No Cover Image

Journal article 70 views 14 downloads

Assessing the predictive power of step selection functions: How social and environmental interactions affect animal space use

Jonathan R. Potts Orcid Logo, Luca Borger Orcid Logo, Bronson K. Strickland, Garrett M. Street Orcid Logo

Methods in Ecology and Evolution, Volume: 13, Issue: 8, Pages: 1805 - 1818

Swansea University Author: Luca Borger Orcid Logo

  • 60414.pdf

    PDF | Version of Record

    © 2022 The Authors. This is an open access article under the terms of the Creative Commons Attribution License

    Download (1.85MB)

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...

Full description

Published in: Methods in Ecology and Evolution
ISSN: 2041-210X 2041-210X
Published: Wiley 2022
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa60414
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2022-07-08T11:23:11Z
last_indexed 2022-07-08T11:23:11Z
id cronfa60414
recordtype SURis
fullrecord <?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>60414</id><entry>2022-07-08</entry><title>Assessing the predictive power of step selection functions: How social and environmental interactions affect animal space use</title><swanseaauthors><author><sid>8416d0ffc3cccdad6e6d67a455e7c4a2</sid><ORCID>0000-0001-8763-5997</ORCID><firstname>Luca</firstname><surname>Borger</surname><name>Luca Borger</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2022-07-08</date><deptcode>SBI</deptcode><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 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.</abstract><type>Journal Article</type><journal>Methods in Ecology and Evolution</journal><volume>13</volume><journalNumber>8</journalNumber><paginationStart>1805</paginationStart><paginationEnd>1818</paginationEnd><publisher>Wiley</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>2041-210X</issnPrint><issnElectronic>2041-210X</issnElectronic><keywords>animal movement, home range, individual-based model, movement ecology, resource selection, spatial ecology, step selection, utilisation distribution</keywords><publishedDay>2</publishedDay><publishedMonth>8</publishedMonth><publishedYear>2022</publishedYear><publishedDate>2022-08-02</publishedDate><doi>10.1111/2041-210x.13904</doi><url/><notes/><college>COLLEGE NANME</college><department>Biosciences</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SBI</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>Agricultural Research Service. Grant Number: #58-0200-0-002</funders><projectreference/><lastEdited>2022-08-03T15:07:48.1213989</lastEdited><Created>2022-07-08T11:50:59.0702564</Created><path><level id="1">College of Science</level><level id="2">Biosciences</level></path><authors><author><firstname>Jonathan R.</firstname><surname>Potts</surname><orcid>0000-0002-8564-2904</orcid><order>1</order></author><author><firstname>Luca</firstname><surname>Borger</surname><orcid>0000-0001-8763-5997</orcid><order>2</order></author><author><firstname>Bronson K.</firstname><surname>Strickland</surname><order>3</order></author><author><firstname>Garrett M.</firstname><surname>Street</surname><orcid>0000-0002-1260-9214</orcid><order>4</order></author></authors><documents><document><filename>60414__24574__f34f4fbd9f24477d9ad1c5c2b5055dcd.pdf</filename><originalFilename>60414.pdf</originalFilename><uploaded>2022-07-13T11:53:00.5585008</uploaded><type>Output</type><contentLength>1937974</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>© 2022 The Authors. This is an open access article under the terms of the Creative Commons Attribution License</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs><OutputDur><Id>105</Id><IsDataAvailableOnline>true</IsDataAvailableOnline><DataNotAvailableOnlineReasonId xsi:nil="true"/><DurUrl>https://nassgeodata.gmu.edu/CropScape/</DurUrl><IsDurRestrictions>false</IsDurRestrictions><DurRestrictionReasonId xsi:nil="true"/><DurEmbargoDate xsi:nil="true"/></OutputDur><OutputDur><Id>106</Id><IsDataAvailableOnline>true</IsDataAvailableOnline><DataNotAvailableOnlineReasonId xsi:nil="true"/><DurUrl>https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13904#mee313904-bib-0051</DurUrl><IsDurRestrictions>false</IsDurRestrictions><DurRestrictionReasonId xsi:nil="true"/><DurEmbargoDate xsi:nil="true"/></OutputDur></OutputDurs></rfc1807>
spelling 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 SBI 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 COLLEGE CODE SBI Swansea University Agricultural Research Service. Grant Number: #58-0200-0-002 2022-08-03T15:07:48.1213989 2022-07-08T11:50:59.0702564 College of Science 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
author_id_str_mv 8416d0ffc3cccdad6e6d67a455e7c4a2
author_id_fullname_str_mv 8416d0ffc3cccdad6e6d67a455e7c4a2_***_Luca Borger
author Luca Borger
author2 Jonathan R. Potts
Luca Borger
Bronson K. Strickland
Garrett M. Street
format Journal article
container_title Methods in Ecology and Evolution
container_volume 13
container_issue 8
container_start_page 1805
publishDate 2022
institution Swansea University
issn 2041-210X
2041-210X
doi_str_mv 10.1111/2041-210x.13904
publisher Wiley
college_str College of Science
hierarchytype
hierarchy_top_id collegeofscience
hierarchy_top_title College of Science
hierarchy_parent_id collegeofscience
hierarchy_parent_title College of Science
department_str Biosciences{{{_:::_}}}College of Science{{{_:::_}}}Biosciences
document_store_str 1
active_str 0
description 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 2022-08-02T15:07:46Z
_version_ 1740149271141810176
score 10.916626