Towards a unified framework for multiple stable states in ecological systems
Jennifer Paige, Denis D. Patterson, Alan Hastings
TLDR
This paper proposes a unified mathematical framework for understanding multiple stable states in ecological systems, synthesizing empirical and theoretical approaches.
Key contributions
- Reviews empirical & theoretical approaches to ecological multiple stable states.
- Synthesizes concepts like stability, tipping, hysteresis, and transient dynamics.
- Proposes a common mathematical framework, highlighting positive feedback loops.
- Discusses implications for ecological restoration and management.
Why it matters
This paper addresses a critical gap in ecology by providing a unified framework for multiple stable states, connecting mechanisms to models. This synthesis is vital for predicting ecosystem shifts and informing effective restoration and management strategies.
Original Abstract
Multiple stable states - the coexistence of two or more distinct ecological configurations under identical environmental conditions - have attracted sustained interest in ecology, yet the field still lacks a unified framework connecting ecological mechanisms to dynamical models. Here, we review empirical and theoretical approaches to multiple stable states, synthesising perspectives on stability, tipping, hysteresis, and transient dynamics, and contextualise these within a common mathematical framework. Drawing on examples of well-known ecosystem models, we highlight the central and necessary role of positive feedback loops and identify other common, unifying features of ecological systems that exhibit multiple stable states. We further discuss the relationship between stable and transient dynamics, the roles of spatial and temporal scales in feedback identification, and the implications for ecological restoration and management. We conclude with open questions and challenges for the field, including extending multistability theory to persistent-transient frameworks and harnessing emerging data-collection technologies to sharpen empirical inference.
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