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Solving the sample size problem for resource selection functions
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
Methods in Ecology and Evolution, Volume: 12, Issue: 12
Swansea University Author: Luca Borger
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DOI (Published version): 10.1111/2041-210x.13701
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...
|Published in:||Methods in Ecology and Evolution|
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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.
Ecological Modelling, Ecology, Evolution, Behavior and Systematics
Faculty of Science and Engineering
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