Solving the sample size problem for resource selection functions
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 and as many relocations per animal N as possible. These thresholds render many RSF-ba...
Published in: | Methods in Ecology and Evolution |
---|---|
Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2021
|
Subjects: | |
Online Access: | https://pure.au.dk/portal/da/publications/solving-the-sample-size-problem-for-resource-selection-functions(c2011a03-0f20-43b8-92df-f304f7e5d021).html https://doi.org/10.1111/2041-210X.13701 https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13701 |
id |
ftuniaarhuspubl:oai:pure.atira.dk:publications/c2011a03-0f20-43b8-92df-f304f7e5d021 |
---|---|
record_format |
openpolar |
institution |
Open Polar |
collection |
Aarhus University: Research |
op_collection_id |
ftuniaarhuspubl |
language |
English |
topic |
bootstrap habitat selection p-value power analysis resource selection function sample size species distribution model validation |
spellingShingle |
bootstrap habitat selection p-value power analysis resource selection function sample size species distribution model validation Street, Garrett M. Potts, Jonathan R. Börger, Luca Beasley, James C. Demarais, Stephen Fryxell, John M. McLoughlin, Philip D. Monteith, Kevin L. Prokopenko, Christina M. Ribeiro, Miltinho C. Rodgers, Arthur R. Strickland, Bronson K. van Beest, Floris M. Bernasconi, David A. Beumer, Larissa T. Dharmarajan, Guha Dwinnell, Samantha P. Keiter, David A. Keuroghlian, Alexine Newediuk, Levi J. Oshima, Júlia Emi F. Rhodes Jr., Olin Schlichting, Peter E. Schmidt, Niels M. Vander Wal, Eric Solving the sample size problem for resource selection functions |
topic_facet |
bootstrap habitat selection p-value power analysis resource selection function sample size species distribution model validation |
description |
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 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 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. |
format |
Article in Journal/Newspaper |
author |
Street, Garrett M. Potts, Jonathan R. Börger, Luca Beasley, James C. Demarais, Stephen Fryxell, John M. McLoughlin, Philip D. Monteith, Kevin L. Prokopenko, Christina M. Ribeiro, Miltinho C. Rodgers, Arthur R. Strickland, Bronson K. van Beest, Floris M. Bernasconi, David A. Beumer, Larissa T. Dharmarajan, Guha Dwinnell, Samantha P. Keiter, David A. Keuroghlian, Alexine Newediuk, Levi J. Oshima, Júlia Emi F. Rhodes Jr., Olin Schlichting, Peter E. Schmidt, Niels M. Vander Wal, Eric |
author_facet |
Street, Garrett M. Potts, Jonathan R. Börger, Luca Beasley, James C. Demarais, Stephen Fryxell, John M. McLoughlin, Philip D. Monteith, Kevin L. Prokopenko, Christina M. Ribeiro, Miltinho C. Rodgers, Arthur R. Strickland, Bronson K. van Beest, Floris M. Bernasconi, David A. Beumer, Larissa T. Dharmarajan, Guha Dwinnell, Samantha P. Keiter, David A. Keuroghlian, Alexine Newediuk, Levi J. Oshima, Júlia Emi F. Rhodes Jr., Olin Schlichting, Peter E. Schmidt, Niels M. Vander Wal, Eric |
author_sort |
Street, Garrett M. |
title |
Solving the sample size problem for resource selection functions |
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 |
publishDate |
2021 |
url |
https://pure.au.dk/portal/da/publications/solving-the-sample-size-problem-for-resource-selection-functions(c2011a03-0f20-43b8-92df-f304f7e5d021).html https://doi.org/10.1111/2041-210X.13701 https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13701 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Tundra |
genre_facet |
Arctic Tundra |
op_source |
Street , G M , Potts , J R , Börger , L , Beasley , J C , Demarais , S , Fryxell , J M , McLoughlin , P D , Monteith , K L , Prokopenko , C M , Ribeiro , M C , Rodgers , A R , Strickland , B K , van Beest , F M , Bernasconi , D A , Beumer , L T , Dharmarajan , G , Dwinnell , S P , Keiter , D A , Keuroghlian , A , Newediuk , L J , Oshima , J E F , Rhodes Jr. , O , Schlichting , P E , Schmidt , N M & Vander Wal , E 2021 , ' Solving the sample size problem for resource selection functions ' , Methods in Ecology and Evolution , vol. 12 , no. 12 , pp. 2421-2431 . https://doi.org/10.1111/2041-210X.13701 |
op_rights |
info:eu-repo/semantics/restrictedAccess |
op_doi |
https://doi.org/10.1111/2041-210X.13701 |
container_title |
Methods in Ecology and Evolution |
_version_ |
1766348763730804736 |
spelling |
ftuniaarhuspubl:oai:pure.atira.dk:publications/c2011a03-0f20-43b8-92df-f304f7e5d021 2023-05-15T15:18:34+02:00 Solving the sample size problem for resource selection functions Street, Garrett M. Potts, Jonathan R. Börger, Luca Beasley, James C. Demarais, Stephen Fryxell, John M. McLoughlin, Philip D. Monteith, Kevin L. Prokopenko, Christina M. Ribeiro, Miltinho C. Rodgers, Arthur R. Strickland, Bronson K. van Beest, Floris M. Bernasconi, David A. Beumer, Larissa T. Dharmarajan, Guha Dwinnell, Samantha P. Keiter, David A. Keuroghlian, Alexine Newediuk, Levi J. Oshima, Júlia Emi F. Rhodes Jr., Olin Schlichting, Peter E. Schmidt, Niels M. Vander Wal, Eric 2021-08 https://pure.au.dk/portal/da/publications/solving-the-sample-size-problem-for-resource-selection-functions(c2011a03-0f20-43b8-92df-f304f7e5d021).html https://doi.org/10.1111/2041-210X.13701 https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13701 eng eng info:eu-repo/semantics/restrictedAccess Street , G M , Potts , J R , Börger , L , Beasley , J C , Demarais , S , Fryxell , J M , McLoughlin , P D , Monteith , K L , Prokopenko , C M , Ribeiro , M C , Rodgers , A R , Strickland , B K , van Beest , F M , Bernasconi , D A , Beumer , L T , Dharmarajan , G , Dwinnell , S P , Keiter , D A , Keuroghlian , A , Newediuk , L J , Oshima , J E F , Rhodes Jr. , O , Schlichting , P E , Schmidt , N M & Vander Wal , E 2021 , ' Solving the sample size problem for resource selection functions ' , Methods in Ecology and Evolution , vol. 12 , no. 12 , pp. 2421-2431 . https://doi.org/10.1111/2041-210X.13701 bootstrap habitat selection p-value power analysis resource selection function sample size species distribution model validation article 2021 ftuniaarhuspubl https://doi.org/10.1111/2041-210X.13701 2021-12-08T23:47:21Z 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 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 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. Article in Journal/Newspaper Arctic Tundra Aarhus University: Research Arctic Methods in Ecology and Evolution |