Sage-grouse Management Categories in Nevada and NE California (August 2014)
Dataset Identification:
Resource Abstract:
Sage-grouse habitat areas divided into proposed management categories within Nevada and California project study boundaries.
<br> <br> MANAGEMENT CATEGORY DETERMINATION <br> <br> The process for category determination was directed by the Nevada Sagebrush
Ecosystem Technical team. Sage-grouse habitat was determined from a statewide resource selection function model and first
categorized into 4 classes: high, moderate, low, and non-habitat. The standard deviations (SD) from a normal distribution
of RSF values created from a set of validation points (10% of the entire telemetry dataset) were used to categorize habitat
quality classes. High quality habitat comprised pixels with RSF values < 0.5 SD, Moderate > 0.5 and < 1.0 SD, Low
< 1.0 and > 1.5, Non-Habitat > 1.5 SD. Proposed Habitat Management Categories were then defined and calculated as
follows. <br> <br> 1) Core habitat: Defined as the intersection between all suitable habitat (high, moderate, and low) and
the 85% Space Use Index (SUI). <br> <br> 2) Priority habitat: Defined as all high quality falling outside the 85% SUI and
all non-habitat falling within the 85% SUI. This was a 2-part process. High quality falling outside the 85% SUI was erased
by the 85% SUI, and non-habitat was clipped by the SUI. <br> <br> 3) General habitat: Defined as moderate and low quality
habitat falling outside the 85% SUI. <br> <br> 4) Non habitat. Defined as non-habitat falling outside the 85% SUI. <br> <br>
SPACE USE INDEX CALCULATION <br> <br> Lek 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. <br> <br> 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. <br>
<br> 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. <br> <br> 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. <br> <br> 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. <br> <br> This dataset
is associated with the following Open-File Report; <br> <br> 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) <br> <br> REFERENCES <br> <br> Beyer HL. 2012. Geospatial Modelling Environment (Version 0.7.2.0).
http://www.spatialecology.com/gme <br> <br> Coates 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. <br> <br> 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#
<br> <br> Duong T. 2012. ks: Kernel smoothing. R package version 1.8.10. http://CRAN.R-project.org/package=ks <br> <br> Horne
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. <br> <br> Silverman BW. 1986. Density estimation
for statistics and data analysis. Chapman & Hall, London, United Kingdom. <br> <br> Vander Wal E, Rodgers AR. 2012. An
individual-based quantitative approach for delineating core areas of animal space use. Ecological Modelling 224: 48-53. <br>
<br> NOTE: This file does not include habitat areas for the Bi-State management area.
Citation
Title Sage-grouse Management Categories in Nevada and NE California (August 2014)
These data provide the Nevada Sagebrush Ecosystem Council, and other wildlife managers, with an additional resource to aid
in planning and management of greater sage-grouse populations.