Linking movement to mortality: a framework for predicting anthropogenic mortality hotspots in wide-ranging animals

Connor Taylor Panter , Arjun Amar

Human activities and their impacts are a major source of mortality for wide-ranging animals, yet conservation efforts often remain reactive because it is difficult to predict where mortality is most likely to occur. Approaches to understand and reduce wildlife mortality typically rely on coarse-scale associations between mortality events and landscape features, limiting their ability to identify spatially concentrated high-risk areas and prioritise effective mitigation. We contend that mortality risk arises from interactions between animal space use and human-modified environments, where movements result in spatial overlap with anthropogenic threats. Here, we outline the Movement–Mortality Framework (MMF), a conceptual framework with practical implications that integrates movement ecology, predictive space-use modelling, and spatial threat mapping to predict where anthropogenic mortality is most likely to occur. The MMF combines predicted space use with spatial threat distributions to estimate exposure, identify mortality hotspots and prioritise targeted conservation interventions. We demonstrate this approach using a worked example for the globally Vulnerable Cape Vulture (Gyps coprotheres) in South Africa. Predicted exposure was highly spatially concentrated: the highest-risk 1% of the landscape contained 41% and 43% of predicted exposure to powerline collision/electrocution and unintentional poisoning, respectively, while the highest-risk 10% contained 78% and 76%. By linking predictive movement models with spatial threat mapping, the MMF provides a transferable framework for identifying mortality hotspots, supporting evidence-based conservation planning and prioritising targeted mitigation for wide-ranging animals facing anthropogenic threats. Practical implication: The MMF provides practitioners with a transferable approach for combining animal space-use predictions with spatial information on anthropogenic threats to identify priority areas for intervention. By targeting locations containing disproportionate levels of predicted exposure, conservation resources can be directed towards areas where mitigation has the greatest potential to reduce anthropogenic mortality.