Predicting phytoplankton community in a large floodplain lake using interpretable machine learning

Floodplain lakes are highly dynamic ecosystems where hydrological fluctuations drive complex phytoplankton responses that are challenging to predict. In this study, a random forest (RF) framework was developed to predict phytoplankton succession in Poyang Lake, the largest freshwater lake in China, using integrated hydrological, hydrodynamic, meteorological, and physicochemical data collected from 2019 to 2021. The RF models exhibited robust predictive performance, with phytoplankton density identified as the most reliable indicator for capturing community variability (R 2 = 0.62–0.75), higher than biomass (R 2 = 0.47–0.72) or Chlorophyll-a (R 2 = 0.70). SHapley Additive exPlanations (SHAP) analysis further revealed that lagged hydrometeorological conditions, specifically 30-day inflow and 15-day cumulative precipitation, were the dominant drivers of total phytoplankton density, whereas suspended solids (SS) and time period primarily influenced community composition. These findings provide novel insights into phytoplankton studies in Poyang Lake, emphasize the key role of hydrological lags in shaping phytoplankton dynamics, and offer theoretical support for eutrophication management in Poyang Lake. • RF-SHAP framework predicted phytoplankton community in a floodplain lake. • Integrated multi-type driving factors into the RF models. • Phytoplankton density indicator outperformed biomass. • Previous hydrometeorological conditions mainly drove total phytoplankton density. • SS dominated phytoplankton community composition variation.