Impact of Fear, Allee Effects, and Harvesting on a Predator-Prey Delay Model with a Modified Beddington–DeAngelis Functional Response

This paper studies the dynamics of a delayed predator-prey model with a modified Beddington-DeAngelis response function influenced by fear factors, Allee effects, and harvesting on the predator population. This paper analyzes the influence of parameters, namely fear factors ($\omega$), Allee effects ($m$), and delay time ($\tau$), on the stability of the model’s equilibrium point. First, an analysis of the existence of the model’s equilibrium point is carried out, then an analysis of the stability and the influence of changes in the model’s parameter values and delay time that can affect the stability of the model’s equilibrium point is carried out. The analysis indicates that the larger the parameters $\omega$, $m$, and $\tau$, the more unstable the coexistence equilibrium point tends to be. Several numerical simulation results are used to validate the analytical results obtained.

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Publication Details

Journal
Statistics Optimization & Information Computing
Published
2025-11-02
DOI
https://doi.org/10.19139/soic-2310-5070-2574
Primary Topic
Mathematical and Theoretical Epidemiology and Ecology Models
Type
article
Field-Weighted Citation Impact
0.00

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article

Impact of Fear, Allee Effects, and Harvesting on a Predator-Prey Delay Model with a Modified Beddington–DeAngelis Functional Response

Statistics Optimization & Information Computing
Mathematical and Theoretical Epidemiology and Ecology Models
article

Impact of Fear, Allee Effects, and Harvesting on a Predator-Prey Delay Model with a Modified Beddington–DeAngelis Functional Response

article en

Abstract

This paper studies the dynamics of a delayed predator-prey model with a modified Beddington-DeAngelis response function influenced by fear factors, Allee effects, and harvesting on the predator population. This paper analyzes the influence of parameters, namely fear factors ($\omega$), Allee effects ($m$), and delay time ($\tau$), on the stability of the model’s equilibrium point. First, an analysis of the existence of the model’s equilibrium point is carried out, then an analysis of the stability and the influence of changes in the model’s parameter values and delay time that can affect the stability of the model’s equilibrium point is carried out. The analysis indicates that the larger the parameters $\omega$, $m$, and $\tau$, the more unstable the coexistence equilibrium point tends to be. Several numerical simulation results are used to validate the analytical results obtained.

Statistics Optimization & Information ComputingVol. 15(3)
Aliah University (IN), Universitas Airlangga (ID)
Universitas Airlangga
Openalex Percentile: Top 31%
Mathematical and Theoretical Epidemiology and Ecology Models
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