Litcius/Paper detail

CROSS-DIFFUSION-DRIVEN PATTERN FORMATION AND SELECTION IN A MODIFIED LESLIE–GOWER PREDATOR–PREY MODEL WITH FEAR EFFECT

Renji Han, Lakshmi Narayan Guin, Binxiang Dai

2020Journal of Biological Systems71 citationsDOI

Abstract

Spatial patterns through diffusion-driven instability are stationary structures that appear spontaneously upon breaking the symmetry of the spatial domain, which results only from the coupling between the reaction and the diffusion processes. This paper is concerned with a modified Leslie–Gower-type model with cross-diffusion and indirect predation effect. We first prove the global existence, non-negativity and uniform boundedness for the considered model. Then the linear stability analysis shows that the cross-diffusion is the key mechanism of spatiotemporal pattern formation. Amplitude equations are derived near Turing bifurcation point under nonlinear cross-diffusion to interpret pattern selection among spot pattern, stripe pattern and the mixture of spot and stripe patterns, which reflects the species’s spatially inhomogeneous distribution, and it is also found that the fear factor has great influence on spatially inhomogeneous distribution of the two species under certain cross-diffusivity, that is, high level of fear can induce striped inhomogeneous distribution, low level of fear can induce spotted inhomogeneous distribution, and the intermediate level of fear can induce the mixture of spotted and striped inhomogeneous distribution. Finally, numerical simulations illustrate the effectiveness of all theoretical results.

Topics & Concepts

Pattern formationStatistical physicsDiffusionInstabilitySpatiotemporal patternNonlinear systemCoupling (piping)Distribution (mathematics)Stability (learning theory)Hopf bifurcationBifurcationMathematicsPhysicsMathematical analysisMechanicsComputer scienceBiologyMaterials scienceMachine learningGeneticsQuantum mechanicsThermodynamicsMetallurgyMathematical and Theoretical Epidemiology and Ecology ModelsEvolution and Genetic DynamicsNonlinear Dynamics and Pattern Formation