Stability, collapse and hyperchaos in a class of tri-trophic predator–prey models

A. McAllister, M. McCartney and D.H. Glass

Physica A: Statistical Mechanics and its Applications, 2023, vol. 628, issue C

Abstract: We present a set of four three-dimensional models inspired by predator–prey systems involving a basal food source, prey, and predator, with each model including a variation in the predator’s behaviour. We investigate these models across a wide range of parameter space and present results for various behaviours, including chaotic and hyper-chaotic behaviour. We find that increased consumption of the basal food source benefits species’ survival for some of our models whilst being detrimental to others. In all of the models, increased consumption reduces the extent to which over-predation leads to the extinction of the prey. Mutation is found to have a more significant impact on stability if the mutant is an omnivore, with no chaotic behaviour occurring in certain regions of parameter space. Connections have been made between each model’s largest Lyapunov Exponent and the Hurst exponent with the Hurst exponent proving to be a reliable indicator of chaos. Machine learning methods have been used to predict the largest Lyapunov exponent using the corresponding Hurst exponent with errors presented for each method.

Keywords: Three dimensional model; Coupled logistic maps; Hurst exponent; Lyapunov exponents; Chaotic dynamics; Machine learning (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:628:y:2023:i:c:s037843712300701x

DOI: 10.1016/j.physa.2023.129146

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