Seasonal Dynamics of Phytoplankton Communities in Relation to Water Quality in Poyang Lake, China

Poyang Lake, China’s largest freshwater lake, is an ecologically significant but increasingly vulnerable system threatened by eutrophication and harmful algal blooms driven by human activities. Phytoplankton organisms, as primary producers and sensitive bioindicators, provide critical insights into these ecological changes; however, comprehensive seasonal assessments remain scarce. This study examined intra-annual phytoplankton dynamics at 15 representative sites, with the objectives of quantifying seasonal and spatial variations in community composition, density, biomass, and diversity, and identifying key environmental drivers. Surface water samples were collected during four seasons. Phytoplankton were identified microscopically, and diversity was quantified using Shannon–Wiener, Pielou’s evenness, and Margalef’s richness indices. Concurrent measurements included water temperature (WT), dissolved oxygen (DO), nutrients (TN, TP, NO3−-N, NO2−-N, NH4+-N), chemical oxygen demand (COD), pH, and transparency. Pearson correlation and redundancy analysis (RDA) were applied to evaluate phytoplankton–environment relationships. A total of 118 phytoplankton species belonging to 7 phyla were identified. Chlorophyta, Cyanobacteria, and Bacillariophyta exhibited the highest species richness. The highest seasonal abundances were observed for Microcystis wesenbergii (0.998) in winter, Aulacoseira granulata var. angustissima (0.780) in spring, and Snowella lacustris (0.520) in autumn, indicating pronounced seasonal shifts in dominant taxa across Poyang Lake. Phytoplankton density and biomass peaked in summer, while diversity indices significantly declined with increasing WT. RDA revealed that WT, DO, TP, and transparency collectively explained 45.7% of the community variation, with DO emerging as the most influential factor. These findings demonstrate that physical drivers, particularly thermal conditions and oxygen availability, exert stronger influences on phytoplankton diversity than nutrients alone, challenging nutrient-centric paradigms. Management should integrate hydrological and oxygen regulation with nutrient control, while long-term monitoring, depth-stratified sampling, and trait-based approaches are recommended to improve predictive models under climate variability.