101. Aggregation as an antipredator strategy in the rock-paper-scissors model
- Author
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J. Menezes, E. Rangel, and B. Moura
- Subjects
Ecology ,Applied Mathematics ,Ecological Modeling ,Populations and Evolution (q-bio.PE) ,FOS: Physical sciences ,Pattern Formation and Solitons (nlin.PS) ,Nonlinear Sciences - Pattern Formation and Solitons ,Nonlinear Sciences - Adaptation and Self-Organizing Systems ,Computer Science Applications ,Computational Theory and Mathematics ,Biological Physics (physics.bio-ph) ,Modeling and Simulation ,FOS: Biological sciences ,Physics - Biological Physics ,Quantitative Biology - Populations and Evolution ,Adaptation and Self-Organizing Systems (nlin.AO) ,Ecology, Evolution, Behavior and Systematics - Abstract
We study a nonhierarchical tritrophic system, whose predator-prey interactions are described by the rock-paper-scissors game rules. In our stochastic simulations, individuals may move strategically towards the direction with more conspecifics to form clumps instead of moving aimlessly on the lattice. Considering that the conditioning to move gregariously depends on the organism's physical and cognitive abilities, we introduce a maximum distance an individual can perceive the environment and a minimum conditioning level to perform the gregarious movement. We investigate the pattern formation and compute the average size of the single-species spatial domains emerging from the grouping behaviour. The results reveal that the defence tactic reduces the predation risk significantly, being more profitable if individuals perceive further distances, thus creating bigger groups. Our outcomes show that the species with more conditioned organisms dominate the cyclic spatial game, controlling most of the territory. On the other hand, the species with fewer individuals ready to perform aggregation strategy gives its predator the chance to fill the more significant fraction of the grid. The spatial interactions assumed in our numerical experiments constitute a data set that may help biologists and data scientists understand how local interactions influence ecosystem dynamics., Comment: 8 pages, 10 figures
- Published
- 2022
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