U.S. Department of the Interior

U.S. Geological Survey

Long-Term Effects of Wildfire on Greater Sage-Grouse—

Integrating Population and Ecosystem Concepts for 

Management in the Great Basin

Open–File Report 2015–1165

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

Top photograph: Sagebrush burning at Hart Mountain National Antelope Refuge in association with a management project 

located 65 miles northeast of Lakeview, Oregon. Photograph by Scott Shaff, U.S. Geological Survey, October 18, 2006. 

Bottom photograph: Cheatgrass invasion following wildfire in Central Nevada north of Austin. Photograph by Caitlin 

Bowman, U.S. Geological Survey, August 2015. 

Inset: Photograph of a male greater sage-grouse performing a courtship display on a lek in northeastern Nevada, 2012.

Photograph courtesy of Tatiana Gettleman. 
Long-Term Effects of Wildfire on Greater Sage-Grouse—

Integrating Population and Ecosystem Concepts for 

Management in the Great Basin

By Peter S. Coates, Mark A. Ricca, Brian G. Prochazka, Kevin E. Doherty, Matthew L. Brooks, and 

Michael L. Casazza

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

Open-File Report 2015–1165

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

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

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The findings and conclusions in this article are those of the author(s) and do not necessarily represent

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Suggested citation:

Coates, P.S., Ricca, M.A., Prochazka, B.G., Doherty, K.E., Brooks, M.L., and Casazza, M.L., 2015, Long-term 

effects of wildfire on greater sage-grouse—Integrating population and ecosystem concepts for management in the 

Great Basin: U.S. Geological Survey Open-File Report 2015–1165, 42 p., http://dx.doi.org/10.3133/ofr20151165.

ISSN 2331-1258 (online)
iii

Preface

This study was conducted to provide timely scientific information regarding the effects of 

wildfire on greater sage-grouse (Centrocercus urophasianus) demography within the Great Basin over 

the last 30 years. Findings are provided to fill a prominent information gap in the threat assessment for 

greater sage-grouse populations as part of the listing decision process by the U.S. Fish and Wildlife 

Service under the Endangered Species Act of 1973. The primary purpose of the current report is to 

present a Bayesian approach to estimate the effects of wildfire on greater sage-grouse population rate of 

change while accounting for influential interactions with climatic conditions (that is, precipitation). We 

also used the derived parameters (medians from posterior probability distributions) of those 

relationships to estimate population size in 2044 relative to measured abundance in 2013–2014. The 

findings of this report were based on multiple long-term datasets, including 30 years (1984 to 2013) of 

wildfire history, and spatially explicit data of climatic conditions, soil moisture and temperature, and lek 

(breeding grounds) counts that were restricted to the 30-year analysis period. A complementary report

using a wildfire dataset that spans the geographic range of greater sage-grouse was concurrently

published with this report. The concurrent study focused on recent spatiotemporal patterns of fire 

regime characteristics during the same 30-year time period with implications for conservation and 

management of the greater sage-grouse (Brooks and others, 2015).
iv

Acknowledgments

We greatly appreciate the cooperation of all State fish and wildlife agencies in the Great Basin 

who graciously provided lek count data under the auspices of the Western Association of Fish and 

Wildlife Agencies (WAFWA). These agencies included Idaho Department of Fish and Game, Nevada 

Department of Wildlife, Oregon Department of Fish and Wildlife, California Department of Fish and 

Wildlife, and Utah Division of Wildlife Resources. We extend particular gratitude to T. Remington 

(WAFWA), who compiled and diligently executed quality assurance and quality control for all lek data 

across agencies. We thank the WAFWA Wildfire and Invasives Work Group, specifically K. Mayer,

J. Chambers, D. Pyke, D. Havlina, S. Espinosa, L. Niell, J. Maestas, T. Koch, and T. Hopkins for 

helpful suggestions on improving this presentation and analysis. We also are grateful to S. Campbell 

(NRCS) for providing soil moisture and temperature data. Reviews of previous manuscript drafts by B. 

Fedy (University of Waterloo), J. Dinkins (University of Wyoming), J. Yee (U.S. Geological Survey), 

and D. Delehanty were particularly helpful. B. Halstead and J. Yee (U.S. Geological Survey) provided

expert statistical advice, and downscaled PRISM precipitation data were graciously provided by P. Flint 

and L. Flint (U.S. Geological Survey). Expert GIS analyses, data management, and problem solving 

were provided by T. Kroeger and K. Mauch (U.S. Geological Survey). 
v

Contents

Preface .......................................................................................................................................................................iii

Acknowledgments.......................................................................................................................................................iv

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

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

Description of Study Area ........................................................................................................................................... 4

Methods...................................................................................................................................................................... 6

Data Sources .......................................................................................................................................................... 6

Lek Counts .......................................................................................................................................................... 6

Annual Wildfire .................................................................................................................................................... 6

Precipitation......................................................................................................................................................... 7

Resilience and Resistance .................................................................................................................................. 7

Model Response and Covariate............................................................................................................................ 10

Population Rate of Change ............................................................................................................................... 10

Density-Dependence......................................................................................................................................... 10

Wildfire .............................................................................................................................................................. 10

Precipitation....................................................................................................................................................... 12

Modeling Wildfire and Climatic Effects on Population Growth............................................................................... 13

Modeling Cumulative Burned Area in Relation to Leks ......................................................................................... 15

Management Scenarios ........................................................................................................................................ 16

Results of Wildfire Effects on Sage-Grouse.............................................................................................................. 18

General 30-Year Wildfire Patterns across the Great Basin ................................................................................... 18

Wildfire and Precipitation Effects on Annual Population Rate of Change.............................................................. 18

Patterns of Cumulative Burned Area and Recovery Near Leks............................................................................. 28

30-Year Predictions of Cumulative Burned Area and Sage-Grouse Population Persistence ................................ 28

Management Scenarios ........................................................................................................................................ 31

Interpretation and Conclusions ................................................................................................................................. 32

References Cited...................................................................................................................................................... 37

Figures

Figure 1. Map of the hydrographic Great Basin, sage-grouse management zones, and locations of 

sage-grouse leks ....................................................................................................................................................... 5

Figure 2. Map of Resilience and Resistance (R&R) index classes in the Great Basin.............................................. 9

Figure 3. Map of estimated sage-grouse core areas and associated distribution of breeding leks in the 

Great Basin.............................................................................................................................................................. 17

Figure 4. Relation between total burned area (acute) and time by low (<32.3 cm) and high (>32.3 cm) 

precipitation years in the Great Basin from 1984 to 2013 ........................................................................................ 19

Figure 5. Increases in burned area (cumulative) by R&R index class across the Great Basin from 

1984 to 2013............................................................................................................................................................ 20

Figure 6. Map of cumulative burned area by R&R index class across the Great Basin as of 2013......................... 21

Figure 7. Effect of distance to burn perimeter on the annual population rate of change (λ) of sage-grouse 

across the Great Basin from 1985 to 2014 .............................................................................................................. 25

Figure 8. Effect of cumulative burned area (square-km) on the annual population rate of change (λ) of 

sage-grouse across the Great Basin from 1985 to 2014.......................................................................................... 26
vi

Figure 9. Three-dimensional display of the interaction effects between precipitation (spring through fall) and 

cumulative burned area on population rate of change (λ) of sage-grouse in the Great Basin from 1985 to 2014 ... 27

Figure 10. Observed and predicted changes in cumulative burned area over time by R&R class .......................... 29

Figure 11. Thirty year predictions for proportion of sage-grouse populations remaining in the Great Basin 

given modeled effects of cumulative fire on rates of change under projected median (50th percentile; A),

low (25th percentile; B), and high (75th percentile; C) amounts of precipitation during spring, summer, and fall..... 30

Figure 12. Examples illustrating how 25, 75, and 99 percent reductions in cumulative burned area in core areas 

relative to no reductions in burned area outside core areas might influence projected sage-grouse population rate 

of change (λ) over the next 30 years under low, median, and high amounts of precipitation................................... 31

Tables

Table 1. Simplified index for overall resilience to disturbance and resistance to cheatgrass invasion (R&R) 

predicted by soil temperature/moisture regimes underlying sagebrush habitats in the Great Basin .......................... 8

Table 2. Summary of fire recovery models used to evaluate how sage-grouse population growth rates 

were best explained by variation in post-recovery times relative to R&R index class .............................................. 11

Table 3. Description of parameters in the mixed effects model for sage-grouse annual population rate of 

change (λ) in the Great Basin from 1985 to 2013, based on density, wildfire, climate, and interaction effects, 

and random effects among lek sites and years........................................................................................................ 14

Table 4. Description of parameters in the model for estimating change in cumulative fire area within 

5 km of leks in the Great Basin from 1984 to 2013 .................................................................................................. 16

Table 5. Descriptive statistics for number of leks counted per state, male lek attendance, corresponding 

number of estimates of annual population rate of change (λ) used as response variables in Bayesian 

modeling of wildfire, precipitation, and density effects on sage-grouse in the Great Basin from 

1985 to 2013............................................................................................................................................................ 22

Table 6. Ranking and selection of models formulated to identify the best combination of wildfire, precipitation, 

and density predictor variables influencing 30-year patterns of sage-grouse population growth across the

Great Basin based on change in Deviance Information Criterion (∆DIC) ................................................................ 22

Conversion Factors

International System of Units to Inch/Pound

Multiply By To obtain

Length

millimeter (mm) 0.03937 inch (in.)

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft)

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2) 10.76 square foot (ft2)

square meter (m2) 0.0002471 acre

hectare (ha) 2.471 acre

square hectometer (hm2) 2.471 acre

square kilometer (km2) 0.3861 square mile (mi2)

square kilometer (km2) 247.1 acre
1

Long-Term Effects of Wildfire on Greater Sage-Grouse—

Integrating Population and Ecosystem Concepts for 

Management in the Great Basin

By Peter S. Coates1, Mark A. Ricca1, Brian G. Prochazka1, Kevin E. Doherty2, Mathew L. Brooks1, and 

Michael L. Casazza1

Abstract

Greater sage-grouse (Centrocercus urophasianus; hereinafter, sage-grouse) are a sagebrush 

obligate species that has declined concomitantly with the loss and fragmentation of sagebrush 

ecosystems across most of its geographical range. The species currently is listed as a candidate for 

federal protection under the Endangered Species Act (ESA). Increasing wildfire frequency and changing 

climate frequently are identified as two environmental drivers that contribute to the decline of sagegrouse populations, yet few studies have rigorously quantified their effects on sage-grouse populations 

across broad spatial scales and long time periods. To help inform a threat assessment within the Great 

Basin for listing sage-grouse in 2015 under the ESA, we conducted an extensive analysis of wildfire and 

climatic effects on sage-grouse population growth derived from 30 years of lek-count data collected 

across the hydrographic Great Basin of Western North America. Annual (1984–2013) patterns of 

wildfire were derived from an extensive dataset of remotely sensed 30-meter imagery and precipitation 

derived from locally downscaled spatially explicit data. In the sagebrush ecosystem, underlying soil 

conditions also contribute strongly to variation in resilience to disturbance and resistance to plant 

community changes (R&R). Thus, we developed predictions from models of post-wildfire recovery and 

chronic effects of wildfire based on three spatially explicit R&R classes derived from soil moisture and 

temperature regimes. We found evidence of an interaction between the effects of wildfire (chronically 

affected burned area within 5 kilometers of a lek) and climatic conditions (spring through fall 

precipitation) after accounting for a consistent density-dependent effect. Specifically, burned areas near 

leks nullifies population growth that normally follows years with relatively high precipitation. In 

models, this effect results in long-term population declines for sage-grouse despite cyclic periods of 

high precipitation. Based on 30-year projections of burn and recovery rates, our population model 

predicted steady and substantial long-term declines in population size across the Great Basin. Further, 

example management scenarios that may help offset adverse wildfire effects are provided by models of 

varying levels of fire suppression and post-wildfire restoration that focus on areas especially important 

to sage-grouse populations. These models illustrate how sage-grouse population persistence likely will 

be compromised as sagebrush ecosystems and sage-grouse habitat are degraded by wildfire, especially 

in a warmer and drier climate, and by invasion of annual grasses that can increase wildfire frequency 

and size in the Great Basin.

1

U.S. Geological Survey.

2

U.S. Fish and Wildlife.
2

Introduction

Contemporary theoretical and applied ecology has focused increasingly on understanding the 

processes and management of alternative ecosystem states, whereby recognizing that disturbances can 

profoundly alter underlying processes that shape ecosystems and reorganizes community composition 

and species abundance (Scheffer and others, 2001; Beisner and others, 2003). Importantly, variation in 

disturbance intensity and effects on underlying processes can either allow an ecosystem to recover and 

ultimately retain its pre-disturbance state (that is, ‘helpful resilience’), or drive it to a persistent 

alternative state with new functional processes once a disturbance threshold has been surpassed (that is,

‘unhelpful resilience’) (Standish and others, 2014). In the latter case, hysteresis can become operative if 

recovery is decoupled from pathways that maintained the pre-disturbance state (Suding and others,

2004). Moreover, lack of resistance to invading species can result in altered species composition 

following disturbance events, which can result in a positive feedback that drives an ecosystem further 

away from its original state (Suding and others, 2004). These state-changing mechanisms ultimately 

may yield an ecosystem that has no historical analog (Seastedt and others, 2008; Hobbs and others,

2009). Accordingly, emphasis recently has been placed on managing ecosystems for resilience to 

disturbance and resistance to invasive species (R&R). In this case, factors that may change ecosystem 

processes and disturbance thresholds are identified so that resources for restoration can be allocated 

most effectively and with a higher probability of success (Seastedt and others, 2008; Chambers and 

others, 2014a; Standish and others, 2014). Equally important is identifying how populations of 

constituent species inhabiting these ecosystems respond to disturbance and subsequent ecosystem 

transitions along ecologically meaningful time-frames (Agrawal and others, 2007). It follows that 

managing for R&R can be improved by integrating concepts from ecosystem and population ecology.

Habitat degradation within the Great Basin ecoregion of the Intermountain West of North 

America is a contemporary example of how disturbance is driving rapid changes in ecosystem structure 

and function across enormous spatial scales. The hydrographic Great Basin comprises more than 72.7 

million ha across five States. The vastness of the affected area results in strong ecological and sociopolitical ramifications. Wildfire has been identified as a primary disturbance leading to shifts in 

ecosystem state and concomitant declines in sagebrush-steppe obligate species in the Great Basin 

(Brooks and Pyke, 2001; Connelly and others, 2011; Miller and others, 2011; Chambers and others, 

2014b). Nearly all species of sagebrush (Artemisia spp.) in the Great Basin are killed by wildfire and do 

not re-sprout (Schlaepfer and others, 2014). At the landscape level, wildfire frequency and magnitude 

have increased significantly subsequent to invasion of annual grasses, particularly cheatgrass (Bromus 

tectorum) and medusahead-rye (Taeniatherum canput-medusae) following disturbance in the Great 

Basin. Annual grass invasion acts as a positive feedback for spreading wildfire to adjacent stands of 

intact sagebrush (that would otherwise be less likely to burn) and subsequent re-burning over shorter 

return intervals (for example, D'Antonio and Vitousek, 1992; Brooks and others, 2004; Chambers and 

others, 2007; Balch and others, 2013; Baruch-Mordo and others, 2013; Chambers and others, 2014a). 

Depending on local soil temperature and moisture regimes influencing variation in R&R (Chambers and 

others, 2014a, 2014b; Brooks and others, in press), the end result can be an ecosystem with novel, albeit 

deleterious and hysteretic, properties (for example, altered water and nutrient cycling, diminished 

wildlife and livestock value, and higher susceptibility to disturbance effects) (Miller and others, 2011). 

From a population ecology perspective, greater sage-grouse (Centrocercus urophasianus, 

hereinafter referred to as “sage-grouse”) are an umbrella or indicator species for the ecological health 

and integrity of sagebrush ecosystems at large spatial scales (Rowland and others, 2006; Knick and 

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

sagebrush ecosystems and sage-grouse now occupy approximately one-half of their former range 
3

(Schroeder and others, 2004; Knick and Connelly, 2011). Following an initial ruling of warranted but 

precluded (U.S. Fish and Wildlife Service, 2010), sage-grouse are now classified as warranted for listing 

under the Endangered Species Act (ESA) with a final determination scheduled for September 2015. 

Increased recognition of risks posed by interactions between wildfire, drought, and invasive annual 

grasses to the ecological and socio-economic integrity of sagebrush ecosystems and threats to the 

persistence of sage-grouse populations led to the recent issuance of Secretarial Order 3336, which aims 

to improve strategies for fire suppression and post-wildfire restoration across the Great Basin (U.S 

Department of the Interior, 2015). 

Critical to these efforts is a sound understanding of how sage-grouse populations respond 

demographically to variation in the frequency and extent of wildfire, post-wildfire recovery times 

related to R&R, and interactions with climate and resource availability across large spatial scales. Most 

studies of sage-grouse–wildfire relations have been site-specific and focused either on effects of 

prescribed fire on sage-grouse population growth (Connelly and others, 2000a), movements and habitat 

associations (Fischer and others, 1996, 1997; Nelle and others, 2000; Rhodes and others, 2010), shorterterm (<10 year) effects of wildfire and climate on population growth (Blomberg and others, 2012) and 

habitat suitability (Davis and Crawford, 2014), or relied primarily on simulations (Pedersen and others,

2003). No analyses to date have linked multi-decadal patterns of wildfire across the Great Basin with 

concomitant data on sage-grouse population dynamics and climate. Such an analysis is important 

because it would provide a means for identifying mechanisms driving prevailing trends in sage-grouse 

population size and predict long-term population change while reducing the chance of mistaken 

inference arising from examining short-term data only. For example, given sage-grouse population 

cycles over time, examining short-term data alone may reveal transient spikes that run counter to actual 

long-term trajectories. Long-term evaluation may identify populations that are most at risk from wildfire 

or changing climate and lead to more effective targeting of management resources for conservation of 

sagebrush and sage-grouse populations. 

Species using central-placed breeding strategies, such as lek breeding sage-grouse, are wellsuited to spatially explicit and large-scale analyses of the effects of environmental and demographic 

stochasticity on population growth rate. One hypothesis of lek evolution posits that leks are located in 

nesting habitat where males are most likely to encounter females for breeding opportunities (Gibson and 

Langen, 1996), and several studies support this mechanism for sage-grouse (Schroeder and White, 1993; 

Gibson, 1996; Holloran and Anderson, 2005; Doherty and others, 2010, 2011; Coates and others, 2013).

Hence, measurements of the extent and persistence of wildfire along with the timing and amount of 

precipitation within biologically relevant distances to leks should be good predictors of sage-grouse 

population growth rates determined from lek counts across the Great Basin. The prior absence of such 

an analysis across broad temporal and spatial scales in the Great Basin in large part is due to the 

difficulty in gathering lek count data, which normally is collected on a state-by-state basis, and forming 

spatially explicit wildfire and climate data from records within State and Federal databases. Now, these 

data have been compiled by the Western Association of Fish and Wildlife Agencies, Monitoring Trends 

in Burn Severity (Eidenshink and others, 2007), and PRISM Climate Group (Daly and others, 2008). In 

addition, spatially explicit data describing landscape-level variation in R&R based on measurable 

environmental gradients of elevation, aspect, and precipitation that influence soil temperature and 

moisture regimes in the Great Basin have been developed recently (Campbell, 2014; Chambers and 

others, 2014b). Hence, sage-grouse population trends can be linked directly to probabilities of 

ecosystem state transition and variation in post-wildfire recovery times derived from wildfire-related 

disturbance activity and underlying R&R, while simultaneously accounting for other sources of 
4

environmental (for example, precipitation) (Blomberg and others, 2012) and demographic (for example,

density dependence) (Garton and others, 2011, 2015) stochasticity that can be similarly strong 

determinants of sage-grouse population growth patterns.

The objectives of the report were 3-fold:

1. Model sage-grouse population growth as a function of wildfire, precipitation, and densitydependence over a 30-year period (1985–2013) at leks with different underlying R&R properties 

across the Great Basin. Our modeling approach identifies influential environmental drivers that 

are thought to explain variation in population growth rates through time, rather than modeling 

growth rate explicitly as a function of time. Thus, factors that are thought to be responsible for

the cyclic patterns (Fedy and Aldridge, 2011) of sage-grouse population growth were modeled 

explicitly. 

2. Predict future (for example, next 30 years) patterns of sage-grouse population rate of change 

based on R&R-specific projections of cumulative burned area within close (that is, 5 km) 

proximity of breeding leks across the Great Basin while accounting for variation in precipitation 

and density-dependence effects.

3. Evaluate projected patterns of sage-grouse population decline across multiple example 

management scenarios that target varying levels of reduction of wildfire in areas with the 

greatest sage-grouse habitat quality and breeding densities. 

Description of Study Area

Our study encompassed nearly 650,000 km2 within the hydrographic boundary of the Great 

Basin. It comprises parts of five States (Nevada [43 percent], Utah [17 percent], Idaho [16 percent],

Oregon [14 percent], and California [10 percent]) and four Sage-Grouse Management Zones (MZ III -

Southern Great Basin [47 percent], MZ IV - Snake River Plain [33 percent], MZ V - Northern Great 

Basin [19 percent], and MZ II - Wyoming Basins [1 percent]) (fig. 1). Across the Great Basin, elevation 

ranges from 400 to 3,000 m within a mosaic of mountain ranges and lowland basins (Miller and others, 

2013). Most precipitation falls in winter, and ranges annually from 150 to 300 mm in southern regions 

and at low elevations, and 300–400 mm in more northern regions and at mid-to-high elevations. Plant 

communities assemble along elevation and precipitation gradients, ranging from salt-desert shrublands

in dry-lowland basins to Wyoming big sagebrush (Artemisia tridentate wyomingensis) associations in 

semi-arid regions to mountain big sagebrush (Artemisia tridentata vaseyana), mixed montane 

shrubland, pinyon pine (Pinus spp.) and juniper (Juniperus spp.) woodland, and coniferous forest

associations in wetter and higher elevation regions.

The sampling area (grain) of our study specifically focused on areas within 5 km of sage-grouse 

leks, areas which primarily are comprised of sagebrush communities. These sagebrush communities 

occur on 63 percent of landscapes within the western range of sage-grouse populations (Brooks and 

others, 2015) and encompass over 80 percent of areas within 5 km of leks. Sagebrush communities can 

be partitioned into sagebrush steppe occupying more northern areas lying within high precipitation 

zones where sagebrush and perennial grasses co-dominate, and Great Basin sagebrush occupying drier 

and warmer southern areas where perennial grasses are less common (Küchler, 1970; Miller and others, 

2011) with relatively low rates of annual net primary production (Noy-Meir, 1973). The timing and 

length of the wildfire season within the geographical range of sage-grouse in the Great Basin can vary 

widely among years, but generally begins in June and ends in September (Brooks and others, 2015). The 

amount of fire area, fire recurrence, and fire rotation also can vary widely among sage-grouse 

management zones and vegetation types in the Great Basin.
5

Figure 1. Map of the hydrographic Great Basin, sage-grouse management zones, and locations of

sage-grouse leks. 
6

Methods

Data Sources

Lek Counts

Counts of male sage-grouse attending breeding leks provide reliable and widely used spatially

explicit data for analyses of population trends (Fedy and Aldridge, 2011). State fish and wildlife 

agencies use established protocols for defining leks, counting male sage-grouse at leks, and 

systematically searching for new leks. Counts of males at leks are typically conducted 3–4 times per 

season and the maximum count is recorded (Connelly and others, 2003). We used data from annual lek 

counts collected by all agencies within the Great Basin from 1985 to 2014. This time frame was selected 

to coincide with the range of available wildfire and climate data (see sections, “Annual Wildfire” and

“Precipitation”). Because protocols and data treatments can vary slightly among agencies, data were 

compiled and subjected to further quality control and assurances (QA/QC) by the Western Association 

of Fish and Wildlife Agencies (2015). These QA/QC included: (1) removing counts obtained by aerial 

survey owing to lower probabilities of detection associated with this method; (2) removing counts 

obtained outside conventional calendar (March 15–May 15) periods and early morning hours (earlier 

than 0.5 hours before sunrise or later 1.5 hours after sunrise); (3) removing counts comprised entirely of 

birds of unknown gender; (4) combining leks within 1.2 km of each other in order to pool likely satellite 

leks into a single lek cluster; and (5) assuming that a recorded count of zero equaled no males were 

observed and a blank value indicated the lek was not counted or not available (NA); and (6) removing 

consecutive zero counts bracketed by non-zero–zero and zero–non-zero counts (see section, “Population 

Rate of Change”). For example, the sequence of counts {50, 0, 0, 0, 100} would become {50, 0, NA, 0, 

100}; another sequence {50, 0, 0, 100} would stay {50, 0, 0, 100}; and {50, 0, 0, 0} would become {50, 

0, NA, NA}.

Annual Wildfire

Spatially explicit data on wildfire extent and severity in the Great Basin extent from 1984 to 

2013 were downloaded from the Monitoring Trends in Burn Severity (MTBS) database 

(http://mtbs.gov/index.html) (Eidenshink and others, 2007). Only burned surface areas greater than 405 

ha as a result of wildfires were included in the Western United States portion of the MTBS database, yet 

these wildfires account for more than 95 percent of burned surface-area in the Western United States

(Eidenshink and others, 2007) and 96 percent are within the range of the sage-grouse (Brooks and 

others, 2015). These data are generated by MTBS using several steps. Scenes are first identified using 

digitized fire histories from agency databases, other fire records, and peak photosynthetic activity 

estimates from Advanced Very High Resolution Radiometer (AVHRR) satellite data. Normalized burn 

ratio (NBR) indices are then calculated using thermal mapping (TM) bands from corresponding 30-m2

Landsat pre- and post-fire imagery, where

NBR = TM4−TM7

TM4+TM7 (1)
7

Bands of red light are measured with TM4 and longer short-wave infrared bands are measured with 

TM7. Values for differenced normalized burn ratios (dNBR) are then calculated by subtracting postfrom pre-fire NBR values. Values for dNBR are then binned into five ecologically relevant fire severity 

classes: (0) increased greenness; (1) unburned to low, (2) low; (3) moderate; and (4) high. Fire 

perimeters are digitized around fire-severity classes 1 through 4, which indicate some minimum level of 

fire-associated changes in vegetation. Because we were most concerned with quantifying effects of 

wildfire stemming from fire-driven changes in vegetation (and not overestimating effects of wildfire on 

sage-grouse population growth), we excluded fire-severity class 1 values that likely result in minimal 

vegetation change, and reclassified fire-severity classes 2, 3, and 4 values into a single binary raster 

using Spatial Analyst in ArcGISTM 10.1. Hence, we erred on the side of reducing commission error 

rather than omission error (that is, we excluded those low-severity areas that may have actually burned 

rather than including low-severity areas that did not burn), and revised fire perimeters were redrawn 

around pixels representing likely vegetation change as a result of wildfire. 

Precipitation

Spatially explicit data for local measurements of precipitation at a spatial resolution of 4 km 

from 1985 to 2013 were obtained from the PRISM Climate Group (http://www.prism.oregonstate.edu/), 

and then downscaled to 800 m (P. Flint and L. Flint, U.S. Geological Survey, unpub. data, 2014). We 

chose to index climatic effects on sage-grouse population rate of change according to variation in annual 

and seasonal precipitation (see section, “Model Covariates,” for rationale). We restricted our analyses to 

include variables related to precipitation to limit the number of covariates in our climatic model set, and 

because effects due to precipitation are more readily explained from a perspective of sage-grouse lifehistory (for example, relations between rainfall, primary productivity, and available resources for 

grouse) than from extremes in temperature, particularly for a cold-adapted gallinaceous species.

Resilience and Resistance

Extensive research effort in recent years has been devoted to determining how sagebrush 

ecosystem structure and function influences resilience to disturbance and resistance to annual grass 

invasion. In general, R&R increases along a gradient based on elevation and aspect that correlates with 

variation in soil moisture and temperature, where corresponding habitats with underlying cold or cool 

and moist soils have higher R&R than habitats with underlying warm and dry soils (Chambers and 

others, 2014a, 2014b). To create a landscape level surface depicting broad patterns in underlying R&R 

processes, three broad classes that index resilience to wildfire-related disturbance and resistance to 

annual grass invasion (that is, high, moderate, and low; table 1, fig. 2) were created by the Fire and 

Invasive Assessment Team (FIAT) from finer scale soil temperature and moisture subclass data 

extracted from maps developed by Campbell (2014). Although data were unavailable to classify some 

areas in the southern portion of the Great Basin, the R&R classification encompassed all sage-grouse 

leks and areas surrounding leks that were relevant to this analysis. We then used R&R index classes to 

calculate different recovery times in calculations of cumulative burned area and chronic wildfire effects 

(as described in section, “Wildfire”).
8

Table 1. Simplified index for overall resilience to disturbance and resistance to cheatgrass invasion (R&R) 

predicted by soil temperature/moisture regimes underlying sagebrush habitats in the Great Basin.

[Derived by the Fire and Invasive Assessment Team based on Chambers and others (2014b) and Campbell (2014).

Sagebrush habitats excludes aquic or oxyaquic soils, which generally are too moist to support sagebrush species, as 

well as ustic (summer moist) moisture regimes (for example, wetlands, lakes)]

Soil temperature/moisture regime - moisture 

subclass Common name R&R index

Cryic/Xeric-Typic Cold/moist High

Cryic/Aridic bordering on Xeric Cold/dry bordering on moist High

Frigid/Xeric-Typic Cool/moist High

Cryic/Aridic-Typic Cold/dry Moderate

Cryic/Xeric bordering on Aridic Cold/moist bordering on dry Moderate

Frigid/Xeric bordering on Aridic Cool/moist bordering on dry Moderate

Frigid/Aridic-Typic Cool/dry Moderate

Frigid/Aridic bordering on Xeric Cool/dry bordering on moist Moderate

Mesic/Xeric-Typic Warm/moist Moderate

Mesic/Aridic bordering on Xeric Warm/dry bordering on moist Low

Mesic/Aridic-Typic Warm/dry Low

Mesic/Xeric bordering on Aridic Warm/moist bordering on dry Low
9

Figure 2. Map of Resilience and Resistance (R&R) index classes in the Great Basin. Classification of R&R were

derived from soil moisture and temperature subclasses from Campbell (2014).
10

Model Response and Covariate

Population Rate of Change

We calculated the instantaneous (or intrinsic) annual per-capita rate of change (r; Gotelli and 

Ellison, 2004) for sage-grouse populations, which took the form:

rii = ll Nii

Nii−1

(2)

where N represents maximum male lek count, i represents lek, j represents year, and j-1 represents a 

previous year. We used r as the response variable in all predictive models for population growth (see 

section, “Modeling Wildfire and Climate Effects on Population Growth”). We added a constant of 0.1 to 

all counts to avoid division by zero when no birds were detected at Nij. Where applicable for illustration 

and interpretation purposes, we convert to finite rates of change (λ; Caswell, 2001; Gotelli and Ellison, 

2004), and expressed as:

λ = er (3)

Density-Dependence

We formulated models to test and account for density-dependent reductions in population 

growth observed within some sage-grouse populations (Garton and others, 2011, 2015). Densitydependence from time-series data are typically evaluated using Ricker (Dennis and Taper, 1994) and 

Gompertz (Dennis and others, 2006) methods that estimate population rate of change as a function of 

raw and logarithmic abundance, respectively. Furthermore, time lags can be incorporated to model 

delayed density-dependent feedbacks that can yield cyclic patterns of population growth often displayed 

by sage-grouse in Wyoming (Fedy and Aldridge, 2011; Fedy and Doherty, 2011). Recent analyses of 

stochastic population growth from 1965 to 2013 found consistent support for Gompertz-type densitydependence lagged one-year log[ N −2]); hereinafter, ‘lag-Gompertz’) across all sage-grouse 

management zones encompassed by our study area (Garton and others, 2015). To allow interpretation 

across studies, we a priori selected lag-Gompertz to be tested against the null model without predictors, 

and, if supported by the data, then carried this effect forward for inclusion in all predictive models. 

Wildfire

Wildfire effects on sage-grouse population growth were indexed with three spatially explicit 

covariates derived from the MTBS data for each year: (1) distance to burned perimeter; (2) burned area; 

and (3) wildfire perimeter-to-area (PA) ratio (an index for amount burned to unburned edge). Distance 

to burned perimeter measured the Euclidean distance between lek centers and nearest burned edge (zero 

distance if lek was inside the fire area), and allowed for estimation of ‘burn over’ effects (for example, 

wildfires that completely burned over lek centers). Burned area and PA ratio within circular buffers of 

5 km (78.5 km2

) and 10 km (314 km2) from lek centers were extracted from raster layers created from 

corresponding moving windows of 5,160 and 10,560 m, respectively. These distances bracketed the 

range of values proposed for limiting surface-disturbance near leks (Coates and others, 2013; Manier 

and others, 2014) and encompassed most nesting habitat as described previously. Burned area 

represented the amount of fire-severity class 2 through 4 within a wildfire perimeter, and PA ratio 

represented the amount of total burned edge relative to its total area.
11

Burned area and PA ratio also were calculated with two methods. Annual time series analyses 

typically measure fire dynamics as a spatially static and ephemeral covariate, whereby landscapes burn 

at particular extents (area) and configurations (PA ratio) in a given year, but burns do not compound 

across years (that is, annual estimates of burn reset to zero at the conclusion of the fire year) (for 

example, Baker, 2006; Miller and others, 2011). We term this an ‘acute’ effect, whereby wildfire

influences on population rate of change were modeled as a discrete process confined to a single year 

(for example, immediate effects of wildfire) (table 2). In contrast, wildfires can have long-lasting effects 

based on longer term reduction of resources necessary to sage-grouse (for example, loss of sagebrush 

cover and annual grass invasion), and the recovery time of these resources were then modeled as a 

function of underlying soil moisture and temperature processes influencing R&R. We term this a 

‘chronic’ effect, whereby persistence of wildfire scars and associated effects on sage-grouse varied 

across years according to the predominant underlying R&R index class within 5 km of leks (for 

example, longer term effects of wildfire) (table 2). Evaluating both acute and chronic effects allowed for 

an investigation of immediate impacts (that is, temporary displacement of sage-grouse) versus those that 

are long-lasting and likely associated with changes in land cover (that is, reduced local persistence). 

Recovery times for chronically affected areas (hereinafter, chronic areas) were based on a published

meta-analysis of post-wildfire recovery in mountain big sagebrush communities that ranged from 1.28

percent of pre-fire sagebrush per year (“slow-track”) to 2.28 percent (“fast-track”) per year (Baker,

2011). Mountain big sagebrush communities are among the most resilient of all big sagebrush types, 

and by assuming their fast-track and slow-track recovery rates for our high and moderate R&R index 

classes, respectively, we modeled a “best case scenario” of sagebrush recovery that likely 

underestimates the length of time to recovery for other sagebrush types (for example, Wyoming big 

sagebrush). 

Table 2. Summary of fire recovery models used to evaluate how sage-grouse population growth rates were best 

explained by variation in post-recovery times relative to R&R index class.

[Years to recovery for high and moderate R&R classes were calculated as 20 percent divided p, where p is the fast-track 

(2.28 percent per year) or slow-track rate (1.28 percent per year) of recovery, respectively]

Fire effect Recovery scenario R&R index class Years to recovery

Acute NA NA 1

Chronic Normal High 9

Moderate 16

Low NA

Accelerated High 5

Moderate 8

Low NA

Decelerated High 18

Moderate 30

Low NA
12

By conservatively defining recovery as a minimum threshold of 20 percent pre-fire sagebrush 

cover within 5 km of leks necessary to fulfill important sage-grouse life history requirements (for 

example, brood-rearing and wintering) (Connelly and others, 2000b), we constructed three scenarios of 

‘normal’, ‘accelerated’, and ‘decelerated’ recovery times for high and moderate R&R pixels. These 

scenarios represent variation in recovery times across a broader range of sagebrush ecosystems (Baker,

2006, 2011; Nelson and others, 2014). Under the ‘normal’ scenario, we calculated recovery time of high 

R&R pixels to 20 percent of pre-fire sagebrush using the fast-track rate (2.28 percent) as 9 years, (that 

is, 20 divided by 2.28, rounded to the nearest whole number) while recovery of moderate R&R pixels to 

20 percent sagebrush using the slow-track rate (1.28 percent) occurred after 16 years (that is, 20 divided 

by 1.28, rounded to the nearest whole number). We cut normal recovery times approximately in onehalf for the ‘accelerated’ scenario (5 and 8 years for high and moderate R&R pixels, respectively). We 

doubled recovery times under the ‘decelerated’ scenario but these were not allowed to exceed the 30-

year span of the data (18 and 30 years for high and moderate R&R pixels, respectively). In all scenarios, 

the ‘recovery clock’ reset to zero if a new wildfire burned over a previously burned pixel with high or 

moderate R&R before recovery occurred. In contrast, we did not allow for any recovery in low R&R 

pixels, and assumed permanent conversion to annual grass following fire. This assumption was based 

on: (1) slow recovery following fire in Wyoming big sagebrush communities that often comprise low 

R&R habitat (for example, < 5 percent of pre-fire sagebrush cover typically returns after 25 years) 

(Miller and others, 2011); and (2) the high likelihood of state-transition without extensive management 

intervention (Chambers and others, 2014a, 2014b). Hence, burned low R&R pixels represented a 

permanent wildfire-scar and loss of resources available to sage-grouse. 

We recognize that full recovery within 5 km of leks can take much longer than our three R&Rbased recovery scenarios suggest. However, modeling full recovery in more resilient mountain big 

sagebrush and perennial grass communities or less resilient and low-elevation Wyoming big sagebrush

communities was untenable because the 30 years of available MTBS data fell short of including the full 

range of years required for full recovery in both communities (ca. 25–120 years) (Baker, 2006, 2011).

Hence, these assumptions allowed for relatively quick return of some recovery-based benefits to sagegrouse (based on an estimated post-fire 20 percent return of sagebrush) that ameliorated the chronic 

wildfire effect for leks dominated by high and moderate R&R, and no recovery in low R&R represented 

by cheatgrass invasion.

Precipitation

Similar to the wildfire covariates, we extracted mean precipitation data within 5- and 10-km 

radius buffers from lek centers using raster layers created from moving windows of 5,160 and 10,560 m, 

respectively. We aggregated precipitation into seasonal (spring, summer, fall, and winter) and annual 

intervals based on a priori hypotheses that synchronized with the timing of sage-grouse life stages. For 

example, spring spanned March to May and indexed resources available during the sage-grouse nesting 

period (for example, forb growth), whereas summer spanned June to August and indexed resources 

available during the brood-rearing period (for example, wet meadow productivity, delay of plant 

senescence). We classified fall as September through November to represent spurts of new growth 

during extended brood-rearing that could increase survival of juveniles entering winter. Winter spanned 

December to February, and indexed precipitation (primarily as snow) that contributed to increased 

runoff that was thought to drive more productive and possibly longer growing seasons. We also formed 

four multi-season groups by combining: (1) spring and summer (nesting + brood rearing); (2) spring, 
13

summer, and fall (nesting + brood rearing + extended brood rearing/juvenile); (3) winter, spring, and 

summer (snowpack runoff + nesting + brood rearing); and (4) annual (total across all seasons). Heavy 

snowfall during winter also can influence overwinter mortality and thus subsequent numbers of birds 

that survive to the following years’ lek count (Anthony and Willis, 2009). However, other studies 

indicate relatively high over winter survival (Blomberg and others, 2013), so we aligned winter at the 

beginning rather than the end of the precipitation year to allow for carry-over effects of winter 

precipitation (for example, snowpack melt) on plant growth and phenology in the subsequent spring that 

should affect successful reproduction and increase the potential number of recruits in the next year. 

Modeling Wildfire and Climatic Effects on Population Growth

A two-step Bayesian mixed-effects model framework was used to investigate densitydependence, precipitation, and wildfire as linear predictors for annual population growth rates from 

1985 to 2013. Our mixed effects model equation is structured as

log(λ) = β0 + ∑i βi Xi + ζLek + ζYear (4)

where Xi represent model covariates to describe density-dependence, climate, and/or wildfire (absent in 

the null model), the β terms represent model coefficient parameters, and the ζ terms represent random 

effects variation among leks and years. Bayesian modeling is well suited for generating projections of 

growth rates and are readily derived from posterior distributions of parameter estimates (Halstead and 

others, 2011). Thus, we chose to use derived distributions for effects of wildfire (estimated while 

accounting for variation in precipitation and sage-grouse density) to forecast how future changes in 

cumulative burned area might influence population trajectories of Great Basin sage-grouse over the next 

30 years. Posterior distributions were derived using Program JAGS within the rjags package (Plummer 

and others, 2015) in R version 3.1.1 (R-Core-Team, 2014). Specifically, posterior distributions of

parameter estimates were generated from three chains of 10,000 iterations each, after a burn-in of 

10,000 (step 1) to 20,000 (step 2) iterations using Markov-chain Monte Carlo (MCMC) methods.

Convergence of MCMC output was assessed visually with history plots and the R-hat statistic, where 

values < 1.1 indicated convergence (Gelman and others, 2014).

Posterior distributions of estimated covariate effects were derived from distributions of 

uninformative prior probabilities (table 3) during both steps, and all models were fitted with the randomeffects structure to account for intraclass correlations associated with leks and years. Step 1 was 

designed to select and carry-forward the covariates with the most support from groups of variables 

representing: (1) density-dependence; (2) wildfire; and (3) precipitation. Input data were aligned so that 

wildfire and precipitation covariates at year j influenced numbers of grouse counted at leks in year j+1. 

Within groups, fitted models representing singular-additive effects were compared against each other, a 

null model (that is, random effects only), and a density-dependence only model (that is, lag-Gompertz 

with random effects; when applicable) using deviance information criterion (DIC, lower is better), a 

goodness-of-fit statistic calculated for every model. Although use of DIC for model selection has 

limitations (Hooten and Hobbs, 2014), this criteria is commonly used to rank competing Bayesian 

models because of its computational ease and similarity to maximum-likelihood based Akaike 

Information Criterion (AIC). Furthermore, we calculated DIC because this criteria is reliable when used 

with models with similar attributes to those in this study (for example, linear and independent data, large 

sample size, low number of parameters). We defined three different ∆DIC values to facilitate 

comparisons among models: ∆DICGroup as DIC minus the minimum DIC value within the same group or 
14

subgroup, to compare models within each group or subgroup; ∆DICDD as DIC minus the DIC of the 

density-dependent-only model, to compare each model to the Gompertz (1-year lag) density-dependent 

model; and ∆DICnull as DIC minus the DIC of the null model to compare each model to the random 

intercept-only model. For each subgroup, single covariate effects were carried forward if they met three 

criteria: (1) consisted of lowest DIC within the subgroup (that is, ∆DICgroup equaled zero); (2) DIC was 

at least two units less than DIC for the null model (that is, ∆DICnull was less than or equal to negative 

two); and (3) DIC was at least two units less than DIC for the lag-Gompertz model (that is, ∆DICDD was

less than or equal to negative two). Covariates from the models that met these DIC criteria for each 

group or subgroup were carried forward to step 2.

Table 3. Description of parameters in the mixed effects model for sage-grouse annual population rate of change 

(λ) in the Great Basin from 1985 to 2013, based on density, wildfire, climate, and interaction effects, and random 

effects among lek sites and years. 

[All parameters are estimated using Bayesian techniques assuming minimal prior information, using Uniform (U) or 

Normal (N) prior distributions, and include baseline (β0), coefficients of effects (βi), random effects (ζ), and their 

standard deviations (σ). All effects relate to population λ according to the equation log(λ) = β0 + ∑i βi Xi + ζLek + ζYear , 

where Xi represent model covariates and the summation includes zero (null model), one (lagged-Gompertz only model), or 

multiple effects]

Symbol Description Prior distribution

σr Standard deviation for annual per capita instantaneous rate of 

increase (model error)

U(0,50)

β0 Baseline annual per capita instantaneous rate of increase U(−20,20)

βDD Coefficient of lagged Gompertz density-dependence effect N(0, σDD)

σDD Prior standard deviation βDD U(0,50)

βFDist Coefficient of fire effect (Distance subgroup) N(0, σFDist)

σFDist Prior standard deviation for βFDist U(0,50)

βFArea Coefficient of fire effect (Area subgroup) N(0, σFArea)

σFArea Prior standard deviation for βFArea U(0,50)

βEdge Coefficient of fire effect (Edge subgroup) N(0, σEdge)

σEdge Prior standard deviation for βFEdge U(0,50)

βPrecip Coefficient of precipitation effect N(0, σPrecip)

σPrecip Prior standard deviation of βPrecip U(0,50)

βInteraction Coefficient of interaction effect between fire and precipitation N(0, σInteraction)

σInteraction Prior standard deviation for βInteraction U(0,50)

ζLek s Random lek effect, for lek s, where s = 1,...,1770 N(0, σLek)

σLek Prior standard deviation for ζLek s U(0,50)

ζYear s Random year effect, for year t, where t = 1985 – 2013. N(0, σYear)

σYear Year standard deviation of ζYear t U(0,50)
15

During step 1, within the density-dependence group, we evaluated a lag-Gompertz model against 

the null model. The DIC was less than that of the null model, so we carried it forward for inclusion in all 

models within the wildfire and precipitation groups to consistently account for density-dependence. 

Within the wildfire group, we created three subgroups of covariates to singularly evaluate the effects of: 

(1) distance to burned perimeter; (2) burned area; and (3) burned edge. Distance to burned perimeter

only included distance to acutely affected burned areas (hereinafter, acute distance). Burned area and 

PA ratio were based on acutely and chronically affected areas at the 5- and 10-km scale. Chronically

affected burn and edge (hereinafter, chronic area and chronic edge) included measurements of 

cumulative area calculated using the different R&R-based recovery rates (that is, normal, accelerated, 

decelerated). Within the precipitation group, we used the same criteria to carry-forward the single-best 

model of seasonal or annual precipitation at the 5- or 10-km scale. Precipitation did not include any 

subgroups. All wildfire and precipitation covariates were centered and standardized using Z-scores (Zar,

1996) to allow for consistent scales of measurement and improve model convergence. Densitydependence variable represented by lag-Gompertz conversion was already log-transformed.

In step 2, we fit two additive models that combined lag-Gompertz with wildfire and precipitation

covariates carried forward from step 1. We also fit a model that included an interaction between the 

effects of precipitation and wildfire to test if wildfire effects on population rate of change varied by 

precipitation. The model with the lowest DIC was chosen for predictive modeling. Multicollinearity 

among covariates (r > |0.65|) was not detected among any step 2 models. We followed the suggestions 

of Kéry (2010) to assess model fit by constructing plots of residuals against predicted values, 

developing posterior predictive distributions, and calculating Bayesian p-values, whereby p ~ 0.5 

indicates good fit. We reduced the number of posterior iterations to 1,000 owing to the large number of 

parameters required for calculating residual plots, and increased the burn-in to 20,000 iterations to 

facilitate better convergence for more complex models. To interpret covariate effects, we report the 

median parameter estimates of the posterior distribution and 95 percent credible intervals (CI; that is,

2.5 and 97.5 percentiles of the posterior distribution that represent the lower and upper bounds, 

respectively, of the CI).

Modeling Cumulative Burned Area in Relation to Leks

A Bayesian linear model was used to describe increases in cumulative burned area through time 

from 1984 to 2014, and resulting posterior distributions were then used to forecast future changes in 

cumulative burned area out to 30 years. We selected the burned area covariate from a subgroup of 

models formed to test different combinations of spatial scale (that is, 5 and 10 km from lek) and acute 

and chronic R&R-based recovery times (that is, accelerated, normal, decelerated) that best described 

variation in annual population change (described in step 1 above), and regressed it against year as a 

continuous fixed effect. Separate models were run for each R&R index class to evaluate changes in 

burned area near leks over time relative to spatially explicit predictions of R&R, and then each model 

was compared to a null (intercept only) model using DIC. Parameter estimates were derived from 

distributions of uninformative prior probabilities (table 4). 
16

Table 4. Description of parameters in the model for estimating change in cumulative fire area within 5-km of leks in 

the Great Basin from 1984 to 2013. 

[All parameters are estimated using Bayesian techniques assuming minimal prior information, using Uniform (U) or Normal 

(N) prior distributions, and include coefficients of trend (βYear ), and standard deviations (σ). Cumulative fire area is 

modeled as βYear Y Y ]

Symbol Description Prior distribution

σr Standard deviation for annual rate of recovery U(0,1.5e7)

βYear Mean effect for year N(0, σYear)

σYear Standard deviation for year effect U(0,1.5e7)

Management Scenarios

Indices of habitat suitability and animal abundance provide useful proxy-based measures for use 

in adaptive management (Coates and others, 2014; Stephens and others, 2015). Doherty and others (U.S. 

Fish and Wildlife Service, written commun., 2015) derived a range-wide population index model for 

sage-grouse using such indices that incorporated sage-grouse habitat suitability generated from Random 

Forest models (Evans and others, 2011), and spatially explicit abundance measures based on fixed 

kernel density functions on lek locations. The kernels were generated using two bandwidth distances 

representing the majority of breeding habitat in relation to leks (6.4 km) and seasonal movements (18.0 

km). We evaluated relationships between abundance indices and the overall population index model to 

demarcate areas that are the most meaningful to sage-grouse populations. Specifically, we clipped the 

range-wide continuous population index layer to the Great Basin study extent (buffered by 10 km), and 

extracted isopleths for the percentage of cumulative volume under the layer at 5 percent increments 

starting at 65 percent. Following Doherty and others (2015), we overlaid locations of active leks (that is, 

greater than two males observed in greater than two of the previous 5 years) and pending leks (that is, 

greater than two males observed only once over the previous 5 years) on the clipped layer and extracted 

the maximum lek count from 2009 to 2014 as an index of population size within each isopleth class. 

Active and pending leks (n = 1,896) had at least one count greater than two males during the time 

interval. For each successive isopleth class, we calculated the ratio of the cumulative increase in 

population size to cumulative area added to the population index model, and rescaled the ratio between 

zero and 100. To determine a cut-point for the population index model that represented where sagegrouse density was most concentrated, we fit an exponential regression between population index model 

isopleth values and the cumulative ratio of increasing population size to area added, and determined 

where the slope of the line equaled 1.0 (Vander Wal and Rodgers, 2012). The slope equaled 1.0 at the 

76.4 percent isopleth, which when rounded to 75 percent was used to demarcate example ‘core areas’ 

(fig. 3). To inform decisions of conservation actions by State and Federal agencies, we ran simulations 

to examine how targeted 25, 75, and 99 percent reductions in the rate (trend) of annual cumulative burned 

area in these core areas might alter sage-grouse population trajectories over the next 30 years. Posterior 

distributions are reported.
17

Figure 3. Map of estimated sage-grouse core areas and associated distribution of breeding

leks in the Great Basin.
18

Results of Wildfire Effects on Sage-Grouse

General 30-Year Wildfire Patterns across the Great Basin

The MTBS-based dataset used for wildfire modeling comprised a pool of 3,102 fire perimeters 

in the Great Basin from 1984 to 2013. Although substantial variation existed among years, total burned 

area increased by an average of 153 km2/yr. Some variation could be explained by annual precipitation 

relative to the 30-year average of precipitation (32.3 cm), where total burned area was greater, on 

average, during years of below-average precipitation (524,134 ha burned per year) compared to those 

years of above average precipitation (239,873 ha burned per year) (fig. 4). Additional variation could be 

explained by R&R index class burned, whereby total burned area tended to be higher for wildfires

burning over land cover with low underlying R&R compared to those with moderate and high 

underlying R&R (fig. 5). When evaluated on a cumulative basis that allowed for normal recovery (based 

on results below) a total of 13,173 km2 of land cover with low underlying R&R burned from 1984 to 

2013 compared to 6,390 km2 of moderate and 4,349 km2 of high underlying R&R during the same 

period (fig. 6). We assumed all land cover types within these generalized R&R classes recovered at the 

same R&R-specific rates as sagebrush for illustrative purposes, and because this calculation is across 

the Great Basin and our analysis is in relation to 5 km of leks, this trend was not used for model 

projections.

Wildfire and Precipitation Effects on Annual Population Rate of Change

After subjecting all lek count data to QA/QC screening, the final dataset used to model wildfire

and precipitation effects on annual population rate of change comprised of 1,770 leks across the Great 

Basin (table 5). Idaho and Nevada had the most leks (69.8 percent of total) and California had the least 

(4.6 percent of total). Average number of males per lek was less than 30. Posterior distributions of 

parameters were then generated from 14,834 estimates of instantaneous population rate of change (r) 

derived from consecutive pairs of lek counts. 

For step 1, Gompertz type density-dependence with a 1-year lag (lag-Gompertz) explained more 

variance in annual population growth than the null (random effects only), and was thus carried forward 

to all models with wildfire and precipitation covariates (table 6). Within the wildfire covariate group, 

the model with distance to burned perimeter had a lower DIC compared to the random intercept model 

(∆DICnull < –72.0) and the lag-Gompertz only model (∆DICDD < –8.0), so this covariate was carried 

forward to step 2. Within the burned area subgroup, chronic area with normal recovery rate within 5 km

of a lek had the lowest DIC compared to the random intercept model, and was 6.8 ∆DICDD units better 

than the lag-Gompertz only model, so this covariate was carried forward to step 2.
19

Figure 4. Relation between total burned area (acute) and time by low (<32.3 cm) and high (>32.3 cm) precipitation 

years in the Great Basin from 1984 to 2013. 
20

Figure 5. Increases in burned area (cumulative) by R&R index class across the Great Basin from 1984 to 2013.
21

Figure 6. Map of cumulative burned area by R&R index class across the Great Basin as of 2013.
22

Table 5. Descriptive statistics for number of leks counted per state, male lek attendance, corresponding number of 

estimates of annual population rate of change (λ) used as response variables in Bayesian modeling of wildfire, 

precipitation, and density effects on sage-grouse in the Great Basin from 1985 to 2013.

State Number 

of leks

Number of males per lek Samples (estimated r) per lek

Average SD Min Max Average SD Min Max Total

Idaho 695 14.4 13.5 0.2 83.7 8.5 7.4 1 29 5,909

Nevada 540 16.5 14.2 0.5 94.9 6.3 6.4 1 29 3,413

Oregon 288 15.7 15.6 0.4 96.1 9.5 7.8 1 29 2,738

Utah 166 16.4 15.7 0.3 106.2 10.8 8.2 1 29 1,797

California 81 22.0 27.2 1.6 135.7 12.1 9.7 1 29 977

All States 1,770 15.8 15.3 0.2 135.7 8.4 7.5 1 29 14,834

Table 6. Ranking and selection of models formulated to identify the best combination of wildfire, precipitation, and 

density predictor variables influencing 30-year patterns of sage-grouse population growth across the Great Basin 

based on change in Deviance Information Criterion (∆DIC).

[∆DICGroup is defined as DIC minus the minimum DIC value within the same group or subgroup, and is used to compare

models within each group or subgroup; ∆DICDD is defined as DIC minus the DIC of the lagged-Gompertz-only model, and is 

used to compare each model to the Gompertz (one year lag) density-dependent model; ∆DICnull is defined as DIC minus the 

DIC of the random-effects only model]

Step Group Subgroup Model DIC ∆DICGroup ∆DICDD ∆DICnull

1 DensitydependenceNA Gompertz (one year lag) 56469.64 0.00 0.00 -63.81

Null 56533.45 63.81 63.81 0.00

Wildfire Distance Distance 56461.19 0.00 -8.45 -72.26

Area Chronic area, normal, 5 km 56462.79 0.00 -6.85 -70.66

Chronic area, accelerated, 5km 56463.24 0.45 -6.40 -70.21

Chronic area, normal, 10 km 56463.33 0.54 -6.31 -70.12

Chronic area, accelerated, 10 km 56464.92 2.13 -4.72 -68.53

Chronic area, decelerated, 5 km 56464.98 2.19 -4.66 -68.47

Chronic area, decelerated, 10 km 56465.99 3.20 -3.65 -67.46

Acute area, 5 km 56472.51 9.72 2.87 -60.94

Acute area, 10 km 56473.57 10.78 3.93 -59.88

Edge Chronic perimeter:area, decelerated, 10 km 56469.78 0.00 0.14 -63.67

Chronic perimeter:area, decelerated, 5 km 56470.95 1.17 1.31 -62.50

Chronic perimeter:area, accelerated, 10 km 56471.58 1.80 1.94 -61.87

Chronic perimeter:area, normal, 10 km 56472.28 2.50 2.64 -61.17

Acute perimeter:area, 10 km 56472.76 2.98 3.12 -60.69

Chronic perimeter:area, accelerated, 5 km 56473.43 3.65 3.79 -60.02

Chronic perimeter:area, normal, 5 km 56473.67 3.89 4.03 -59.78

Acute perimeter: area, 5 km 56473.83 4.05 4.19 -59.62
23

Step Group Subgroup Model DIC ∆DICGroup ∆DICDD ∆DICnull

Precipitation NA Spr-Sum-Fall, 10 km 56467.06 0.00 -2.58 -66.39

Spr-Sum, 10 km 56468.81 1.75 -0.83 -64.64

Sum, 5 km 56468.84 1.78 -0.80 -64.61

Annual, 1 km 56468.93 1.87 -0.71 -64.52

Spr-Sum-Fall, 5 km 56469.11 2.05 -0.53 -64.34

Sum, 10 km 56469.12 2.06 -0.52 -64.33

Fall, 10 km 56469.30 2.24 -0.34 -64.15

Spr, 10 km 56469.91 2.85 0.27 -63.54

Win-Spr-Sum, 10 km 56470.04 2.98 0.40 -63.41

Spr-Sum, 5 km 56470.73 3.67 1.09 -62.72

Annual, 5 km 56470.79 3.73 1.15 -62.66

Spr, 5 km 56471.30 4.24 1.66 -62.15

Fall, 5 km 56471.92 4.86 2.28 -61.53

Win-Spr-Sum, 5 km 56472.02 4.96 2.38 -61.43

Win, 10 km 56472.75 5.69 3.11 -60.70

Win, 5 km 56473.42 6.36 3.78 -60.03

2 Combined 

(Interaction)

NA Acute distance + Chronic area, 5 km * SprSum-Fall, 10 km + Gompertz (1-year lag)56457.16 0.00 NA NA

Combined 

(Additive)

NA Acute distance + Chronic area, 5 km + 

Gompertz (1-year lag)

56458.59 1.43 NA NA

Acute distance + Chronic area, 5 km + SprSum-Fall, 10 km + Gompertz (1-year lag)56458.90 1.74 NA NA
24

Competing evidence (∆DICgroup < 2.0) existed for chronic area with normal and accelerated 

recovery rate within 10 and 5 km of a lek, respectively, but we only carried forward the single best 

model to limit complexity of step 2. Notably, all models of acute burned area had a poorer DIC than the 

lag-Gompertz only model (∆DICDD > 0.0). Within the burned edge subgroup, no models were supported 

by the data better than the lag-Gompertz only model. Hence, no PA ratio covariates were carried 

forward to step 2. Finally, within the precipitation group, the model with precipitation within 10 km of a 

lek during spring, summer, and fall had the lowest DIC, and improved significantly on the lagGompertz only model (∆DICDD < –2), and this covariate was carried forward to step 2. No other 

precipitation group models satisfied the rule of ∆DICDD < –2.0, so no other precipitation covariates were 

carried forward. Convergence criteria was met for all models (R-hat < 1.1).

For step 2, main effects models containing additive effects of density-dependence, wildfire, and 

precipitation explained less variation in λ compared to the model containing interaction effects between 

wildfire and precipitation (table 6). The equation for the final model including all carried-forward 

variables took the form:

log(λt) = β0 + βDD log(Nt−1) + βFDistXFDist + βFAreaXFArea +

βPrecipXPrecip + βInteractionXFAreaXPrecip + ζLek + ζYear (4)

where βs represent coefficients for intercept (0), lag-Gompertz density-dependence (DD), distance to 

perimeter burned (FDist), cumulative burned area (FArea), and seasonal precipitation (Precip; spring 

through fall), ζs represent random effects for lek and year. Descriptions are provided in table 3. Fit of 

the final model was excellent (Bayesian P = 0.496), and indicated that effects of wildfire varied 

inconsistently across the range of precipitation while accounting for density-dependence. Posterior 

probability distributions of the parameters for wildfire effects indicated that variation in population rate 

of change was positively related to distance away from the burned perimeter and negatively related to 

cumulative burned area with normal recovery rate within 5 km of a lek. On average, rate of change

increased by approximately 1.1 percent (posterior median = 0.011, 95-percent CI = 0.007–0.015) with 

every 10 km increase in distance to burned perimeter (fig. 7), and decreased by 2.1 percent (0.021, 95-

percent CI = 0.012–0.031) with every 10 km2 increase in cumulative burned area (fig. 8). Interactive

effects between cumulative wildfire and total precipitation during spring, summer, and fall indicated 

intriguing and complex patterns (fig. 9). Increasing precipitation correlated with increasing population 

size, whereby λ was predicted to exceed 1.0 during years of above-average precipitation if cumulative 

burned area was not too large. However, the positive effect of precipitation on population growth

decreased as cumulative burned area increased, whereby λ was predicted to be below 1.0 during years 

with high precipitation at leks associated with relatively large amounts of cumulative burned area.
25

Figure 7. Effect of distance to burn perimeter on the annual population rate of change (λ) of sage-grouse across 

the Great Basin from 1985 to 2014. Ten thousand samples from the posterior distributions of model parameters 

were used to represent the range of uncertainty in this relationship. 
26

Figure 8. Effect of cumulative burned area (square-km) on the annual population rate of change (λ) of sage-grouse 

across the Great Basin from 1985 to 2014. Ten thousand samples from the posterior distributions of model 

parameters were used to represent the range of uncertainty in this relationship.
27

Figure 9. Three-dimensional display of the interaction effects between precipitation (spring through fall) and 

cumulative burned area on population rate of change (λ) of sage-grouse in the Great Basin from 1985 to 2014. 
28

Patterns of Cumulative Burned Area and Recovery Near Leks

Based on results from the burned area subgroup test in step 1 above, we modeled cumulative 

burned area per year within 5 km of leks using the normal burn recovery rate to describe past and 

predict future changes in the amount of burned areas, which explained more variation than the null 

model (∆DIC = 6.85) and adequately converged (R-hat < 1.1). The inter-annual trend estimate (median 

value of posterior probability distribution) indicated that 3.4 km2 (0.4 percent) per lek burned annually 

when averaged across all three R&R classes (fig. 10). When expressed as cumulative averages for the 

burned proportional area within 5 km of a lek for each resilience and resistance index classes: high R&R 

burned at 0.36 percent per year (95-percent CI = 0.0034–0.0039), moderate R&R burned at 0.34 percent

per year (95-percent CI = 0.0031–0.0037 percent), and low R&R burned at 0.53 percent (95-percent CI 

= 0.0050–0.0056; fig. 10). By 2013, an average of 89.1, 89.7, and 84.2 percent of unburned area with 

high, moderate, and low R&R, respectively, remained within 5 km of a lek.

30-Year Predictions of Cumulative Burned Area and Sage-Grouse Population Persistence

We estimated future cumulative burned area based on normal recovery rate for each R&R class 

by extracting 30,000 samples from the posterior probability distribution for area burn rate and 

extrapolating future cumulative burned area over 30 more years. By 2044, an average of 77.8, 79.1, and 

67.8 percent of unburned area with high, moderate, and low R&R, respectively, was projected to remain 

within 5 km of a lek (fig. 10). We then took each sample of the posterior probability distribution for the 

projected cumulative burned area for each year, and multiplied it by the effect of cumulative burned

area on annual population λ (derived from step 2) to project proportional changes in population size 

separately by R&R index class over the next 30 years. Because the effect of cumulative burned area was 

confounded by the interaction with precipitation, we generated projections under normal (50th

percentile), below-average (25th percentile), and above-average (75th percentile) levels of precipitation 

from spring through fall. We assumed a stable population (λ = 1.0) at the start of projections in order to 

better mimic environmental conditions facing sage-grouse in the absence of wildfire. Starting 

population size for projections was based on the mean of the 2014 lek counts within each R&R class, 

and subsequent annual changes in population size were expressed as a proportion of the population in 

2014. 

Sage-grouse populations under normal precipitation conditions declined steadily, resulting in 48, 

52, and 30 percent of grouse in high, moderate, and low R&R leks, respectively, projected to remain by 

2044 according to the 50th percentile parameter (median) of the posterior probability distribution (fig. 

11A). Sage-grouse populations declined more sharply under below-average precipitation conditions, for 

which 36, 38, and 22 percent of grouse in high, moderate, and low R&R leks, respectively, were 

projected to remain by 2044 on a median basis (fig. 11B). In contrast, sage-grouse populations in high 

and moderate R&R leks remained stable under above-average precipitation conditions through 

approximately 2025 on a median basis, and some upper percentile distributions showed some 

population growth. However, high and moderate R&R leks began to decline after 2025, and only 67 and 

71 percent, respectively, remained by 2044. Population decline was invariant to higher precipitation for 

low R&R leks, and only 40 percent remained by 2044 (fig. 11C).
29

Figure 10. Observed and predicted changes in cumulative burned area over time by R&R class. Y-axis values 

represent the average proportion of a 5 km circular lek buffer.
30

Figure 11. Thirty year predictions for proportion of sage-grouse populations remaining in the Great Basin given modeled effects of cumulative fire on 

rates of change under projected median (50th percentile; A), low (25th percentile; B), and high (75th percentile; C) amounts of precipitation during 

spring, summer, and fall.
31

Management Scenarios

Slowing forecasted population declines of Great Basin sage-grouse over the next 30 years may 

depend on intensity of fire suppression efforts in population ‘core areas’ and long-term patterns of 

precipitation (fig. 12). On a median basis, based on our simulations, reducing the rate of annual 

cumulative burned area by 25 percent in defined ‘core areas’ under all three modeled precipitation 

conditions did little to prevent population declines, and trajectories tracked those outside core areas 

where models allowed wildfires to continue to burn at the original modeled rate. However, reducing the 

cumulative burned area by 75 percent substantially slowed the rate of population decline under belowaverage precipitation conditions, stabilized population growth under normal precipitation conditions, 

and resulted in population growth under above-average precipitation conditions. Near complete fire 

suppression (99 percent) in core areas resulted in slightly increased population growth under all 

precipitation conditions on a median basis, especially for normal and above-average precipitation.

Figure 12. Examples illustrating projected sage-grouse population rate of change (λ) over the next 30 years under 

example management scenarios of 25, 75, and 99 percent reductions in annual average cumulative burned area 

within 5 km of lek sites under low, median, and high amounts of precipitation. Solid black line indicates stable 

population growth (λ = 1.0).
32

Interpretation and Conclusions

We quantified relatively long-term effects of wildfire and precipitation on sage-grouse 

population growth across the Great Basin, a significant proportion of the species range. Our results 

indicate that wildfire has persistently and negatively impacted sage-grouse population growth over the 

past three decades. Although wildfire is a natural process in sagebrush ecosystems, burn frequency and 

size of wildfires within the boundaries of the hydrographic Great Basin have increased artificially over 

the past few decades in response to the range expansion of invasive annual grasses (that is, primarily 

cheatgrass native to Eurasia) and changes in land-management practices (Baker, 2006, 2011; Brooks 

and others, 2015; Ielmini and others, 2015). However, trends in burn frequency, wildfire size, and other 

attributes have been shown to vary substantially across the range of sage-grouse (Brooks and others,

2015). Within the Great Basin, wildfire is highly probable when cover of annual grass increases to 

values greater than 45 percent (Link and others, 2006; cited by Miller and others, 2011), and a large 

majority of sagebrush ecosystems within the Great Basin are at high risk of wildfire if cheatgrass 

expansion continues at its unfettered pace (Suring and others, 2005; cited by Miller and others, 2011). 

Importantly, evidence of the chronic effect suggests that the adverse impacts associated with wildfire on 

sage-grouse populations are primarily driven by replacement of sagebrush following fire to invasive 

grasses. These findings indicate that model-predicted increases in cumulative burned area (that is,

wildfire-scars) over the next 30 years will have significant negative impacts on sage-grouse habitat and 

long-term sage-grouse population trajectory. 

Given current rates of cumulative burned area within 5 km of leks, model-based projections 

indicate continued declines for sage-grouse in the Great Basin over the next three decades under most 

scenarios. Across all R&R classes within the Great Basin, we projected a median of 44 percent

(95-percent confidence limit; 22 to 69 percent) of current sage-grouse population numbers will remain 

by the mid-2040s. Our projections are similar to those of another recent study conducted across the four 

sage-grouse management zones within the Great Basin, which projected a 50-percent reduction (range 

32−60 percent) in carrying capacity (a surrogate for population size) by 2043 (Garton and others, 2015). 

These consensus findings were reached despite distinctly different methodologies between these studies. 

For example, our study employed Bayesian inferences derived from individual lek complexes modeled 

with density-dependence, wildfire, and precipitation covariates while implementing random effects to 

account for random variation among site and year. Because these environmental conditions (that is,

wildfire and precipitation) often change over time and can demonstrate cyclical patterns (Fedy and 

Doherty, 2011), our modeling approach allowed for temporal variation in rate of change as a function of 

short-term changes in those environmental factors over the past 30 years. In other words, our population 

rate of change was not constrained to fit a linear time trend. However, our 30-year projections were 

based on predicted average annual changes in cumulative burned area over time (that is, linear change) 

under different precipitation scenarios. In contrast, Garton and others (2015) applied maximum 

likelihood estimates derived from larger regional clusters of leks modeled with density-dependence and 

temporal (that is, year) effects, instead of environmental covariates. Year effects also can serve as a 

proxy for environmental conditions that change over time. Thus, similar projections among studies, 

coupled with exceptional fit of our model, indicate that wildfire and precipitation patterns explain 

temporal patterns in sage-grouse population size in the Great Basin. Although estimation of growth rate 

is sensitive to variation in absolute population size relative to carrying capacity and cyclic dynamics 

(May, 1974), similar conclusions reached by both studies indicate that estimated effects are not spurious 

artifacts of a specific modeling technique. 
33

Sage-grouse in the Great Basin essentially inhabit a cold desert where ecosystem productivity is 

tied strongly to water availability (Noy-Meir, 1973). It follows that periods of above-average 

precipitation result in food and cover resources that boost survival of young sage-grouse and contribute 

to population recruitment (Blomberg and others, 2012; Guttery and others, 2013). Precipitation supports 

moisture recharge of upland mesic sites where forbs and invertebrates become abundant as food for 

young grouse (Casazza and others, 2011). Thus, precipitation may provide a population-level buffer 

against subsequent periods of drought, where prolonged drought is likely to have deleterious impacts on 

population persistence, independent of wildfire. In our study, sage-grouse population growth responded 

positively to increases in total precipitation during spring, summer, and fall months, supporting grouse 

reproduction. However, this positive effect was counteracted as cumulative burned area in relation to 

leks increased across the Great Basin, and was nullified where burned areas were the largest. Blomberg 

and others (2012) found a similar pattern at a local study site located within central Nevada. Hence, 

wildfire acts to negate the positive effects of cyclically favorable weather on sage-grouse population 

growth and appears to do so across multiple spatial scales.

Our modeling approach has two informative and novel aspects. The first of which is our spatially 

explicit incorporation of different recovery times for sagebrush with range-wide concepts of resilience 

and resistance that are currently a cornerstone of sagebrush ecosystem management. Habitat in the low 

R&R comprises approximately 40 percent of all habitat with mapped R&R values in the Great Basin, 

and an ecosystem that is highly susceptible to cheatgrass invasion following disturbance. Accordingly, 

current R&R-based management strategies call for protecting intact low R&R habitat “of high 

conservation value” from wildfire, and enhancing R&R in moderate and high R&R habitats in the early 

stages of cheatgrass invasion where restoration success is high because, in part, of increasing cover of 

perennial grasses and forbs and reducing large-woody fuel loads such as encroaching pinyon-juniper 

(Chambers and others, 2014a, 2014b). Our model-simulated conversion of burned habitat with low 

R&R to a permanent burn-scar integrates the rather high likelihood of a hysteretic transition to a 

cheatgrass-invaded state without intensive (and sometimes ineffective) management intervention. It 

follows that wildfire had the most substantial impact on sage-grouse inhabiting these habitats where 

projected population declines were greatest. 

However, these R&R based projections also predicted 30-year declines for sage-grouse 

inhabiting moderate and high R&R habitats. Although moderate and high R&R habitats may recover 

relatively quickly in ecological time and restoration of sagebrush ecosystems are thought to have a high 

likelihood of success (Chambers and others, 2014a, 2014b), our findings suggest that wildfires within 

these habitats still can be harmful to sage-grouse survival and recruitment, especially if wildfires 

increase in size and frequency. Hence, relatively fast ecosystem recovery times in moderate and high 

R&R habitats under the current burn trends in the Great Basin may be asynchronous with sage-grouse 

demographic response to post-wildfire recovery of sagebrush. Management of wildfire in sagebrush 

from a perspective of plant-soil dynamics and disturbance regimes can benefit from a greater 

understanding of sage-grouse demographic responses to wildfire provided by our modeling.

The second informative and novel aspect of our approach stems from our example management 

scenarios, which simulated focused reductions in cumulative burned area near leks within sage-grouse 

‘core areas’ across the Great Basin. These areas are most meaningful to sage-grouse based on response 

indices of habitat suitability and abundance (Doherty and others, 2015). For example, high resolution 

maps depicting habitat suitability for sage-grouse have been developed recently in Nevada and northeast 

California (Coates and others, 2014). Core areas calculated in this Nevada and California region overlap 

with approximately 20 percent of overall sage-grouse habitat (48 percent of high quality) but encompass 

about 90 percent of the males counted. It follows that targeted fire suppression in these core areas could 
34

help conserve large blocks of the ‘best’ areas for sage-grouse in the Great Basin. A straightforward 

calculation between males counted per hectare within core versus non-core areas, revealed an 

approximate 32.8-fold increase (3,180 percent increase) in ‘return’ by focusing on core opposed to noncore areas, and a 4-fold increase (298 percent increase) in return compared to random area of sagegrouse. In our example scenarios, reducing cumulative burned area per year by 25 percent in core areas 

under current regimes of median precipitation slows, but does not halt, projected rates of overall sagegrouse decline based on the 50th percentile of the posterior distributions of model derived parameters. 

However, reducing this area by 75 percent in core area under the current median or below average 

precipitation regimes may result in locally stable sage-grouse populations. 

Importantly, our analysis incorporated recovery times as a function of their corresponding R&R 

index values. Thus, reduction in cumulative burned area can be achieved through management actions 

aimed at fire suppression as well as those that accelerate sagebrush recovery. We recognize that federal 

wildfire managers have reported that 97 percent of fires burned less than 1,000 acres of area, which may 

have been the result of initial attack fire suppression activities, and less than 1 percent were greater than 

10,000 acres (Havlina and others, 2015). Although additional modeling would help us achieve reliable 

estimates of suppression rates and, perhaps, understand relationships between fire suppression rates

versus burned areas in the Great Basin, increased suppression could lead to a significant reduction in 

cumulative burned area if potentially large wildfires (that is, mega-wildfires) are suppressed before they 

grow to unmanageable sizes. Importantly, approximately 32 percent of the estimated sage-grouse 

population within core areas (based on the maximum of lek counts between 2009 and 2014) is 

comprised primarily of low R&R habitat within 5 km of leks. Because these sites are highly susceptible 

to invasive grass following wildfire and, thus, permanent loss of sagebrush, management actions 

focused on suppression and protection should be most effective. However, it follows that core area leks 

surrounded by moderate or high R&R may benefit from recovery actions, as well as suppression. This is 

important because sage-grouse core areas comprised 35 and 39 percent of high and moderate R&R 

classes, respectively. It is also important to recognize that the predicted ‘success’ of reducing 

cumulative burned area varies with amounts of precipitation. Although climate models predict future 

increases in winter precipitation in latitudes above 40°, the Great Basin as a whole is predicted to 

receive lower precipitation in upcoming years (Intergovernmental Panel on Climate Change, 2014; 

Schlaepfer and others, 2014). Thus, predicted outcomes of increasing sage-grouse populations with 

reductions of cumulative wildfire in core areas under high precipitation conditions will likely be most 

effective in northern areas where R&R is relatively high and conditions generally are colder and wetter. 

However, reductions may be less effective in more southern areas of the Great Basin if climate model 

predictions hold.

Several caveats need to be considered when interpreting or implementing our results. First, 

recovery times specific to R&R classes did not take implemented restoration efforts into account (for 

example, Emergency Stabilization and Rehabilitation; Pilliod and Welty, 2013). However, restoration 

success is highly variable (Arkle and others, 2014), and often dependent on local conditions (for 

example, wet sites with established perennial grasses) or intensity of restoration (for example, drill 

seeding) (Knutson and others, 2014). Thus, modeling restoration efficacy and success was beyond the 

scope of our Great Basin-wide analysis of wildfire effects on sage-grouse, but may contribute to

additional variation in sage-grouse demographic response to wildfire. Similarly, post-wildfire recovery 

times for sagebrush can vary widely along elevation and precipitation gradients across the species’ 

range. For example, Baker (2011) estimated that post-fire recovery times in mountain big sagebrush 

communities can take either fast (25–35 years) or slow (75–100 years) tracks for recovery. In contrast, 

recovery in arid Wyoming big sagebrush communities can be more prolonged (for example, 50–120 
35

years, Baker, 2006). The temporal duration of our time series (that is, 30 years) precluded testing of 

different recovery times to complete pre-wildfire conditions, so we focused on recovery times for 

minimal resources necessary (that is, 20 percent sagebrush return) to meet some sage-grouse life history 

requirements based on published estimates. Notably, our slower ‘decelerated’ recovery model showed 

less support from the data than the ‘normal’ recovery model. Collection of field-data to quantify a 

chronological sequence of wildfire-related vegetation change or recovery among R&R index classes 

could prove beneficial to these and other analyses, and is currently underway. 

The second caveat is that our analysis did not parse the effects of prescribed fire versus wildfire, 

or potential beneficial effects of fire in sagebrush ecosystems. As stated previously in this report, most 

studies of fire effects on sage-grouse and their habitat have focused on prescribed fire. Connelly and 

others (2000a) described a significant decline in lek attendance following a prescribed fire that removed 

nearly 60 percent of existing sagebrush, while other studies have reported prescribed fire-related 

reductions in forage availability and quality (Nelle and others, 2000; Rhodes and others, 2010). 

However, low intensity prescribed fire followed by higher-than-normal annual precipitation has been 

associated with increased flowering and extended phenology of several forb taxa selected by sagegrouse and their chicks (Wrobleski and Kauffman, 2003). In addition, approximately 66 percent of sagegrouse leks tend to occur in sagebrush habitats with low to moderate R&R that are being subjected to 

larger and more persistent wildfire scars according to our models. Yet, this pattern may not pertain to 

montane habitats at high elevation that our model did not specifically measure (Brooks and others,

2015), as well as other areas outside the Great Basin but within the range of sage-grouse with a 

preponderance of high R&R habitats (for example, sage-grouse management zones I and II). Historical 

wildfire may have been beneficial in these wet and productive montane habitats where resilience is 

intrinsically high (Brooks and others, 2015). Suppression of wildfire at higher elevations also may have 

the unintended consequence of facilitating encroachment of pinyon-juniper woodlands across the 

Intermountain West (Miller and Rose, 1999; Miller and others, 2013), which decreases resilience to 

wildfire (Chambers and others, 2014a) and substantially reduces sage-grouse habitat suitability (Casazza 

and others, 2011; Baruch-Mordo and others, 2013). 

Finally, several aspects of our empirical modeling and future projections incorporated ‘best case’ 

assumptions that require additional discussion of caveats. First, our 30-year projections began with 

stable populations (λ = 1.0) without explicit incorporation of modeled density-dependent or climatic 

parameter estimates. Rather, and for the sake of focusing on the primary question of interest (that is,

fire), we drew from posterior distributions of parameter estimates for the effect of burned area that 

accounted for variation in density and precipitation. Second, we modeled and projected increases in 

cumulative burned area as a linear function with time (year). Notably, empirical data points for burned

area near the end of the time-series fell outside the upper tail of the 95-percent prediction interval (fig. 

10), which appears to take an exponential form. We chose to conservatively model a linear relationship 

owing to uncertainty in future rates of fire suppression and restoration efficacy. 
36

A third point that likely led to more optimistic predictions was our estimation of post-fire 

recovery rates for R&R index classes based on meta-analyses for mountain and Wyoming big sagebrush 

communities, where we assumed mountain big sagebrush comprised high and moderate R&R, and 

Wyoming big sagebrush comprised low R&R. Recovery for leks classified as high and moderate R&R 

could be biased high if they also comprised some proportion of Wyoming big sagebrush. Moreover, our 

‘recovered’ criteria of 20 percent return of pre-fire sagebrush cover only meets partial life history 

requirements, and may be too low to maintain adequate sage-grouse survival or recruitment. High 

sagebrush cover is not always associated with winter and brood rearing habitat, but large expanses of 

intact sagebrush often associates with high nesting success, and tall sagebrush is critical for providing 

forage in deep snow conditions (Connelly and others, 2000b; P. Coates and M. Casazza, U.S. 

Geological Survey, unpub. data, 2008). 

We additionally defined burned area based on MTBS severity classes most indicative of 

vegetation change resulting from fire (that is, severity class > 2), yet MTBS severity classes are prone to 

errors of omission and commission. A recent analysis of MTBS fires within big sagebrush, black and 

low sagebrush, and grassland vegetation types across the range of the sage-grouse indicated that 70

percent of fires were classified using “initial assessment” imagery from the same growing season as the 

fire (typically a few months later) and the other 30 percent used “extended assessment” imagery from 

the first growing season after the fire (Brooks and others, 2015). In areas where the latter imagery is 

used, vegetation recovery prior to the imagery acquisition in the following growing season can result in 

lower dNBR values than if initial assessment imagery were used. Among all the 30-m MTBS pixels 

within the fires evaluated in Brooks and others (2015), initial assessment fires contained 13 percent fireseverity class 1 pixels, whereas extended assessment fires contained 35 percent fire-severity class 1 

pixels. We do not know the specific proportions of initial versus extended assessment fires, and thus a

potential underestimation of fire area is possible using the fires in this current study. However, by 

focusing on MTBS fires (that is, greater than 405 ha), we likely omitted approximately 5 percent of the 

total fire area that would have otherwise been represented by smaller fires (Eidenshink and others, 2007; 

Brooks and others, 2015). Thus, the absolute fire area values reported in the current study should be 

considered conservative underestimates. Departure from these ‘best case’ aforementioned assumptions 

or realization of caveats would likely lead to more rapid projected population declines and accentuated 

negative effects on sage-grouse.

Using large-scale datasets and well-founded R&R concepts, our study identifies clear and 

predictable patterns of major sagebrush habitat loss associated with cumulative wildfire effects. R&R 

concepts, which has often resulted in major losses of sagebrush habitat. Importantly, even under ‘best 

case’ assumptions, we demonstrate that adverse impacts associated with accelerating positive feedbacks 

between wildfire and invasive annual grass on sage-grouse populations are non-trivial in the Great Basin 

and, under some conditions, wildfire can nullify positive effects on sage-grouse that are normally 

associated with increased precipitation. Balancing management resources between post-wildfire 

restoration actions versus those of fire suppression is a challenging task facing land and wildlife 

agencies. Dependent on sagebrush ecosystem concepts and population responses of sage-grouse, our 

work provides an initial modeling framework and decision support tool to help land and wildlife 

managers sustain sage-grouse populations within the Great Basin.
37

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