资源环境科技发展态势分析平台

Forecasting changes to ecological communities is one of the central challenges in ecology. However, nonlinear dependencies, biotic interactions and data limitations have limited our ability to assess howpredictable communities are. Here, we used a machine learning approach and environmental monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton celldensity in one lake across an unprecedented range of time-scales. Communities were highly predictable over hours to months: model R-2 decreased from 0.89 at 4hours to 0.74 at 1month, and in a long-term dataset lacking fine spatial resolution, from 0.46 at 1month to 0.32 at 10years. When cyanobacterial and eukaryotic algal cell densities were examined separately, model-inferred environmental growth dependencies matched laboratory studies, and suggested novel trade-offs governing their competition. High-frequency monitoring and machine learning can set prediction targets for process-based models and help elucidate the mechanisms underlying ecological dynamics.