Supervised machine learning for understanding and predicting the status of bistable eukaryotic plankton community in...

Understanding and predicting the ecological status of urbanized rivers is crucial for their restoration and management. However, the complex and nonlinear nature of ecological responses poses a challenge to the development of predictive models. Here, the study investigated and predicted the status of eukaryotic plankton communities in urbanized rivers by coupling environmental DNA metabarcoding, the alternative stable states theory, and supervised machine learning (SML) models. The results revealed two distinct states of eukaryotic plankton communities under similar environmental conditions: one state was characterized by the enrichment of a diverse phytoplankton population and the high relative abundance of protozoa, whereas the alternative state was characterized by abundant phytoplankton and fungi with an associated risk of algal blooms. Turbidity was identified as a key driver based on the SML model and Mantel test. Potential analysis demonstrated that the response pattern of eukaryotic plankton communities to turbidity was thresholds with hysteresis (Threshold1 = 17 NTU, Threshold2 = 24 NTU). A reduction in turbidity induced a regime shift in the eukaryotic plankton community toward an alternative state associated with a risk of algal blooms. In the prediction of ecological status, both SML models showed excellent performance (R² > 0.80, RMSE < 0.1, Kappa > 0.70). Additionally, SHapley Additive exPlanations analysis identified turbidity, chlorophyll-a, chemical oxygen demand (COD), ammonia nitrogen and green algae's amplicon sequence variants as crucial features for prediction, with turbidity and COD showing a synergistic effect on ecological status. A framework was further proposed to enhance the understanding and prediction of ecological status in urbanized rivers. The obtained results of this study demonstrated the feasibility of using SML models to predict and explain the ecological status of urbanized rivers with alternative stable states. This provides valuable insights for the application of SML models in the restoration and management of urbanized rivers.