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Diseased prey predator model with general Holling type interactions
Banshidhar Sahoo1, Swarup Poria1
1Department of Applied Mathematics, University of Calcutta, Kolkata, West Bengal, India.
Summary
This study introduces a novel diseased prey-predator model using general Holling type interactions. It reveals that specific interaction function choices can lead to disease-free ecosystems and complex dynamics like chaos.
Area of Science:
- Mathematical Biology
- Ecology
- Theoretical Ecology
Background:
- Food chain modeling relies heavily on interaction function selection.
- Existing diseased prey-predator models often use limited Holling type interactions (I, II, III).
- A gap exists in models incorporating general Holling type interactions for diseased prey-predator systems.
Purpose of the Study:
- To develop and analyze a diseased prey-predator model with general Holling type interactions.
- To investigate the stability, permanence, and dynamic behaviors of the proposed model.
- To explore bifurcation scenarios and the potential for disease eradication through parameter selection.
Main Methods:
- Mathematical modeling of a diseased prey-predator system.
- Derivation of local stability conditions for equilibrium points.
- Analysis of system permanence and impermanence conditions.
- Investigation of global stability.
- Bifurcation analysis using the MATCONT package for general Holling parameters and infection rates.
Main Results:
- The model exhibits rich dynamics, including limit cycles, period-2, higher periodic oscillations, and chaotic behavior.
- Permanence and impermanence conditions for the system were established.
- Bifurcation analysis revealed complex scenarios with variations in interaction parameters and infection rates.
- Demonstrated that specific choices of interaction functions can transition a diseased system to a disease-free state.
Conclusions:
- The general Holling type interaction model provides a more realistic framework for food chain dynamics.
- Parameter selection is crucial for controlling ecosystem stability and potentially eliminating disease.
- Findings offer insights for constructing more accurate and predictive ecological models.
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