Non-equilibrium early-warning signals for critical transitions in ecological systems

Xu, Li; Patterson, Denis; Levin, Simon Asher; Wang, Jin

Abstract

Complex systems can exhibit sudden transitions or regime shifts from one stable state to another, typically referred to as critical transitions. It becomes a great challenge to identify a robust warning sufficiently early that action can be taken to avert a regime shift. We employ landscape-flux theory from nonequilibrium statistical mechanics as a general framework to quantify the global stability of ecological systems and provide warning signals for critical transitions. We quantify the average flux as the nonequilibrium driving force and the dynamical origin of the nonequilibrium transition while the entropy production rate as the nonequilibrium thermodynamic cost and thermodynamic origin of the nonequilibrium transition. Average flux, entropy production, nonequilibrium free energy, and time irreversibility quantified by the difference in cross-correlation functions forward and backward in time can serve as early warning signals for critical transitions much earlier than other conventional predictors. We utilize a classical shallow lake model as an exemplar for our early warning prediction. Our proposed method is general and can be readily applied to assess the resilience of many other ecological systems. The early warning signals proposed here can potentially predict critical transitions earlier than established methods and perhaps even sufficiently early to avert catastrophic shifts.

Citation

Xu, L., Patterson, D., Levin, S. A., & Wang, J. (2023). Non-equilibrium early-warning signals for critical transitions in ecological systems. Proceedings of the National Academy of Sciences, 120(5), Article e2218663120. https://doi.org/10.1073/pnas.2218663120

• Journal Article Type: Article
• Acceptance Date: Dec 21, 2022
• Online Publication Date: Jan 23, 2023
• Publication Date: Jan 31, 2023
• Deposit Date: Jun 6, 2024
• Journal: Proceedings of the National Academy of Sciences
• Print ISSN: 0027-8424
• Electronic ISSN: 1091-6490
• Publisher: National Academy of Sciences
• Peer Reviewed: Peer Reviewed
• Volume: 120
• Issue: 5
• Article Number: e2218663120
• DOI: https://doi.org/10.1073/pnas.2218663120
• Public URL: https://durham-repository.worktribe.com/output/2474798