EcoGraph: A Graph-Based Species Interaction Modeling Approach for Understanding Wildlife Community Stability

Abstract

Understanding the stability of wildlife communities remains a central challenge in ecology, particularly as habitats face increasing fragmentation and disturbance. Traditional models of species interactions often fail to capture the structural complexity underlying community resilience. This paper introduces EcoGraph, a graph-based modeling framework that represents species and their interactions as nodes and weighted edges, enabling quantitative analysis of community stability through network metrics such as connectivity, centrality, and modularity. Unlike conventional population-based approaches, EcoGraph incorporates dynamic interaction data to predict how communities respond to perturbations such as species loss or habitat fragmentation. The framework is evaluated across multiple case-study ecosystems, demonstrating improved interpretability and predictive capability compared to baseline stability models. Results indicate that network-level structural properties strongly influence ecosystem resilience, offering ecologists a scalable tool for conservation planning and biodiversity monitoring. This work contributes a unified graph-theoretic methodology bridging network science and wildlife ecology, with implications for proactive ecosystem management.