Predicting the way toward Nature’s recovery

Predictive biodiversity models are emerging as essential tools to translate global, regional, and national biodiversity strategies into effective actions. By explicitly linking conservation measures to expected ecological outcomes, these models allow us to evaluate alternative policy pathways, anticipate biodiversity responses to different management strategies, reveal trade-offs such as balancing habitat protection with agricultural needs, and ensure that conservation actions are effective, efficient, and equitable. As many conservation actions can take years or even decades to unfold, forward-looking models are essential to anticipate whether current actions will suffice to meet biodiversity targets. Yet their potential remains underused in planning and policy processes worldwide. In this talk, we briefly summarise recent advances in predictive biodiversity modelling and illustrate how models can help bridge the gap between biodiversity goals and action. We draw from a wide range of examples, such as uncertainty-aware “blacklisting’’ of invasive plants that supports prevention and early-detection strategies; assessments of EU agricultural and biodiversity measures and their effectiveness for maintaining farmland bird populations; model-based analyses of restoration success and genetic connectivity in recovering bison populations; and global and regional biodiversity model intercomparisons that help identify which ecological processes most strongly drive biodiversity change. Together, these case studies show how predictive approaches can reveal hidden trade-offs, quantify uncertainties, and fill key knowledge gaps highlighted by recent global assessments. We also emphasise the importance of easy-to-use modelling toolboxes, participatory approaches that engage stakeholders and policy makers throughout the modelling process, and capacity-building initiatives that enable broader adoption and co-development of predictive tools worldwide. These elements increase transparency and mutual understanding, and ensure that modelling outputs are tailored to real-world decision needs. Taken together, our examples demonstrate how predictive modelling can help move biodiversity science from diagnosis toward solution, supporting the design and evaluation of strategies that are scientifically robust, societally relevant, and capable of guiding long-term conservation success.