Universality of noise-induced resilience restoration in spatially-extended ecological systems

TL;DR

This paper develops new mathematical tools using nucleation theory to analyze resilience restoration in spatially-extended ecological systems under stochastic conditions, revealing phase behaviors and scaling laws.

Contribution

It introduces a novel approach employing nucleation theory to study resilience restoration in high-dimensional stochastic systems, bridging a key analytical gap.

Findings

Systems exhibit single-cluster or multi-cluster phases depending on size and noise.

A new scaling law for restoration time in two-dimensional systems.

The approach applies broadly beyond ecological systems.

Abstract

Many systems may switch to an undesired state due to internal failures or external perturbations, of which critical transitions toward degraded ecosystem states are a prominent example. Resilience restoration focuses on the ability of spatially-extended systems and the required time to recover to their desired states under stochastic environmental conditions. While mean-field approaches may guide recovery strategies by indicating the conditions needed to destabilize undesired states, these approaches are not accurately capturing the transition process toward the desired state of spatially-extended systems in stochastic environments. The difficulty is rooted in the lack of mathematical tools to analyze systems with high dimensionality, nonlinearity, and stochastic effects. We bridge this gap by developing new mathematical tools that employ nucleation theory in spatially-embedded systems to advance resilience restoration. We examine our approach on systems following mutualistic dynamics and diffusion models, finding that systems may exhibit single-cluster or multi-cluster phases depending on their sizes and noise strengths, and also construct a new scaling law governing the restoration time for arbitrary system size and noise strength in two-dimensional systems. This approach is not limited to ecosystems and has applications in various dynamical systems, from biology to infrastructural systems.