Mosang Wang’s research while affiliated with Chinese Academy of Fishery Sciences and other places

This study provides new insights for the hatchery released Chinese shrimp (Fenneropenaeus chinensis), including proportion, dynamic migration route, after they were released into nature for stock enhancement using a new strategy quite different than ever. Chinese shrimp were sampled at 22 survey stations during two investigation voyages acrossing 74 survey stations in the Bohai Sea from July 16 to August 9 in 2015. Among 289 sampled individuals during the second voyage, totally 155 shrimps were identified as hatchery shrimp released into the Laizhou Bay at mid-May in 2015 based on finger-print of eight SSR (simple sequence repeats) markers, and the proportion of hatchery released shrimp in recapture samples were from 41.30%–85.71% in each station with an average value 53.63%, which verified a previous view point that up to 90% of autumn season Chinese shrimp landing in the Bohai Sea were composed of hatchery released. Meanwhile, the dynamic migration route of hatchery released shrimp revealed that part of released shrimp migrated heading northwest along the west coast of the Bohai Sea up to the Bohai Bay but just remained at the Laizhou Bay until over-wintering migration at mid-October when they initiate over-wintering migration. Present unnatural spring season shrimp fishing model cut the throat of spawner shrimp chance to swim back to their respective spawning plants at each spring, it still no chance to clarify whether the hatchery released shrimp could replenish to the reproduce population and complete a whole life cycle as same as their natural relatives.

Eight microsatellite markers were used to analyze genetic diversity, level of inbreeding, and effective population size of spawner and recaptured populations of Chinese shrimp (Fenneropenaeus chinensis) during stock enhancement in the Bohai Bay in 2013. A total of 254 and 238 alleles were identified in the spawner and recaptured populations, respectively, and the numbers of alleles (Na) were 8–63 and 6–60, respectively. The numbers of effective alleles (Ne) were 2.52–21.60 and 2.67–20.72, respectively. The polymorphism information content ranged from 0.529 to 0.952. The observed heterozygosity (Ho) values (0.638–0.910 and 0.712–0.927) were lower than the expected heterozygosity (He) values (0.603–0.954 and 0.625–0.952), which indicated that the two populations possessed a rich genetic diversity. In 16 tests (2 populations×8 loci), 13 tests deviated from the Hardy-Weinberg equilibrium. Fis values were positive at seven loci and the inbreeding coefficients (F) of the two populations estimated by trioML were 13.234% and 11.603%, suggesting that there was a relatively high degree of inbreeding. A certain level of inbreeding depression had occurred in the Chinese shrimp population. Fst values ranged from 0 to 0.059, with a mean of 0.028, displaying a low level of genetic differentiation in the two populations. Effective population sizes (3 060.2 and 3 842.8) were higher than the minimum number suggested for retaining the evolutionary potential to adapt to new environmental conditions. For enhancement activity in 2014, the ideal number of captured shrimp spawners should have ranged from 7 686 to 19 214 to maintain genetic diversity and effective population size. Further strategies to adjust the balance of economic cost, fishing effort and ideal number of shrimp spawners to maintain a satisfactory effective population size for ensuring the sustainability of Chinese shrimp are proposed.

Ten microsatellite markers were used to analyze the levels of genetic diversity and inbreeding in a hatchery release population of Rhopilema esculentum Kishinouye (Scyphozoa: Rhizostomatidae). A total of 85 alleles were detected in 600 individuals. Within-population levels of observed (H o) and expected (H e) heterozygosity ranged from 0.152 to 0.839 (mean=0.464) and from 0.235 to 0.821 (mean=0.618), respectively. The polymorphism information content (PIC) of each marker ranged from 0.207 to 0.795 with an average of 0.580, indicating that the hatchery population maintained a high level of genetic diversity. Inbreeding levels were estimated in the hatchery population and the inbreeding coefficient was 0.203. This result revealed that a certain level of inbreeding occurred within the population. Meanwhile, we also determined genetic diversity at the clone level. Several polyps from the same scyphistomae were genotyped at the ten microsatellite loci and there was virtually no difference in their genotypes. Furthermore, we calculated the probabilities of exclusion. When both parents were known, the average exclusion probability of ten loci was 99.99%. Our data suggest that the ten microsatellite markers can not only be used to analyze the identity of individuals but they can also be applied to parentage identification. Our research provides a theoretical basis and technical support for genetic diversity detection and reasonable selection of R. esculentum hatchery populations. These findings support the use of releasing studies and conservation of R. esculentum germplasm resources.

Estimates of heritability and genotype-by-environment (G × E) interactions for three common traits, such as harvest weight (HW), body length (BL), and condition factor (K) were estimated for harvest-size turbot in an industrial farming system (IFS) and a similar system maintained at a lower temperature (IFSLT). The only difference between the two environments was the variation in water temperature. The experimental population (69 families) was composed of 16 maternal half-sib family groups and 17 full-sib families generated by artificial mating. After 15-months of rearing, 2125 and 2925 individuals from the IFS and IFSLT environments were evaluated, respectively. The genetic analysis was based on an animal model with mean family weight at tagging as a covariate, test tank effect as a fixed effect, and an additive genetic effect plus an effect common to full-sib families as random effects using the restricted maximum likelihood method. Heritability estimates within the IFS environment were medium for HW and BL (0.34 ± 0.12 and 0.34 ± 0.10) but very low for K (0.009 ± 0.03). Heritability estimates within the IFSLT environment for HW, BL, and K were 0.16 ± 0.05, 0.17 ± 0.05, and 0.04 ± 0.04, respectively. The genetic correlations between HW and BL in both environments were very high (0.99) with small standard errors. However, the genetic correlations between K and other two traits (HW and BL) were both not significant. The genetic coefficients of variations for HW in the IFS and IFSLT environments were 20.16 and 9.62, and those for BL were 6.68 and 3.70. The genetic correlations for HW and BL between environments were 0.97 ± 0.15 and 0.90 ± 0.12, respectively. Our results suggest weak re-ranking of genotypes and heterogeneity of additive genetic variation across environments for HW and BL. The genetic correlation (0.78) for K was near the break-even point but with a high standard error (0.77). This is the first report on G × E interactions across environments for turbot growth traits, which will be of great value to optimize a turbot selective breeding program. Statement of relevance Our paper offers guideline to select breeding strategy.

Bayesian and restricted maximum likelihood (REML) approaches were used to estimate the genetic parameters in a cultured turbot Scophthalmus maximus stock. The data set consisted of harvest body weight from 2 462 progenies (17 months old) from 28 families that were produced through artificial insemination using 39 parent fish. An animal model was applied to partition each weight value into a fixed effect, an additive genetic effect, and a residual effect. The average body weight of each family, which was measured at 110 days post-hatching, was considered as a covariate. For Bayesian analysis, heritability and breeding values were estimated using both the posterior mean and mode from the joint posterior conditional distribution. The results revealed that for additive genetic variance, the posterior mean estimate (σa2=9 320) was highest but with the smallest residual variance, REML estimates (σa2=8 088) came second and the posterior mode estimate (σa2=7 849) was lowest. The corresponding three heritability estimates followed the same trend as additive genetic variance and they were all high. The Pearson correlations between each pair of the three estimates of breeding values were all high, particularly that between the posterior mean and REML estimates (0.996 9). These results reveal that the differences between Bayesian and REML methods in terms of estimation of heritability and breeding values were small. This study provides another feasible method of genetic parameter estimation in selective breeding programs of turbot.