Detecting and distinguishing tipping points using spectral early warning signals
T M Bury1,2, C T Bauch1, M Anand2
1Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada ON N2L 3G1.
Journal of the Royal Society, Interface
|September 30, 2020
Summary
New spectral early warning signals (EWS) can now better predict critical transitions in complex systems. These advanced EWS offer improved sensitivity and robustness, helping to prevent ecosystem collapse.
Area of Science:
- Complex Systems Science
- Dynamical Systems Theory
- Ecology
Background:
- Complex systems can undergo critical transitions, abrupt shifts to new states.
- Current early warning signals (EWS) lack specificity and robustness, failing to predict transition type or handle noise.
Purpose of the Study:
- Develop novel spectral EWS for improved prediction of critical transitions.
- Enhance robustness to noise and differentiate between bifurcation types.
Main Methods:
- Utilized Ornstein-Uhlenbeck theory to derive analytic approximations for EWS.
- Developed new spectral EWS sensitive to transition proximity and type.
- Applied EWS in concert with conventional methods to a population model and experimental data.
Main Results:
- New spectral EWS demonstrate higher sensitivity and robustness to noise and bifurcation type.
- Successfully identified characteristic signals preceding Hopf bifurcations in experimental data.
- Showed that combining spectral and conventional EWS improves transition prediction.
Conclusions:
- Novel spectral EWS offer a significant advancement in predicting critical transitions.
- These improved EWS can help differentiate transition types and manage risks in complex systems.
- Enhanced prediction capabilities are crucial for managing critical transitions in the Anthropocene.
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