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.

To investigate the spatial–temporal distribution characteristics of Harpadon nehereus in the Yangtze River Estuary (YRE) and its relationship with environmental factors, this study used the data from resource and environmental surveys conducted in the YRE and adjacent waters during August (summer) and November (autumn), 2017–2022. Generalized additive models (GAM) were employed to analyze the relationships between the relative resources of H. nehereus and environmental factors and to predict the spatial–temporal distribution of H. nehereus resources in 2022. Our results revealed that the best model deviance explained in summer and autumn was 64.89% and 49.90%, with average effect sizes of 0.75 and 0.70, respectively, for cross-validated regression slopes. Water temperature and salinity were identified as the key environmental factors influencing the relative resources of H. nehereus in the YRE. Overall, there were notable seasonal differences in the relationship between the relative resources of H. nehereus and environmental factors. The relative resources of H. nehereus in the YRE were higher in the summer than in autumn. In summer, both water temperature and salinity exhibited multi-wave nonlinear relationships with the relative resources of H. nehereus, while in autumn, the relative resources of H. nehereus showed a positive linear relationship with water temperature and a non-linear relationship with salinity. Additionally, the predicted and observed values of the relative resources of H. nehereus in 2022 showed similar spatial distribution patterns. The relative resources of H. nehereus was higher in the northern branch than in the southern branch and the offshore regions compared to the near-estuary regions. Altogether, our study provides a scientific basis for conservation management and sustainable utilization of H. nehereus resources in the YRE, thereby contributing to the restoration and management of fishery resources in the region.

Fish communities play an important role in determining the dynamics of marine ecosystems, while the evaluation and formulation of protective measures for these fish communities depends on the quality and quantity of data collected from well-designed sampling programs. The ecological model was used first to predict the distribution of the demersal fish community as the “true” population for the sampling design. Four sampling designs, including simple random sampling, systematic sampling, and stratified sampling with two sampling effort allocations (proportional allocation and Neyman allocation), were compared to evaluate their performance in estimating the richness and biodiversity indices of the demersal fish community. The impacts of two different temperature change scenarios, uniform temperature and non-uniform temperature increase on the performance of the sampling designs, were also evaluated. The proportional allocation yielded the best estimates of fish community richness and biodiversity relative to a synthetic baseline. However, its performance was not always robust relative to the simulated temperature change. When the water temperature changed unevenly, systematic sampling tended to perform the best. Thus, it is important to adjust the strata for a stratified sampling when the habitat experiences large changes. This suggests that we need to carefully evaluate the appropriateness of stratification when temperature change-induced habitat changes are large enough to result in substantial changes in the fish community.

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.

Effective fishery management in coastal waters requires accurate assessments of species–environment relationships, particularly in data-rich but zero-inflated contexts (i.e., datasets with an excess of zero catches). Here, we used fishery-independent trawl survey data collected from 2018 to 2019 in the offshore waters of southern Zhejiang Province of China to investigate the spatio-temporal distribution of Setipinna taty (scaly hairfin anchovy) and its environmental determinants. Given the high frequency of zero catches, we fitted both zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models and selected the best-performing approach using the Akaike information criterion (AIC). Cross-validation indicated that the ZINB model (RMSE: 199.1, R²; 0.25) outperformed ZIP model (RMSE: 239.4, R²; 0.23). Temperature, depth, and salinity were key predictors of S. taty abundance, which generally occurred at depths of 20–40 m and salinities of 26–34 psu. We then applied the optimal ZINB model to predict S. taty distributions in spring, summer, and autumn of 2020. The predictions indicated a summer peak in abundance and a nearshore-to-offshore decreasing gradient, and were broadly consistent with the spatial distribution trends observed in the 2020 survey data. The highest predicted densities were located in nearshore areas off Wenzhou and Taizhou, west of 122° E. By clarifying the key environmental factors shaping S. taty distribution and applying zero-inflated count models to account for an excess of zero catches, which occur more frequently than expected under standard negative binomial models, this study provides an improved basis for effective conservation and sustainable utilization of S. taty resources in the southern offshore waters of Zhejiang; nevertheless, predictive performance could be further improved by incorporating additional environmental and biotic covariates together with extended spatio-temporal data.