Dataset Identification:
Resource Abstract:
- description: This shapefile represents habitat suitability categories (High, Moderate, Low, and Non-Habitat) derived from
a composite, continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada and northeastern California
during the winter season, and is a surrogate for habitat conditions during periods of cold and snow. Summary of steps to create
Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial
distribution and created using ArcGIS 10.2.2) that contrasted data from multiple environmental factors at used sites (telemetry
locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at
multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation
data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002,
LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include
high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground
(C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper,
P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014);
additional telemetry location data from field sites in 2014 were added to the dataset. The dataset was then split according
calendar date into three seasons (spring, summer, winter). Winter included telemetry locations (n = 4862) from November to
March. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations
from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in
winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI
values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e.,
nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout
the study area extent. A Resource Selection Function (RSF) was calculated for each subregion using R software (v 3.13) and
using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models.
Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide
HSI whereby a relative probability of occurrence was calculated for each raster cell during the spring season. In order to
account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south
extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric
regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between
zero and one, then mosaicked once more into a state-wide extent. HABITAT CATEGORIZATION: Using the same ecoregion boundaries
described above, the habitat classification dataset (an independent data set comprising 10% of the total telemetry location
sample) was split into locations falling within respective north and south regions. HSI values from the composite and relativized
statewide HSI surface were then extracted to each classification dataset location within the north and south region. The distribution
of these values were used to identify class break values corresponding to 0.5 (high), 1.0 (moderate), and 1.5 (low) standard
deviations (SD) from the mean HSI. These class breaks were used to classify the HSI surface into four discrete categories
of habitat suitability: High, Moderate, Low, and Non-Habitat. In terms of percentiles, High habitat comprised greater than
30.9 % of the HSI values, Moderate comprised 15 30.9%, Low comprised 6.7 15%, and Non-Habitat comprised less than 6.7%.The
classified north and south regions were then clipped by the boundary layer and mosaicked to create a statewide categorical
surface for habitat selection . Each habitat suitability category was converted to a vector output where gaps within polygons
less than 1.2 million square meters were eliminated, polygons within 500 meters of each other were connected to create corridors
and polygons less than 1.2 million square meters in one category were incorporated to the adjacent category. The final step
was to mask major roads that were buffered by 50m (Census, 2014), lakes (Peterson, 2008) and urban areas, and place those
masked areas into the non-habitat category. The existing urban layer (Census 2010) was not sufficient for our needs because
it excluded towns with a population lower than 1,500. Hence, we masked smaller towns (populations of 100 to 1500) and development
with Census Block polygons (Census 2015) that had at least 50% urban development within their boundaries when viewed with
reference imagery (ArcGIS World Imagery Service Layer). REFERENCES: California Forest and Resource Assessment Program (CFRAP).
2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php
Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html
Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html
Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html
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) Comer,
P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western
United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G.,
Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression
Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation
14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior,
Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency ProgramMethods For Estimating
Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared
by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas,
Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for
Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program,
Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization
of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300.
NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison
to Coates et al. 2014; abstract: This shapefile represents habitat suitability categories (High, Moderate, Low, and Non-Habitat)
derived from a composite, continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada and northeastern
California during the winter season, and is a surrogate for habitat conditions during periods of cold and snow. Summary of
steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified
by binomial distribution and created using ArcGIS 10.2.2) that contrasted data from multiple environmental factors at used
sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation
communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness,
and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch
(Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was
updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation,
and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily
pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates
et al. (2014); additional telemetry location data from field sites in 2014 were added to the dataset. The dataset was then
split according calendar date into three seasons (spring, summer, winter). Winter included telemetry locations (n = 4862)
from November to March. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum
of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven
subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed
of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females
(i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons
throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion using R software (v
3.13) and using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of
additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall
statewide HSI whereby a relative probability of occurrence was calculated for each raster cell during the spring season. In
order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north
and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic
and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale
between zero and one, then mosaicked once more into a state-wide extent. HABITAT CATEGORIZATION: Using the same ecoregion
boundaries described above, the habitat classification dataset (an independent data set comprising 10% of the total telemetry
location sample) was split into locations falling within respective north and south regions. HSI values from the composite
and relativized statewide HSI surface were then extracted to each classification dataset location within the north and south
region. The distribution of these values were used to identify class break values corresponding to 0.5 (high), 1.0 (moderate),
and 1.5 (low) standard deviations (SD) from the mean HSI. These class breaks were used to classify the HSI surface into four
discrete categories of habitat suitability: High, Moderate, Low, and Non-Habitat. In terms of percentiles, High habitat comprised
greater than 30.9 % of the HSI values, Moderate comprised 15 30.9%, Low comprised 6.7 15%, and Non-Habitat comprised less
than 6.7%.The classified north and south regions were then clipped by the boundary layer and mosaicked to create a statewide
categorical surface for habitat selection . Each habitat suitability category was converted to a vector output where gaps
within polygons less than 1.2 million square meters were eliminated, polygons within 500 meters of each other were connected
to create corridors and polygons less than 1.2 million square meters in one category were incorporated to the adjacent category.
The final step was to mask major roads that were buffered by 50m (Census, 2014), lakes (Peterson, 2008) and urban areas, and
place those masked areas into the non-habitat category. The existing urban layer (Census 2010) was not sufficient for our
needs because it excluded towns with a population lower than 1,500. Hence, we masked smaller towns (populations of 100 to
1500) and development with Census Block polygons (Census 2015) that had at least 50% urban development within their boundaries
when viewed with reference imagery (ArcGIS World Imagery Service Layer). REFERENCES: California Forest and Resource Assessment
Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire
Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban
Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census
2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html
Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html
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) Comer,
P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western
United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G.,
Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression
Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation
14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior,
Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency ProgramMethods For Estimating
Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared
by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas,
Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for
Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program,
Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization
of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300.
NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison
to Coates et al. 2014
Citation
- Title Winter Season Habitat Categories for Greater Sage-grouse in Nevada and northeastern California.
-
- creation Date
2018-05-21T09:57:15.081475
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2018-08-06T23:06:49Z
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on a transform by Damian Ulbricht. Run on 2018-08-06T23:06:49Z
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