Prepared in cooperation with the State of Nevada Sagebrush Ecosystem Program, Bureau of 

Land Management, Nevada Department of Wildlife, and California Department of Fish and Wildlife

Spatially Explicit Modeling of Greater Sage-Grouse 

(Centrocercus urophasianus) Habitat in Nevada and 

Northeastern California: A Decision Support Tool 

for Management

U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2014-1163
Cover: Photograph of a male greater sage-grouse performing a courtship display on a lek in Nevada. 

Photograph courtesy of Tatiana Gettleman. 
Spatially Explicit Modeling of Greater Sage-Grouse 

(Centrocercus urophasianus) Habitat in Nevada and 

Northeastern California—A Decision-Support Tool for 

Management

By Peter S. Coates, Michael L. Casazza, Brianne E. Brussee, Mark A. Ricca, K. Benjamin Gustafson, Cory T. 

Overton, Erika Sanchez-Chopitea, Travis Kroger, Kimberly Mauch, Lara Niell, Kristy Howe, Scott Gardner, 

Shawn Espinosa, and David J. Delehanty

Prepared in cooperation with the State of Nevada Sagebrush Ecosystem Program, Bureau of Land Management, 

Nevada Department of Wildlife, and California Department of Fish and Wildlife

Open-File Report 2014–1163

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2014

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visit http://www.usgs.gov/pubprodhttp://www.usgs.gov/pubprod

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Suggested citation:

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 California—A 

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)

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

Although this information product, for the most part, is in the public domain, it also may contain

copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be 

secured from the copyright owner.
iii

Contents

Abstract ......................................................................................................................................................................... 1

Introduction.................................................................................................................................................................... 2

Methods and Results ..................................................................................................................................................... 3

Overview and Conceptual Models.............................................................................................................................. 3

Habitat Suitability Model Development....................................................................................................................... 5

Delineating the Region-Wide Scale........................................................................................................................ 5

Sage-Grouse Telemetry Data................................................................................................................................. 5

Delineating Subregions. ......................................................................................................................................... 7

Classification of Landscape Habitat Features. ....................................................................................................... 9

Distance Metrics, and Topographic Indices.......................................................................................................... 12

RSF Analyses .......................................................................................................................................................... 13

Subregional RSF Modeling................................................................................................................................... 13

Summary of Subregional RSF Results................................................................................................................. 14

Region-Wide Habitat Suitability Index and Implementation for Conservation Planning............................................ 17

Region-Wide Average Habitat Suitability Surface Map......................................................................................... 17

Implementation of the Region-Wide HSI Map for Conservation Planning—An Example ......................................... 17

HSI Classification and Validation—An Example................................................................................................... 18

Space Use Index .................................................................................................................................................. 25

Developing a Decision-Support Tool—Combining RSF Categories with Space Use ........................................... 28

Conclusion................................................................................................................................................................... 31

Acknowledgments........................................................................................................................................................ 31

References Cited......................................................................................................................................................... 32

Appendix A. Supplemental Material for Buffalo-Skedaddle RSF Modeling.................................................................. 35

Appendix B. Supplemental material for Butte-Buck-White Pine RSF Modeling ........................................................... 40

Appendix C. Supplemental Material for Cortez RSF Modeling .................................................................................... 45

Appendix D. Supplemental Material for Desert-Tuscarora RSF Modeling ................................................................... 49

Appendix E. Supplemental Material for Gollaher-O’Neil RSF Modeling....................................................................... 53

Appendix F. Supplemental Material for Lincoln-Schell-Snake RSF Modeling.............................................................. 57

Appendix G. Supplemental Material for Lone Willow RSF Modeling ........................................................................... 61

Appendix H. Supplemental Material for Midway RSF Modeling................................................................................... 65

Appendix I. Supplemental Material for Sheldon RSF Modeling .................................................................................. 69

Appendix J. Supplemental material for South Fork-Ruby Valley RSF modeling.......................................................... 72

Appendix K. Supplemental Material for Toiyabe RSF Modeling .................................................................................. 76

Appendix L. Supplemental Material for Virginia Mountains RSF Modeling .................................................................. 80
iv

Figures

Figure 1. Diagram showing conceptual model for a statewide greater sage-grouse (Centrocercus urophasianus) 

habitat suitability model and habitat management scenario map, Nevada and northeastern California .................... 4

Figure 2. Map showing project area, which included the segment of greater sage-grouse (Centrocercus 

urophasianus) range in Nevada and northeastern California, excluding the Bi-State Distinct Population 

Segment (Bi-State DPS)............................................................................................................................................ 6

Figure 3. Map showing telemetry points (colored dots) comprising greater sage grouse (Centrocercus 

urophasianus) locations available for use in resource selection function modeling, Nevada and northeastern 

California ................................................................................................................................................................... 8

Figure 4. Map showing 12 subregions with suitable greater sage-grouse (Centrocercus urophasianus) 

location data for resource selection function analyses, Nevada and northeastern California .................................. 10

Figure 5. Map showing region-wide, model-averaged suitability map of greater sage-grouse (Centrocercus 

urophasianus) habitat derived from 12 subregions, Nevada and northeastern California........................................ 19

Figure 6. Map and graph (resource selection function [RSF] and probability distribution) showing example 

region-wide distribution of categorized high suitability habitat for greater sage grouse (Centrocercus 

urophasianus), Nevada and northeastern California................................................................................................ 20

Figure 7. Map and graph (resource selection function [RSF] and probability distribution) showing example 

region-wide distribution of categorized moderate suitability habitat for greater sage grouse (Centrocercus 

urophasianus), Nevada and northeastern California ................................................................................................ 21

Figure 8. Map and graph (resource selection function [RSF] and probability distribution) showing example 

region-wide distribution of categorized low suitability habitat for greater sage grouse (Centrocercus urophasianus), 

Nevada and northeastern California ........................................................................................................................ 22

Figure 9. Map showing example region-wide distribution of categorized non-suitable habitat for greater 

sage grouse (Centrocercus urophasianus), Nevada and northeastern California.................................................... 23

Figure 10. Relation between ratio of added potential habitat to added telemetry points validated and SD cutoff .. 24

Figure 11. Map showing overlay of radio-telemetry data and lek locations used to validate habitat suitability 

classes for greater sage grouse (Centrocercus urophasianus), Nevada and northeastern California ..................... 27

Figure 12. Map showing a space use index (SUI) that was developed compiling data on greater sage-grouse 

(Centrocercus urophasianus) use and distribution of leks, Nevada and northwestern California ............................ 29

Figure 13. Map showing habitat management classes that can be determined based on the intersection of 

habitat suitability classes and space use index categories for greater sage grouse (Centrocercus urophasianus), 

Nevada and northeastern California ........................................................................................................................ 30

Tables

Table 1. Proposed variables assessed in resource selection function model development for each subregion, 

Nevada and northeastern California ........................................................................................................................ 11

Table 2. Magnitude of significant model-averaged effects among 12 subregional resource selection function 

models for all proposed variables included in modeling of greater sage-grouse (Centrocercus urophasianus) 

habitat, Nevada and northeastern California............................................................................................................ 15

Table 3. Summary of habitat suitability model validation tests used to evaluate habit suitability classes for 

greater sage-grouse (Centrocercus urophasianus), Nevada and northeastern California ....................................... 26

Table 4. Rubric for determining habitat management classes from habitat suitability and space use categories.... 28
v

Conversion Factors and Datums 

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Area

acre 4,047 square meter (m2)

acre 0.4047 hectare (ha)

acre 0.4047 square hectometer (hm2)

SI to Inch/Pound

Multiply By To obtain

Length

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

meter (m) 1.094 yard (yd) 

Area

square meter (m2) 0.0002471 acre

hectare (ha) 2.471 acre

square hectometer (hm2) 2.471 acre

square meter (m2) 10.76 square foot (ft2)

Datums 

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD83).

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 (NAVD 88)”

Elevation, as used in this report, refers to distance above the vertical datum.
vi

Acronyms and Abbreviations

AICc Akaike’s information criterion with second-order bias correction

ΔAICc difference between model of interest and most parsimonious model 

CDFW California Department of Fish and Wildlife

GIS Geographic Information System 

GPS Global Positioning System 

HSI habitat suitability index

K number of parameters 

κ Cohen’s kappa coefficient

LANDFIRE Landscape Fire and Resource Management Planning Tools

MCP Minimum Convex Polygon 

PMU population management unit 

PTT Platform Transmitter Terminal

NDOW Nevada Department of Wildlife

r correlation coefficient

RSF resource selection function 

SD standard deviation 

SUI space use index

TPI topographic position index 

UTM Universal Transverse Mercator
1

Spatially Explicit Modeling of Greater Sage-Grouse 

(Centrocercus urophasianus) Habitat in Nevada and 

Northeastern California—A Decision-Support Tool for 

Management

By Peter S. Coates1, Michael L. Casazza1, Brianne E. Brussee1, Mark A. Ricca1, K. Benjamin Gustafson1, Cory T. 

Overton1, Erika Sanchez-Chopitea1, Travis Kroger1, Kimberly Mauch1, Lara Niell2,3, Kristy Howe1, Scott Gardner4, 

Shawn Espinosa3, and David J. Delehanty5 

Abstract

Greater sage-grouse (Centrocercus urophasianus, hereafter referred to as “sage-grouse”) 

populations are declining throughout the sagebrush (Artemisia spp.) ecosystem, including millions of 

acres of potential habitat across the West. Habitat maps derived from empirical data are needed given 

impending listing decisions that will affect both sage-grouse population dynamics and human land-use 

restrictions. This report presents the process for developing spatially explicit maps describing relative 

habitat suitability for sage-grouse in Nevada and northeastern California. Maps depicting habitat 

suitability indices (HSI) values were generated based on model-averaged resource selection functions 

informed by more than 31,000 independent telemetry locations from more than 1,500 radio-marked 

sage-grouse across 12 project areas in Nevada and northeastern California collected during a 15-year 

period (1998–2013). Modeled habitat covariates included land cover composition, water resources, 

habitat configuration, elevation, and topography, each at multiple spatial scales that were relevant to 

empirically observed sage-grouse movement patterns. We then present an example of how the HSI can 

be delineated into categories. Specifically, we demonstrate that the deviation from the mean can be used 

to classify habitat suitability into three categories of habitat quality (high, moderate, and low) and one 

non-habitat category. The classification resulted in an agreement of 93–97 percent for habitat versus 

non-habitat across a suite of independent validation datasets. Lastly, we provide an example of how 

space use models can be integrated with habitat models to help inform conservation planning. In this 

example, we combined probabilistic breeding density with a non-linear probability of occurrence 

relative to distance to nearest lek (traditional breeding ground) using count data to calculate a composite 

space use index (SUI). The SUI was then classified into two categories of use (high and low-to-no) and 

intersected with the HSI categories to create potential management prioritization scenarios based on 

1

U.S. Geological Survey, Western Ecological Research Center, 800 Business Park Drive, Suite D, Dixon, California 95620

2

Nevada Sagebrush Ecosystem Program, 201 South Roop Street, Suite 101 Carson City, Nevada 89701 3

Nevada Department of Wildlife, 1100 Valley Road, Reno, Nevada 89515 4

California Department of Fish and Wildlife, 1812 9th Street, Sacramento, California 95814 5

Department of Biological Sciences, Idaho State University, Pocatello, Idaho 83209
2

information about sage-grouse occupancy coupled with habitat suitability. This provided an example of

a conservation planning application that uses the intersection of the spatially-explicit HSI and 

empirically-based SUI to identify potential spatially explicit strategies for sage-grouse management. 

Importantly, the reported categories for the HSI and SUI can be reclassified relatively easily to employ

alternative conservation thresholds that may be identified through decision-making processes with 

stake-holders, managers, and biologists. Moreover, the HSI/SUI interface map can be updated readily as 

new data become available. 

Introduction

Greater sage-grouse (Centrocercus urophasianus, hereafter referred to as “sage-grouse”) are 

considered an umbrella (Rich and Altman, 2001; Rich and others, 2005; Rowland and others, 2006) or 

indicator species for the ecological integrity of sagebrush (Artemisia spp.) ecosystems due to the 

dependence of sage-grouse on sagebrush habitat, as well as their propensity to occupy sagebrush habitat 

across large spatial scales during the course of seasonal self-maintenance needs and reproduction (Knick 

and Connelly, 2011). Sage-grouse populations have declined concomitantly with the loss, degradation, 

and fragmentation of sagebrush ecosystems (Knick and Connelly, 2011), and currently (circa 2014) 

occupy slightly more than one-half of their former range across Western North America (Schroeder and 

others, 2004; Miller and others, 2011). Accordingly, sage-grouse have been identified as a candidate 

species for listing under the Endangered Species Act (U.S. Fish and Wildlife Service, 2010). 

Sage-grouse in Nevada and northeastern California represent more than 25 percent of the present

range-wide distribution of the species. Hence, empirically-based analytical tools that inform 

management decisions within Nevada and California are needed, especially where state and local 

resource managers have site-specific information that could be incorporated into a data-driven analytical 

tool for managers. One approach to aid sage-grouse management would be to develop an analytical tool

that uses data replicated across broad geographical ranges to inform landscape level decisions, but that 

also can then be downscaled to inform local management decisions. 

Currently available computational tools now allow for greater quantification of probabilistic 

habitat use at multiple spatial scales. In particular, habitat suitability indices (HSIs) generated from 

resource selection functions (RSFs) are powerful empirical quantifications that simultaneously consider 

habitat characteristics with animal distribution. Furthermore, these values can be measured at multiple 

local sites and then be used to project the relative probability of species occurrence across broader and 

un-sampled areas as a function of habitat characteristics (Boyce and McDonald, 1999; Manly and 

others, 2002). 

The utility of Geographic Information System (GIS)-derived HSIs for understanding sagegrouse ecology has been facilitated by the increased availability of remotely sensed imagery used to 

accurately classify vegetation types across large geographic areas in conjunction with the availability of 

large datasets of sage-grouse locations generated from monitoring radio-marked sage-grouse (Aldridge 

and others, 2012). Moreover, coupling HSI values with predictions of sage-grouse occurrence based on 

space use models and indices (for example, Doherty and others, 2010a; Coates and others, 2013) will 

assist managers in recognizing the relative importance of particular areas to sage-grouse. For example, 

an understanding of the distribution and density of breeding sage-grouse at lek sites (that is, traditional 

breeding grounds) can aid in region-wide management of sage-grouse by targeting actions in areas with 

high quality habitat coupled with information on how sage-grouse use the habitat spatially and 

seasonally. 
3

This report describes the process used to develop a region-wide habitat suitability map for sagegrouse within the southwestern portion of sage-grouse range (that is, Nevada and northeastern 

California). Because, variation in habitat composition across the sage-grouse range influences sagegrouse populations, this map was generated using averaged parameter estimates from RSFs informed by 

telemetry location data across 12 project areas with data that range 15 years. We then describe a method 

for applying a quantitative approach to conservation planning based on the simultaneous consideration 

of objectively categorized HSI values and space use indices (SUIs) derived from lek location data. The 

goal was to employ empirical information to evaluate different management scenarios for areas where 

sage-grouse conservation is a management goal. These scenarios can take many forms. For example, 

quantification could help to identify core areas to conserve sagebrush habitat with an emphasis on areas 

occupied frequently by sage-grouse, or help to identify conservation priorities and management actions 

for less suitable habitat, or suitable yet unoccupied habitat. Importantly, the indices created through the 

analytical processes described can be reclassified easily to incorporate alternative thresholds for 

conservation such as those that arise through decision-making processes with stake-holders, managers, 

and biologists. This work was completed in partnership with the State of Nevada Sagebrush Ecosystem 

Technical Team, the Nevada Department of Wildlife (NDOW), the Bureau of Land Management, and 

the California Department of Fish and Wildlife (CDFW). 

Methods and Results

Overview and Conceptual Models

The quantitative approach to develop a spatially explicit support tool for conservation planning 

consisted of multiple steps that we describe in detail below and outline in a conceptual model (fig. 1). 

The overall modeling framework comprised input data sets (blue rectangular boxes) that were subjected 

to a series of processing steps (black rounded boxes) to produce interim and final spatially explicit maps 

(red parallelograms) (fig. 1).

First, we compiled sage-grouse telemetry location data from multiple areas across Nevada and 

northeastern California, and divided these data into three independent sets for the purposes of model 

training (80 percent of locations), mapping classification (10 percent), and map validation (10 percent) 

(see: ‘Habitat Suitability Model Development’). The training data set was linked spatially with 

corresponding environmental covariates to enable calculation of population-level RSFs (Manly and 

others, 2002) within 12 subregions with adequate data. To achieve this, we first identified the relevant 

spatial scale and linear relationships of environmental characteristics. Next, model-averaged parameter 

estimates for influential covariates among all candidate models were calculated to account for model 

selection uncertainty (Burnham and Anderson, 2002) (see ‘RSF Analyses’). We then used those 

estimates to develop spatially explicit models reflecting the relative probability of selection at each 

subregion. For each of the 12 subregional RSF models, we: (1) transformed the model into an HSI; (2) 

extrapolated the HSI across the extent of the region; and (3) averaged the HSI predictions generated 

from each subregion to provide an unbiased region-wide HSI map. The independent classification 

telemetry data set was then used to extract the region-wide HSI predictions and categorize the 

continuous HSI value based on the mean and variance of the extracted data that resulted in a regionwide categorical habitat map grouped into four hierarchical classes of descending probability of 

selection. The third independent data set was used to validate the region-wide map by calculating the 
4

proportion of locations within each category. We calculated these proportions for telemetry data within 

each of the training subregions and telemetry data from multiple independent subregions (that is, nonRSF subregions). Data from independent subregions were used for the purpose of assessing the map in 

interpolated areas. Locations of active leks were used as an additional dataset for map validation (see: 

‘Region-wide Habitat Suitability Index and implementation for Conservation Planning’). 

, 

Figure 1. Diagram showing conceptual model for a statewide greater sage-grouse (Centrocercus urophasianus) 

habitat suitability model and habitat management scenario map, Nevada and northeastern California. Input 

datasets (blue rectangular boxes) were subjected to a series of processing steps (black rounded boxes) to produce 

interim and final spatially explicit maps (red parallelograms). HSI, habitat suitability index; RSF, resource selection 

function; %, percent.
5

From the RSFs, information about the probability of selection was produced solely on predicted 

associations of sage-grouse with environmental covariates. However, the model does not incorporate 

knowledge of sage-grouse abundance and density that represents space occupied currently by sagegrouse. Therefore, a SUI was created based on lek count data and existing information regarding how 

sage-grouse use space in relation to leks. Specifically, the SUI integrated information on lek density, lek 

size (that is, average number of males attending leks), and the non-linear relation between probability of 

space use and distance to lek, which was then used to create categories of high use or low-to-no use 

across the region. To provide a modeling tool that can aid conservation planning, the region-wide HSI 

(categorized into high, moderate, low, and non-habitat based on the variance distribution of HSI values) 

and high and low-to-no use SUI categories were combined into a single region-wide map. This map 

simultaneously reflects both the presence of sage-grouse and the presence of habitat features associated 

with sage-grouse occupancy, and can then be used to prioritize areas for different management 

scenarios. The strength of this map is to account for characteristics that describe the quality of the 

environment for sage-grouse as well as an index of population abundance. This technique can be used to 

aid decision-making processes across the landscape (see ‘Implementation of the Region-wide HSI map 

for Conservation Planning: An Example’).

Habitat Suitability Model Development 

Delineating the Region-Wide Scale. 

The region-wide extent of the project area was defined by using the outer perimeter of all 

combined sage-grouse Population Management Units (PMU; Nevada Department of Wildlife, 2014) in 

Nevada and northeastern California plus a 10-km buffer (fig. 2). This approached yielded an area of 

21.5 million hectares that approximated the total potential sage-grouse range in Nevada and California

(excluding the Bi-State Distinct Population Segment on the eastern side of the central Sierra Nevada 

Mountains). The purpose of the buffers was to ensure adequate representation of available habitats to 

sage-grouse at and near the PMU boundaries. Floristically, the region was typical of the Great Basin 

with Wyoming big sagebrush (Artemesia tridentata ssp. wyomingensis) and black (Artemesia nova) and 

low (Artemesia arbuscula) sagebrush occurring at elevations below 2100 m, and with mountain big 

sagebrush (Artemesia tridentata ssp. Vaseyana) occurring more frequently at higher elevations. 

Common non-sagebrush shrubs included rabbitbrush (Chrysothamnus ssp.), Mormon tea (Ephedra 

viridis), snowberry (Symphoricarpos ssp.), western serviceberry (Amelanchier alnifolia), and antelope 

bitterbrush (Purshia tridentata). Conifer forests were most frequently comprised of single-leaf pinyon 

pine (Pinus monophylla) and Utah juniper (Juniperus osteosperma) (hereafter, “pinyon-juniper”). Nonnative and highly invasive annual grasses included cheatgrass (Bromus tectorum) and medusahead rye 

(Taeniatherum caput-medusae). Native perennial grasses included needle and thread (Hesperostipa 

comata), Indian ricegrass (Achnatherum hymenoides), and squirreltail (Elymus elymoides). 

Sage-Grouse Telemetry Data. 

Data used in the study were generated from several sage-grouse telemetry studies across Nevada 

and northeastern California conducted from 1998 through 2013 by USGS, NDOW, CDFW, Idaho State 

University, University of Idaho, and University of Nevada-Reno. Field data collection protocols for 

tracking and locating sage-grouse were generally consistent across sites and years. Data were excluded 

from the analyses in situations where data collection procedures or supporting information differed 

substantially from norms. For example, telemetry data were removed from the analyses when a unique 

bird identifier or location date was absent or birds had less than two locations total. 
6

Figure 2. Map showing project area, which included the segment of greater sage-grouse (Centrocercus 

urophasianus) range in Nevada and northeastern California, excluding the Bi-State Distinct Population Segment 

(Bi-State DPS). 
7

Generally, sage-grouse were captured in close proximity to leks in spring (March–April) and at 

various areas where sage-grouse congregate in autumn (October–December) using spotlighting 

techniques at night (Giesen and others, 1982; Wakkinen and others, 1992). Captured sage-grouse were 

outfitted with necklace-style VHF radio-transmitters (Kolada and others, 2009). Over the 15-year period 

(1998–2013), personnel across agencies and organizations conducted on-the-ground monitoring of sagegrouse. In lieu of VHF only radio-transmitters, a subsample of sage-grouse at some sites during 2012 

and 2013 were outfitted with a combined Global Positioning Systems (GPS) - Platform Transmitter 

Terminals (PTTs; North Star Science and Technology, LLC, King George, Virginia) and VHF 

transmitter system. This system had a combined weight or less than 3 percent of sage-grouse body mass. 

The purpose of the GPS transmitter was to collect locations remotely, and the PTT transmitted stored 

location data via satellite communication to a central database. The VHF marked sage-grouse were 

relocated using hand-held radio receivers and antennas, whereby ground observers circled sage-grouse 

at a radius of 30–50 m and used the loudest signal method to minimize location error. Location 

coordinates for VHF-marked sage-grouse were obtained using a hand-held GPS (Universal Transverse 

Mercator, UTM).

 Both VHF and GPS - PTT telemetry data were used in our analyses, and were screened for 

completeness and comparability prior to inclusion in models. GPS - PTT transmitters were programmed 

to collect 9–12 locations per day. To prevent autocorrelation among GPS - PTT location data, only a 

single random location per day (during daylight hours) was used in our analyses, and the remaining 

daily locations were removed. In total, 35,883 telemetry locations from 1,612 sage-grouse were 

compiled into a region-wide database for all analyses. The majority of locations from marked sagegrouse were obtained within a single year (that is, few unique grouse were marked across multiple 

years). All locations were generated from adult sage-grouse (that is, older than 1 year of age) of each 

sex across all seasonal life stages.

Sage-grouse telemetry locations were divided into three independent data subsets for use in 

different steps of model processing and validation. These data sets were considered independent in that 

no telemetry locations across sets were shared by the same individual. Thus, different sage-grouse were 

used for each data set. These data sets consisted of: (1) an RSF model training subset employing 80 

percent of location data; (2) a classification subset employing 10 percent of location data to delineate 

areas of differing habitat quality; and (3) a validation subset employing 10 percent of location data from 

RSF subregions to assess predictiveness and consistency of habitat quality areas. Individual sage-grouse 

were randomly assigned to these three categories at the given proportions. 

Delineating Subregions. 

Spatial associations between marked sage-grouse and existing PMU boundaries (Nevada 

Department of Wildlife, 2014) were used as an initial starting point for delineating subregions for 

habitat selection analyses and naming conventions across Nevada and northeastern California (fig. 3). 

Ultimately, the data were partitioned into 19 subregions based on movement patterns of individual 

radio-marked sage-grouse for habitat analyses, with each grouse occupying one subregion only. Some 

subregions contained too few marked sage-grouse for sufficient training data to develop a habitat model, 

which resulted in the exclusion of seven subregions with fewer than 20 marked sage-grouse or less than 

100 telemetry locations. However, data from these excluded ‘non-RSF’ subregions were sufficient to 

provide further validation of the region-wide model in areas that were not used to train the model (see 
8

Figure 3. Map showing telemetry points (colored dots) comprising greater sage grouse (Centrocercus 

urophasianus) locations available for use in resource selection function modeling, Nevada and northeastern 

California. Names refer to locations associated with NDOW Population Management Units.
9

‘HSI Classification and Validation’). After data-screening, we included telemetry data from 12 

subregions in the habitat training models: Buffalo-Skedaddle, Butte-Buck-White Pine, Cortez, DesertTuscarora, Gollaher-O’Neil, Lincoln-Schell-Snake, Lone Willow, Midway, Sheldon, South Fork-Ruby 

Valley, Toiyabe, and Virginia Mountains (fig. 4). The spatial extent of habitat availability for use in 

habitat modeling was defined by first calculating a minimum convex polygon (MCP) that encompassed 

all telemetry locations within each subregion, and then buffering each MCP by the maximum average 

daily sage-grouse movement (1,451 m). Using the MCP to identify the study extent is a common and 

useful approach for habitat studies (Aebischer and others, 1993), and buffering by the maximum 

average daily movement helps ameliorate underestimation of habitat availability. 

Classification of Landscape Habitat Features. 

A broad suite of biotic and abiotic variables potentially associated with sage-grouse occurrence 

was quantified to provide inputs to estimated HSIs as spatially explicit environmental covariates. Landcover types representing the dominant vegetation within 30 x 30 m pixels were classified into binary 

raster layers using existing mapping products. For Nevada, detailed land cover classes were derived 

from the Nevada SynthMap (Peterson, 2008). Land cover classes were then reclassified into broad 

habitat categories that were guided by classification levels from NatureServe (NatureServe, 2013), 

Landscape Fire and Resource Management Planning Tools (LANDFIRE) (2010), and The Nature 

Conservancy. Land cover classes for the northeastern California portion of the project area were derived 

from LANDFIRE, SageStitch (Comer and others, 2002), and California Department of Forestry and 

Fire Protection (2006) data sets. To facilitate region-wide compatibility across land cover classes, each 

data set was reclassified into the broadest categories used to reclassify the Nevada SynthMap, and then 

compared across pixels. Pixel values that matched for at least two of the data sets were chosen, whereas 

the reclassified LANDFIRE value was used when no agreement occurred. The final Nevada and 

northeastern California layers were then merged. The final set of non-sagebrush land cover classes used 

in the analysis comprised annual grass, perennial grass, lowland non-sagebrush shrub, upland nonsagebrush shrub, wet meadow, riparian, pinyon-juniper conifer, non-pinyon-juniper conifer (forest), 

agricultural cropland, and bare ground. All sagebrush species (for example, Wyoming big, mountain 

big, low, and three-tip [Artemisia tripartita]) were ultimately condensed into a single “sagebrush” land 

cover class for analysis (table 1).

Because variation in sage-grouse habitat selection can be strongly scale-dependent (Aldridge and 

Boyce, 2007; Doherty and others, 2008; Casazza and others, 2011; Aldridge and others, 2012), the 

analysis was performed on each land cover raster at three different spatial scales relevant to sage-grouse 

movement patterns. Specifically, the scale-dependent analysis used a circular moving window 

(neighborhood analysis tool, ArcGISTM Spatial Analyst) with a radius of 167.9 m (8.7 ha), 439.5 m (61.5 

ha), or 1,451.7 m (661.4 ha) that represented averages across sage-grouse of the minimum, mean, and 

maximum daily distance traveled by sage-grouse in this study, respectively, to calculate the proportion 

of a particular habitat within a respective spatial scale. Other land cover related variables measured at 

the three spatial scales included variety of land cover types (that is, the number of unique land cover

types), variety of edge types (that is, the number of unique combinations of adjacent land cover types), 

and the amount of edge, quantified as the total number of pixels that represented interface of two 

adjacent cover types (hereafter, “edge effect”) (table 1). 
10

Figure 4. Map showing 12 subregions with suitable greater sage-grouse (Centrocercus urophasianus) location 

data for resource selection function analyses, Nevada and northeastern California. 
11

Table 1. Proposed variables assessed in resource selection function model development for each subregion, 

Nevada and northeastern California.

[ha, hectare; m, meter]

Variable type Scales

Land cover

Annual grass 8.7 ha 61.5 ha 661.4 ha

Agriculture 8.7 ha 61.5 ha 661.4 ha

Bare ground 8.7 ha 61.5 ha 661.4 ha

Sagebrush 8.7 ha 61.5 ha 661.4 ha

Forest 8.7 ha 61.5 ha 661.4 ha

Lowland shrubs 8.7 ha 61.5 ha 661.4 ha

Perennial grass 8.7 ha 61.5 ha 661.4 ha

Pinyon-juniper 8.7 ha 61.5 ha 661.4 ha

Riparian 8.7 ha 61.5 ha 661.4 ha

Upland shrubs 8.7 ha 61.5 ha 661.4 ha

Wet meadow 8.7 ha 61.5 ha 661.4 ha

Habitat configuration

Edge effects 8.7 ha 61.5 ha 661.4 ha

Variety of edge types 8.7 ha 61.5 ha 661.4 ha

Variety of land cover types 8.7 ha 61.5 ha 661.4 ha

Distance to edge Linear Exponential decay

Distance to agriculture Linear Exponential decay

Water sources

Any stream Linear Exponential decay

Perennial stream Linear Exponential decay

Intermittent stream Linear Exponential decay

Spring Linear Exponential decay

Water body Linear Exponential decay

Wet meadow Linear Exponential decay

Topography

Elevation Linear

Roughness index 1 ha

Topographic position index 510 m 2,010 m

 
12

Distance Metrics, and Topographic Indices.

Distances to landscape features that may affect the probability of sage-grouse use were 

calculated from the GIS (table 1). These landscape features included various water features, agricultural 

development, and habitat edge (table 1). The influence of distance to water was measured using multiple 

landscape features from the National Hydrography Dataset (U.S. Geological Survey, 2014) that 

included perennial streams, intermittent streams, springs, and open water bodies. Distance to wet 

meadows was also measured, as identified by the land cover maps. For all landscape features, linear 

distance was calculated as a simple Euclidean distance from a used or available point using the Distance 

tool in Spatial Analyst (ArcGISTM 10.1). Non-linear relationships were assessed with an exponential 

decay function, e

-d/α , where d was the Euclidean distance from a used or available point to a landscape 

feature, and α was the mean linear distance from that feature. This decay function allowed estimation of

the degree to which the effect of a habitat feature strengthened or weakened with increasing distance 

from that feature. A metric estimating the distance to road was also calculated but not included in the set 

of variables because the sage-grouse locations obtained by hand-held VHF were closer to roads than 

those obtained by GPS-PTT and could result in biased results across data sources (P.S. Coates, U.S. 

Geological Survey, unpub. data, 2014).

Topographic characteristics were calculated to assess the probability of sage-grouse use with 

several indices. Elevation and topographic roughness (within 30 x 30 m pixels) were determined from 

the National Elevation Dataset (U.S. Geological Survey, 2009). Topographic roughness, which 

measures variance in elevation change (Riley and others, 1999), was calculated using the 

Geomorphometry and Gradient Metrics Toolbox (Evans and Oakleaf, 2012) and normalized by dividing 

each pixel value by the maximum value. Topographic position indices (TPI; Jenness, 2006) were 

calculated as the difference between elevation at a central point and the surrounding average elevation 

within radii of 510 and 2,010 m. Positive and negative TPI values indicated central point elevations that 

were higher and lower than the surrounding area, respectively, and depressions or valleys can represent 

areas of increased moisture (De Reu and others, 2013). 

Values of all landscape habitat features, distance metrics, and topographic indices were extracted

from the GIS for input into the habitat selection analyses (see ‘Subregional RSF Modeling’) at used 

locations (telemetry data) and random locations. The purpose of generating random locations was to 

characterize the environment available to sage-grouse populations. Five random locations within the 

buffered MCP were generated for every used location to account for heterogeneity of available land 

cover types (Aldridge and others, 2012). 
13

RSF Analyses

Subregional RSF Modeling. 

Resource selection functions (RSFs) are calculated frequently using data from wildlife telemetry 

studies. Typically, selection and avoidance for particular landscape features are estimated by contrasting 

measurements at used locations (telemetry data) with measurements at random locations that represent 

areas available to all individuals within a population (Boyce and McDonald, 1999; Manly and others, 

2002; Johnson and others, 2006). We estimated population-level RSFs using generalized linear models 

with a binomial error distribution and specified logit-link function (that is, logistic regression) in a 

mixed effects model framework, where environmental variables (described above) were modeled as 

explanatory covariates (predictors). The number of sample locations was not equal across individual 

sage-grouse. Therefore, the individual sage-grouse was treated as a random effect (that is, random 

intercept) to account for potential autocorrelation among locations associated with each individual 

(Gillies and others, 2006). Year was also included as a random effect for those subregions with more 

than 1 year of telemetry data to account for temporal intraclass correlation. A weight of 0.2 was 

specified in the model structure for each random location that was used to characterize available habitat. 

This value allowed equal influence by used (weight = 1) and random points because 5 random points 

were generated per actual grouse location. To avoid seasonal sampling bias, we also added an additional 

weight to each location based on the proportion of use occurring during spring/summer (March–

August), fall (September–November), and winter (December–February). The seasonal weight allowed 

all seasons to be represented equally. We fit all models using the lme4 package (Bates and others, 2012) 

in Program R (R-Core-Team, 2012). 

A two-part selection procedure was employed to reduce the number of covariates. This 

procedure relied on bias-corrected Akaike’s information criterion (AICc) (Burnham and Anderson, 

2002) to identify the most parsimonious RSF model for each subregion. In the first part, proposal 

covariates (table 1; appendixes A– L) were used to determine the spatial scale, distance function, or 

topographic index that best approximated the probability of selection for each corresponding covariate 

relative to a null model (that is, random effect only) in an information-theoretic framework. The most 

appropriate fit for percent cover estimated at the three spatial scales (8.7 ha, 61.5 ha, or 661.4 ha) was 

evaluated for each land cover type. The most relevant distance function (linear or exponential decay) 

was evaluated for water features, edge habitat, and agriculture. All topographic measures were evaluated 

relative to a null model. Candidate covariates from models that represented the best performing 

scale/distance function were then carried forward providing that the model also outperformed the null 

model by greater than 2.0 ∆AICc units (Burnham and Anderson, 2002). 

The second part of model development comprised a series of additive models containing all 

possible 2-covariate combinations of our “candidate” covariates carried over from the first stage. 

Models in this set estimated the effect (slope) of a covariate on probability of selection while accounting 

for the presence of all other covariates. We sought to reduce multicollinearity by constructing 

correlation matrices and removing models with evidence of correlated effects (r ≥ | 0.65 |). We then 

calculated model-averaged parameter estimates (βs; appendixes A–L) for each covariate across the set 
14

of additive models to account for model selection uncertainty (Burnham and Anderson, 2002). The 

purpose of this stage was not to develop the most parsimonious additive model with multiple covariates, 

but instead estimate the effect of each covariate and use the model-averaged parameter estimates to 

calculate an RSF. Covariates were excluded when their model-averaged 95 percent confidence intervals 

overlapped zero. The RSF took the form: 

w(x) = exp (β1X1 + β2X2+, ... , +βkXk) (1)

where

w(x) is the resource selection function (RSF), and 

 β is the averaged parameter estimate for each covariate (X1 ,..., Xk) (Manly 

 and others, 2002). 

Although the RSF cannot be considered an absolute probability because unused areas were not known, 

the RSF is useful as a representation for the probability of selection (Manly and others, 2002). 

Summary of Subregional RSF Results.

A total of 25 covariates were modelled in 12 subregional RSF analyses (table 2; appendixes 

A–L). Sixteen of these covariates were present in a majority of the subregional RSF models consisting 

of: (1) seven covariates characterizing land cover – sagebrush, lowland shrub, upland shrub, riparian, 

forest, pinyon-juniper, and perennial grass relative cover; (2) four covariates characterizing distance to 

water sources – nearest water body of any type, nearest spring, nearest wet meadow, and nearest 

perennial stream; and (3) six covariates characterizing land cover interfaces or abiotic features – amount 

of edge habitat, number of adjacent habitat types, topographic roughness, topographic position, and 

elevation. 

While coefficient direction and magnitude for several covariates varied across subregions, some 

consistent patterns were evident in covariate coefficients that indicated use or avoidance of a covariate 

by sage-grouse (table 2; appendixes A–L). On average, 6.5 land cover covariates were present in final 

models for each subregion. The Sheldon subregion was the only notable exception, where just one cover 

type was retained in the final model. Sagebrush was selected by sage-grouse in all eleven subregions 

containing the sagebrush covariate, and pinyon-juniper and forest were avoided by sage-grouse in the 

subregions that contained those covariates (9 and 7 subregions, respectively). Strong correlations 

between pinyon-juniper and sagebrush did not occur among any subregions (r < 0.65). Final RSF 

models in every subregion indicated selection for at least one type of water source. Some 

inconsistencies occurred among coefficients that represented relationships with agriculture across 

subregions, whereby use of agriculture ranged from selection, proportional use to availability, and 

avoidance. Effects related to either the amount of or distance to edge habitat were evident in RSFs for 

every subregion, but the magnitude of these effects varied among subregions. Edge types or land cover

types were present in RSFs for all subregions except for the South Fork/Ruby Valley, and the direction 

of these effects were similar among subregions. A covariate that represented distance to some source of 

water was influential and, thus, included in every subregional RSF. 
15

Table 2. Magnitude of significant model-averaged effects among 12 subregional resource selection function models for all proposed variables 

included in modeling of greater sage-grouse (Centrocercus urophasianus) habitat, Nevada and northeastern California.

[Symbols: +, positive RSF coefficient; -, negative RSF coefficient; 0, RSF coefficient confidence interval overlaps zero; V, topographic position index 

coefficient indicates selection for valleys]

Subregion

Group Covariate BuffaloSkedaddle

ButteBuckWhite 

Pine

Cortez DesertTuscarora

Gollaher 

- O'Neil

LincolnSchellSnake

Lone 

Willow Midway Sheldon

South 

ForkRuby 

Valley

Toyiabe Virginia

Land cover Annual 

grass

0 0 0 - 0 0 0 - 0 - - + 

Bare ground - - - - 0 0 + 0 0 0 0 0 

Cropland + 0 - 0 0 + 0 + 0 + 0 0 

Forest - - 0 - - - 0 - 0 - 0 0 

Lowland 

shrub - - - - 0 - + + 0 - - + 

Perennial 

grass + + + - 0 + 0 - 0 + 0 0 

Pinyon- juniper - - - - 0 - 0 - 0 - - - 

Riparian - + - - - - - 0 0 + -

Sagebrush + + + + + + + + + + +

Upland 

shrub - + - - - - - 0 - 0 + 0 

Wet 

meadow - 0 0 0 0 0 0 0 0 0 0 0 

Agriculture

Distance to 

cropland 0 + 0 + 0 0 0 0 0 0 - - 

Edge Effects Edge effects 0 + + 0 - + + - - - 0 + 

Distance to 

edge - 0 0 + 0 0 0 0 0 0 + 0 

Landscape 

Variation

Variety of 

edge types 0 0 0 0 - 0 + 0 0 0 0 - 
16

Subregion

Group Covariate BuffaloSkedaddle

ButteBuckWhite 

Pine

Cortez DesertTuscarora

Gollaher 

- O'Neil

LincolnSchellSnake

Lone 

Willow Midway Sheldon

South 

ForkRuby 

Valley

Toyiabe Virginia

Variety of 

land cover

types - + + - 0 + 0 + - 0 + 0 

Water 

Source

Distance to 

water body + + + + + + + + + - + -

Distance to 

spring - - + + - + + + 0 - + + 

Distance to 

wet meadow 0 - + + 0 + + + 0 0 + + 

Distance to 

nearest 

stream

0 0 0 - 0 0 0 0 0 + 0 0 

Distance to 

perennial 

stream - 0 + 0 0 + + + - 0 + - 

Distance to 

intermittent 

stream

0 + 0 0 - 0 0 0 0 0 0 0 

Topography Roughness 

index - + + - - - - - 0 - + - 

Topographic 

position 

index

0 V 0 V V V V V V V 0 V 

 Elevation - + + - - + + - + + + +
17

Region-Wide Habitat Suitability Index and Implementation for Conservation Planning

Region-Wide Average Habitat Suitability Surface Map. 

The final RSF equation was applied for each subregion across all pixels in the region-wide 

extent using the Raster Calculator in Spatial Analyst. Because the subregional RSF consisted of extreme 

values, a monotonic transformation of the RSF was conducted, expressed as:

HSI = w(x)

1+w(x)

 , (2)

that resulted in subregional HSI surfaces. These HSI surfaces provided a relative metric of habitat 

quality for any given pixel where habitat quality reflects range-wide mean propensity to be used by 

sage-grouse given the attributes of the pixel. The HSI equation is equivalent to a logistic transformation 

on the βkXk, for each covariate Xk. However, the function was used only to express relative influence 

among different RSF values by expressing influence as a value between 0 and 1. Although we did not 

assume that HSI values represent absolute probabilities, an increase in HSI corresponds to an increase in 

the probability of selection. The subregional HSIs were averaged across each pixel to calculate a single 

continuous surface for the region. This was an appropriate technique for developing a region-wide HSI 

because it reduces the potential for non-typical selection patterns at a local site to influence HSI values 

elsewhere within the region. Further refinement by averaging across subregions at smaller scales is 

possible; however, we suspect that representation from 12 subregions is not broad enough to warrant 

further delineation. Our end result was a region-wide HSI surface that accounts for variability in 

predicted HSI values from each of the subregional areas (fig. 5).

Implementation of the Region-Wide HSI Map for Conservation Planning—An Example

Effective conservation planning is an inherent stakeholder-driven process, and stakeholders may 

use quantitative tools to aid decision making. Here, an example is provided for how a HSI continuous 

surface map can be used as a tool to aid conservation planning and the decision-making process. In this 

example, two categorized sources of information are employed to identify spatially explicit management 

areas: (1) suitability of landscape characteristics; and (2) likelihood of sage-grouse occurrence.
18

HSI Classification and Validation—An Example

The relative suitability of habitat occurring in an area may be obtained directly from the regionwide HSI map. However, the continuous index at each 30 x 30 m pixel provided by the map is an 

unwieldy mechanism for decision-making related to distinct areas, especially at relatively large scales. 

Therefore, it can be valuable to categorize the region-wide HSI surface into classes that represent habitat 

quality at larger spatial scales. To do this, pixels that represented large bodies of water (for example,

lakes and reservoirs) identified from Landsat land cover classifications (that is, SynthMap) were first 

masked from the region-wide HSI. The region-wide HSI was then objectively binned into four discrete 

categories in multiple steps. First, HSI values were extracted using the 10 percent of independent 

telemetry locations that were intentionally withheld from the data set used to develop subregion RSFs. 

This data set comprised the ‘map classification data set’, as it represented data that were statistically 

independent of RSF outcomes. Second, four suitability categories were developed using cutoff values 

based on the standard deviation (SD) from the mean HSI (x̅) derived from the map classification data 

set. For these purposes, we assumed the data arose from a normal distribution. High suitability habitat 

was comprised of all HSI values greater than 0.5 SD below x̅. This constituted a percentile rank range of 

30.9–100.0 percent of HSI values (fig. 6). Moderate suitability habitat was comprised of HSI values 

between 1.0 and 0.5 SD below x̅, constituting a percentile rank range of 15.0–30.9 percent (fig. 7). Low 

suitability habitat was comprised of HSI values between 1.5 and 1.0 SD below x̅, constituting a 

percentile rank range of 6.7–15.0 percent (fig. 8). Non-suitable habitat was comprised of HSI values 1.5 

SD below x̅(less than 6.7 percent; fig. 9). The cutpoint of 1.5 SD was identified as the most appropriate 

value to determine the lowest threshold (non-habitat vs. habitat) using an ancillary analysis. 

Specifically, habitat areas were generated using cutpoints from SD of 0.5, 1.0, 1.5, and 2.0 below x̅. For 

each successive SD cutpoint, we then calculated the ratio of percent potential habitat area gained to the 

percent of RSF telemetry points added. A curvilinear line between this ratio (y-axis) and SD cutpoint (xaxis) was fitted, and the point where the line intercepted 1.0 on the y-axis was closest to the 1.5 SD. 

This analysis indicated that cutpoints beyond 1.5 SD incorporated disproportionately fewer telemetry 

points per unit area (fig. 10). As further rationale for 1.5 SD, this value represents the 6.7 percentile of 

the HSI distribution. Assuming that sage grouse locations are normally distributed in relation to habitat 

quality and sampling was random, this value roughly corresponds to the 5-10 percent of time that sagegrouse may spend moving between seasonal habitats. 
19

Figure 5. Map showing region-wide, model-averaged suitability map of greater sage-grouse (Centrocercus 

urophasianus) habitat derived from 12 subregions, Nevada and northeastern California. 
20

Figure 6. Map and graph (resource selection function [RSF] and probability distribution) showing example regionwide distribution of categorized high suitability habitat for greater sage grouse (Centrocercus urophasianus), 

Nevada and northeastern California. xpri = classification as priority habitat; x� = mean of the suitability values derived 

from classification data set; σ = standard deviation of suitability values derived from classification data set.

xpri > x� − σ

2
21

Figure 7. Map and graph (resource selection function [RSF] and probability distribution) showing example regionwide distribution of categorized moderate suitability habitat for greater sage grouse (Centrocercus urophasianus), 

Nevada and northeastern California. xmod = classification as moderate habitat; x� = mean of the suitability values 

derived from classification data set; σ = standard deviation of suitability values derived from classification data set.

x� − σ > xmod < x� − σ

2
22

Figure 8. Map and graph (resource selection function [RSF] and probability distribution) showing example regionwide distribution of categorized low suitability habitat for greater sage grouse (Centrocercus urophasianus), Nevada 

and northeastern California. xlow = classification as low habitat; x� = mean of the suitability values derived from 

classification data set; σ = standard deviation of suitability values derived from classification data set.

x� − 1. 5σ > xlow < x� − σ
23

Figure 9. Map showing example region-wide distribution of categorized non-suitable habitat for greater sage 

grouse (Centrocercus urophasianus), Nevada and northeastern California. xnon = classification as non-habitat; x� = 

mean of the suitability values derived from classification data set; σ = standard deviation of suitability values 

derived from classification data set.

 

xnon < x� − 1. 5σ
24

Figure 10. Relation between ratio of added potential habitat to added telemetry points validated and SD cutoff. 

SD, standard deviation
25

We used three data sets to assess the accuracy of the habitat suitability categories. The first set 

was comprised of locations from the 10 percent validation set within RSF regions (n = 3,124). The 

second set was comprised of all locations from non-RSF subregions with insufficient sample size for 

inclusion in the original RSF analyses (n = 609, subregions = 7). The third set was comprised of 

locations for active leks (see Spatial Use Index for data source). Locations from all validation sets were 

overlaid onto the categorized HSI map, and then evaluated for agreement between percentages of 

locations falling within each habitat category and SD percentile classes used for the habitat 

classification (fig. 11). In addition, Cohen’s Kappa coefficient (κ) was used to assess agreement 

between the frequencies of observed (actual) validated HSI values versus expected values based on SD 

percentile bins. Cohen’s Kappa is a more robust measure than a simple percentage of agreement because 

κ takes into account the agreement that can occur by chance alone. Values of κ greater than 0.75 

constitute excellent agreement, 0.40–0.75 are acceptable, and less than 0.40 are poor (Fleiss, 1981). 

Relatively good agreement occurred among the validation data and habitat categories based on both 

percentages and κ (table 3). Agreement across all categories was exceptionally strong within the RSF 

subregion validation set, and acceptable for the non-RSF subregional set. On a cumulative basis, 79 

percent, 94 percent, and 97 percent of leks occurred within the categories: (1) high only; (2) moderate 

and high; and 3) high, moderate, and low habitat, respectively. Acceptable agreement occurred for the 

lek validation set, but more leks occurred in high and moderate habitat than expected, and fewer leks 

occurred in low and non-habitat than expected.

Space Use Index 

Habitat suitability categories provide a crucial piece of information to support decision-making.

The second source of information that we used incorporated data regarding lek sites to estimate use of 

areas by sage-grouse across the landscape. We developed a composite SUI that combined the density of 

lek sites (breeding density) with the non-linear probability of space use relative to distance to lek 

(distance). Lek locations were the basis for both indices for multiple reasons. Leks are ideal locations to 

conduct space use analyses because they are considered hubs for nesting (Autenrich, 1985; Connelly 

and others, 2004) and generally are centered within seasonal use areas, meaning lek location provides an 

appropriate focal point for areas critical to all life phases of sage-grouse (Doherty and others, 2010a; 

Coates and others, 2013). Leks also are detectable using standard survey procedures and established 

protocols exist for counting male sage-grouse at these sites (Connelly and others, 2004), whereby males 

at leks were typically counted 3–4 times per season and the maximum count was recorded. Spatial 

coordinates for leks and associated data on sage-grouse abundance and activity were obtained from 

databases compiled by the NDOW and CDFW. Although 3–4 counts were typically conducted for 

counted leks, not all leks were counted every year across the project area. For our analyses, all included 

leks were classified by agency personnel as “active” (that is, leks with known male attendance) within 

the last 5–6 years (2009–2013) and “pending” (that is, leks with no males observed or leks that had not 

been surveyed adequately). Pending leks were included to allow for a more robust likelihood of sagegrouse occupancy across the landscape given the uncertainty associated with whether or not a pending 

lek had actually become inactive. 
26

Table 3. Summary of habitat suitability model validation tests used to evaluate habit suitability classes for greater 

sage-grouse (Centrocercus urophasianus), Nevada and northeastern California. 

[Three independent sets used for validation included: (1) radio telemetry data selected from within the subregions where 

RSFs (RSF subregions); (2) telemetry data outside the subregions (Non-RSF subregions); and (3) Active leks. Percent, %; 

Values for Cohen’s kappa coefficient (κ) are in parentheses]

Habitat Suitability

Classification

Expected 

%

Validation Sets

RSF subregions

% (κ)

Non - RSF subregions

% (κ)

Active leks

% (κ)

High 69 68 (0.97) 56 (0.50) 79 (0.73)

Moderate 15 20 (0.83) 34 (0.37) 15 (0.98)

Low 9 7 (0.89) 3 (0.61) 3 (0.50)

Non-Habitat 7 5 (0.81) 7 (0.85) 3 (0.57)

To estimate density of lek sites (breeding density), we used a kernel density analysis (Silverman, 

1986) and estimated the smoothing parameter (that is, bandwidth) using likelihood based crossvalidation (Horne and Garton, 2006). Because substantial variation in lek size (number of attending 

males) existed among lek sites, individual leks were weighted by the most recent 5-year average for 

maximum male attendance per year. Therefore, breeding density was a function of lek distribution on 

the landscape (that is, proximity to each other) and lek size. Parameter estimation was conducted using 

Geospatial Modeling Environment (Beyer and others, 2010) and in Program R (R-Core-Team, 2012) 

with the ‘ks’ package (Duong, 2012). 

The other component of the SUI consisted of adjusting for the use of space around lek sites (lek 

distance index), largely because leks are considered points on the landscape whereas sage-grouse use 

areas in relation to lek sites. Because the probability of occurrence is not likely to be a linear 

relationship with the Euclidean distance from a lek, we used a non-linear effect based on an average 

space use response curve derived by Coates and others (2013) from nearby populations of sage-grouse 

within the Bi-State Distinct Population Segment. Specifically, the curve was derived from quantification 

of the volume of population level utilization distribution (vUD) within a range of areas that varied in 

size and were centered on leks, up to a distance of 30 km. Utilization distributions were represented by 

an individual probability density function for each of 193 sage-grouse totaling nearly 11,878 sagegrouse locations. To obtain the distance index for our purposes, we simply subtracted the derived vUD 

value from one for every 30 m distance away from leks up to 30 km. Therefore, the lek point received a 

value of one, and as distance increased the value declined exponentially until it flattened at distances of 

5–8 km. This calculated value provided a relative likelihood of occurrence based on previously 

published probability density functions from radio-telemetry data for sage-grouse. The curve developed 

for the Bi-State was appropriate to adopt for this analysis for multiple reasons because the curve: (1)

accounted for seasonal patterns; (2) represented multiple isolated populations; (3) represented a 

relatively large spatial extent; and (4) likely represents other areas of the Great Basin because it 

consisted of substantial variation among populations as described in Coates and others (2013). 
27

Figure 11. Map showing overlay of radio-telemetry data and lek locations used to validate habitat suitability 

classes for greater sage grouse (Centrocercus urophasianus), Nevada and northeastern California. RSF, resource 

selection function.
28

To create the SUI, grid-cell (30 x 30 m) values for lek density index and lek distance index were 

first normalized by dividing by the maximum of their respective index, and then averaged across all grid 

cells. The SUI, therefore, is a continuous, spatially explicit relative measure of sage-grouse occurrence 

weighted by local population size. For development of the example decision support tool, the SUI was 

categorized into two categories: “high use” and “low-to-no use” areas. High use areas consisted of areas 

that included up to 85 percent of the highest SUI density (cumulative density values). Low-to-no use 

areas of the landscape consisted of areas with less than 15 percent of the cumulative SUI density (fig.

12). The identification of high use regions allowed for spatial connectivity among areas of likely sagegrouse use and is consistent with previously used standards for sage-grouse breeding density (for 

example, Doherty and others, 2010b). 

Developing a Decision-Support Tool—Combining RSF Categories with Space Use 

To promote clear and effective policy decisions, it is often desirable to simplify a suite of 

important considerations regarding habitats or populations into a few non-overlapping classes, each of 

which are subject to specific rules, valuations, or interpretation for aiding in the decision-making 

process. The following is an example of how the intersection between habitat quality (a function of 

environmental attributes) and sage-grouse space use (a function of sage-grouse occurrence) can provide 

spatially explicit information to policymakers. Four habitat management classes were developed from 

the intersection of HSI and SUI categories (table 4). The rubric used to develop management classes and 

rationale is as follows:

1. Core Areas (fig. 13): Defined as the intersection between all suitable habitats (high, moderate, and 

low categories) and the high use SUI category. This habitat management class is intended to 

incorporate all suitable habitats that have relatively high certainty of current sage-grouse occupancy.

2. Priority Areas (fig. 13): Defined as both high suitability habitat that is present within the low-to-no 

use SUI category or non-suitable habitat occurring within the high use SUI category. This habitat 

management class encompasses: (1) high-quality habitats based on environmental covariates with a 

lower potential for occupancy given the current distribution of sage-grouse; and (2) sage-grouse 

incursion into areas of low quality habitat that is potentially important for local populations (for 

example, corridors of non-habitat connecting higher quality habitat). 

3. General Areas (fig. 13): Defined as moderate and low habitat suitability that is present within the 

low-to-no use SUI category. This habitat management class represents areas with appropriate 

environmental conditions for sage-grouse, but are less frequently used by sage-grouse. 

4. Non-habitat Areas (fig. 13): Defined as non-suitable habitat that is present within the low-to-no use 

SUI. This scenario represents habitat of marginal value to sage-grouse populations. 

Table 4. Rubric for determining habitat management classes from habitat suitability and space use categories.

Space Use Index Category

Region-wide RSF Category High Use Area Low-to-No Use Areas

High Habitat Suitability Core Area Priority Area

Moderate Habitat Suitability Core Area General Area

Low Habitat Suitability Core Area General Area

Non-suitable Habitat Priority Area Non Habitat Area
29

Figure 12. Map showing a space use index (SUI) that was developed compiling data on greater sage-grouse

(Centrocercus urophasianus) use and distribution of leks, Nevada and northwestern California. Areas that

contained 85 percent (%) of the total SUI density were identified as “high use” areas (reddish-brown).
30

Figure 13. Map showing habitat management classes that can be determined based on the intersection of habitat 

suitability classes and space use index categories for greater sage grouse (Centrocercus urophasianus), Nevada 

and northeastern California. 
31

Conclusion

This report presents a ‘first of its kind’ spatially explicit map of greater sage-grouse 

(Centrocercus urophasianus) habitat suitability across Nevada and northeastern California. Importantly, 

the map was informed by resource selection functions derived from data across multiple site-specific 

studies of sage-grouse and scaled up to a region-wide level as a habitat suitability index. The power of 

this approach rests within the map output that can be downscaled back to the local level that may help 

inform specific, “on the ground”, habitat-management decisions. However, it is important to recognize 

that field data and other sources of information should be used in conjunction with inferences from this 

model. 

The example of incorporating information about space use further improves the utility of the 

model for conservation planning. Merging sage-grouse space use with habitat characteristics helps to 

identify areas with the highest likelihood of occurrence coupled with suitable habitat so that biologically 

significant areas can receive conservation priority. However, it must be stressed that the habitat 

categories and management scenarios presented serve only as examples for the types of output that can 

be created with this conservation planning method. Levels of habitat suitability and frequency of space 

use can be reclassified readily, or other space use models could be employed (that is, other home range 

estimators) as might be deemed appropriate through a structured decision making process among 

various stakeholders in sage-grouse management. In addition, either the provided or newly generated 

map can be updated readily as new data become available. Further estimation of variance in habitat 

selection associated with life history specific habitat requirements (for example, nesting, brood rearing, 

overwinter) and anthropogenic disturbances (for example, power lines, energy development) would be 

beneficial and could also be incorporated into this model framework. 

Acknowledgments

We thank the countless biologists and technicians who captured and tracked radio-marked sagegrouse and whose data that made this modeling effort possible. A special “thanks” to M. Atamian, 

E. Blomberg, and D. Gibson from the University of Nevada Reno (UNR); D. Davis and K. Reese from 

the University of Idaho; Z. Lockyer, J. Dudko, and S. Matthews from Idaho State University; 

B. Prochazka and J. Hull from the University of California Davis; and K. Andrle, T. Tam, T. Allen, and 

J. Ragni from the U.S. Geological Survey, Western Ecological Research Center. 

We thank Cory Hunt from the Nevada State Governors Office for his support. We also thank T. 

Rubald, J. Copeland, M. Faigeles, and K. McGowan from the Nevada State Sagebrush Ecosystem 

Technical Team and all members of the Sagebrush Ecosystem Council. We thank J. Tull (NDOW), B. 

Halstead (USGS), and J. Yee (USGS) for important statistical consultation and constructive comments 

on previous draft reports. We are also thankful for the help of S. Abele (U.S. Fish and Wildlife Service), 

R. Mazur (U.S. Department of Agriculture, Forest Service), S. Brewer and P. Zieglar (Bureau of Land 

Management), and J. Sedinger (UNR) as part of an expert review team that periodically provided 

feedback on this project. This work was a collaborative effort between the Nevada Department of 

Wildlife, California Department of Fish and Wildlife, and Bureau of Land Management, and was 

funded by the Nevada State Sagebrush Ecosystem Program. 
32

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35

Appendix A. Supplemental Material for Buffalo-Skedaddle RSF Modeling 

Table A1. Variable selection results from the “proposal set” of variables from the Buffalo-Skedaddle subregion, 

Nevada and northeastern California. 

[The top-ranked variable in each set was retained in the suite of candidate variables for resource selection function (RSF) 

modeling if they performed better than the null model and if confidence intervals around estimated mean effects did not 

overlap zero] 

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Land cover Annual grass 661.4 ha 4 -34,181.4 0.0 1.0

61.5 ha 4 -34,379.3 395.6 0.0

Null 3 -34,400.9 436.9 0.0

8.7 ha 4 -34,400.7 438.5 0.0

Bare ground 61.5 ha 4 -34,387.8 0.0 1.0

661.4 ha 4 -34,399.6 23.6 0.0

Null 3 -34,400.9 24.1 0.0

8.7 ha 4 -9,1626.0 114,476.3 0.0

Cropland 661.4 ha 4 -33,830.5 0.0 1.0

61.5 ha 4 -34,304.6 948.2 0.0

8.7 ha 4 -34,348.4 1,035.8 0.0

Null 3 -34,400.9 1,138.8 0.0

Forest 661.4 ha 4 -33,470.6 0.0 1.0

61.5 ha 4 -33,742.4 543.6 0.0

8.7 ha 4 -33,948.6 956.1 0.0

Null 3 -34,400.9 1,858.7 0.0

Lowland shrub 661.4 ha 4 -34,374.1 0.0 0.93

61.5 ha 4 -34,376.7 5.2 0.07

8.7 ha 4 -34,388.6 28.9 0.0

Null 3 -34,400.9 51.6 0.0

Perennial grass 661.4 ha 4 -33,325.8 0.0 1.0

61.5 ha 4 -34,014.7 1,377.9 0.0

8.7 ha 4 -34,241.1 1,830.6 0.0

Null 3 -34,400.9 2,148.3 0.0

Pinyon-juniper 661.4 ha 4 -32,755.0 0.0 1.0

61.5 ha 4 -32,976.5 443.1 0.0

8.7 ha 4 -33,261.8 1,013.6 0.0

Null 3 -34,400.9 3,289.8 0.0
36

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Land- cover Riparian 661.4 ha 4 -32,879.2 0.0 1.0

61.5 ha 4 -33,578.4 1,398.5 0.0

8.7 ha 4 -33,980.8 2,203.2 0.0

Null 3 -34,400.9 3,041.5 0.0

Sagebrush 8.7 ha 4 -33,784.8 0.0 0.98

61.5 ha 4 -33,788.9 8.1 0.02

661.4 ha 4 -34,245.8 921.9 0.0

Null 3 -34,400.9 1,230.1 0.0

Upland shrub 8.7 ha 4 -34,253.2 0.0 1.0

61.5 ha 4 -34,338.3 170.2 0.0

661.4 ha 4 -34,350.3 194.1 0.0

Null 3 -34,400.9 293.4 0.0

Wet meadow 8.7 ha 4 -34,193.4 0.0 0.8

661.4 ha 4 -34,194.8 2.8 0.2

61.5 ha 4 -34,212.9 39.1 0.0

Null 3 -34,400.9 413.1 0.0

Agriculture Distance to cropland Expon. decay 4 -33,898.1 0.0 1.0

Linear 4 -34,072.0 347.7 0.0

Null 3 -34,400.9 1,003.6 0.0

Edge Edge effects 661.4 ha 4 -31,631.3 0.0 1.0

61.5 ha 4 -31,815.2 367.8 0.0

8.7 ha 4 -32,406.5 1,550.4 0.0

Null 3 -34,400.9 5,537.3 0.0

Distance to edge Expon. decay 4 -31,271.3 0.0 1.0

Linear 4 -31,727.5 912.4 0.0

Null 3 -34,400.9 6,257.1 0.0

Landscape 

variation Variety of edge types 61.5 ha 4 -33,070.8 0.0 1.0

8.7 ha 4 -33,230.3 318.9 0.0

661.4 ha 4 -33,624.9 1,108.1 0.0

Null 3 -34,400.9 2,658.1 0.0

Variety of land cover types 61.5 ha 4 -32,507.1 0.0 1.0

8.7 ha 4 -32,973.0 931.7 0.0

661.4 ha 4 -33,854.0 2,693.7 0.0

Null 3 -34,400.9 3,785.6 0.0

Water 

sources

Distance to spring Linear 4 -30,963.6 0.0 1.0

Distance to spring Expon. decay 4 -31,349.9 772.6 0.0
37

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Distance to perennial stream Expon. decay 4 -32,313.8 2,700.3 0.0

Distance to perennial stream Linear 4 -32,541.7 3,156.2 0.0

Water 

sources

Distance to intermittent stream Expon. decay 4 -32,549.2 3,171.2 0.0

Distance to intermittent stream Linear 4 -33,156.8 4,386.5 0.0

Distance to water body Linear 4 -33,800.3 5,673.5 0.0

Distance to nearest stream Expon. decay 4 -33,835.4 5,743.5 0.0

Distance to wet meadow Linear 4 -34,196.7 6,466.2 0.0

Distance to water body Expon. decay 4 -34,210.3 6,493.5 0.0

Distance to nearest stream Linear 4 -34,284.3 6,641.4 0.0

Distance to wet meadow Expon. decay 4 -34,300.4 6,673.7 0.0

Null Null 3 -34,400.9 6,872.6 0.0

Topographic Roughness index 1 ha 4 -28,466.5 0.0 1.0

Elevation Linear 4 -33,099.8 9286.3 0.0

Topographic position index 510 m 4 -34,399.8 11,866.7 0.0

Null 3 -34,400.9 11,866.8 0.0

 Topographic position index 2,010 m 4 -34,456.6 11,980.2 0.0
38

Table A2. Model averaged parameter estimates and 95-percent confidence intervals for candidate variables 

included in the Buffalo-Skedaddle subregional resource selection function (RSF) model, Nevada and northeastern 

California. 

Variable

Scale/distance 

function

Model averaged estimate (95-

percent confidence interval) Selection/Avoidance

Bare ground 61.5 ha -2.89 (-3.63, -2.15) Avoidance

Cropland 661.4 ha 1.59 (1.21, 1.97) Selection

Forest 61.5 ha -11.84 (-14.18, -9.51) Avoidance

Lowland shrub 661.4 ha -1.24 (-1.48, -1.00) Avoidance

Perennial grass 661.4 ha 6.05 (5.30, 6.79) Selection

Pinyon-juniper 661.4 ha -2.47 (-2.73, -2.22) Avoidance

Riparian 661.4 ha -57.51 (-62.68, -52.34) Avoidance

Sagebrush 8.7 ha 1.10 (1.03, 1.17) Selection

Upland shrub 8.7 ha -2.00 (-2.24, -1.75) Avoidance

Wet meadow 8.7 ha -11.93 (-13.69, -10.16) Avoidance

Variety of land cover types 61.5 ha -0.25 (-0.26, -0.24) Avoidance

Distance to edge

Exponential

decay -1.77 (-1.85, -1.69) Avoidance

Distance to perennial stream

Exponential

decay -1.89 (-1.99, -1.80) Avoidance

Distance to spring Linear 0.18 (0.17, 0.20) Avoidance

Distance to water body Linear -0.07 (-0.09, -0.05) Selection

Roughness index 1 ha -12.68 (-13.00, -12.36) Avoidance

Elevation Linear -1.53 (-1.70, -1.37)

Selection for lower 

elevations
39

Table A3. Buffalo-Skedaddle subregional resource selection function (RSF) variable means and standard errors 

within all available habitats and habitats used by greater sage-grouse (Centrocercus urophasianus), Nevada and 

northeastern California. 

Available habitats Used habitats

Variable Scale Mean

Standard

error Mean

Standard

error

Bare ground 61.5 ha 0.007 0.0003 0.004 0.0005

Cropland 661.4 ha 0.010 0.0004 0.024 0.0017

Forest 61.5 ha 0.014 0.0009 0.001 0.0002

Lowland shrub 661.4 ha 0.025 0.0009 0.020 0.0016

Perennial grass 661.4 ha 0.006 0.0002 0.017 0.0010

Pinyon-juniper 661.4 ha 0.068 0.0012 0.027 0.0021

Riparian 661.4 ha 0.003 0.0001 0.001 0.0001

Sagebrush 8.7 ha 0.823 0.0032 0.899 0.0058

Upland shrub 8.7 ha 0.015 0.0010 0.009 0.0015

Wet meadow 8.7 ha 0.007 0.0007 0.001 0.0003

Variety of land cover types 61.5 ha 3.80 0.0191 2.78 0.0371

Distance to edge km 0.25 0.0035 0.54 0.0124

Distance to perennial 

stream km 4.34 0.0003 5.88 0.0014

Distance to spring km 2.69 0.0185 4.20 0.0468

Distance to water body km 2.03 0.0001 1.60 0.0004

Roughness index 1 ha 0.14 0.0008 0.07 0.0014

Elevation km 1.64 0.0001 1.57 0.0001
40

Appendix B. Supplemental material for Butte-Buck-White Pine RSF Modeling

Table B1. Variable selection results from the “proposal set” of variables from the Butte-Buck-White Pine subregion, 

Nevada. 

[The top-ranked variable in each set was retained in the suite of candidate variables for resource selection function (RSF) 

modeling if they performed better than the null model and if confidence intervals around estimated mean effects did not 

overlap zero] 

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Landcover

Annual grass 661.4 ha 4 -43,980.9 0.0 1.0

61.5 ha 4 -44,106.0 250.1 0.0

8.7 ha 4 -44,167.1 372.3 0.0

Null 3 -44,236.7 509.5 0.0

Bare ground 8.7 ha 4 -44,224.4 0.0 1.0

61.5 ha 4 -44,234.4 20.0 0.0

Null 3 -44,236.7 22.6 0.0

661.4 ha 4 -44,236.2 23.7 0.0

Cropland 661.4 ha 4 -43,256.3 0.0 1.0

61.5 ha 4 -43,551.9 591.3 0.0

8.7 ha 4 -43,802.2 1,091.8 0.0

Null 3 -44,236.7 1,958.8 0.0

Forest 8.7 ha 4 -44,185.5 0.0 1.0

61.5 ha 4 -44,220.9 71.0 0.0

661.4 ha 4 -44,232.5 94.1 0.0

Null 3 -44,236.7 100.4 0.0

Lowland shrub 61.5 ha 4 -43,475.8 0.0 1.0

8.7 ha 4 -43,584.9 218.2 0.0

661.4 ha 4 -43,619.9 288.1 0.0

Null 3 -44,236.7 1,519.7 0.0

Perennial grass 61.5 ha 4 -44,168.3 0.0 1.0

661.4 ha 4 -44,202.2 67.7 0.0

8.7 ha 4 -44,214.0 91.5 0.0

Null 3 -44,236.7 134.7 0.0

Pinyon-juniper 61.5 ha 4 -37,257.8 0.0 1.0

8.7 ha 4 -37,911.6 1,307.6 0.0

661.4 ha 4 -38,225.0 1,934.5 0.0

Null 3 -44,236.7 13,955.7 0.0

Riparian 661.4 ha 4 -44,052.0 0.0 1.0

61.5 ha 4 -44,193.9 283.7 0.0

8.7 ha 4 -44,235.5 366.9 0.0
41

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Null 3 -44,236.7 367.2 0.0

Sagebrush 661.4 ha 4 -37,390.3 0.0 1.0

61.5 ha 4 -37,804.4 828.2 0.0

8.7 ha 4 -38,632.4 2,484.2 0.0

Null 3 -44,236.7 13,690.6 0.0

Upland shrub 661.4 ha 4 -44,126.9 0.0 1.0

61.5 ha 4 -44,198.1 142.5 0.0

8.7 ha 4 -44,224.4 195.2 0.0

Null 3 -44,236.7 217.6 0.0

Wet meadow 661.4 ha 4 -44,078.5 0.0 1.0

61.5 ha 4 -44,173.1 189.2 0.0

8.7 ha 4 -44,200.2 243.5 0.0

Null 3 -44,236.7 314.3 0.0

Agriculture Distance to cropland Expon. decay 4 -41,961.8 0.0 1.0

Linear 4 -42,223.4 523.0 0.0

Null 3 -44,236.7 4,547.6 0.0

Edge Edge effects 661.4 ha 4 -43,957.8 0.0 1.0

61.5 ha 4 -44,118.4 321.2 0.0

8.7 ha 4 -44,224.8 534.1 0.0

Null 3 -44,236.7 555.8 0.0

Distance to edge Linear 4 -44,235.3 0.0 0.44

Null 3 -44,236.7 0.8 0.29

Expon. decay 4 -44,235.7 0.9 0.27

Landscape 

variation Variety of edge types 61.5 ha 4 -44,075.0 0.0 1.0

8.7 ha 4 -44,164.9 179.7 0.0

661.4 ha 4 -44,231.6 313.1 0.0

Null 3 -44,236.7 321.2 0.0

Variety of land cover types 661.4 ha 4 -43,246.1 0.0 1.0

61.5 ha 4 -44,174.6 1857.0 0.0

8.7 ha 4 -44,221.9 1,951.5 0.0

Null 3 -44,236.7 1,979.1 0.0

Water 

sources

Distance to intermittent stream Linear 4 -42,153.8 0.0 1.0

Distance to nearest stream Linear 4 -42,219.2 130.9 0.0

Distance to intermittent stream Expon. decay 4 -42,781.3 1,255.0 0.0

Distance to perennial stream Expon. decay 4 -42,821.9 1,336.2 0.0

Distance to nearest stream Expon. decay 4 -42,879.6 1,451.7 0.0
42

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Distance to spring Exponential 

decay

4 -43,134.6 1,961.7 0.0

Water sources Distance to perennial stream Linear 4 -43,253.8 2,200.0 0.0

Distance to spring Linear 4 -43,784.2 3,260.8 0.0

Distance to wet meadow Linear 4 -44,144.7 3,981.8 0.0

Distance to water body Linear 4 -44,164.5 4,021.4 0.0

Distance to water body Expon. decay 4 -44,189.3 4,071.1 0.0

Distance to wet meadow Expon. decay 4 -44,218.7 4,129.9 0.0

Null Null 3 -44,236.7 4,163.7 0.0

Topographic Roughness index 1 ha 4 -43,199.6 0.0 1.0

Topographic position index 510 m 4 -44,159.3 1,919.3 0.0

Elevation Linear 4 -44,202.1 2,005.0 0.0

Topographic position index 2010 m 4 -44,236.7 2,072.1 0.0

 Null 3 -44,243.3 2,087.3 0.0
43

Table B2. Model averaged parameter estimates and 95-percent confidence intervals for candidate variables 

included in the Butte-Buck-White Pine subregional resource selection function (RSF) model, Nevada. 

Variable Scale/distance function

Model averaged estimate (95% 

confidence interval) Selection/Avoidance

Bare ground 8.7 ha -3.48 (-4.43, -2.54) Avoidance

Forest 8.7 ha -1.50 (-1.81, -1.19) Avoidance

Lowland shrub 61.5 ha -3.66 (-3.80, -3.51) Avoidance

Perennial grass 61.5 ha 14.90 (13.94, 15.85) Selection

Pinyon-juniper 61.5 ha -4.17 (-4.26, -4.07) Avoidance

Riparian 661.4 ha 40.74 (38.74, 42.74) Selection

Sagebrush 661.4 ha 5.47 (5.37, 5.57) Selection

Upland shrub 661.4 ha 8.37 (7.83, 8.91) Selection

Edge effect 661.4 ha 11.47 (11.12, 11.82) Selection

Variety of land cover types 661.4 ha 0.50 (0.49, 0.51) Selection

Distance to cropland Exponential decay 2.24 (2.17, 2.31) Selection

Distance to intermittent stream Linear -2.42 (-2.50, -2.34) Selection

Distance to spring Exponential decay 2.84 (2.76, 2.91) Avoidance

Distance to water body Linear -0.10 (-0.11, -0.10) Selection

Distance to wet meadow Linear 0.04 (0.03, 0.04) Avoidance

Roughness index 1 ha 2.28 (2.04, 2.52) Selection

Topographic position index 510 m 0.009 (0.007, 0.01)

Selected ridges / 

Avoided valleys

Elevation Linear 1.57 (1.49, 1.65)

Selection for higher 

elevation
44

Table B3. Butte-Buck-White Pine subregional resource selection function (RSF) variable means and standard 

errors within all available habitats and habitats used by greater sage-grouse (Centrocercus urophasianus), Nevada. 

Available habitats Used habitats

Variable Scale Mean

Standard

error Mean

Standard

error

Bare ground 8.7 ha 0.002 0.0002 0.0004 0.0002

Edge effect 661.4 ha 0.117 0.0005 0.150 0.0013

Forest 8.7 ha 0.009 0.0006 0.007 0.0006

Lowland shrub 61.5 ha 0.076 0.0018 0.036 0.0020

Perennial grass 61.5 ha 0.003 0.0002 0.009 0.0005

Pinyon-juniper 61.5 ha 0.316 0.0033 0.072 0.0036

Riparian 661.4 ha 0.010 0.0001 0.012 0.0003

Sagebrush 661.4 ha 0.558 0.0026 0.754 0.0040

Upland shrub 661.4 ha 0.009 0.0003 0.018 0.0007

Distance to cropland Km 5.30 0.0325 3.54 0.0663

Variety of land cover types 661.4 ha 5.39 0.0147 6.65 0.0381

Distance to intermittent stream Km 0.32 0.0033 0.17 0.1207

Distance to spring Km 5.41 0.0327 3.18 0.0639

Distance to wet meadow Km 10.34 0.0538 9.12 0.1207

Distance to water body Km 4.16 0.0227 3.38 0.0438

Roughness index 1 ha 0.16 0.0008 0.13 0.0015

Topographic position index 510 m -0.02 0.1430 1.37 0.2656

Elevation Km 2.06 0.0018 2.13 0.0062
45

Appendix C. Supplemental Material for Cortez RSF Modeling 

Table C1. Variable selection results from the “proposal set” of variables from the Cortez subregion, Nevada. 

[The top-ranked variable in each set was retained in the suite of candidate variables for resource selection function (RSF) 

modeling if they performed better than the null model and if confidence intervals around estimated mean effects did not 

overlap zero] 

Group Variable Scale/distance 

function

K Log 

likelihood

ΔAICc Model 

weight

Land cover Annual grass 661.4 ha 4 -77,225.4 0.0 1.0

61.5 ha 4 -77,774.8 1,098.8 0.0

8.7 ha 4 -77,904.0 1,357.2 0.0

Null 3 -78,138.5 1,824.1 0.0

Bare ground 661.4 ha 4 -75,612.3 0.0 1.0

61.5 ha 4 -75,973.4 722.1 0.0

8.7 ha 4 -76,322.2 1,419.7 0.0

Null 3 -78,138.5 5,050.3 0.0

Cropland 661.4 ha 4 -77,406.3 0.0 1.0

61.5 ha 4 -77,572.6 332.7 0.0

8.7 ha 4 -77,674.5 536.3 0.0

Null 3 -78,138.5 1,462.4 0.0

Forest 61.5 ha 4 -78,008.3 0.0 1.0

8.7 ha 4 -78,089.1 161.6 0.0

661.4 ha 4 -78,092.0 167.4 0.0

Null 3 -78,138.5 258.4 0.0

Lowland shrub 661.4 ha 4 -76,255.9 0.0 1.0

61.5 ha 4 -76,781.9 1,052.0 0.0

8.7 ha 4 -77,006.2 1,500.7 0.0

Null 3 -78,138.5 3,763.2 0.0

Perennial grass 61.5 ha 4 -78,125.2 0.0 1.0

8.7 ha 4 -78,136.3 22.1 0.0

Null 3 -78,138.5 24.5 0.0

661.4 ha 4 -78,137.7 24.9 0.0

Pinyon-juniper 61.5 ha 4 -76,096.2 0.0 1.0

661.4 ha 4 -76,108.8 25.0 0.0

8.7 ha 4 -76,324.8 457.1 0.0

Null 3 -78,138.5 4,082.5 0.0

Riparian 661.4 ha 4 -77,558.5 0.0 1.0

61.5 ha 4 -77,626.5 136.1 0.0

8.7 ha 4 -77,766.0 415.1 0.0

Null 3 -78,138.5 1,158.0 0.0
46

Group Variable Scale/distance 

function

K Log 

likelihood

ΔAICc Model 

weight

Land cover Sagebrush 661.4 ha 4 -74,476.9 0.0 1.0

61.5 ha 4 -74,551.1 148.3 0.0

8.7 ha 4 -74,759.7 565.5 0.0

Null 3 -78,138.5 7,321.1 0.0

Upland shrub 8.7 ha 4 -77,990.3 0.0 1.0

61.5 ha 4 -78,029.8 78.9 0.0

661.4 ha 4 -78,137.4 294.1 0.0

Null 3 -78,138.5 294.3 0.0

Wet meadow 661.4 ha 4 -78,057.5 0.0 1.0

Null 3 -78,138.5 160.0 0.0

8.7 ha 4 -78,137.7 160.3 0.0

61.5 ha 4 -78,137.7 160.4 0.0

Agriculture Distance to cropland Expon. decay 4 -78,091.8 0.0 1.0

Linear 4 -78,128.9 74.2 0.0

Null 3 -78,138.5 91.4 0.0

Edge Edge effects 661.4 ha 4 -77,328.9 0.0 1.0

61.5 ha 4 -77,743.5 829.2 0.0

8.7 ha 4 -77,918.1 1,178.5 0.0

Null 3 -78,138.5 1,617.1 0.0

Distance to edge Linear 4 -77,676.7 0.0 1.0

Expon. decay 4 -77,838.2 323.0 0.0

Null 3 -78,138.5 921.5 0.0

Landscape 

variation

Variety of edge types 661.4 ha 4 -77,168.2 0.0 1.0

61.5 ha 4 -77,887.7 1,439.0 0.0

8.7 ha 4 -78,062.8 1,789.2 0.0

Null 3 -78,138.5 1,938.6 0.0

Variety of land cover types 661.4 ha 4 -77,243.7 0.0 1.0

8.7 ha 4 -78,116.6 1,745.8 0.0

61.5 ha 4 -78,128.4 1,769.4 0.0

Null 3 -78,138.5 1,787.6 0.0

Water 

sources

Distance to perennial stream Linear 4 -74,243.3 0.0 1.0

Distance to perennial stream Expon. decay 4 -74,549.4 612.2 0.0

Distance to spring Expon. decay 4 -75,276.4 2,066.3 0.0

Distance to spring Linear 4 -75,799.1 3,111.6 0.0

Distance to nearest stream Linear 4 -76,307.6 4,128.5 0.0

Distance to intermittent stream Linear 4 -76,628.3 4,770.0 0.0
47

Group Variable Scale/distance 

function

K Log 

likelihood

ΔAICc Model 

weight

Distance to nearest stream Expon. decay 4 -77,301.1 6,115.6 0.0

Distance to intermittent stream Expon. decay 4 -77,626.9 6,767.1 0.0

Distance to wet meadow Expon. decay 4 -77,768.8 7,051.0 0.0

Distance to wet meadow Linear 4 -77,955.0 7,423.5 0.0

Distance to water body Linear 4 -77,985.9 7,485.2 0.0

Distance to water body Expon. decay 4 -78,132.4 7,778.2 0.0

Null Null 3 -78,138.5 7,788.4 0.0

Topography Elevation Linear 4 -75,718.7 0.0 1.0

Roughness index 1 ha 4 -77,228.9 3,020.4 0.0

Topographic position index 510 m 4 -78,136.9 4,836.3 0.0

Null 3 -78,138.5 4,837.5 0.0

Topographic position index 2010 m 4 -78,284.9 5,132.3 0.0

Table C2. Model averaged parameter estimates and 95-percent confidence intervals for candidate variables 

included in the Cortez subregional resource selection function (RSF) model, Nevada

Variable Scale/distance function

Model averaged estimate (95% 

confidence interval) Selection/Avoidance

Bare ground 661.4 ha -15.24 (-16.00, -14.48) Avoidance

Cropland 661.4 ha -6.39 (-6.83, -5.94) Avoidance

Lowland shrub 661.4 ha -6.27 (-6.47, -6.06) Avoidance

Perennial grass 61.5 ha 0.71 (0.55, 0.87) Selection

Pinyon-juniper 61.5 ha -2.89 (-2.96, -2.82) Avoidance

Riparian 661.4 ha -33.51 (-34.93, -32.09) Avoidance

Sagebrush 661.4 ha 2.35 (2.29, 2.41) Selection

Upland shrub 8.7 ha -6.13 (-6.89, -5.38) Avoidance

Edge effect 661.4 ha 4.84 (4.68, 4.99) Selection

Variety of edge types 661.4 ha 0.12 (0.116, 0.123) Selection

Distance to perennial stream Linear -0.14 (-0.15, -0.14) Selection

Distance to spring Expon. decay 2.05 (2.01, 2.10) Selection

Distance to water body Linear -0.09 (-0.10, -0.09) Selection

Distance to wet meadow Linear -2.19 (-2.27, -2.11) Selection

Roughness index 1 ha 3.76 (3.63, 3.90) Selection

Elevation Linear 3.26 (3.19, 3.33)

Selection for higher 

elevations
48

Table C3. Cortez subregional resource selection function (RSF) variable means and standard errors within all 

available habitats and habitats used by greater sage-grouse (Centrocercus urophasianus), Nevada

Available habitats Used habitats

Variable Scale Mean Standard

error

Mean Standard

error

Bare ground 661.4 ha 0.048 0.0012 0.003 0.0003

Cropland 661.4 ha 0.020 0.0006 0.004 0.0003

Lowland shrub 661.4 ha 0.050 0.0007 0.015 0.0007

Perennial grass 61.5 ha 0.020 0.0005 0.015 0.0009

Pinyon-juniper 61.5 ha 0.135 0.0018 0.034 0.0018

Riparian 661.4 ha 0.006 0.0001 0.004 0.0001

Sagebrush 661.4 ha 0.686 0.0019 0.855 0.0025

Upland shrub 8.7 ha 0.003 0.0003 0.0006 0.0001

Edge effect 661.4 ha 0.116 0.0006 0.127 0.0013

Variety of edge types 661.4 ha 6.98 0.0285 7.45 0.0607

Distance to perennial stream Km 7.43 0.0309 5.55 0.0602

Distance to spring Km 3.07 0.0150 2.50 0.0294

Distance to water body Km 4.97 0.0208 5.09 0.0374

Distance to wet meadow Km 9.56 0.0290 10.03 0.0558

Roughness 1 ha 0.14 0.0007 0.16 0.0011

Elevation Km 1.92 0.0013 1.99 0.0032
49

Appendix D. Supplemental Material for Desert-Tuscarora RSF Modeling 

Table D1. Variable selection results from the “proposal set” of variables from the Desert-Tuscarora subregion, 

Nevada. 

[The top-ranked variable in each set was retained in the suite of candidate variables for resource selection function (RSF) 

modeling if they performed better than the null model and if confidence intervals around estimated mean effects did not 

overlap zero] 

Group Variable Scale/distance 

function

K Log likelihood ΔAICc Model 

weight

LandcoverAnnual grass 661.4 ha 4 -52,893.7 0.0 1.0

61.5 ha 4 -54,780.3 3,773.1 0.0

8.7 ha 4 -55,275.5 4,763.6 0.0

Null 3 -56,394.5 6,999.5 0.0

Bare ground 661.4 ha 4 -56,239.7 0.0 1.0

61.5 ha 4 -56,353.4 227.4 0.0

8.7 ha 4 -56,385.8 292.2 0.0

Null 3 -56,394.5 307.5 0.0

Cropland 661.4 ha 4 -55,243.5 0.0 1.0

61.5 ha 4 -55,675.4 863.9 0.0

8.7 ha 4 -55,832.3 1,177.5 0.0

Null 3 -56,394.5 2,299.9 0.0

Forest 61.5 ha 4 -55,515.5 0.0 1.0

661.4 ha 4 -55,542.9 54.8 0.0

8.7 ha 4 -55,597.7 164.3 0.0

Null 3 -56,394.5 1,755.9 0.0

Lowland shrub 661.4 ha 4 -56,172.6 0.0 1.0

61.5 ha 4 -56,273.9 202.6 0.0

8.7 ha 4 -56,308.7 272.3 0.0

Null 3 -56,394.5 441.8 0.0

Perennial grass 661.4 ha 4 -56,115.9 0.0 1.0

61.5 ha 4 -56,245.4 258.9 0.0

8.7 ha 4 -56,324.8 417.8 0.0

Null 3 -56,394.5 555.1 0.0

Pinyon-juniper 61.5 ha 4 -55,831.4 0.0 1.0

661.4 ha 4 -55,880.9 99.0 0.0

8.7 ha 4 -55,968.8 274.8 0.0

Null 3 -56,394.5 1,124.1 0.0

Riparian 661.4 ha 4 -56,344.3 0.0 1.0

61.5 ha 4 -56,362.0 35.2 0.0

8.7 ha 4 -56,384.9 81.1 0.0

Null 3 -56,394.5 98.2 0.0
50

Group Variable Scale/distance 

function

K Log likelihood ΔAICc Model 

weight

Land cover Sagebrush 8.7 ha 4 -56,373.7 0.0 0.9986

61.5 ha 4 -56,380.3 13.2 0.0014

661.4 ha 4 -56,392.9 38.5 0.0

Null 3 -56,394.5 39.6 0.0

Upland shrub 661.4 ha 4 -56,242.2 0.0 1.0

61.5 ha 4 -56,383.6 282.7 0.0

Null 3 -56,394.5 302.5 0.0

8.7 ha 4 -

1,466,256

2,820,027.0 0.0

Wet meadow 661.4 ha 4 -56,280.8 0.0 1.0

8.7 ha 4 -56,390.7 219.7 0.0

61.5 ha 4 -56,391.7 221.7 0.0

Null 3 -56,394.5 225.3 0.0

Agriculture Distance to cropland Linear 4 -54,776.6 0.0 1.0

Expon. Decay 4 -54,809.9 66.5 0.0

Null 3 -56,394.5 3,233.7 0.0

Edge Edge effects 61.5 ha 4 -56,239.3 0.0 1.0

8.7 ha 4 -56,273.1 67.5 0.0

661.4 ha 4 -56,300.9 123.1 0.0

Null 3 -56,394.5 308.2 0.0

Distance to edge Linear 4 -56,201.2 0.0 1.0

Expon. Decay 4 -56,393.3 384.2 0.0

Null 3 -56,394.5 384.6 0.0

Landscape 

variation

Variety of edge types 61.5 ha 4 -56,274.4 0.0 1.0

8.7 ha 4 -56,307.5 66.2 0.0

661.4 ha 4 -56,376.0 203.2 0.0

Null 3 -56,394.5 238.0 0.0

Variety of land cover types 61.5 ha 4 -56,339.6 0.0 1.0

661.4 ha 4 -56,365.3 51.4 0.0

8.7 ha 4 -56,370.0 60.8 0.0

Null 3 -56,394.5 107.8 0.0

Water 

sources

Distance to wet meadow Linear 4 -48,894.1 0.0 1.0

Distance to nearest stream Linear 4 -53,448.4 9,108.5 0.0

Distance to wet meadow Expon. Decay 4 -53,712.7 9,637.2 0.0

Distance to nearest stream Expon. Decay 4 -54,668.6 11,548.9 0.0

Distance to intermittent stream Expon. Decay 4 -55,166.7 12,545.2 0.0

Distance to intermittent stream Linear 4 -55,269.2 12,750.2 0.0

Distance to spring Linear 4 -55,453.6 13,118.9 0.0
51

Group Variable Scale/distance 

function

K Log likelihood ΔAICc Model 

weight

Water sources Distance to water body Expon. Decay 4 -55,500.6 13,213.0 0.0

Distance to perennial Stream Expon. Decay 4 -55,548.7 13,309.1 0.0

Distance to spring Expon. Decay 4 -56,185.9 14,583.5 0.0

Distance to perennial stream Linear 4 -56,315.5 14,842.7 0.0

Distance to water body Linear 4 -56,353.2 14,918.3 0.0

Null Null 3 -56,394.5 14,998.7 0.0

Topography Roughness index 1 ha 4 -53,854.4 0.0 1.0

Elevation Linear 4 -56,043.5 4,378.1 0.0

Topographic position index 510 m 4 -56,190.2 4,671.6 0.0

Null 3 -56,394.5 5,078.1 0.0

Topographic position index 2010 m 4 -56,429.1 5,149.3 0.0

Table D2. Model averaged parameter estimates and 95-percent confidence intervals for candidate variables 

included in the Desert-Tuscarora subregional resource selection function (RSF) model, Nevada.

Variable Scale/distance function

Model averaged estimate (95% 

confidence interval) Selection/Avoidance

Annual grass 661.4 ha -42.68 (-44.82, -40.55) Avoidance

Bare ground 661.4 ha -40.89 (-46.19, -35.58) Avoidance

Forest 61.5 ha -10.67 (-11.30, -10.03) Avoidance

Lowland shrub 661.4 ha -3.21 (-4.21, -2.20) Avoidance

Perennial grass 661.4 ha -6.51 (-7.10, -5.92) Avoidance

Pinyon-juniper 61.5 ha -6.24 (-6.61, -5.86) Avoidance

Riparian 661.4 ha -3.34 (-3.83, -2.86) Avoidance

Sagebrush 8.7 ha 0.65 (0.60, 0.70) Selection

Upland shrub 661.4 ha -18.16 (-19.99, -16.33) Avoidance

Variety of edge types 61.5 ha -0.09 (-0.09, -0.08) Avoidance

Distance to cropland Linear -0.17 (-0.18, -0.16) Selection

Distance to edge Linear -0.74 (-0.84, -0.64) Selection

Distance to nearest stream Linear 2.69 (2.61, 2.77) Avoidance

Distance to spring Linear -0.10 (-0.11, -0.09) Selection

Distance to water body Expon. Decay -2.56 (-2.64, -2.48) Selection

Distance to wet meadow Linear -0.13 (-0.14, -0.13) Selection

Roughness index 1 ha -5.49 (-5.69, -5.29) Avoidance

Topographic position index 510 m 0.009 (0.009, 0.01)

Selected ridges / 

Avoided valleys

Elevation Linear -2.24 (-2.33, -2.14)

Selection for Lower 

Elevations
52

Table D3. Desert-Tuscarora subregional resource selection function (RSF) variable means and standard errors 

within all available habitats and habitats used by greater sage-grouse (Centrocercus urophasianus), Nevada.

Available habitats Used habitats

Variable Scale Mean Standard

error

Mean Standard

error

Annual grass 661.4 ha 0.014 0.0003 0.004 0.0001

Bare ground 661.4 ha 0.002 0.0001 0.0008 0.0000

Forest 61.5 ha 0.015 0.0005 0.003 0.0003

Lowland shrub 661.4 ha 0.004 0.0002 0.0004 0.0002

Perennial grass 661.4 ha 0.027 0.0002 0.026 0.0004

Pinyon-juniper 61.5 ha 0.022 0.0004 0.013 0.0005

Riparian 661.4 ha 0.023 0.0003 0.027 0.0005

Upland shrub 661.4 ha 0.003 0.0001 0.001 0.0001

Sagebrush 8.7 ha 0.854 0.0019 0.845 0.0050

Distance to cropland Km 2.30 0.0129 1.78 0.0279

Variety of edge types 61.5 ha 3.83 0.0197 3.63 0.0427

Distance to edge Km 0.14 0.0013 0.14 0.0022

Distance to nearest stream Km 0.20 0.0012 0.30 0.0038

Distance to spring Km 2.00 0.0130 1.72 0.0169

Distance to water body Km 3.23 0.0168 3.36 0.0266

Distance to wet meadow Km 13.22 0.0654 6.96 0.0614

Roughness index 1 ha 0.20 0.0006 0.16 0.0012

Topographic position index 510 m -0.07 0.1459 2.69 0.2543

Elevation Km 1.93 0.0015 1.90 0.0022
53

Appendix E. Supplemental Material for Gollaher-O’Neil RSF Modeling

Table E1. Variable selection results from the “proposal set” of variables from the Gollaher-O’Neil subregion, 

Nevada. 

[The top-ranked variable in each set was retained in the suite of candidate variables for resource selection function (RSF) 

modeling if they performed better than the null model and if confidence intervals around estimated mean effects did not 

overlap zero] 

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Land cover Annual grass 661.4 ha 4 -40,679.6 0.0 1.0

61.5 ha 4 -40,836.8 314.4 0.0

8.7 ha 4 -40,845.8 332.4 0.0

Null 3 -40,861.0 360.9 0.0

Bare ground 661.4 ha 4 -40,238.0 0.0 1.0

61.5 ha 4 -40,756.0 1,035.9 0.0

8.7 ha 4 -40,832.0 1,187.9 0.0

Null 3 -40,861.0 1,244.0 0.0

Cropland 661.4 ha 4 -39,985.7 0.0 1.0

61.5 ha 4 -40,535.5 1,099.6 0.0

8.7 ha 4 -40,635.0 1,298.6 0.0

Null 3 -40,861.0 1,748.7 0.0

Forest 661.4 ha 4 -39,276.3 0.0 1.0

61.5 ha 4 -39,770.1 987.6 0.0

8.7 ha 4 -40,176.3 1,800.0 0.0

Null 3 -40,861.0 3,167.5 0.0

Lowland shrub 661.4 ha 4 -39,179.8 0.0 1.0

61.5 ha 4 -39,803.7 1,247.8 0.0

8.7 ha 4 -40,216.0 2,072.4 0.0

Null 3 -40,861.0 3,360.4 0.0

Perennial grass 661.4 ha 4 -40,102.6 0.0 1.0

8.7 ha 4 -40,573.1 941.1 0.0

61.5 ha 4 -40,611.8 1,018.4 0.0

Null 3 -40,861.0 1,514.9 0.0

Pinyon-juniper 661.4 ha 4 -39,837.9 0.0 1.0

61.5 ha 4 -40,733.0 1,790.2 0.0

8.7 ha 4 -40,825.3 1,974.8 0.0

Null 3 -40,861.0 2,044.3 0.0

Riparian 661.4 ha 4 -39,795.3 0.0 1.0

61.5 ha 4 -39,817.2 43.7 0.0

8.7 ha 4 -40,116.6 642.5 0.0

Null 3 -40,861.0 2,129.4 0.0
54

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Land cover Sagebrush 661.4 ha 4 -39,375.7 0.0 1.0

61.5 ha 4 -39,429.8 108.1 0.0

8.7 ha 4 -39,809.8 868.0 0.0

Null 3 -40,861.0 2,968.6 0.0

Upland shrub 661.4 ha 4 -40,728.1 0.0 1.0

61.5 ha 4 -40,784.0 111.9 0.0

8.7 ha 4 -40,829.4 202.7 0.0

Null 3 -40,861.0 263.9 0.0

Agriculture Distance to cropland Linear 4 -38,573.2 0.0 1.0

Expon. decay 4 -40,091.9 3,037.4 0.0

Null 3 -40,861.0 4,573.7 0.0

Edge Edge effects 61.5 ha 4 -39,709.7 0.0 1.0

661.4 ha 4 -39,792.5 165.6 0.0

8.7 ha 4 -40,109.4 799.4 0.0

Null 3 -40,861.0 2,300.6 0.0

Distance to edge Expon. decay 4 -39,801.5 0.0 1.0

Linear 4 -40,179.1 755.1 0.0

Null 3 -40,861.0 2,117.0 0.0

Landscape 

variation Variety of edge types 661.4 ha 4 -39,413.5 0.0 1.0

61.5 ha 4 -39,572.8 318.6 0.0

8.7 ha 4 -39,738.0 649.0 0.0

Null 3 -40,861.0 2,893.1 0.0

Variety of land cover types 8.7 ha 4 -39,554.7 0.0 1.0

61.5 ha 4 -39,741.3 373.3 0.0

661.4 ha 4 -40,614.3 2,119.2 0.0

Null 3 -40,861.0 2,610.7 0.0

Water 

source Distance to intermittent stream Linear 4 -37,343.6 0.0 1.0

Distance to intermittent stream Expon. decay 4 -37,854.2 1,021.3 0.0

Distance to wet meadow Linear 4 -38,690.8 2,694.4 0.0

Distance to nearest stream Linear 4 -38,705.3 2,723.4 0.0

Distance to nearest stream Expon. decay 4 -39,289.1 3,891.0 0.0

Distance to wet meadow Expon. decay 4 -39,427.8 4,168.4 0.0

Distance to spring Expon. decay 4 -40,084.8 5,482.5 0.0

Distance to water body Linear 4 -40,368.7 6,050.2 0.0

Distance to spring Linear 4 -40,501.4 6,315.6 0.0

Distance to water body Expon. decay 4 -40,638.4 6,589.6 0.0
55

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Distance to perennial stream Linear 4 -40,758.3 6,829.3 0.0

Null Null 3 -40,861.0 7,032.9 0.0

Distance to perennial stream Expon. decay 4 -40,861.0 7,034.9 0.0

Topography Elevation Linear 4 -39,028.3 0.0 1.0

Roughness Index 1 ha 4 -39,744.7 1,432.8 0.0

Topographic position index 2010 m 4 -40,600.8 3,144.9 0.0

Topographic position index 510 m 3 -40,803.9 3,551.3 0.0

Null 4 -40,861.0 3,663.5 0.0

Table E2. Model averaged parameter estimates and 95-percent confidence intervals for candidate variables 

included in the Gollaher-O’Neil subregional resource selection function (RSF) model, Nevada. 

Variable

Scale/distance 

function

Model averaged estimate (95% 

confidence interval) Selection/Avoidance

Forest 661.4 ha -19.56 (-20.62, -18.49) Avoidance

Riparian 661.4 ha -21.72 (-22.55, -20.89) Avoidance

Sagebrush 661.4 ha 9.09 (8.75, 9.42) Selection

Upland shrub 661.4 ha -12.09 (-13.62, -10.57) Avoidance

Edge effects 61.5 ha -4.59 (-4.78, -4.39) Avoidance

Variety of edge types 661.4 ha -0.21 (-0.21, -0.20) Avoidance

Distance to cropland Linear -0.34 (-0.35, -0.33) Selection

Distance to intermittent stream Linear 2.81 (2.74, 2.88) Avoidance

Distance to spring Exponential decay -1.06 (-1.15, -0.98) Avoidance

Distance to water body Linear -0.12 (-0.14, -0.11) Selection

Roughness index 1 ha -5.66 (-5.90, -5.43) Avoidance

Topographic position index 2010 m 0.005 (0.004, 0.005)

Selected ridges / 

Avoided valleys

Elevation Linear -1.99 (-2.08, -1.90)

Selection for Lower 

Elevations
56

Table E3. Gollaher-O’Neil subregional resource selection function (RSF) variable means and standard errors within 

all available habitats and habitats used by greater sage-grouse (Centrocercus urophasianus), Nevada. 

Available habitats Used habitats

Variable Scale Mean

Standard

error Mean

Standard

error

Forest 661.4 ha 0.023 0.0007 0.003 0.0003

Riparian 661.4 ha 0.026 0.0004 0.012 0.0004

Sagebrush 661.4 ha 0.890 0.0012 0.933 0.0010

Upland shrub 661.4 ha 0.005 0.0001 0.004 0.0003

Edge effects 61.5 ha 0.12 0.0010 0.08 0.0018

Variety of edge types 661.4 ha 5.96 0.0287 4.77 0.0490

Distance to cropland Km 2.49 0.0188 1.58 0.0202

Distance to intermittent stream Km 0.25 0.0023 0.39 0.0078

Distance to spring Km 2.49 0.0163 2.62 0.0336

Distance to water body Km 2.07 0.0127 1.95 0.0212

Roughness index 1 ha 0.18 0.0008 0.16 0.0015

Topographic position index 2010 m -0.27 0.3952 6.17 0.6330

Elevation Km 1.94 0.0023 1.84 0.0037
57

Appendix F. Supplemental Material for Lincoln-Schell-Snake RSF Modeling

Table F1. Variable selection results from the “proposal set” of variables from the Lincoln-Schell-Snake subregion, 

Nevada. 

[The top-ranked variable in each set was retained in the suite of candidate variables for resource selection function (RSF) 

modeling if they performed better than the null model and if confidence intervals around estimated mean effects did not 

overlap zero] 

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Land cover Annual grass 661.4 ha 4 -13,552.1 0.0 1.0

61.5 ha 4 -13,568.1 31.9 0.0

8.7 ha 4 -13,581.8 59.4 0.0

Null 3 -13,606.5 106.7 0.0

Bare ground 661.4 ha 4 -13,171.4 0.0 1.0

61.5 ha 4 -13,244.4 146.0 0.0

8.7 ha 4 -13,383.1 423.4 0.0

Null 3 -13,606.5 868.2 0.0

Cropland 661.4 ha 4 -11,792.0 0.0 1.0

61.5 ha 4 -12,162.7 741.4 0.0

8.7 ha 4 -12,383.0 1,181.9 0.0

Null 3 -13,606.5 3,626.9 0.0

Forest 661.4 ha 4 -12,920.6 0.0 1.0

61.5 ha 4 -13,036.1 231.0 0.0

8.7 ha 4 -13,135.5 429.9 0.0

Null 3 -13,606.5 1,369.8 0.0

Lowland shrub 8.7 ha 4 -13,544.2 0.0 1.0

61.5 ha 4 -13,554.5 20.7 0.0

Null 3 -13,606.5 122.6 0.0

661.4 ha 4 -13,606.4 124.5 0.0

Perennial grass 661.4 ha 4 -12,535.4 0.0 1.0

61.5 ha 4 -12,692.8 314.8 0.0

8.7 ha 4 -13,009.3 947.7 0.0

Null 3 -13,606.5 2,140.1 0.0

Pinyon-juniper 61.5 ha 4 -11,412.7 0.0 1.0

8.7 ha 4 -11,520.4 215.5 0.0

661.4 ha 4 -11,559.2 293.0 0.0

Null 3 -13,606.5 4385.5 0.0

Riparian 661.4 ha 4 -13,262.4 0.0 1.0

8.7 ha 4 -13,372.6 220.5 0.0

61.5 ha 4 -13,389.6 254.4 0.0

Null 3 -13,606.5 686.1 0.0
58

Group Variable Scale/distance 

function K Log Likelihood ΔAICc Model Weight

Sagebrush 661.4 ha 4 -12,227.2 0.0 1.0

61.5 ha 4 -12,597.8 741.2 0.0

8.7 ha 4 -12,703.4 952.4 0.0

Null 3 -13,606.5 2,756.5 0.0

Upland shrub 61.5 ha 4 -13,545.1 0.0 1.0

8.7 ha 4 -13,561.6 33.0 0.0

661.4 ha 4 -13,573.6 57.1 0.0

Null 3 -13,606.5 120.8 0.0

Wet Meadow 661.4 ha 4 -12,561.4 0.0 1.0

61.5 ha 4 -13,063.7 1,004.5 0.0

8.7 ha 4 -13,385.3 1,647.8 0.0

Null 3 -13,606.5 2,088.2 0.0

Agriculture Distance to cropland Expon. decay 4 -11,806.1 0.0 0.98

Linear 4 -11,810.3 8.3 0.02 

Null 3 -13,606.5 3,598.7 0.0

Edge Edge effects 661.4 ha 4 -13,348.6 0.0 1.0

61.5 ha 4 -13,575.3 453.4 0.0

8.7 ha 4 -13,603.1 509.0 0.0

Null 3 -13,606.5 513.8 0.0

Distance to edge Expon. decay 4 -13,574.8 0.0 0.64

Linear 4 -13,575.3 1.1 0.36

Null 3 -13,606.5 61.5 0.0

Landscape 

variation Variety of edge types 661.4 ha 4 -13,013.2 0.0 1.0

61.5 ha 4 -13,371.7 717.1 0.0

8.7 ha 4 -13,563.4 1,100.4 0.0

Null 3 -13,606.5 1,184.6 0.0

Variety of land cover types 661.4 ha 4 -12,769.2 0.0 1.0

61.5 ha 4 -13,455.1 1,371.8 0.0

8.7 ha 4 -13,571.8 1,605.2 0.0

Null 3 -13,606.5 1,672.5 0.0

Water 

source

Distance to water body Linear 4 -11,831.9 0.0 1.0

Distance to water body Expon. decay 4 -11,945.6 2,27.3 0.0

Distance to wet meadow Expon. decay 4 -12,845.2 2,026.5 0.0

Distance to wet meadow Linear 4 -13,198.9 2,733.8 0.0

Distance to perennial stream Linear 4 -13,441.2 3,218.4 0.0

Distance to spring Linear 4 -13,473.8 3,283.7 0.0

Distance to intermittent stream Expon. decay 4 -13,546.7 3,429.5 0.0
59

Group Variable Scale/distance 

function K Log Likelihood ΔAICc Model Weight

Distance to Spring Expon. decay 4 -13,547.5 3,431.0 0.0

Distance to Perennial Stream Expon. decay 4 -13,552.0 3,440.1 0.0

Distance to Nearest Stream Expon. decay 4 -13,585.8 3,507.8 0.0

Distance to Nearest Stream Linear 4 -13,603.9 3,543.9 0.0

Null Null 3 -13,606.5 3,547.1 0.0

Distance to Intermittent Stream Linear 4 -13,606.3 3,548.7 0.0

Topography Roughness Index 1 ha 4 -12,298.3 0.0 1.0

Elevation Linear 4 -13,001.8 1,380.5 0.0

Topographic Position Index 510 m 4 -13,590.3 2,583.9 0.0

Null 3 -13,606.5 2,614.4 0.0

 Topographic Position Index 2010 m 4 -13,635.4 2,674.3 0.0

Table F2. Model averaged parameter estimates and 95-percent confidence intervals for candidate variables 

included in the Lincoln-Schell-Snake subregional resource selection function (RSF) model, Nevada. 

Variable Scale/distance function

Model averaged estimate (95% 

confidence interval) Selection/Avoidance

Cropland 661.4 ha 21.42 (20.58, 22.26) Selection

Forest 661.4 ha -5.96 (-6.35, -5.56) Avoidance

Lowland shrub 8.7 ha -1.35 (-1.45, -1.25) Avoidance

Perennial grass 661.4 ha 37.62 (35.99, 39.26) Selection

Pinyon-juniper 61.5 ha -3.79 (-3.97, -3.61) Avoidance

Riparian 661.4 ha -8.25 (-9.72, -6.78) Avoidance

Sagebrush 661.4 ha 4.68 (4.53, 4.84) Selection

Upland shrub 61.5 ha -4.27 (-5.01, -3.52) Avoidance

Edge effects 661.4 ha 12.58 (11.97, 13.18) Selection

Variety of land cover types 661.4 ha 0.47 (0.45, 0.49) Selection

Distance to perennial stream Linear -0.12 (-0.13, -0.11) Selection

Distance to spring Linear -0.17 (-0.18, -0.16) Selection

Distance to water body Linear -0.57 (-0.59, -0.55) Selection

Distance to wet meadow Exponential decay 3.41 (3.28, 3.54) Selection

Roughness index 1 ha -10.87 (-11.28, -10.45) Avoidance

Topographic position index 510 m 0.005 (0.003, 0.007)

Selected ridges / 

Avoided valleys

Elevation Linear -1.21 (-1.31, -1.11)

Selection for Higher 

Elevations
60

Table F3. Lincoln-Schell-Snake subregional resource selection function (RSF) variable means and standard errors 

within all available habitats and habitats used by greater sage-grouse (Centrocercus urophasianus), Nevada. 

Available habitats Used habitats

Variable Scale Mean

Standard

error Mean

Standard

error

Cropland 661.4 ha 0.008 0.0005 0.115 0.0045

Forest 661.4 ha 0.081 0.0026 0.012 0.0016

Lowland shrub 8.7 ha 0.138 0.0049 0.115 0.0083

Perennial grass 661.4 ha 0.005 0.0003 0.047 0.0026

Pinyon-juniper 61.5 ha 0.314 0.0059 0.033 0.0044

Riparian 661.4 ha 0.017 0.0004 0.011 0.0007

Sagebrush 661.4 ha 0.405 0.0045 0.563 0.0093

Upland shrub 61.5 ha 0.013 0.0010 0.005 0.0011

Edge effects 661.4 ha 0.11 0.0009 0.15 0.0022

Variety of land cover types 661.4 ha 5.39 0.0289 7.39 0.0810

Distance to perennial stream Km 4.62 0.0512 3.58 0.0814

Distance to spring Km 3.43 0.0404 2.51 0.0624

Distance to water body Km 4.03 0.0353 1.75 0.0555

Distance to wet meadow Km 11.90 0.1191 7.38 0.2442

Roughness index 1 ha 0.18 0.0016 0.10 0.0021

Topographic position index 510 m -0.53 0.3133 0.70 0.3097

Elevation Km 2.11 0.0053 1.94 0.0088
61

Appendix G. Supplemental Material for Lone Willow RSF Modeling

Table G1. Variable selection results from the “proposal set” of variables from the Lone Willow subregion, Nevada. 

[The top-ranked variable in each set was retained in the suite of candidate variables for resource selection function (RSF) 

modeling if they performed better than the null model and if confidence intervals around estimated mean effects did not 

overlap zero] 

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Land cover Annual grass 661.4 ha 4 -3,066.1 0.0 1.00

61.5 ha 4 -3,113.8 95.4 0.00

8.7 ha 4 -3,131.2 130.2 0.00

Null 3 -3,188.5 242.8 0.00

Bare ground 61.5 ha 4 -3,170.1 0.0 0.90

661.4 ha 4 -3,172.2 4.3 0.10

8.7 ha 4 -3,180.1 20.1 0.00

Null 3 -3,188.5 34.8 0.00

Cropland 661.4 ha 4 -3,116.0 0.0 1.00

61.5 ha 4 -3,129.4 26.9 0.00

8.7 ha 4 -3,131.7 31.5 0.00

Null 3 -3,188.5 143.0 0.00

Forest 8.7 ha 4 -3,174.2 0.0 0.56

661.4 ha 4 -3,174.7 1.0 0.34

61.5 ha 4 -3,176.0 3.5 0.10

Null 3 -3,188.5 26.6 0.00

Lowland shrub 661.4 ha 4 -3,120.4 0.0 1.00

61.5 ha 4 -3,157.8 74.9 0.00

8.7 ha 4 -3,161.9 83.0 0.00

Null 3 -3,188.5 134.2 0.00

Perennial grass 661.4 ha 4 -3,121.1 0.0 1.00

61.5 ha 4 -3,158.4 74.6 0.00

8.7 ha 4 -3,177.5 112.8 0.00

Null 3 -3,188.5 132.7 0.00

Pinyon-juniper 661.4 ha 4 -3,146.9 0.0 1.00

61.5 ha 4 -3,180.9 67.9 0.00

Null 3 -3,188.5 81.1 0.00

8.7 ha 4 -3,188.5 83.1 0.00

Riparian 61.5 ha 4 -3,186.1 0.0 0.47

8.7 ha 4 -3,186.4 0.5 0.37

Null 3 -3,188.5 2.7 0.12

661.4 ha 4 -3,188.5 4.7 0.04
62

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Sagebrush 661.4 ha 4 -3,149.4 0.0 1.00

8.7 ha 4 -3,158.4 18.0 0.00

61.5 ha 4 -3,167.6 36.3 0.00

Null 3 -3,188.5 76.1 0.00

Upland shrub 661.4 ha 4 -3,097.0 0.0 1.00

61.5 ha 4 -3,130.7 67.4 0.00

8.7 ha 4 -3,154.4 114.9 0.00

Null 3 -3,188.5 180.9 0.00

Wet meadow 661.4 ha 4 -3,129.0 0.0 1.00

61.5 ha 4 -3,168.9 79.9 0.00

8.7 ha 4 -3,184.0 110.0 0.00

Null 3 -3,188.5 117.0 0.00

Agriculture Distance to cropland Linear 4 -3,184.2 0.0 0.91

Expon. decay 4 -3,187.0 5.7 0.05

Null 3 -3,188.5 6.6 0.03

Edge Edge effects 661.4 ha 4 -2,930.4 0.0 1.00

61.5 ha 4 -3,062.3 263.7 0.00

8.7 ha 4 -3,114.0 367.1 0.00

Null 3 -3,188.5 514.1 0.00

Distance to edge Expon. decay 4 -3,129.3 0.0 0.63

Linear 4 -3,129.8 1.1 0.37

Null 3 -3,188.5 116.3 0.00

Landscape 

variation Variety of edge types 661.4 ha 4 -3,034.4 0.0 1.00

61.5 ha 4 -3,090.4 112.0 0.00

8.7 ha 4 -3,101.5 134.3 0.00

Null 3 -3,188.5 306.2 0.00

Variety of land cover types 661.4 ha 4 -3,049.3 0.0 1.00

8.7 ha 4 -3,115.6 132.6 0.00

61.5 ha 4 -3,134.2 169.7 0.00

Null 3 -3,188.5 276.3 0.00

Water 

source

Distance to wet meadow Linear 4 -3,002.3 0.0 1.00

Distance to spring Linear 4 -3,014.1 23.7 0.00

Distance to spring Expon. decay 4 -3,024.5 44.4 0.00

Distance to wet meadow Expon. decay 4 -3,046.5 88.5 0.00

Distance to water body Linear 4 -3,076.2 147.9 0.00

Distance to water body Expon. decay 4 -3,108.7 212.9 0.00

Distance to perennial stream Linear 4 -3,129.7 254.8 0.00

Distance to perennial stream Expon. decay 4 -3,134.8 265.1 0.00
63

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Distance to nearest stream Expon. decay 4 -3,181.5 358.5 0.00

Distance to intermittent stream Expon. decay 4 -3,182.6 360.7 0.00

Null Null 3 -3,188.5 370.4 0.00

Distance to nearest stream Linear 4 -3,188.4 372.3 0.00

Distance to intermittent stream Linear 4 -3,188.5 372.4 0.00

Topography Elevation Linear 4 -2,759.4 0.0 1.00

Topographic position index 2010 m 4 -3,144.6 770.3 0.00

Roughness index 1 ha 4 -3,181.1 843.3 0.00

Topographic position index 510 m 4 -3,186.7 854.6 0.00

Null 3 -3,188.5 856.1 0.00

Table G2. Model averaged parameter estimates and 95-percent confidence intervals for candidate variables 

included in the Lone Willow subregional resource selection function (RSF) model, Nevada. 

Variable Scale/distance function

Model averaged estimate (95% 

confidence interval) Selection/Avoidance

Annual grass 661.4 ha -1.26 (-1.70, -0.82) Avoidance

Bare ground 61.5 ha 5.29 (3.63, 6.94) Selection

Lowland shrub 661.4 ha 2.28 (1.25, 3.31) Selection

Sagebrush 661.4 ha 4.72 (4.16, 5.29) Selection

Upland shrub 661.4 ha -1.59 (-3.04, -0.13) Avoidance

Edge effects 661.4 ha 7.52 (6.47, 8.57) Selection

Variety of edge types 661.4 ha 0.13 (0.11, 0.15) Selection

Distance to perennial stream Linear -0.09 (-0.11, -0.08) Selection

Distance to spring Linear -0.21 (-0.27, -0.15) Selection

Distance to water body Linear -0.27 (-0.33, -0.22) Selection

Distance to wet meadow Linear -0.20 (-0.26, -0.13) Selection

Roughness index 1 ha -1.28 (-2.07, -0.48) Avoidance

Topographic position index 2010 m -0.003 (-0.005, -0.002)

Avoided ridges / 

Selected valleys

Elevation Linear 4.06 (3.70, 4.43)

Selection for Higher 

Elevations
64

Table G3. Lone Willow subregional resource selection function (RSF) variable means and standard errors within all 

available habitats and habitats used by greater sage-grouse (Centrocercus urophasianus), Nevada. 

Available habitats Used habitats

Variable Scale Mean

Standard

error Mean

Standard

error

Annual grass 661.4 ha 0.107 0.0059 0.039 0.0064

Bare ground 61.5 ha 0.007 0.0009 0.011 0.0031

Edge effects 661.4 ha 0.129 0.0020 0.174 0.0037

Lowland shrub 661.4 ha 0.034 0.0028 0.014 0.0032

Sagebrush 661.4 ha 0.693 0.0070 0.749 0.0094

Upland shrub 661.4 ha 0.018 0.0012 0.037 0.0036

Variety of edge types 661.4 ha 7.38 0.0975 8.82 0.1664

Distance to perennial stream Km 4.89 0.1048 3.82 0.2056

Distance to spring Km 1.98 0.0376 1.40 0.0632

Distance to water body Km 2.38 0.0378 1.86 0.0597

Distance to wet meadow Km 1.91 0.0369 1.28 0.0571

Roughness index 1 ha 0.17 0.0025 0.17 0.0047

Topographic position index 2010 m -0.95 1.4400 13.77 3.1791

Elevation Km 1.68 0.059 1.84 0.0110
65

Appendix H. Supplemental Material for Midway RSF Modeling

Table H1. Variable selection results from the “proposal set” of variables from the Midway subregion, Nevada. 

[The top-ranked variable in each set was retained in the suite of candidate variables for resource selection function (RSF) 

modeling if they performed better than the null model and if confidence intervals around estimated mean effects did not 

overlap zero] 

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Land- cover Annual grass 61.5 ha 3 -8,485.2 0.0 0.9582

8.7 ha 3 -8,488.7 6.9 0.0303

Null 2 -8,491.1 9.7 0.0074

661.4 ha 3 -8,490.7 11.0 0.0040 

Bare ground 8.7 ha 4 -8,489.0 0.0 0.4243

Null 2 -8,491.1 0.1 0.4034

61.5 ha 4 -8,490.3 2.7 0.1115

661.4 ha 4 -8,490.9 3.9 0.0609

Cropland 661.4 ha 4 -7,629.6 0.0 0.9992

61.5 ha 4 -7,636.7 14.1 0.0008

8.7 ha 4 -7,858.7 458.1 0.0000

Null 2 -8,491.1 1,718.9 0.0000

Forest 61.5 ha 3 -8,356.3 0.0 1.0000

8.7 ha 3 -8,399.8 87.0 0.0000

661.4 ha 3 -8,435.0 157.5 0.0000

Null 2 -8,491.1 267.6 0.0000

Lowland shrub 661.4 ha 3 -7,753.5 0.0 1.0000

61.5 ha 3 -8,246.8 986.5 0.0000

8.7 ha 3 -8,276.4 1,045.7 0.0000

Null 2 -8,491.1 1,473.1 0.0000

Perennial grass 61.5 ha 3 -8,486.2 0.0 0.9108

661.4 ha 3 -8,488.9 5.4 0.0618

Null 2 -8,491.1 7.6 0.0199

8.7 ha 3 -8,491.0 9.6 0.0074

Pinyon-juniper 661.4 ha 3 -7,115.7 0.0 1.0000

61.5 ha 3 -7,143.4 55.5 0.0000

8.7 ha 3 -7,388.0 544.6 0.0000

Null 2 -8,491.1 2,748.7 0.0000

Riparian 61.5 ha 3 -8,400.8 0.0 1.0000

8.7 ha 3 -8,438.7 75.7 0.0000

661.4 ha 3 -8,475.7 149.8 0.0000

Null 2 -8,491.1 178.5 0.0000

Sagebrush 661.4 ha 4 -8,457.8 0.0 1.0000
66

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

8.7 ha 4 -8,478.5 41.4 0.0000

61.5 ha 4 -8,481.8 48.0 0.0000

Null 2 -8,491.1 62.5 0.0000

Upland shrub 61.5 ha 3 -8,429.7 0.0 1.0000

8.7 ha 3 -8,466.1 72.6 0.0000

661.4 ha 3 -8,488.0 116.5 0.0000

Null 2 -8,491.1 120.6 0.0000

Wet meadow 661.4 ha 3 -7,922.4 0.0 1.0000

61.5 ha 3 -8,313.6 782.4 0.0000

8.7 ha 3 -8,490.0 1,135.2 0.0000

Null 2 -8,491.1 1,135.3 0.0000

Agriculture Distance to cropland Expon. decay 3 -8,341.2 0.0 1.0000

Linear 3 -8,371.7 61.0 0.0000

Null 2 -8,491.1 297.8 0.0000

Edge Edge effects 661.4 ha 3 -,8442.5 0.0 1.0000

8.7 ha 3 -8,480.3 75.5 0.0000

61.5 ha 3 -8,486.5 88.0 0.0000

Null 2 -8,491.1 95.1 0.0000

Distance to edge Expon. decay 3 -8,449.5 0.0 1.0000

Linear 3 -8,474.7 50.4 0.0000

Null 2 -8,491.1 81.2 0.0000

Landscape 

variation Variety of edge types 61.5 ha 3 -8,452.1 0.0 0.9989

661.4 ha 3 -8,459.0 13.8 0.0010

8.7 ha 3 -8,462.0 19.8 0.0001

Null 2 -8,491.0 75.8 0.0000

Variety of land cover

types 661.4 ha 3 -8,222.5 0.0 1.0000

8.7 ha 3 -8,374.2 303.4 0.0000

61.5 ha 3 -8,377.6 310.1 0.0000

Null 2 -8,491.1 535.1 0.0000

Water 

sources

Distance to perennial 

stream Expon. decay 3 -6,874.0 0.0 1.0000

Distance to perennial 

stream Linear 3 -6,898.7 49.2 0.0000

Distance to water body Expon. decay 3 -7,302.3 856.5 0.0000

Distance to water body Linear 3 -7,519.7 1,291.3 0.0000

Distance to spring Expon. decay 3 -8,227.0 2,706.0 0.0000

Distance to spring Linear 3 -8,312.4 2,876.7 0.0000

Distance to wet Meadow Expon. decay 3 -8,330.9 2,913.8 0.0000

Distance to Wet Meadow Linear 3 -8,348.9 2,949.7 0.0000
67

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Distance to Nearest 

Stream Linear 3 -8,361.8 2,975.4 0.0000

Distance to Nearest 

Stream Expon. decay 3 -8,418.1 3,088.2 0.0000

Distance to Intermittent 

Stream Linear 3 -8,445.9 3,143.7 0.0000

Distance to Intermittent 

Stream Expon. decay 3 -8,480.2 3,212.4 0.0000

Null Null 2 -8,491.1 3,232.0 0.0000

Topography Roughness Index 1 ha 3 -7,521.8 0.0 1.0000

Elevation Linear 3 -7,983.2 922.8 0.0000

Topographic Position 

Index 2010 m 3 -8,392.4 1,741.1 0.0000

Topographic Position 

Index 510 m 3 -8,486.5 1,929.5 0.0000

 Null 2 -8,491.1 1,936.5 0.0000

Table H2. Model averaged parameter estimates and 95-percent confidence intervals for candidate variables 

included in the Midway subregional resource selection function (RSF) model, Nevada. 

Variable Scale/distance function

Model averaged estimate (95% 

confidence interval) Selection/Avoidance

Annual grass 61.5 ha -6.32 (-11.82, -0.82) Avoidance

Cropland 661.4 ha 14.20 (12.94, 15.47) Selection

Forest 61.5 ha -42.07 (-51.78, -32.37) Avoidance

Lowland shrubs 661.4 ha 1.92 (1.75, 2.09) Selection

Perennial grass Linear -7.82 (-10.95, -4.69) Avoidance

Pinyon-juniper Linear -13.63 (-14.57, -12.68) Avoidance

Riparian Linear -2.68 (-4.38, -0.98) Avoidance

Sagebrush 661.4 ha 0.98 (0.8, 1.15) Selection

Edge effects 661.4 ha -7.51 (-8.43, -6.58) Avoidance

Distance to edge Expon. Decay -0.37 (-0.50, -0.24) Avoidance

Variety of land cover types 661.4 ha 0.26 (0.24, 0.28) Selection

Distance to perennial stream Expon. Decay 5.21 (5, 5.43) Selection

Distance to spring Expon. Decay 3.91 (3.70, 4.13) Selection

Distance to water body Expon. Decay 3.95 (3.76, 4.15) Selection

Distance to wet meadow Expon. Decay 17.86 (16.21, 19.50) Selection

Roughness index 1 ha -14.59 (-15.37, -13.81) Avoidance

Topographic position index 2010 m -0.006 (-0.007, -0.004)

Avoided Ridges / 

Selected Valleys

Elevation Linear -4.06 (-4.32, -3.79)

Selection for Lower 

Elevations
68

Table H3. Midway subregional resource selection function (RSF) variable means and standard errors within all 

available habitats and habitats used by greater sage-grouse (Centrocercus urophasianus), Nevada. 

Available habitats Used habitats

Variable Scale Mean

Standard

error Mean

Standard

error

Annual grass 61.5 ha 0.001 0.0002 0.0007 0.0001

Cropland 661.4 ha 0.006 0.0005 0.038 0.0016

Forest 61.5 ha 0.010 0.0009 0.0003 0.0001

Lowland shrubs 661.4 ha 0.169 0.0034 0.347 0.0072

Perennial grass 61.5 ha 0.003 0.0003 0.004 0.0005

Pinyon-juniper 661.4 ha 0.177 0.0033 0.014 0.0012

Riparian 61.5 ha 0.004 0.0002 0.010 0.0011

Sagebrush 661.4 ha 0.624 0.0039 0.584 0.0067

Edge effects 661.4 ha 0.13 0.0007 0.14 0.0017

Distance to edge Km 0.15 0.0025 0.13 0.0054

Variety of land cover types 661.4 ha 5.24 0.0206 6.16 0.0755

Distance to perennial stream Km 8.49 0.0656 3.71 0.1068

Distance to spring Km 4.72 0.0341 3.82 0.0795

Distance to water body Km 4.42 0.0407 2.25 0.0611

Distance to wet meadow Km 10.55 0.1796 6.85 0.0656

Roughness index 1 ha 0.15 0.0012 0.09 0.0015

Topographic position index 2010 m -0.63 0.5626 -8.80 0.4314

Elevation Km 2.00 0.0027 1.90 0.0055
69

Appendix I. Supplemental Material for Sheldon RSF Modeling

Table I1. Variable selection results from the “proposal set” of variables from the Sheldon subregion, Nevada. 

[The top-ranked variable in each set was retained in the suite of candidate variables for resource selection function (RSF) 

modeling if they performed better than the null model and if confidence intervals around estimated mean effects did not 

overlap zero] 

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Land cover Annual grass 61.5 ha 4 -712.8 0.0 0.2921

661.4 ha 4 -712.9 0.1 0.2722

Null 3 -713.9 0.3 0.2527

8.7 ha 4 -713.2 0.9 0.1830 

Bare ground 8.7 ha 4 -711.2 0.0 0.6708

661.4 ha 4 -712.5 2.7 0.1724

Null 3 -713.9 3.5 0.1150 

61.5 ha 4 -713.9 5.6 0.0418

Cropland 661.4 ha 4 -709.1 0.0 0.9649

Null 3 -713.9 7.7 0.0203

61.5 ha 4 -713.2 8.4 0.0147

8.7 ha No Cropland at 9ha scale

Forest 61.5 ha 4 -712.0 0.0 0.5465

Null 3 -713.9 1.9 0.2084

8.7 ha 4 -713.2 2.4 0.1617

661.4 ha 4 -713.8 3.8 0.0834

Lowland shrub Null 3 -713.9 0.0 0.4027

661.4 ha 4 -713.4 1.0 0.2415

61.5 ha 4 -713.7 1.6 0.1811

8.7 ha 4 -713.8 1.7 0.1746

Perennial grass 8.7 ha 4 -712.3 0.0 0.3720

61.5 ha 4 -712.7 0.9 0.2396

661.4 ha 4 -712.9 1.3 0.1945

Null 3 -713.9 1.3 0.1939

Pinyon-juniper 661.4 ha 4 -712.6 0.0 0.3884

Null 3 -713.9 0.6 0.2875

61.5 ha 4 -713.2 1.1 0.2199

8.7 ha 4 -713.9 2.6 0.1042

Riparian Null 3 -713.9 0.0 0.3553

8.7 ha 4 -713.2 0.6 0.2573

661.4 ha 4 -713.3 0.8 0.2382

61.5 ha 4 -713.8 1.7 0.1492
70

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Sagebrush 661.4 ha 4 -712.8 0.0 0.3451

Null 3 -713.9 0.2 0.3163

8.7 ha 4 -713.3 0.9 0.2241

61.5 ha 4 -713.9 2.2 0.1145

Upland shrub 661.4 ha 4 -711.0 0.0 0.6913

8.7 ha 4 -712.6 3.3 0.1359

Null 3 -713.9 3.9 0.0963

61.5 ha 4 -713.2 4.4 0.0766

Wet meadow Null 3 -713.9 0.0 0.3135

8.7 ha 4 -713.0 0.1 0.2931

61.5 ha 4 -713.3 0.8 0.2127

661.4 ha 4 -713.5 1.1 0.1807

Agriculture Distance to cropland Expon. decay 4 -711.6 0.0 0.6977

Null 3 -713.9 2.6 0.1876

Linear 4 -713.4 3.6 0.1147

Edge Edge effects 661.4 ha 4 -706.4 0.0 0.9193

61.5 ha 4 -709.0 5.1 0.0729

8.7 ha 4 -711.4 9.9 0.0065

Null 3 -713.9 13.0 0.0014

Distance to edge Expon. decay 4 -710.8 0.0 0.8100

Null 3 -713.9 4.3 0.0960

Linear 4 -712.9 4.3 0.0940

Landscape 

variation Variety of edge types 8.7 ha 4 -707.7 0.0 0.5500

661.4 ha 4 -708.0 0.5 0.4326

61.5 ha 4 -711.4 7.3 0.0144

Null 3 -713.9 10.4 0.0030

Variety of land cover types 8.7 ha 4 -704.9 0.0 0.9954

661.4 ha 4 -710.7 11.7 0.0029

61.5 ha 4 -711.4 13.1 0.0014

Null 3 -713.9 16.1 0.0003

Water 

source

Distance to perennial stream Expon. decay 4 -709.2 0.0 0.4544

Distance to water body Expon. decay 4 -710.1 1.8 0.1830

Distance to water body Linear 4 -710.9 3.4 0.0833

Distance to spring Expon. decay 4 -710.9 3.4 0.0819

Distance to wet meadow Linear 4 -711.2 4.0 0.0608

Distance to spring Linear 4 -711.8 5.2 0.0329

Distance to perennial stream Linear 4 -711.8 5.3 0.0317

Distance to wet meadow Expon. decay 4 -712.5 6.6 0.0165
71

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Null Null 3 -713.9 7.5 0.0106

Distance to intermittent 

stream

Expon. decay 4 -713.7 9.0 0.0051

Distance to nearest stream Expon. decay 4 -713.7 9.1 0.0048

Distance to intermittent 

stream Linear 4 -713.9 9.5 0.0040

Distance to nearest stream Linear 4 -713.9 9.5 0.0038

Topography Topographic position index 2010 m 4 -707.6 0.0 0.8649

Elevation Linear 4 -709.6 4.0 0.1175

Roughness index 1 ha 4 -712.1 9.0 0.0098

Null 3 -713.9 10.7 0.0041

Topographic position index 510 m 4 -713.0 10.9 0.0037

Table I2. Model averaged parameter estimates and 95-percent confidence intervals for candidate variables 

included in the Sheldon subregional resource selection function (RSF) model, Nevada. 

Variable Scale/distance function

Model averaged estimate (95% 

confidence interval) Selection/Avoidance

Upland shrubs 661.4 ha -5.25 (-8.31, -2.19) Avoidance

Edge effects 661.4 ha -5.11 (-7.48, -2.75) Avoidance

Variety of land cover types 8.7 ha -0.39 (-0.55, -0.22) Avoidance

Distance to perennial stream Exponential decay -1.15 (-1.86, -0.44) Avoidance

Distance to water body Exponential decay 0.77 (0.21, 1.33) Selection

Topographic position index 2010 m 0.01 (0.005, 0.015)

Selected ridges/ 

Avoided valleys

Elevation Linear 4.52 (2.87, 6.16)

Selection for Higher 

Elevations

Table I3. Sheldon subregional resource selection function (RSF) variable means and standard errors within all 

available habitats and habitats used by greater sage-grouse (Centrocercus urophasianus), Nevada.

Available habitats Used habitats

Variable Scale Mean

Standard

error Mean

Standard

error

Upland shrubs 661.4 ha 0.021 0.0028 0.017 0.0052

Edge effects 661.4 ha 0.063 0.0029 0.058 0.0057

Variety of land cover types 8.7 ha 1.59 0.0425 1.48 0.0836

Distance to perennial stream Km 8.31 0.1930 8.80 0.3469

Distance to spring Km 3.25 0.0980 3.23 0.2042

Distance to water body Km 1.82 0.0669 1.66 0.1500

Topographic position index 2010 m 0.26 1.234 6.09 3.1746

Elevation Km 1.84 0.0052 1.87 0.0042
72

Appendix J. Supplemental material for South Fork-Ruby Valley RSF modeling 

Table J1. Variable selection results from the “proposal set” of variables from the South Fork-Ruby Valley 

subregion, Nevada. 

[The top-ranked variable in each set was retained in the suite of candidate variables for resource selection function (RSF) 

modeling if they performed better than the null model and if confidence intervals around estimated mean effects did not 

overlap zero] 

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Land cover Annual grass 661.4 ha 4 -4,771.8 0.0 1.0000

61.5 ha 4 -4,806.2 68.8 0.0000

8.7 ha 4 -4,815.9 88.0 0.0000

Null 3 -4,879.6 213.5 0.0000

Bare ground 661.4 ha 4 -4,524.7 0.0 1.0000

61.5 ha 4 -4,697.9 346.5 0.0000

8.7 ha 4 -4,762.2 475.0 0.0000

Null 3 -4,879.6 707.8 0.0000

Cropland 8.7 ha 4 -4,858.7 0.0 1.0000

661.4 ha 4 -4,875.0 32.6 0.0000

61.5 ha 4 -4,877.5 37.5 0.0000

Null 3 -4,879.6 39.7 0.0000

Forest 661.4 ha 4 -4,676.0 0.0 0.9999

61.5 ha 4 -4,685.4 18.7 0.0001

8.7 ha 4 -4,697.6 43.1 0.0000

Null 3 -4,879.6 405.1 0.0000

Lowland shrub 661.4 ha 4 -4,792.9 0.0 1.0000

61.5 ha 4 -4,807.1 28.4 0.0000

8.7 ha 4 -4,820.7 55.7 0.0000

Null 3 -4,879.6 171.4 0.0000

Perennial grass 661.4 ha 4 -4,839.3 0.0 1.0000

61.5 ha 4 -4,871.9 65.1 0.0000

8.7 ha 4 -4,872.8 67.0 0.0000

Null 3 -4,879.6 78.5 0.0000

Pinyon-juniper 61.5 ha 4 -4,723.8 0.0 1.0000

661.4 ha 4 -4,759.7 71.8 0.0000

8.7 ha 4 -4,767.9 88.2 0.0000

Null 3 -4,879.6 309.5 0.0000

Riparian 661.4 ha 4 -4,756.2 0.0 1.0000

61.5 ha 4 -4,788.6 64.9 0.0000

8.7 ha 4 -4,842.3 172.1 0.0000

Null 3 -4,879.6 244.7 0.0000
73

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Sagebrush 661.4 ha 4 -4,329.6 0.0 1.0000

61.5 ha 4 -4,387.8 116.4 0.0000

8.7 ha 4 -4,411.1 163.0 0.0000

Null 3 -4,879.6 1097.9 0.0000

Upland shrub 8.7 ha 4 -4,751.7 0.0 1.0000

661.4 ha 4 -4,762.3 21.2 0.0000

61.5 ha 4 -4,826.4 149.3 0.0000

Null 3 -4,879.6 253.7 0.0000

Wet meadow 61.5 ha 4 -4,858.7 0.0 0.9982

661.4 ha 4 -4,865.0 12.6 0.0018

8.7 ha 4 -4,875.1 32.8 0.0000

Null 3 -4,879.6 39.8 0.0000

Agriculture Distance to cropland Expon. decay 4 -4,877.6 0.0 0.5677

Linear 4 -4,878.6 1.9 0.2180

Null 3 -4,879.6 1.9 0.2143

Edge Edge effects 661.4 ha 4 -4,666.1 0.0 1.0000

61.5 ha 4 -4,779.4 226.5 0.0000

8.7 ha 4 -4,796.0 259.6 0.0000

Null 3 -4,879.6 424.9 0.0000

Distance to edge Linear 4 -4,863.8 0.0 0.5357

Expon. decay 4 -4,863.9 0.3 0.4643

Null 3 -4,879.6 29.6 0.0000

Landscape variation Variety of edge types 8.7 ha 4 -4,825.0 0.0 0.9801

61.5 ha 4 -4,828.9 7.8 0.0199

661.4 ha 4 -4,861.4 72.7 0.0000

Null 3 -4,879.6 107.1 0.0000

Variety of land cover

types 8.7 ha 4 -4,772.8 0.0 1.0000

61.5 ha 4 -4,818.2 90.7 0.0000

661.4 ha 4 -4,874.0 202.4 0.0000

Null 3 -4,879.6 211.6 0.0000

Water source Distance to water body Expon. decay 4 -4,760.0 0.0 1.0000

Distance to nearest stream Expon. decay 4 -4,787.2 54.4 0.0000

Distance to spring Linear 4 -4,836.6 153.2 0.0000

Distance to water body Linear 4 -4,839.3 158.6 0.0000

Distance to nearest stream Linear 4 -4,844.4 168.9 0.0000

Distance to intermittent 

stream Linear 4 -4,846.1 172.2 0.0000

Distance to intermittent 

stream Expon. decay 4 -4,847.0 174.1 0.0000
74

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Distance to wet meadow Linear 4 -4,856.5 192.9 0.0000

Distance to spring Expon. decay 4 -4,866.1 212.1 0.0000

Distance to perennial 

stream

Expon. decay 4 -4,873.8 227.6 0.0000

Distance to wet meadow Expon. decay 4 -4,877.6 235.2 0.0000

Null Null 3 -4,879.6 237.2 0.0000

Distance to perennial 

stream Linear 4 -4,879.4 238.9 0.0000

Topography Roughness index 1 ha 4 -4,788.5 0.0 1.0000

Elevation Linear 4 -4,843.9 111.0 0.0000

Topographic position 

index 2010 m 4 -4,871.7 166.5 0.0000

Null 3 -4,879.6 180.2 0.0000

 Topographic position 

index 510 m 4 -4,879.1 181.2 0.0000

Table J2. Model averaged parameter estimates and 95-percent confidence intervals for candidate variables 

included in the South Fork-Ruby Valley subregional resource selection function (RSF) model, Nevada.

Variable Scale/distance function

Model averaged estimate (95% 

confidence interval) Selection/Avoidance

Annual grass 661.4 ha -18.77 (-22.97, -14.57) Avoidance

Cropland 8.7 ha 0.74 (0.41, 1.08) Selection

Forest 661.4 ha -3.98 (-5.16, -2.80) Avoidance

Lowland shrubs 661.4 ha -0.86 (-1.57, -0.15) Avoidance

Perennial grass 661.4 ha 2.13 (0.84, 3.42) Selection

Pinyon-juniper 61.5 ha -17.31 (-21.23, -13.39) Avoidance

Sagebrush 661.4 ha 3.77 (3.50, 4.04) Selection

Distance to nearest stream Exponential decay 0.94 (0.76, 1.13) Selection

Distance to spring Linear 0.01 (0.002, 0.020) Avoidance

Distance to water body Exponential decay -0.35 (-0.59, -0.11) Avoidance

Roughness index 1 ha -2.34 (-2.94, -1.73) Avoidance

Topographic position index 2010 m -0.002 (-0.002, -0.001)

Avoided ridges / 

Selected valleys

Elevation Linear 0.44 (0.22, 0.67)

Selection for higher 

elevations
75

Table J3. South Fork-Ruby Valley subregional resource selection function (RSF) variable means and standard 

errors within all available habitats and habitats used by greater sage-grouse (Centrocercus urophasianus), Nevada. 

Available habitats Used habitats

Variable Scale Mean

Standard

error Mean

Standard

error

Annual grass 661.4 ha 0.0233 0.0024 0.0034 0.0007

Cropland 8.7 ha 0.0624 0.0074 0.0357 0.0114

Forest 661.4 ha 0.0575 0.0033 0.0150 0.0025

Lowland shrubs 661.4 ha 0.0725 0.0055 0.0209 0.0047

Perennial grass 661.4 ha 0.0334 0.0019 0.0176 0.0031

Pinyon-juniper 61.5 ha 0.0375 0.0045 0.0046 0.0016

Sagebrush 661.4 ha 0.6043 0.0088 0.8105 0.0142

Edge effects 661.4 ha 0.17 0.0029 0.13 0.0045

Distance to water body Km 2.50 0.0716 2.95 0.0641

Distance to nearest stream Km 0.45 0.0096 0.55 0.0222

Distance to spring Km 5.24 0.1617 6.15 0.4236

Roughness index 1 ha 0.16 0.004 0.12 0.0055

Topographic position index 2010 m -1.94 2.1853 -8.00 2.2769

Elevation Km 2.00 0.0110 1.94 0.0101
76

Appendix K. Supplemental Material for Toiyabe RSF Modeling

Table K1. Variable selection results from the “proposal set” of variables from the Toiyabe subregion, Nevada. 

[The top-ranked variable in each set was retained in the suite of candidate variables for resource selection function (RSF) 

modeling if they performed better than the null model and if confidence intervals around estimated mean effects did not 

overlap zero] 

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Land cover Annual grass 8.7 ha 4 -48,876.8 0.0 1.0000

61.5 ha 4 -48,886.9 20.2 0.0000

661.4 ha 4 -48,892.2 30.8 0.0000

Null 3 -48,901.5 47.5 0.0000

Bare ground 661.4 ha 4 -48,735.8 0.0 1.0000

61.5 ha 4 -48,752.3 32.9 0.0000

8.7 ha 4 -48,844.0 216.3 0.0000

Null 3 -48,901.5 329.5 0.0000

Cropland 61.5 ha 4 -48,818.2 0.0 0.9999

8.7 ha 4 -48,827.9 19.4 0.0001

661.4 ha 4 -48,899.5 162.7 0.0000

Null 3 -48,901.5 164.6 0.0000

Forest 661.4 ha 4 -48,840.4 0.0 1.0000

8.7 ha 4 -48,860.8 40.8 0.0000

61.5 ha 4 -48,884.4 88.0 0.0000

Null 3 -48,901.5 120.3 0.0000

Lowland shrub 661.4 ha 4 -45,850.6 0.0 1.0000

61.5 ha 4 -46,806.1 1,911.1 0.0000

8.7 ha 4 -47,254.2 2,807.2 0.0000

Null 3 -48,901.5 6,099.9 0.0000

Perennial grass 61.5 ha 4 -48,849.8 0.0 1.0000

8.7 ha 4 -48,867.4 35.2 0.0000

661.4 ha 4 -48,897.0 94.4 0.0000

Null 3 -48,901.5 101.4 0.0000

Pinyon-juniper 61.5 ha 4 -44,548.9 0.0 1.0000

661.4 ha 4 -44,793.6 489.4 0.0000

8.7 ha 4 -45,162.5 1,227.2 0.0000

Null 3 -48,901.5 8,703.3 0.0000

Riparian 61.5 ha 4 -48,453.7 0.0 1.0000

661.4 ha 4 -48,582.4 257.4 0.0000

8.7 ha 4 -48,621.0 334.6 0.0000

Null 3 -48,901.5 893.7 0.0000

Sagebrush 661.4 ha 4 -44,570.0 0.0 1.0000
77

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

61.5 ha 4 -45,334.9 1,529.8 0.0000

8.7 ha 4 -46,099.1 3,058.1 0.0000

Null 3 -48,901.5 8,661.0 0.0000

Upland shrub 661.4 ha 4 -47,877.1 0.0 1.0000

61.5 ha 4 -48,675.2 1,596.1 0.0000

8.7 ha 4 -48,737.9 1,721.6 0.0000

Null 3 -48,901.5 2,046.9 0.0000

Wet meadow 61.5 ha 4 -48,805.2 0.0 1.0000

661.4 ha 4 -48,845.9 81.2 0.0000

8.7 ha 4 -48,877.2 143.9 0.0000

Null 3 -48,901.5 190.6 0.0000

Agriculture Distance to cropland Linear 4 -46,777.2 0.0 1.0000

Exponential 

decay

4 -47,546.0 1,537.5 0.0000

Null 3 -48,901.5 4,246.6 0.0000

Edge Edge effects 661.4 ha 4 -47,995.8 0.0 1.0000

61.5 ha 4 -48,023.2 54.7 0.0000

8.7 ha 4 -48,142.1 292.6 0.0000

Null 3 -48,901.5 1,809.4 0.0000

Distance to edge Linear 4 -47,791.3 0.0 1.0000

Exponential 

decay

4 -48,112.6 642.6 0.0000

Null 3 -48,901.5 2,218.5 0.0000

Landscape 

variation Variety of edge types 661.4 ha 4 -48,121.7 0.0 1.0000

61.5 ha 4 -48,141.7 40.0 0.0000

8.7 ha 4 -48,277.1 310.8 0.0000

Null 3 -48,901.5 1,557.7 0.0000

Variety of land cover types 661.4 ha 4 -47,448.5 0.0 1.0000

61.5 ha 4 -48,161.9 1,426.8 0.0000

8.7 ha 4 -48,425.5 1,954.1 0.0000

Null 3 -48,901.5 2,904.2 0.0000

Water source Distance to spring Linear 4 -43,299.1 0.0 1.0000

Distance to spring Expon. decay 4 -43,787.5 976.8 0.0000

Distance to wet meadow Linear 4 -44,519.0 2,439.7 0.0000

Distance to wet meadow Expon. decay 4 -45,892.0 5,185.8 0.0000

Distance to perennial stream Linear 4 -46,280.0 5,961.9 0.0000

Distance to water body Linear 4 -46,990.7 7,383.2 0.0000

Distance to water body Expon. decay 4 -47,811.0 9,023.9 0.0000

Distance to perennial stream Expon. decay 4 -48,196.7 9,795.2 0.0000
78

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Distance to intermittent 

stream Linear 4 -48,727.5 10,856.9 0.0000

Distance to nearest stream Linear 4 -48,829.5 11,060.8 0.0000

Distance to intermittent 

stream Expon. decay 4 -48,858.0 11,117.8 0.0000

Distance to nearest stream Expon. decay 4 -48,893.2 11,188.2 0.0000

Null Null 3 -48,901.5 11,202.9 0.0000

Topography Elevation Linear 4 -46,678.2 0.0 1.0000

Roughness index 1 ha 4 -48,509.2 3,662.1 0.0000

Topographic position index 510 m 4 -48,899.1 4,441.8 0.0000

Null 3 -48,901.5 4,444.7 0.0000

 Topographic position index 2010 m 4 -48,918.4 4,480.4 0.0000

Table K2. Model averaged parameter estimates and 95-percent confidence intervals for candidate variables 

included in the Toiyabe subregional resource selection function (RSF) model, Nevada. 

Variable Scale/distance function

Model averaged estimate (95% 

confidence interval) Selection/Avoidance

Annual grass 8.7 ha -1.85 (-2.20, -1.49) Avoidance

Lowland shrubs 661.4 ha -35.06 (-36.84, -33.28) Avoidance

Pinyon-juniper 61.5 ha -6.16 (-6.32, -6.00) Avoidance

Riparian 61.5 ha 14.08 (13.43, 14.72) Selection

Sagebrush 661.4 ha 4.73 (4.62, 4.84) Selection

Upland shrubs 661.4 ha 15.36 (14.81, 15.91) Selection

Distance to cropland Exponential decay -0.21 (-0.22, -0.20) Avoidance

Distance to edge Linear -2.66 (-2.74, -2.59) Selection

Variety of land cover types 661.4 ha 0.40 (0.39, 0.41) Selection

Distance to perennial stream Linear -0.24 (-0.24, -0.23) Selection

Distance to spring Linear -0.57 (-0.59, -0.56) Selection

Distance to water body Linear -0.21 (-0.22, -0.21) Selection

Distance to wet meadow Linear -0.18 (-0.18, -0.18) Selection

Roughness index 1 ha 8.61 (8.38, 8.85) Selection

Elevation Linear 4.21 (4.11, 4.31)

Selection for Higher 

Elevations
79

Table K3. Toiyabe subregional resource selection function (RSF) variable means and standard errors within all 

available habitats and habitats used by greater sage-grouse (Centrocercus urophasianus), Nevada. 

Available habitats Used habitats

Variable Scale Mean

Standard

error Mean

Standard

error

Annual grass 8.7 ha 0.0043 0.0004 0.0025 0.0006

Lowland shrubs 661.4 ha 0.0417 0.0009 0.0018 0.0002

Pinyon-juniper 61.5 ha 0.193 0.0023 0.055 0.0015

Riparian 61.5 ha 0.013 0.0002 0.035 0.0015

Sagebrush 661.4 ha 0.727 0.0020 0.842 0.0023

Upland shrubs 661.4 ha 0.008 0.0002 0.032 0.0010

Variety of land cover types 661.4 ha 5.08 0.0130 6.12 0.0264

Distance to edge Km 0.23 0.0022 0.10 0.0024

Distance to cropland Km 4.54 0.0230 3.34 0.0316

Distance to perennial stream Km 5.48 0.0317 2.86 0.0339

Distance to spring Km 3.27 0.0213 1.34 0.0188

Distance to water body Km 6.05 0.0235 5.13 0.0389

Distance to wet meadow Km 12.83 0.0442 8.59 0.0820

Roughness index 1 ha 0.17 0.0007 0.21 0.0013

Elevation Km 2.08 0.0015 2.25 0.0041
80

Appendix L. Supplemental Material for Virginia Mountains RSF Modeling

Table L1. Variable selection results from the “proposal set” of variables from the Virginia Mountains, Nevada. 

[The top-ranked variable in each set was retained in the suite of candidate variables for resource selection function (RSF) 

modeling if they performed better than the null model and if confidence intervals around estimated mean effects did not 

overlap zero] 

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Land cover Annual grass 661.4 ha 4 -2,086.4 0.0 1.0000

61.5 ha 4 -2,155.5 138.3 0.0000

8.7 ha 4 -2,186.3 199.9 0.0000

Null 3 -2,452.4 730.0 0.0000

Bare ground 661.4 ha 4 -2,347.4 0.0 1.0000

61.5 ha 4 -2,419.7 144.6 0.0000

8.7 ha 4 -2,421.1 147.4 0.0000

Null 3 -2,452.4 207.9 0.0000

Cropland 661.4 ha 4 -2,322.6 0.0 1.0000

61.5 ha 4 -2,384.8 124.3 0.0000

8.7 ha 4 -2,412.1 179.0 0.0000

Null 3 -2,452.4 257.5 0.0000

Forest 661.4 ha 4 -2,429.1 0.0 1.0000

Null 3 -2,452.4 44.5 0.0000

8.7 ha 4 -2,451.8 45.4 0.0000

61.5 ha 4 -2,452.1 46.0 0.0000

Lowland shrub 8.7 ha 4 -2,435.4 0.0 0.9958

61.5 ha 4 -2,440.8 10.9 0.0042

661.4 ha 4 -2,448.9 27.0 0.0000

Null 3 -2,452.4 31.9 0.0000

Perennial grass 8.7 ha 4 -2,445.1 0.0 0.6819

661.4 ha 4 -2,445.9 1.5 0.3162

Null 3 -2,452.4 12.5 0.0013

61.5 ha 4 -2,452.2 14.3 0.0005

Pinyon-juniper 8.7 ha 4 -2,422.1 0.0 0.9999

61.5 ha 4 -2,431.5 18.9 0.0001

661.4 ha 4 -2,442.4 40.7 0.0000

Null 3 -2,452.4 58.6 0.0000

Riparian 8.7 ha 4 -2,439.6 0.0 0.9921

61.5 ha 4 -2,444.7 10.0 0.0066

661.4 ha 4 -2,446.3 13.3 0.0013

Null 3 -2,452.4 23.4 0.0000
81

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Sagebrush 8.7 ha 4 -2,429.8 0.0 0.9574

61.5 ha 4 -2,432.9 6.2 0.0425

661.4 ha 4 -2,439.4 19.1 0.0001

Null 3 -2,452.4 43.1 0.0000

Upland shrub 61.5 ha 4 -2,443.7 0.0 0.9928

661.4 ha 4 -2,448.7 10.1 0.0065

Null 3 -2,452.4 15.3 0.0005

8.7 ha 4 -2,452.1 16.7 0.0002

Wet meadow 8.7 ha 4 -2,438.4 0.0 0.9922

661.4 ha 4 -2,443.2 9.7 0.0077

61.5 ha 4 -2,450.8 24.8 0.0000

Null 3 -2,452.4 26.0 0.0000

Agriculture Distance to cropland Expon. decay 4 -2,349.8 0.0 1.0000

Linear 4 -2,388.7 77.6 0.0000

Null 3 -2,452.4 203.0 0.0000

Edge Edge effects 661.4 ha 4 -2,004.1 0.0 1.0000

61.5 ha 4 -2,174.7 341.1 0.0000

8.7 ha 4 -2,302.1 595.9 0.0000

Null 3 -2,452.4 894.5 0.0000

Distance to edge Linear 4 -2,236.0 0.0 1.0000

Expon. decay 4 -2,364.6 257.2 0.0000

Null 3 -2,452.4 430.7 0.0000

Landscape 

variation Variety of edge types 8.7 ha 4 -2,331.8 0.0 1.0000

61.5 ha 4 -2,360.1 56.8 0.0000

661.4 ha 4 -2,447.6 231.7 0.0000

Null 3 -2,452.4 239.2 0.0000

Variety of land cover types 8.7 ha 4 -2,424.0 0.0 0.9997

61.5 ha 4 -2,432.3 16.6 0.0003

Null 3 -2,452.4 54.7 0.0000

661.4 ha 4 -2,452.2 56.5 0.0000

Water 

source

Distance to spring Expon. decay 4 -2,322.0 0.0 1.0000

Distance to spring Linear 4 -2,364.5 85.1 0.0000

Distance to wet meadow Linear 4 -2,399.1 154.2 0.0000

Distance to water body Expon. decay 4 -2,403.7 163.5 0.0000

Distance to perennial stream Expon. decay 4 -2,416.7 189.5 0.0000

Distance to intermittent stream Linear 4 -2,417.9 191.9 0.0000
82

Group Variable Scale/distance 

function K Log likelihood ΔAICc Model weight

Distance to water body Linear 4 -2,426.8 209.6 0.0000

Distance to perennial stream Linear 4 -2,429.2 214.4 0.0000

Distance to wet meadow Expon. decay 4 -2,439.6 235.2 0.0000

Distance to nearest stream Expon. decay 4 -2,445.1 246.3 0.0000

Distance to nearest stream Linear 4 -2,449.6 255.3 0.0000

Distance to intermittent stream Expon. decay 4 -2,450.3 256.7 0.0000

Null Null 3 -2,452.4 258.8 0.0000

Topography Elevation Linear 4 -2,112.4 0.0 1.0000

Topographic position index 2010 m 4 -2,320.7 416.7 0.0000

Topographic position index 510 m 4 -2,424.7 624.6 0.0000

Roughness index 1 ha 4 -2,437.1 649.5 0.0000

 Null 3 -2,452.4 677.9 0.0000

Table L2. Model averaged parameter estimates and 95-percent confidence intervals for candidate variables 

included in the Virginia Mountains subregional resource selection function (RSF) model, Nevada. 

Variable Scale/distance function

Model averaged estimate (95% 

confidence interval) Selection/Avoidance

Annual grass 661.4 ha 5.42 (4.80, 6.03) Selection

Lowland shrubs 8.7 ha 0.68 (0.42, 0.94) Selection

Pinyon-juniper 8.7 ha -3.67 (-4.00, -3.33) Avoidance

Riparian 8.7 ha -20.31 (-26.14, -14.48) Avoidance

Sagebrush 8.7 ha 0.72 (0.52, 0.92) Selection

Distance to cropland Exponential decay -1.24 (-1.60, -0.87) Avoidance

Edge effects 661.4 ha 17.91 (16.65, 19.17) Selection

Variety of edge types 8.7 ha -0.22 (-0.3, -0.13) Avoidance

Distance to perennial stream Exponential decay -2.90 (-3.26, -2.54) Avoidance

Distance to spring Exponential decay 1.06 (0.71, 1.41) Selection

Distance to water body Exponential decay -2.88 (-3.26, -2.49) Avoidance

Distance to wet meadow Linear -0.07 (-0.10, -0.04) Selection

Roughness index 1 ha -1.30 (-2.2, -0.39) Avoidance

Topographic position index 2010 m 0.008 (0.007, 0.009)

Selected ridges/ 

Avoided valleys

Elevation Linear 5.05 (4.70, 5.40)

Selection for higher 

elevations
83

Table L3. Virginia Mountains subregional resource selection function (RSF) variable means and standard errors 

within all available habitats and habitats used by greater sage-grouse (Centrocercus urophasianus), Nevada. 

Available habitats Used habitats

Variable Scale Mean

Standard

error Mean

Standard

error

Annual grass 661.4 ha 0.038 0.0013 0.321 0.0051

Lowland shrubs 8.7 ha 0.150 0.0052 0.015 0.0038

Pinyon-juniper 8.7 ha 0.235 0.0058 0.211 0.0100

Riparian 8.7 ha 0.005 0.0005 0.003 0.0006

Sagebrush 8.7 ha 0.416 0.0065 0.354 0.0097

Edge effects 661.4 ha 0.14 0.0011 0.27 0.0018

Variety of edge types 8.7 ha 1.35 0.0179 2.37 0.0351

Distance to cropland Km 3.62 0.0385 4.77 0.0575

Distance to perennial stream Km 5.51 0.0618 4.91 0.0999

Distance to spring Km 2.51 0.0300 1.12 0.0224

Distance to water body Km 2.63 0.0269 2.29 0.0454

Distance to wet meadow Km 5.26 0.0480 3.90 0.0467

Roughness index 1 ha 0.20 0.0017 0.22 0.0022

Topographic position index 2010 m 0.59 0.9103 31.65 2.5552

Elevation Km 1.57 0.0049 2.03 0.0079
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Coates and others—Spatially Explicit Modeling of Greater Sage-Grouse Habitat in Nevada and Northeastern California—Open-File Report 2014–1163

ISSN 2331-1258 (online)

http://dx.doi.org/10.3133/ofr20141163