Systematic analysis and optimization of early warning signals for critical transitions

Proverbio, D.; Skupin, A.; Goncalves, J.

doi:10.1101/2022.11.04.515178

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bioRxiv · 10.1101/2022.11.04.515178

Systematic analysis and optimization of early warning signals for critical transitions

Proverbio, D.·Skupin, A.·Goncalves, J.

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Abstract

Abrupt shifts between alternative regimes occur in complex systems, from cell regulation to brain functions to ecosystems. Several model-free Early Warning Signals (EWS) have been proposed to detect impending transitions, but failure or poor performance in some systems have called for better investigation of their generic applicability. In particular, there are still ongoing debates whether such signals can be successfully extracted from data. In this work, we systematically investigate properties and performance of dynamical EWS in different deteriorating conditions, and we propose an optimised combination to trigger warnings as early as possible, eventually verified on experimental data. Our results explain discrepancies observed in the literature between warning signs extracted from simulated models and from real data, provide guidance for EWS selection based on desired systems and suggest an optimised composite indicator to alert for impending critical transitions. HighlightsO_LIHow to extract early warning signals (EWS) against critical transitions from data is still poorly understood C_LIO_LIA mathematical framework assesses and explains the performance of EWS in noisy deteriorating conditions C_LIO_LIComposite indicators are optimised to alert for impending shifts C_LIO_LIThe results are applicable to wide classes of systems, as shown with models and on empirical data. C_LI

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Proverbio, D., Skupin, A., Goncalves, J.. 2022-11-04. Systematic analysis and optimization of early warning signals for critical transitions. https://doi.org/10.1101/2022.11.04.515178

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