Issue |
EPL
Volume 139, Number 5, September 2022
|
|
---|---|---|
Article Number | 57002 | |
Number of page(s) | 7 | |
Section | Biological and soft matter physics | |
DOI | https://doi.org/10.1209/0295-5075/ac817a | |
Published online | 17 August 2022 |
Adaptive movement strategy may promote biodiversity in the rock-paper-scissors model
1 School of Science and Technology, Federal University of Rio Grande do Norte 59072-970, P.O. Box 1524, Natal, RN, Brazil
2 Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam Science Park 904, 1098 XH Amsterdam, The Netherlands
(a) jmenezes@ect.ufrn.br (corresponding author)
Received: 22 March 2022
Accepted: 15 July 2022
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 intermediate 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.
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