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

Area of Science:

• Ecology
• Dynamical Systems Theory
• Statistical Modeling

Background:

• Ecological systems often exhibit multiple stable states (regimes).
• Sudden shifts between these regimes, triggered by environmental changes, pose significant challenges for conservation and management.
• Predicting these regime shifts, especially under unobserved environmental conditions, remains a critical unsolved problem.

Purpose of the Study:

• To develop a novel method for predicting dynamical regimes in ecological systems.
• To forecast population fluctuations and regime shifts without prior knowledge of the system's governing equations.
• To provide a tool for proactive conservation and management strategies.

Main Methods:

• Integration of time-series data with environmental driver information.
• Application of Gaussian Process (GP) regression to model system dynamics.
• Validation using simulated population dynamics models (fixed-point, cyclic, chaotic) and real-world ecological data.

Main Results:

• Accurate prediction of dynamical regimes (fixed-point, cyclic, chaotic) under novel environmental driver levels in simulations.
• Successful characterization of post-tipping point regimes for models with abrupt population collapse.
• Reconstruction of transitions away from chaos in a microbial food web experiment.
• Anticipation of oligotrophic regime dynamics in a lake planktonic food web.

Conclusions:

• Gaussian Process regression effectively predicts ecological regime shifts using time-series data and driver information.
• The approach surpasses traditional early warning signals by characterizing future states.
• This predictive capability is crucial for informed decision-making in ecosystem management and preventing or preparing for regime shifts.