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How to scale up from animal movement decisions to spatiotemporal patterns: An approach via step selection

Jonathan R. Potts Orcid Logo, Luca Borger Orcid Logo

Journal of Animal Ecology, Volume: 92, Issue: 1, Pages: 16 - 29

Swansea University Author: Luca Borger Orcid Logo

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DOI (Published version): 10.1111/1365-2656.13832

Abstract

Uncovering the mechanisms behind animal space use patterns is of vital importance for predictive ecology, thus conservation and management of ecosystems. Movement is a core driver of those patterns so understanding how movement mechanisms give rise to space use patterns has become an increasingly ac...

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Published in: Journal of Animal Ecology
ISSN: 0021-8790 1365-2656
Published: Wiley 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa62065
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first_indexed 2022-11-28T10:46:27Z
last_indexed 2023-01-21T04:12:04Z
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spelling 2023-01-20T18:03:29.7895426 v2 62065 2022-11-28 How to scale up from animal movement decisions to spatiotemporal patterns: An approach via step selection 8416d0ffc3cccdad6e6d67a455e7c4a2 0000-0001-8763-5997 Luca Borger Luca Borger true false 2022-11-28 SBI Uncovering the mechanisms behind animal space use patterns is of vital importance for predictive ecology, thus conservation and management of ecosystems. Movement is a core driver of those patterns so understanding how movement mechanisms give rise to space use patterns has become an increasingly active area of research.This study focuses on a particular strand of research in this area, based around step selection analysis (SSA). SSA is a popular way of inferring drivers of movement decisions, but, perhaps less well appreciated, it also parametrises a model of animal movement. Of key interest is that this model can be propagated forwards in time to predict the space use patterns over broader spatial and temporal scales than those that pertain to the proximate movement decisions of animals.Here, we provide a guide for understanding and using the various existing techniques for scaling up step selection models to predict broad-scale space use patterns. We give practical guidance on when to use which technique, as well as specific examples together with code in R and Python.By pulling together various disparate techniques into one place, and providing code and instructions in simple examples, we hope to highlight the importance of these techniques and make them accessible to a wider range of ecologists, ultimately helping expand the usefulness of SSA. Journal Article Journal of Animal Ecology 92 1 16 29 Wiley 0021-8790 1365-2656 home range, individual-based models, integro-difference equations, movement ecology, partial differential equations, space use, step selection, utilisation distribution 4 1 2023 2023-01-04 10.1111/1365-2656.13832 COLLEGE NANME Biosciences COLLEGE CODE SBI Swansea University Engineering and Physical Sciences Research Council, Grant/Award Number: EP/V002988/1 2023-01-20T18:03:29.7895426 2022-11-28T10:40:59.6112943 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Biosciences Jonathan R. Potts 0000-0002-8564-2904 1 Luca Borger 0000-0001-8763-5997 2 62065__25924__c8e84ddf04644fe0979a3cece8cf0d60.pdf 62065.pdf 2022-11-28T10:45:19.9360682 Output 1978278 application/pdf Version of Record true © 2022 The Authors. 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 How to scale up from animal movement decisions to spatiotemporal patterns: An approach via step selection
spellingShingle How to scale up from animal movement decisions to spatiotemporal patterns: An approach via step selection
Luca Borger
title_short How to scale up from animal movement decisions to spatiotemporal patterns: An approach via step selection
title_full How to scale up from animal movement decisions to spatiotemporal patterns: An approach via step selection
title_fullStr How to scale up from animal movement decisions to spatiotemporal patterns: An approach via step selection
title_full_unstemmed How to scale up from animal movement decisions to spatiotemporal patterns: An approach via step selection
title_sort How to scale up from animal movement decisions to spatiotemporal patterns: An approach via step selection
author_id_str_mv 8416d0ffc3cccdad6e6d67a455e7c4a2
author_id_fullname_str_mv 8416d0ffc3cccdad6e6d67a455e7c4a2_***_Luca Borger
author Luca Borger
author2 Jonathan R. Potts
Luca Borger
format Journal article
container_title Journal of Animal Ecology
container_volume 92
container_issue 1
container_start_page 16
publishDate 2023
institution Swansea University
issn 0021-8790
1365-2656
doi_str_mv 10.1111/1365-2656.13832
publisher Wiley
college_str Faculty of Science and Engineering
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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 Uncovering the mechanisms behind animal space use patterns is of vital importance for predictive ecology, thus conservation and management of ecosystems. Movement is a core driver of those patterns so understanding how movement mechanisms give rise to space use patterns has become an increasingly active area of research.This study focuses on a particular strand of research in this area, based around step selection analysis (SSA). SSA is a popular way of inferring drivers of movement decisions, but, perhaps less well appreciated, it also parametrises a model of animal movement. Of key interest is that this model can be propagated forwards in time to predict the space use patterns over broader spatial and temporal scales than those that pertain to the proximate movement decisions of animals.Here, we provide a guide for understanding and using the various existing techniques for scaling up step selection models to predict broad-scale space use patterns. We give practical guidance on when to use which technique, as well as specific examples together with code in R and Python.By pulling together various disparate techniques into one place, and providing code and instructions in simple examples, we hope to highlight the importance of these techniques and make them accessible to a wider range of ecologists, ultimately helping expand the usefulness of SSA.
published_date 2023-01-04T04:21:23Z
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score 11.016235

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