Demographic noise slows down cycles of dominance. (7th November 2017)

• Record Type: Journal Article
• Authors: Yang, Qian; Rogers, Tim; Dawes, Jonathan H.P.
• Is Part Of: Journal of theoretical biology. Volume 432(2017)
• Page Start: 157
• Page End: 168
• Publication Date: 2017-11-07
• Subjects: Cyclic dominance ecology -- Limit cycle -- Mean field model -- Replicator equation -- Stochastic differential equation -- Stochastic simulation; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2017.07.025
• Languages: English
• ISSNs: 0022-5193

Abstract

Highlights: We propose a stochastic population model to study noisy limit cycles in the rock-paper-scissors game. Stochastic simulations show that the oscillation period of the limit cycle is increased by noise. We apply Markov-chain theory valid in the limit of large populations and low mutation rate. We identify a cross-over regime in which both deterministic and stochastic effects are relevant. We describe the regime boundaries in terms of mutation rate and population size. Abstract: We study the phenomenon of cyclic dominance in the paradigmatic Rock–Paper–Scissors model, as occurring in both stochastic individual-based models of finite populations and in the deterministic replicator equations. The mean-field replicator equations are valid in the limit of large populations and, in the presence of mutation and unbalanced payoffs, they exhibit an attracting limit cycle. The period of this cycle depends on the rate of mutation; specifically, the period grows logarithmically as the mutation rate tends to zero. We find that this behaviour is not reproduced in stochastic simulations with a fixed finite population size. Instead, demographic noise present in the individual-based model dramatically slows down the progress of the limit cycle, with the typical period growing as the reciprocal of the mutation rate. Here we develop a theory that explains these scaling regimes and delineates them in terms of population size and mutation rate. We identify a further intermediate regime in which we construct a stochastic differential equation model describing the transition between stochastically-dominated and mean-field behaviour.