Approximate probabilistic cellular automata for the dynamics of single-species populations under discrete logisticlike growth with and without weak Allee effects

J. Ricardo G. Mendonça and Yeva Gevorgyan
Phys. Rev. E 95, 052131 – Published 18 May 2017

Abstract

We investigate one-dimensional elementary probabilistic cellular automata (PCA) whose dynamics in first-order mean-field approximation yields discrete logisticlike growth models for a single-species unstructured population with nonoverlapping generations. Beginning with a general six-parameter model, we find constraints on the transition probabilities of the PCA that guarantee that the ensuing approximations make sense in terms of population dynamics and classify the valid combinations thereof. Several possible models display a negative cubic term that can be interpreted as a weak Allee factor. We also investigate the conditions under which a one-parameter PCA derived from the more general six-parameter model can generate valid population growth dynamics. Numerical simulations illustrate the behavior of some of the PCA found.

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  • Received 3 March 2017
  • Revised 4 April 2017

DOI:https://doi.org/10.1103/PhysRevE.95.052131

©2017 American Physical Society

Physics Subject Headings (PhySH)

Interdisciplinary PhysicsStatistical Physics & ThermodynamicsPhysics of Living SystemsNonlinear Dynamics

Authors & Affiliations

J. Ricardo G. Mendonça*

  • Escola de Artes, Ciências e Humanidades, Universidade de São Paulo Rua Arlindo Bettio 1000, Ermelino Matarazzo, 03828-000 São Paulo, SP, Brazil

Yeva Gevorgyan

  • Departamento de Matemática Aplicada, Instituto de Matemática e Estatística, Universidade de São Paulo Rua do Matão 1010, Cidade Universitária, 05508-090 São Paulo, SP, Brazil

  • *jricardo@usp.br
  • yeva@ime.usp.br

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Issue

Vol. 95, Iss. 5 — May 2017

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