Solving the sample size problem for resource selection functions
1. 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 M ≥ 30 and as many relocations per animal N as possible. These thresholds render many RSF-b...
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ftleedsuniv:oai:eprints.whiterose.ac.uk:177281 2023-05-15T15:11:59+02:00 Solving the sample size problem for resource selection functions 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, O. Schlichting, P.E. Schmidt, N.M. Vander Wal, E. 2021-12-01 text https://eprints.whiterose.ac.uk/177281/ https://eprints.whiterose.ac.uk/177281/1/hma_streetetal_accepted.pdf en eng Wiley https://eprints.whiterose.ac.uk/177281/1/hma_streetetal_accepted.pdf Street, G.M., Potts, J.R. orcid.org/0000-0002-8564-2904 , Börger, L. et al. (22 more authors) (2021) Solving the sample size problem for resource selection functions. Methods in Ecology and Evolution, 12 (12). pp. 2421-2431. ISSN 2041-210X Article PeerReviewed 2021 ftleedsuniv 2023-01-30T22:40:37Z 1. 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 M ≥ 30 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. 2. 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). 3. 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 M = 30 animals. 4. We identify several critical steps in implementing these equations, including (i) a priori selection of expected model coefficients, and (ii) regular sampling of ... Article in Journal/Newspaper Arctic Tundra White Rose Research Online (Universities of Leeds, Sheffield & York) Arctic |
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Open Polar |
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White Rose Research Online (Universities of Leeds, Sheffield & York) |
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ftleedsuniv |
language |
English |
description |
1. 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 M ≥ 30 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. 2. 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). 3. 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 M = 30 animals. 4. We identify several critical steps in implementing these equations, including (i) a priori selection of expected model coefficients, and (ii) regular sampling of ... |
format |
Article in Journal/Newspaper |
author |
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, O. Schlichting, P.E. Schmidt, N.M. Vander Wal, E. |
spellingShingle |
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, O. Schlichting, P.E. Schmidt, N.M. Vander Wal, E. Solving the sample size problem for resource selection functions |
author_facet |
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, O. Schlichting, P.E. Schmidt, N.M. Vander Wal, E. |
author_sort |
Street, G.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 |
publisher |
Wiley |
publishDate |
2021 |
url |
https://eprints.whiterose.ac.uk/177281/ https://eprints.whiterose.ac.uk/177281/1/hma_streetetal_accepted.pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Tundra |
genre_facet |
Arctic Tundra |
op_relation |
https://eprints.whiterose.ac.uk/177281/1/hma_streetetal_accepted.pdf Street, G.M., Potts, J.R. orcid.org/0000-0002-8564-2904 , Börger, L. et al. (22 more authors) (2021) Solving the sample size problem for resource selection functions. Methods in Ecology and Evolution, 12 (12). pp. 2421-2431. ISSN 2041-210X |
_version_ |
1766342748034564096 |