Cannibalism and Harvesting in Tritrophic Chains: Insights from Mathematical and Artificial Neural Network Analysis

In this study, we introduce a novel tri-trophic food chain model that integrates cannibalism among basal prey and harvesting behaviors in the top predator, aiming to understand ecosystem dynamics comprehensively. Objectives encompass assessing system boundedness, computing fixed points, and determining stability characteristics using mathematical frameworks. The Routh-Hurwitz criteria and Lyapunov function are employed for local and global stability analyses of coexistence equilibrium points. Graphical interpretations elucidate relationships among pivotal parameters: prey growth rate, cannibalism intensity, and predator predation rate. Phase portraits and time series solutions illustrate parameter impacts. To enhance analytical depth and predictive capabilities, we utilize artificial neural networks (ANNs). Methods include connecting ANNs to computational proficiency for insights into the model's behavior over time. Findings demonstrate system boundedness, computed fixed points, and stability characteristics. Graphical interpretations reveal parameter impacts on system dynamics. ANNs offer predictive insights into model behavior. This study's novelty lies in integrating cannibalism and harvesting behaviors into a tri-trophic food chain model, employing mathematical analyses and ANNs to understand ecosystem dynamics comprehensively. Improvements include predictive capabilities and deeper analytical insights. Doi: 10.28991/ESJ-2024-08-04-02 Full Text: PDF