Generalized linear Bayesian (GLBM) non-hierarchical and hierarchical models were developed for standardization of catch per unit effort (CPUE). The GLBM containing the covariates of month, latitude, sea surface temperature (SST), sea surface salinity (SSS) and sea level height (SLH) had the best fit for the Chinese squid-jigging fishery of Ommastrephes bartramii in the northwest Pacific Ocean based on deviance information criteria. This best-fitting model tends to be more ecologically sound than other CPUE standardization models, such as generalized linear models and generalized additive models. GLBM was also used to deal with the problems of estimating stock abundance index (i.e. standardized CPUE) resulting from increased spatial heterogeneity of spatial dynamics of fishing efforts in the squid fishery by predicting the standardized CPUE for unfished areas. The standardized CPUE based on data including predicted CPUE of unfished areas was lower than the derived CPUE based on data with observed CPUE alone, in particular during the fishing peak of August to October. This study indicates that it is more appropriate to use the standardized CPUE derived from data including both predicted CPUE of unfished areas and observed CPUE of fished area as a stock abundance index. We suggest that the proposed method be used in CPUE standardization to account for impacts of large spatial heterogeneity of fishing efforts in fisheries

In order to accurately explore the relationship between the density of Engraulis japonicus and environmental factors, five types of models, including Tweedie-Generalized Additive Model (GAM), two-stage GAM, Ad hoc-GAM, and Generalized Additive Mixing Model (GAMM), were compared based on the survey data in offshore waters of southern Zhejiang, China from 2015 to 2021 in this study. The results showed the best goodness of fit for two-stage GAM when processing the data of E. japonicus resource density. The deviance explained of GAM1 and GAM2 were 19.9 and 53.8%, respectively. According to this study, water temperature and salinity are important environmental factors affecting the distribution of E. japonicus, which are also closely related to latitude. In general, the resource density of E. japonicus decreases gradually with the increase in water temperature. When the salinity was between 26 ppt and 34 ppt, the resource density was higher. Also, there were some differences in the spatial distribution of E. japonicus in different seasons. The relationship between the resource density of E. japonicus and environmental factors was analyzed through various models to provide a scientific basis for the conservation management of E. japonicus in offshore waters of southern Zhejiang, China.

The relationships between environmental factors and fish density are closely related, and species distribution models (SDMs) have been widely used in exploring these relationships and predicting the spatial distribution of fishery resources. When exploring the prediction of the spatial distribution of species in different seasons, the method of choosing the appropriate approach to the season will help to improve the predictive performance of the model. Based on data collected from 2015 to 2020 during a survey off southern Zhejiang, the Tweedie-GAM was used to establish the relationship between the density of Decapterus maruadsi and environmental factors at different modeling approaches. The results showed that water temperature, salinity and depth were the main factors influencing D. maruadsi, and they operated through different mechanisms and even resulted in opposite trends of density in different seasons. Spatially, the two modeling approaches also differed in predicting the spatial distribution of D. maruadsi, with the seasonal model showing a higher density trend in inshore waters than in offshore waters in spring but showing the opposite trend in summer and autumn, which was more consistent with the actual spatial distribution of the resource. By analyzing the effects of two different approaches on the prediction of fishery resources, this study aims to provide research ideas and references for improving the predictive performance of SDMs.

The characteristics of zooplankton communities and the relationships with the aquatic environment in the shallow waters of northern Liaodong Bay were investigated. Spot sampling surveys were carried out in April, June, September, and November 2018 to assess zooplankton species composition and diversity, abundance, biomass, and dominant species, and the associated relationships with environmental factors. A total of 45 species of zooplankton were recorded in the survey, comprising 18 Copepoda, 2 Amphipoda, 1 Mysidacea, 1 Decapoda, 1 Chaetognatha, 7 Hydrozoa, 1 Tunicate, and 14 planktonic larvae. Overall, the most dominant species was Aidanosagitta crassa (Tokioka, 1938), with copepods and planktonic larvae also dominating the zooplankton community. However, there was a seasonal alternation of species dominance. A cluster analysis showed that the zooplankton community in spring differed from other seasons and was mostly influenced by suspended particulate matter. Bioenv analysis indicated the main environmental factor affecting the zooplankton community in spring was suspended particulate matter. In summer, the determining variables were temperature, dissolved inorganic nitrogen (DIN), nitrate, and sediment pH. In autumn, temperature, DIN, and nitrate were determining variables, and dissolved oxygen (DO) and DIN in winter. Zooplankton abundance and biomass were influenced by salinity, suspended particulate matter, chemical oxygen demand (COD), chlorophyll, and water and sediment pH. In general, the shallow sea area north of Liaodong Bay is rich in zooplankton species and exhibits significant seasonal variations. Human activities have disturbed the biological community to a certain extent, and the environmental factors in this area are closely related to the diversity of zooplankton species.