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Solving the sample size problem for resource selection functions

Journal Article · · Methods in Ecology and Evolution (Online)
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [8];  [9];  [10];  [11];  [5];  [12];  [4];  [12];  [4];  [13];  [4];  [14];  [9] more »;  [10];  [4];  [4];  [12];  [9] « less
  1. Department of Wildlife, Fisheries, and Aquaculture Mississippi State University Mississippi State MS USA, Quantitative Ecology and Spatial Technologies Laboratory Mississippi State University Mississippi State MS USA
  2. School of Mathematics and Statistics University of Sheffield Sheffield UK
  3. Department of Biosciences Swansea University Swansea UK, Centre for Biomathematics Swansea University Swansea UK
  4. Savannah River Ecology Laboratory University of Georgia Aiken SC USA
  5. Department of Wildlife, Fisheries, and Aquaculture Mississippi State University Mississippi State MS USA
  6. Department of Integrative Biology University of Guelph Guelph ON Canada
  7. Department of Biology University of Saskatchewan Saskatoon SK Canada
  8. Haub School of Environment and Natural Resources University of Wyoming Laramie WY USA
  9. Department of Biology Memorial University of Newfoundland St. John’s NL Canada
  10. Instituto de Biosciências Universidad Estadual Paulista Rio Claro, São Paulo Brazil
  11. Centre for Northern Forest Ecosystem Research Ontario Ministry of Natural Resources and Forestry ON Canada
  12. Department of Bioscience Aarhus University Aarhus Denmark
  13. Wyoming Cooperative Fish and Wildlife Research Unit University of Wyoming Laramie WY USA
  14. IUCN/SSC Peccary Specialist Group Campo Grande Brazil
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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.

Sponsoring Organization:
USDOE
OSTI ID:
1814655
Journal Information:
Methods in Ecology and Evolution (Online), Journal Name: Methods in Ecology and Evolution (Online) Journal Issue: 12 Vol. 12; ISSN 2041-210X
Publisher:
Wiley-BlackwellCopyright Statement
Country of Publication:
United Kingdom
Language:
English

References (27)

The importance of sample size in marine megafauna tagging studies journal July 2019
MMI: Multimodel inference or models with management implications?: Multimodel Inference and Models for Management journal May 2015
An Introduction to Statistical Learning book January 2013
On territorial behavior and other factors influencing habitat distribution in birds: I. Theoretical development journal March 1969
Effects of spatial scale and sample size in GPS-based species distribution models: are the best models trivial for red deer management? journal August 2011
Habitat functional response mitigates reduced foraging opportunity: implications for animal fitness and space use journal March 2016
Relating populations to habitats using resource selection functions journal July 1999
Distinguishing technology from biology: a critical review of the use of GPS telemetry data in ecology journal July 2010
A ‘How to’ guide for interpreting parameters in habitat‐selection analyses journal March 2021
Species distribution models predict rare species occurrences despite significant effects of landscape context journal June 2016
A generalized residual technique for analysing complex movement models using earth mover's distance journal October 2014
Power analysis for generalized linear mixed models in ecology and evolution journal December 2014
Improving rigour and efficiency of use-availability habitat selection analyses with systematic estimation of availability journal November 2012
Functional responses in habitat selection are density dependent in a large herbivore journal July 2015
Application of random effects to the study of resource selection by animals: Random effects in resource selection journal June 2006
Considering ecological dynamics in resource selection functions journal January 2010
Effects of sample size on the performance of species distribution models journal September 2008
Equivalence of MAXENT and Poisson Point Process Models for Species Distribution Modeling in Ecology: Equivalence of MAXENT and Poisson Point Process Models journal February 2013
Designing studies to detect differences in species occupancy: power analysis under imperfect detection: Power analysis for occupancy studies journal June 2012
Species Distribution Models: Ecological Explanation and Prediction Across Space and Time journal December 2009
Seasonal variation in preference dictates space use in an invasive generalist journal July 2018
Functional Responses in Habitat use: Availability Influences Relative use in Trade-Off Situations journal June 1998
Wolves Influence elk Movements: Behavior Shapes a Trophic Cascade in Yellowstone National park journal May 2005
Generalized functional responses for species distributions journal March 2011
Introduction to the Special Section on Resource Selection journal April 2006
More suggestions on raising the bar for conservation—a response to Anderson et al journal June 2004
The Comparison of Usage and Availability Measurements for Evaluating Resource Preference journal February 1980

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