U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2015–1158

Prepared in cooperation with the U.S. Fish and Wildlife Service

Range-Wide Network of Priority Areas for 

Greater Sage-Grouse—A Design for Conserving 

Connected Distributions or Isolating Individual Zoos?

U.S. Department of the Interior

U.S. Geological Survey

Range-Wide Network of Priority Areas for 

Greater Sage-Grouse—A Design for Conserving 

Connected Distributions or Isolating Individual Zoos?

By Michele R. Crist, Steven T. Knick, and Steven E. Hanser

Prepared in cooperation with the U.S. Fish and Wildlife Service

Open-File Report 2015–1158
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2015

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Suggested citation:

Crist, M.R., Knick, S.T., and Hanser, S.E., 2015, Range-wide network of priority areas for greater sagegrouse—A design for conserving connected distributions or isolating individual zoos?: 

U.S. Geological Survey Open-File Report 2015-1158, 34 p., http://dx.doi.org/10.3133/ofr20151158. 

ISSN 2331-1258 (online)
iii

Contents

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

Introduction.................................................................................................................................................... 1

Description of Study Area.............................................................................................................................. 3

Methods......................................................................................................................................................... 5

Priority Areas as a Spatial Network............................................................................................................ 5

Movement Potential among Priority Areas................................................................................................. 6

Network Analysis ..................................................................................................................................... 10

Relative Isolation...................................................................................................................................... 10

Results......................................................................................................................................................... 11

Priority Areas as a Spatial Network.......................................................................................................... 11

Connectivity among Priority Areas ........................................................................................................... 14

Relative Isolation...................................................................................................................................... 16

Discussion ................................................................................................................................................... 16

Priority Areas as a Spatial Network.......................................................................................................... 16

Connectivity among Priority Areas ........................................................................................................... 17

Relative Isolation...................................................................................................................................... 18

Synthesis and Application........................................................................................................................ 18

Acknowledgments ....................................................................................................................................... 19

References Cited......................................................................................................................................... 20

Appendix A. Crosswalk Table Depicting Priority Area Identifiers, U.S. Fish and Wildlife Service Unique 

Identifiers, Sage-Grouse Population Name, and Management Zone........................................................... 24

Appendix B. Centrality Results for Degree and Betweenness Metrics for Each Priority Area...................... 30

Figures

Figure 1. Study area and designated priority areas across the sage-grouse range in western 

North America represented as a network of nodes and links.........................................................................4

Figure 2. Habitat similarity index (HSI) values for greater sage-grouse across their historical range. ..........9

Figure 3. Priority area importance and connectivity for betweenness centrality and ranked across the 

network........................................................................................................................................................ 12

Figure 4. Relative contribution of each priority area to range-wide cumulative percent betweenness 

centrality ...................................................................................................................................................... 13

Figure 5. Cumulative distribution of each priority area’s contribution to total betweenness centrality ......... 14

Figure 6. Relative isolation of priority areas based on estimated potential for sage-grouse movement 

(Circuitscape; McRae and Shah, 2008)....................................................................................................... 15

Tables

Table 1. Partitions (k) in a Mahalanobis D2 model describing ecological minimums for the range-wide

distribution of greater sage-grouse ................................................................................................................8

Table 2. Summary statistics calculated for degree and betweenness centrality, and effective 

resistance and maximum current densities from Circuitscape (McRae and others, 2008)........................... 11
iv

Conversion Factors

International System of Units to Inch/Pound

Multiply By To obtain

Length

kilometer (km) 0.6214 mile (mi)

Area

hectare (ha) 2.471 acre

square hectometer (hm2) 2.471 acre

square kilometer (km2) 247.1 acre

square kilometer (km2) 0.3861 square mile (mi2)

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

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Elevation, as used in this report, refers to distance above the vertical datum.
1 

Range-Wide Network of Priority Areas for 

Greater Sage-Grouse—A Design for Conserving 

Connected Distributions or Isolating Individual Zoos?

By Michele R. Crist, Steven T. Knick, and Steven E. Hanser

Abstract

The network of areas delineated in 11 Western States for prioritizing management of 

greater sage-grouse (Centrocercus urophasianus) represents a grand experiment in conservation 

biology and reserve design. We used centrality metrics from social network theory to gain 

insights into how this priority area network might function. The network was highly centralized. 

Twenty of 188 priority areas accounted for 80 percent of the total centrality scores. These 

priority areas, characterized by large size and a central location in the range-wide distribution, 

are strongholds for greater sage-grouse populations and also might function as sources. Midranking priority areas may serve as stepping stones because of their location between large 

central and smaller peripheral priority areas. The current network design and conservation 

strategy has risks. The contribution of almost one-half (n = 93) of the priority areas combined for 

less than 1 percent of the cumulative centrality scores for the network. These priority areas 

individually are likely too small to support viable sage-grouse populations within their boundary. 

Without habitat corridors to connect small priority areas either to larger priority areas or as a 

clustered group within the network, their isolation could lead to loss of sage-grouse within these 

regions of the network. 

Introduction

Greater sage-grouse (Centrocercus urophasianus; hereinafter, sage-grouse) is an endemic 

Galliform to arid and semiarid sagebrush (Artemisia spp.) landscapes of Western North America 

(Schroeder and others, 1999). Sage-grouse currently occupy approximately one-half of their 

presettlement habitat distribution and have recently received much attention for their long-term 

population declines (Schroeder and others, 2004; Garton and others, 2011). The U.S. Fish and 

Wildlife Service listed sage-grouse as a candidate species under the Endangered Species Act in 

2010 concluding that protection was warranted although immediate conservation actions were 

precluded due to other higher priority species (U.S. Fish and Wildlife Service, 2010). Broadscale habitat loss and fragmentation from synergistic cycles of wildfire and conversion to 

invasive plant communities as well as from human land use is the primary cause of population 

declines (Knick and Connelly, 2011). The most pressing challenge to long-term sage-grouse 

persistence is conservation of remaining large and intact sagebrush landscapes (Stiver and others, 

2006). 
2 

The U.S. Fish and Wildlife Service, faced with legal challenges for delaying full 

protection under the Endangered Species Act, is currently reviewing the bird’s status and is 

scheduled to issue an updated listing decision in September 2015. In an effort to avoid listing, the 

11 Western States and Federal management agencies within the sage-grouse range have 

developed conservation plans embracing the concept of core or priority areas (Priority Areas for 

Conservation, PACs [U.S. Fish and Wildlife Service, 2013], or equivalent terms designated in 

individual State agency plans)—allowable spatial area of disturbance due to human land use, 

such as energy development, is tightly regulated and conservation actions are focused in areas 

with the highest number of sage-grouse and potentially the greatest benefit to the species. Land 

use is allowed to continue outside of priority areas under normal regulations. 

The delineation of an entire species range spanning more than 2 million km2

 (excluding 

the Canadian portion) into a binary division of priority and nonpriority areas may represent one 

of the largest experiments in conservation reserve design for a single species. Individual priority 

areas range in size from less than 1 to more than 83,000 km2 and encompass the broad spectrum 

of reserve design paradigms from single large to several small reserves. Although we do not 

know the minimum area required, the largest priority areas likely can support viable sage-grouse 

populations completely within their boundaries. However, the smallest priority areas clearly 

enclose much less than the annual range of a sage-grouse (4–615 km2

; Connelly and others, 

2011). Much scientific literature addressing conservation reserve design has stressed the 

importance of the inclusion and protection of habitat connectivity between conservation reserves 

to ensure individual movements, opportunity to shift habitats when needed, and facilitate genetic 

exchange (Crooks and Sanjayan, 2006). Therefore, numerous connected priority areas also may 

be necessary to provide seasonal habitats that can be separated by up to 160 km (Connelly and 

others, 2011; Smith, 2013). The two primary factors that influence populations, area and 

isolation (MacArthur and Wilson, 1967; Hanski and Gilpin, 1991; Hanski, 1999), are important 

metrics in understanding the efficacy of this conservation approach. 

We used social network theory and centrality metrics (Moreno, 1932, 1934; Freeman, 

1979, 2004) to quantify and understand the potential for the delineated priority areas to function 

as a connected network to conserve sage-grouse populations. Our objectives were to: 

1. Identify high-ranking priority areas within the network based on their location and 

number of connections to other priority areas, 

2. Estimate the ability of other lower ranking priority areas within the network to function 

as stepping stones for maintaining connectivity among clusters of priority areas, and 

3. Model relative isolation among priority areas based on movement potential in their 

surrounding environment.
3 

Description of Study Area

We included 2,030,230 km2 of the Western United States in our analysis of designated 

priority areas across the current sage-grouse range (Schroeder and others, 2004). The sagegrouse range is divided into seven management zones based on similar floristic and 

environmental characteristics (Stiver and others, 2006). The area contains a diversity of 

shrubland types of which landscapes dominated by sagebrush are the most important to sagegrouse. Mountain ranges, forest communities, and agricultural regions, particularly in broad 

plains of large river systems, are not used by sage-grouse and can act as barriers to their 

movements (Fedy and others, 2014). Lands used by sage-grouse are of mixed ownership (Knick, 

2011). Public lands are dominant in the Western States and are managed primarily by the Bureau 

of Land Management and U.S. Forest Service for multiple uses. Private lands, characteristically 

those fertile lands with deep soils and access to water, constitute the greatest proportion of 

ownership in the northern and eastern parts of the sage-grouse range and can comprise more than 

two-thirds of the landscape used by sage-grouse (Doherty and others, 2010; Knick, 2011). 

Each State used different criteria for delineating boundaries of priority areas but each 

generally incorporated metrics for sage-grouse populations (lek locations and breeding bird 

densities [Doherty and others, 2010]) and habitat areas (identified from known sage-grouse 

distributions or seasonal habitats for breeding, nesting, brood-rearing, or wintering areas derived 

from observations or telemetry data). In some cases, States also adjusted boundaries to exclude 

private lands, Federal lands approved for or in the process of being developed for energy and 

other management activities, and pre-existing development. The current range-wide management 

strategy, if not the ecological reality, is that each priority area bounds a homogeneous patch and 

that all priority areas are of equal importance. 

We created a range-wide map of priority areas by combining the spatial boundaries of 

priority areas as delineated by the 11 States in Western North America (fig. 1, appendix A). 

Boundaries of polygons were merged between States when shared but followed State lines when 

adjoining priority areas did not match across borders. We also merged or removed priority areas 

less than 1 km2 that typically were slivers left after the State’s original delineations and 

subsequent edits. The final map contained 188 priority areas ranging in size from approximately 

1.1 to 83,000 km2 (appendix B). Mean size was approximately 16,600 km2. The frequency 

distribution consisted predominantly of smaller priority areas; 50 percent of the priority areas 

were less than 125 km2 and 90 percent were less than 3,300 km2

. Total area included within 

priority areas was approximately 310,000 km2

 and included 15 percent of our study area. 
4 

Figure 1. Study area and designated priority areas across the sage-grouse range in Western North 

America represented as a network of nodes and links. Background map is from U.S. Geological Survey 

National Elevation Data (NED; 2011; http://seamless.usgs.gov).
5 

Methods

Priority Areas as a Spatial Network

We described the spatial network of priority areas as a graph structured by nodes and 

connecting links (Diestel, 2005). To identify adjacencies, we delineated individual polygons 

around each priority area by creating Thiessen polygons where boundaries encompassed grid 

cells closest to each priority area relative to all other priority areas. Shared boundaries between 

Thiessen polygons identified neighboring priority areas. We then added links between each 

priority area and its neighbor’s centroid. Thus, the network of conservation reserves currently 

designed for the sage-grouse range was represented by nodes and links across all priority areas 

(fig. 1).

We used analyses derived from social network theory (Wasserman and Faust, 2004) to 

identify priority areas that were highly important for connectivity within the range-wide network. 

Social network theory combines graph theory and centrality indices to characterize network 

structures by mapping and measuring relationships and flows (links) between people, groups, 

organizations, computers, and other entities (nodes) (Freeman, 2004; Wasserman and Faust, 

2004; Diestel, 2005; Newman, 2010). Quantifying network centrality provides insight into the 

overall structure, connection, and function of a network, and is considered to be the fundamental 

characteristic describing a node’s position in a network. Relative importance in social networks 

is measured by centrality metrics that emphasize number of connections to indicate relative 

position within the network. Networks can range from highly centralized, dominated by a few 

highly connected nodes, to more widely dispersed configurations in which connections are 

equally shared among all nodes.

We used two centrality metrics, degree and betweenness, to assess the relative 

importance of individual priority areas based on their position and number of connections within 

the overall range-wide network (Freeman, 1977, 2004; Wasserman and Faust, 2004; Newman, 

2010). Relative importance estimated by degree centrality is based simply on number of 

connections to other nodes in the network; more connections indicate greater influence and a 

more central position in the network (Erdos and Gallai, 1960; Diestel, 2005). However, a priority 

area also might be important because its relative position connects clusters or groups of priority 

areas located in close proximity. Betweenness centrality quantifies the number of times a node 

acts as a bridge along the shortest path between two other nodes, thus indicating its importance 

in maintaining the network (Freeman, 1977; Freeman and others, 1991; Estrada, 2007; Brandes, 

2008; González-Pereira and others, 2010). Nodes with high scores of betweenness centrality 

represent the primary foundation of the network’s structure because a disproportionately high 

number of the shortest pathways go through them. These nodes funnel movement not only from 

adjacent nodes but also from nodes that could be located far away in the landscape (Bodin and 

Norberg, 2007). 
6 

Movement Potential among Priority Areas

Connections in ecological networks are not without dimensions (as in social networks) 

but rather have a distance and environmental cost to move between nodes (Bunn and others, 

2000; McRae and others, 2008; Carroll and others, 2012; LaPointe and others, 2013). To assess 

the relative importance of priority areas, we needed to combine number of connections with the 

ability to traverse the interstitial landscape matrix.

We calibrated movement potential by sage-grouse through the landscape by mapping a 

model of ecological minimum requirements (Knick and others, 2013). Sage-grouse may perceive 

a landscape quite differently when moving within a range, moving between seasonal ranges, or 

when dispersing. Similarly, connectivity for individual movements obtained from telemetry data 

might be different than connectivity derived from genetic information. For our study, we made 

the basic assumption that movement would be more likely in suitable environments that could be 

modeled across the entire range.

An ecological minimum, in concept, represents a multivariate construct of the basic 

requirements for a species. The model was developed from 23 variables describing land cover, 

fire history (area burned from 1980 to 2013), terrain (topographic accessibility [Sappington and 

others, 2007]), climate, edaphic, and anthropogenic features measured at our minimum mapping 

unit of 1-km2

 resolution across the sage-grouse range. Land-cover variables consisted of 

combined Landfire Existing Vegetation Type (http://www.landfire.gov/; Rollins, 2009) for big 

sagebrush, low sagebrush, salt desert shrub, exotic grassland, native grassland, pinyon-juniper 

woodland, conifer forest, and riparian associations. Climate variables were obtained from the 

PRISM Climate Group (Daly and others, 2004; Oregon State University, 2011) measured from 

1998 to 2010 and included mean annual maximum and minimum temperatures, and mean annual 

precipitation. We described soils using available water capacity, salinity, and depth to rock (U.S. 

Department of Agriculture, 2011). Anthropogenic features included agriculture and development 

land cover (http://www.landfire.gov/), transmission lines, tall structures (communication towers, 

wind towers), roads, pipelines, and oil and gas wells. We produced a smoothed, continuous 

surface for most variables by averaging individual cell values within a 5-km radius moving 

window. We used mapped values for soils, which were in vector format, measured at the center 

of each 1-km grid cell in the map. 

We derived estimates of the ecological minimums using a partitioned Mahalanobis D2

model of presence only data (Dunn and Duncan, 2000; Rotenberry and others, 2002). Lek 

(breeding area) locations were used to indicate presence for a previous model of sage-grouse 

ecological minimums across their western range (Knick and others, 2013). However, we did not 

have permission to use lek data from all States across the sage-grouse range. Therefore, we 

assumed that the priority areas delineated by States captured higher quality habitat than occurred 

outside, despite having some areas excluded because of ownership or forecasted disturbance, and 

a large proportion of the sage-grouse population. We randomly selected 1,669 points within 

individual priority areas as presence data and extracted values for corresponding variables to 

calibrate models. Total number of presence points was obtained by proportional area expansion 

to the eastern part of the sage-grouse range after an initial 1,000 point random sample in a 

preliminary comparison of results from priority areas compared to the lek-based map in our 

previous study of the western range (Knick and others, 2013). We then performed a principal 

components analysis on 1,000 iterative samples created by bootstrapping the calibration data. 

The final model was created by subsequently averaging the PCA output after correcting for sign 

ambiguity (Bro and others, 2008) across all iterations. 
7 

We evaluated model performance from the area under the curve (AUC) for a receiver 

operating characteristic (ROC) to assess sensitivity (fraction of habitat points correctly 

classified) and specificity (fraction of non-habitat points predicted as habitat) (Fielding and Bell, 

1997). To generate presence data, we overlaid the 100 percent sage-grouse breeding densities 

(Doherty and others, 2010) representing spatial locations of all known sage-grouse breeding sites 

with our map of ecological minimums and selected all values that fell within the density 

boundaries. For absence data, we selected all values that fell outside of the breeding density 

boundaries. To calculate the AUC, we randomly sampled 5,000 presence points and 20,000 

absence points. We also created a null presence/absence dataset by randomly sampling 20,000 

points 1,000 times from the ecological minimums map. For each iteration, we divided the 

resulting sample into two datasets (null presence and null absence) based on a relatively equal 

proportion of the total rows and columns. We then sampled 10,000 points from each of the two 

datasets and computed a mean AUC score and distribution from all null samples. Means and 

distributions for model and null AUC scores then were used in a t-test for significance.

Principal component partition 14 met our criteria of having an eigenvalue <1, a relative 

difference in eigenvalues among adjacent partitions (table 1), performance against evaluation 

data (AUC = 0.80; null AUC = 0.50; 95% CI = 0.49 and 0.50; t-test between the null AUC and 

true AUC = -3,775.0; p << 0.001), and our subjective assessment of mapped results from 

different model partitions. We rescaled the mapped output to range continuously from 0 to 1 

based on a χ2 distribution of the D2 distance; a value of 1 indicated environmental conditions 

identical to the mean vector of ecological minimum requirements, whereas a value near 0 

indicated very dissimilar conditions (fig. 2). 

We used circuit theory (McRae and others, 2008) to model movement pathways between 

all priority areas across our network. We assumed that sage-grouse moved more readily through 

areas meeting their ecological minimum requirements and used a scaled inverse of our mapped 

scores as a resistance surface (McRae and others, 2008; Spear and others, 2010; Beier and others, 

2011; Zeller and others, 2012). Our resistance surface was calculated by multiplying habitat 

values by 100 and using the following function: ((habitat value – maximum habitat value) * -1) + 

minimum habitat value). Resistance values ranged from 1, representing the lowest 

resistance/highest habitat value, to 100 (high resistance/lowest habitat value). We ran 

Circuitscape (Circuitscape version 4.0, http://www.circuitscape.org; McRae and Shah, 2008) 

using the pairwise mode to calculate connectivity between all pairs of priority areas. We treated 

priority areas as focal patches instead of individual nodes in the modeling process: evaluating 

habitat pathways from priority area polygon boundaries rather than nodes captured the influence 

of priority area structure and size in influencing current flow. Effective resistance distances, the 

relative distance that incorporates the resistance to a species movements across a heterogeneous 

landscape and used as an estimate of connectivity, were calculated iteratively between all priority 

area pairs and maps of current densities. We calculated electric current flowing through the 

resistance landscape between each pair to produce cumulative and maximum current densities 

across all pair-wise combinations. Our approach thus incorporated multiple dispersal pathways 

and landscape heterogeneity. 
8 

Table 1. Partitions (k) in a Mahalanobis D2 model 

describing ecological minimums for the range-wide 

distribution of greater sage-grouse.

Partition (k) Eigenvalue

1 3.18

2 2.89

3 1.87

4 1.76

5 1.68

6 1.42

7 1.31

8 0.99

9 0.96

10 0.93

11 0.85

12 0.80

13 0.76

14 0.63

15 0.59

16 0.49

17 0.44

18 0.40

19 0.34

20 0.32

21 0.24

22 0.14

23 0.04
9 

Figure 2. Habitat similarity index (HSI) values for greater sage-grouse across their historical range. HSI 

values represent the relationship of environmental values at map locations to the multivariate mean vector

of minimum requirements for sage-grouse defined by land cover, anthropogenic variables, soil, topography, 

and climate.
10 

Network Analysis

We applied the effective resistance distances calculated between priority areas to our 

priority area network. We exported the attribute table of our line network shapefile and built a 

matrix based on priority area IDs, where “0” was assigned to indicate non-adjacency for a 

priority area pair (where the two priority areas are not linked in the network), and a “1” indicates 

adjacency (priority area pairs are linked). We assigned the resulting pair-wise effective resistance 

distances as a cost between adjacent priority areas in the matrix to all priority pair adjacencies 

labeled with a “1”. For example, a low effective resistance distance represents a relative 

capability for sage-grouse movements between the priority area pair based on similarity to 

habitats within priority areas. We rebuilt our node and link network from our matrices using the 

igraph package in R (Csardi and Nepusz, 2006; R Core Team, 2013) to calculate centrality. 

Links connecting adjacent priority areas represent a relation between the priority areas based on 

the effective resistance distance. The final graph represented the priority area network’s spatial 

structure of connectivity. 

We calculated our centrality metrics, degree and betweenness, using the igraph package 

and computed summary statistics of our centrality results using R (Csardi and Nepusz, 2006; R 

Core Team, 2013). We also computed a cumulative distribution curve for resulting betweenness 

centrality values and used the incremental contribution by each priority areas to assess its 

contribution to overall network centrality. We used the distribution to rank and identify central 

priority areas that contribute the most in maintaining a connected network and to identify priority 

areas that function as stepping stones in promoting connectivity to the central priority areas. 

Relative Isolation

Our final objective was to model the relative isolation of priority areas across the 

historical range of sage-grouse. Results from Circuitscape were used to map habitat linkages 

among all priority areas and identify clusters of connected priority areas within our network. 

Circuit theory is advantageous for quantifying connectivity in this manner because of its ability 

to simultaneously evaluate the combined contributions of multiple pathways to dispersal in 

heterogeneous landscapes, and identify areas important for connectivity conservation (McRae, 

2006; McRae and others, 2008). We used visual observations of the maximum current densities 

and computed effective resistance distances resulting from Circuitscape to identify areas where 

habitat connectivity is high or low between priority areas. We chose to evaluate the maximum 

current density map because maximum values help to remove the confounding effects of 

network configuration (halo effect) in the Circuitscape results. Again, greater connectivity 

among priority areas was reflected by a larger number of connected pathways and lower 

effective resistance distance values (McRae and Shah, 2008; McRae and others, 2008). We also 

visually identified locations of high current densities that may function as bottlenecks (pinch 

points) to sage-grouse movements where alternative pathways are not available (McRae and 

others, 2008). These locations may represent conservation priorities for sage-grouse because 

their loss may disrupt connectivity among the priority area network. 
11 

Results

Priority Areas as a Spatial Network

Average number of connections from each priority area to adjacent neighbors averaged 

11 and ranged from 2 to 50 (fig. 3). Betweenness centrality scores ranged from 0 to 11,414 (table 

2; appendix B); the average betweenness centrality was 475 (table 2), indicating that most 

priority areas were contributing little to the range-wide centrality. The largest priority area 

(Priority Area ID 48), which combined individual State polygons in northeastern Oregon, 

western California, northern Nevada, southern Idaho, and western Utah, exhibited the highest 

number of adjacent neighbors (n = 50) and highest betweenness centrality value, signifying its 

importance in connecting the network. 

Twenty priority areas explained 80 percent of the total betweenness centrality value and 

were likely the central priority areas for maintaining a connected reserve network (fig. 4). Two 

priority areas that exhibited the highest betweenness centrality scores and explained 20 percent 

of total centrality were the largest single polygon (Priority Area ID 48) and a priority area 

centrally located in Wyoming (Priority Area ID 110). Priority areas that were within the 80–99 

percent cumulative distribution scored lower in centrality compared to the central priority areas 

but may still contribute largely by functioning as stepping stones maintaining connections across 

the most central 20 priority areas. These priority areas typically were located between the 

highest- and lowest-scoring priority areas, mid-sized in area, and were distributed across the 

entire range rather than having a more central location.

Ninety-three priority areas that scored a 0 for betweenness centrality were characterized 

by small size (averaging approximately 350 km2

), and were either isolated between large priority 

areas where the shape of the surrounding large priority areas limited the number of connections, 

or were located on the periphery of the range. Although these small priority areas were not 

central in maintaining the overall network, most scored low in the effective resistance distance 

results (figs. 4 and 5; appendix B) indicating high connectivity to their neighboring priority 

areas.

Table 2. Summary statistics calculated for degree and betweenness centrality, and effective resistance and 

maximum current densities from Circuitscape (McRae and others, 2008).

Priority Areas Mean Minimum Maximum

Distance between Priority Areas (km) 99.3 2.7 843.3

Degree Centrality Metric 10.6 2.0 50.0

Betweenness Centrality Metric 475 0 11,414

Priority Area Effective Resistance 4.4 <0.1 35.8

Maximum Current Densities 0.1 0.0 1.0
12 

Figure 3. Priority area importance and connectivity for betweenness centrality and ranked across the 

network. Potential for sage-grouse movements was estimated between priority areas and used to 

determine each priority area’s centrality based on the number of movement pathways available between 

priority areas. Current densities were displayed using a histogram equalize stretch. 
13 

Figure 4. Relative contribution of each priority area to range-wide cumulative percent betweenness 

centrality. Priority area colors in map correspond with figure 5.
14 

Figure 5. Cumulative distribution of each priority area’s contribution to total betweenness centrality. Graph 

colors correspond to mapped priority areas in figure 4. 

Connectivity among Priority Areas

Movement potential, estimated by Circuitscape current densities (fig. 6), coupled with the 

relatively low mean for priority area effective resistance distance (mean = 4.4; table 2) indicated 

a high degree of connectivity across the network characterized by numerous and multiple 

pathways between most of the priority areas. The map reflecting maximum current densities 

highlighted areas of high current flow between priority areas that may be important habitat 

linkages (pinch points). Their loss may result in disconnections across the entire network or 

result in the use of less efficient (more costly) habitat pathways connecting priority areas (fig. 6). 

A number of linkages have portions of high current densities that depict pinch points where 

connectivity is high but constrained due to either natural or anthropogenic barriers to sage-grouse 

movements surrounding the pinch points. Our map of maximum current densities also 

highlighted priority area clusters where current flow was high between priority areas and low 

surrounding a group of priority areas. 
15 

Figure 6. Relative isolation of priority areas based on estimated potential for sage-grouse movement 

(Circuitscape; McRae and Shah, 2008). Inverted HSI values were used as a measure of landscape 

resistance. Six clusters of priority areas are circled where connectivity between priority areas was high in 

comparison to surrounding environment. High to medium current densities represent pinch points.
16 

Relative Isolation

Low current densities highlighted areas where habitat quality was more fragmented or 

where barriers for sage-grouse movements may exist. Mean maximum current density across the 

study area was low (mean = 0.1; table 2) because the study area included large expanses for high 

elevation mountain ranges, forested communities, highly populated areas, agriculture 

development, and other areas of low habitat value for sage-grouse that composed a large portion 

of our study area. For example, the Snake River Plain in southern Idaho, which contains 

Interstate 84 and large areas of developed private lands, may function as a barrier for sage-grouse 

movements between two adjoining priority areas. The Snake River Plain also has experienced 

significant areas of cheatgrass (Bromus tectorum) invasions and recent fire activity resulting in 

higher habitat loss and fragmentation in comparison to other regions across the historical range.

Discussion

The strategy currently implemented for conserving greater sage-grouse is based on 

designated priority areas in each of the 11 States across its range (U.S. Fish and Wildlife Service, 

2013). Focusing conservation actions on a relatively small (<15 percent) total area containing a 

large proportion of the range-wide population can have the greatest benefit with limited 

resources. However, continued management under normal regulations in regions surrounding 

priority areas can potentially lead to a spatially disjunct set of areas that retain the characteristics 

necessary to sustain sage-grouse populations. We assumed that the priority areas serve as a 

system of reserves and function within the context of island biogeography theory (MacArthur 

and Wilson, 1967; Wiens, 1997). 

We used two primary factors, size and connectivity of priority areas, to understand how 

this network functions. We ranked priority areas for their relative importance within the network 

and identified important habitat linkages that may help maintain connected sage-grouse 

populations across their range. However, our approach was a simple metric based on a social 

theory relating importance to number of connections. The critical component to assessing 

viability is not just size of priority area and number of connections but how individuals are 

linked together to function as a viable population. 

Priority Areas as a Spatial Network

Centrality measures derived from social network theory provided an interpretable 

analysis for characterizing the importance of priority areas within a network. Centrality measures 

also produced a ranking metric for identifying key areas to conserve to minimize network 

connectivity loss (Freeman, 2004; Blazquez-Cabrera and others, 2014). A highly centralized 

network is dominated by one or a few very central nodes. If these nodes are removed, the 

network may quickly fragment into unconnected sub-networks by isolating individual or clusters 

of nodes. In contrast, a less centralized network might be more resilient because many links or 

nodes can fail while allowing the remaining nodes to remain connected through other network 

paths. 
17 

High centrality scores for 20 of 188 priority areas indicated that the network was highly 

centralized. Highly ranked priority areas were characterized by large size, a more central spatial 

location within the network, and were surrounded by many other priority areas of various sizes. 

The highest ranked priority area (Priority Area ID 48) was the largest and most centrally located 

in the network. Large size also correlates with longer boundaries that allow for more dispersal 

opportunity with adjacent priority areas. Similarly, a central position in the network facilitates 

movement to reach numerous other priority areas, thus increasing overall connectivity across the 

network. Loss or fragmentation of these large priority areas, or their associated connections, 

would have a disproportionally large influence across the entire network. Delineating priority 

areas with these characteristics may be important in further conservation strategies because they 

play a strong role in the range-wide network connectivity. 

Approximately 80 percent of the priority areas scored betweenness centrality values of 

(near) zero despite being well-connected to surrounding priority areas. These priority areas 

generally were smaller and were distributed across the network surrounding the central larger 

priority areas. Although these individual priority areas were small, their total area contained a 

large amount of the habitat across the entire sage-grouse range. Their size and location likely 

allows them to function as stepping stones and may be critical for individuals moving from 

larger neighboring priority areas needed to maintain smaller sage-grouse populations (Bodin and 

others, 2006; Saura and others, 2014). 

Connectivity among Priority Areas

Maintaining connectivity by conserving habitat between separated populations or 

reserves is an important strategy to mitigate against impacts of land-use change. Landscape 

connectivity is often assessed in the form of least-cost paths, corridors, and graph networks to 

identify critical habitat connections where, if severed, could potentially isolate populations 

(Bunn and others, 2000; Urban and Keitt, 2001; LaPoint and others, 2013). Our primary 

objective was to evaluate the capability of the network of priority areas to serve as a connected 

reserve network for sage-grouse. To do that, we also needed to produce the first range-wide 

landscape-scale analysis to quantify habitat quality and connectivity across their range. This 

approach, incorporating an effective resistance surface, enhanced our assessment of the priority 

area network by permitting multiple dispersal pathways and recognizing landscape heterogeneity 

in estimating movement cost. Our maps highlighted important habitat corridors and pinch points 

between priority areas that land managers can target for conservation to help ensure sage-grouse 

seasonal and dispersal movements. These locations also might be considered for future priority 

areas to ensure connectivity.

We emphasize that the parameters defining connectivity in our study were based on a 

habitat suitability metric measured at a 1-km2 resolution. The interpretation of connectivity 

requires an understanding of genetic, individual, and population levels as well as recognizing 

behavioral differences between seasonal and dispersal movements. Connectivity to maintain 

genetic diversity might have different requirements than the connectivity necessary to recolonize 

areas or augment declining populations. Characteristics of sage-grouse dispersal are relatively 

unknown (Connelly and others, 2011); patterns from telemetry, satellite, and genetic studies 

would provide valuable information in assessing landscape-scale connectivity for conservation 

planning. 
18 

Relative Isolation

The cost of movement across a landscape is a combined function of distance and 

resistance to movement (McRae, 2006). Connectivity, measured by the effective resistance 

distance, varied widely across the sage-grouse range. Some geographically distant priority areas 

were highly connected to the network through corridors of low resistance to movement. In 

contrast, other priority areas in close proximity were disconnected because of resistance created 

by unsuitable environments. 

The formal conservation strategy focused on priority areas did not designate connecting 

corridors among priority areas, which could effectively isolate priority areas or regions. 

Therefore, we identified linkages and pinch-points that may be important for sustaining sagegrouse movements among priority areas (Bengtsson and others, 2003; Beier and others, 2011; 

Dickson and others, 2013; LaPoint and others, 2013). Most techniques for analyzing landscape 

connectivity identify one primary route based on a least cost pathway that becomes the focus for 

conservation efforts. Our approach for characterizing connectivity based on a resistance surface 

and circuit theory allowed for the quantitative and simultaneous evaluation of multiple 

alternative habitat linkages important for maintaining connected sage-grouse populations 

(McRae and others, 2008; Knick and others, 2013).

Synthesis and Application

The current network of priority areas has many important characteristics for maintaining 

sage-grouse populations. This network contained a range of large and small sizes of priority 

areas that might provide different functions. The structure of the network of priority areas for 

conserving greater sage-grouse was highly centralized. A relatively few large and more central 

priority areas accounted for a large proportion of cumulative centrality ranking. These large 

priority areas likely can self-sustain viable sage-grouse populations because of the large 

sagebrush regions within their boundaries. Large priority areas also might function as sources to 

augment adjacent populations, either those in priority areas too small to support persistent sagegrouse populations or in nonpriority areas. 

The network also contained connected clusters of priority areas that otherwise might be 

too small individually to sustain viable populations. For example, a cluster of priority areas in 

Wyoming were highly connected and centered on one large priority area. A priority area cluster 

in Montana appears geographically isolated but is highly connected to the Wyoming cluster 

through habitat linkages in North and South Dakota. High current densities between priority 

areas in Oregon connect with priority areas across Idaho, Nevada, and California. The Bi-State 

cluster on the border of Nevada and California was isolated from all other clusters but exhibited 

a high degree of connectivity among the priority areas within it. Although our analysis focused 

on the range-wide network, there is likely a hierarchical system of networks for both priority 

areas and metapopulations of sage-grouse. These smaller clusters might function independently 

and an analysis of these smaller clusters as networks might provide important insights into 

regional centrality and linkages. 
19 

Designating clustered areas in close proximity is one of the central tenets of reserve 

design (Diamond, 1975; Williams and others, 2004). Clustering helps to promote frequent 

dispersal movements for genetic exchange. Clustering also might enhance migration that might

rescue declining or isolated populations, allow for seasonal movements, or egress away from 

areas that have become degraded or lost (Cabeza and Moilanen, 2001). Maintaining connectivity 

within and among the clusters potentially allows for dispersal to augment declining populations 

and maintain genetic exchange across the entire network reducing the chance for the creation of 

isolated or genetically distinct populations in the long-term (Crooks and Sanjayan, 2006). 

Priority areas that scored lower in the centrality metrics were mid-sized and widely 

distributed across the entire range. Their function as stepping stones to reduce overall distance 

for sage-grouse movements among the central priority areas is an important consideration for 

sustaining a connected network. 

Adopting a range-wide conservation plan for sage-grouse based on a network of priority 

areas has risks. Different conservation and management priorities among administrative units 

could disrupt the metapopulation structure leading to greater isolation and potentially initiate or 

accelerate population declines. Many priority areas share a boundary on State jurisdictional lines 

and many important habitat linkages presented here occur across State and Federal jurisdictional 

boundaries. Yet, priorities and land use plans often differ among State and Federal management 

agencies both within and outside of the proposed priority area structure (Copeland and others, 

2014). Understanding the functions of the priority area network and recognizing the importance 

of connecting corridors can help sustain sage-grouse populations. 

Designing reserve networks is challenging because of combined needs to protect the 

largest habitat or population areas in a landscape, ensure that those areas are close enough to 

sustain effective dispersal rates, and also ensure that a sufficient number of areas exist so that 

individual losses can be absorbed within the entire network (Diamond, 1975; Cabeza and 

Moilanen, 2001; Williams and others, 2004). Our centrality results may help predict impacts to 

connectivity when priority areas are lost, degraded, or fragmented. Numerous factors, both 

natural and anthropogenic, make it unlikely that the current network of priority areas can be 

sustained (Knick and Connelly, 2011). Focusing conservation actions on important and highly 

connected priority areas and corresponding habitat linkages may help to mitigate future 

landscape change and enhance the long-term viability of sage-grouse populations. 

Acknowledgments

Western Association of Fish and Wildlife Agencies within the sage-grouse range 

provided spatial data. U.S. Geological Survey provided ancillary support. D.E. Naugle, 

University of Montana, was responsible for the zoo metaphor. S.L. Phillips, U.S. Geological 

Survey, reviewed an early draft of the manuscript. 
20 

References Cited

Beier, P., Spencer, W., Baldwin, R., and McRae, B. H., 2011, Best science practices for 

developing regional connectivity maps: Conservation Biology, v. 25, p. 879–892. 

Bengtsson, J., Angelstam, P., Elmqvist, T., Emanuelsson, U., Folke, C., Ihse, M., Moberg, F., 

and Nyström, M., 2003, Reserves, resilience and dynamic landscapes: Ambio, v. 32, p. 389–

396. 

Blazquez-Cabrera, S., Bodin, Ö., and Saura, S., 2014, Indicators of the impacts of habitat loss on 

connectivity and related conservation priorities—Do they change when habitat patches are 

defined at different scales?: Ecological Indicators, v. 60, p. 704–716. 

Bodin, Ö., and Norberg, J., 2007, A network approach for analyzing spatially structured 

populations in fragmented landscapes: Landscape Ecology, v. 22, p. 31–44. 

Bodin, Ö., Tengö, M., Norman, A., Lundberg, J., and Elmqvist, T., 2006, The value of small 

size—Loss of forest patches and ecological thresholds in southern Madagascar: Ecological 

Applications, v. 16, p. 440–451. 

Brandes, U., 2008, On variants of shortest-path betweenness centrality and their generic 

computation: Social Networks, v. 30, p. 136–145. 

Bro, R., Acar, E., and Kolda, T.G., 2008, Resolving the sign ambiguity in the singular value 

decomposition: Journal of Chemometrics, v. 22, p. 135–140. 

Bunn, A.G., Urban, D.L., and Keitt, T.H., 2000, Landscape connectivity—A conservation 

application of graph theory: Journal of Environmental Management, v. 59, p. 265–278. 

Cabeza, M., and Moilanen, A., 2001, Design of reserve networks and the persistence of 

biodiversity: Trends in Ecology and Evolution, v. 16, p. 242–248. 

Carroll, C., McRae, B., and Brookes, A., 2012, Use of linkage mapping and centrality analysis 

across habitat gradients to conserve connectivity of gray wolf populations in western North 

America: Conservation Biology, v. 26, p. 78–87. 

Connelly, J.W., Rinkes, E.T., and Braun, C.E., 2011, Characteristics of sage-grouse habitats, in

Knick, S.T., and Connelly, J.W., eds., Greater sage-grouse—Ecology and conservation of a 

landscape species and its habitats—Studies in avian biology: Berkeley, University of

California Press, p. 69–83. 

Copeland, H.E., Sawyer, H., Monteith, K.L., Naugle, D.E., Pocewicz, A., Graf, N., and 

Kauffman, M.J., 2014, Conserving migratory mule deer through the umbrella of sage-grouse: 

Ecosphere, v. 5, issue 9, article 117, DOI:10.1890/ ES14-00186.1. 

Crooks, K.R., and Sanjayan, M.A., 2006, Connectivity conservation: Cambridge, United 

Kingdom, Cambridge University Press, 712 p., DOI:10.1017/CBO9780511754821. 

Csardi, G., and Nepusz, T., 2006, The igraph software package for complex network research, 

InterJournal, Complex Systems 1695, http://igraph.org.

Daly, C., Gibson, P., Doggett, M., Smith, J., and Taylor, G., 2004, Up-to-date climate monthly 

maps for the conterminous United States: Proceedings, 14th American Meteorological Society

Conference on Applied Climatology, 84th American Meteorological Society Annual Meeting 

Combined Preprints,, January 13–16, 2004, Seattle, Washington Paper P5.1 [CD-ROM]. 

Diamond, J., 1975, Assembly of species communities, in Cody, M.L., and Diamond, J., eds., 

Ecology and evolution of communities: Cambridge, Massachusetts, Belknap Press, p. 342–

444. 

Dickson, B.G., Roemer, G.W., McRae, B.H., and Rundall, J.M., 2013, Models of regional 

habitat quality and connectivity for pumas (Puma concolor) in the Southwestern United States: 

PLoS ONE, v. 8, e81898, DOI:10.1371/journal.pone.0081898. 
21 

Diestel, R., 2005, Graph theory (3rd ed.): Heidelberg, Germany, Springer-Verlag, v. 173, 431 p., 

ISBN 3-540-26182-6. 

Doherty, K.E., Tack, J.D., Evans, J.S., and Naugle, D.E., 2010, Mapping breeding densities of 

greater sage grouse—A tool for range-wide conservation planning: Bureau of Land 

Management Completion Report, Interagency Agreement # L10PG00911. 

Dunn, J.E., and Duncan, L., 2000, Partitioning Mahalanobis D2 to sharpen GIS classification, in

Brebbia, C.A., and Pascolo, P., eds., Management Information Systems 2000—GIS and 

remote sensing: Southampton, United Kingdom, WIT Press, p. 195–204. 

Erdos, P., and Gallai, T., 1960, Graphs with prescribed degrees of vertices: Matematikai Lapok , 

v. 11, p. 264–274 [in Hungarian]. 

Estrada, E., 2007, Topological structural classes of complex networks: Physical Review E, v. 75, 

016103-1–016103-12. 

Fedy, B.C., Doherty, K.E., Aldridge, C.L., O’Donnell, M., Beck, J.L., Bedrosian, B., Gummer, 

D., Holloran, M.J., Johnson, G.D., Kaczor, N.W., Kirol, C.P., Mandich, C.A., Marshall, D., 

Mckee, G., Olson, C., Pratt, A.C., Swanson, C.C., and Walker, B.L., 2014, Habitat 

prioritization across large landscapes, multiple seasons, and novel areas: an example using 

greater sage-grouse in Wyoming: Wildlife Monographs 190, 39 p. 

Fielding, A.H., and Bell, J.F., 1997, A review of methods for the assessment of prediction errors 

in conservation presence/absence models: Environmental Conservation, v. 24, p. 38–49. 

Freeman, L.C., 1977, A set of measures of centrality based on betweenness: Sociometry, v. 40, p. 

35–41, DOI:10.2307/3033543. 

Freeman, L.C., 1979, Centrality in social networks—Conceptual clarification: Social Networks, 

v. 1, p. 215–239. 

Freeman, L.C., 2004, The development of social network analysis—A study in the sociology of 

science: Vancouver, British Columbia, Empirical Press, 218 p. 

Freeman, L.C., Borgatti, S.P., and White, D.R., 1991, Centrality in valued graphs—A measure of 

betweenness based on network flow: Social Networks, v. 13, p. 41–154. 

Garton, E.O., Connelly, J.W., Horne, J.S., Hagen, C.A., Moser, A., and Schroeder, M.A., 2011, 

Greater sage-grouse populations trends and probability of persistence, in Knick, S.T., and 

Connelly, J.W., eds., Greater sage-grouse—Ecology and conservation of a landscape species 

and its habitats: Berkeley, University of California Press, p. 293–381. 

González-Pereira, B., Guerrero-Bote, V.P., and Moya-Anegón, F., 2010, A new approach to the 

metric of journals’ scientific prestige—The SJR indicator: Journal of Informetrics, v. 4, p. 

379–391. 

Hanski, I., 1999, Habitat connectivity, habitat continuity and metapopulations in dynamic 

landscapes: Oikos, v. 87, p. 209–219. 

Hanski, I., and Gilpin, M., 1991, Metapopulation dynamics—Brief history and conceptual 

domain: Biological Journal of the Linnean Society, v. 42, p. 3–16. 

Knick, S.T., 2011, Historical development, principal federal legislation, and current management 

of sagebrush habitats—Implications for conservation, in Knick, S.T., and Connelly, J.W., eds., 

Greater sage-grouse—Ecology and conservation of a landscape species and its habitats: 

Berkeley, University of California Press, p. 13–31. 

Knick, S.T., and Connelly, J.W., 2011, Greater sage-grouse and sagebrush—An introduction to 

the landscape, in Knick, S.T., and Connelly, J.W., eds., Greater sage-grouse—Ecology and 

conservation of a landscape species and its habitats: Berkeley, University of California Press,

p. 1–9. 
22 

Knick, S.T., Hanser, S.E., and Preston, K.L., 2013, Modeling ecological minimum requirements 

for distribution of greater sage-grouse leks—Implications for population connectivity across 

their western range, U.S.A.: Ecology and Evolution, v. 3, p. 1539–1551. 

LaPoint, S., Gallery, P., Wikelski, M., and Kays, R., 2013, Animal behavior, cost-based corridor 

models, and real corridors: Landscape Ecology, v. 28, p. 1615–1630. 

MacArthur, R.H., and Wilson, E.O., 1967, The theory of island biogeography: Princeton, New 

Jersey, Princeton University Press, 224 p. 

McRae, B.H., 2006, Isolation by resistance: Evolution, v. 60, p. 1551–1561. 

McRae, B.H., Dickson, B.G., Keitt, T.H., and Shah, V.B., 2008, Using circuit theory to model 

connectivity in ecology, evolution, and conservation: Ecology, v. 89, p. 2712–2724. 

McRae, B.H., and Shah, V.B., 2008, Circuitscape user's guide: Santa Barbara, University of 

California, www.circuitscape.org/userguide. 

Moreno, J.L., 1932, Application of the group method to classification: New York, National 

Committee on Prisons and Prison Labor, 104 p. 

Moreno, J.L., 1934, Who shall survive?: Washington, D.C., Nervous and Mental Disease 

Publishing Company, 440 p. 

Newman, M.E.J., 2010, Networks—An introduction: Oxford, United Kingdom, Oxford 

University Press, 784 p. 

Oregon State University, 2011, PRISM digital temperature and precipitation data: PRISM 

Climate Group database, http://www.prism.oregonstate.edu.

R Core Team, 2013, R—A language and environment for statistical computing: Vienna, Austria, 

R Foundation for Statistical Computing, http://www.R-project.org/, ISBN 3-900051-07-0. 

Rollins, M.G., 2009, LANDFIRE—A nationally consistent vegetation, wildland fire, and fuel 

assessment: International Journal of Wildland Fire, v. 18, p. 235–249. 

Rotenberry, J.T., Knick, S.T., and Dunn, J.E., 2002, A minimalist approach to mapping species’ 

habitat—Pearson’s planes of closest fit, in Scott, J.M., Heglund, P.J., Morrison, M.L., Haufler, 

J.B., Raphael, M.G., Wall, W.A., and Samson, F.B., eds., Predicting species occurrences—

Issues of accuracy and scale: Washington, D.C., Island Press, p. 281–289. 

Sappington, J.M., Longshore, K.M., and Thompson, D.B., 2007, Quantifying landscape 

ruggedness for animal habitat analysis—A case study using desert bighorn sheep in the 

Mojave Desert: Journal of Wildlife Management, v. 71, p. 1419–1426. 

Saura, S., Bodin, Ö., and Fortin, M.J., 2014, Stepping stones are crucial for species’ longdistance dispersal and range expansion through habitat networks: Journal of Applied Ecology, 

v. 51, p. 171–182, DOI:10.1111/1365-2664.12179. 

Schroeder, M.A., Aldridge, C.L., Apa, A.D., Bohne, J.R., Braun, C.E., Bunnell, S.D., Connelly, 

J.W., Deibert, P.A., Gardner, S.C., Hilliard, M.A., Kobriger, G.D., McAdam, S.M., McCarthy, 

C.W., McCarthy, J.J., Mitchell, D.L., Rickerson, E.V., and Stiver, S.J., 2004, Distribution of 

sage-grouse in North America: Condor, v. 106, p. 363–376. 

Schroeder, M.A., Young, J.R., and Braun, C.E., 1999, Sage Grouse (Centrocercus 

urophasianus), in Poole, A., and Gill, F., eds., The birds of North America: Philadelphia, 

Pennsylvania, and Washington, D.C., The Academy of Natural Sciences and The American 

Ornithologists, p. 32. 

Smith, R.E., 2013, Conserving Montana’s sagebrush highway: long distance migration in sagegrouse: University of Montana, Missoula, M.S. thesis, 47 p. 
23 

Spear, S.F., Balkenhol, N., Fortin, M.J., McRae, B.H., and Scribner, K., 2010, Use of resistance 

surfaces for landscape genetic studies: Considerations of parameterization and analysis: 

Molecular Ecology, v. 19, no. 17, p. 3,576-3,591. 

Stiver, S.J., Apa, A.D., Bohne, J.R., Bunnell, S.D., Deibert, P.A., Gardner, S.C., Hilliard, M.A., 

McCarthy, C.W., and Schroeder, M.A., 2006, Greater sage-grouse comprehensive 

conservation strategy: Cheyenne, Wyoming: Western Association of Fish and Wildlife 

Agencies, accessed August 18, 2015, at http://wafwa.05-

one.net/Documents%20and%20Settings/37/Site%20Documents/News/GreaterSagegrouseConservationStrategy2006.pdf. 

Urban, D., and Keitt, T., 2001, Landscape connectivity—A graph-theoretic perspective: Ecology, 

v. 82, p. 1205–1218. 

U.S. Department of Agriculture, 2011, Soil Survey staff—Natural Resources Conservation 

Service: U.S. Department of Agriculture General Soils Map (STATSGO) database, accessed 

December 28, 2011, at http://soildatamart.nrcs.usda.gov. 

U.S. Fish and Wildlife Service, 2010, Endangered and threatened wildlife and plants; 12-month 

findings for petitions to list the greater sage-grouse (Centrocercus urophasianus) as threatened 

or endangered: Federal Register , v. 75, no. 55, p. 13909–14014, accessed August 2015, at 

https://www.federalregister.gov/articles/2010/03/23/2010-5132/endangered-and-threatenedwildlife-and-plants-12-month-findings-for-petitions-to-list-the-greater.

U.S. Fish and Wildlife Service, 2013, Greater sage-grouse (Centrocercus urophasianus) 

conservation objectives—Final report: U.S. Fish and Wildlife Service, Denver, Colorado. 

Wasserman, S., and Faust, K., 2004, Social network analysis—Methods and applications: 

Cambridge, United Kingdom, and New York, Cambridge University Press, 857 p. 

Wiens, J.A., 1997, Metapopulation dynamics and landscape ecology, in Hanski, I., and Gilpin, 

M., eds., Metapopulation dynamics—Ecology, genetics, and evolution: New York, Academic 

Press, p. 43–62. 

Williams, J.C., ReVelle, C.S., and Levin, S.A., 2004, Using mathematical optimization models 

to design nature reserves: Frontiers in Ecology and the Environment, v. 2, p. 98–105, 

DOI:10.1890/1540-9295(2004)002[0098:UMOMTD]2.0.CO;2. 

Zeller, K.A., McGarigal, K., and Whiteley, A.R., 2012, Estimating landscape resistance to 

movement: a review: Landscape Ecology, v. 27, p. 777-797. 
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25 

Appendix A. Crosswalk Table Depicting Priority Area Identifiers, U.S. Fish 

and Wildlife Service Unique Identifiers, Sage-Grouse Population Name, 

and Management Zone

[Data for crosswalk table was obtained from the U.S. Fish and Wildlife Service (FWS). ID, identifier]

Priority Area 

ID

FWS Unique 

ID

Sage-grouse Population Management 

Zone

FWS Name

1 401 Bi-State MZ3 401-Bi-State-MZ3

2 395 Bi-State MZ3 395-Bi-State-MZ3

3 358 Bi-State MZ3 358-Bi-State-MZ3

4 396 Bi-State MZ3 396-Bi-State-MZ3

5 334 Parachute Piceance Roan MZ7 334-Parachute Piceance Roan-MZ7

6 353 Bi-State MZ3 353-Bi-State-MZ3

7 354 Bi-State MZ3 354-Bi-State-MZ3

8 332 Parachute Piceance Roan MZ7 332-Parachute Piceance Roan-MZ7

9 352 Bi-State MZ3 352-Bi-State-MZ3

10 351 Bi-State MZ3 351-Bi-State-MZ3

11 385 Bi-State MZ3 385-Bi-State-MZ3

12 362 Bi-State MZ3 362-Bi-State-MZ3

13 399 Bi-State MZ3 399-Bi-State-MZ3

14 360 Bi-State MZ3 360-Bi-State-MZ3

15 350 Bi-State MZ3 350-Bi-State-MZ3

16 388 Bi-State MZ3 388-Bi-State-MZ3

17 391 Bi-State MZ3 391-Bi-State-MZ3

18 390 Bi-State MZ3 390-Bi-State-MZ3

19 345 Bi-State MZ3 345-Bi-State-MZ3

20 386 Bi-State MZ3 386-Bi-State-MZ3

21 349 Bi-State MZ3 349-Bi-State-MZ3

22 383 Bi-State MZ3 383-Bi-State-MZ3

23 387 Bi-State MZ3 387-Bi-State-MZ3

24 356 Bi-State MZ3 356-Bi-State-MZ3

25 355 Bi-State MZ3 355-Bi-State-MZ3

26 357 Bi-State MZ3 357-Bi-State-MZ3

27 359 Bi-State MZ3 359-Bi-State-MZ3

28 394 Bi-State MZ3 394-Bi-State-MZ3

29 393 Bi-State MZ3 393-Bi-State-MZ3

30 382 Bi-State MZ3 382-Bi-State-MZ3

31 389 Bi-State MZ3 389-Bi-State-MZ3

32 384 Bi-State MZ3 384-Bi-State-MZ3

33 381 Bi-State MZ3 381-Bi-State-MZ3

34 374 Bi-State MZ3 374-Bi-State-MZ3

35 344 Bi-State MZ3 344-Bi-State-MZ3

36 369 Bi-State MZ3 369-Bi-State-MZ3
26 

Priority Area 

ID

FWS Unique 

ID

Sage-grouse Population Management 

Zone

FWS Name

37 372 Bi-State MZ3 372-Bi-State-MZ3

38 370 Bi-State MZ3 370-Bi-State-MZ3

39 341 Bi-State MZ3 341-Bi-State-MZ3

40 347 Bi-State MZ3 347-Bi-State-MZ3

41 346 Bi-State MZ3 346-Bi-State-MZ3

42 375 Bi-State MZ3 375-Bi-State-MZ3

43 314 Western Great Basin MZ5 314-Western Great Basin-MZ5

44 343 Bi-State MZ3 343-Bi-State-MZ3

45 317 Klamath OR/CA MZ5 317-Klamath OR/CA-MZ5

46 368 Bi-State MZ3 368-Bi-State-MZ3

47 367 Bi-State MZ3 367-Bi-State-MZ3

48 316 Western Great Basin MZ5 316-Western Great Basin-MZ5

49 309 Western Great Basin MZ5 309-Western Great Basin-MZ5

50 312 Western Great Basin MZ5 312-Western Great Basin-MZ5

51 223 Eagle/S Routt CO MZ2 223-Eagle/S Routt CO-MZ2

52 156 Wyoming Basin MZ2 156-Wyoming Basin-MZ2

53 363 Bi-State MZ3 363-Bi-State-MZ3

54 306 Central MZ5 306-Central-MZ5

55 322 Yakama Indian Nation MZ6 322-Yakama Indian Nation-MZ6

56 253 Snake, Salmon, and 

Beaverhead

MZ4 253-Snake, Salmon, and 

Beaverhead-MZ4

57 279 Northern Great Basin MZ4 279-Northern Great Basin-MZ4

58 329 Parachute Piceance Roan MZ7 329-Parachute Piceance Roan-MZ7

59 340 Parachute Piceance Roan MZ7 340-Parachute Piceance Roan-MZ7

60 331 Parachute Piceance Roan MZ7 331-Parachute Piceance Roan-MZ7

61 224 Eagle/S Routt CO MZ2 224-Eagle/S Routt CO-MZ2

62 239 Panguitch MZ3 239-Panguitch-MZ3

63 398 Bi-State MZ3 398-Bi-State-MZ3

64 242 Southern Great Basin MZ3 242-Southern Great Basin-MZ3

65 243 Southern Great Basin MZ3 243-Southern Great Basin-MZ3

66 241 Southern Great Basin MZ3 241-Southern Great Basin-MZ3

67 232 Sheeprock Mountains MZ3 232-Sheeprock Mountains-MZ3

68 237 Carbon MZ3 237-Carbon-MZ3

69 361 Bi-State MZ3 361-Bi-State-MZ3

70 400 Bi-State MZ3 400-Bi-State-MZ3

71 214 Middle Park CO MZ2 214-Middle Park CO-MZ2

72 221 Eagle/S Routt CO MZ2 221-Eagle/S Routt CO-MZ2

73 222 Eagle/S Routt CO MZ2 222-Eagle/S Routt CO-MZ2

74 220 Eagle/S Routt CO MZ2 220-Eagle/S Routt CO-MZ2

75 326 Meeker - White River MZ7 326-Meeker - White River-MZ7

76 327 Parachute Piceance Roan MZ7 327-Parachute Piceance Roan-MZ7

77 323 Meeker - White River MZ7 323-Meeker - White River-MZ7
27 

Priority Area 

ID

FWS Unique 

ID

Sage-grouse Population Management 

Zone

FWS Name

78 238 Parker Mountain-Emery MZ3 238-Parker Mountain-Emery-MZ3

79 153 Wyoming Basin MZ2 153-Wyoming Basin-MZ2

80 152 Wyoming Basin MZ2 152-Wyoming Basin-MZ2

81 235 Strawberry MZ3 235-Strawberry-MZ3

82 154 Wyoming Basin MZ2 154-Wyoming Basin-MZ2

83 183 Wyoming Basin MZ2 183-Wyoming Basin-MZ2

84 219 Eagle/S Routt CO MZ2 219-Eagle/S Routt CO-MZ2

85 204 Wyoming Basin MZ2 204-Wyoming Basin-MZ2

86 198 Wyoming Basin MZ2 198-Wyoming Basin-MZ2

87 160 Wyoming Basin MZ2 160-Wyoming Basin-MZ2

88 193 Wyoming Basin MZ2 193-Wyoming Basin-MZ2

89 199 Wyoming Basin MZ2 199-Wyoming Basin-MZ2

90 195 Wyoming Basin MZ2 195-Wyoming Basin-MZ2

91 158 Wyoming Basin MZ2 158-Wyoming Basin-MZ2

92 191 Wyoming Basin MZ2 191-Wyoming Basin-MZ2

93 182 Wyoming Basin MZ2 182-Wyoming Basin-MZ2

94 266 Snake, Salmon, and 

Beaverhead

MZ4 266-Snake, Salmon, and 

Beaverhead-MZ4

95 213 North Park MZ2 213-North Park-MZ2

96 142 Wyoming Basin MZ2 142-Wyoming Basin-MZ2

97 159 Wyoming Basin MZ2 159-Wyoming Basin-MZ2

98 157 Wyoming Basin MZ2 157-Wyoming Basin-MZ2

99 178 Wyoming Basin MZ2 178-Wyoming Basin-MZ2

100 169 Wyoming Basin MZ2 169-Wyoming Basin-MZ2

101 139 Wyoming Basin MZ2 139-Wyoming Basin-MZ2

102 143 Wyoming Basin MZ2 143-Wyoming Basin-MZ2

103 114 Powder River Basin MZ1 114-Powder River Basin-MZ1

104 141 Wyoming Basin MZ2 141-Wyoming Basin-MZ2

105 148 Wyoming Basin MZ2 148-Wyoming Basin-MZ2

106 150 Wyoming Basin MZ2 150-Wyoming Basin-MZ2

107 264 Snake, Salmon, and 

Beaverhead

MZ4 264-Snake, Salmon, and 

Beaverhead-MZ4

108 144 Wyoming Basin MZ2 144-Wyoming Basin-MZ2

109 149 Wyoming Basin MZ2 149-Wyoming Basin-MZ2

110 145 Wyoming Basin MZ2 145-Wyoming Basin-MZ2

111 146 Wyoming Basin MZ2 146-Wyoming Basin-MZ2

112 366 Bi-State MZ3 366-Bi-State-MZ3

113 244 NW-Interior NV MZ3 244-NW-Interior NV-MZ3

114 267 Snake, Salmon, and 

Beaverhead

MZ4 267-Snake, Salmon, and 

Beaverhead-MZ4

115 263 Snake, Salmon, and 

Beaverhead

MZ4 263-Snake, Salmon, and 

Beaverhead-MZ4

116 138 Wyoming Basin MZ2 138-Wyoming Basin-MZ2
28 

Priority Area 

ID

FWS Unique 

ID

Sage-grouse Population Management 

Zone

FWS Name

117 126 Jackson Hole WY MZ2 126-Jackson Hole WY-MZ2

118 246 Southwest Montana MZ4 246-Southwest Montana-MZ4

119 245 Southwest Montana MZ4 245-Southwest Montana-MZ4

120 275 Northern Great Basin MZ4 275-Northern Great Basin-MZ4

121 273 Northern Great Basin MZ4 273-Northern Great Basin-MZ4

122 248 Snake, Salmon, and 

Beaverhead

MZ4 248-Snake, Salmon, and 

Beaverhead-MZ4

123 269 Northern Great Basin MZ4 269-Northern Great Basin-MZ4

124 277 Northern Great Basin MZ4 277-Northern Great Basin-MZ4

125 310 Western Great Basin MZ5 310-Western Great Basin-MZ5

126 247 Southwest Montana MZ4 247-Southwest Montana-MZ4

127 115 Powder River Basin MZ1 115-Powder River Basin-MZ1

128 121 Powder River Basin MZ1 121-Powder River Basin-MZ1

129 108 Yellowstone Watershed MZ1 108-Yellowstone Watershed-MZ1

130 147 Wyoming Basin MZ2 147-Wyoming Basin-MZ2

131 104 Yellowstone Watershed MZ1 104-Yellowstone Watershed-MZ1

132 117 Powder River Basin MZ1 117-Powder River Basin-MZ1

133 137 Wyoming Basin MZ2 137-Wyoming Basin-MZ2

134 106 Yellowstone Watershed MZ1 106-Yellowstone Watershed-MZ1

135 116 Powder River Basin MZ1 116-Powder River Basin-MZ1

136 120 Powder River Basin MZ1 120-Powder River Basin-MZ1

137 107 Yellowstone Watershed MZ1 107-Yellowstone Watershed-MZ1

138 110 Yellowstone Watershed MZ1 110-Yellowstone Watershed-MZ1

139 119 Powder River Basin MZ1 119-Powder River Basin-MZ1

140 123 Powder River Basin MZ1 123-Powder River Basin-MZ1

141 135 Wyoming Basin MZ2 135-Wyoming Basin-MZ2

142 134 Wyoming Basin MZ2 134-Wyoming Basin-MZ2

143 128 Wyoming Basin MZ2 128-Wyoming Basin-MZ2

144 130 Wyoming Basin MZ2 130-Wyoming Basin-MZ2

145 105 Yellowstone Watershed MZ1 105-Yellowstone Watershed-MZ1

146 102 Northern Montana MZ1 102-Northern Montana-MZ1

147 113 Dakotas MZ1 113-Dakotas-MZ1

148 118 Powder River Basin MZ1 118-Powder River Basin-MZ1

149 305 Central MZ5 305-Central-MZ5

150 101 Northern Montana MZ1 101-Northern Montana-MZ1

151 111 Dakotas MZ1 111-Dakotas-MZ1

152 321 Yakama Training Center MZ6 321-Yakama Training Center-MZ6

153 397 Bi-State MZ3 397-Bi-State-MZ3

154 392 Bi-State MZ3 392-Bi-State-MZ3

155 365 Bi-State MZ3 365-Bi-State-MZ3

156 380 Bi-State MZ3 380-Bi-State-MZ3

157 379 Bi-State MZ3 379-Bi-State-MZ3
29 

Priority Area 

ID

FWS Unique 

ID

Sage-grouse Population Management 

Zone

FWS Name

158 377 Bi-State MZ3 377-Bi-State-MZ3

159 378 Bi-State MZ3 378-Bi-State-MZ3

160 348 Bi-State MZ3 348-Bi-State-MZ3

161 376 Bi-State MZ3 376-Bi-State-MZ3

162 373 Bi-State MZ3 373-Bi-State-MZ3

163 371 Bi-State MZ3 371-Bi-State-MZ3

164 364 Bi-State MZ3 364-Bi-State-MZ3

165 342 Bi-State MZ3 342-Bi-State-MZ3

166 308 Western Great Basin MZ5 308-Western Great Basin-MZ5

167 270 Northern Great Basin MZ4 270-Northern Great Basin-MZ4

168 276 Northern Great Basin MZ4 276-Northern Great Basin-MZ4

169 272 Northern Great Basin MZ4 272-Northern Great Basin-MZ4

170 274 Northern Great Basin MZ4 274-Northern Great Basin-MZ4

171 304 Central MZ5 304-Central-MZ5

172 300 Central MZ5 300-Central-MZ5

173 302 Central MZ5 302-Central-MZ5

174 271 Northern Great Basin MZ4 271-Northern Great Basin-MZ4

175 303 Central MZ5 303-Central-MZ5

176 301 Central MZ5 301-Central-MZ5

177 268 Baker MZ4 268-Baker-MZ4

178 320 Crab Creek MZ6 320-Crab Creek-MZ6

179 319 Moses Coulee MZ6 319-Moses Coulee-MZ6

180 298 Northern Great Basin MZ4 298-Northern Great Basin-MZ4

181 140 Wyoming Basin MZ2 140-Wyoming Basin-MZ2

182 136 Wyoming Basin MZ2 136-Wyoming Basin-MZ2

183 132 Wyoming Basin MZ2 132-Wyoming Basin-MZ2

184 131 Wyoming Basin MZ2 131-Wyoming Basin-MZ2

185 129 Wyoming Basin MZ2 129-Wyoming Basin-MZ2

186 133 Wyoming Basin MZ2 133-Wyoming Basin-MZ2

187 315 Western Great Basin MZ5 315-Western Great Basin-MZ5

188 288 Northern Great Basin MZ4 288-Northern Great Basin-MZ4
30 

Appendix B. Centrality Results for Degree and Betweenness Metrics for 

Each Priority Area

[Priority areas are ranked from highest to lowest betweenness centrality value. Cumulative percent of betweenness 

centrality was calculated to provide each priority area’s contribution to total betweenness centrality. ID, identifier]

Priority Area 

ID

Area 

(km2) 

Degree

Centrality

Betweenness

Centrality

Betweenness 

Centrality Rank

Cumulative 

Percent

48 78,218 50 11,414 1 12.8

110 7,673 20 6,820 2 20.4

101 18,607 24 6,740 3 27.9

39 440 22 5,537 4 34.1

111 608 8 5,178 5 39.9

19 1,847 32 5,072 6 45.6

35 717 24 5,048 7 51.3

83 7,316 48 4,455 8 56.2

65 33,892 26 3,000 9 59.6

181 11,999 24 2,554 10 62.5

21 40 16 2,415 11 65.2

114 9,548 14 2,024 12 67.4

107 6,133 18 2,009 13 69.7

166 2,570 18 1,907 14 71.8

20 24 16 1,400 15 73.4

80 5,593 22 1,291 16 74.8

169 1,760 14 1,093 17 76.0

105 950 10 1,068 18 77.2

58 839 28 1,048 19 78.4

109 753 8 1,029 20 79.6

160 560 18 992 21 80.7

7 132 14 961 22 81.7

69 33 12 948 23 82.8

148 493 8 945 24 83.9

138 7,677 16 926 25 84.9

182 2,601 10 794 26 85.8

131 4,448 16 711 27 86.6

119 1,894 14 670 28 87.3

137 7,376 20 626 29 88.0

167 1,132 6 577 30 88.7

14 48 14 574 31 89.3

98 554 10 542 32 89.9

176 1,788 18 524 33 90.5

9 82 12 491 34 91.1

3 400 22 458 35 91.6

123 1,492 16 400 36 92.0
31 

Priority Area 

ID

Area 

(km2) 

Degree

Centrality

Betweenness

Centrality

Betweenness 

Centrality Rank

Cumulative 

Percent

64 5,783 10 366 37 92.4

125 1,336 14 366 38 92.8

171 56 10 364 39 93.3

141 585 10 338 40 93.6

74 50 10 325 41 94.0

157 2 16 295 42 94.3

134 1,422 8 268 43 94.6

62 4,606 8 257 44 94.9

6 541 14 233 45 95.2

133 1,260 10 225 46 95.4

177 1,362 14 222 47 95.7

118 3,264 12 218 48 95.9

24 82 12 210 49 96.2

78 4,563 12 210 50 96.4

144 2,464 18 199 51 96.6

139 3,122 14 198 52 96.8

170 669 16 193 53 97.0

178 3,273 12 193 54 97.3

122 316 14 192 55 97.5

146 6,796 10 185 56 97.7

72 37 12 180 57 97.9

95 1,529 10 178 58 98.1

120 336 10 174 59 98.3

135 284 10 172 60 98.5

55 1,285 8 164 61 98.7

142 147 8 155 62 98.8

27 26 14 149 63 99.0

68 1,442 14 148 64 99.2

185 523 10 142 65 99.3

61 81 10 128 66 99.5

10 120 12 60 67 99.5

33 23 12 60 68 99.6

84 214 12 60 69 99.7

112 24 12 51 70 99.7

158 17 14 38 71 99.8

25 24 12 30 72 99.8

143 1,487 12 29 73 99.8

121 227 8 24 74 99.9

12 14 12 23 75 99.9

127 79 12 18 76 99.9

26 82 12 16 77 99.9
32 

Priority Area 

ID

Area 

(km2) 

Degree

Centrality

Betweenness

Centrality

Betweenness 

Centrality Rank

Cumulative 

Percent

161 4 10 14 78 99.9

129 965 12 13 79 100.0

184 661 8 12 80 100.0

162 2 10 7 81 100.0

174 1,492 14 7 82 100.0

51 27 10 6 83 100.0

81 1,309 10 4 84 100.0

71 888 10 3 85 100.0

108 117 8 3 86 100.0

152 1,933 8 2 87 100.0

86 8 8 1 88 100.0

91 78 8 1 89 100.0

175 81 8 1 90 100.0

1 2 6 0 91 100.0

2 1 4 0 92 100.0

4 1 6 0 93 100.0

5 5 6 0 94 100.0

8 8 10 0 95 100.0

11 4 8 0 96 100.0

13 4 4 0 97 100.0

15 153 12 0 98 100.0

16 1 12 0 99 100.0

17 2 8 0 100 100.0

18 4 8 0 101 100.0

22 2 12 0 102 100.0

23 7 6 0 103 100.0

28 2 12 0 104 100.0

29 2 6 0 105 100.0

30 2 8 0 106 100.0

31 3 6 0 107 100.0

32 2 2 0 108 100.0

34 1 4 0 109 100.0

36 2 10 0 110 100.0

37 5 12 0 111 100.0

38 5 6 0 112 100.0

40 27 8 0 113 100.0

41 15 8 0 114 100.0

42 3 10 0 115 100.0

43 103 4 0 116 100.0

44 5 2 0 117 100.0

45 658 10 0 118 100.0
33 

Priority Area 

ID

Area 

(km2) 

Degree

Centrality

Betweenness

Centrality

Betweenness 

Centrality Rank

Cumulative 

Percent

46 2 6 0 119 100.0

47 2 6 0 120 100.0

49 128 6 0 121 100.0

50 845 8 0 122 100.0

52 108 6 0 123 100.0

53 2 6 0 124 100.0

54 172 10 0 125 100.0

56 4,967 8 0 126 100.0

57 200 4 0 127 100.0

59 1 2 0 128 100.0

60 15 6 0 129 100.0

63 2 8 0 130 100.0

66 399 10 0 131 100.0

67 2,474 14 0 132 100.0

70 2 10 0 133 100.0

73 1 4 0 134 100.0

75 1 8 0 135 100.0

76 31 8 0 136 100.0

77 58 14 0 137 100.0

79 6 2 0 138 100.0

82 648 6 0 139 100.0

85 1 4 0 140 100.0

87 145 12 0 141 100.0

88 12 12 0 142 100.0

89 2 4 0 143 100.0

90 3 4 0 144 100.0

92 1 2 0 145 100.0

93 6 4 0 146 100.0

94 1,046 10 0 147 100.0

96 891 14 0 148 100.0

97 7 4 0 149 100.0

99 1 2 0 150 100.0

100 2 4 0 151 100.0

102 109 12 0 152 100.0

103 37 10 0 153 100.0

104 2,960 8 0 154 100.0

106 697 4 0 155 100.0

113 1,504 4 0 156 100.0

115 7 6 0 157 100.0

116 2,071 12 0 158 100.0

117 342 18 0 159 100.0
34 

Priority Area 

ID

Area 

(km2) 

Degree

Centrality

Betweenness

Centrality

Betweenness 

Centrality Rank

Cumulative 

Percent

124 2 6 0 160 100.0

126 555 6 0 161 100.0

128 357 8 0 162 100.0

130 352 10 0 163 100.0

132 48 8 0 164 100.0

136 481 4 0 165 100.0

140 556 6 0 166 100.0

145 125 4 0 167 100.0

147 316 4 0 168 100.0

149 7 10 0 169 100.0

150 2,456 4 0 170 100.0

151 2,121 10 0 171 100.0

153 2 8 0 172 100.0

154 2 10 0 173 100.0

155 7 10 0 174 100.0

156 2 6 0 175 100.0

159 5 8 0 176 100.0

163 1 8 0 177 100.0

164 8 12 0 178 100.0

165 21 6 0 179 100.0

168 11 6 0 180 100.0

172 145 6 0 181 100.0

173 1,044 10 0 182 100.0

179 4,437 4 0 183 100.0

180 490 6 0 184 100.0

183 105 10 0 185 100.0

186 199 12 0 186 100.0

187 6 4 0 187 100.0

188 17 2 0 188 100.0
Publishing support provided by the U.S. Geological Survey

Science Publishing Network, Tacoma Publishing Service Center

For more information concerning the research in this report, contact the

 Director, Forest and Rangeland Ecosystem Science Center

U.S. Geological Survey

777 NW 9th St., Suite 400

Corvallis, Oregon 97330

http://fresc.usgs.gov/
Crist and others—Range-Wide Network of Priority Areas for Greater Sage-Grouse—Open-File Report 2015–1158

ISSN 2331-1258 (online)

http://dx.doi.org/10.3133/ofr20151158