Solving the Sample Size Problem for Resource Selection Analysis
Resource selection analysis (RSA) is a cornerstone approach for understanding animal distributions, yet there exists no rigorous quantification of sample sizes required to obtain reliable results. We provide closed-form mathematical expressions for both the number of animals and relocations per anim...
Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , |
---|---|
Format: | Other/Unknown Material |
Language: | unknown |
Published: |
Authorea, Inc.
2022
|
Subjects: | |
Online Access: | http://dx.doi.org/10.22541/au.164865115.53827873/v1 |
id |
crwinnower:10.22541/au.164865115.53827873/v1 |
---|---|
record_format |
openpolar |
spelling |
crwinnower:10.22541/au.164865115.53827873/v1 2024-06-02T08:15:25+00:00 Solving the Sample Size Problem for Resource Selection Analysis Street, Garrett Potts, Jonathan Börger, Luca Beasley, James Demarais, Stevew Fryxell, John McLoughlin, Philip Monteith, Kevin Prokopenko, Christina Ribeiro, Milton Rodgers, Arthur Strickland, Bronson Beest, Floris van Bernasconi, David Beumer, Larissa Dharmarajan, Guha Dwinnel, Samantha Keiter, David Keuroghlian, Alexine Newediuk, Levi Oshima, Júlia Rhodes, Olin Schlichting, Peter Schmidt, Neils Wal, Eric Vander 2022 http://dx.doi.org/10.22541/au.164865115.53827873/v1 unknown Authorea, Inc. posted-content 2022 crwinnower https://doi.org/10.22541/au.164865115.53827873/v1 2024-05-07T14:19:12Z Resource selection analysis (RSA) is a cornerstone approach for understanding animal distributions, yet there exists no rigorous quantification of sample sizes required to obtain reliable results. We provide closed-form mathematical expressions for both the number of animals and relocations per animal required for parameterising RSA to a given degree of precision. Required sample sizes depend on just two quantities: habitat selection strength and an index of landscape complexity, which we define rigorously. We validate our solutions using 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores, and herbivores from boreal, temperate, and tropical forests, montane woodlands, swamps, and tundra). Our results contradict conventional wisdom by showing that environmental effects on distributions can often be estimated with fewer animals and relocations than assumed, with far-reaching implications for ecologists, conservationists, and natural resource managers. Other/Unknown Material Tundra The Winnower |
institution |
Open Polar |
collection |
The Winnower |
op_collection_id |
crwinnower |
language |
unknown |
description |
Resource selection analysis (RSA) is a cornerstone approach for understanding animal distributions, yet there exists no rigorous quantification of sample sizes required to obtain reliable results. We provide closed-form mathematical expressions for both the number of animals and relocations per animal required for parameterising RSA to a given degree of precision. Required sample sizes depend on just two quantities: habitat selection strength and an index of landscape complexity, which we define rigorously. We validate our solutions using 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores, and herbivores from boreal, temperate, and tropical forests, montane woodlands, swamps, and tundra). Our results contradict conventional wisdom by showing that environmental effects on distributions can often be estimated with fewer animals and relocations than assumed, with far-reaching implications for ecologists, conservationists, and natural resource managers. |
format |
Other/Unknown Material |
author |
Street, Garrett Potts, Jonathan Börger, Luca Beasley, James Demarais, Stevew Fryxell, John McLoughlin, Philip Monteith, Kevin Prokopenko, Christina Ribeiro, Milton Rodgers, Arthur Strickland, Bronson Beest, Floris van Bernasconi, David Beumer, Larissa Dharmarajan, Guha Dwinnel, Samantha Keiter, David Keuroghlian, Alexine Newediuk, Levi Oshima, Júlia Rhodes, Olin Schlichting, Peter Schmidt, Neils Wal, Eric Vander |
spellingShingle |
Street, Garrett Potts, Jonathan Börger, Luca Beasley, James Demarais, Stevew Fryxell, John McLoughlin, Philip Monteith, Kevin Prokopenko, Christina Ribeiro, Milton Rodgers, Arthur Strickland, Bronson Beest, Floris van Bernasconi, David Beumer, Larissa Dharmarajan, Guha Dwinnel, Samantha Keiter, David Keuroghlian, Alexine Newediuk, Levi Oshima, Júlia Rhodes, Olin Schlichting, Peter Schmidt, Neils Wal, Eric Vander Solving the Sample Size Problem for Resource Selection Analysis |
author_facet |
Street, Garrett Potts, Jonathan Börger, Luca Beasley, James Demarais, Stevew Fryxell, John McLoughlin, Philip Monteith, Kevin Prokopenko, Christina Ribeiro, Milton Rodgers, Arthur Strickland, Bronson Beest, Floris van Bernasconi, David Beumer, Larissa Dharmarajan, Guha Dwinnel, Samantha Keiter, David Keuroghlian, Alexine Newediuk, Levi Oshima, Júlia Rhodes, Olin Schlichting, Peter Schmidt, Neils Wal, Eric Vander |
author_sort |
Street, Garrett |
title |
Solving the Sample Size Problem for Resource Selection Analysis |
title_short |
Solving the Sample Size Problem for Resource Selection Analysis |
title_full |
Solving the Sample Size Problem for Resource Selection Analysis |
title_fullStr |
Solving the Sample Size Problem for Resource Selection Analysis |
title_full_unstemmed |
Solving the Sample Size Problem for Resource Selection Analysis |
title_sort |
solving the sample size problem for resource selection analysis |
publisher |
Authorea, Inc. |
publishDate |
2022 |
url |
http://dx.doi.org/10.22541/au.164865115.53827873/v1 |
genre |
Tundra |
genre_facet |
Tundra |
op_doi |
https://doi.org/10.22541/au.164865115.53827873/v1 |
_version_ |
1800739597581287424 |