Revealing unseen dynamical regimes of ecosystems from population time-series data
Many ecosystems can exist in alternative dynamical regimes for which small changes in an environmental driver can cause sudden jumps between regimes. However, predicting the dynamics of regimes that occur under unobserved levels of the environmental driver has remained an unsolved challenge in ecology with important implications for conservation and management. Here we show that integrating population time-series data and information on the putative driver into an empirical dynamic model allows us to predict new dynamical regimes without the need to specify a population dynamics model. As a proof of concept, we demonstrate that we can accurately predict fixed-point, cyclic, or chaotic dynamics under unseen driver levels for a range of simulated models. For a model with an abrupt population collapse, we show that our approach can anticipate the regime that follows the tipping point. We then apply our approach to data from an experimental microbial ecosystem and from a lake planktonic ecosystem. We find that we can reconstruct transitions away from chaos in the experimental ecosystem and anticipate the dynamics of the oligotrophic regime in the lake ecosystem. These results lay the groundwork for making rational decisions about preventing, or preparing for, regime shifts in natural ecosystems.