Dataset Identification:
Resource Abstract:
- description: SPACE USE INDEX CALCULATIONLek coordinates and associated trend count data were obtained from the 2013 Nevada
Sage-grouse Lek Database compiled by the Nevada Department of Wildlife (NDOW, S. Espinosa, 9/10/2013). We queried the database
for leks with a LEKSTATUS field classified as Active or Pending . Active leks comprised leks with breeding males observed
within the last 5 years. Pending leks comprised leks without consistent breeding activity during the prior 3 5 surveys or
had not been surveyed during the past 5 years; these leks typically trended towards inactive . A sage-grouse management area
(SGMA) was calculated by buffering Population Management Units developed by NDOW by 10km. This included leks from the Buffalo-Skedaddle
PMU that straddles the northeastern California Nevada border, but excluded leks for the Bi-State Distinct Population Segment.
The 5-year average (2009 2013) for the number of males grouse (or unknown gender if males were not identified) attending each
lek was calculated. The final dataset comprised 907 leks. Utilization distributions describing the probability of lek occurrence
were calculated using fixed kernel density estimators (Silverman 1986) with bandwidths estimated from likelihood based cross-validation
(CVh) (Horne and Garton 2006). UDs were weighted by the 5-year average (2009 2013) for the number of males grouse (or unknown
gender if males were not identified) attending leks. UDs and bandwidths were calculated using Geospatial Modelling Environment
(Beyer 2012) and the ks package (Duong 2012) in Program R. Grid cell size was 30m. The resulting raster was clipped by the
SGMA polygon, and values were re-scaled between zero and one by dividing by the maximum pixel value.The non-linear effect
of distance to lek on the probability of grouse spatial use was estimated using the inverse of the utilization distribution
curves described by Coates et al. (2013), where essentially the highest probability of grouse spatial use occurs near leks
and then declines precipitously as a non-linear function. Euclidean distance was first calculated in ArcGIS, reclassified
into 30-m distance bins (ranging from 0 30,000m), and bins reclassified according to the non-linear curve in Coates et al.
(2013). The resulting raster was clipped by the SGMA polygon, and re-scaled between zero and one by dividing by the maximum
pixel value.A Spatial Use Index (SUI) was calculated taking the average of the lek utilization distribution and non-linear
distance to lek rasters in ArcGIS, and re-scaled between zero and 1 by dividing by the maximum pixel value.The volume of the
SUI at cumulative 5% increments (isopleths) was extracted in Geospatial Modelling Environment (Beyer 2012) with the command
isopleth . Interior polygons (i.e., donuts > 1.2 km2) representing no probability of use within a larger polygon of
use were erased from each isopleth. The relationship between percent land area within each isopleth and isopleth volume (VanderWal
and Rodgers 2012) indicated statistically concentrated use at the 70% isopleth. The 85% isopleth, which provided greater spatial
connectivity and consistency with previously used agency standards (e.g., Doherty et al. 2010), was ultimately recommended
by the Sagebrush Ecosystem Technical Team. The 85% SUI isopleth was clipped by the SGMA clipped by the Nevada state boundary,
which only included habitat within the state of Nevada.Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson,
K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty,
D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern
CaliforniaA decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163.
ISSN 2331-1258 (online)REFERENCES Beyer HL. 2012. Geospatial Modelling Environment (Version 0.7.2.0). http://www.spatialecology.com/gmeCoates
PS, Casazza ML, Blomberg EJ, Gardner SC, Espinosa SP, Yee JL, Wiechman L, Halstead BJ. 2013. Evaluating greater sage-grouse
seasonal space use relative to leks: Implications for surface use designations in sagebrush ecosystems. The Journal of Wildlife
Management 77: 1598-1609.Doherty KE, Tack JD, Evans JS, Naugle DE. 2010. Mapping breeding densities of greater sage-grouse:
A tool for range-wide conservation planning. Bureau of Land Management. Report Number: L10PG00911. Accessed at: http://www.conservationgateway.org/ConservationByGeography/NorthAmerica/Pages/sagegrouse.aspx#
Duong T. 2012. ks: Kernel smoothing. R package version 1.8.10. http://CRAN.R-project.org/package=ksHorne JS, Garton EO. 2006.
Likelihood cross-validation versus least squares cross-validation for choosing the smoothing parameter in kernel home-range
analysis. Journal of Wildlife Management 70: 641-648.Silverman BW. 1986. Density estimation for statistics and data analysis.
Chapman & Hall, London, United Kingdom.Vander Wal E, Rodgers AR. 2012. An individual-based quantitative approach for
delineating core areas of animal space use. Ecological Modelling 224: 48-53.NOTE: This file does not include habitat areas
for the Bi-State management area.; abstract: SPACE USE INDEX CALCULATIONLek coordinates and associated trend count data were
obtained from the 2013 Nevada Sage-grouse Lek Database compiled by the Nevada Department of Wildlife (NDOW, S. Espinosa, 9/10/2013).
We queried the database for leks with a LEKSTATUS field classified as Active or Pending . Active leks comprised leks with
breeding males observed within the last 5 years. Pending leks comprised leks without consistent breeding activity during the
prior 3 5 surveys or had not been surveyed during the past 5 years; these leks typically trended towards inactive . A sage-grouse
management area (SGMA) was calculated by buffering Population Management Units developed by NDOW by 10km. This included leks
from the Buffalo-Skedaddle PMU that straddles the northeastern California Nevada border, but excluded leks for the Bi-State
Distinct Population Segment. The 5-year average (2009 2013) for the number of males grouse (or unknown gender if males were
not identified) attending each lek was calculated. The final dataset comprised 907 leks. Utilization distributions describing
the probability of lek occurrence were calculated using fixed kernel density estimators (Silverman 1986) with bandwidths estimated
from likelihood based cross-validation (CVh) (Horne and Garton 2006). UDs were weighted by the 5-year average (2009 2013)
for the number of males grouse (or unknown gender if males were not identified) attending leks. UDs and bandwidths were calculated
using Geospatial Modelling Environment (Beyer 2012) and the ks package (Duong 2012) in Program R. Grid cell size was 30m.
The resulting raster was clipped by the SGMA polygon, and values were re-scaled between zero and one by dividing by the maximum
pixel value.The non-linear effect of distance to lek on the probability of grouse spatial use was estimated using the inverse
of the utilization distribution curves described by Coates et al. (2013), where essentially the highest probability of grouse
spatial use occurs near leks and then declines precipitously as a non-linear function. Euclidean distance was first calculated
in ArcGIS, reclassified into 30-m distance bins (ranging from 0 30,000m), and bins reclassified according to the non-linear
curve in Coates et al. (2013). The resulting raster was clipped by the SGMA polygon, and re-scaled between zero and one by
dividing by the maximum pixel value.A Spatial Use Index (SUI) was calculated taking the average of the lek utilization distribution
and non-linear distance to lek rasters in ArcGIS, and re-scaled between zero and 1 by dividing by the maximum pixel value.The
volume of the SUI at cumulative 5% increments (isopleths) was extracted in Geospatial Modelling Environment (Beyer 2012) with
the command isopleth . Interior polygons (i.e., donuts > 1.2 km2) representing no probability of use within a larger
polygon of use were erased from each isopleth. The relationship between percent land area within each isopleth and isopleth
volume (VanderWal and Rodgers 2012) indicated statistically concentrated use at the 70% isopleth. The 85% isopleth, which
provided greater spatial connectivity and consistency with previously used agency standards (e.g., Doherty et al. 2010), was
ultimately recommended by the Sagebrush Ecosystem Technical Team. The 85% SUI isopleth was clipped by the SGMA clipped by
the Nevada state boundary, which only included habitat within the state of Nevada.Coates, P.S., Casazza, M.L., Brussee, B.E.,
Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S.,
Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat
in Nevada and northeastern CaliforniaA decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163,
83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online)REFERENCES Beyer HL. 2012. Geospatial Modelling Environment
(Version 0.7.2.0). http://www.spatialecology.com/gmeCoates PS, Casazza ML, Blomberg EJ, Gardner SC, Espinosa SP, Yee JL, Wiechman
L, Halstead BJ. 2013. Evaluating greater sage-grouse seasonal space use relative to leks: Implications for surface use designations
in sagebrush ecosystems. The Journal of Wildlife Management 77: 1598-1609.Doherty KE, Tack JD, Evans JS, Naugle DE. 2010.
Mapping breeding densities of greater sage-grouse: A tool for range-wide conservation planning. Bureau of Land Management.
Report Number: L10PG00911. Accessed at: http://www.conservationgateway.org/ConservationByGeography/NorthAmerica/Pages/sagegrouse.aspx#
Duong T. 2012. ks: Kernel smoothing. R package version 1.8.10. http://CRAN.R-project.org/package=ksHorne JS, Garton EO. 2006.
Likelihood cross-validation versus least squares cross-validation for choosing the smoothing parameter in kernel home-range
analysis. Journal of Wildlife Management 70: 641-648.Silverman BW. 1986. Density estimation for statistics and data analysis.
Chapman & Hall, London, United Kingdom.Vander Wal E, Rodgers AR. 2012. An individual-based quantitative approach for
delineating core areas of animal space use. Ecological Modelling 224: 48-53.NOTE: This file does not include habitat areas
for the Bi-State management area.
Citation
- Title Space Use Index (SUI) for the Greater Sage-grouse in Nevada and California (August 2014).
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- creation Date
2018-05-20T02:17:45.591126
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2018-08-06T21:17:23Z
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on a transform by Damian Ulbricht. Run on 2018-08-06T21:17:23Z
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