KS_C1 (Table 4 and Table 5). The allele frequency pattern was similar between the wild and hatchery populations (Figure 3). Genetic differences among the hatchery populations were generally larger than those among the wild populations; however, high gene flow still existed among the hatchery populations (Nm = 10.895; Table 6 and Table 7). This indicates maintenance of a high degree of genetic diversity among cultured black sea bream, and this can avoid inbreeding effects. The hatcheries used to produce juveniles for the release project during 2013–2015 (KS_C1, KS_C2, and KS_C3) imported new stock from an unknown source. Although significant changes were observed in the genetic structure, fish larvae from the unknown hatchery (PR_C1, PC_C2, and KS_C1) exhibited small genetic differences. Therefore, hatchery information is not always reliable, especially because fish larvae may come from several hatcheries simultaneously, or broodstock may change after a natural disaster. As expected, smaller genetic differences were observed among hatcheries in Taiwan than among hatcheries in Taiwan’s offshore islands, southern China, and northern China. Generally, inbreeding effects easily arise in hatchery populations due to their small effective population size, which clearly means that hatchery larvae released into the wild could reduce the genetic diversity of wild populations. For example, in one of the world’s largest marine stock early programs involving the red sea bream (Pagrus major) in Kagoshima Bay, Japan, the released hatchery fish clearly reduced the genetic diversity of the wild population [48]. Due to the application of a special type of stock enhancement in Taiwan (fish from private farms not from official institutes), more hatcheries (stock) could contribute to the genetic diversity, thus preventing inbreeding.
When hatchery fish are cultured, high gene flow among hatchery populations and between wild populations cannot usually be maintained. As broodstocks are not changed each year, no random mating occurs. After several generations, hatchery populations tend to show different genetic structures to wild populations [12]. Our study of the silver sea bream (Rhabdosargus sarba) presented two distinct clusters (hatchery and wild population clusters) [12]. As silver sea bream and black sea bream are both Sparidae fish and have a similar culture-related history in Taiwan, they are generally considered to have the same genetic structure. Unexpectedly, unlike silver sea bream, wild and hatchery black sea bream cannot be clearly separated into two clusters. The genetic structure of black sea bream did not have the same pattern among and between hatchery and wild populations. This may have had several causes; one is that the hatcheries of silver sea bream are few, located only in southern Taiwan, and maintain high communication (gene flow among silver sea bream hatcheries is high at 32.677). However, there are more hatcheries of black sea bream and they are located in different areas (i.e., southern Taiwan, offshore islands, and even China). Moreover, low but effective communication is maintained among and between populations in black sea bream hatcheries (Nm = 10.895). Second, black sea bream is more abundant and widely distributed in Taiwan’s coastal waters than silver sea bream. The stock population was initially established independently and from different areas and may have helped to maintain genetic diversity.

4.2. Dramatic Change in Genetic Structure after Fish Release

We collected 10 wild populations from the following: Japan (as an outgroup population)—JP_W (Nagasaki City); Taiwan’s offshore islands—KM_W (Kinmen) and PH_W (Penghu); northern Taiwan—ML_W1, ML_W2, ML_W3 (Miaoli City), and TP_W (Taipei City); and southern Taiwan—YL_W (Yunlin City), CY_W (Chiayi City), and TN_W (Tainan City). The fish from Taiwan’s offshore islands, northern Taiwan, and southern Taiwan were expected to have a clear genetic structure such as one population (e.g., silver sea bream) [12] or two populations (northern Taiwan vs. Penghu–southern Taiwan; e.g., rabbitfish, Siganus fuscescens) [49]. However, no clear pattern of genetic structure was observed among wild populations and the three wild populations (namely ML_W2, PH_W, and CY_W) that showed higher pairwise FST. Notably, the hatchery populations KS_C1, PR_C1, and PR_C2 exhibited a lower pairwise FST with other wild populations than with ML_W2, PH_W, and CY_W. This indicated that the hatchery populations KS_C1, PR_C1, and PR_C2 had high gene flow with the wild populations, and changes in their genetic structure were mainly caused by fish release.
Stock enhancement has been found to induce genetic structure changes in fishes such as brown trout and red sea bream [48,50]. When the genetic differences between hatchery and wild populations are considerable, the genetic structure changes greatly with a greater number of releases and longer release duration. By contrast, when the genetic difference (FST) between KS_C1 and ML_W1 is 0, the effect on the genetic structure should be minor. However, according to the present STRUCTURE analysis, KS_C1 was different from ML_W1, and stock enhancement led to evident genetic changes over three consecutive years (ML_W1, ML_W2, and ML_W3; Figure 2). In addition, the genetic structure of sea bream from the sampling sites was inconsistent with their geographical distribution along the western coast of Taiwan. CY_W is geographically close to YL_W, TN_W, and PH_W, but the genetic structures of fish from these populations were found to be different (Figure 2). For YL_W, TN_W, and PH_W, several records of the official release from 2004 to 2015 were available. CY_W, YL_W, and TN_W were probably affected by stock enhancement and therefore exhibited no clear clusters with relative complexity in STRUCTURE analysis. For sampling sites ML_W, TP_W, and KM_W, which belonged to the northern region, several official releases were noted during 2004–2015. Long geographical distance and stock enhancement may have led to the lack of consistent and clear clusters in the STRUCTURE analysis. In addition to the large number of juveniles released over the past decade, escapes were another potential source of continued gene flow. Earth pond farming aquaculture is mainly performed in the coastal waters of southern Taiwan, and escapes happen after typhoons. Escaped farmed fish may affect natural populations and the broodstock (Holmer). One- to two-year-old silver sea bream can mature and undergo protandrous (male-to-female) sex changes later [23]. Escaped farmed fish are relatively big at 1–2 years of age and have a higher survival rate than juveniles used for stock enhancement. However, those fish were all traced to hatchery-reared stock (Figure 1). In general, fish were released over the entire western coast and outer islands of Taiwan from 2004 to 2015, resulting in complex genetic structures of wild populations that are inconsistent with their geographic distribution. Among them, no release history was found for only CY_W, which presented a simple cluster exhibiting few or no effects of stock enhancement (Figure 2).

4.3. Stock Enhancement of Black Sea Bream in Taiwan

Through a literature review, Araki and Schmid [51] summarized 50 years of data about the effects of hatcheries on fish and stock enhancement. They reported a clear reduction in genetic variation in hatchery populations. However, this result is completely different from the result of stock enhancement of black sea bream in Taiwan. In this study, we investigated 19 hatchery and wild populations and found that the frequency distribution of microsatellites in the hatchery and wild populations was similar, and the allele number remained at a high level (Figure 3). Additionally, we used pairwise relatedness to prepare a kinship network for evaluating the genetic relationship among each population, all hatchery populations, and all wild populations. We considered individuals with a relatedness (r) of >0.4 as related, almost excluding unrelated individuals. Weng et al. [52] used 11 microsatellites for parentage analysis in giant grouper (Epinephelus lanceolatus), and their relatedness value (r) of >0.25 accurately excluded unrelated individuals. In each population, we could not find obvious inbreeding groups, and further guaranteed stock enhancement should not reduce the diversity of the wild population (Figure 4). However, the kinship network indicated that the relationships among hatchery individuals were closer than those among wild individuals (Figure 5). Pairwise relatedness must be introduced to monitor stock enhancement programs and avoid unexpected inbreeding in hatcheries and large-scale programs.
Stock enhancement of black sea bream in Hiroshima Bay, Japan, is a successful example [53]. Gonzalez et al. [54] estimated that hatchery black sea bream contributed 12.5% and 13.5% to the wild population, and even as high as 58.9% in Jeong et al. [30]. In case of stock enhancement in Daya Bay, China, the contribution rate was low (approximately 1.18%), as assessed by Wang et al. [55]. Thus, stock enhancement varies widely depending on the location and method used for assessing it. In this study, the contribution rate of stock enhancement in ML_W was between 3% and 13% (Table 9). Private release (religious release) in Taiwan, another major contributor of stock, is estimated to have contributed approximately one-third of the total number of released fish. On evaluating two batches of religious release, Lee et al. [56] found that hatchery fish contributed 61% to the wild population. Regardless of the contribution rate, this study found that frequent large-scale release (official and private) in Taiwan has significantly changed the genetic structure of wild populations. Chiayi (CY_W), the main oyster production area, is a location in which no release has been recorded. Considering the influence of the oyster industry, no official stock enhancement is performed in this area, which enables investigation of the genetic structure of an area with no or few stock enhancements in Taiwan. As oyster farms are also a crucial reproductive base for black sea bream [57], the stock enhancement of black sea bream in Hiroshima Bay may have negatively affected oysters and other fishes [51,58]. Thus, not only the survival and contribution rates of released fish, but also the impacts of the release on the environment and ecology should be determined [7,59].

5. Conclusions

In Taiwan, official stock enhancement and private religious release of black sea bream are conducted frequently and on a large scale. Such diverse and unpredictable fish larvae prevent the decline of overall diversity. Although determining the short-term effect of stock enhancement in Taiwan is difficult, the contribution of stock enhancement to wild populations is evidenced by changes in the genetic structure and the inconsistency of such structure.

Author Contributions

Conceptualization, T.-H.H., H.-T.L. and C.-W.H.; methodology, T.-H.H.; software, T.-H.H.; validation, T.-H.H., H.-T.L., H.-J.L., C.-H.L., H.-Y.G. and C.-W.H.; formal analysis, T.-H.H., H.-T.L. and C.-W.H.; investigation, T.-H.H. and H.-T.L.; resources, T.-H.H., H.-T.L., H.-J.L., C.-H.L., H.-Y.G. and C.-W.H.; data curation, T.-H.H.; writing—original draft preparation, T.-H.H. writing—review and editing, T.-H.H., H.-T.L. and C.-W.H.; visualization, T.-H.H.; supervision, C.-W.H.; project administration, T.-H.H., C.-H.L. and C.-W.H.; and funding acquisition, T.-H.H., C.-H.L. and C.-W.H.; All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by grants from the Center of Excellence for the Oceans (National Taiwan Ocean University), which were financially supported by the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education, ROC (Taiwan). This work was also supported by the Fisheries Agency, Council of Agriculture, Executive Yuan, ROC (Taiwan) and the Taiwan Ocean Conservation and Fisheries Sustainability Foundation (109toffrest001, 110toffrest001, 111toffrest001).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors are grateful to Jou-Yun Chen for technical support.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Biology 11 00554 g001 550
Figure 1. Sampling, hatchery, and stock enhancement information for black sea bream.
Figure 1. Sampling, hatchery, and stock enhancement information for black sea bream.
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Figure 2. Sampling locations and structure analysis of 19 black sea bream populations. KS: Kaohsiung City, private hatchery which provides juveniles for official release; PR: Kaohsiung City, from unknown hatchery for private release; TP: Taipei city; ML: Miaoli County; YL: Yunlin County; CY: Chiayi County; PH: Penghu County; TN: Tainan City; KM: Kinmen County; MT: Matsu islands; XM: Xiamen city; QD: Qingdao city; and JP: Nagasaki Prefecture. The estimated population structure based on the highest probability structure run at K = 8. Each individual is represented by a thin vertical line, which is partitioned into K colored segments that represent individuals’ estimated likelihood of membership in each of the K clusters.
Figure 2. Sampling locations and structure analysis of 19 black sea bream populations. KS: Kaohsiung City, private hatchery which provides juveniles for official release; PR: Kaohsiung City, from unknown hatchery for private release; TP: Taipei city; ML: Miaoli County; YL: Yunlin County; CY: Chiayi County; PH: Penghu County; TN: Tainan City; KM: Kinmen County; MT: Matsu islands; XM: Xiamen city; QD: Qingdao city; and JP: Nagasaki Prefecture. The estimated population structure based on the highest probability structure run at K = 8. Each individual is represented by a thin vertical line, which is partitioned into K colored segments that represent individuals’ estimated likelihood of membership in each of the K clusters.
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Figure 3. Allele frequency of nine microsatellite loci in hatchery (n = 388) and wild (n = 739) black sea bream populations in Taiwan. Hatchery: KS_C1, KS_C2, KS_C3, PR_C1, PR_C2, KM_C, and MT_C; Wild: ML_W1, ML_W2, ML_W3, YL_W, PH_W, TN_W, TP_W, KM_W, and CY_W.
Figure 3. Allele frequency of nine microsatellite loci in hatchery (n = 388) and wild (n = 739) black sea bream populations in Taiwan. Hatchery: KS_C1, KS_C2, KS_C3, PR_C1, PR_C2, KM_C, and MT_C; Wild: ML_W1, ML_W2, ML_W3, YL_W, PH_W, TN_W, TP_W, KM_W, and CY_W.
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Figure 4. Kinship network based on pairwise relatedness within 19 populations. Wild populations: ML_W1, ML_W2, ML_W3, YL_W, PH_W, TN_W, TP_W, KM_W, CY_W, and JP_W; Cultured populations: KS_C1, KS_C2, KS_C3, PR_C1, PR_C2, KM_C, MT_C, XM_C, and QD_C. Pairwise relatedness® > 0.4.
Figure 4. Kinship network based on pairwise relatedness within 19 populations. Wild populations: ML_W1, ML_W2, ML_W3, YL_W, PH_W, TN_W, TP_W, KM_W, CY_W, and JP_W; Cultured populations: KS_C1, KS_C2, KS_C3, PR_C1, PR_C2, KM_C, MT_C, XM_C, and QD_C. Pairwise relatedness® > 0.4.
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Figure 5. Kinship network based on pairwise relatedness: (a) seven wild populations and (b) nine cultured populations. Wild populations: ML_W1, YL_W, PH_W, TN_W, TP_W, KM_W, and CY_W; Cultured populations: KS_C1, KS_C2, KS_C3, PR_C1, PR_C2, KM_C, and MT_C. Pairwise relatedne®(r) > 0.4.
Figure 5. Kinship network based on pairwise relatedness: (a) seven wild populations and (b) nine cultured populations. Wild populations: ML_W1, YL_W, PH_W, TN_W, TP_W, KM_W, and CY_W; Cultured populations: KS_C1, KS_C2, KS_C3, PR_C1, PR_C2, KM_C, and MT_C. Pairwise relatedne®(r) > 0.4.
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Table
Table 1. Summary of sample information. n = number of fish.
Table 1. Summary of sample information. n = number of fish.
0nYearSampling LocationFish TypesSource Information
Cultured populations
KS_C1942015Kaohsiung, TaiwanbloodstockFor release project during 2013–2015
KS_C2462016--Import new stock from an unknown source
KS_C3482017--Import new stock from an unknown source
PR_C1392015unknown hatcheryjuvenilesPrivate (religious) release
PR_C2412015---
KM_C962015Kinmen, Taiwansubadult/adultFarm fish from offshore islands of Taiwan
MT_C242015Matsu, Taiwan-Farm fish from offshore islands of Taiwan
XM_C482015Xiamen, China-Farm fish from southern China
non-native
QD_C222015Qingdao, China-Farm fish from northern China for comparison
Total458
Wild populations
ML_W1622015Miaoli, Taiwansubadult/adultRelease project during 2013–2015
ML_W21062016---
ML_W31922017---
YL_W472015Yunlin, Taiwan-Release history during 2004–2015
PH_W482015Penghu, Taiwan--
TN_W472015Tainan, Taiwan--
TP_W472015Taipei, Taiwan--
KM_W942015Kinmen, Taiwan--
CY_W962015Chiayi, Taiwan-No release history during 2004–2015
non-native
JP_W342015Nagasaki, Japan-Wild population from Japan for comparison
Total773
Table
Table 2. Nine selected microsatellite loci of black sea bream.
Table 2. Nine selected microsatellite loci of black sea bream.
LocusPrimer Sequences (5′–3′)Repeat MotifTa (°C)Size Range (bp)Accession No.Reference
AS144F: CGACGTGATGGGTTATTCTTAGAC
R: GCCATTCCACAGATTTCTTTCTC		(AC)n6096–128GU121415Kim et al., 2010 [27]
AS194F: GATCCTGTCCAGTTGCCCAGTA
R: TCCACAGCTGAAACACGACTACAT		(AC)n60122–192GU121416
AS324F: CCCAAAAACTACGTAATGCACCTT
R: GCCGGATGAAGATTCTGCTC		(GT)n60168–238GU121417
AS392F: AACCTGACCAGCCTGGCTCTTC
R: ACCTCCTCTGATGCTTTTGTGTGC		(AC)nAT(AC)n60124–188GU121420
CL011F: CCATCGCTTGACACTAGCAC
R: GCCACACTTGAGCCTTTCTC		(GATA)nGATG
(GATA)n		60212–256FJ554545Reid et al., 2012 [28]
SaI10F: TCACGGGGGACCAAGACTG
R: CTCACACTGCCTAATTAGCACAGA		(GT)n60173–211AY322107Liu et al., 2007 [29]
SaI19F: ATTCTTCACAGGCCCAACACAAA
R: GAAAACACCGGCCCAGTACGA		(GT)n60232–278AY322111
ACS-4F: TTTACACACCGGGAGCTCAA
R: GTAAAGATCCATGGAGGTGC		(GT)n6076–112AB095009Jeong et al., 2007 [30]
AC229F: TGTCCGTTCTGCTTTGCTC
R: TGCGGTAGTGCCTTCTCTG		(TG)n60297–327GU166144Yang et al., 2014 [31]
Table
Table 3. Number of alleles (Na) and effective alleles (Ne) at nine microsatellite loci in 19 black sea bream populations.
Table 3. Number of alleles (Na) and effective alleles (Ne) at nine microsatellite loci in 19 black sea bream populations.
Pop (n) AS144AS194AS324AS392CL011SaI10SaI19ACS-4AC229Total
All hatchery in Taiwan (388)Na12 11 29 14 11 13 20 15 14 139
Ne2.4203.16515.2093.2226.5141.7925.8535.1144.227Mean 5.280
All hatchery in this study (458)Na14 13 29 14 11 13 20 15 15 144
Ne2.4203.23116.0363.1746.4411.7775.9035.1194.148Mean 5.361
All wild in Taiwan (739)Na16 17 27 15 10 13 19 17 15 149
Ne2.1743.48414.3173.0545.5081.8125.8455.7194.122Mean 5.115
All wild in this study (773)Na161829151113191715153
Ne2.1903.47314.6633.0235.5201.8545.7135.7304.227Mean 5.155
All populations (1231)Na172031181216211817170
Ne2.2753.39116.0943.1015.8901.8265.7965.5484.213Mean 5.348
Table
Table 4. Summary statistics for genetic variation at nine microsatellite loci in nine cultured black sea bream populations.
Table 4. Summary statistics for genetic variation at nine microsatellite loci in nine cultured black sea bream populations.
Pop (n) AS144AS194AS324AS392CL011SaI10SaI19ACS-4AC229Average
KS_C1 (94)Na662081081591110.3
Ne2.0743.01012.2643.0486.5502.1156.1004.9823.6714.868
Ho0.4680.7340.9260.6060.9150.5320.8620.8400.6170.722
He0.5180.6680.9180.6720.8470.5270.8360.7990.7280.724
FIS0.096 NS−0.099 NS−0.008 ***0.098 **−0.080 NS−0.009 ***−0.031 ***−0.051 ***0.152 ***0.008
KS_C2 (46)Na9820610513889.7
Ne2.3323.33813.4783.1326.0031.7165.6583.9924.2404.877
Ho0.5430.7610.8910.6300.9350.4350.7390.8040.7610.722
He0.5710.7000.9260.6810.8330.4170.8230.7500.7640.718
FIS0.048 ***−0.086 NS0.037 NS0.074 **−0.122 NS−0.042 NS0.102 NS−0.073 NS0.004 NS−0.006
KS_C3 (48)Na761967410698.2
Ne1.9832.70312.6593.3884.1741.4753.8824.0675.5254.428
Ho0.5830.7290.9170.6670.7710.3540.7710.6880.8540.704
He0.4960.6300.9210.7050.7600.3220.7420.7540.8190.683
FIS−0.177 NS−0.157 NS0.005 *0.054 NS−0.014 NS−0.100 NS−0.038 NS0.088 ***−0.043 NS−0.042
PR_C1 (39)Na57198859998.8
Ne2.2223.04510.8642.4775.7071.7835.0125.7504.5274.599
Ho0.5640.6920.9490.6410.7180.3080.7950.6670.8970.692
He0.5500.6720.9080.5960.8250.4390.8000.8260.7790.711
FIS−0.026 NS−0.031 NS−0.045 *−0.075 ***0.130 *0.299 ***0.007 ***0.193 ***−0.152 NS0.033
PR_C2 (41)Na11919977127910.0
Ne2.9573.92811.2072.4786.1802.0796.3805.0034.1974.934
Ho0.6100.7320.8050.4630.6340.2930.7800.7800.8050.656
He0.6620.7450.9110.5960.8380.5190.8430.8000.7620.742
FIS0.079 ***0.018 ***0.116 ***0.223 ***0.243 ***0.436 ***0.074 NS0.025 NS−0.057 ***0.129
KM_C (96)Na741697415798.7
Ne3.1433.0989.9742.8056.0021.3875.6354.2663.4884.422
Ho0.6770.6670.7400.7290.7810.2400.8020.8020.7600.689
He0.6820.6770.9000.6440.8330.2790.8230.7660.7130.702
FIS0.007 NS0.016 NS0.178 ***−0.133 NS0.063 NS0.141 NS0.025 NS−0.048 NS−0.066 NS0.020
MT_C (24)Na6415986111078.4
Ne1.7252.4778.1134.2205.9082.6184.1296.4003.1824.308
Ho0.5000.6250.9580.8330.7500.4170.6670.8750.6670.699
He0.4200.5960.8770.7630.8310.6180.7580.8440.6860.710
FIS−0.190 NS−0.048 NS−0.093 NS−0.092 ***0.097 NS0.326 **0.120 NS−0.037 ***0.028 NS0.012
XM_C (48)Na1052179411999.4
Ne1.9873.55012.2882.5535.4081.5155.6264.9342.6734.504
Ho0.5210.6880.9170.5000.6670.2920.2920.7710.4380.565
He0.4970.7180.9190.6080.8150.3400.8220.7970.6260.683
FIS−0.048 ***0.043 NS0.002 NS0.178 ***0.182 NS0.142 NS0.645 ***0.033 NS0.301 ***0.164
QD_C (22)Na57114645676.1
Ne2.9602.8988.5662.3105.6612.1274.1554.1024.1374.102
Ho0.4090.3640.8640.7270.7730.7270.7730.3181.0000.662
He0.6620.6550.8830.5670.8230.5300.7590.7560.7580.711
FIS0.382 ***0.445 ***0.022 ***−0.282 NS0.061 ***−0.372 NS−0.018 *0.579 ***−0.319 *0.055
n = number of samples, Na = allele number, Ne = allele richness, He = expected heterozygosity, Ho = observed heterozygosity, FIS = fixation index. NS = not significant, * p < 0.05, ** p < 0.01, *** p < 0.001 in Hardy–Weinberg equilibrium test.
Table
Table 5. Summary statistics for genetic variation at nine microsatellite loci in 10 wild black sea bream populations.
Table 5. Summary statistics for genetic variation at nine microsatellite loci in 10 wild black sea bream populations.
Pop (n) AS144AS194AS324AS392CL011SaI10SaI19ACS-4AC229Average
ML_W1 (62)Na7619795127109.1
Ne2.6442.88712.8352.7335.1421.8835.5394.7544.2884.745
Ho0.5970.7100.9190.7580.7100.4680.7260.7420.8230.717
He0.6220.6540.9220.6340.8060.4690.8190.7900.7670.720
FIS0.040 NS−0.086 NS0.003 ***−0.195 NS0.119 NS0.003 NS0.114 ***0.060 NS−0.073 NS−0.002
ML_W2 (106)Na910179951581210.444
Ne2.2543.0127.5313.2054.8581.4624.6405.2475.3684.2
Ho0.5660.7360.8770.6890.7550.3300.7920.7740.6890.690
He0.5560.6680.8670.6880.7940.3160.7840.8090.8140.700
FIS−0.017 NS−0.102 NS−0.012 NS−0.001 ***0.050 NS−0.045 NS−0.010 NS0.044 NS0.154 ***0.007
ML_W3 (192)Na10102110971481311.333
Ne2.3563.70610.6022.9275.3381.8835.2966.1643.9324.7
Ho0.5210.8390.5940.5470.6720.3960.7450.6410.6460.622
He0.5760.7300.9060.6580.8130.4690.8110.8380.7460.727
FIS0.095 ***−0.148 ***0.344 ***0.169 ***0.173 **0.156 ***0.082 ***0.235 ***0.134 ***0.138
YL_W (47)Na6619796121099.333
Ne1.7302.84711.5053.0815.8442.0214.5694.7663.8094.5
Ho0.3190.6170.8940.7020.9150.4470.4680.8090.6810.650
He0.4220.6490.9130.6750.8290.5050.7810.7900.7370.700
FIS0.244 *0.049 NS0.021 *−0.040 **−0.104 NS0.116 NS0.401 ***−0.023 ***0.077 NS0.082
PH_W (48)Na862168696108.889
Ne2.1022.57013.9211.8485.8702.5255.1834.5355.0204.8
Ho0.4580.5830.8960.3330.8330.5630.7920.6880.8130.662
He0.5240.6110.9280.4590.8300.6040.8070.7800.8010.705
FIS0.126 ***0.045 ***0.035 NS0.273 ***−0.004 NS0.069 *0.019 NS0.118 NS−0.015 NS0.074
TN_W (47)Na651778510688.0
Ne2.2772.89511.8452.0925.3361.6874.9814.7813.8324.414
Ho0.5960.7450.8720.4260.7450.4470.8940.7230.7020.683
He0.5610.6550.9160.5220.8130.4070.7990.7910.7390.689
FIS−0.062 NS−0.138 NS0.047 ***0.185 ***0.084 ***−0.097 NS−0.118 NS0.085 NS0.050 NS0.004
TP_W (47)Na781878611799.0
Ne1.6604.06411.4752.7805.2471.5785.0325.3753.1444.484
Ho0.4040.9360.8720.5740.8720.4040.9790.7450.7020.721
He0.3970.7540.9130.6400.8090.3660.8010.8140.6820.686
FIS−0.017 ***−0.242 ***0.044 *0.103 ***−0.078 **−0.104 ***−0.221 ***0.085 *−0.030 NS−0.051
KM_W (94)Na1010207881681110.9
Ne2.4133.84810.5073.2525.4971.5116.1754.7964.0614.673
Ho0.5740.7340.7550.5320.8190.2770.7660.7130.6060.642
He0.5860.7400.9050.6930.8180.3380.8380.7910.7540.718
FIS0.019 NS0.008 ***0.165 ***0.232 ***−0.001 NS0.182 ***0.086 ***0.099 NS0.195 ***0.110
CY_W (96)Na7619109713131010.4
Ne1.7413.8899.9583.1055.5692.0496.3386.2273.3984.697
Ho0.4380.6460.8850.6250.9480.4580.9170.8330.7710.725
He0.4260.7430.9000.6780.8200.5120.8420.8390.7060.718
FIS−0.028 NS0.131 NS0.016 NS0.078 NS−0.155 ***0.105 ***−0.088 **0.007 ***−0.092 ***−0.003
JP_W (34)Na871959456108.1
Ne2.5323.06610.5572.3884.4292.1833.2564.5875.9284.325
Ho0.6180.7650.8820.5880.5000.1470.7350.7060.9120.650
He0.6050.6740.9050.5810.7740.5420.6930.7820.8310.710
FIS−0.021 NS−0.135 NS0.025 NS−0.012 NS0.354 ***0.729 ***−0.061 NS0.097 **−0.097 NS0.098
n = number of samples, Na = allele number, Ne = allele richness, He = expected heterozygosity, Ho = observed heterozygosity, FIS = fixation index. NS = not significant, * p < 0.05, ** p < 0.01, *** p < 0.001 in Hardy–Weinberg equilibrium test.
Table
Table 6. Pairwise FST values (below the diagonal) and associated p values (above the diagonal) among 19 black sea bream populations collected from Taiwanese hatcheries and the wild.
Table 6. Pairwise FST values (below the diagonal) and associated p values (above the diagonal) among 19 black sea bream populations collected from Taiwanese hatcheries and the wild.
KS_C1KS_C2KS_C3PR_C1PR_C2KM_CMT_CXM_CQD_CML_W1ML_W2ML_W3YL_WPH_WTN_WTP_WKM_WCY_WJP_W
KS_C1-0 *0 *0.0250.0360 *0 *0 *0 *0.4650 *0 *0 *0 *0 *0 *0 *0 *0 *
KS_C20.023-0 *0 *0 *0 *0 *0.001 0 *0 *0 *0 *0.168 0 *0 *0 *0 *0 *0 *
KS_C30.0180.023-0 *0 *0 *0 *0 *0 *0 *0 *0 *0 *0 *0 *0 *0 *0 *0 *
PR_C10.0050.0250.027-0.065 0 *0.003 0 *0.001 0.044 0 *0 *0 *0 *0.001 0 *0 *0 *0.001
PR_C20.0050.0280.0240.006-0 *0 *0 *0.040 0.061 0 *0 *0 *0.001 0 *0 *0 *0 *0 *
KM_C0.0240.0280.0370.0190.023-0 *0 *0 *0 *0 *0 *0 *0 *0 *0 *0 *0 *0 *
MT_C0.0120.0290.0310.0150.0260.029-0 *0 *0 *0 *0 *0 *0 *0 *0 *0 *0 *0 *
XM_C0.0240.0120.0340.0280.0310.0300.028-0 *0 *0 *0 *0.022 0 *0 *0.001 0 *0 *0 *
QD_C0.0220.0450.0370.0220.0120.0430.0470.052-0 *0 *0 *0 *0 *0 *0 *0 *0 *0 *
ML_W10.0000.0260.0120.0050.0050.0240.0210.0310.022-0 *0 *0 *0.001 0 *0 *0 *0 *0 *
ML_W20.0230.0310.0170.0220.0250.0230.0270.0320.0490.018-0 *0 *0 *0 *0 *0 *0 *0 *
ML_W30.0150.0230.0240.0120.0160.0190.0190.0220.0410.0130.007-0 *0 *0 *0 *0.001 0 *0 *
YL_W0.0130.0030.0150.0160.0220.0270.0190.0080.0370.0160.0170.014-0 *0 *0 *0 *0 *0 *
PH_W0.0100.0290.0260.0190.0140.0390.0330.0310.0290.0100.0340.0230.017-0.040 0 *0 *0 *0 *
TN_W0.0110.0160.0250.0120.0140.0240.0290.0160.0290.0120.0260.0130.0140.006-0.001 0 *0 *0 *
TP_W0.0170.0230.0190.0190.0200.0270.0210.0120.0480.0200.0220.0120.0170.0240.011-0 *0 *0 *
KM_W0.0190.0210.0220.0150.0220.0140.0230.0190.0470.0150.0070.0050.0140.0320.0200.014-0 *0 *
CY_W0.0110.0290.0290.0170.0150.0310.0240.0250.0460.0170.0280.0160.0240.0290.0200.0100.019-0 *
JP_W0.0220.0510.0520.0180.0320.0540.0320.0560.0420.0200.0490.0350.0400.0340.0420.0510.0410.041-
p values (above diagonal) and their significance after Bonferroni corrections at an alpha level of 5% (p = 0.05/342 = 0.0001). * p < 0.0001.
Table
Table 7. Analysis of molecular variance (AMOVA) among different black sea bream groups.
Table 7. Analysis of molecular variance (AMOVA) among different black sea bream groups.
SourceDfSum of SquaresMean SquaresVariance% Total
19 populations (All)
Among sampling localities18224.66812.4820.0712.17
Among individuals12124139.4263.4150.1895.74
Within individuals12313738.0003.0373.03792.09
Total24618102.094 3.297100
Average FST value = 0.022 (p = 0 < 0.001); Nm = 11.291
17 populations (All without JP_W, QD_C)
Among sampling localities16190.13511.8830.0631.90
Among individuals11583946.5533.4080.1845.59
Within individuals11753573.0003.0413.04192.51
Total23497709.689 3.287100
Average FST value = 0.019 (p = 0 < 0.001); Nm = 12.882
9 populations (TW wild)
Among sampling localities891.50611.4380.0511.55
Among individuals7302508.4363.4360.2106.42
Within individuals7392228.5003.0163.01692.04
Total14774828.442 3.277100
Average FST value = 0.015 (p = 0 < 0.001), Nm = 15.929
8 populations (TW cultured)
Among sampling localities778.30411.1860.0742.24
Among individuals4281438.1173.3600.1384.19
Within individuals4361344.5003.0843.08493.56
Total8712860.921 3.296100
Average FST value = 0.022 (p = 0 < 0.001), Nm = 10.895
Table
Table 8. Hierarchical AMOVA of 19 black sea bream populations collected from hatcheries and the wild, with analysis performed using SAMOVA.
Table 8. Hierarchical AMOVA of 19 black sea bream populations collected from hatcheries and the wild, with analysis performed using SAMOVA.
Region GroupingsΦCTp% Variance
among Groups
19 pops (All)
K = 2(18pops); (JP_W)0.0210.0512.05
K = 3(17pops); (JP_W); (QD_C)0.0210.0092.09
K = 4(16pops); (JP_W); (QD_C); (PH_W)0.0150.0031.47
17 pops (no JP_W, QD_C)
K = 14(PH_W; TN_W); (PR_C1; PR_C2); (ML_W2; ML_W3); etc…0.0120.0011.24
K = 15(PH_W; TN_W); (KS_C2; YL_W); etc…0.0150.0011.47
K = 16(PH_W; TN_W); etc…0.0130.0441.33
9 pops (TW wild)
K = 5(ML_W2; ML_W3; KM_W); (PH_W; TN_W); (TP_W; CY_W); (ML_W1); (YL_W)0.0110.0011.15
K = 6(ML_W2; ML_W3; KM_W); (PH_W; TN_W); (TP_W); (CY_W); (ML_W1); (YL_W)0.01201.20
K = 7(ML_W2; ML_W3; KM_W); (PH_W); (TN_W); (TP_W); (CY_W); (ML_W1); (YL_W)0.0120.0111.17
8 pops (TW cultured)
K = 4(KS_C; PR_C1; PR_C2; MT_C); (KS_C2; XM_C); (KS_C3); (KM_C)0.01601.64
K = 6(KS_C); (PR_C1; PR_C2); (MT_C); (KS_C2; XM_C); (KS_C3); (KM_C)0.0140.0151.44
K = 7(KS_C); (PR_C1; PR_C2); (MT_C); (KS_C2); (XM_C); (KS_C3); (KM_C)0.0170.0171.65
Table
Table 9. Parentage analysis of black sea bream collected from hatcheries (KS_C1) and the wild (ML_W3), with analysis performed using Cervus.
Table 9. Parentage analysis of black sea bream collected from hatcheries (KS_C1) and the wild (ML_W3), with analysis performed using Cervus.
LevelConfidenceCritical LODAssignmentsAssignments
Observed%Expected%
Strict955.01123226
Relaxed802.504913349
Unassigned--3358735091
Total--384100384100
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Hsu, T.-H.; Lee, H.-T.; Lu, H.-J.; Liao, C.-H.; Gong, H.-Y.; Huang, C.-W. Maintenance of Genetic Diversity of Black Sea Bream despite Unmonitored and Large-Scale Hatchery Releases. Biology 2022, 11, 554. https://doi.org/10.3390/biology11040554

AMA Style

Hsu T-H, Lee H-T, Lu H-J, Liao C-H, Gong H-Y, Huang C-W. Maintenance of Genetic Diversity of Black Sea Bream despite Unmonitored and Large-Scale Hatchery Releases. Biology. 2022; 11(4):554. https://doi.org/10.3390/biology11040554

Chicago/Turabian Style

Hsu, Te-Hua, Hung-Tai Lee, Hsueh-Jung Lu, Cheng-Hsin Liao, Hong-Yi Gong, and Chang-Wen Huang. 2022. "Maintenance of Genetic Diversity of Black Sea Bream despite Unmonitored and Large-Scale Hatchery Releases" Biology 11, no. 4: 554. https://doi.org/10.3390/biology11040554

APA Style

Hsu, T. -H., Lee, H. -T., Lu, H. -J., Liao, C. -H., Gong, H. -Y., & Huang, C. -W. (2022). Maintenance of Genetic Diversity of Black Sea Bream despite Unmonitored and Large-Scale Hatchery Releases. Biology, 11(4), 554. https://doi.org/10.3390/biology11040554

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