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Understanding and predicting animal movements and distributions in the Anthropocene

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

Journal of Animal Ecology, Volume: 94, Issue: 6, Pages: 1146 - 1164

Swansea University Author: Luca Borger Orcid Logo

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DOI (Published version): 10.1111/1365-2656.70040

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 pre...

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Published in: Journal of Animal Ecology
ISSN: 0021-8790 1365-2656
Published: Wiley 2025
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To accurately predict the effects of anthropogenic change, it is important to first identify the defining features of human&#x2010;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&#x2010;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&#x2010;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&#x2010;based management and policy decisions. 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L.C.E. was supported by the Natural Environment Research Council Grant (award number: NE/V006916/1). Z.O. was funded by the Regional Government of Andalusia and NextGenerationEU. P.H. received support from the Faculty of Tropical AgriSciences&#x2014;Czech University of Life Sciences Prague (award number: IGA20243107). C.J.G.R. was supported by the Natural Environment Research Council and the ARIES Doctoral Training Partnership (award number: NE/S007334/1). C.R. acknowledges funding from the Gordon and Betty Moore Foundation (GBMF9881) and the National Geographic Society (NGS-82515R-20). 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spelling 2025-06-05T17:13:40.6558912 v2 69288 2025-04-14 Understanding and predicting animal movements and distributions in the Anthropocene 8416d0ffc3cccdad6e6d67a455e7c4a2 0000-0001-8763-5997 Luca Borger Luca Borger true false 2025-04-14 BGPS 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. Journal Article Journal of Animal Ecology 94 6 1146 1164 Wiley 0021-8790 1365-2656 biologging, conservation, human-modified landscapes, modelling, movement ecology 1 6 2025 2025-06-01 10.1111/1365-2656.70040 Review COLLEGE NANME Biosciences Geography and Physics School COLLEGE CODE BGPS Swansea University Another institution paid the OA fee V.B.A. received support from the São Paulo Research Foundation (processes number: 2020/07586-4). L.C.E. was supported by the Natural Environment Research Council Grant (award number: NE/V006916/1). Z.O. was funded by the Regional Government of Andalusia and NextGenerationEU. P.H. received support from the Faculty of Tropical AgriSciences—Czech University of Life Sciences Prague (award number: IGA20243107). C.J.G.R. was supported by the Natural Environment Research Council and the ARIES Doctoral Training Partnership (award number: NE/S007334/1). C.R. acknowledges funding from the Gordon and Betty Moore Foundation (GBMF9881) and the National Geographic Society (NGS-82515R-20). K.F.W. was supported by the Scottish Government's Rural and Environment Science and Analytical Services Division (RESAS). 2025-06-05T17:13:40.6558912 2025-04-14T12:14:39.2714439 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Biosciences Sara Gomez 0000-0003-1299-7509 1 Holly M. English 0000-0002-8854-6707 2 Vanesa Bejarano Alegre 0000-0002-3899-291x 3 Paul G. Blackwell 0000-0002-3141-4914 4 Anna M. Bracken 0000-0002-5183-3139 5 Eloise Bray 6 Luke C. Evans 0000-0001-8649-0589 7 Jelaine L. Gan 8 W. James Grecian 0000-0002-6428-719x 9 Catherine Gutmann Roberts 0000-0002-8506-3355 10 Seth M. Harju 0000-0003-0444-7881 11 Pavla Hejcmanová 0000-0001-9547-4302 12 Lucie Lelotte 13 Benjamin Michael Marshall 0000-0001-9554-0605 14 Jason Matthiopoulos 0000-0003-3639-8172 15 AichiMkunde Josephat Mnenge 16 Bernardo Brandao Niebuhr 0000-0002-0453-315x 17 Zaida Ortega 0000-0002-8167-1652 18 Christopher J. Pollock 0000-0002-5859-9437 19 Jonathan R. Potts 0000-0002-8564-2904 20 Charlie J. G. Russell 0000-0002-4271-0700 21 Christian Rutz 0000-0001-5187-7417 22 Navinder J. Singh 0000-0002-5131-0004 23 Katherine F. Whyte 0000-0003-3388-9603 24 Luca Borger 0000-0001-8763-5997 25 69288__34409__3bb8e548148d4f9f8069885131626067.pdf 69288.VOR.pdf 2025-06-05T17:11:06.6712798 Output 1063470 application/pdf Version of Record true © 2025 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License. true eng http://creativecommons.org/licenses/by/4.0/
title Understanding and predicting animal movements and distributions in the Anthropocene
spellingShingle Understanding and predicting animal movements and distributions in the Anthropocene
Luca Borger
title_short Understanding and predicting animal movements and distributions in the Anthropocene
title_full Understanding and predicting animal movements and distributions in the Anthropocene
title_fullStr Understanding and predicting animal movements and distributions in the Anthropocene
title_full_unstemmed Understanding and predicting animal movements and distributions in the Anthropocene
title_sort Understanding and predicting animal movements and distributions in the Anthropocene
author_id_str_mv 8416d0ffc3cccdad6e6d67a455e7c4a2
author_id_fullname_str_mv 8416d0ffc3cccdad6e6d67a455e7c4a2_***_Luca Borger
author Luca Borger
author2 Sara Gomez
Holly M. English
Vanesa Bejarano Alegre
Paul G. Blackwell
Anna M. Bracken
Eloise Bray
Luke C. Evans
Jelaine L. Gan
W. James Grecian
Catherine Gutmann Roberts
Seth M. Harju
Pavla Hejcmanová
Lucie Lelotte
Benjamin Michael Marshall
Jason Matthiopoulos
AichiMkunde Josephat Mnenge
Bernardo Brandao Niebuhr
Zaida Ortega
Christopher J. Pollock
Jonathan R. Potts
Charlie J. G. Russell
Christian Rutz
Navinder J. Singh
Katherine F. Whyte
Luca Borger
format Journal article
container_title Journal of Animal Ecology
container_volume 94
container_issue 6
container_start_page 1146
publishDate 2025
institution Swansea University
issn 0021-8790
1365-2656
doi_str_mv 10.1111/1365-2656.70040
publisher Wiley
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Biosciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Biosciences
document_store_str 1
active_str 0
description 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.
published_date 2025-06-01T05:26:33Z
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score 11.089572

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