Successful forecasting of biodiversity change in natural plankton communities

Ecologists have made remarkable discoveries toward a mechanistic understanding of ecosystem dynamics. However, in light of the unprecedented rate of degradation experienced by ecosystems worldwide, just understanding is not enough. We must deliver quantitative predictions. Thanks to time series of both abiotic and biotic drivers, combined with non-parametric modeling, here we provide accurate predictions of biodiversity turnover in the plankton community of a peri-alpine lake. Our results show that not only we can successfully generate anticipatory forecasts of biodiversity change, but we were able to evaluate the relative influence of species interactions, community stability, temperature, light, and nutrients. These results promise a way forward that would allow us to have real-time forecasts of plankton communities using automatized sampling methods.

Primary Presenter: Luis Gilarranz, Eawag (luis.gilarranz@eawag.ch)

Authors:

Ewa Merz, Eawag  (e2merz@ucsd.edu)

Francesco Pomati, Eawag  (francesco.pomati@eawag.ch)