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Solving the sample size problem for resource selection functions

Garrett M. Street, Jonathan R. Potts, Luca Borger Orcid Logo, James C. Beasley, Stephen Demarais, John M. Fryxell, Philip D. McLoughlin, Kevin L. Monteith, Christina M. Prokopenko, Miltinho C. Ribeiro, Arthur R. Rodgers, Bronson K. Strickland, Floris M. Beest, David A. Bernasconi, Larissa T. Beumer, Guha Dharmarajan, Samantha P. Dwinnell, David A. Keiter, Alexine Keuroghlian, Levi J. Newediuk, Júlia Emi F. Oshima, Olin Rhodes, Peter E. Schlichting, Niels M. Schmidt, Eric Vander Wal

Methods in Ecology and Evolution, Volume: 12, Issue: 12

Swansea University Author: Luca Borger Orcid Logo

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DOI (Published version): 10.1111/2041-210x.13701

Abstract

Sample size sufficiency is a critical consideration for estimating resource selection functions (RSFs) from GPS-based animal telemetry. Cited thresholds for sufficiency include a number of captured animals urn:x-wiley:2041210X:media:mee313701:mee313701-math-0001 and as many relocations per animal N...

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Published in: Methods in Ecology and Evolution
ISSN: 2041-210X 2041-210X
Published: Wiley 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa57807
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These thresholds render many RSF-based studies misleading if large sample sizes were truly insufficient, or unpublishable if small sample sizes were sufficient but failed to meet reviewer expectations. We provide the first comprehensive solution for RSF sample size by deriving closed-form mathematical expressions for the number of animals M and the number of relocations per animal N required for model outputs to a given degree of precision. The sample sizes needed depend on just 3 biologically meaningful quantities: habitat selection strength, variation in individual selection and a novel measure of landscape complexity, which we define rigorously. The mathematical expressions are calculable for any environmental dataset at any spatial scale and are applicable to any study involving resource selection (including sessile organisms). We validate our analytical solutions using globally relevant empirical data including 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores and herbivores living in boreal, temperate and tropical forests, montane woodlands, swamps and Arctic tundra). Our analytic expressions show that the required M and N must decline with increasing selection strength and increasing landscape complexity, and this decline is insensitive to the definition of availability used in the analysis. Our results demonstrate that the most biologically relevant effects on the utilization distribution (i.e. those landscape conditions with the greatest absolute magnitude of resource selection) can often be estimated with much fewer than urn:x-wiley:2041210X:media:mee313701:mee313701-math-0002 animals. We identify several critical steps in implementing these equations, including (a) a priori selection of expected model coefficients and (b) regular sampling of background (pseudoabsence) data within a given definition of availability. We discuss possible methods to identify a priori expectations for habitat selection coefficients, effects of scale on RSF estimation and caveats for rare species applications. 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Biosciences</level></path><authors><author><firstname>Garrett M.</firstname><surname>Street</surname><order>1</order></author><author><firstname>Jonathan R.</firstname><surname>Potts</surname><order>2</order></author><author><firstname>Luca</firstname><surname>Borger</surname><orcid>0000-0001-8763-5997</orcid><order>3</order></author><author><firstname>James C.</firstname><surname>Beasley</surname><order>4</order></author><author><firstname>Stephen</firstname><surname>Demarais</surname><order>5</order></author><author><firstname>John M.</firstname><surname>Fryxell</surname><order>6</order></author><author><firstname>Philip D.</firstname><surname>McLoughlin</surname><order>7</order></author><author><firstname>Kevin L.</firstname><surname>Monteith</surname><order>8</order></author><author><firstname>Christina M.</firstname><surname>Prokopenko</surname><order>9</order></author><author><firstname>Miltinho C.</firstname><surname>Ribeiro</surname><order>10</order></author><author><firstname>Arthur R.</firstname><surname>Rodgers</surname><order>11</order></author><author><firstname>Bronson K.</firstname><surname>Strickland</surname><order>12</order></author><author><firstname>Floris M.</firstname><surname>Beest</surname><order>13</order></author><author><firstname>David A.</firstname><surname>Bernasconi</surname><order>14</order></author><author><firstname>Larissa T.</firstname><surname>Beumer</surname><order>15</order></author><author><firstname>Guha</firstname><surname>Dharmarajan</surname><order>16</order></author><author><firstname>Samantha P.</firstname><surname>Dwinnell</surname><order>17</order></author><author><firstname>David A.</firstname><surname>Keiter</surname><order>18</order></author><author><firstname>Alexine</firstname><surname>Keuroghlian</surname><order>19</order></author><author><firstname>Levi J.</firstname><surname>Newediuk</surname><order>20</order></author><author><firstname>J&#xFA;lia Emi F.</firstname><surname>Oshima</surname><order>21</order></author><author><firstname>Olin</firstname><surname>Rhodes</surname><order>22</order></author><author><firstname>Peter E.</firstname><surname>Schlichting</surname><order>23</order></author><author><firstname>Niels M.</firstname><surname>Schmidt</surname><order>24</order></author><author><firstname>Eric Vander</firstname><surname>Wal</surname><order>25</order></author></authors><documents><document><filename>57807__20789__a3d13b7c32ad4976bad326ca2635a937.pdf</filename><originalFilename>57807_AAM.pdf</originalFilename><uploaded>2021-09-08T15:58:27.2606826</uploaded><type>Output</type><contentLength>615591</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><embargoDate>2022-08-13T00:00:00.0000000</embargoDate><copyrightCorrect>true</copyrightCorrect><language>eng</language></document><document><filename>57807__20790__950c3e643de14554b1ed1c97635f723e.pdf</filename><originalFilename>57807_Supplementary.pdf</originalFilename><uploaded>2021-09-08T15:59:04.7256184</uploaded><type>Output</type><contentLength>791658</contentLength><contentType>application/pdf</contentType><version>Supplemental material</version><cronfaStatus>true</cronfaStatus><embargoDate>2022-08-13T00:00:00.0000000</embargoDate><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling 2022-10-26T13:53:34.7825698 v2 57807 2021-09-07 Solving the sample size problem for resource selection functions 8416d0ffc3cccdad6e6d67a455e7c4a2 0000-0001-8763-5997 Luca Borger Luca Borger true false 2021-09-07 SBI Sample size sufficiency is a critical consideration for estimating resource selection functions (RSFs) from GPS-based animal telemetry. Cited thresholds for sufficiency include a number of captured animals urn:x-wiley:2041210X:media:mee313701:mee313701-math-0001 and as many relocations per animal N as possible. These thresholds render many RSF-based studies misleading if large sample sizes were truly insufficient, or unpublishable if small sample sizes were sufficient but failed to meet reviewer expectations. We provide the first comprehensive solution for RSF sample size by deriving closed-form mathematical expressions for the number of animals M and the number of relocations per animal N required for model outputs to a given degree of precision. The sample sizes needed depend on just 3 biologically meaningful quantities: habitat selection strength, variation in individual selection and a novel measure of landscape complexity, which we define rigorously. The mathematical expressions are calculable for any environmental dataset at any spatial scale and are applicable to any study involving resource selection (including sessile organisms). We validate our analytical solutions using globally relevant empirical data including 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores and herbivores living in boreal, temperate and tropical forests, montane woodlands, swamps and Arctic tundra). Our analytic expressions show that the required M and N must decline with increasing selection strength and increasing landscape complexity, and this decline is insensitive to the definition of availability used in the analysis. Our results demonstrate that the most biologically relevant effects on the utilization distribution (i.e. those landscape conditions with the greatest absolute magnitude of resource selection) can often be estimated with much fewer than urn:x-wiley:2041210X:media:mee313701:mee313701-math-0002 animals. We identify several critical steps in implementing these equations, including (a) a priori selection of expected model coefficients and (b) regular sampling of background (pseudoabsence) data within a given definition of availability. We discuss possible methods to identify a priori expectations for habitat selection coefficients, effects of scale on RSF estimation and caveats for rare species applications. We argue that these equations should be a mandatory component for all future RSF studies. Journal Article Methods in Ecology and Evolution 12 12 Wiley 2041-210X 2041-210X Ecological Modelling, Ecology, Evolution, Behavior and Systematics 23 8 2021 2021-08-23 10.1111/2041-210x.13701 COLLEGE NANME Biosciences COLLEGE CODE SBI Swansea University Mississippi Department of Wildlife, Fisheries, and Parks Identifier: FundRef 10.13039/100014490 Parks Canada Identifier: FundRef 10.13039/100014612 Animal and Plant Health Inspection Service Identifier: FundRef 10.13039/100009168 U.S. Department of Energy Identifier: FundRef 10.13039/100000015 U.S. Forest Service Identifier: FundRef 10.13039/100006959 U.S. Fish and Wildlife Service Identifier: FundRef 10.13039/100000202 Ontario Ministry of Natural Resources and Forestry Identifier: FundRef 10.13039/100008138 2022-10-26T13:53:34.7825698 2021-09-07T17:02:31.9519445 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Biosciences Garrett M. Street 1 Jonathan R. Potts 2 Luca Borger 0000-0001-8763-5997 3 James C. Beasley 4 Stephen Demarais 5 John M. Fryxell 6 Philip D. McLoughlin 7 Kevin L. Monteith 8 Christina M. Prokopenko 9 Miltinho C. Ribeiro 10 Arthur R. Rodgers 11 Bronson K. Strickland 12 Floris M. Beest 13 David A. Bernasconi 14 Larissa T. Beumer 15 Guha Dharmarajan 16 Samantha P. Dwinnell 17 David A. Keiter 18 Alexine Keuroghlian 19 Levi J. Newediuk 20 Júlia Emi F. Oshima 21 Olin Rhodes 22 Peter E. Schlichting 23 Niels M. Schmidt 24 Eric Vander Wal 25 57807__20789__a3d13b7c32ad4976bad326ca2635a937.pdf 57807_AAM.pdf 2021-09-08T15:58:27.2606826 Output 615591 application/pdf Accepted Manuscript true 2022-08-13T00:00:00.0000000 true eng 57807__20790__950c3e643de14554b1ed1c97635f723e.pdf 57807_Supplementary.pdf 2021-09-08T15:59:04.7256184 Output 791658 application/pdf Supplemental material true 2022-08-13T00:00:00.0000000 true eng
title Solving the sample size problem for resource selection functions
spellingShingle Solving the sample size problem for resource selection functions
Luca Borger
title_short Solving the sample size problem for resource selection functions
title_full Solving the sample size problem for resource selection functions
title_fullStr Solving the sample size problem for resource selection functions
title_full_unstemmed Solving the sample size problem for resource selection functions
title_sort Solving the sample size problem for resource selection functions
author_id_str_mv 8416d0ffc3cccdad6e6d67a455e7c4a2
author_id_fullname_str_mv 8416d0ffc3cccdad6e6d67a455e7c4a2_***_Luca Borger
author Luca Borger
author2 Garrett M. Street
Jonathan R. Potts
Luca Borger
James C. Beasley
Stephen Demarais
John M. Fryxell
Philip D. McLoughlin
Kevin L. Monteith
Christina M. Prokopenko
Miltinho C. Ribeiro
Arthur R. Rodgers
Bronson K. Strickland
Floris M. Beest
David A. Bernasconi
Larissa T. Beumer
Guha Dharmarajan
Samantha P. Dwinnell
David A. Keiter
Alexine Keuroghlian
Levi J. Newediuk
Júlia Emi F. Oshima
Olin Rhodes
Peter E. Schlichting
Niels M. Schmidt
Eric Vander Wal
format Journal article
container_title Methods in Ecology and Evolution
container_volume 12
container_issue 12
publishDate 2021
institution Swansea University
issn 2041-210X
2041-210X
doi_str_mv 10.1111/2041-210x.13701
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 Sample size sufficiency is a critical consideration for estimating resource selection functions (RSFs) from GPS-based animal telemetry. Cited thresholds for sufficiency include a number of captured animals urn:x-wiley:2041210X:media:mee313701:mee313701-math-0001 and as many relocations per animal N as possible. These thresholds render many RSF-based studies misleading if large sample sizes were truly insufficient, or unpublishable if small sample sizes were sufficient but failed to meet reviewer expectations. We provide the first comprehensive solution for RSF sample size by deriving closed-form mathematical expressions for the number of animals M and the number of relocations per animal N required for model outputs to a given degree of precision. The sample sizes needed depend on just 3 biologically meaningful quantities: habitat selection strength, variation in individual selection and a novel measure of landscape complexity, which we define rigorously. The mathematical expressions are calculable for any environmental dataset at any spatial scale and are applicable to any study involving resource selection (including sessile organisms). We validate our analytical solutions using globally relevant empirical data including 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores and herbivores living in boreal, temperate and tropical forests, montane woodlands, swamps and Arctic tundra). Our analytic expressions show that the required M and N must decline with increasing selection strength and increasing landscape complexity, and this decline is insensitive to the definition of availability used in the analysis. Our results demonstrate that the most biologically relevant effects on the utilization distribution (i.e. those landscape conditions with the greatest absolute magnitude of resource selection) can often be estimated with much fewer than urn:x-wiley:2041210X:media:mee313701:mee313701-math-0002 animals. We identify several critical steps in implementing these equations, including (a) a priori selection of expected model coefficients and (b) regular sampling of background (pseudoabsence) data within a given definition of availability. We discuss possible methods to identify a priori expectations for habitat selection coefficients, effects of scale on RSF estimation and caveats for rare species applications. We argue that these equations should be a mandatory component for all future RSF studies.
published_date 2021-08-23T04:13:49Z
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score 11.021648

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