U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2014–1010

Gunnison Sage-Grouse Lek Site Suitability Modeling
Front cover: Male Gunnison Sage-Grouse in full strut. Photograph by Karen Tucker, Bureau of Land Management, Uncompahgre Field Office, Montrose, Colorado.

Back cover:

Top: Male Gunnison Sage-Grouse dancing on the Lek. Photograph by Douglas Ouren, U.S. Geological Survey, Fort Collins Science Center, Fort Collins, Colorado.

Bottom: Male and Female Gunnison Sage-Grouse on the Lek. Photograph by Karen Tucker, Bureau of Land Management, Uncompahgre Field Office, Montrose, 

Colorado.
Prepared in cooperation with the National Audubon Society, the Bureau of Land Management, 

the National Park Service, the U.S. Department of Agriculture Forest Service, Colorado Parks 

and Wildlife, and the Habitat Partnership Program

Gunnison Sage-Grouse Lek Site Suitability Modeling

By Douglas S. Ouren, Drew A. Ignizio, Melissa Siders, Theresa Childers, Karen Tucker, and Nathan Seward

Open-File Report 2014–1010

U.S. Department of the Interior

U.S. Geological Survey
ii

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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secured from the copyright owner. 

Suggested citation:

Ouren, D.S., Ignizio, D.A., Siders, Melissa, Childers, Theresa, Tucker, Karen, and Seward, Nathan, 2014, 

Gunnison sage-grouse lek site suitability modeling: U.S. Geological Survey Open-File Report 2014–1010, 18 p.,

http://dx.doi.org/10.3133/ofr20141010. 

ISSN 2331-1258 (online)
iii

Contents

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

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

Problem Statement .................................................................................................................................................... 2

Objectives .............................................................................................................................................................. 2

Study Area..................................................................................................................................................................... 3

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

Modeling Approach .................................................................................................................................................... 5

Presence Points......................................................................................................................................................... 6

Environmental Predictor Variables............................................................................................................................. 6

Training Site and Variables Used in the Maxent Model.............................................................................................. 7

Final Model Development .......................................................................................................................................... 8

Results..........................................................................................................................................................................13

Model-Guided Sampling Efforts ................................................................................................................................15

Discussion ....................................................................................................................................................................15

Acknowledgments ........................................................................................................................................................17

References ...................................................................................................................................................................17
iv

Figures

Figure 1. The Crawford study area, including jurisdictional boundaries.................................................................... 4

Figure 2. Vegetation types in the Crawford study area considered potentially suitable for sage-grouse 

lekking activity ............................................................................................................................................................... 9

Figure 3. The range of elevation values in the Crawford study area and which were considered potentially 

suitable (2,310―2,420 m) for lekking activity .............................................................................................................. 10

Figure 4. The range of slope values in the Crawford study area and which regions were considered potentially 

suitable (8 degrees or less) for lekking activity ............................................................................................................ 11

Figure 5. Areas considered potentially suitable for lekking activity (white areas) in the Crawford study area, 

based on slope, elevation, and vegetation type present at known leks ....................................................................... 12

Tables

Table 1. Environmental predictor variables used in final Maxent model of lek suitability for 

Gunnison sage-grouse .................................................................................................................................................. 7

Table 2. Environmental predictor variables and the range of values considered acceptable for each 

variable in the final model.............................................................................................................................................. 8

Table 3. Maxent output values, by class, associated rank assignments, and rank interpretations. ....................... 13

Table 4. Suitability of the Crawford study area (percent) for lekking activity as modeled by Maxent, 

by jurisdiction, before application of the binary filter .................................................................................................... 14

Table 5. Suitability of the Crawford study area (percent) for lekking activity, as modeled by Maxent, 

by jurisdiction, after applying the binary filter............................................................................................................... 15

Table 6. Relative contribution of environmental predictor variables used in the Maxent model for lek 

site suitability ............................................................................................................................................................... 16

Conversion Factors

SI to Inch/Pound

Multiply By To obtain

Length

kilometer (km) 0.6214 mile (mi)

meter (m) 1.094 yard (yd)

Area

square kilometer (km2) 247.1 acre

square kilometer (km2) 0.3861 square mile (mi2)

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).
1

Gunnison Sage-Grouse Lek Site Suitability Modeling

By Douglas S. Ouren1, Drew A. Ignizio1, Melissa Siders2, Theresa Childers3, Karen Tucker2, and Nathan Seward4

Abstract

In order to better understand and protect species with minimal or decreasing populations, it is 

imperative to determine their actual existing population size. The focal species for this project is the 

Gunnison sage-grouse (GUSG), which became a proposed endangered species under the Endangered 

Species Act, thus confirming the need for better population estimates. Lek site counting during mating 

season has historically been the primary method for estimating population size since the grouse are very 

difficult to count at other times of the year. The objective of this project was to use historical data and 

available technology to identify additional potential lekking sites. This was done by determining areas 

throughout the study area that have the same landscape characteristics as those where known lekking 

activities occur. More accurate population counts could be the outcome of locating more lek sites. 

One of the remaining seven GUSG populations, the Crawford population (estimated at 128 

individuals) exists in an area that includes the Gunnison Gorge National Conservation Area and the 

northern portion of the Black Canyon of the Gunnison National Park (our study area). While the 

Crawford population is small, it is still considered a self-sustaining population; the persistence and 

growth of this population directly contribute to genetic diversity conservation of this declining species. 

To date, only observational and anecdotal information about the Crawford population’s range, 

movements, and seasonal habitat use exist. 

From 1978 to the present, GUSG population monitoring has been accomplished through annual 

lek counts conducted each spring during GUSG mating season. Although this method has provided 

information on GUSG population trends, it is somewhat limited because counts are based only on 

known lekking sites and historically minimal efforts have been made to identify additional lek sites. To 

meet the objective of locating more potential lekking sites, we used a suite of spatial data, geographic 

information system tools, and maximum entropy species distribution tools. Based on expert knowledge 

and landscape variables, the modeling process evolved into a hybrid approach for delineating areas that 

would have a significant probability for supporting GUSG lekking activities. Based on model results, a 

sampling protocol was developed for model verification. The results of this project provide wildlife 

managers with a more sophisticated methodology to evaluate GUSG habitat for potential lekking sites.

Introduction

Prior to the early 1980s, it was assumed that there was one species of sage-grouse throughout 

North America. In 1991, however, researchers noted that the plumage and vocalization characteristics of 

sage-grouse inhabiting the Gunnison River Basin of Colorado differed from those of sage-grouse 

 

1 U.S. Geological Survey, Fort Collins Science Center, Fort Collins, Colo.

2 Bureau of Land Management, Uncompahgre Field Office, Montrose, Colo. 3 National Park Service, Curecanti National Recreation Area, Black Canyon of the Gunnison National Park, Gunnison, Colo.

4 Colorado Parks and Wildlife, Gunnison, Colo. 
2

occurring elsewhere (Hupp and Braun, 1991). In 1991, Drs. Clait Braun and Jessica Young proposed 

that the Gunnison sage-grouse (GUSG) was indeed a unique species. 

Based on long-term monitoring (since 1978) of sage-grouse lekking activity (leks are display 

areas where mating takes place), populations of GUSG are declining. As of August 2012, the GUSG 

was considered a species of special concern by all Federal and state natural resource management 

agencies throughout its range. One of the remaining seven populations, the Crawford population 

(estimated at 128 individuals), inhabits the Bureau of Land Management’s (BLM) Gunnison Gorge 

National Conservation area and the North Rim of the Black Canyon of the Gunnison National Park, 

which we refer to herein as the “Crawford study area” (fig. 1). Although the Crawford population is 

small, it is considered a “genetically discrete unit and can be considered as a distinct population” 

(Oyler-McCance and others, 2005); thus, the persistence and growth of this population directly 

contributes to conserving the overall genetic diversity of this declining species. 

Alteration and loss of sage-steppe habitat are suspected of causing declines in GUSG 

populations. For example, since European settlement in the Gunnison Basin, a lack of fire (due to fire 

suppression), and mechanical disturbance (hydro axing) have led to encroachment of Rocky Mountain 

juniper (Juniperus scopulorum) in this sagebrush ecosystem (Crawford and others, 2004). To date, 

however, there is only observational and anecdotal information about the Crawford population’s range, 

movements, and seasonal habitat use. To effectively target areas for GUSG conservation work, such as 

habitat-management prescriptions, decisionmakers and planners need empirical evidence of the 

Crawford population’s range and movements. 

Problem Statement

Sage-grouse lekking activity has been monitored in the Crawford study area since 1978, and to 

date, 10 lek sites have been identified. It is not clear, however, whether the Crawford population uses 

additional lek sites. Managers need this kind of information to better protect and manage for the 

population’s persistence and growth. Data acquired through the long-term lek monitoring, as well as 

new research on sage-grouse ecology, have implications for GUSG management. For example, activity 

at lek sites and associated lek counts are used to determine GUSG population trends. In turn, this 

information can be used by management agencies to identify areas of critical concern for preserving 

GUSG populations. 

Objectives

The overall objective of this project was to use the existing long-term records of GUSG lekking 

activity (specifically, the lek site locations) and existing environmental data (for example, vegetation 

type, topographic details) that characterize those lek sites for developing a lek site suitability map for 

the entire Crawford study area. This information will serve as the foundation for future inventory and 

monitoring, management, and research efforts on National Park Service (NPS), Bureau of Land 

Management (BLM), State of Colorado, and private (such as those enrolled in Natural Resources 

Conservation Service programs) lands. More specifically, our objectives were to: 

1. use existing data to map the locations of known GUSG lek sites; 

2. develop spatially explicit models to identify areas with characteristics similar to those of the known 

lek sites;

3. combine model results with expert knowledge about GUSG ecology to help identify other locations 

in the Crawford study area that may be suitable for GUSG lekking activity; and
3

4. establish sampling points in areas identified as suitable for lekking activity to use in assessing and 

validating model outputs and informing future research. 

Study Area

The Crawford study area consists of approximately 880 square kilometers (km2) in southwestern 

Colorado, and includes parts of Delta, Montrose, and Gunnison counties. The region is managed by 

various Federal and state agencies and private landowners, including the NPS, the BLM, and the U.S. 

Department of Agriculture Forest Service (FS), as shown in figure 1. 
4

Figure 1. The Crawford study area, including jurisdictional boundaries. 
5

The study area provides habitat for a wide variety of species, and it supports multiple uses, 

including livestock grazing and resource extraction. Elevations in the area range from 2,130–2,880 

meters (m). Vegetation in the lowest elevations (2,130 m to approximately 2,500 m) is dominated by 

saltbush (Atriplex spp.) and greasewood (Sarcobatus vermiculatus). From elevations of 2,420–2,880 m, 

short grasses, yucca (Yucca baccata), Wyoming big sagebrush (Artemisia tridentata ssp. 

wyomingensis), and Rocky Mountain juniper (Juniperus scopulorum) comprise the majority of the 

vegetation. 

Methods

Modeling Approach

The modeling approach for this project was to use the maximum entropy species distribution 

modeling package, or Maxent (Phillips and others, 2006), to help identify and classify (for ranking) 

areas possessing ecological characteristics similar to those of the known GUSG lek sites. Maxent is a 

statistical model that estimates the distribution of a phenomenon based on a certain set of conditions 

(Phillips and others, 2006). Using existing records of a species’ presence (locations) within a given 

landscape, the software can be used to estimate the probability of that species’ occurrence elsewhere 

within the same landscape. The decision to use the Maxent software for the analysis was based on its 

wide use in species-distribution modeling (Austin, 2007; Elith and Graham, 2009; Hernandez and 

others, 2006) and the fact that it is one of the few modeling packages designed to use presence-only 

data, a feature of the data used in this project (Phillips and others, 2006). 

A user must provide at least two inputs to the Maxent model: (1) point locations of the species’ 

known occurrences (referred to as “presence points”), and (2) a set of environmental predictor variables 

(hereafter variables) that collectively help to explain the species’ distribution. The set of variables 

should be informed by the species’ ecological needs and typically includes elevation, vegetation type, 

and other crucial landscape characteristics (Phillips and others, 2006). Users have the option of 

configuring parameters that determine how the Maxent model is trained and run, including the extent of 

the training area, the extent of the variability it captures for each variable, and the number of model runs 

to be conducted. Using an algorithmic approach, Maxent combines information about the characteristics 

of each variable at the presence points to score novel areas in the landscape. Areas exhibiting 

characteristics more similar to those at the presence points are assigned higher scores. In the context of 

this study, higher scores represent a higher modeled likelihood of suitability for lekking activity. 

We ran several different preliminary models to evaluate various strategies for establishing 

presence points and selecting the appropriate variables, with varying degrees of success. After 

reviewing these outputs, we identified an approach, described below, that maximized our confidence in 

the modeled results given the limitations of the existing lek data. The final lek suitability analysis of the 

Crawford study area entailed a hybrid approach that refines, a posteriori, the output of a simplified 

Maxent model by applying a binary filter to ensure that all areas modeled met certain minimum criteria. 

More specifically, the binary filter, which was based on expert knowledge about the general 

characteristics of known lek sites, was used to exclude areas that did not meet the minimum criteria for 

lekking activity, thus restricting the overall area for which model scores were generated. Then we 

ranked the remaining areas according to their suitability (modeled likelihood scores) for lekking 

activity.
6

Presence Points

A digital dataset representing the boundaries (polygons) of 10 known lek sites within the 

Crawford study area was used for generating the presence points used in the modeling effort. The lek 

sites were reviewed visually by using ArcMap (ESRI, 2010) desktop geographic information system 

(GIS) software in conjunction with a basemap (aerial imagery at 1-m resolution) from the ESRI online 

data library (http://www.esri.com/data/free-data/index.html) and a raster layer (30-m resolution) 

depicting slope. Although all of the polygons in the lek dataset generally encompassed areas that 

appeared to have lek site potential, many of the polygons also included wide swaths of land that were 

deemed unlikely to be suitable for lekking activity (for example, areas characterized by heavy tree 

cover). Presence points were generated by manually placing them within the polygons outlining known 

leks. These points were recorded as latilong values. A total of 11 points were generated, 1 in each of 9 

lek polygons and 2 in a particularly large and irregularly shaped lek polygon. Rather than randomly 

generating points within the polygons, points were specifically placed within the lek polygons in areas 

with sparse vegetation and low slope values. We did this so that the presence points would represent 

locations that possess the characteristics most representative of known sage-grouse lek sites. These 

points were used as the presence locations in the Maxent model.

Environmental Predictor Variables

The final set of environmental predictor variables was selected on the basis of their availability 

as spatial data and a desire to use a concise but relevant group of variables that collectively could help to 

identify areas that are relatively flat and sparsely vegetated—characteristics believed to be shared by 

most leks. Three of the variables, wetness, greenness, and brightness, were GIS layers derived from 

tasseled cap transformation (Crist and Cicone, 1984) of Landsat Thematic Mapper (TM) imagery (path 

35, row 33, capture date May 11, 2007). Wetness represents soil and canopy moisture, greenness is a 

general indicator of vegetation, and brightness is a measure of bare soil. From the Landsat TM imagery 

we also derived a raster layer of the normalized difference vegetation index (NDVI) to see whether it 

could help us identify differences in vegetation. We included distance to water to determine whether 

water features on the landscape affect suitability for lekking activity. Elevation and slope were included 

based on the generally accepted belief that areas occupied by sage-grouse are restricted to a certain 

range of elevations and that lekking takes place in primarily flat areas. The final Maxent model was run 

using the variables shown in table 1.
7

Table 1. Environmental predictor variables used in final Maxent model of lek suitability for Gunnison sage-grouse.

Preparation of the spatial variables and the Maxent modeling analysis was conducted with the 

VisTrails software package (Callahan and others, 2006), in which we used a custom module developed 

at the U.S. Geological Survey (USGS) Fort Collins Science Center. This module facilitated the 

processing of all the environmental predictor raster layers prior to their use in Maxent. All layers were 

re-projected into, and analysis was conducted in, the Universal Transverse Mercator (UTM) zone 13 

North projection, based on the North American Datum of 1983. The resolution of all inputs was 30 m, 

either because their native resolution was 30 m or the data were re-sampled to yield a 30-m resolution.

The custom VisTrails module also allowed us to review the degree of correlation between each 

variable and drop from the model any layers that were highly correlated. We conducted Pearson, 

Spearman, and Kendall correlation tests and considered the largest coefficient values for assessing the 

degree of correlation between variables. We used a cutoff value of 0.7 to exclude individual layers from 

highly correlated pairs of variables. The raster image of the NDVI was dropped from the Maxent model 

because it correlated strongly with the greenness index (r = 0.77). 

Training Site and Variables Used in the Maxent Model

An essential step to implementing the Maxent modeling package is training the model. Within 

the Maxent software, a user has the option to customize the area or geographic extent in which the 

software will train the model, as well as the number of “background points” that Maxent casts 

throughout the training area to capture the range of variability for each variable. Comparing the values 

of the variables at all of the background points to their values at the presence points helps the model 

determine which predictor variables may be influencing the distribution of the lek sites. 

Environmental 

predictor variable Source Raster resolution (m) Date of source material

Digital Elevation 

Model (DEM) National Elevation Dataset 1 arc second (~30 m) 2009

Slope National Elevation Dataset 1 arc second (~30 m) 2009

Distance to water BLM, Uncompahgre Field 

Office 30 2009

Vegetation type

Colorado Vegetation 

Classification Map from the 

Colorado Division of Wildlife

25

Source imagery 

originally captured 

1993–1997

Wetness

Derived from tasseled cap 

transformation of Landsat TM 

imagery (Path: 35, Row: 33)

30 May 11, 2007

Greenness

Derived from tasseled cap 

transformation of Landsat TM 

imagery (Path: 35, Row: 33)

30 May 11, 2007

Brightness

Derived from tasseled cap 

transformation of Landsat TM 

imagery (Path: 35, Row: 33)

30 May 11, 2007
8

All 10 of the known lek sites are clustered together in a relatively small area located near the 

center of the Crawford study area (fig. 1), thus the presence points are also spatially clustered together. 

To overcome potential spatial bias created by this clustering, the Maxent model was trained within a 

rectangular area of 6.6 x 1.5 kilometers (approximately 10.5 km2 ) immediately surrounding the existing 

lek polygons and then applied to the rest of the study area. 

A total of 1,000 background points were randomly distributed within the training area. Eleven 

replicate model runs were performed in Maxent with the “cross-validate” option. In cross-validating 

each model run, each presence point was dropped one at a time to overcome any sensitivity the model 

might have to extraneous or outlier values at a single presence point. The final Maxent model output 

values (scores) represent the average of all 11 model runs. 

Final Model Development

After training and running the model, the results were projected onto the entire polygon of the 

Crawford study area. Additional refinement was subsequently performed outside of Maxent to account 

for trends witnessed in the training area and to include general knowledge about the known lek sites 

within the study area. We addressed these considerations by masking the Maxent results based on the 

criteria listed below and restricted model predictions to a region defined by some general expectations 

about sage-grouse lekking activity. 

The mask we used to refine the Maxent results was based on the conditions present at known lek 

sites. The mask was developed by using a binary filter comprising three variables used to exclude 

completely unsuitable areas from the modeled results. The three variables used to refine the areas 

potentially suitable for lekking activity and the range of values considered acceptable for each variable 

are shown in table 2.

Table 2. Environmental predictor variables and the range of values considered acceptable for each variable in the 

final model.

For vegetation type, we masked out areas not classified as grass forb rangeland, sagebrush 

community, or sagebrush/grass mix, (the known Crawford lek sites and the training area were vegetated 

almost entirely with these types). For elevation, we retained only areas between 2,310 and 2,420 m 

(mean elevation in the model training area was 2,364.5 m with a standard deviation of 27.24), and we 

derived this range of values by adding two standard deviations above and below the mean elevation. For 

slope, we masked out areas where the slope was greater than 8 degrees (mean slope of the known lek 

sites was 6.4 degrees, the standard deviation was 1.5, and the upper slope threshold was derived by 

adding one standard deviation to the mean). Figures 2–4 depict the areas that met the vegetation type, 

elevation, and slope criteria, respectively). Areas that met all three criteria were determined to be 

potentially suitable for lekking activity (fig. 5). The Maxent results were clipped to the area within the 

Crawford study area that met all three criteria; areas that did not meet the criteria were not scored. 

Environmental predictor variable Inclusive values

Vegetation type Sagebrush/grass mix, sagebrush community, or grass forb 

rangeland 

Elevation 2,310―2,420 m

Slope Less than 8 degrees
9

Figure 2. Vegetation types in the Crawford study area considered potentially suitable for sage-grouse lekking 

activity. 
10

Figure 3. The range of elevation values in the Crawford study area and which were considered potentially 

suitable (2,310―2,420 m) for lekking activity (hatching indicates areas excluded from the model).
11

Figure 4. The range of slope values in the Crawford study area (shown in the inset) and which regions were 

considered potentially suitable (8 degrees or less) for lekking activity (shown as brown in the large polygon). 
12

Figure 5. Areas considered potentially suitable for lekking activity (white areas) in the Crawford study area, based 

on slope, elevation, and vegetation type present at known leks.
13

Results

Output values from the Maxent model range from 0 to 1, with higher values representing a 

higher likelihood for occurrence of the phenomenon modeled—in this case, conditions suitable for 

lekking activity. For analysis purposes, the results of the Maxent model were divided into five classes 

for ranking them according to their level of suitability. The classes were obtained by applying five equal 

intervals to the final range of output values in the Maxent (table 3). These five classes, and their 

associated ranks, are shown in table 3. 

Table 3. Maxent output values, by class, associated rank assignments, and rank interpretations.

Maxent output value classes Ranks Description

0.00―0.19 1 Low suitability for lekking activity

0.20―0.39 2 Low-to-medium suitability for lekking activity

0.40―0.58 3 Medium suitability for lekking activity

0.59―0.77 4 Medium-to-high suitability for lekking activity

0.78―0.96 5 High suitability for lekking activity

The results of this project will be used by management agencies, including the BLM, NPS, and 

the FS, to assist in developing monitoring strategies for locating currently unknown lekking areas. We 

ranked the Maxent results to help land managers identify priority regions and areas of potential interest 

within their domains. Tables 4 and 5 provide a breakdown of the model results by suitability category 

and jurisdiction before and after the application of the binary filter. 

Initial Maxent output before applying the filter represented an area of 862 km2 (slightly less than 

the whole Crawford study area; there were a few missing regions of Landsat TM data, so Maxent did 

not generate results for these areas). After applying the binary filter, the area was reduced to 105 km2

(fig. 5). Although the entire 105 km2 area represents potentially suitable lekking habitat, the predicted 

suitability for GUSG lekking activity is greater in areas with higher ranks (classes 4–5) and less for 

areas with lower ranks (classes <4).
14

Table 4. Suitability of the Crawford study area (percent) for lekking activity as modeled by Maxent, by jurisdiction, 

before application of the binary filter. To help resource managers separate the more suitable sites from those 

less suitable, the Maxent output scores were assigned to five equal classes, or ranks, with a rank of 1 

representing areas least suitable and a rank of 5 representing the areas most suitable for lekking activity.

Maxent Class 1 Maxent Class 2 Maxent Class 3 Maxent Class 4 Maxent Class 5

Jurisdiction Percent Area

(km2) Percent Area(km2) Percent Area(km2) Percent Area(km2) Percent Area(km2)

Private 41.05 274.57 46.42 30.00 51.82 18.70 56.57 16.38 49.33 31.49

Bureau of Land 

Management 24.96 166.99 44.64 28.85 41.07 14.82 36.74 10.63 30.91 19.73

Forest Service 14.07 94.14 4.34 2.80 4.68 1.69 4.65 1.35 18.71 11.95

Colorado State 

Parks 0.42 2.81 0.19 0.12 0.19 0.07 0.10 0.03 0.05 0.03

National Park 

Service 19.14 128.05 4.40 2.84 2.24 0.81 1.94 0.56 0.99 0.63

Colorado Parks 

and Wildlife 0.35 2.37 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Total 100.00 668.92 100.00 64.62 100.00 36.08 100.00 28.95 100.00 63.84

Percent of total 

modeled area 77.56 7.49 4.18 3.36 7.40
15

Table 5. Suitability of the Crawford study area (percent) for lekking activity, as modeled by Maxent, by jurisdiction, 

after applying the binary filter. To help resource managers separate the more suitable sites from those less 

suitable, the Maxent output scores were assigned to five classes, or ranks, with a rank of 1 representing areas 

least suitable and a rank of 5 representing the areas most suitable for lekking activity.

Maxent Class 1 Maxent Class 2 Maxent Class 3 Maxent Class 4 Maxent Class 5

Land Manager Percent Area 

(km2) Percent Area (km2) Percent Area (km2) Percent Area(km2) Percent Area(km2)

Private 45.62 37.69 13.10 2.02 10.99 0.56 16.92 0.21 35.56 0.13

Bureau of Land 

Management 35.74 29.52 80.76 12.46 86.82 4.45 81.73 1.01 62.07 0.23

Forest Service 3.30 2.73 0.06 0.01 0.00 0.00 0.00 0.00 0.00 0.00

National Park 

Service 14.46 11.95 6.07 0.94 2.20 0.11 1.35 0.02 2.37 0.01

Colorado 

Division 

of Wildlife 0.88 0.73 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Total 100.00 82.61 100.00 15.42 100.00 5.13 100.00 1.23 100.00 0.37

Percent of total 

modeled area 78.86 14.72 4.90 1.17 0.35

Model-Guided Sampling Efforts

In addition to being relevant for land management concerns, the development of this classified 

suitability surface may be used to guide the design of sampling methods for finding additional active 

GUSG leks within the Crawford study area. After reviewing the model output, 200 sample points 

separated by at least 200 m were scattered throughout the entire area in which output scores were 

assigned. Because such a small proportion of the total scored area was ranked as highly suitable (4 or 5), 

an additional 50 sampling points were scattered in the two areas ranked as 4 or 5. Due to the small sizes 

of these areas, a minimum point-separation distance was not enforced when generating these additional 

points. These points have since been provided to field crews as way to help guide sampling efforts in the 

field and are useful method for concentrating observation efforts in a relatively large study area. 

Furthermore, the results of the field crews’ sampling efforts will help to validate the Maxent model 

results and inform any future modeling approaches. 

Discussion

The Maxent model provided two metrics for assessing which variables were most important in 

determining model results: the percent contribution and the permutation importance. Generally 

speaking, the percent contribution is a measure of how much a single variable contributed to the results 

for a single model run, while permutation importance provides a measure of how consistently a variable 

contributed to the results across multiple model runs (table 6).
16

Table 6. Relative contribution of environmental predictor variables used in the Maxent model for lek site suitability.

After investigating the percent contribution of each variable to the model output, it appears that 

while vegetation type is widely recognized as a factor in determining lek site distribution, Maxent did 

not identify the vegetation data layer used in this analysis as a significant indicator for lek presence. 

This may indicate that the vegetation data layer (categorical data at a 30-m resolution) does a poor job 

of representing the actual land cover characteristics within the landscape, and/or it may indicate that at 

the point level, vegetation functions poorly as a predictor for lek sites. Future research might evaluate 

the contribution of landscape composition at various scales (that is, analysis of vegetation and terrain 

characteristics within different distances from an observed lek). Because sage-grouse likely select lek 

sites based on the overall characteristics of an area rather than on the single vegetation type assigned to 

a 30-m cell, this approach would yield results from a more informed ecological perspective. It should be 

noted that although Maxent did not identify vegetation type as a predictor for lek sites, this does not 

mean that vegetation characteristics at the landscape level were not relevant in the Maxent model 

output. The brightness and greenness variables derived from the Landsat TM imagery capture valuable 

information about vegetation composition, and both of these datasets were used in Maxent to generate 

predicted lek suitability scores.

Application of the binary filter was an effective means of removing regions that appeared to 

have been assigned illogically high suitability scores by the Maxent model (this is illustrated by 

comparing tables 4 and 5). The land cover type of some areas to which Maxent assigned high suitability 

scores is unlikely to be used for lekking activity. These seemingly spurious Maxent results may be 

explained by the fact that the categorical vegetation predictor was effectively excluded from 

consideration in model. The contribution of brightness to the model also helps to explain these results. 

Brightness, which was the single most influential predictor layer used in the Maxent model, is generally 

effective at identifying the sparsely vegetated areas associated with lek activity, but it also effectively 

identifies other land cover types (such as agricultural areas or snow-covered peaks) unlikely to be used 

for lekking activity. Also, probably due to the relatively minimal elevational variation in the training 

area, Maxent did not identify elevation as having a strong role in distinguishing between presence points 

and background points. Consequently, the Maxent model identified some improbable regions at very 

high elevations as being highly suitable for lekking activity.

Combining the Maxent results with existing knowledge about the known lek sites allowed us to 

refine the results and restrict model scores to regions that met certain conditions needed for GUSG 

lekking activity. This hybrid technique provides the benefits of a simple binary model (regions are 

classified as usable or not based on an established set of criteria) and a traditional Maxent-based 

approach (a continuously scored output is produced, allowing distinction between the different areas 

identified as being potentially suitable).

In any modeling application, projecting results into a region different from where the model was 

trained or developed can present a challenge. Such a scenario however, can be difficult to avoid when 

Environmental predictor variable Percent contribution Permutation importance

Brightness 90.1 88.6

Slope 8.4 9.2

Greenness 0.9 1.5

Elevation 0.3 0.2

Distance to water 0.2 0.5

Vegetation type 0.1 0
17

the recorded observations used to develop a model are limited in number or spatial distribution 

throughout a landscape. With the dataset representing known lek locations used in this project, these 

were both known limitations. The unexpected Maxent predictions in certain regions are most likely 

attributable to these issues. The various measures included to address these shortcomings include the 

use of cross validation, limiting the training area for the model to a region defined by the spatial bias in 

the lek locations, eliminating highly correlated variables, and applying the binary filter outlined above to 

the final model results. It is also worth noting that the range or threshold of filter values selected was 

based only on the known lek sites in the Crawford region. While it is assumed that sage-grouse in other 

regions will select for similar areas, they may exhibit slightly different behaviors or select for landscape 

characteristics we have not captured here. 

While the final output product of any model is only as good as the quality of the initial data, we 

used the best data currently available to conduct a valuable investigation of lek site suitability in the 

Crawford area. We are confident that our results provide the information needed to develop sampling 

transects for identifying additional GUSG lek sites. The next step in the process would be to use the 

results of new monitoring efforts to validate the model output and determine whether we can find any 

other areas used by GUSG for lekking activity within the Crawford Study area. 

Acknowledgments

This project was supported by funding from the National Audubon Society. We are most 

grateful to Doug Homan for providing the history of GUSG monitoring and other information. We also 

would like to thank the Bureau of Land Management, National Park Service, and the Colorado Division 

of Parks and Wildlife for their assistance on this project. 

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Ouren and others—Gunnison Sage-Grouse Lek Site Suitability Modeling—Open-File Report 2014–1010

ISSN 2331-1258 (online)

http://dx.doi.org/10.3133/ofr20141010