ECONET has advanced biodiversity science by developing a predictive framework for ecological resilience that moves beyond descriptive network approaches. By integrating species traits, interactions, and occurrences, I created new metrics—rewiring capacity and rewiring potential—that quantify how ecological networks reorganize under environmental change. This provides, for the first time, a globally applicable and data-efficient method to anticipate ecosystem responses even in data-poor regions. Our case study on plant–hummingbird pollination networks in Americas shows that species exhibit high functional flexibility, indicating that many ecosystems may retain pollination services despite environmental change if habitat diversity is maintained.
Complementing this, my global analysis of seed disperser communities introduces a three-dimensional vulnerability framework, combining exposure, sensitivity, and adaptability to assess how human habitat modification affects ecosystem function. The framework identifies priority areas for targeted interventions—from low-intensity management in adaptable systems to rewilding and restoration in highly vulnerable regions (e.g. Southeast Asia, Madagascar).
Potential impacts:
Early-warning indicators of ecosystem function loss.
Data-driven prioritization of conservation and rewilding.
Integration of rewiring and vulnerability metrics into conservation.
To ensure further use of the work, future steps include expanding trait and interaction databases and applying the concepts and methods across additional ecosystems and interaction types. Overall, the project sets a new benchmark for predictive ecology, equipping policymakers and practitioners with actionable tools to safeguard biodiversity and ecosystem functions under global change.
