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\begin{document}

\title{Greater sage-grouse face tradeoffs between predation risk and thermal
exposure in selecting habitat}



\author[1]{Aidan Beers}%
\author[2]{Shandra Frey}%
\affil[1]{Montana State University}%
\affil[2]{Utah State University}%


\vspace{-1em}



  
  \date{May 23, 2024}


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\selectlanguage{english}
\begin{abstract}
Climate change is likely to drive widespread species range shifts and
extirpations, mostly on the warm distribution edges, where habitat tends
to be fragmented, of lower quality, support lower population density,
and at greater risk from extreme weather events. In the Intermountain
West, future climate will likely be warmer and drier, driving a
reduction in sagebrush (Artemesia sp.) and other shrubland cover. Among
the species threatened by more xeric climate is the Greater sage-grouse
(Centrocercus urophasianus), which depends on sagebrush for forage and
shelter, though their response to temperature is not well studied. We
deployed 75 data loggers across two valleys in southern Utah and Nevada,
near the southern edge of sage-grouse distribution, and collected
temperature data for 27 months. We used random forest models to test the
impacts of temperature, land cover, and topography on sage-grouse
habitat selection and found that temperature influenced selection in all
seasons and both sites. In Utah, the warmer site, sage-grouse selected
areas near trees during the extremes of both winter and summer. In
autumn and spring those extremes were rarer and sage-grouse avoided
habitat near trees. Conversely, sage-grouse in the cooler Nevada site
selected contiguous patches of sagebrush in extremes periods but only
selected habitat near trees during winter cold, avoiding trees during
summer. Our findings show that extreme temperatures drive sage-grouse to
select habitat near trees despite the risk likely posed by avian
predators. The difference between the Utah and Nevada sites suggests
that sage-grouse prefer sagebrush as thermal shelter but that it may be
inadequate during the hottest times, forcing riskier selection. These
models point toward a more mechanistic understanding of how sage-grouse
distribution may retract at its warm edges. This will refine our
understanding of seasonal habitat requirements and inform management
decisions to prioritize thermal refugia for an imperiled species.%
\end{abstract}%



\sloppy


Title: Greater sage-grouse face tradeoffs between predation risk and
thermal exposure in selecting habitat

Authors: Aidan T. Beers\textsuperscript{1, 2}, S. Nicki
Frey\textsuperscript{1}

Author affiliations: \textsuperscript{1}Department of Wildland
Resources, Utah State University, Logan, UT USA

\textsuperscript{2}Present affiliation: Department of Ecology, Montana
State University, Bozeman, MT USA

\subsection*{ABSTRACT}

{\label{abstract}}

Climate change is likely to drive widespread species range shifts and
extirpations, mostly on the warm distribution edges, where habitat tends
to be fragmented, of lower quality, support lower population density,
and at greater risk from extreme weather events. In the Intermountain
West, future climate will likely be warmer and drier, driving a
reduction in sagebrush (\emph{Artemesia sp.} ) and other shrubland
cover. Among the species threatened by more xeric climate is the Greater
sage-grouse (\emph{Centrocercus urophasianus} ), which depends on
sagebrush for forage and shelter, though their response to temperature
is not well studied. We deployed 75 data loggers across two valleys in
southern Utah and Nevada, near the southern edge of sage-grouse
distribution, and collected temperature data for 27 months. We used
random forest models to test the impacts of temperature, land cover, and
topography on sage-grouse habitat selection and found that temperature
influenced selection in all seasons and both sites. In Utah, the warmer
site, sage-grouse selected areas near trees during the extremes of both
winter and summer. In autumn and spring those extremes were rarer and
sage-grouse avoided habitat near trees. Conversely, sage-grouse in the
cooler Nevada site selected contiguous patches of sagebrush in extremes
periods but only selected habitat near trees during winter cold,
avoiding trees during summer. Our findings show that extreme
temperatures drive sage-grouse to select habitat near trees despite the
risk likely posed by avian predators. The difference between the Utah
and Nevada sites suggests that sage-grouse prefer sagebrush as thermal
shelter but that it may be inadequate during the hottest times, forcing
riskier selection. These models point toward a more mechanistic
understanding of how sage-grouse distribution may retract at its warm
edges. This will refine our understanding of seasonal habitat
requirements and inform management decisions to prioritize thermal
refugia for an imperiled species.

KEYWORDS: greater sage-grouse, habitat selection, thermal ecology, Basin
and Range, random forest, range margins, \emph{Centrocercus
urophasianus}

COVER LETTER:

Allen Moore, Andrew Beckerman, Chris Foote, Gareth Jenkins, Marcus
Lashley, and Zhaoxue Ma

Editors-in-chief

\emph{Ecology and Evolution}

Dear Editors,

We are very pleased to submit our article, \emph{Greater sage-grouse
face tradeoffs between predation risk and thermal exposure in selecting
habitat} , for consideration at \emph{Ecology and Evolution} .

In this research, we used GPS transmitters to track the habitat
selection of greater sage-grouse in the fragmented habitat of their
southern range margin. As sagebrush habitat specialists, greater
sage-grouse are more vulnerable to predation in areas of greater habitat
fragmentation. It is clear that encroaching conifer forests provide
perches for avian predators and threaten sage-grouse habitat, and
previous research suggests that sage-grouse select more rugged terrain
when near trees. However, it is unclear what may compel sage-grouse to
select habitat near trees rather than avoiding them altogether. Here, we
present evidence that along their southern range margin, greater
sage-grouse may be forced by high temperatures to seek thermal refuge in
tree cover when sagebrush is inadequate shelter. This has important
implications for how we understand the risks faced by this imperiled
species and the factors land managers must consider for their
conservation, especially in the face of ongoing climate change.

We believe that \emph{Ecology and Evolution} would be an excellent means
to disseminate our research. Sage-grouse are a species of conservation
concern that may serve as an indicator species for sagebrush ecosystems
and the challenges they face are emblematic of those faced by other
habitat specialists and of conservation efforts in general.

We declare no conflicts of interest and would be happy to correspond
further at aidan.beers@montana.edu or aidantb@gmail.com.

Thank you for your time and consideration.

Dr. Aidan T. Beers

Postdoctoral Researcher

Department of Ecology

Montana State University

Co-author

Dr. S. Nicki Frey

Associate Professor

Department of Wildland Resources

Utah State University

nicki.frey@usu.edu

\subsection*{INTRODUCTION}

{\label{introduction}}

Ongoing climate change is forcing species redistributions and local
extirpations, driving shifts in habitat suitability and connectivity and
compelling wildlife to shift their range or modify their behavior to
avoid extirpation (Thomas et al. 2004a, Parmesan 2006, Chen et al.
2011b, Varner et al. 2016, Beever et al. 2017, Pecl et al. 2017).
However, the rates of change in habitat suitability are heterogeneous
across a species' range, as climate is not always the dominant driver of
range limits (Arntzen and Espregueira Themudo 2008, Balzotti et al.
2016, Oldfather et al. 2020). Habitat fragmentation (sometimes as an
effect of climate) has been implicated as a primary driver that both has
direct effects and can exacerbate the impacts of climate (Opdam and
Wascher 2004). Furthermore, local- and micro-scale climates can be
decoupled from regional trends, especially by topography, complicating
predictions of population persistence and connectivity (Dobrowski 2011,
Ashcroft et al. 2012, Gollan et al. 2015). That decoupling can create
microrefugia (Rull 2009, Hannah et al. 2014) where suitable habitat
persists longer than expected at macroecological scales. This
complicates and can limit our understanding of how species and
ecosystems will respond to climate change and can reduce the capacity to
plan for and manage change for sensitive species.

Microrefugia and their impact on species are especially important at
species' lagging range margin, where habitat is likely to be fragmented
and of lower quality. Even for mobile wildlife species, suitable
microhabitat can provide essential refuge from thermal stress and
extreme events that otherwise drive local extirpations at range margins
(Parmesan 2006, Seabrook et al. 2014, Lima et al. 2016). For species of
conservation concern, studying their limiting factors at range margins
can provide insight into their capacity to shift their range or
behaviorally adapt to new conditions. In particular, the lagging range
margin can be used as a natural laboratory to evaluate the environmental
factors limiting the defining range limits and portend the conditions
likely to become more common at their current range core (Travis and
Dytham 2004, Keith et al. 2008, Seabrook et al. 2014). It is therefore
critical to identify the mechanisms limiting habitat suitability for
sensitive species at their lagging range margin at multiple scales (Vale
et al. 2014).

Large-scale patterns in species distribution often do not scale down and
can neglect variation in habitat suitability at finer scales, especially
at range margins where species distribution models (SDMs) tend to be
less accurate (Hannah et al. 2014, Vale et al. 2014). While much of the
research on range limitations focuses on occupancy, studying wildlife
habitat selection may offer further insight into how individuals are
compelled to exploit microhabitat in response to thermal stress. Shifts
in wildlife behavior in response to thermal stress or other climatic
drivers often precede detectable shifts in distribution or population
processes (Berger-Tal et al. 2011, Beever et al. 2017). By focusing on
tendencies in individual habitat selection, we are able to identify the
environmental factors that foster suitable microhabitat and better
inform management at local scales for sensitive species. In combination
with large scale distributions, understanding limits on habitat
selection at range margins can provide more accurate estimates of
wildlife response and sensitivity to climate change.

The range of sagebrush of western North America has declined rapidly due
primarily to land conversion, improper grazing management, fire,
invasive species, and loss to grassland and forest (Connelly and Braun
1997, Connelly et al. 2004). Sagebrush species (\emph{Artemesia sp.} )
will likely have varied responses to ongoing climate change, but at
their southern range limit they are likely to decrease in cover in
response to climate warming (Tredennick et al. 2016, Kleinhesselink and
Adler 2018, Renwick et al. 2018).

As a sagebrush obligate, greater sage-grouse (\emph{Centrocercus
urophasianus} , hereafter ``sage-grouse'') range has declined in
response to loss of sagebrush-dominated habitats (Braun 1998, Connelly
et al. 2004, Schroeder et al. 2004). Sage-grouse are a species of
conservation concern emblematic of the sagebrush system and may serve as
an indicator of ecosystem change there (Rowland et al. 2006, Hanser and
Knick 2011, Runge et al. 2019, Ricca and Coates 2020). Habitat
specialists like sage-grouse are less able to adapt to novel conditions
(Hampe and Petit 2005), so studying their habitat selection along their
lagging (southern) range margin provides an opportunity to assess the
factors likely to limit suitable habitat and portend future changes to
their distribution and to sagebrush habitats.

For sage-grouse, it is clear that sagebrush extent is an essential
driver of their habitat, but it is not the only limitation, as sagebrush
range extends far south of that of sage-grouse. So while SDMs for
sage-grouse likely explicitly include sagebrush (Balzotti et al. 2016),
a habitat selection framework can elucidate important points of stress
and cryptic fragmentation that would be overlooked at coarser scales or
by focusing on occupancy. In particular, it is important to assess the
effects of direct thermal stress on selection, as extreme weather could
preclude using otherwise suitable habitat. While other gallinaceous
birds are sensitive to temperature (Patten et al. 2007, Hovick et al.
2014, Londe et al. 2021), thermal effects on sage-grouse are not clear.
Pratt et al. (2017) used relatively coarse scale PRISM data (4 km
resolution: {[}PRISM Climate Group 2020{]}) to study the role of
temperature in triggering sage-grouse seasonal migration; that study
indicated sage-grouse make coarse scale decisions about their habitat in
response to temperature, but it did not address the degree to which
sage-grouse select habitat within seasons in response to thermal stress
or how seemingly intact habitat can be or will become untenable due to
temperature.

In addition to sagebrush extent, sage-grouse habitat selection and
long-term persistence is strongly impacted by tree cover, especially
encroaching forests of pinyon pine (\emph{Pinus monophyla} and \emph{P.
edulis} ) and juniper (\emph{Juniperus spp.} ), as conifers can replace
sagebrush cover and may provide perches for avian predators (Frey et al.
2013, Prochazka et al. 2017, Severson et al. 2017b, 2017a, Olsen et al.
2021). Large-scale studies of sage-grouse lek persistence and population
trends suggest that tree cover can be among the greatest threats to
sage-grouse and other sagebrush obligates (Davies et al. 2011,
Baruch-Mordo et al. 2013, Knick et al. 2013), though to our knowledge
only one study has directly linked conifer cover with decreased survival
(Prochazka et al. 2017). Yet despite the poorer habitat quality and
likely risk of avian predators, sage-grouse sometimes select habitat
near trees, possibly mitigating that risk by exploiting rugged
topography to block predator sightlines (Dinkins et al. 2014, Beers and
Frey 2022a). The reason for this apparent incongruity between some
observed selection and population processes is unclear. However, it has
been suggested that sage-grouse may be prone to ecological traps or
maladaptive selection, wherein they select areas of greater risk to
exploit its resources in spite of negative fitness impacts (Kirol et al.
2015, Coates et al. 2017, Pratt and Beck 2021). The reasons for that
potentially risky selection have not been explored.

It is likely that sage-grouse will be extirpated from large swathes of
their current southern range if warming and drying trends continue,
resulting in sagebrush conversion to grassland, increased fire
frequency, and decreased soil moisture and mesic resources (Schlaepfer
et al. 2012a, Kleinhesselink and Adler 2018). To best conserve
sage-grouse, it is therefore important to assess the role that thermal
stress plays in driving their habitat selection in the fragmented
habitat of their southern range edge. In identifying the direct impact
of temperature on selection, we will be better able to predict the local
and regional variation in habitat suitability along the lagging range
margin and to inform conservation efforts to foster potential
microrefugia. Knowledge of selection for suitable microhabitat can
complement larger scale efforts and inform ecosystem management to
better identify areas at multiple scales that are most likely to support
that microhabitat and to take actions to foster or create it (Kirol et
al. 2015).

In this study, we sought to identify trends in sage-grouse habitat
selection within each season in response to near-surface temperature, to
determine when and where sage-grouse select habitat in response to
temperature and identify where temperature is most likely to limit
habitat suitability. We hypothesized that sage-grouse would make
micro-scale habitat selection in response to extreme temperatures within
their home range and within otherwise suitable sagebrush habitat.

\subsection*{MATERIALS AND METHODS}

{\label{materials-and-methods}}

\subsubsection*{Study areas}

{\label{study-areas}}

We performed this study in two valleys near the sage-grouse southern
range margin. Both valleys were a mosaic of sagebrush and grasses
bordered by mountains (Figure 1). In this region sagebrush is
largely\emph{Artemisia tridentata wyomingensis} with patches of other
\emph{A. tridentata} subspecies and some patches of \emph{A. nova} in
the more xeric areas. The mountainous areas included some patches of
sagebrush, but were largely dominated by mixed pinyon-juniper forest
(\emph{P. monophylla} and \emph{J. osteosperma} ), mountain mahogany
(\emph{Cercocarpus sp.} ), and occasional stands of aspen (\emph{Populus
tremuloides} ). In each valley, the pinyon-juniper forest was expanding
farther into the valley, and in each there had been management actions
to remove some of that expansion.

Buckskin Valley and Bear Valleys in Utah are in the Panguitch
Sage-grouse Management Area (SGMA; Utah Public Lands Policy Coordination
Office 2019). This Bear-Buckskin complex (hereafter, Buckskin) was the
smaller of the two study areas (\textasciitilde{}220
km\textsuperscript{2}), located farther south, and had a smaller
elevation range used by sage-grouse (2100 -- 2500 m). The highest areas
were the ridge between the two valleys and the lowest was the open, flat
center of Buckskin Valley. There were large patches of dense sagebrush
as well as large extents with little to no sagebrush, which was covered
by grasses (annuals and native bunchgrasses) and bare ground. Over the
30-year period (1991 -- 2020) used to define PRISM data climate normal,
Buckskin had a mean annual temperature of 7.3 \selectlanguage{ngerman}°C and received 435 mm of
precipitation. A large portion of that precipitation (127mm) came during
the spring, March -- May, though the second-wettest month on average was
August and the second-wettest three-month period was December --
February (109 mm). There was a mean monthly difference in maximum and
minimum temperatures of 18.1 °C. During the study period, Buckskin had a
mean annual temperature of 8.6 °C and an average of 345 mm of
precipitation each year. The hottest month was August
(T\textsubscript{mean} = 19.8 °C) and the coldest was February
(T\textsubscript{mean} = -3.9 °C). There was a mean monthly difference
between maximum and minimum temperatures of 15.2 °C. The northern half
of Buckskin is divided by Utah State Highway 20, which sees moderate
traffic. Bear Valley had a few small houses and ranch buildings, but
there were none in Buckskin. Buckskin and Bear Valleys each had a few
small gravel roads through them.

Steptoe Valley is part of the Steptoe/Cave Population Management Unit in
Nevada (Emm et al. 2019). It is larger than Buckskin
(\textasciitilde{}540 km\textsuperscript{2}) and is interspersed with
patches of grasslands throughout the valley, commonly including
cheatgrass (\emph{Bromus tectorum} ), crested wheatgrass
(\emph{Agropyron cristatum} ), Sandberg's bluegrass (\emph{Poa secunda}
or \emph{Poa sandbergii} ), bluebunch wheatgrass (\emph{Pseudoroegneria
spicata} ), and Indian ricegrass (\emph{Oryzopsis hymenoides} ). Within
this study area, grouse were generally located at higher elevations than
in Buckskin, Utah (2000 -- 2700 m). Buckskin had a greater seasonality
in its precipitation and tended to be warmer than Steptoe in each of
their respective wettest and driest quarters of the year.

Compared to Buckskin, Steptoe was drier and of about the same
temperature across the entire year, despite have less seasonality. From
1981 -- 2010, Steptoe had a mean annual temperature of 7.3 °C and 317 mm
of annual precipitation. Like in Buckskin, most of the precipitation
fell in March -- May (87 mm) and the second wettest quarter was December
- February (78 mm). In Steptoe, the warmest month (July) had an average
maximum temperature of 30.3 °C and the coldest month (December) an
average minimum temperature of -11.1 °C. There was a mean monthly
difference in maximum and minimum temperatures of 18.1 °C. During the
study period, Steptoe received an average of 233 mm of precipitation.
The hottest month was July (T\textsubscript{mean} = 19.6 °C) and the
coldest was February (T\textsubscript{mean} = -3.9 °C). During the study
period there was a mean monthly difference in maximum and minimum
temperatures of 19.3 °C. In Steptoe, there was a dirt road on either
side of the valley and the two met where the low area of the valley
narrowed to about 3 km across. Where the valley is close to 10 -- 12 km
across, there are a few permanent structures on the west side, mostly
clustered together. There is also a small state park, Ward Charcoal
Ovens State Historic Park, near the edge of the treeline with several
stone charcoal ovens. Like much of sage-grouse habitat in the Great
Basin, both Buckskin and Steptoe are used for cattle ranching and
sage-grouse could encounter both cattle and cattle-grazed habitat in
almost any part of each study area.

\subsubsection*{Landscape covariate data}

{\label{landscape-covariate-data}}

We downloaded 30 m resolution land cover data from the Landscape Fire
and Resource Management Planning Tools Project database (LANDFIRE;
Rollins 2009) to build rasters of cover by sagebrush and trees. We first
generalized the land cover types, reclassifying all cover types that
included the words ``tree,'' ``woodland'', ``forest'', ``conifer'', and
``juniper'' as tree cover, and any type described as ``sagebrush'' or
``\emph{Artemesia} '' as sagebrush cover. We created metrics of tree
cover---the density of ``tree'' pixels within 400 m and 800 m radii
(TREEDEN400 and TREEDEN800) and the distance to any ``tree'' pixel
(TREEDIST). Because the size and configuration of sagebrush patches can
be important for sage-grouse survival, we used
the\emph{landscapemetrics} package in R (Hesselbarth et al. 2019) to
calculate the contiguity (CONTIG) and core area index (CAI) of sagebrush
patches. Pixels that fell outside of sagebrush cover received a score of
0 for each of those metrics. CONTIG measures the degree to which pixels
within a patch of a single cover type are connected and values can range
from zero to one. CAI measures the percentage of pixels of cover type
patch that are not adjacent to pixels of a different cover type. As a
patch increases in size and interior area, CAI approaches 100.

Because topography plays a role in sage-grouse survival and habitat
selection (Aldridge et al. 2008, Knick et al. 2013, Dinkins et al. 2014,
Picardi et al. 2020, Beers and Frey 2022a), We also included metrics of
topographic position and heterogeneity. We first used the R
package\emph{elevatr} (Hollister et al. 2017) to download a 10 m
resolution digital elevation model (DEM) for each study area, then used
that DEM to calculate indices of topographic position (TPI) and
heterogeneity (THI) within moving window sizes of 50 m, 200 m, and 400
m. TPI (Jenness et al. 2013) is a measure of how high or low any DEM
cell is compared to the cells around it within a user-defined radius.
Cells with negative values are lower than the terrain around them and
positive values indicate a high point or ridge. THI is a measure of
overall ruggedness, calculated by summing the absolute value of TPI at
every cell within moving window sizes of 50 m, 200 m, and 400 m.

\subsubsection*{Temperature data}

{\label{temperature-data}}

We deployed HOBO Pendant Temperature/Light data loggers (Onset
Corporation, Bourn, MA, USA, \#UA-002-64) in a stratified random
distribution in each valley, placed within areas of known sage-grouse
use, collecting data every 30 minutes from June 2018 -- November 2020.
We attached the loggers to sagebrush or other shrubs where available to
minimize exposure to direct sunlight, on the north side of the shrub. At
points where there was no shrub available, we attached the logger to an
aluminum tent stake and drove it into the ground on the north side of a
bunchgrass. At each type of location, we positioned the logger 15 -- 25
cm above the ground to mimic the conditions a sage-grouse would
experience.

In July 2019 we picked up the loggers to download the data, install a
new battery, and redeploy them in a different random configuration. In
both years some of these loggers failed or were destroyed (seemingly by
cows, ravens, and coyotes). Of the loggers deployed in 2018, we were
able to use data from 31 from Steptoe and 22 Buckskin. Of those deployed
in 2019, we used data from 40 loggers from Steptoe and 28 loggers from
Buckskin. For each logger, we excluded any data where light intensity
was \textgreater{} 10,000 lumens to exclude warming from direct
sunlight. This filtering left a total of 1,016,833 data points. In
combination with loggers lost to extreme cold and animals, data omitted
due to direct sunlight, and the loggers being deployed in summer, in
both Buckskin and Steptoe we had the most temperature data points in
Autumn (Buckskin \emph{n} = 100,362; Steptoe \emph{n} = 168,901),
followed by Summer (Buckskin \emph{n} = 94,264; Steptoe \emph{n} =
156,610), Winter (Buckskin \emph{n} = 66,914; Steptoe \emph{n} =
112,597), and Spring (Buckskin \emph{n} = 56,816; Steptoe \emph{n} =
95,823). From those points, we pulled the daily maximum, minimum, and
difference (T\textsubscript{max}, T\textsubscript{min},
T\textsubscript{diff}, respectively) and then calculated the monthly
average for each of those metrics for each logger.

After calculating the T\textsubscript{max}, T\textsubscript{min}, and
T\textsubscript{diff} for each logger and each month in the study
period, we created an interpolated surface at a 100 m resolution and
aggregated the monthly averages by season. We performed the
interpolation using the interpolation tools in ArcMap version 10.6
(Environmental Science Research Institute (ESRI), Inc., Redlands,
California, USA), co-kriging across the extent of the study area in each
valley assuming that temperature varied with elevation. While elevation
is not the only driver of temperature at local scales, it is an
important factor (Dobrowski 2011, Ashcroft and Gollan 2012), thus we did
not include a separate measure of elevation in the habitat selection
analysis to avoid problems of variable collinearity. We grouped
September -- November as Autumn, December -- February as Winter, March
-- May as Spring, and June -- August as Summer.

\subsubsection*{GPS data}

{\label{gps-data}}

For sage-grouse locations, we tracked individual birds using
rump-mounted GPS transmitters (22 g Solar Argos/GPS PTT-100, Microwave
Telemetry Inc., Columbia, MD; 22 g GPS-PTT, GeoTrak, Inc., Apex, NC). We
captured sage-grouse at night with little to no moon illumination using
spotlights and dip nets, searching on foot in groups of 2 -- 4 in areas
of known or suspected sage-grouse use (after Giesen et al. 1982). While
handling the sage-grouse, we assessed their age, sex, mass, and body
condition. We declined to put a transmitter on any grouse with an injury
or a mass less than 1 kg. We released grouse at the capture site and
monitored their departure flight to ensure that bird was moving
naturally. The sage-grouse included in this study were captured in years
2017-2019. This research protocol was reviewed and approved by the Utah
State University IACUC ( \#10175, \#11161).

The GPS transmitters logged four locations per day. For this study, we
removed from the dataset any points from within 48 hours of a
sage-grouse's capture date, points for any grouse with fewer than 100
successful GPS fixes, and points that fell outside of the spatial extent
of the data loggers in each study area or outside the study period of
June 2018 -- November 2020. This left a total of 8163 data points from
14 birds in Buckskin and 7209 locations from 15 birds in Steptoe. We
calculated a 90\% home range for each sage-grouse from a kernel density
estimator using the R package \emph{adehabitatHR} (Calenge 2017). Within
that home range, we randomly generated points to sample the landscape as
``available habitat'' at a 1:10 ratio in a used-available design
(Johnson 1980, McDonald et al. 2013). However, for each run of the
models we randomly selected from within the available habitat point
dataset for a 1:1 ratio between used GPS detection points and available
habitat sampling points. This 1:1 ratio avoids problems that can arise
from oversampling from one class of the response variable in a
classification method like random forest classification (Chen et al.
2004, MacKenzie 2005, Reisinger et al. 2021, but see Street et al.
2021).

\subsubsection*{Model Construction}

{\label{model-construction}}

We grouped the data within the two study areas and within four seasons,
creating eight groups of data for analysis. For each of those data
groups, we built models both including and excluding temperature to
assess how temperature affected model performance, analogous to using a
null model for comparison. For each analysis, we built random forests
(RF) (Breiman 2001), a simple machine learning algorithm that has been
used successfully with complex ecological datasets (De'ath 2007, Yu et
al. 2020), including presence-only and animal habitat selection data
(McDonald et al. 2013, Mi et al. 2017, Zhang et al. 2019, Picardi et al.
2020, Rather et al. 2020). RF is a tree-based classification model that
uses a bootstrap sample of the data provided to train a model and a
withheld sample to test each iteration of the tree. It has outperformed
a traditional logistic regression approach in a used-available
framework, including wildlife habitat selection (Cushman et al. 2010, Mi
et al. 2017, Cushman and Wasserman 2018, Shoemaker et al. 2018, Rather
et al. 2020). We used the R packages \emph{ranger} (Wright et al. 2021)
and \emph{caret} (Kuhn 2016) to grow the RF with a leave-group-out cross
validation (LGOCV) grouped by sage-grouse ID to build these models. We
used 70\% of the data for initial model training with a random subset of
30\% of the data withheld for model validation. We tuned the models in
the training process by allowing the number of features selected for
testing at each node (\emph{mtry} ) to vary between 2, 3, 4, 5, 8, and
10. To minimize the chance of overfitting, we also set the minimum node
size at 50 points, which prevents each decision tree in the model from
making inferences on too little data (Valavi et al. 2021).

To evaluate each model's performance, we used the \emph{caret} package
(Kuhn 2016) in R to predict the withheld 30\% of the data and measured
model performance by the true skill statistic (TSS), Cohen's kappa,
model sensitivity, and used-habitat calibration (Fieberg et al. 2018).
TSS measures both model specificity and sensitivity while being
insensitive to prevalence (Fielding and Bell 1997, Allouche et al.
2006). There is also an argument that model evaluation metrics for
presence-background (i.e., used-available) data should not be
prevalence-insensitive (Stephanie et al. 2001, Lawson et al. 2014), so
we also included Cohen's kappa in model validation. Kappa ranges from -1
to 1, where higher values indicate greater model performance or strength
of agreement between withheld data and the model's predictions (Cohen
1960). A guideline for evaluating kappa suggests that a range 0.41 --
0.6 suggests ``moderate'' agreement, 0.61 -- 0.80 ``substantial''
agreement, and 0.81 -- 1.00 ``almost perfect'' agreement (Landis and
Koch 1977). The same guideline applies to TSS. Further, because the
available points in a used-available design do not necessarily represent
species absence, we also calculated the model's performance in
predicting only the true presence points (the model's sensitivity) and
calculated the correlation in used-habitat calibration. We repeated the
process of dataset division ten times for each study area and season and
report the average model performance metrics.

We also measured variable importance in each model using the mean
decrease in Gini node purity (Calle and Urrea 2011), which measures each
variable's contribution to the RF model's ability to distinguish between
response variable classes. We examined the impact of different variables
using partial dependence (R package \emph{pdp} : Greenwell 2017), which
is useful for interpreting RF models and others modeling methods that
measure nonlinear effects (Elith et al. 2008, Robinson et al. 2017). In
our models, partial dependence plots visualize the marginal effect of an
independent variable on the model's predicted used vs available outcome
at every value of that independent variable when the effects of all
other covariates are held at their mean value. Partial dependence plots
are also useful for showing the interaction of two variables in
predicted selection or avoidance, where two independent variables are on
adjacent axes and the dependent variable is represented by a color
gradient in the two-dimensional space of the plot.

\subsection*{RESULTS}

{\label{results}}

\subsubsection*{Temperature data}

{\label{temperature-data-1}}

At a broad spatial scale, the average ambient temperatures of Buckskin
and Steptoe were nearly equal, though Steptoe experienced both warmer
maximum and colder minimum temperatures while being drier (PRISM Climate
Group 2020). However, the data collected from the data loggers indicated
that Buckskin was slightly warmer in all four seasons (all
t-test\emph{p} \textless{} 0.001, Table 1). Buckskin had a higher mean
temperature in summer months; Steptoe had a colder mean winter minimum
temperature.

\subsubsection*{Model performance}

{\label{model-performance}}

We generated sixteen different model combinations of study area, season,
and data logger temperature inclusion. By comparing the performance of
models in the same study area and season with and without temperature
metrics, we evaluated the degree to which temperature drives sage-grouse
habitat selection in each of those situations. Most study area -- season
model combinations performed adequately or better, regardless of whether
the model included temperature data (Table 2). Here, we report a ``mean
performance'' for each model by averaging the value of each performance
metric, which is a simple way to initially describe model performance.

In Buckskin, Utah, all models that included data logger temperature
performed moderately to very well, showing ``substantial'' (0.61
\textless{} kappa \textless{} 0.80) to ``near-perfect'' (kappa
\textgreater{} 0.81) agreement between training data model predictions
and withheld testing data. The best performing model was in Summer,
which performed best by all metrics (mean performance = 0.940), followed
by Spring (0.840), Winter (0.828), and Autumn (0.825) (Table 2). For
each evaluation metric, the Summer model's performance was in the range
of ``near perfect'' agreement between model predictions and withheld
data, and including temperature covariates improved model performance
most in Summer (Table 2). The performance of the poorest models that
included temperature still suggest adequate or good performance. When
excluding temperature, mean model performance was again highest in
Summer (0.856), followed by Winter (0.824), Spring (0.815), and Autumn
(0.811). Notably, model performance was slightly higher without
temperature covariates than with them by at least one evaluation metric
in Winter (sensitivity), Spring (kappa), and Autumn (kappa), suggesting
less impact of temperature on selection in those cooler seasons (Table
2).

In Steptoe, Nevada, the best performing model that included temperature
covariates was also in Summer (mean performance = 0.815). Mean
performance was lower but still good in Spring (0.774), Winter (0.788),
and Autumn (0.804). For each seasonal model with temperature covariates,
TSS was greater than 0.63, kappa was greater than 0.62, sensitivity was
greater than 0.78, and UHC correlation was greater than 0.96 (Table 2).
Steptoe models excluding temperature covariates also performed at least
moderately well by each evaluation metric, with acceptable mean model
performance in Summer (0.746), Winter (0.727), Autumn (0.726), and
Spring (0.711). The greatest change in model performance in predicting
withheld data due to including temperature was in Autumn (\selectlanguage{greek}Δ \selectlanguage{english}mean model
performance = 0.064). The next largest changes in performance due to
temperature were in Spring (0.061), Winter (0.056), and Summer (0.058).

By including temperature in Steptoe's Summer and Winter models, when
temperatures were most extreme and therefore most likely to be limiting,
model performance improved by model sensitivity = 0.057 and 0.047,
respectively. Similarly, the same comparisons in Buckskin showed a
change in sensitivity of 0.134 in Summer and -0.037 in Winter due to
including temperature in the models. This shows a greater impact of
temperature in both study areas during Summer than in Winter.
Furthermore, there was a proportionally larger impact in Buckskin than
in Steptoe. Including temperature in Buckskin had a greater absolute
impact on model sensitivity, and the difference in improvement caused by
adding temperature was greater in Buckskin (0.171) than in Steptoe
(0.010).

\subsubsection*{Variable importance}

{\label{variable-importance}}

Our RF models of sage-grouse habitat selection showed that temperature
metrics played an important role in each model combination of study area
and season as measured by the mean decrease in Gini index. Compared to
the other variables included, temperature was the most important in
Summer in both Steptoe and Buckskin (Table 3). Although of less
influence in Winter, temperature variables were still important to model
fit. In Buckskin, sagebrush patch contiguity was among the three most
important variables in every model whether or not temperature was
included. In contrast, sagebrush contiguity was not among the most
important variables in Steptoe in any model. Distance to trees
(TREEDIST) was more important than sagebrush patch contiguity and core
area index in every model. In all seasonal models excluding temperature
covariates, distance to trees was among the three most important
variables.

\subsubsection*{Response to temperature}

{\label{response-to-temperature}}

Examining the partial dependence of the temperature variables in our
models suggest that sage-grouse avoided extreme temperatures. Partial
dependence plots showed that within each season, sage-grouse were most
likely to select moderate temperatures and avoided extremes. In
Buckskin, the probability of sage-grouse selecting areas of the
landscape dropped quickly and approached zero where temperatures in
Summer exceeded roughly 35 \selectlanguage{ngerman}°C, or 28 °C in Autumn (Figure 2). In
Steptoe, the effect was similar but not as clear (Figure 3). Sage-grouse
also selected locations with moderate minimum temperature in Summer,
Autumn, and Spring in both study areas. In Winter, the probability of
sage-grouse selecting habitat decreased rapidly where temperatures were
below a minimum temperature of -17 °C but then plateaued (Figure 4).
Similarly, maximum temperature in Steptoe was not as limiting to the
landscape selected by sage-grouse as in Buckskin---the rate of change of
partial dependence was slower across the available temperature range in
each season and the range of temperatures where selection was most
likely was less distinct.

These results indicate the importance of temperature in sage-grouse
seasonal habitat selection, but do not in themselves show how the birds
respond to temperature. The two-way partial dependence plots we built
demonstrate the choices sage-grouse tend to make during thermal
extremes. In Summer, measures of partial dependence show that
sage-grouse used areas closer to trees when maximum temperature was
high, especially when it was greater than \textasciitilde{}25 °C.
(Figure 5). Though sage-grouse rely on sagebrush, our results indicate
that they did not select large or contiguous patches of sagebrush during
high summer heat in Buckskin (Figure 6). Sage-grouse likewise tended to
avoid the coldest temperatures during Winter, but during these
temperatures, they were more likely to be nearer to trees. In
particular, when Winter minimum temperature was less than -16 °C,
sage-grouse were likely to be less than 50 m from trees (Figure 7). In
contrast, sage-grouse in Steptoe were more likely to avoid treed areas
during extreme heat trees---when Summer maximum temperature was above 30
°C, selection was most likely \textgreater{} 1500 m from trees (Figure
8). Instead, at those higher temperatures Steptoe sage-grouse were
likely to select areas of moderate to high sagebrush patch CAI, though
that trend was weaker than selection for trees in Buckskin (Figure 9).
Further, sage-grouse in Buckskin did not show strong selection for areas
near trees during the highest or lowest temperatures of Autumn and
Spring, when those high and low temperatures were less extreme than
Summer and Winter (Figure 11).

There was also an effect of topography interacting with temperature on
bird locations. In Buckskin, sage-grouse selected areas of greater
topographic heterogeneity during Summer heat (Figure 12a). When maximum
temperature was above 30 °C, selection was most likely at moderate to
high values of heterogeneity (THI400 \textgreater{} 4800). The effect of
topographic heterogeneity was less clear in Steptoe during Summer, where
sage-grouse selected moderately rugged terrain but with less difference
in selection across the ranges in maximum temperature and heterogeneity,
with the highest selection rate where THI400 was 7000 -- 12000 (Figure
12b). More rugged terrain exists in both study areas than is represented
in the GPS location dataset, especially in Steptoe, but fell outside of
the home ranges used to define ``available'' for this
3\textsuperscript{rd} order selection process.

\subsection*{DISCUSSION}

{\label{discussion}}

\subsubsection*{Temperature differences in study
areas}

{\label{temperature-differences-in-study-areas}}

The temperature data we recorded revealed differences in our Buckskin
(Utah) and Steptoe (Nevada) study areas that were not clear using the
coarser-scale PRISM data. PRISM data suggested that the two were nearly
identical in average temperature and that Steptoe was drier. We did not
measure precipitation, but measurements collected from temperature data
loggers suggested that Buckskin was slightly warmer than Steptoe on
average across the entire year. This suggests that while data like PRISM
is critical for understanding many broad-scale patterns, including for
sage-grouse, it is also essential to understand how temperature varies
and drives ecological phenomena at biologically relevant scales. For a
study of third-order habitat selection where individual home ranges may
not cover more than a few pixels of PRISM data, there may be variation
in temperature at finer scales that drives individuals' choices that
would be missed by coarser-scale data. For example, if simply
considering PRISM data, Steptoe may have appeared to be the less
suitable of the two areas, though we did not build RF models of
selection using PRISM data for comparison.

Furthermore, temperatures in both study areas during the study period
(June 2018 -- November 2020) were higher than the period currently used
to define climatic norms (1990 -- 2020). The difference was small but
given the differences we found in habitat selection between study areas,
it may be enough to reach a threshold in thermal stress beyond which
sage-grouse select habitat differently. Ongoing climate change is likely
to drive shifts in sagebrush distribution and ecosystem composition
(Schlaepfer et al. 2012b, Evers et al. 2013, Kleinhesselink and Adler
2018, Snyder et al. 2019), and species in Great Basin lowlands are
likely to face extirpation without adequate thermal refuge (Warren et
al. 2014). As that process continues, it will be increasingly important
to identify potential environmental thresholds, how sensitive species
like sage-grouse are likely to respond, the habitat that may provide
refuge in times and places that exceed these thresholds, and how
managers can plan for and mitigate negative impacts.

\subsubsection*{Response to temperature}

{\label{response-to-temperature-1}}

Our results suggest that sage-grouse select habitat in response to
temperature and that thermal extremes may be limiting. However, we also
found that sage-grouse use land cover---and to a lesser extent,
topography---as shelter from those extremes. When temperatures were
highest, sage-grouse were more likely to select habitat in either more
contiguous sagebrush or nearer to trees. In the warmer study area,
Buckskin, sage-grouse selected habitat nearer to trees while in Steptoe
they selected sagebrush cover. To our knowledge, this is the first time
that any study has documented how sage-grouse habitat selection varies
in response to temperature, though other research has detected wildlife
responding to temperature at similarly fine scales (Varner and Dearing
2014), including Galliformes (Hovick et al. 2014, Londe et al. 2021).
Where temperature at fine scales can be decoupled from larger patterns
and provide suitable thermal refugia, it is critical to identify the
characteristics of the landscape that foster suitable microhabitat
(Rodhouse et al. 2010, Varner and Dearing 2014). Some of the clearest
evidence of the influence of temperature in this study is through
measures of variable importance and model performance. In each of the
eight models that included temperature, all three temperature covariates
were among the five most important variables. Further, including
temperature consistently improved model performance compared to models
without temperature covariates, especially in Buckskin in Summer. While
it is clear that climate informs sage-grouse distributions and
populations (Blomberg et al. 2012, Coates et al. 2016b, 2018, Acevedo
2021) and climate change is likely to negatively impact sagebrush cover
in the southern Great Basin (Kleinhesselink and Adler 2018), it is
important to explore potential mechanisms of individual habitat
selection that drive those larger scale patterns as we have in this
study.

In examining two-way partial dependence plots in combination with
measures of variable importance and model performance, the impact of
temperature on selection and where sage-grouse and characteristics of
thermal refugia are clear. Combined, our results indicate that
sage-grouse respond to temperature, but that other variables play a
strong role in selection. If they did not, there would be no interaction
between temperature and other variables, and at extreme temperatures
there would always be low selection. On the contrary, sage-grouse are
likely forced to make decisions that balance resource acquisition and
the potentially competing risks of predation and thermal stress, similar
to the tradeoffs faced by greater prairie chickens (\emph{Tympanuchus
cupido} {[}Londe et al. 2021{]}). For example, sage-grouse may be
exposing themselves to greater risk of predation by spending time near
the cool shade of trees, balanced against the risk of hyperthermia in
sagebrush patches during high temperatures, which may explain some past
findings of apparent high risk selection by sage-grouse (Cutting et al.
2019). Our data clearly support this, especially in Buckskin. In Spring
and Autumn, when thermal extremes were less common, sage-grouse in
Buckskin showed less selection for areas near trees than during the
higher maximum temperature in Summer. While metrics of vegetation cover
and activity such as Normalized Difference Vegetation Index (NDVI) are
important for sage-grouse (Dinkins et al. 2017, Stoner et al. 2020), the
grouse in this study generally selected areas with moderate temperatures
and avoided extremes where possible, suggesting that temperature also
drives selection. In Steptoe and Buckskin, most of the mesic habitat,
which sage-grouse tend to select during late brood rearing (summer), is
not treed riparian areas like in some areas of sage-grouse distribution,
and there is likely little direct correlation between tree cover and
mesic resources. Were sage-grouse primarily selecting based on NDVI and
mesic resources, then we would not have detected sage-grouse selection
for areas close to trees during higher temperatures, as those areas are
not rich in mesic resources. Instead, sage-grouse would have continued
to avoid trees because they could exploit the resources of mesic areas
without incurring the risk of predation near trees. Furthermore, the
fact that sage-grouse in our two study areas did not select for the same
land cover in response to thermal extremes suggests that vegetation
activity (e.g., NDVI) is not their only limitation, and there is cryptic
fragmentation of suitable sagebrush habitat in Buckskin, while in
Steptoe the contiguous sagebrush provides enough thermal cover that
sage-grouse there are not forced to shelter near trees.

Similarly, several previous studies have found negative effects of
terrain ruggedness on sage-grouse (Doherty et al. 2008, 2010b, Knick et
al. 2013, Dinkins et al. 2017). Those have largely examined larger-scale
processes such as population size or lek persistence and captured a
broader spatial sample of the ``available'' landscape. On the other
hand, other studies focused on individual habitat selection have found
that in some conditions, sage-grouse select more rugged terrain than
expected, especially in marginal habitat (Dinkins et al. 2014, Beers and
Frey 2022a). Like those, this study found that sage-grouse in some cases
select more rugged terrain than expected. This may be in part because
some of the more heterogeneous topography in these study areas tended to
be near valley edges, where sage-grouse appeared to use taller and
denser land cover (trees and dense sagebrush) for thermal refuge.
However, the fact that measures of topographic heterogeneity were often
among the most important variables in the RF models shows that the
terrain itself also featured in sage-grouse selection. It may be that
moderately rugged terrain fosters snow deposition, accumulation, and
retention in winter and spring (Winstral et al. 2002, Jost et al. 2007).
In cold extremes, sage-grouse could use that snow as thermal cover. That
retained snow may then allow the persistence of more mesic microhabitat
during summer. Our study was focused at smaller scales and did not
sample from a large enough area to include the mountainous terrain
surrounding the study areas that might have been defined as available
habitat in a 2\textsuperscript{nd} or 1\textsuperscript{st} order
selection process, which may have allowed me to detect the effects of
topography on habitat selection at fine scales.

It is important to note that in our models we did not use known
temperature data at the exact location of each sage-grouse GPS
detection. We also did not estimate temperature at each of those points
based on interpolating temperature between the nearest data loggers.
Instead, we used modeled outputs that represent detected trends of
temperature within each study area and season, hypothesizing that
sage-grouse selection trends will correspond to those of temperature.
This may mean that these temperature data lack precision in their
interpolation and there would be a benefit to implementing a temperature
interpolation method that allows us to model the impacts on individual
sage-grouse movements. In the case of both summer heat and winter cold,
when sage-grouse make selections to avoid thermal stress, they are
likely to experience even more extreme temperatures than we detected. We
intentionally positioned data loggers to avoid direct sun exposure and
removed data points where the logger nonetheless received direct
sunlight. Yet, sage-grouse experience heating from direct sunlight and
must make decisions to avoid it if stressed, seeking shade from land
cover or otherwise moving to a cooler area, such as by changing
elevation or habitat type. Similarly, in both sites there were some data
loggers that appeared to have been covered in snow for periods of the
winter given their small diel range in light intensity detected compared
to that of other loggers. Because the data loggers were therefore
insulated, these data likely do not capture all of the coldest events,
and modeled Winter minimum temperature may be higher than what occurred.
However, the loggers also likely reflect the temperatures that
sage-grouse experience, as they are known to burrow into snow for
shelter during extreme winter events (Beck 1977, Back et al. 1987).

\subsubsection*{Conservation
implications}

{\label{conservation-implications}}

Our findings may point toward a mechanism limiting the extent of the
sage-grouse distribution on their warm range margin---inadequate refuge
from thermal stress and a cryptic fragmentation that inadequacy creates.
In both Buckskin and Steptoe, there are large areas of contiguous
sagebrush. In Buckskin, those areas are mostly at lower elevations
within Buckskin Valley and the cooler, high elevation available habitat
is dominated by trees: pinyon pine, juniper, Gambel oak, mountain
mahogany, and some aspen. In Steptoe, there are much larger patches of
contiguous sagebrush in both the valley bottom and in a few patches at
mid to upper elevations that have more area far from dense tree cover.
While Steptoe grouse avoided tree cover during high summer temperatures,
generally selecting habitat more than 1800 m from trees, in Buckskin
there is little area that is more than 800 m from tree cover. Therefore,
Steptoe sage-grouse have more habitat in which to escape from thermal
stress without incurring greater predation risk, while in the Buckskin
site they more often choose to shelter in riskier habitat. While
sage-grouse in some areas of Steptoe likely also face that tradeoff,
that valley is much larger and there is more area where sage-grouse do
not have to choose between thermal stress and predation risk. Buckskin
may therefore act as a portent for what may occur in Steptoe given
continued warming, sagebrush loss, and conifer encroachment.

Sage-grouse have been observed using trees in the past or using areas
with tree cover great enough to reduce survival (Baruch-Mordo et al.
2013, Coates et al. 2017, Beers and Frey 2022a), but the reason for that
risky choice has been unclear. A potential explanation suggested by our
results is that in areas where temperature is limiting, especially in
summer heat and fragmented habitat, sage-grouse may be impelled to incur
the risk of predation to avoid thermal stress.

In contrast with the long-term climate data from PRISM, our data
indicated that Utah's Buckskin Valley is marginally warmer than Nevada's
Steptoe Valley. In combination with the fact that our results suggest
that sage-grouse respond to extreme operative temperatures in habitat
selection, this underscores the fact that temperature varies at multiple
scales, and that it is potentially hazardous to infer fine-scale
processes in either temperature or wildlife response to it based on
larger patterns (Gillingham et al. 2012, Gollan et al. 2015). Instead,
studies should evaluate potential predictor variables at more
biologically relevant scales. Though preference at fine scales for
moderate temperatures has been demonstrated for other potentially
threatened species and environments (Scherrer and Korner 2011, Varner
and Dearing 2014), including for greater prairie chicken (Hovick et al.
2014, Londe et al. 2021), it has not been demonstrated for greater
sage-grouse. Sage-grouse in the southern great basin are likely to face
a future with a more xeric environment and declining sagebrush cover
(Tredennick et al. 2016, Kleinhesselink and Adler 2018). Because
sagebrush is sensitive to climate (Schlaepfer et al. 2014, Tredennick et
al. 2016, Renwick et al. 2018), as a sagebrush obligate, sage-grouse
will be negatively impacted by its range retracting along the southern
range margin. Their range limit, therefore, may be defined by a
combination of sagebrush cover, exposure to extreme heat, and the extent
of trees.

As the climate continues to change, it will be important to identify or
even foster potential microrefugia for sage-grouse. Hotter summers and
less sagebrush cover will likely make their current southern range
margin even less tenable through thermal stress and loss of forage.
Compounded by increased threat of avian predation due to ongoing
pinyon-juniper encroachment and potential ecological traps (Coates et
al. 2017, Pratt and Beck 2021), sage-grouse will face greater threats in
the future where thermal stress drives them to make risky habitat
selection. Even on their fragmented southern range margin, there may be
some suitable microrefugia or holdouts as climate changes if there are
large enough areas of contiguous sagebrush and some decoupling from
regional climate at local scales (Dobrowski 2011, Hannah et al. 2014).
Yet that is only if the limiting climatic factors for sage-grouse
decouple from regional trends in the landscapes they occupy and if
ecosystem managers take steps to foster suitable microhabitats (Hylander
et al. 2015, Selwood et al. 2019). That may mean adopting a
comprehensive, pragmatic approach to identify potential microrefugia
(Ashcroft et al. 2012), evaluate ecosystem resistance and resilience
(Chambers et al. 2007, Ricca et al. 2018), and assess local to regional
scale factors to determine what actions are appropriate in different
areas of sage-grouse habitat (Doherty et al. 2016, Lynch et al. 2021).
For sage-grouse, that may mean creating some areas of mesic resources to
offer thermal refuge during extreme heat (Donnelly et al. 2018).
Ironically, it may also entail leaving some tree cover where sage-grouse
will be exposed to thermal stress and do not have other adequate cover.
Conifer removal efforts should prioritize areas where sage-grouse are
likely to experience thermal stress and do not have shelter from avian
predators. In particular, sage-grouse would likely be most vulnerable
during thermal extremes in flatter, more open terrain with less intact
sagebrush patches where they are less able to hide from avian predators
(Dinkins et al. 2017). For that purpose, lone trees would likely remain
dangerous and provide little shelter, but small clusters of dense trees
could be useful.

\subsubsection*{Conclusion}

{\label{conclusion}}

In this study we identified when temperature impact sage-grouse habitat
selection and described their response to mitigate thermal stress.
Though this has been a focus of study for other species of conservation
concern, this is the first study to address temperature effects on
sage-grouse habitat selection at a fine scale. We found that extreme
temperatures may be limiting, but that sage-grouse response to those
temperatures likely depend on what refuge habitat is available. In the
larger study area with greater extents of contiguous sagebrush,
marginally cooler temperatures, and more patches of sagebrush at higher
elevations, sage-grouse tended to select those patches during high
summer temperatures. In the smaller study area with less apparent
refuge, sage-grouse used areas close to trees when temperatures were
most extreme. Selection depends on the local environment and always
involves tradeoffs---in this case it appears there may be some threshold
in the combination of thermal exposure and sagebrush availability beyond
which sage-grouse are more likely to risk exposure to avian predators.
This suggests thermal stress contributing to cryptic fragmentation as a
mechanism limiting greater sage-grouse in areas of their southern range
margin and shows that ecosystem management in the Great Basin must
account for regional and local factors of climate and sagebrush loss and
fragmentation to protect the sagebrush and its imperiled species into
the future.

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{\label{literature-cited}}

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\subsection*{FIGURES AND TABLES}

{\label{figures-and-tables}}

Table 1. Mean temperatures (degC) in each study area and season and the
p-value for a t-test evaluating the differences in temperatures between
study areas.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Season & Steptoe mean & Buckskin mean & p-value\tabularnewline
\midrule
\endhead
All & 1.85 & 3.50 & \textless{}0.0001\tabularnewline
Autumn & 1.83 & 3.48 & \textless{}0.0001\tabularnewline
Winter & -11.32 & -10.02 & \textless{}0.0001\tabularnewline
Spring & -2.39 & -1.77 & \textless{}0.0001\tabularnewline
Summer & 13.92 & 15.81 & \textless{}0.0001\tabularnewline
\bottomrule
\end{longtable}

Table 2. Model performance for each Study Area -- Season combination,
including and excluding temperature covariates, measured by true skill
statistic (TSS), Cohen's kappa, sensitivity, correlation in used-habitat
calibration (UHC), and the mean of those metrics. Mean performance
difference shows the mean difference in model performance for a Study
Area -- Season model when including temperature covariates compared to
excluding them.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
Study Area & Season & Temperature covariates & TSS & Kappa & Sensitivity
& UHC correlation & Mean Performance & Mean Performance
Difference\tabularnewline
\midrule
\endhead
Steptoe & Autumn & Yes & 0.6988 & 0.6825 & 0.8544 & 0.9516 & 0.7968 &
0.0636\tabularnewline
Steptoe & Autumn & No & 0.5792 & 0.5492 & 0.8190 & 0.9853 & 0.7332
&\tabularnewline
Steptoe & Winter & Yes & 0.7125 & 0.7024 & 0.7854 & 0.9423 & 0.7857 &
0.0518\tabularnewline
Steptoe & Winter & No & 0.6271 & 0.6049 & 0.7387 & 0.9648 & 0.7339
&\tabularnewline
Steptoe & Spring & Yes & 0.6389 & 0.6289 & 0.8594 & 0.9606 & 0.7720 &
0.0539\tabularnewline
Steptoe & Spring & No & 0.5640 & 0.5312 & 0.8248 & 0.9523 & 0.7181
&\tabularnewline
Steptoe & Summer & Yes & 0.7017 & 0.7298 & 0.8429 & 0.9501 & 0.8061 &
0.0599\tabularnewline
Steptoe & Summer & No & 0.6221 & 0.6321 & 0.7861 & 0.9446 & 0.7462
&\tabularnewline
Buckskin & Autumn & Yes & 0.7053 & 0.7284 & 0.9012 & 0.9659 & 0.8252 &
0.0143\tabularnewline
Buckskin & Autumn & No & 0.6937 & 0.8089 & 0.7820 & 0.9590 & 0.8109
&\tabularnewline
Buckskin & Winter & Yes & 0.7269 & 0.8080 & 0.8213 & 0.9559 & 0.8280 &
0.0038\tabularnewline
Buckskin & Winter & No & 0.7239 & 0.7822 & 0.8579 & 0.9327 & 0.8242
&\tabularnewline
Buckskin & Spring & Yes & 0.8358 & 0.6759 & 0.9318 & 0.9154 & 0.8397 &
0.0250\tabularnewline
Buckskin & Spring & No & 0.8160 & 0.7109 & 0.8349 & 0.8970 & 0.8147
&\tabularnewline
Buckskin & Summer & Yes & 0.9224 & 0.9123 & 0.9550 & 0.9713 & 0.9403 &
0.0844\tabularnewline
Buckskin & Summer & No & 0.8269 & 0.8349 & 0.8215 & 0.9401 & 0.8559
&\tabularnewline
\bottomrule
\end{longtable}

Table Top five most important variables for each Study Area -- Season
combination model with and without temperature covariates, measured by
the mean decrease in Gini index (node impurity). The possible variables
included temperature minimum, maximum, and difference
(T\textsubscript{min}, T\textsubscript{max}, T\textsubscript{diff});
topographic position index (TPI) and topographic heterogeneity index
(THI) at scales of 50 m, 200 m, and 400 m; sagebrush patch core area
index (CAI) and contiguity (CONTIG); distance to tree cover (TREEDIST);
and density of tree cover (TREEDEN) within 400 m and 800 m.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Study Area & Season & Temperature covariates & Top five important
variables\tabularnewline
\midrule
\endhead
Steptoe & Autumn & Yes & T\textsubscript{min}, T\textsubscript{max},
T\textsubscript{diff}, TREEDIST, CONTIG\tabularnewline
Steptoe & Winter & Yes & T\textsubscript{min}, T\textsubscript{diff},
TREEDEN800, T\textsubscript{max}, THI400\tabularnewline
Steptoe & Spring & Yes & T\textsubscript{max}, T\textsubscript{diff},
TREEDIST, T\textsubscript{min}, THI400, THI200\tabularnewline
Steptoe & Summer & Yes & T\textsubscript{min}, T\textsubscript{diff},
TREEDIST, CONTIG, THI400\tabularnewline
Steptoe & Autumn & No & TREEDIST, THI400, THI200, TREEDEN800,
CONTIG\tabularnewline
Steptoe & Winter & No & TREEDEN800, THI400, TREEDEN400, TREEDIST,
THI200\tabularnewline
Steptoe & Spring & No & TREEDIST, THI400, THI200, TPI400,
TREEDEN800\tabularnewline
Steptoe & Summer & No & TREEDIST, THI400, CONTIG, TREEDEN800,
THI200\tabularnewline
Buckskin & Autumn & Yes & CONTIG, TREEDEN800, T\textsubscript{min},
TREEDEN400, TREEDIST\tabularnewline
Buckskin & Winter & Yes & T\textsubscript{min}, T\textsubscript{diff},
TREEDEN800, T\textsubscript{max}, THI400\tabularnewline
Buckskin & Spring & Yes & CONTIG, TREEDEN800, TREEDIST,
T\textsubscript{min}, T\textsubscript{diff}\tabularnewline
Buckskin & Summer & Yes & Tmin, T\textsubscript{max},
T\textsubscript{diff}, TREEDIST, CONTIG\tabularnewline
Buckskin & Autumn & No & CONTIG, TREEDEN800, TREEDEN400, TREEDIST,
THI400\tabularnewline
Buckskin & Winter & No & TREEDEN800, CONTIG, TREEDEN400, TREEDIST,
THI400\tabularnewline
Buckskin & Spring & No & CONTIG, TREEDEN800, TREEDIST, TREEDEN400,
THI400\tabularnewline
Buckskin & Summer & No & CONTIG, TREEDEN400, TREEDEN400, TREEDIST,
THI400\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1. Study area locations in the western United States---Steptoe
Valley, Nevada and Buckskin Valley, Utah---marked in dark blue with a
minimum convex polygon around sage-grouse GPS data points collected
there June 2019 -- November 2020.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2. Marginal effect of average daily maximum temperature on
sage-grouse habitat selection in Buckskin Valley, Utah across seasons,
measured by partial dependence. Partial dependence measures the marginal
effect of one covariate on the response variable when the effects all
other covariates are held at their mean value. A higher partial
dependence indicates a higher probability of selection.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3. Marginal effect of average daily maximum temperature on
sage-grouse habitat selection in Steptoe Valley, Nevada across seasons,
measured by partial dependence. Partial dependence measures the marginal
effect of one covariate on the response variable when the effects all
other covariates are held at their mean value. A higher partial
dependence indicates a higher probability of selection.

.

\textbf{a)}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{b)}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Figure 4. Marginal effect of average daily minimum temperature on
sage-grouse habitat selection in \textbf{a)} Buckskin Valley, Utah
and\textbf{b)} Steptoe Valley, Nevada across seasons, measured by
partial dependence. Partial dependence measures the marginal effect of
one covariate on the response variable when the effects all other
covariates are held at their mean value. A higher partial dependence
indicates a higher probability of selection.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Figure 5. Marginal effects (partial dependence: PD) of average daily
maximum temperature and distance to tree cover on sage-grouse habitat
selection during summer in Buckskin Valley, Utah.



\textbf{a)}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{b)}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

Figure 6. Marginal effects (partial dependence: PD) of average daily
maximum temperature and \textbf{a)} sagebrush patch contiguity index
(CONTIG) and \textbf{b)} sagebrush patch core area index (CAI) on
sage-grouse habitat selection during summer in Buckskin Valley, Utah.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

Figure 7. Marginal effects (partial dependence: PD) of average daily
minimum temperature and distance to tree cover on sage-grouse habitat
selection during winter in Buckskin Valley, Utah.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

Figure 8. Marginal effects (partial dependence: PD) of average daily
maximum temperature and distance to tree cover on sage-grouse habitat
selection during summer in Steptoe Valley, Nevada.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image11/image11}

\end{center}
\end{figure}

Figure 9. Marginal effects (partial dependence: PD) of average daily
maximum temperature and sagebrush patch core area index (CAI) on
sage-grouse habitat selection during summer in Steptoe Valley, Nevada.

\textbf{a)}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/imagf0/imagf0}

\end{center}
\end{figure}

\textbf{b)}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/imagf1/imagf1}

\end{center}
\end{figure}

Figure 10. Marginal effects (partial dependence: PD) of average daily
maximum temperature and \textbf{a)} sagebrush patch contiguity index
(CONTIG) and \textbf{b)} sagebrush patch core area index (CAI) on
sage-grouse habitat selection during summer in Buckskin Valley, Utah.

\textbf{a)}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image12/image12}

\end{center}
\end{figure}

\textbf{b)}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image13/image13}

\end{center}
\end{figure}

Figure 11. Marginal effects (partial dependence: PD) of average daily
maximum temperature and distance to trees on sage-grouse habitat
selection in Buckskin Valley, Utah during \textbf{a)} autumn
and\textbf{b)} spring.

\textbf{a)}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image14/image14}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image15/image15}

\end{center}
\end{figure}

\textbf{b)}

Figure 12. Marginal effects (partial dependence: PD) of average daily
maximum temperature and topographic heterogeneity in a 400 m window on
sage-grouse habitat selection during summer in \textbf{a)} Buckskin
Valley, Utah and \textbf{b)} Steptoe Valley, Nevada.

COMPETING INTERESTS STATEMENT:

We declare no conflict of interest.

DATA ACCESSIBILITY STATEMENT:

Data on sage-grouse locations are sensitive, concerning a threatened
species, owned by state agencies, used through a memorandum of
understanding, and cannot be provided here. Others interested in using
those data may request them from the Utah Division of Wildlife Resources
with a Government Records Access and Management Act request and from
Nevada using a Nevada Department of Wildlife public records request. The
LANDFIRE data may be accessed through landfire.gov. Elevation data were
sourced using the elevatr R package from
https://registry.opendata.aws/terrain-tiles/.We downloaded LANDFIRE and
elevation data over the spatial extent of our study areas with a 1-km
buffer around each side.

AUTHOR CONTRIBUTIONS:

ATB led project development, contributed to sage-grouse capture,
collected temperature data, and led data analysis and manuscript
writing.

SNF acquired funding, contributed to project development, led
sage-grouse capture, contributed to data analysis, and contributed
significantly to manuscript writing and revising.

ACKNOWLEDGMENTS:

We acknowledge our many partners and supporters through many years of
research. This work would not have been possible without the Utah
Division of Wildlife Resources (UDWR), Utah State University (USU)
Extension, S. J. Quinney College of Natural Resources (CNR) USU, Nevada
Department of Wildlife (NDOW), U.S. Department of Interior Bureau of
Land Management (BLM), and many research technicians and graduate
students. Funding was provided by grants from NDOW and the BLM.

\selectlanguage{english}
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