Mitigating Tipping Points in Ecological Networks through Game-Theoretic Approaches

Ecosystems worldwide face escalating pressures from anthropogenic exploitation and climate change, pushing many closer to abrupt and potentially irreversible tipping points. These can lead to catastrophic biodiversity loss, collapse of ecosystems and long-term socio-ecological instability. Current theoretical frameworks often fall short in capturing the adaptive behaviours of organisms and humans that both influence and respond to environmental change. This project proposes an innovative, interdisciplinary framework that integrates evolutionary game theory, ecological network analysis, and environmental feedbacks to advance our understanding of tipping dynamics and inform robust mitigation strategies. By explicitly modelling dynamic strategy evolution, payoff uncertainty, and feedback-driven interactions among species within structured ecological networks, the project aims to find mechanisms that promote resilience and prevent collapse. We will develop and analyse coupled systems of differential equations incorporating adaptive interactions, stochasticity and nonlinear resource dynamics. The research will identify critical thresholds and characterize the conditions under which targeted, minimal interventions can stabilize ecosystems. A key innovation is the introduction of payoff uncertainty and heterogeneous risk preferences into eco-evolutionary game dynamics, enabling more realistic modelling of individual and collective responses to environmental signals. The project will be empirically anchored through case studies in coral reef and alpine ecosystems, where shifts in species interactions and environmental stressors are well documented. The outcomes of this research are expected to combine theoretical insight with practical relevance, offering new tools for adaptive conservation policy, early warning signal detection and ecosystem management under uncertainty, contributing to the EU’s mission to foster sustainability and resilience in socio-ecological systems.