Revealing unseen dynamical regimes of ecosystems from population time-series data.

ABSTRACT: Many dynamical systems can exist in alternative regimes for which small changes in an environmental driver can cause sudden jumps between regimes. In ecology, predicting the regime of population fluctuations under unobserved levels of an environmental driver has remained an unsolved challenge with important implications for conservation and management. Here, we show that integrating time-series data and information on a putative driver into a Gaussian Process regression model for the system's dynamics allows us to predict dynamical regimes without the need to specify the equations of motion of the system. As a proof of concept, we demonstrate that we can accurately predict fixed-point, cyclic, or chaotic dynamics under unseen levels of a control parameter for a range of simulated populatio

SUBMITTER: Medeiros LP 

PROVIDER: S-EPMC12184501 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature