Spatio-temporal dynamics in a delayed prey–predator model with nonlinear prey refuge and harvesting

Sarif, Nawaj; Kumar, Arjun; Anshu,; Sarwardi, Sahabuddin; Dubey, Balram

doi:10.1016/j.chaos.2024.115247

Chaos, Solitons & Fractals

2024

DOI: 10.1016/j.chaos.2024.115247

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Spatio-temporal dynamics in a delayed prey–predator model with nonlinear prey refuge and harvesting

Nawaj Sarif,

Arjun Kumar,

Anshu

et al.

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Cited by 3 publications

References 47 publications

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Understanding population dynamics and management strategies for a newly emerging pest Carea sp. in Eucalyptus plantations in Indonesia through a mathematical model

Zevika,

Utami,

Tjahjono

et al. 2024

Chaos, Solitons & Fractals

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Understanding population dynamics and management strategies for a newly emerging pest Carea sp. in Eucalyptus plantations in Indonesia through a mathematical model

Zevika,

Utami,

Tjahjono

et al. 2024

Chaos, Solitons & Fractals

View full text Add to dashboard Cite

Dynamical analysis of a prey-predator-tourist model: Environmental preferences and optimal fee control

Delpini,

Melis,

Russu

2025

Chaos, Solitons & Fractals

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Fourth Order Runge-Kutta and Gill Methods in Numerical Analysis of Predator-Prey Models

Elpianora,

Berou,

Kong

et al. 2024

Intv. Ind. J. of. Math. Ed

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Purpose of the study: This study aims to solve the numerical solution of the Predator-Prey model using the fourth-order Runge-Kutta and Gill methods, and to determine the profile of the Predator-Prey model solved numerically using the fourth-order Runge-Kutta and Gill methods. Methodology: Schematically, the steps taken in this study are starting from a literature review of the Predator-Prey Model, then solving the Predator-Prey Model using the Fourth-Order Runge-Kutta and Gill Methods, then the program creation step which is continued with program simulation, and finally analysis of the simulation results. Main Findings: From the results of the analysis of the difference in estimates of the fourth-order Runge-Kutta and Gill for predators and prey, there is no significant difference between the two methods in determining a better method in solving the Predator-Prey model. Because the Predator-Prey model cannot be solved analytically, the difference between the two methods cannot be seen from the analytical solution approach. The simulation results using the fourth-order Runge-Kutta and Gill methods show that the greater the value of b, the prey population increases with a value of α > β, and the smaller the values of α and β given, the interaction process between the two populations will slow down and the prey population will increase. Novelty/Originality of this study: can provide information about the profile of the Predator-Prey model which is solved numerically using the fourth-order Runge-Kutta and Gill methods. The combination of these two methods to solve the Predator-Prey model is the novelty of this study

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Spatio-temporal dynamics in a delayed prey–predator model with nonlinear prey refuge and harvesting

Cited by 3 publications

References 47 publications

Understanding population dynamics and management strategies for a newly emerging pest Carea sp. in Eucalyptus plantations in Indonesia through a mathematical model

Understanding population dynamics and management strategies for a newly emerging pest Carea sp. in Eucalyptus plantations in Indonesia through a mathematical model

Dynamical analysis of a prey-predator-tourist model: Environmental preferences and optimal fee control

Fourth Order Runge-Kutta and Gill Methods in Numerical Analysis of Predator-Prey Models

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