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Assessing the predictive power of step selection functions: How social and environmental interactions affect animal space use
Methods in Ecology and Evolution, Volume: 13, Issue: 8, Pages: 1805 - 1818
Swansea University Author:
Luca Borger
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DOI (Published version): 10.1111/2041-210x.13904
Abstract
The ability to predict animal space use patterns is a fundamental concern in changing environments. Such predictions require a detailed understanding of the movement mechanisms from which spatial distributions emerge. However, these are typically complex, multifaceted, and therefore difficult to unc...
Published in: | Methods in Ecology and Evolution |
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ISSN: | 2041-210X 2041-210X |
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Wiley
2022
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URI: | https://cronfa.swan.ac.uk/Record/cronfa60414 |
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2022-08-03T15:07:48.1213989 v2 60414 2022-07-08 Assessing the predictive power of step selection functions: How social and environmental interactions affect animal space use 8416d0ffc3cccdad6e6d67a455e7c4a2 0000-0001-8763-5997 Luca Borger Luca Borger true false 2022-07-08 BGPS The ability to predict animal space use patterns is a fundamental concern in changing environments. Such predictions require a detailed understanding of the movement mechanisms from which spatial distributions emerge. However, these are typically complex, multifaceted, and therefore difficult to uncover.Here, we provide a methodological framework for uncovering the movement mechanisms necessary for building predictive models of animal space use. Our procedure begins by parametrising a movement model of each individual in a population using step selection analysis, from which we build an individual-based model (IBM) of interacting individuals, derive predicted broad-scale space use patterns from the IBM and then compare the predicted and empirical patterns. Importantly, discrepancies between these predicted and empirical patterns are used to formulate new hypotheses about the drivers of animal movement decisions and thus iteratively improve the model's predictive power. We demonstrate our method on a population of feral pigs in Mississippi, USA.Our technique incorporates both social interactions between individuals and environmental drivers of movement. At each iteration of model construction, we were able to identify missing features to improve model prediction by analysing the IBM output. These include overuse-avoidance effects of self-attractive mechanisms (i.e. attraction to previously visited sites becomes repulsion if there have been multiple visits in quick succession), which were vital for ensuring predicted occurrence distributions do not become vanishingly small.Overall, we have provided a general method for iteratively improving the predictive power of step selection models. This will enable future researchers to maximise the information obtained from step selection analyses and to highlight potentially missing data for uncovering the drivers of movement decisions and emergent space use patterns. Ultimately, this provides a fundamental step towards the general aim of constructing predictive models of animal space use. Journal Article Methods in Ecology and Evolution 13 8 1805 1818 Wiley 2041-210X 2041-210X animal movement, home range, individual-based model, movement ecology, resource selection, spatial ecology, step selection, utilisation distribution 2 8 2022 2022-08-02 10.1111/2041-210x.13904 COLLEGE NANME Biosciences Geography and Physics School COLLEGE CODE BGPS Swansea University Agricultural Research Service. Grant Number: #58-0200-0-002 2022-08-03T15:07:48.1213989 2022-07-08T11:50:59.0702564 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 Bronson K. Strickland 3 Garrett M. Street 0000-0002-1260-9214 4 60414__24574__f34f4fbd9f24477d9ad1c5c2b5055dcd.pdf 60414.pdf 2022-07-13T11:53:00.5585008 Output 1937974 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/ 105 true https://nassgeodata.gmu.edu/CropScape/ false 106 true https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13904#mee313904-bib-0051 false |
title |
Assessing the predictive power of step selection functions: How social and environmental interactions affect animal space use |
spellingShingle |
Assessing the predictive power of step selection functions: How social and environmental interactions affect animal space use Luca Borger |
title_short |
Assessing the predictive power of step selection functions: How social and environmental interactions affect animal space use |
title_full |
Assessing the predictive power of step selection functions: How social and environmental interactions affect animal space use |
title_fullStr |
Assessing the predictive power of step selection functions: How social and environmental interactions affect animal space use |
title_full_unstemmed |
Assessing the predictive power of step selection functions: How social and environmental interactions affect animal space use |
title_sort |
Assessing the predictive power of step selection functions: How social and environmental interactions affect animal space use |
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8416d0ffc3cccdad6e6d67a455e7c4a2_***_Luca Borger |
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Luca Borger |
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Jonathan R. Potts Luca Borger Bronson K. Strickland Garrett M. Street |
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Methods in Ecology and Evolution |
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The ability to predict animal space use patterns is a fundamental concern in changing environments. Such predictions require a detailed understanding of the movement mechanisms from which spatial distributions emerge. However, these are typically complex, multifaceted, and therefore difficult to uncover.Here, we provide a methodological framework for uncovering the movement mechanisms necessary for building predictive models of animal space use. Our procedure begins by parametrising a movement model of each individual in a population using step selection analysis, from which we build an individual-based model (IBM) of interacting individuals, derive predicted broad-scale space use patterns from the IBM and then compare the predicted and empirical patterns. Importantly, discrepancies between these predicted and empirical patterns are used to formulate new hypotheses about the drivers of animal movement decisions and thus iteratively improve the model's predictive power. We demonstrate our method on a population of feral pigs in Mississippi, USA.Our technique incorporates both social interactions between individuals and environmental drivers of movement. At each iteration of model construction, we were able to identify missing features to improve model prediction by analysing the IBM output. These include overuse-avoidance effects of self-attractive mechanisms (i.e. attraction to previously visited sites becomes repulsion if there have been multiple visits in quick succession), which were vital for ensuring predicted occurrence distributions do not become vanishingly small.Overall, we have provided a general method for iteratively improving the predictive power of step selection models. This will enable future researchers to maximise the information obtained from step selection analyses and to highlight potentially missing data for uncovering the drivers of movement decisions and emergent space use patterns. Ultimately, this provides a fundamental step towards the general aim of constructing predictive models of animal space use. |
published_date |
2022-08-02T08:03:52Z |
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11.052532 |