The integrated assessment of ecological quality in estuarine ecosystems holds significant importance for environmental management. Previous monitoring programs predominantly focused on environmental data, lacking a comprehensive quality assessment approach. To address this gap, this study aimed to integrate environmental factors with macrofaunal community information to evaluate the ecological quality status of the Yellow River Estuary. A total of 13 stations were routinely monitored in August for four consecutive years to collect environmental and biological data. Candidate indicators were screened based on variation coefficients, distribution ranges, and redundancy analysis, identifying 16 indicators belonging to three categories (i.e., seawater, sediment, and biology). The model fit and the interrelationship of the components were determined using structural equation modelling (SEM). The main results were as follows. (1) A total of 144 macrofaunal taxa, belonging to eight animal phyla and 98 families, were identified, with a dominance of Annelida (37.8%) and Mollusca (33.3%). The environmental variables most strongly correlated with the macrofaunal community were TOC, DO, Cd, and Md. (2) NO₂ and heavy metals represented the two most direct factors of environmental pollution, while the factor load of biodiversity indices (H’, J, and D) was large in the biology category. (3) The evaluation results indicated that 78.85% of the total samples were between the average and upper levels of ecological quality, but only 7.69% of samples were at the “high” level. The framework system for the evaluation of ecological quality constructed in this study provides a theoretical and practical basis for the evaluation of the effectiveness of conservation management of the Yellow River Estuary.

As transitional ecosystems between land and sea, estuaries are characterized by a unique environment that supports complex and diverse microbial communities. A comprehensive analysis of microbial diversity and ecological processes at different trophic levels is crucial for understanding the ecological functions of estuarine ecosystems. In this study, we systematically analyzed the diversity patterns, community assembly, and environmental adaptability of bacterial and protist communities using high-throughput sequencing techniques. The results revealed a higher alpha diversity for the bacteria than for protists, and the beta diversity pattern was dominated by species turnover in both communities. In addition, the two community assemblages were shown to be dominated by deterministic and stochastic processes, respectively. Furthermore, our results emphasized the influence of the local species pool on microbial communities and the fact that, at larger scales, geographic factors played a more significant role than environmental factors in driving microbial community variation. The study also revealed differences in environmental adaptability among different microbial types. Bacteria exhibited strong adaptability to salinity, while protists demonstrated greater resilience to variations in dissolved oxygen, nitrate, and ammonium concentrations. These results suggested differences in environmental adaptation strategies among microorganisms at different trophic levels, with bacteria demonstrating a more pronounced environmental filtering effect.

Marine organisms that episodically aggregate near coastal nuclear power plant water intakes pose a substantial risk to cooling water security. Predicting the spatial distribution of such high-risk species remains challenging because their occurrence is shaped not only by environmental conditions but also by complex trophic interactions. In this study, we model the habitat distribution of three high-risk nektonic species, Dotted gizzard shad (Konosirus punctatus), Japanese swimming crab (Charybdis japonica) and squid (Loligo sp.), in the cooling water intake area of a coastal nuclear power plant in eastern Liaodong Bay using generalized linear models (GLMs) and joint species distribution models (JSDMs). Based on summer surveys conducted in 2024–2025, we explicitly incorporated trophic linkages among target species, their prey, and predators within JSDMs. Model performance was evaluated using cross-validation based on AUC, RMSE, and coefficient of determination (R²). Our results indicate that water depth was the dominant environmental driver for all three species, while chlorophyll-a concentration and distance to the intake exerted species-specific effects. By incorporating interspecific trophic associations and environmental responses, JSDMs showed consistently improved predictive performance relative to GLMs, with approximately 1.5-fold higher R² values and 10–30% lower RMSE, while offering enhanced ecological interpretability. The models revealed strong positive associations between target species and both lower-trophic prey and higher-trophic predators, suggesting that top–down and bottom–up processes jointly regulate aggregation dynamics. This study demonstrates that integrating trophic interactions into species distribution modeling substantially improves predictions of high-risk marine species near coastal infrastructure and provides an ecological basis for proactive management of cooling water intake systems.