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Research Article | Open Access

Memory-based prey-taxis and environmental stress shape spatiotemporal predator-prey dynamics

Dazhuo Liu1( )Xuewen Tan2
College of Education and Psychology Science, Minnan Normal University, Zhangzhou Fujian 363000, China
School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650504, China
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Abstract

This paper investigates a diffusive predator–prey system incorporating memory-based prey-taxis, the Allee effect, and environmental stressors. After establishing global well-posedness and existence conditions for equilibria, we use prey-taxis sensitivity and memory delay as parameters to identify Turing and Hopf bifurcation thresholds. Our results show that strong memory-based prey-taxis suppresses diffusion-driven Turing patterns and restores spatial homogeneity. However, this spatial stabilization does not necessarily imply greater delay tolerance: The critical Hopf delay may decrease with taxis sensitivity, revealing a trade-off between taxis-induced spatial stabilization and delay-induced temporal oscillations. Additionally, we analyze environmental stress, revealing a predator release effect where moderate stress disproportionately suppresses predators, which indirectly leads to an increase in the prey's density. We also identify a mode-jumping phenomenon in the critical delay threshold during stress-induced transitions from ordinary differential equation (ODE) to Turing instability. Finally, the numerical simulations provide a two-parameter stability map delineating four dynamic regions: Stable homogeneous states, stationary Turing patterns, spatially nonhomogeneous periodic solutions, and complex spatiotemporal dynamics from interacting instabilities.

CLC number: 92D25, 35B36

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AIMS Mathematics
Pages 17880-17916

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Cite this article:
Liu D, Tan X. Memory-based prey-taxis and environmental stress shape spatiotemporal predator-prey dynamics. AIMS Mathematics, 2026, 11(6): 17880-17916. https://doi.org/10.3934/math.2026729

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Received: 19 March 2026
Revised: 10 May 2026
Accepted: 27 May 2026
Published: 15 June 2026
©2026 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)