Biodiversity forecasting in natural plankton communities reveals temperature and biotic interactions as key predictors

As natural ecosystems face unprecedented human-made degradation, it is urgent to provide quantitative forecasts of changes in biodiversity and identify relevant biotic and abiotic predictors. Forecasting natural ecosystems has proven challenging due to their complexity, chaotic nonlinear nature, and lack of adequate data. In this study, we investigate the predictability of lake plankton biodiversity using four years of daily data of environmental predictors and community metrics derived from state-dependent models. Our findings show that presence-absence-based biodiversity metrics are more predictable than abundance-based metrics. For short-term forecasts, the most significant predictor of species richness is prior richness, while the key predictors for Jaccard dissimilarity are prior richness and prior Jaccard dissimilarity. In long-term forecasts of both metrics, water temperature emerges as the primary predictor, with community connectance (number of interactions) also contributing to improved predictions. We found that richness, connectance, and water temperature can interact in nonlinear and synergistic ways depending on the forecast horizon, enhancing each other's effects on richness and Jaccard dissimilarity. These results underscore the challenges of forecasting biodiversity in natural ecosystems and highlight the importance of monitoring key community metrics and abiotic predictors to anticipate long-term changes.