Abstract

Prey–predator models with variable carrying capacity are proposed. These models are more realistic in modeling population dynamics in an environment that undergoes changes. In particular, prey–predator models with Holling type I and type II functional responses, incorporating the idea of a variable carrying capacity, are considered. The carrying capacity is modeled by a logistic equation that increases sigmoidally between an initial value κ0>κ1 (a lower bound for the carrying capacity) and a final value κ1+κ2 (an upper bound for the carrying capacity). In order to examine the effect of the variable carrying capacity on the prey–predator dynamics, the two models were analyzed qualitatively using stability analysis and numerical solutions for the prey, and the predator population densities were obtained. Results on global stability and Hopf bifurcation of certain equilibrium points have been also presented. Additionally, the effect of other model parameters on the prey–predator dynamics has been examined. In particular, results on the effect of the handling parameter and the predator’s death rate, which has been taken to be the bifurcation parameter, are presented.

Highlights

  • Prey–predator dynamic is an essential tool in mathematical ecology, for our understanding of interacting populations in the natural environment

  • The rest of the paper is organized as follows: in the second section, we present and analyze a prey–predator model with Holling type I functional response and variable carrying capacity

  • Our aim in this paper is to examine the effect of variable carrying capacity on the prey–predator dynamics

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Summary

Introduction

Prey–predator dynamic is an essential tool in mathematical ecology, for our understanding of interacting populations in the natural environment This relationship will continue to be one of the dominant themes in both ecology and mathematical ecology due to its universal existence and importance. Meyer et al [8] proposed the carrying capacity to be modeled by a logistic equation that increases sigmoidally between an initial value κ0 > κ1 and a final value κ1 + κ2 They studied the effect of this dynamic carrying capacity on the trajectories of simple growth models, and they use the new model to Mathematics 2018, 6, 102; doi:10.3390/math6060102 www.mdpi.com/journal/mathematics. The rest of the paper is organized as follows: in the second section, we present and analyze a prey–predator model with Holling type I functional response and variable carrying capacity.

Model Building
Mathematical Analysis of the Model
Numerical Simulation and Discussion
Prey–Predator Model with Holling Type II Functional Response
Conclusions
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