Understanding and predicting animal movements and distributions in the Anthropocene

Gomez, Sara; English, Holly M.; Bejarano Alegre, Vanesa; Blackwell, Paul G.; Bracken, Anna M.; Bray, Eloise; Evans, Luke C.; Gan, Jelaine L.; Grecian, W. James; Gutmann Roberts, Catherine; Harju, Seth M.; Hejcmanová, Pavla; Lelotte, Lucie; Marshall, Benjamin Michael; Matthiopoulos, Jason; Mnenge, AichiMkunde Josephat; Niebuhr, Bernardo Brandao; Ortega, Zaida; Pollock, Christopher J.; Potts, Jonathan R.; Russell, Charlie J. G.; Rutz, Christian; Singh, Navinder J.; Whyte, Katherine F.; Börger, Luca

Abstract

Predicting animal movements and spatial distributions is crucial for our comprehension of ecological processes and provides key evidence for conserving and managing populations, species and ecosystems. Notwithstanding considerable progress in movement ecology in recent decades, developing robust predictions for rapidly changing environments remains challenging. To accurately predict the effects of anthropogenic change, it is important to first identify the defining features of human‐modified environments and their consequences on the drivers of animal movement. We review and discuss these features within the movement ecology framework, describing relationships between external environment, internal state, navigation and motion capacity. Developing robust predictions under novel situations requires models moving beyond purely correlative approaches to a dynamical systems perspective. This requires increased mechanistic modelling, using functional parameters derived from first principles of animal movement and decision‐making. Theory and empirical observations should be better integrated by using experimental approaches. Models should be fitted to new and historic data gathered across a wide range of contrasting environmental conditions. We need therefore a targeted and supervised approach to data collection, increasing the range of studied taxa and carefully considering issues of scale and bias, and mechanistic modelling. Thus, we caution against the indiscriminate non‐supervised use of citizen science data, AI and machine learning models. We highlight the challenges and opportunities of incorporating movement predictions into management actions and policy. Rewilding and translocation schemes offer exciting opportunities to collect data from novel environments, enabling tests of model predictions across varied contexts and scales. Adaptive management frameworks in particular, based on a stepwise iterative process, including predictions and refinements, provide exciting opportunities of mutual benefit to movement ecology and conservation. In conclusion, movement ecology is on the verge of transforming from a descriptive to a predictive science. This is a timely progression, given that robust predictions under rapidly changing environmental conditions are now more urgently needed than ever for evidence‐based management and policy decisions. Our key aim now is not to describe the existing data as well as possible, but rather to understand the underlying mechanisms and develop models with reliable predictive ability in novel situations.

Citation

Gomez, S., English, H. M., Bejarano Alegre, V., Blackwell, P. G., Bracken, A. M., Bray, E., Evans, L. C., Gan, J. L., Grecian, W. J., Gutmann Roberts, C., Harju, S. M., Hejcmanová, P., Lelotte, L., Marshall, B. M., Matthiopoulos, J., Mnenge, A. J., Niebuhr, B. B., Ortega, Z., Pollock, C. J., Potts, J. R., …Börger, L. (online). Understanding and predicting animal movements and distributions in the Anthropocene. Journal of Animal Ecology, https://doi.org/10.1111/1365-2656.70040

• Journal Article Type: Article
• Acceptance Date: Mar 17, 2025
• Online Publication Date: Apr 4, 2025
• Deposit Date: Apr 16, 2025
• Publicly Available Date: Apr 16, 2025
• Journal: Journal of Animal Ecology
• Print ISSN: 0021-8790
• Electronic ISSN: 1365-2656
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1111/1365-2656.70040
• Keywords: biologging, conservation, human‐modified landscapes, movement ecology, modelling
• Public URL: https://durham-repository.worktribe.com/output/3780954