Vertical distribution patterns of phytoplankton during abnormal algal proliferation in the Danjiangkou Reservoir

To elucidate the vertical distribution characteristics of phytoplankton and their environmental drivers during abnormal algal proliferation in the Danjiangkou Reservoir, this study conducted intensive monitoring from July to September 2025. By integrating manual stratified sampling with high-resolution in situ fluorescence profiling, we collected phytoplankton and physicochemical data across multiple water depths. Redundancy analysis, Spearman correlation, and partial least squares path modeling were applied to explore key influencing factors. The results revealed pronounced thermal stratification dynamics throughout the study period. Phytoplankton exhibited a distinct “increase-then-decrease” pattern along the vertical profile, with peak biomass generally occurring in the sub-surface layer, driven by variations in water depth and thermocline structure.Dominant taxa, including Aphanocapsa, Raphidiopsis, Leptolyngbya, and Pseudanabaena, primarily resided below the surface, with community succession shifting from Aphanocapsa and Raphidiopsis during the early stage to Leptolyngbya and Pseudanabaena in the later stage of the study period. Correlation and path analysis indicated that water temperature, dissolved oxygen, and other physicochemical factors exerted both direct positive effects on phytoplankton growth and indirect effects via regulation of nutrient availability. Notably, phosphate concentrations were negatively correlated with phytoplankton density, suggesting potential nutrient limitation during peak proliferation. Overall, thermal stratification, light availability, nutrient conditions, and species-specific traits jointly shape the vertical distribution patterns of phytoplankton These findings emphasize the importance of shifting from conventional surface-based algal monitoring to integrated three-dimensional profiling in the Danjiangkou Reservoir. Establishing automated vertical monitoring and early-warning systems is essential for improving the detection and management of algal proliferation.