Data from: The predictability of a lake phytoplankton community, over time-scales of hours to years

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 how predictable communities are. We used a machine learning approach and environmental monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton cell density in one lake across an unprecedented range of time scales. Communities were highly predictable over hours to months: model R2 decreased from 0.89 at 4 hours to 0.75 at 1 month, and in a long-term dataset lacking fine spatial resolution, from 0.46 at 1 month to 0.32 at 10 years. When cyanobacterial and eukaryotic algal cell density 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 help elucidate the mechanisms underlying ecological dynamics and set prediction targets for process-based models.

预测生态群落的动态变化，是生态学领域的核心挑战之一。然而，非线性依赖关系、生物间相互作用以及数据限制，制约了我们评估群落可预测性的能力。本研究采用机器学习方法与环境监测数据（涵盖生物学、物理学与化学维度），针对单一湖泊内的浮游植物（phytoplankton）细胞密度可预测性展开评估，覆盖了前所未有的时间尺度范围。该生态群落具备极高的可预测性，时间尺度覆盖数小时至数月：模型决定系数R²从4小时时的0.89降至1个月时的0.75；在缺乏精细空间分辨率的长期数据集内，该系数则从1个月时的0.46降至10年时的0.32。当分别针对蓝藻（cyanobacterial）与真核藻类（eukaryotic algae）的细胞密度开展分析时，模型推导得出的环境生长依赖关系与实验室研究结果一致，且揭示了调控二者竞争关系的全新权衡机制。高频监测与机器学习技术，有助于阐明生态动态背后的核心机制，并为基于过程的模型设定预测目标。