Noise-guided evolution within cyclical interactions

TL;DR

This paper investigates how Gaussian noise influences predator-prey dynamics on a lattice, revealing noise-guided evolution driven by spatial correlations and threshold mechanisms, with implications for evolutionary game theory.

Contribution

It introduces the concept of noise-guided evolution via threshold duality and demonstrates its relevance beyond predator-prey models, supported by simulations and mean-field approximations.

Findings

Noise significantly impacts evolutionary outcomes.

Short-range spatial correlations are crucial for noise effects.

The concept applies to evolutionary game models.

Abstract

We study a stochastic predator-prey model on a square lattice, where each of the six species has two superior and two inferior partners. The invasion probabilities between species depend on the predator-prey pair and are supplemented by Gaussian noise. Conditions are identified that warrant the largest impact of noise on the evolutionary process, and the results of Monte Carlo simulations are qualitatively reproduced by a four-point cluster dynamical mean-field approximation. The observed noise-guided evolution is deeply routed in short-range spatial correlations, which is supported by simulations on other host lattice topologies. Our findings are conceptually related to the coherence resonance phenomenon in dynamical systems via the mechanism of threshold duality. We also show that the introduced concept of noise-guided evolution via the exploitation of threshold duality is not limited to predator-prey cyclical interactions, but may apply to models of evolutionary game theory as well, thus indicating its applicability in several different fields of research.