Using spatial statistics and point‐pattern simulations to assess the spatial dependency between greater sage‐grouse and anthropogenic features

Abstract The greater sage‐grouse (Centrocercus urophasianus; hereafter, sage‐grouse), a candidate species for listing under the Endangered Species Act, has experienced population declines across its range in the sagebrush (Artemisia spp.) steppe ecosystems of western North America. One factor contri...

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Main Authors: Jeffrey K. Gillan, Eva K. Strand, Jason W. Karl, Kerry P. Reese, Tamara Laninga
Format: Article
Language:English
Published: Wiley 2013-06-01
Series:Wildlife Society Bulletin
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Online Access:https://doi.org/10.1002/wsb.272
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author Jeffrey K. Gillan
Eva K. Strand
Jason W. Karl
Kerry P. Reese
Tamara Laninga
author_facet Jeffrey K. Gillan
Eva K. Strand
Jason W. Karl
Kerry P. Reese
Tamara Laninga
author_sort Jeffrey K. Gillan
collection DOAJ
description Abstract The greater sage‐grouse (Centrocercus urophasianus; hereafter, sage‐grouse), a candidate species for listing under the Endangered Species Act, has experienced population declines across its range in the sagebrush (Artemisia spp.) steppe ecosystems of western North America. One factor contributing to the loss of habitat is the expanding human population with associated development and infrastructure. Our objective was to use a spatial‐statistical approach to assess the effect of roads, power transmission lines, and rural buildings on sage‐grouse habitat use. We used the pair correlation function (PCF) spatial statistic to compare sage‐grouse radiotelemetry locations in west‐central Idaho, USA, to the locations of anthropogenic features to determine whether sage‐grouse avoided these features, thus reducing available habitat. To determine significance, we compared empirical PCFs with Monte Carlo simulations that replicated the spatial autocorrelation of the sampled sage‐grouse locations. We demonstrate the implications of selecting an appropriate null model for the spatial statistical analysis by comparing results using a spatially random and a clustered null model. Results indicated that sage‐grouse avoided buildings by 150 m and power transmission lines by 600 m, because their PCFs were outside the bounds of a 95% significance envelope constructed from 1,000 iterations of a null model. Sage‐grouse exhibited no detectable avoidance of major and minor roads. The methods used here are broadly applicable in conservation biology and wildlife management to evaluate spatial relationships between species occurrence and landscape features. Our results can directly inform planning of infrastructure and other development projects in or near sage‐grouse habitat. © 2013 The Wildlife Society.
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spelling doaj-art-65d59141c7fe4e21895376a274f6e7b72025-08-20T02:49:14ZengWileyWildlife Society Bulletin2328-55402013-06-0137230131010.1002/wsb.272Using spatial statistics and point‐pattern simulations to assess the spatial dependency between greater sage‐grouse and anthropogenic featuresJeffrey K. Gillan0Eva K. Strand1Jason W. Karl2Kerry P. Reese3Tamara Laninga4Department of Forest, Rangeland and Fire SciencesUniversity of Idaho709 Deakin AvenueMoscow, ID83844‐9802USADepartment of Forest, Rangeland and Fire SciencesUniversity of Idaho709 Deakin AvenueMoscow, ID83844‐9802USANew Mexico State University Las CrucesNM88003‐8003USADepartment of Fish and Wildlife ResourcesUniversity of Idaho709 Deakin AvenueMoscow, ID83844‐9802USADepartment of Conservation Social SciencesUniversity of Idaho709 Deakin AvenueMoscow, ID83844‐9802USAAbstract The greater sage‐grouse (Centrocercus urophasianus; hereafter, sage‐grouse), a candidate species for listing under the Endangered Species Act, has experienced population declines across its range in the sagebrush (Artemisia spp.) steppe ecosystems of western North America. One factor contributing to the loss of habitat is the expanding human population with associated development and infrastructure. Our objective was to use a spatial‐statistical approach to assess the effect of roads, power transmission lines, and rural buildings on sage‐grouse habitat use. We used the pair correlation function (PCF) spatial statistic to compare sage‐grouse radiotelemetry locations in west‐central Idaho, USA, to the locations of anthropogenic features to determine whether sage‐grouse avoided these features, thus reducing available habitat. To determine significance, we compared empirical PCFs with Monte Carlo simulations that replicated the spatial autocorrelation of the sampled sage‐grouse locations. We demonstrate the implications of selecting an appropriate null model for the spatial statistical analysis by comparing results using a spatially random and a clustered null model. Results indicated that sage‐grouse avoided buildings by 150 m and power transmission lines by 600 m, because their PCFs were outside the bounds of a 95% significance envelope constructed from 1,000 iterations of a null model. Sage‐grouse exhibited no detectable avoidance of major and minor roads. The methods used here are broadly applicable in conservation biology and wildlife management to evaluate spatial relationships between species occurrence and landscape features. Our results can directly inform planning of infrastructure and other development projects in or near sage‐grouse habitat. © 2013 The Wildlife Society.https://doi.org/10.1002/wsb.272Centrocercus urophasianusMonte Carlopair correlation functionpoint patternRipley's Ksage‐grouse
spellingShingle Jeffrey K. Gillan
Eva K. Strand
Jason W. Karl
Kerry P. Reese
Tamara Laninga
Using spatial statistics and point‐pattern simulations to assess the spatial dependency between greater sage‐grouse and anthropogenic features
Wildlife Society Bulletin
Centrocercus urophasianus
Monte Carlo
pair correlation function
point pattern
Ripley's K
sage‐grouse
title Using spatial statistics and point‐pattern simulations to assess the spatial dependency between greater sage‐grouse and anthropogenic features
title_full Using spatial statistics and point‐pattern simulations to assess the spatial dependency between greater sage‐grouse and anthropogenic features
title_fullStr Using spatial statistics and point‐pattern simulations to assess the spatial dependency between greater sage‐grouse and anthropogenic features
title_full_unstemmed Using spatial statistics and point‐pattern simulations to assess the spatial dependency between greater sage‐grouse and anthropogenic features
title_short Using spatial statistics and point‐pattern simulations to assess the spatial dependency between greater sage‐grouse and anthropogenic features
title_sort using spatial statistics and point pattern simulations to assess the spatial dependency between greater sage grouse and anthropogenic features
topic Centrocercus urophasianus
Monte Carlo
pair correlation function
point pattern
Ripley's K
sage‐grouse
url https://doi.org/10.1002/wsb.272
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