Functional groups are an effective method for assessing water quality. From January 2020 to December 2023, the phytoplankton and environmental variables at five sites in Baihua Reservoir (BHR) were investigated once a month. The succession rate (SR) and the average variation degree (AVD) of the functional groups were determined, and the corresponding driving factors were analyzed by using the Random Forest model, hierarchical partitioning, and Mantel test. A total of 95 phytoplankton species belonging to 7 taxonomic categories were identified, which can be divided into 27 functional groups and 8 dominant functional groups (B, D, L₀, P, S1, W1, W2, Y). B, L₀, and Y occupied dominant positions in spatiotemporal succession, indicating that the water body was in a moderate to eutrophication state. Water temperature, total nitrogen, and transparency were the key factors driving the functional groups’ succession. Total nitrogen, total phosphorus, permanganate index, and dissolved oxygen were significantly positively correlated with AVD (n = 230; p < 0.01). SR not only directly positively affected AVD (n = 230; p < 0.05) but also indirectly affected AVD by affecting physicochemical factors. Understanding the relationship between the succession, stability, and environmental factors of functional groups is of great significance for algae management and the prevention of water bloom.

Baihua Reservoir, a typical large waterbody in the karst region of southwestern China and an essential drinking water source, is characterized by a high carbonate buffering capacity that profoundly shapes the structure and function of its phytoplankton community. This study systematically elucidates the multi-dimensional driving mechanisms underlying seasonal phytoplankton community assembly in karst reservoirs by integrating multiple analytical models—including the Neutral Community Model, β-diversity decomposition, co-occurrence network analysis, XGBoost-SHAP machine learning, and Partial Least Squares Path Modeling—based on monthly sampling at five sites from 2020 to 2024. The results revealed that: (1) Stochastic processes dominated community assembly across all four seasons, while deterministic processes played a crucial role in local species turnover. (2) The co-occurrence network structure showed significant seasonal dynamics, with the composition of keystone species adaptively shifting in response to changing environmental conditions. (3) The key environmental factors influencing the phytoplankton community exhibited clear seasonal patterns, primarily pH, NH₃-N, and COD_Mn in spring; water temperature, COD_Mn, and NH₃-N in summer; TN, TP, and pH in autumn; and pH, water temperature, and DO in winter. To support the sustainable management of karst reservoirs, we propose seasonally differentiated strategies derived from our phytoplankton community analysis: target COD_Mn reduction in spring and summer, focus on TN and TP load control in autumn, prioritize water column stability in winter, and maintain hydrological connectivity and pH monitoring year-round. This approach enhances phytoplankton community stability, safeguards drinking water safety, and provides a targeted management model for similar reservoir ecosystems globally.