PT  - JOURNAL ARTICLE
AU  - Mridul K. Thomas
AU  - Simone Fontana
AU  - Marta Reyes
AU  - Michael Kehoe
AU  - Francesco Pomati
TI  - The predictability of a lake phytoplankton community, from hours to years
AID  - 10.1101/230722
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 230722
4099  - http://biorxiv.org/content/early/2017/12/07/230722.short
4100  - http://biorxiv.org/content/early/2017/12/07/230722.full
AB  - Forecasting anthropogenic 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. Here 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.