Forecasting Bifurcations from Large Perturbation Recoveries in Feedback Ecosystems.

Area of Science:

• Ecological resilience and tipping point dynamics.
• Environmental management and system preservation.

Background:

• Forecasting critical transitions in ecological systems is crucial for management and preservation.
• Predicting the distance to tipping points and post-transition states remains a significant challenge.

Purpose of the Study:

• To present a novel model-less method for forecasting bifurcations and post-bifurcation dynamics.
• To address the challenge of anticipating critical transitions and predicting system states after breaches.

Main Methods:

• A model-less approach based on monitoring system recoveries from large perturbations.
• Utilizes recovery data from at least two parameter values before the critical point.
• Evaluated performance under varying levels of measurement noise using two established ecological models.

Main Results:

• The proposed method successfully predicts both the bifurcation point and post-bifurcation dynamics.
• Demonstrated effectiveness across different noise levels and with ecological models of vegetation and shallow lake ecosystems.

Conclusions:

• The new method offers a promising tool for anticipating ecological tipping points and their consequences.
• Discusses potential applications in complex scenarios and outlines data requirements for empirical implementation.