Beta diversity represents the spatial or temporal variation of species diversity among sampling sites and may be composed of two elements: turnover (Brepl, replacement of species assemblages) and nestedness (Brich, loss or gain of species assemblages). Knowledge of the mechanisms driving beta diversity contributes to an understanding of the variation in aquatic ecosystem community structures. We sampled zooplankton assemblages at 24 sites in 11 rivers in Shanghai City and conducted a nutrient addition experiment to elucidate the effects of various environmental variables on the community structure and beta diversity patterns of the zooplankton. The zooplankton assemblages in the rivers differed significantly at ammonia nitrogen (NH₃-N) concentrations below (Group I) and above (Group II) 1.03 mg·L⁻¹. The nutrient addition experiment further demonstrated that the composition of the zooplankton assemblages changed markedly along an ammonia concentration gradient (0.2 to 5.0 mg N·L⁻¹). The total beta diversity of Group I was considerably higher than that of Group II, indicating that high nutrient (ammonia) pollution led to biotic homogeneity. Overall, turnover was the key factor determining the total beta diversity of the two groups, suggesting the key importance of replacement of zooplankton assemblages. Furthermore, we found a correlation between environmental factors (mainly nitrogen content) and the beta diversity of zooplankton, and beta diversity (Brepl and Btotal) decreased with increasing trophic state. These findings provide further insight into the changing characteristics of the beta diversity patterns of zooplankton in river networks and may help to guide managers dealing with conservation strategies for aquatic biodiversity preservation in urban river ecosystems.

Frequent algal blooms in lakes pose a serious threat to aquatic ecosystems. It is of great significance to quickly and accurately monitor the distribution of algae in lakes for the regulation of algal blooms. While remote sensing techniques and machine learning methods can be used in combination to identify algae and analyze their spatial and temporal distribution, these methods still face challenges in practical applications due to uncertainties in lake boundaries and imbalances between algae and non-algae. In order to overcome these difficulties, we studied the dynamic open water range of Ulansuhai Lake and used a non-equilibrium data processing method to identify its algae. We also performed a spatiotemporal analysis of the algal range over a long time series. The results show that (1) the spectral characteristics of Landsat 8 images are very suitable for algal identification based on remote sensing, especially in the random forest method, where the fourth band plays an important role. (2) Among various machine learning methods, the accuracy of the random forest method on the training set and validation set is more than 90%. This indicates that the random forest method is suitable for the long-term monitoring of algal blooms. This study provides scientific and technical support for the management of Ulansuhai Lake, which will be helpful in guiding future management and control work.

Ecological water replenishment is crucial for alleviating ecological degradation of lakes in cold and arid areas. However, especially if high-nitrogen water is used as a water source for replenishment, the water volume in lakes effectively increases, resulting in new environmental pressures, posing potential threats to aquatic ecosystems. Clarifying the response characteristics of plankton communities to high nitrogen replenishment is crucial for lake management. Under high nitrogen replenishment conditions, we focused on the ecological niche characteristics (including niche width and niche overlap), interspecies relationships and interaction networks of dominant phytoplankton and zooplankton species in Ulansuhai Lake in Inner Mongolia, with the aim of exploring the potential associations and response patterns of the plankton community. The results indicated that under high nitrogen replenishment conditions, the structure of plankton communities generally remained stable, with no significant fluctuations in species composition, density, biomass, or diversity indices. The niche width values of the dominant species were mostly above 0.6, suggesting that these species adopted niche-broadening strategies to enhance their environmental adaptability. Within the phytoplankton community, species exhibited a significant positive correlation, reflecting strong co-occurrence and niche differentiation. In contrast, interspecific relationships among zooplankton were not significant, reflecting asynchronous dynamics and different developmental trends under fluctuating environments. Co-occurrence network analysis further indicates that both phytoplankton and zooplankton form complex networks characterized by a high degree of modularity. Nevertheless, the phytoplankton and zooplankton networks in 2024 exhibited a reduction in size, an increase in negative cohesion (indicating heightened competition), and a decline in network robustness compared to 2023. The findings of this study provide a theoretical basis for the scientific implementation of ecological water replenishment and ecosystem management in cold and arid lakes under high-nitrogen conditions.