Shrimp community structure and influence of environmental variables in the East China Sea and Yellow Sea in spring.

Based on data collected from research vessel cruises performed in May 2020 off the East China Sea (ECS) and the southern Yellow Sea (YS) (26°30'-35°00' N, 120°30'-127°00' E), we analyzed the shrimp community and its relationships with environmental variables by using index of relative importance, biodiversity indices, and multivariate techniques. A total of 29 species were recorded, belonging to 11 families and 19 genera. The dominant species were Metapenaeopsis longirostris, Leptochela gracilis, Solenocera melantho, Crangon hakodatei, Parapenaeus fissuroides, Plesionika izumiae, and Trachypenaeus curvirostris, which together accounted for 82.9% of the total biomass and 90.8% of the total abundance of shrimps. Results of Cluster and NMDS analyses showed that three groups were identified for the shrimp community in the ECS and YS in spring, including group A (inshore of northern ECS and YS group), group B (offshore of northern ECS group) and group C (southern ECS group). ANOSIM and SIMPER analysis showed significant differences between group A and B, gourp A and C, and group B and C, with the dissimilarity of 92.2%, 95.8% and 91.6%, respectively. The typical species were T. curvirostris, C. hakodatei, L. gracilis and Palaemon gravieri in group A, and S. melantho in group B, and M. longirostris, P. fissuroides, P. izumiae and Solenocera alticarinata in group C. Significant differences were also detected in biomass, diversity index, species richness index and evenness index among groups, with significantly greater values in group C than those in A and B. Environmental variables and the substrate also displayed significant differences among groups. Results of canonical correspondence analysis showed that bottom temperature, bottom salinity, depth, and the substrate were the main environmental variables affecting spatial structure of shrimp community. Water mass characteristics and substrate type had important influences on the distribution of shrimp community in the ECS and YS in spring.

Stage-specific influence of temperature on the growth rate of Japanese Spanish mackerel (Scomberomorus niphonius) in early life.

Since 1850, each successive decade has been warmer than any preceding one. Warming could make a major contribution to the growth of fish larvae. To evaluate the influence of water temperature on the growth of larvae who spawned in later spring and early summer, we selected Scomberomorus niphonius, which has important ecological and economic value as a sample fish species. We conducted high-resolution spatiotemporal surveys during the 2015 spawning season at an important spawning ground in China. We found that the temperature required for larval survival was stricter than that for spawning. Within the appropriate temperature range, a rapid rise in water temperature was favourable for larval hatching, but S. niphonius hatched at relatively low temperature exhibited a faster growth rate in the yolk-sac and pre-flexion stages. The accumulated temperature and hatching temperature significantly affected the growth rate of S. niphonius larvae. The model that considered developmental stages provided a better explanation of the data than the model that only considered the temperature effect. The model improvement in terms of variance explained was higher for the early developmental stages than for the later developmental stages, suggesting that stage-specific temperature influences were prominent in the earlier stages, like the yolk-sac stage, and then reduced. Our results implied that water temperature anomalies could be hazardous to fish larvae, especially for fish spawning in late spring and early summer. Given that early-life stage fish are highly sensitive to water temperature, it is imperative to incorporate the potential effects of climate change into fisheries management.

[Overwintering distribution and its environmental determinants of small yellow croaker based on ensemble habitat suitability modeling].

Small yellow croaker is a trans-boundary fish resource shared by China and South Korea. Information on the distribution and preferred habitats of overwintering populations is lacking, parti-cularly in southern waters of Yellow Sea where the species is regulated together by China and South Korea. We simulated the geographic distribution under current condition with eight species distribution models (SDM) based on the presence-absence data and five environmental variables. The performance of model's prediction was evaluated using the area under the receiver operating characteris-tic curve (AUC) based on 5-fold cross-validation. Ensemble SDMs were constructed using a weighted average of eight habitat suitability model types to identify core areas with high probability of small yellow croaker occurrence. The results suggested that predictions based on presence-absence data generally perform better than those based on presence-only data and classical regression models under-performed compared to machine learning approaches. Among all the approaches that supported presence-absence data, support vector machine was the best performing technique and GLM was the worst. The ensemble model outperformed individual SDM models, demonstrating higher effectiveness of ensemble modelling approaches than individual models in reducing the predictive uncertainty. Salinity and temperature were important factors in predicting the overwintering distribution of small yellow croaker. The core areas with high probability of occurrence were concentrated in three areas, the open waters of southern Yellow Sea, the open waters of northern East China Sea, and the coastal sea of Zhejiang Province. Coastal waters in southern Yellow Sea and open waters in central and southern East China Sea were not suitable for overwintering of small yellow croaker. Our results provided a basis for predicting the potential overwintering distribution to guide spatial planning in support of sustainable utilization of small yellow croaker.

[Inter-decadal variation of spatial aggregation of Trichiurus japonicus in East China Sea based on spatial autocorrelation analysis]. / åŸºäºŽç©ºé—´è‡ªç›¸å ³çš„ä¸œæµ·å¸¦é±¼èšé›†ç‰¹å¾å¹´ä»£é™ å˜åŒ–.

Marine fish shows high heterogeneity in spatial aggregation. We analyzed the inter-deca-dal variations of stock density for Trichiurus japonicus in East China Sea (ECS) using geo-statistical approaches such as spatial autocorrelation and hotspot analysis, based on the data of T. japonicus from both bottom trawl fishery and research surveys in the open waters of ECS during 1971 to 2011, combined with the sea surface temperature (SST) and surface salinity data in the PN section in August. The global spatial autocorrelation statistics showed that Moran's I firstly decreased and then went up, indicating that the spatial aggregation patterns of T. japonicus was weakened in the beginning and then increased during 1971 to 2011. The surface salinity in the PN section displayed the opposite trend during the same period. The local spatial autocorrelation statistics showed that the population firstly moved to the southern ECS and then to the northern ECS except in 1971 in which the population concentrated in the middle of ECS because of the restriction of offshore fishing ground. The movement of hotspot areas of T. japonicus adaptively varied with the first EOF mode of SST in summer (sumEOF1), which indicated that the hotspot areas first moved southeastward with decreasing sumEOF1, and moved northeastward with increasing sumEOF1, but all of the hotspot areas were close to the northward branch of the Kuroshio Current.

[Effect of climate change on the fisheries conununity pattern in the overwintering ground of open waters of northern East China Sea].

Data sets of 26 fisheries target species from the fishery-depen-dent and fishery-independent surveys in the overwintering ground of open waters of northern East China Sea (OW-NECS), combined sea surface temperature (SST), were used to examine the links between diversity index, pattern of common variability and climate changes based on the principal component analysis (PCA) and generalized additive model (GAM). The results showed that the shift from a cold regime to a warm regime was detected in SST during the 1970s-2011 with step changes around 1982/ 1983. SST increased during the cold regime and the warm regime before 1998 (warming trend period, 1972-1998), and decreased during the warm regime after 1998 (cooling trend period, 1999-2011). Shannon diversity index was largely dependent on the filefish, which contributed up to 50% of the total production as a single species, with low diversity in the waters of the OW-NECS, during the late 1980s and early 1990s. Excluding the filefish, the diversity index linearly increased and decreased during 1972-1998 and 1999-2011, respectively. The variation pattern generally corresponds with the trend in water temperature, strongly suggesting the effect of the SST on the diversity. The first two components (PC1 and PC2) of PCA for target species, which accounted for 32.43% of the total variance, showed evident decadal variation patterns with a step change during 1992-1999 and inter-annual variability with short-period fluctuation, respectively. It seems that PC1 was associated with large scale climatic change, while PC2 was related to inter-annual oceanographic variability such as ENSO events. Linear fitting results showed winEOF1 had significant effect on PC1, and GAM analysis for PC1 showed that winter EOF1 (winEOF1) and summer EOF2 (sumEOF2) can explain 88.9% of the total variance. Nonlinear effect was also found between PC2 and win EOF1, indicating that the fish community structure, which had predominantly decadal/inter-annual variation patterns, was influenced by inter-annual variations in oceanographic conditions.

[Effect of stock abundance and environmental factors on the recruitment success of small yellow croaker in the East China Sea].

Multiple hypotheses are available to explain recruitment rate. Model selection methods can be used to identify the best model that supports a particular hypothesis. However, using a single model for estimating recruitment success is often inadequate for overexploited population because of high model uncertainty. In this study, stock-recruitment data of small yellow croaker in the East China Sea collected from fishery dependent and independent surveys between 1992 and 2012 were used to examine density-dependent effects on recruitment success. Model selection methods based on frequentist (AIC, maximum adjusted R2 and P-values) and Bayesian (Bayesian model averaging, BMA) methods were applied to identify the relationship between recruitment and environment conditions. Interannual variability of the East China Sea environment was indicated by sea surface temperature ( SST) , meridional wind stress (MWS), zonal wind stress (ZWS), sea surface pressure (SPP) and runoff of Changjiang River ( RCR). Mean absolute error, mean squared predictive error and continuous ranked probability score were calculated to evaluate the predictive performance of recruitment success. The results showed that models structures were not consistent based on three kinds of model selection methods, predictive variables of models were spawning abundance and MWS by AIC, spawning abundance by P-values, spawning abundance, MWS and RCR by maximum adjusted R2. The recruitment success decreased linearly with stock abundance (P < 0.01), suggesting overcompensation effect in the recruitment success might be due to cannibalism or food competition. Meridional wind intensity showed marginally significant and positive effects on the recruitment success (P = 0.06), while runoff of Changjiang River showed a marginally negative effect (P = 0.07). Based on mean absolute error and continuous ranked probability score, predictive error associated with models obtained from BMA was the smallest amongst different approaches, while that from models selected based on the P-value of the independent variables was the highest. However, mean squared predictive error from models selected based on the maximum adjusted R2 was highest. We found that BMA method could improve the prediction of recruitment success, derive more accurate prediction interval and quantitatively evaluate model uncertainty.

[Spatial heterogeneity in body condition of small yellow croaker in Yellow Sea and East China Sea based on mixed-effects model and quantile regression analysis].

By using the 2008-2010 investigation data about the body condition of small yellow croaker in the offshore waters of southern Yellow Sea (SYS), open waters of northern East China Sea (NECS), and offshore waters of middle East China Sea (MECS), this paper analyzed the spatial heterogeneity of body length-body mass of juvenile and adult small yellow croakers by the statistical approaches of mean regression model and quantile regression model. The results showed that the residual standard errors from the analysis of covariance (ANCOVA) and the linear mixed-effects model were similar, and those from the simple linear regression were the highest. For the juvenile small yellow croakers, their mean body mass in SYS and NECS estimated by the mixed-effects mean regression model was higher than the overall average mass across the three regions, while the mean body mass in MECS was below the overall average. For the adult small yellow croakers, their mean body mass in NECS was higher than the overall average, while the mean body mass in SYS and MECS was below the overall average. The results from quantile regression indicated the substantial differences in the allometric relationships of juvenile small yellow croakers between SYS, NECS, and MECS, with the estimated mean exponent of the allometric relationship in SYS being 2.85, and the interquartile range being from 2.63 to 2.96, which indicated the heterogeneity of body form. The results from ANCOVA showed that the allometric body length-body mass relationships were significantly different between the 25th and 75th percentile exponent values (F=6.38, df=1737, P<0.01) and the 25th percentile and median exponent values (F=2.35, df=1737, P=0.039). The relationship was marginally different between the median and 75th percentile exponent values (F=2.21, df=1737, P=0.051). The estimated body length-body mass exponent of adult small yellow croakers in SYS was 3.01 (10th and 95th percentiles = 2.77 and 3.1, respectively). The estimated body length-body mass relationships were significantly different from the lower and upper quantiles of the exponent (F=3.31, df=2793, P=0.01) and the median and upper quantiles (F=3.56, df=2793, P<0.01), while no significant difference was observed between the lower and median quantiles (F=0.98, df=2793, P=0.43).