The Role of Stochasticity in Noise-Induced Tipping Point Cascades: A Master Equation Approach

TL;DR

This paper investigates how stochastic noise can trigger cascading tipping points in interconnected ecological populations, using a Markov chain model to analyze recovery and collapse scenarios.

Contribution

It introduces a novel Markov chain approach to simplify and analyze stochastic tipping cascades in a two-population ecological model.

Findings

Identifies conditions for population recovery versus collapse.

Demonstrates how noise influences tipping cascades.

Provides a reduced model for predicting system resilience.

Abstract

Tipping points have been shown to be ubiquitous, both in models and empirically in a range of physical and biological systems. The question of how tipping points cascade through systems has been less well studied and is an important one. A study of noise-induced tipping, in particular, could provide key insights into tipping cascades. Here, we consider a specific example of a simple model system that could have cascading tipping points. This model consists of two interacting populations with underlying Allee effects and stochastic dynamics, in separate patches connected by dispersal, which can generate bistability. From an ecological standpoint, we look for rescue effects whereby one population can prevent the collapse of a second population. As a way to investigate the stochastic dynamics, we use an individual-based modeling approach rooted in chemical reaction network theory. Then, using continuous-time Markov chains and the theory of first passage times, we essentially approximate, or emulate, the original high-dimensional model by a Markov chain with just four states, where each state corresponds to a combination of population thresholds. Analysis of this reduced model shows when the system is likely to recover, as well as when tipping cascades through the whole system.