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Study of memory-dependent dynamics of a predator-prey model with hyperbolic mortality
1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, India, 632014.
This study analyzes a fractional-order prey-predator model using the Caputo fractional derivative, proving its well-posedness and stability. Numerical simulations confirm the model
Area of Science:
- Mathematical Biology
- Fractional Calculus
- Ecology
Background:
- Ecological models often simplify complex species interactions.
- Fractional calculus offers a powerful framework to model memory effects in biological systems.
- Hyperbolic mortality and memory effects are crucial for realistic prey-predator dynamics.
Purpose of the Study:
- To analyze a fractional-order prey-predator model with hyperbolic mortality and memory effects.
- To establish the well-posedness (existence, uniqueness, positivity, boundedness) of the model's solutions.
- To investigate the local stability and parameter sensitivity of the ecological system.
Main Methods:
- Application of the Caputo fractional derivative.
- Fixed-point theory for proving solution properties.
- Jacobian matrix and fractional-order stability criteria for local stability analysis.
- Numerical simulations and error analysis for validation.
- Sensitivity analysis to assess parameter impact.
Main Results:
- The fractional-order prey-predator model is proven to be well-posed.
- Local stability of the system is rigorously analyzed.
- Numerical simulations confirm theoretical findings.
- The numerical scheme demonstrates convergence and precision.
- Parameter sensitivity analysis reveals key drivers of system dynamics.
Conclusions:
- The fractional-order model with memory effects provides a more realistic representation of prey-predator interactions.
- The established theoretical framework ensures reliable analysis of the model.
- Numerical methods effectively validate the model's behavior and stability.
- Understanding parameter sensitivity is vital for ecological management and prediction.
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