Adaptive movement strategy may promote biodiversity in the rock-paper-scissors model

TL;DR

This study investigates how adaptive movement strategies influence biodiversity in a rock-paper-scissors model, showing that such strategies can enhance coexistence even when not all individuals adapt.

Contribution

It introduces the role of adaptive movement in cyclic models, demonstrating its effect on biodiversity and coexistence probabilities through stochastic simulations.

Findings

Adaptive movement increases biodiversity in the model.

Biodiversity benefits even when only some individuals adapt.

Higher sensory range enhances the positive effect of adaptation.

Abstract

We study the role of the adaptive movement strategy in promoting biodiversity in cyclic models described by the rock-paper-scissors game rules. We assume that individuals of one out of the species may adjust their movement to escape hostile regions and stay longer in their comfort zones. Running a series of stochastic simulations, we calculate the alterations in the spatial patterns and population densities in scenarios where not all organisms are physically or cognitively conditioned to perform the behavioural strategy. Although the adaptive movement strategy is not profitable in terms of territorial dominance for the species, it may promote biodiversity. Our findings show that if all individuals are apt to move adaptively, coexistence probability increases for intermediary mobility. The outcomes also show that even if not all individuals can react to the signals received from the neighbourhood, biodiversity is still benefited, but for a shorter mobility range. We find that the improvement in the coexistence conditions is more accentuated if organisms adjust their movement intensely and can receive sensory information from longer distances. We also discover that biodiversity is slightly promoted for high mobility if the proportion of individuals participating in the strategy is low. Our results may be helpful for biologists and data scientists to understand adaptive process learning in system biology.