Using Predictive Models to Explore Routes to Biodiversity Recovery

As Europe advances major policy initiatives - including the Nature Restoration Law and the EU Biodiversity Strategy for 2030 - there is growing demand for biodiversity models that can directly support the design, targeting, and evaluation of restoration actions. This requires innovation beyond existing indicators (e.g. national farmland bird indicators), which have been instrumental for describing retrospective biodiversity trends and raising awareness of biodiversity declines. However,  policymakers now require predictive, spatially explicit, ecologically grounded models that can identify where interventions should be prioritised, anticipate potential trade-offs, and assess the likely outcomes of alternative policy pathways. Meeting these needs calls for a new generation of biodiversity models that link local ecological processes to national and continental targets in a coherent and decision-focused way.

In this study, we develop and apply a flexible, large-scale modelling framework that synthesises tens of millions of bird abundance time-series across Europe. The framework couples spatially explicit population models with environmental, climatic, and land-use predictors, while accounting for temporal dependence, spatial structure, imperfect detection, and phylogenetic relatedness. This models integrates best-practice statistical theory and tools to operate coherently across continental spatial extents and hundreds of species, producing local-scale predictions through time to help shape policy decisions.

Analyses reveal highly heterogeneous population responses to climate warming, protected area status, and anthropogenic land-use change. These patterns suggest that conservation actions may produce trade-offs: interventions that facilitate recovery in some species may inadvertently accelerate declines in others. Understanding this heterogeneity is essential for designing targeted restoration actions, prioritising locations for investment, and assessing the feasibility of reaching policy-defined targets. We further demonstrate how this framework can be used to re-evaluate existing bird biodiversity indicators, including farmland bird indices and pan-European trend metrics, by linking local population responses to aggregated national and continental indicators. This enables more accurate tracking of progress towards restoration goals and supports scenario-based assessments of alternative policy pathways.

Overall, our work highlights how scalable predictive models can strengthen biodiversity decision-support systems - informing restoration planning, guiding conservation investments, and supporting cohesive national and EU-level efforts to meet biodiversity targets.