Original data can be downloaded from here. Another online version of the data can be found HERE.This version presented and hosted by CPAWS-NL allows for data extraction and analysis within ArcGIS Online Map Viewer."Kernel density estimation (KDE) utilizes spatially explicit data to model the distribution of a variable of interest. It is a simple non-parametric neighbor-based smoothing function that relies on few assumptions about the structure of the observed data. It has been used in ecology to identify hotspots, that is, areas of relatively high biomass/abundance, and in 2010 was used by Fisheries and Oceans Canada to delineate significant concentrations of corals and sponges. The same approach has been used successfully in the Northwest Atlantic Fisheries Organization (NAFO) Regulatory Area. Here, we update the previous analyses with the catch records from up to 5 additional years of trawl survey data from Eastern Canada, including the Gulf of St. Lawrence. We applied kernel density estimation to create a modelled biomass surface for each of sponges, small and large gorgonian corals, and sea pens, and applied an aerial expansion method to identify significant concentrations of theses taxa. We compared our results to those obtained previously and provided maps of significant concentrations as well as point data co-ordinates for catches above the threshold values used to construct the significant area polygons. The borders of the polygons can be refined using knowledge of null catches and species distribution models of species presence/absence and/or biomass." (DOI: 10.17632/dtk86rjm86.2)

The mxd_file folder contains the maps for the empirical analysis. The transect_addin folder contains the add-in transect tools that can be installed within ArcGIS. The transect_data folder contains the data used for making the maps in the mxd_file.

Please note that this data was selected from a larger dataset for use in the San Diego Ocean Planning Partnership, a collaborative pilot project between the California State Lands Commission and the Port of San Diego. For more information about the Partnership, please visit: https://www.sdoceanplanning.org/ The data was retrieved in May 2018 from OceanSpaces.org and is now available at https://data.cnra.ca.gov/dataset/spatial-and-economic-human-uses-california-south-coast-mpa-baseline-study-1992-to-2012 (Chen et al. An Economic and Spatial Baseline of Coastal Recreation in the South Coast of California. OceanSpaces.org. Retrieved May 2018).These data are a Kernel Density layers produced using ArcGIS. The Kernel analysis is a nonparametric statistical method for estimating probability densities from a set of points. Conceptually, a smooth raster surface is fitted over each point. The surface value is highest at the location of the point and diminishes with increasing distance (i.e.search radius), eventually reaching zero. A default value is calculated by the analysis tool for the search radius based on the input data; increasing the radius has little affect on the density value. Although more points will fall inside a larger search radius the number will be divided by a larger area when calculating density resulting in a more generalized output raster. The volume under the surface equals the weighted value for the point. The weights were created by Knowledge Networks and applied to the points based on demographics. The density of the output raster is calculated by adding the values of all the individual surfaces where they overlap. The point's weighted value determines the number of times to count the point. For example, a weighted value of 1.5 would cause the point to be counted one and half times. The resulting dataset is a smooth raster surface depicting the intensity use or density of an activity. Based on previous experience and after conducting some tests, all of the activity datasets were given a search radius of one mile. In discussing a similar project with the Oregon Department of Parks and Recreation we discovered that most visitors to the coast stay within a mile of their activity location. This distance also proved to be a good match to the mapped activities.

This study uses the methods of kernel density analysis and service area analysis in GIS to quantify the accessibility of public service facilities for residents within 15-minute living circles. This study takes the Ciqikou block as an example to analyze the distribution of public facilities and the polarization of services in historical blocks, quantifies and evaluates the accessibility of public facilities and the influencing factors of accessibility for residents' walking, and makes an assessment contribution to the sustainable renewal of social transportation and the maintenance of social equity in historical blocks in the later stage, with the aim of providing a useful reference for sustainable urban renewal.

This data gathers socioeconomic drivers at 1km2 grid cell spatial resolution to predict forest fires in a region in Spain. The response variable was fire density by grid cell from kernel density methods. It was produced under the Firemap project (https://geogra.uah.es/firemap/) by using GIS, spatial and statistical data sources at regional level in Spain. The resulting work was published at Vilar del Hoyo L, Martín Isabel MP, Martínez Vega FJ. 2011. Logistic regression models for human-caused wildfire risk estimation: analysing the effect of the spatial accuracy in fire occurrence data. European Journal of Forest Research. 130:983-96. doi: 10.1007/s10342-011-0488-2

Analysis condicted by ABR Inc.–Environmental Research & Services.Data from ADFG/BLM/NSB and ConocoPhillips Alaska Inc.For Brownian Bridge Movement Models - Conducted dynamic Brownian Bridge Movement Models (dBBMM) to delineate movmeents on seasonal herd ranges. dBBMM models were run using the move package for r using the following methods.1. Locations within 30 days of first collaring were removed from the analysis. 2. Selected females from PTT and GPS collars during the date range July 1 2012–June 30 2017 and individuals having more than 30 locations per season.3. ran a dBBMMM model for each individual during each season using 1 km pixels. 4. Calculate the 95% isopleth for each individual.5. Overlap all 95% isopleths and calculate the proportion of animals using (as defined by 95% isopleth) each pixel. Value shown is proportion times 1000. Seasons used: Winter (Dec 1-Apr 15); Spring (Apr 16-May 31); Calving (June 1-15); postcalving (June 16-30); Mosquito (July 1-15); Oestrid Fly (July 16-Aug 7); late summer (August 8-Sept 15); Fall Migration (Sept 16-Nov 30). For Kernel Density Estimates - Conducted Kernel Density Estimation (KDE) to delineate seasonal herd ranges. Kernels were run using the ks package for r and the plugin bandwidth estimator. 1. Locations within 30 days of first collaring were removed from the analysis. 2. The mean latitiude and longitude for each animal was calculated for each day.3. A KDE utilization distribution was calculated for Julian day of the season (all years combined). 4. The daily KDE uds were averaged across the season. This method accounts for individual's movements during the seasons without the overfitting that results from using autocorrelated lcoations from individuals.Seasons used: Winter (Dec 1-Apr 15); Spring (Apr 16-May 31); Calving (June 1-15); postcalving (June 16-30); Mosquito (July 1-15); Oestrid Fly (July 16-Aug 7); late summer (August 8-Sept 15); Fall Migration (Sept 16-Nov 30).

Created using ArcGIS Pro Geoprocessing tools (Create Space Time Cube, Emerging Hot Spot Analysis, and Enrich Layer) and the ArcGIS R Bridge. The EBest function, part of the spdep package was used to calculate an Empirical Bayes smoothed crime rate with 2016 population estimates. This procedure is presented as part of the R-ArcGIS Workflow Demo on GeoNet.Relative Burglary Risk is the natural log (Ln) of the kernel density of burglaries g(x) divided by the kernel density of households g(y) calculated using CrimeStat. Note: Ten months of burglary data (the minimum required) were used for this initial analysis. Also Note: These locations are one-half kilometer square polygons. It will be updated in the future as more data from the Albuquerque Police Department is obtained (see ABQ Data).Please see the web map for another similar way to present these results.More information at (http://www.unm.edu/~lspear/other_nm.html).

Summary of the data by year.

description: These data are a Kernel Density layers produced using ArcGIS. The Kernel analysis is a nonparametric statistical method for estimating probability densities from a set of points. Conceptually, a smooth raster surface is fitted over each point. The surface value is highest at the location of the point and diminishes with increasing distance (i.e. search radius), eventually reaching zero. A default value is calculated by the analysis tool for the search radius based on the input data; increasing the radius has little affect on the density value. Although more points will fall inside a larger search radius the number will be divided by a larger area when calculating density resulting in a more generalized output raster. The volume under the surface equals the weighted value for the point. The weights were created by Knowledge Networks and applied to the points based on demographics. The density of the output raster is calculated by adding the values of all the individual surfaces where they overlap. The point's weighted value determines the number of times to count the point. For example, a weighted value of 1.5 would cause the point to be counted one and half times. The resulting dataset is a smooth raster surface depicting the intensity use or density of an activity. Based on previous experience and after conducting some tests, all of the activity datasets were given a search radius of one mile. In discussing a similar project with the Oregon Department of Parks and Recreation we discovered that most visitors to the coast stay within a mile of their activity location. This distance also proved to be a good match to the mapped activities.Link to the Dataset - ftp://ftp.dfg.ca.gov/R7_MR/NONCONSUMPTIVE/NCCSR/Cars.zip; abstract: These data are a Kernel Density layers produced using ArcGIS. The Kernel analysis is a nonparametric statistical method for estimating probability densities from a set of points. Conceptually, a smooth raster surface is fitted over each point. The surface value is highest at the location of the point and diminishes with increasing distance (i.e. search radius), eventually reaching zero. A default value is calculated by the analysis tool for the search radius based on the input data; increasing the radius has little affect on the density value. Although more points will fall inside a larger search radius the number will be divided by a larger area when calculating density resulting in a more generalized output raster. The volume under the surface equals the weighted value for the point. The weights were created by Knowledge Networks and applied to the points based on demographics. The density of the output raster is calculated by adding the values of all the individual surfaces where they overlap. The point's weighted value determines the number of times to count the point. For example, a weighted value of 1.5 would cause the point to be counted one and half times. The resulting dataset is a smooth raster surface depicting the intensity use or density of an activity. Based on previous experience and after conducting some tests, all of the activity datasets were given a search radius of one mile. In discussing a similar project with the Oregon Department of Parks and Recreation we discovered that most visitors to the coast stay within a mile of their activity location. This distance also proved to be a good match to the mapped activities.Link to the Dataset - ftp://ftp.dfg.ca.gov/R7_MR/NONCONSUMPTIVE/NCCSR/Cars.zip

These data are a Kernel Density layers produced using ArcGIS. The Kernel analysis is a nonparametric statistical method for estimating probability densities from a set of points. Conceptually, a smooth raster surface is fitted over each point. The surface value is highest at the location of the point and diminishes with increasing distance (i.e.search radius), eventually reaching zero. A default value is calculated by the analysis tool for the search radius based on the input data; increasing the radius has little affect on the density value. Although more points will fall inside a larger search radius the number will be divided by a larger area when calculating density resulting in a more generalized output raster. The volume under the surface equals the weighted value for the point. The weights were created by Knowledge Networks and applied to the points based on demographics. The density of the output raster is calculated by adding the values of all the individual surfaces where they overlap. The point's weighted value determines the number of times to count the point. For example, a weighted value of 1.5 would cause the point to be counted one and half times. The resulting dataset is a smooth raster surface depicting the intensity use or density of an activity. Based on previous experience and after conducting some tests, all of the activity datasets were given a search radius of one mile. In discussing a similar project with the Oregon Department of Parks and Recreation we discovered that most visitors to the coast stay within a mile of their activity location. This distance also proved to be a good match to the mapped activities

This is a heatmap (a graphical representation of data where the individual values contained in a matrix are represented as colors) of 2013 deer hunt kills within the California Department of Fish & Wildlife (CDFW) North Central Region (Region 2). The data was compiled from 2013 CDFW Automated Licensing Data System (ALDS) tables. Text descriptions from hunters were approximated and placed with geographic coordinates. The resulting point data was converted to a heatmap using Kernel Density Tool in ArcGIS 10.1

These data are a Kernel Density layers produced using ArcGIS. The Kernel analysis is a nonparametric statistical method for estimating probability densities from a set of points. Conceptually, a smooth raster surface is fitted over each point. The surface value is highest at the location of the point and diminishes with increasing distance (i.e.search radius), eventually reaching zero. A default value is calculated by the analysis tool for the search radius based on the input data; increasing the radius has little affect on the density value. Although more points will fall inside a larger search radius the number will be divided by a larger area when calculating density resulting in a more generalized output raster. The volume under the surface equals the weighted value for the point. The weights were created by Knowledge Networks and applied to the points based on demographics. The density of the output raster is calculated by adding the values of all the individual surfaces where they overlap. The point's weighted value determines the number of times to count the point. For example, a weighted value of 1.5 would cause the point to be counted one and half times. The resulting dataset is a smooth raster surface depicting the intensity use or density of an activity. Based on previous experience and after conducting some tests, all of the activity datasets were given a search radius of one mile. In discussing a similar project with the Oregon Department of Parks and Recreation we discovered that most visitors to the coast stay within a mile of their activity location. This distance also proved to be a good match to the mapped activities.Link to the Dataset - ftp://ftp.dfg.ca.gov/R7_MR/NONCONSUMPTIVE/NCCSR/Photo.zip

description: These data are a Kernel Density layers produced using ArcGIS. The Kernel analysis is a nonparametric statistical method for estimating probability densities from a set of points. Conceptually, a smooth raster surface is fitted over each point. The surface value is highest at the location of the point and diminishes with increasing distance (i.e.search radius), eventually reaching zero. A default value is calculated by the analysis tool for the search radius based on the input data; increasing the radius has little affect on the density value. Although more points will fall inside a larger search radius the number will be divided by a larger area when calculating density resulting in a more generalized output raster. The volume under the surface equals the weighted value for the point. The weights were created by Knowledge Networks and applied to the points based on demographics. The density of the output raster is calculated by adding the values of all the individual surfaces where they overlap. The point's weighted value determines the number of times to count the point. For example, a weighted value of 1.5 would cause the point to be counted one and half times. The resulting dataset is a smooth raster surface depicting the intensity use or density of an activity. Based on previous experience and after conducting some tests, all of the activity datasets were given a search radius of one mile. In discussing a similar project with the Oregon Department of Parks and Recreation we discovered that most visitors to the coast stay within a mile of their activity location. This distance also proved to be a good match to the mapped activities.Link to the Dataset - ftp://ftp.dfg.ca.gov/R7_MR/NONCONSUMPTIVE/NCCSR/BeachActivities.zip; abstract: These data are a Kernel Density layers produced using ArcGIS. The Kernel analysis is a nonparametric statistical method for estimating probability densities from a set of points. Conceptually, a smooth raster surface is fitted over each point. The surface value is highest at the location of the point and diminishes with increasing distance (i.e.search radius), eventually reaching zero. A default value is calculated by the analysis tool for the search radius based on the input data; increasing the radius has little affect on the density value. Although more points will fall inside a larger search radius the number will be divided by a larger area when calculating density resulting in a more generalized output raster. The volume under the surface equals the weighted value for the point. The weights were created by Knowledge Networks and applied to the points based on demographics. The density of the output raster is calculated by adding the values of all the individual surfaces where they overlap. The point's weighted value determines the number of times to count the point. For example, a weighted value of 1.5 would cause the point to be counted one and half times. The resulting dataset is a smooth raster surface depicting the intensity use or density of an activity. Based on previous experience and after conducting some tests, all of the activity datasets were given a search radius of one mile. In discussing a similar project with the Oregon Department of Parks and Recreation we discovered that most visitors to the coast stay within a mile of their activity location. This distance also proved to be a good match to the mapped activities.Link to the Dataset - ftp://ftp.dfg.ca.gov/R7_MR/NONCONSUMPTIVE/NCCSR/BeachActivities.zip

Snake River Plain Play Fairway Analysis - Phase 1 CRS Raster Files. This dataset contains raster files created in ArcGIS. These raster images depict Common Risk Segment (CRS) maps for HEAT, PERMEABILITY, AND SEAL, as well as selected maps of Evidence Layers. These evidence layers consist of either Bayesian krige functions or kernel density functions, and include: (1) HEAT: Heat flow (Bayesian krige map), Heat flow standard error on the krige function (data confidence), volcanic vent distribution as function of age and size, groundwater temperature (equivalue interval and natural breaks bins), and groundwater T standard error. (2) PERMEABILTY: Fault and lineament maps, both as mapped and as kernel density functions, processed for both dilational tendency (TD) and slip tendency (ST), along with data confidence maps for each data type. Data types include mapped surface faults from USGS and Idaho Geological Survey data bases, as well as unpublished mapping; lineations derived from maximum gradients in magnetic, deep gravity, and intermediate depth gravity anomalies. (3) SEAL: Seal maps based on presence and thickness of lacustrine sediments and base of SRP aquifer. Raster size is 2 km. All files generated in ArcGIS.

Number of incidents counted within census tracts based on different spatial join approaches.

Characteristics of participants in largest components of the sociospatial network.

The temple gardens are an important human landscape and have an important position in the Chinese garden system. Using GIS analysis tools, primarily the Nearest Neighbor Index, Kernel Density Estimation, and Spatial Autocorrelation, and employing a Geographic Detector model, we analyzed the spatial distribution characteristics and influencing factors of 4,317 temples and gardens in Jiangxi Province. Research shows that: 1) The spatial distribution type of temple gardens in Jiangxi Province is agglomeration type, with large spatial differences in distribution, forming a spatial distribution pattern of “generally dispersed and concentrated in some areas”; 2) the distribution of temple gardens in Jiangxi Province is uneven. They are mostly distributed in five prefecture-level cities: Ganzhou, Jiujiang, Shangrao, Fuzhou, and Nanchang; 3) The overall spatial distribution of temple gardens in Jiangxi Province has positive autocorrelation characteristics, and prefecture-level cities have significant proximity characteristics, forming a “high-high” “agglomeration” and “low-low agglomeration” distribution patterns; 4) Temple gardens in various regions are affected by geomorphological factors, and are mostly concentrated in the lower altitude range of 0–500 m and the gentle slope of 0°–30°. Most of the distribution density of temple gardens in various prefecture-level cities is within the buffer zone distance of the road network within the range of 0–1.5 km. 5) Economic, cultural, demographic, and historical factors have affected the development of temple gardens. Areas with more active economies have a denser number of temple gardens. The unique regional culture affects the distribution of temples and gardens in different regions. In places where the modern population is densely distributed, there are fewer temples and gardens, while in places where the population is less densely distributed, there are more temples and gardens. 6) The use of geographical detectors to detect influencing factors shows that the greatest impact on the spatial distribution of temple gardens in Jiangxi Province is the road network, followed by elevation, slope, GDP, and water systems. The research is conducive to scientific understanding of the distribution of temple gardens among prefecture-level cities in Jiangxi Province, and provides reference for strengthening the protection of temple gardens and exploring the tourism characteristics of temple gardens.

This shapefile contains four levels of occurrence (frequent, common, occasional, and rare range) for the Florida black bear (Ursus americanus floridanus) throughout the state of Florida. Range extent and levels of occurrence were created using research, management, and public-generated location data of black bears from 2009-2018. The four levels of occurrence were estimated by 90% kernel density estimator (KDE) isopleth (range of frequent occurrence), 97.5% KDE isopleth (common), concave hull model (occasional), and the remainder of Florida (rare).

This hosted feature layer has been published in RI State Plane Feet NAD 83. This dataset was developed as an aid in the identification and protection of plant and animal species listed in the RI Natural Heritage Data.The Natural Heritage Areas were developed from a kernel density analysis of Heritage data element occurrences (EO). The calculation, based on a 30 meter pixel size, determines the mean number of EOs per square kilometer for each pixel within a one kilometer search radius. Non-statistically significant areas were eliminated and the remaining areas converted to a polygon dataset. Element Occurrences are discreet observations of a community or nesting site of State or Federally listed rare or threatened species OR species deemed noteworthy by the State.

This shapefile represents proposed management categories (Core, Priority, General, and Non-Habitat) derived from the intersection of habitat suitability categories and lek space use. Habitat suitability categories were derived from a composite, continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada and northeastern California formed from the multiplicative product of the spring, summer, and winter HSI surfaces. Summary of steps to create Management 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) as well as additional telemetry location data from field sites in 2014. The dataset was then split according to calendar date into three seasons. Spring included telemetry locations (n = 14,058) from mid-March to June; summer included locations (n = 11,743) from July to mid-October; winter included 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 season using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. For each season, 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. The three seasonal HSI rasters were then multiplied to create a composite annual HSI. 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). SPACE USE INDEX CALCULATION: Updated lek coordinates and associated trend count data were obtained from the 2015 Nevada Sage-grouse Lek Database compiled by the Nevada Department of Wildlife (NDOW, S. Espinosa, 9/20/2015). Leks count data from the California side of the Buffalo-Skedaddle and Modoc PMU's that contributed to the overall space-use model were obtained from the Western Association of Fish and Wildlife Agencies (WAFWA), and included count data up to 2014. We used NDOW data for border leks (n = 12), and WAFWA data for those fully in California and not consistently surveyed by NDOW. 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 (through the 2014 breeding season). 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’, or newly discovered leks with at least 2 males. 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 (2011 - 2015) for the number of male grouse (or NDOW classified 'pseudo-males' if males were not clearly identified but likely) attending each lek was calculated. Compared to the 2014 input lek dataset, 36 leks switched from pending to inactive, and 74 new leks were added for 2015 (which included pending ‘new’ leks with one year of counts. A total of 917 leks were used for space use index calculation in 2015 compared to 878 leks in 2014. 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 (2011 - 2015) 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 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 re-scaled between zero and one by dividing by the maximum cell value. A Spatial Use Index (SUI) was calculated by taking the average of the lek utilization distribution and non-linear distance-to-lek rasters in ArcGIS, and re-scaled between zero and one by dividing by the maximum cell value. The volume of the SUI at cumulative at specific 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 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 Nevada state boundary. MANAGEMENT CATEGORIES: The process for category determination was directed by the Nevada Sagebrush Ecosystem Technical team. Sage-grouse habitat was categorized into 4 classes: High, Moderate, Low, and Non-Habitat as described above, and intersected with the space use index to form the following management categories . 1) Core habitat: Defined as the intersection between all suitable habitat (High, Moderate, and Low) and the 85% Space Use Index (SUI). 2) Priority habitat: Defined as all high quality habitat