Ecosystem Modeling, Network Approaches & Complexity
Abstract:
Ecosystem functioning emerges from intricate webs of biotic and abiotic interactions, which are inherently nonlin ear and often display features of complexity such as resilience, tipping points, and self-organization. Traditional models based on compartmental flows, such as Lotka–Volterra dynamics, provide useful insights but struggle to capture higher-order interactions and structural dependencies. In recent decades, network-based approaches have become powerful tools to model ecosystems as complex adaptive systems. This paper integrates theoretical per spectives, empirical analyses, and computational methods to evaluate the role of network topology and complexity metrics in shaping ecosystem stability. Two hypotheses are tested: (1) modularity in ecological networks enhances ecosystem resilience to perturbations, and (2) network connectance positively correlates with biodiversity stability only up to a critical threshold, beyond which instability emerges. Using simulated data and advanced statistical modeling—including regression with modularity indices, nonlinear modeling of connectance, and network visual ization—we provide evidence supporting both hypotheses. The results highlight the necessity of embracing complex systems frameworks in ecological modeling.
