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.