Improving the Classification of Wildlife Conservation Status to Support Nature Protection in the European Union

ABSTRACT Ensuring that species of conservation concern achieve favorable conservation status (FCS) is central to European Union (EU) biodiversity conservation targets. A key criterion for FCS is exceeding the favorable reference range (FRR)—the range extent needed to support long‐term species stability across sufficient environmental variation. However, FRRs are often unknown, undermining their applicability. We developed a machine‐learning approach based on the assumption that species with similar traits and habitats share comparable range requirements to estimate and standardize FRRs across the EU. Applied to amphibians, mammals, and reptiles, our method provided FRRs for 99.5% species of conservation concern, compared to 17.5% previously available (with satisfactory modeling performance: R 2 0.75). We compared current ranges with estimated FRRs, finding that only 34.8% of cases meet or exceed FRR expectations, notably fewer than reported in official documentation (79%). Our approach may support periodic FCS reassessments and help refine the targets of EU conservation policies.