The Journal of Wildlife Management 77(4):791–801; 2013; DOI: 10.1002/jwmg.493
Habitat Relations
Spatial Heterogeneity in Habitat Selection:
Nest Site Selection by Greater
Prairie-Chickens
LANCE B. MCNEW,1,2 Division of Biology, Kansas State University, Manhattan, KS 66506, USA
ANDREW J. GREGORY,3 Division of Biology, Kansas State University, Manhattan, KS 66506, USA
BRETT K. SANDERCOCK, Division of Biology, Kansas State University, Manhattan, KS 66506, USA
ABSTRACT Ecological relationships of animals and their environments are known to vary spatially and
temporally across scales. However, common approaches for evaluating resource selection by animals assume
that the processes of habitat selection are stationary across space. The assumption that habitat selection is
spatially homogeneous may lead to biased inference and ineffective management. We present the first
application of geographically weighted logistic regression to habitat selection by a wildlife species. As a case
study, we examined nest site selection by greater prairie-chickens at 3 sites with different ecological
conditions in Kansas to assess whether the relative importance of habitat features varied across space.
We found that 1) nest sites were associated with habitat conditions at multiple spatial scales, 2) habitat
associations across spatial scales were correlated, and 3) the influence of habitat conditions on nest site
selection was spatially explicit. Post hoc analyses revealed that much of the spatial variability in habitat
selection processes was explained at a regional scale. Moreover, habitat features at local spatial scales were
more strongly associated with nest site selection in unfragmented grasslands managed intensively for cattle
production than they were in fragmented grasslands within a matrix of farmland. Female prairie-chickens
exhibited spatial variability in nest site selection at multiple spatial scales, suggesting plasticity in habitat
selection behavior. Our results highlight the importance of accounting for spatial heterogeneity when
evaluating the ecological effects of habitat components. ß 2013 The Wildlife Society.
KEY WORDS geographically weighted regression (GWR), greater prairie-chicken, habitat selection, nest site
selection, resource selection function (RSF), Tympanuchus cupido.
Ecological patterns are a function of multiple interacting
processes operating at different spatial and temporal scales,
and the explicit spatial nature of organism–environment
associations is a growing area of research (Mitchell et al.
2001, Thogmartin et al. 2004, Miller and Hanham 2011).
Multiscale evaluations of ecological relationships have shed
new light onto the ecological processes determining species
distribution, abundance, and behavior (Wiens and Milne
1989, Mitchell et al. 2001, Gregory et al. 2011). However,
less effort has addressed the effects of spatial heterogeneity in
ecological processes, and standard approaches of evaluating
the interactions between animals and environments are often
spatially stationary because they assume the same relationship at all spatial scales (Fotheringham et al. 1996). For
Received: 17 May 2012; Accepted: 27 September 2012
Published: 24 January 2013
Additional supporting information may be found in the online version of
this article.
1
E-mail: lmcnew@usgs.gov
2
Present address: U.S. Geological Survey, Alaska Science Center, 4210
University Dr., Anchorage, AK 99508, USA.
3
Present address: School of Forestry, Northern Arizona University,
Flagstaff, AZ 86011-5018, USA.
Mcnew et al. Spatially Explicit Habitat Selection
example, when evaluating habitat selection processes of
animals, managers often assume that the relationships being
modeled are the same everywhere. Therefore, estimates of
habitat selection parameters are averages of the effects over
researcher-defined spatial extents. Relying on average
parameter estimates, or spatially stationary coefficients,
may lead to a failure to detect influential variables affecting
ecological processes of interest. If selection is positively
associated with an environmental variable in 1 area but
negatively associated with the same variable in another,
spatially stationary approaches, such as standard resource
selection functions (RSFs), may predict no overall relationship. Thus, spatial stationarity has major implications for
inference because potentially influential variables may be
obscured by averaging and thus overlooked (Fotheringham
et al. 2002). Several techniques have been developed to
address the limitation of stationarity in standard models,
including the spatial expansion method (Casetti 1972),
spatially adaptive filtering (Foster and Gorr 1986), and
moving window regressions (Fotheringham et al. 1996).
More recently, geographically weighted regression (GWR)
was introduced as a method to model spatially heterogeneous
or spatially explicit processes (Fotheringham et al. 2002).
791
This method allows for the exploration of spatial variation
in the relationships between dependent and independent
variables by weighting data points by their proximity to a
regression point and then moving the regression point across
the study area to make regional calibrations. Environmental
data are weighted differently by distance for each location so
that calibration results are unique to a particular location
(Fotheringham et al. 2002). Recent studies have successfully
used GWR to evaluate whether vegetation–environment
relationships are spatially explicit (Bickford and Laffan
2006, Kupfer and Farris 2007), but the method has not
been applied to investigations of habitat selection by animals.
Greater prairie-chickens (Tympanuchus cupido; hereafter
prairie-chickens) are a good candidate for examining
spatially explicit habitat selection because they are widely
distributed, occur over a variety of grassland habitats, and
exhibit regional differences in demography and population
dynamics that appear to be related to local habitat conditions
(Svedarsky et al. 2000, Patten et al. 2005, McNew et al.
2012a). Even within the core of the species’ extant
distribution in Kansas, considerable variation exists in
both landscape heterogeneity and management regimes.
Prairie-chickens occupy habitats ranging from unfragmented
but heavily grazed rangelands in the southern Flint Hills
ecoregion to moderately fragmented but lightly grazed grasslands in the Smoky Hills ecoregion (McNew et al. 2011).
Variation in vital rates such as nest survival appears to be
determined by spatial variation in grazing regimes and landscape patterning (Robbins et al. 2002, McNew et al. 2012a).
Our objective was to evaluate spatial heterogeneity and
scale of habitat selection mechanisms using geographically
weighted logistic regression. As a case study, we examined
nest site selection by female greater prairie-chickens at 3 sites
having different ecological conditions. We examined nest
site selection with a multiscaled hierarchical modeling
approach and a modified GWR technique. We addressed
4 main questions. 1) What nest-site, core area, and homerange scale habitat features are associated with nest sites of
prairie-chickens? 2) Are habitat associations correlated
among different spatial scales? 3) At which spatial scales
does nest site selection occur? 4) Are the ecological factors
associated with female prairie-chickens during nest site
selection global in nature and exhibit spatial stationarity,
or local and exhibit spatial heterogeneity?
STUDY AREA
We conducted our field study at 3 discrete research areas in
Kansas: 2 areas located in the Flint Hills ecoregion and 1 area
in the Smoky Hills ecoregion (see Fig. A.1 available online at
www.onlinelibrary.wiley.com). The 3 study areas were spatially heterogeneous and differed in landscape composition
and pattern, as well as rangeland management practices. The
South area was located in the southern Flint Hills, had
landcover of 90% grassland and 3% cropland with a road
density of 0.32 km of roads per km2. A majority of the area
was burned annually in the spring, and grazed intensively
by early (Apr–Jul) stocking of yearling steers (IESB, 1
head/0.8 ha for 90 days; Smith and Owensby 1978). The
792
North area was located in the north-central Flint Hills, had
landcover of 81% grassland and 10% cropland and a road
density of 0.57 km per km2. Annual spring burning was
common and lands were managed with a mixture of IESB
and season-long grazing of steers (SLSB; 1 head per 1.6 ha
for 180 days). The Smoky area was located in the Smoky
Hills ecoregion and was more fragmented with landcover of
53% grassland and 38% cropland and a greater road density
of 1.4 km per km2. Cultivated crops included sorghum,
corn, wheat, and soybeans. Native grass pastures at the
Smoky area were burned infrequently at fire return intervals
>1 year and grazed at low intensity (1 head per 2 ha for
90–180 days), and cattle stocking occurred later in the season
than at the other 2 study areas.
METHODS
Field Techniques
We captured prairie-chickens at lek sites during the spring
with walk-in traps and drop-nets during 2006–2009 (Silvy
et al. 1990, Schroeder and Braun 1991). We sexed captured
birds by plumage characteristics (Henderson et al. 1967), and
fitted females with 11-g necklace-style very high frequency
(VHF) radio transmitters with mortality switches and an
expected battery life of 12 months (Model RI-2B, Holohil
Systems Ltd, Ontario, Canada). We monitored radiomarked females 3 times/week during the nesting period
(Apr–Aug). We used a portable radio receiver and a handheld Yagi antenna to locate the incubating female and nest
site. We recorded locations of nests with handheld Global
Positioning System (GPS) units and uploaded them to a
Geographic Information System (GIS) using ArcMap
(ver. 10; Environmental Systems Research Institute,
Redlands, CA). We conducted GIS analyses at spatial
extents 100 m to ensure that inference was not confounded
by GPS location estimation error (<10 m). Field methods
were approved by Kansas State University’s Institutional
Animal Care and Use Committee (Protocol numbers
2474 and 2781).
Habitat Sampling
We evaluated habitat conditions at 3 nested spatial scales: the
nest site (0.01 ha), the core use area (13 ha), and the home
range (310 ha; see below). We measured 13 habitat characteristics at nest sites and random points. We quantified
vegetation structure at each nest site within 3 days of hatching or failure. We recorded the average of 4 visual obstruction
readings at the nest from a distance of 2 m and a height of
0.5 m (Robel et al. 1970b). We estimated non-overlapping
vegetation cover (% grass, forbs, shrub, and bare ground) at
12 subsampling locations within 6 m of each nest using a
20-cm 50-cm frame (Daubenmire 1959). We also measured the heights of the tallest grass and forb plant within
5 cm of the nest and the height and the distance of the
nearest woody plant. We calculated the distance from each
nest to the nearest state highway and local county road and
the distance to the nearest non-grassland habitat type using
ArcMap 10. State highways were paved and had higher
traffic volume than local gravel roads. We used the same
The Journal of Wildlife Management 77(4)
set of protocols to conduct parallel sampling at available
points selected randomly within each study area.
We based the home-range scale (310 ha) on the estimated
home range sizes of female prairie-chickens during
the breeding season (300–400 ha; Robel et al. 1970a,
Augustine and Sandercock 2010), assuming it contained
the range of resources used by females during the nesting
season. We delineated the home range area by centering a
circular plot with a 1-km radius on the nest site or random
point. Locations of female prairie-chickens were generally
limited to a 10–15-ha area around the nest during the nesting
period (L. B. McNew, Kansas State University, unpublished
data). Therefore, we defined core use areas (13 ha) by a
circular plot with a radius of 200 m, which represented
habitat immediately available for nesting within a female’s
home range. We assessed habitat variables at the home-range
and core area scales using remotely sensed data and ArcMap
10. For landcover analyses, we used the 30-m resolution
land cover map depicting 11 biologically relevant landcover
classes in Kansas in 2005 (Whistler et al. 2006). We also
included state and non-state road system datasets for Kansas
in 2006 (Kansas Department of Transportation: Bureau of
Transportation Planning). Land use changes were minimal
and remote imagery from 2005 to 2006 was a good measure
of landscape conditions during our study period of 2006 to
2009. We used the Patch Analyst Extension in ArcMap 10 to
measure the proportion of areas in grassland, grassland shape
complexity, and patch fractal dimension at each spatial scale.
We hypothesized that females may avoid areas with greater
densities of edges and roads because of increased predation
risk (O’Leary and Nyberg 2000, Winter et al. 2000, Kuehl
and Clark 2002, Bollinger and Gavin 2004). Therefore, at
both the core area and home range extents, we quantified
total grassland edge by measuring the total perimeter length
of grassland patches, and measured densities (linear km of
road per km2) of state highways and local county roads.
Data Analysis
Prior to model fitting, we conducted a series of multivariate
correlation analyses and univariate comparisons of habitat
variables between nest sites and random points to assess
within-scale correlations. We evaluated correlations for
each pair of habitat variables. If habitat metrics within a
spatial scale were highly correlated (r 0.5, P < 0.05), we
used single factor logistic regression to determine which of
the 2 variables accounted for most of the variation in the site
selection data. We considered the variable with lower residual model deviance to be the primary habitat variable in the
model and the other variable was a correlated secondary
factor.
We located nests by telemetry monitoring and did not
conduct searches for unmarked prairie-chickens within
core areas or simulated home ranges around random points.
Thus, we were unable to determine with certainty that
random sites were unused by nesting females. Therefore,
our study design fit sampling protocol b of Manly et al.
(2001); where used and available resource units were independently sampled. We transformed habitat data when
Mcnew et al. Spatially Explicit Habitat Selection
required to meet the assumption of normality (e.g., angular
transformed for proportion data). We employed an exploratory and hierarchical approach to model selection. We
began by fitting a series of geographically weighted logistic
regression models at each spatial scale using nest versus
random site as the binary response. We weighted parameters
based on their geographic location. The basic spatially explicit logistic model took the form:
yi ðuÞ
¼
expfb1 ðui ; vi Þx1 þ b2 ðui ; vi Þx2 þ þ bm ðui ; vi Þxm g
1 þ expfb1 ðui ; vi Þx1 þ b2 ðui ; vi Þx2 þ þ bm ðui ; vi Þxm g
where (ui, vi) denotes the geographical coordinates of the
ith regression point and bk(ui, vi) describes the localized
effect of covariate xk (Fotheringham et al. 2002). In
GWR, each data point is weighted by a distance-decay
function or kernel where data points closer to the regression
point (ui, vi) are weighted more heavily than those farther
away. Regression points are the spatial locations relative to
which habitat variables are weighted and coincided with the
locations of nest and random points in our analysis. The
result was a spatially explicit logistic regression evaluated at
each observation in the sample. The GWR method fits
potentially different coefficient values for each observation
as a function of a spatial kernel weighing scheme determined
by a bandwidth parameter, the distance within which other
data points have influence (see Fotheringham et al. 2002).
The bandwidth can be set at a fixed distance (Kupfer and
Farris 2007), but we chose an adaptive spatial kernel weighting function where the kernel bandwidth was allowed to vary
depending upon the spatial density of data points. Model
parameter estimates are, in part, dependent upon the bandwidth of the spatial-weighting function. Narrow bandwidths
result in high model fit at the expense of increased degrees of
freedom, whereas broad bandwidths result in model coefficients that approach those of a spatially homogenous model. Therefore, a method of deriving a bandwidth that
provides a tradeoff between goodness of fit and degrees of
freedom is recommended (Fotheringham et al. 2002). We
used Akaike’s Information Criterion (AIC) to select the
appropriate bandwidth size at each regression point
(Fotheringham et al. 2002).
Ideally, we would consider GWR models where all parameters were allowed to vary spatially as well as mixed models in
which some coefficients were spatially explicit and others
stationary and not allowed to vary over space (Fotheringham
et al. 2002). Mixed model calibrations of logistic GWR were
not possible using the most recent version of GWR (ver. 3;
S. Fotheringham, National Centre for Geocomputation,
National University of Ireland, Maynooth, personal communication). Moreover, formal tests are not yet available for
calculating the statistical significance of spatial variability in
parameter estimates for logistic GWR. We examined whether or not parameters exhibited spatial heterogeneity by comparing the interquartile range of parameter estimates from
spatially explicit calibrations of a model with a range of values
at 1 standard deviation of the parameter estimate of the
793
equivalent spatially stationary model (Fotheringham et al.
2002). We considered a parameter to be spatially explicit if
the interquartile range of spatially explicit parameter estimates was greater than 2 standard errors (1 SD) of the
stationary parameter estimate. We retained variables in the
model if their effects on nest site selection were significant in
stationary models (t > 2.1, P 0.05), or if their effect on
selection exhibited significant non-stationarity.
We selected the most parsimonious models from all possible combinations of the candidate habitat variables measured
at each spatial scale using AIC adjusted for small sample sizes
(AICc; Burnham and Anderson 2002). We then used all of
the variables included in each of the top models for each
spatial scale to build a full model representing nest site
selection at all scales. We considered models with differences
of AICc (DAICc) values 2 from the best-fit model equally
parsimonious (Burnham and Anderson 2002), and in such
cases included variables from all models with DAICc 2 in
the multiscale analysis.
Probabilities obtained from a logistic regression model are
not appropriate to describe the true probability of use in a
study design based on used versus available habitats (Manly
et al. 2001:100). Therefore, we estimated the relative probability of use of our study areas where we estimated the slope
coefficients (bi) using the corresponding coefficients from
the logistic regression. The sampling probabilities of used
and available habitat units were unknown and the intercept
term of (b0) of a resource selection probability function could
not be estimated (Manly et al. 2001).
We validated our top model of RSF with a holdout data set
consisting of 31 nest sites and 31 random points that we
selected at random from our entire dataset (20% of data;
Boyce et al. 2002). We used the top performing model to
calculate RSF values for each nest observation in the training
data set and the holdout data set. To compare the performance of spatially stationary models with spatially explicit
models, we also calculated RSF values for the holdout data
set using the spatially stationary calibration of the top model.
We categorized raw RSF values into quantile bins representing increasing likelihood of points being classified as a nest
site. Bin 1 contained the lowest 20% of raw RSF values, and
bin 5 contained the highest 20%. We regressed the observed
proportion of test nest locations in each RSF bin for both
spatially stationary and spatially explicit calibrations of the
top model against the proportion of nests categorized in each
bin for the original training data set. A good model fit leads
to a high R2 value, a slope not different from 1.0, and an
intercept not different from zero when comparing training
and test data sets using linear regression (Johnson et al.
2006).
We used variance decomposition to separate variation in
nest site selection into 2 types of components: pure variance
attributed to habitat variables at a single spatial scale, and
variance attributed to groups of habitat variables across
multiple spatial scales (Borcard et al. 1992, Lawler and
Edwards 2006, Doherty et al. 2010). Specifically, we isolated
the variation in the binary response with habitat attributes at
3 spatial scales: within a home range, within the core area,
794
and attributes at the nest site. We then identified the proportion of the variation that was shared by 1) a combination
of home range and core area attributes, 2) a combination of
home range and nest site attributes, 3) a combination of core
area and nest site attributes, and 4) a combination of attributes measured at all 3 scales (Lawler and Edwards 2006,
Doherty et al. 2010).
We created maps of spatially explicit slope coefficients from
the top performing model. We connected spatially explicit
mean parameter estimates to their respective regression
point at nest sites and random points. We then used the
Geostatistical Analyst Tool in ArcMap 10 to apply an inverse
distance weighting interpolation across study sites.
Interpolated rasters were masked by study site boundaries
defined by a minimum convex polygon drawn around the
nest and random points at each site.
Models depicting spatial variability at a local scale may be
interesting from an ecological perspective, but may not have
application because habitat management at the scale of an
individual nest site is not practical. Therefore, if spatially
explicit effects on nest site selection were supported, we
conducted a series of post hoc analyses to evaluate whether
spatial variability in environmental effects on nest site selection could be accounted for by variation among study sites.
First, we compared the spatial variation of model effects from
the slope coefficients for regression points within each study
site to the variation of effects when study sites were pooled.
Next, we conducted an analysis of variance (ANOVA) of
each spatially explicit effect grouping by study site. We used
coefficients of determination (r2) to evaluate the proportion
of spatial variation in effects explained by study site. We were
also interested in assessing whether spatial variation in selection of habitat features such as visual obstruction at nest
sites may be related to the other habitat conditions, including
distance to the nearest habitat edge. Therefore, we regressed
spatially explicit slope coefficients from the most parsimonious RSF onto all measured habitat variables. Significant
relationships would suggest that the relative importance of
1 habitat feature were conditional upon other factors when
prairie-chickens selected nest sites (r2 0.5, P 0.05).
RESULTS
We captured and radio-marked 286 female greater prairiechickens and located 299 nests during our 4-year study from
2006 to 2009 (230 first nests, 69 known renests). We located
72, 94, and 133 nests at the South, North, and Smoky study
areas, respectively, and conducted habitat sampling at all 3
field sites. In addition, we randomly selected 341 points
(South ¼ 90, North ¼ 78, Smoky ¼ 173) within the 3
study areas. Habitat conditions at available nesting sites
varied among the 3 study areas (Table 1). Compared to
the South and North sites, random points at the Smoky
site had greater vegetation height and visual obstruction,
were closer to local roads and habitat edges, and occurred
in areas that were more fragmented by agricultural and
forested habitats (Table 1).
The Journal of Wildlife Management 77(4)
Table 1. Mean (SD) habitat measurements assessed at greater prairie-chicken nest locations and random points at the South, North, and Smoky study areas in
Kansas, 2006–2009.
South
Nest
(n ¼ 72)
Nest-site variables (0.01 ha)
Elevation (m)
417 76
30 13
VOR (cm)a
Grass height (cm)
54 21
Forb height (cm)
36 16
% Grass
57 21
% Forb
19 13
% Bare
12 16
Distance to shrub (m)
150 284
Distance to state road (km)
5.7 2.3
Distance to local road (km)
1.0 0.9
Distance to lek (km)
1.5 2.1
Distance to edge (km)
0.4 0.2
Core area variables (13 ha)
% Grass
100 2
Total grassland edge (m)
11 49
GSIb
1.0 0.02
c
MGFD
1.2 0.003
Road density (m/ha)
0.004 0.01
Home range scale variables (310 ha)
% Grass
98 2
Total grassland edge (km)
2.1 1.6
GSI
1.3 0.3
MGFD
1.2 0.02
Road density (m/ha)
0.002 0.004
North
Random
(n ¼ 90)
Nest
(n ¼ 94)
419
15
35
22
48
15
19
59
5.4
1.5
1.8
0.4
67
14
19
14
20
13
20
127
3.4
0.9
0.9
0.3
427
28
42
35
52
25
9
60
3.3
0.7
1.9
0.3
48
13
20
17
23
21
7
92
2.3
0.6
1.9
0.2
97
94
1.1
1.2
0.002
1.0
191
0.2
0.03
0.01
97
90
1.1
1.2
0.004
97
2.6
1.4
1.2
0.001
4
2.4
0.4
0.04
0.003
96
3.8
1.6
1.2
0.002
Smoky
Random
(n ¼ 78)
407
16
27
29
50
17
21
56
3.0
1.1
2.0
0.3
69
19
17
20
23
15
22
76
2.0
0.8
1.1
0.2
12
199
0.2
0.03
0.01
96
162
1.1
1.2
0.003
6
3.2
0.5
0.04
0.004
96
4.0
1.6
1.2
0.002
Nest
(n ¼ 133)
455
24
40
26
49
11
13
48
5.5
0.4
1.5
0.3
73
15
23
21
24
12
20
88
3.6
0.2
1.1
0.2
11
280
0.3
0.03
0.01
94
133
1.1
1.2
0.006
7
3.6
0.6
0.04
0.004
86
5.4
1.9
1.2
0.004
Random
(n ¼ 173)
438
28
39
27
38
15
30
67
5.6
0.3
5.2
0.1
57
30
29
29
30
22
31
99
4.2
0.3
4.1
0.1
33
240
0.3
0.2
0.12
62
374
1.3
1.2
0.011
38
372
0.6
0.4
0.02
20
3.7
0.7
0.05
0.004
64
8.0
2.6
1.3
0.008
27
3.7
0.9
0.06
0.005
a
Visual obstruction reading.
Grassland Shape Index; a metric of grassland patch shape complexity. GSI ¼ 1 when all patches are circular and increases with increasing shape irregularity.
c
Mean Grassland Fractal Dimension; another measure of shape complexity. Fractal dimension approaches 1 for shapes with simple perimeters and
approaches 2 for complex shapes.
b
Nest Site Conditions
We evaluated habitat covariates at each nest site and random
point. We found a significant positive correlation between
visual obstruction and grass height (r ¼ 0.53, P < 0.001), as
well as a negative correlation between percent grass cover
and bare ground (r ¼ 0.50, P < 0.001). A logistic model
with visual obstruction as the explanatory variable had lower
residual deviance (deviance ¼ 817) than one with grass
height (deviance ¼ 850). Likewise, a model with proportion
bare ground had lower residual deviance (deviance ¼ 832)
than one with grass cover (deviance ¼ 865). Thus, we reduced the number of explanatory variables from 13 to 11
prior to model fitting by dropping grass height and proportion grass cover from the nest site scale.
For environmental variables evaluated at the nest site,
spatially explicit models out-performed stationary models
(ratio of AICc weights: wspatially explicit/wstationary > 0.99/
0.01). The most parsimonious model indicated that female
prairie-chickens selected nest sites relative to 6 fine-scale
environmental features: proportion bare ground, visual
obstruction, distance to both state and local roads, distance
to nearest lek, and distance to the nearest habitat edge
(Table 2). Comparisons of the standard deviations of spatially stationary effects with the interquartile distances of
spatially explicit calibrations suggested that the effects of all 6
of the environmental variables in the top model were best
explained as spatially explicit (Table 3).
Mcnew et al. Spatially Explicit Habitat Selection
Core Area and Home Range
At the core area scale (13 ha), the configuration of grassland
patches was positively correlated with both proportion
grassland cover (r ¼ 0.55, P < 0.001) and complexity of
grassland patches (r ¼ 0.99, P < 0.001). Single variable
logistic models indicated that the proportion of the core
area in grassland cover explained more of the variation in
the binominal response (deviance ¼ 821) than the grassland
complexity metric (deviance ¼ 876) so we removed grassland complexity from further consideration. Three of 5
variables assessed at the home-range scale (310 ha) were
uncorrelated and retained: proportion grassland cover, total
grassland edge, and road density. The 2 measures of grassland shape complexity, grassland shape complexity and
mean fractal dimension of grassland patches, were positively
correlated with each other (r ¼ 0.96, P < 0.001), and with
total grassland edge (r ¼ 0.72, P < 0.001 and r ¼ 0.64,
P < 0.001, respectively), suggesting that grassland patch
shape complexity increases with increasing grassland edge
at the home range scale. We retained the measure of total
grassland edge of grassland because it was uncorrelated with
proportion of grassland cover.
Spatially explicit models out-performed models without
spatial structure at both the core area and home range scales
(ratio of AICc weights: wspatially explicit/wstationary > 0.99/
0.01). The most parsimonious model (AICc ¼ 646,
wi ¼ 0.95) included 2 environmental variables at the core
795
Table 2. Model selection results based on minimization of Akaike’s Information Criterion adjusted for small sample sizes (AICc) to identify the best resource
selection function for nest site selection of greater prairie-chickens at 3 spatially discrete areas in Kansas, 2006–2009. Only models having Akaike weights
(wi) 0.01 are presented except for a null model.
Model typea
Kb
Deviance
AICc
Explicit
42
379
470
0
0.93
Explicit
49
371
476
6
0.05
Explicit
54
359
478
8
0.02
Explicit
Explicit
Explicit
22
14
28
646
666
640
690
696
700
0
6
10
0.95
0.05
0.01
Explicit
Explicit
20
14
670
687
712
716
0
4
0.88
0.12
þ
Explicit
53
338
457
0
0.72
þ
Explicit
60
327
460
3
0.16
þ
Explicit
48
356
461
4
0.10
þ
Explicit
59
333
464
7
0.02
Stationary
1
800
803
346
0.00
Model
Habitat model nest site (<0.01 ha)
% Bare þ VORc þ Distance to state road þ
Distance to local road þ Distance to lek þ
Distance to edge
% Bare þ Forb height þ VOR þ Distance to state road þ
Distance to local road þ Distance to lek þ Distance to edge
% Bare þ Forb height þ VOR þ Distance to shrub þ
Distance to state road þ Distance to local road þ Distance to lek þ
Distance to edge
Habitat model core area (13 ha)
% Grass þ Road density
% Grass
% Grass þ Total grassland edge þ Road density
Habitat model home range (310 ha)
% Grass þ Total grassland edge
% Grass
Multiscale habitat modeld
% Bare þ VOR þ Distance to state road þ Distance to local road
Distance to lek þ Distance to edge þ % grass CA þ % grass HR
% Bare þ VOR þ Distance to state road þ Distance to local road
Distance to lek þ Distance to edge þ % grass CA þ
Road density CA þ % grass HR
% Bare þ VOR þ Distance to state road þ Distance to local road
Distance to lek þ Distance to edge þ % grass CA
% Bare þ VOR þ Distance to state road þ Distance to local road
Distance to lek þ Distance to edge þ % grass CA þ
Road density CA þ Total grassland edge HR
Constant model (intercept only)
DAICc
wi
a
Model type: Explicit designates that model structure was spatially explicit, allowing model parameters to vary across space. Stationary designates that the
model parameters were ubiquitous or global over all areas.
b
K ¼ Effective number of parameters. For spatially explicit calibrations, K is a function of the number of habitat variables and the estimated bandwidth for
each model run (Fotheringham et al. 2002).
c
Visual obstruction reading.
d
Variables at the core area scale are followed by CA, variables at the home range scale are followed by HR, all others are at the nest site scale.
area scale as factors for nest selection by female prairiechickens: proportion grassland cover and road density
(Table 2). At the home range scale, the top model
(AICc ¼ 670, wi ¼ 0.88) included the effects of proportion
of grassland cover and total grassland edge (Table 2).
Multiscale Model
A multiscale model that combined environmental factors
from the top models at 3 spatial scales included 10 variables:
percentage of the nest site canopy that was bare, visual
obstruction at the nest site, distance from the nest to the
Table 3. Comparison of standard deviations (SD) of effects of the spatially stationary parameterization of the most parsimonious model predicting nest site
selection of greater prairie-chickens at 3 spatially discrete areas in Kansas, 2006–009 and the interquartile distance (IQD) of effects from the spatially explicit
parameterization of the same model. Interquartile distances greater than 1 SD (IQD/SD ratio > 1) suggest effects are better explained as spatially explicit and
are different among local sites.
Spatial scale
Nest site
% Bare
VORa
Distance to state road
Distance to local road
Distance to lek
Distance to edge
Core area
% Grass
Home range
% Grass
a
SD
Lower quartile
Upper quartile
IQD
IQD/SD ratio
Result
0.71
0.69
0.50
0.46
0.76
0.33
1.92
0.21
0.40
1.69
6.99
0.78
3.51
8.21
4.43
0.16
2.22
0.51
5.43
8.00
4.83
1.53
4.77
1.29
7.6
11.6
9.7
3.3
6.3
4.0
Local
Local
Local
Local
Local
Local
1.68
0.87
10.2
9.33
5.6
Local
1.34
2.33
4.28
3.2
Local
1.95
Visual obstruction reading.
796
The Journal of Wildlife Management 77(4)
nearest state highway, distance from the nest to the nearest
county road, distance to the nearest lek, distance to the
nearest habitat edge, percent of the core area composed of
grassland, density of roads in the core area, percent of the
home range area composed of grassland, and the total linear
length of grassland edge within the home range area. Model
selection revealed that multiscale models had nearly all of the
statistical support (wi ¼ 0.99).
A single spatially explicit multiscale model received
the majority of model support (AICc ¼ 457, wi ¼ 0.72;
Table 2). Standard deviations of slope coefficients from a
spatially homogenous calibration of the top model were
always smaller than the interquartile distances of spatially
varying coefficients, suggesting that effects at all 3 spatial
scales were better explained as spatially explicit than as
stationary (Table 3). Indeed, effects of environmental variables on selection processes varied substantially across our 3
different study areas. For example, female prairie-chickens
typically placed nests at sites with greater vertical nesting
cover but the relative influence of vertical cover on nest
placement varied greatly across space (interquartile distance
of effect coefficient ¼ 8.0; Table 3). The effect of distance to
the nearest state highway was negative at some parts of the
study area (lower quartile of effect ¼ 0.4) but positive at
others (upper quartile of effect ¼ 4.4), suggesting that
females avoided state highways when initiating nests in
some areas but not in others (Table 3). We produced
maps depicting the spatial variability of all effects from
the top multiscale model (Appendix A, available online at
www.onlinelibrary.wiley.com).
Variance decomposition analysis showed that 39% of variation was purely associated with nest site scale variables,
whereas core area variables and home range scale variables
alone explained only 4% and 1% of variation in nest site
selection, respectively. Nest site and core area variables combined accounted for the 22% of shared variation, whereas
nest site and home range variables accounted for 20% of
shared variation. Variables evaluated at the core area and
home range scales accounted for none of the shared variance.
A combination of attributes at all 3 scales accounted for 14%
of shared variance (Fig. 1).
Model Validation
We withheld 20% of our data for model validation, including
an equal number of independent nest sites and random
points. The top spatially explicit multiscale model correctly
classified 25 of 31 (81%) hold out nest observations as nests.
Regression validations showed a high coefficient of determination (r2 ¼ 0.95), an intercept overlapping zero (95% CI:
2 to 15), and a slope coefficient close to 1.0 (b ¼ 0.68, 95%
CI: 0.4 to 1.0; Fig. 2). Conversely, the spatially stationary
calibration of the top model had low predictive accuracy.
Regression showed a low coefficient of determination value
(r2 ¼ 0.22) and a slope coefficient similar to 1.0 but highly
variable (b ¼ 0.68, 95% CI: 0.5 to 1.0), although the
intercept overlapped zero (95% CI: 7 to 38).
Mcnew et al. Spatially Explicit Habitat Selection
39%
Nest site
20%
NS+HR
14%
All scales
1%
Home range
22%
NS+CA
4%
Core area
Figure 1. Relationship of 3 components of variation associated with nest
site selection by greater prairie-chickens in Kansas during 2006–2009. The
circles represent the proportions of explained variation in habitat selection
associated with sets of factors measured at each of 3 spatial scales, nest site
(NS), core area (CA), and home range (HR). Shared variation across scales is
reflected where circles overlap.
Post Hoc Analyses
Variation in mean parameter estimates was always smaller for
spatially explicit models within study areas when compared
to models pooled across study areas. In addition, the effect of
study area explained more than 50% of the variation in slope
A
B
Figure 2. Percentage of nest locations in 5 bins of increasing resource
selection function values that we used to train (black bars, n ¼ 269) and
test (gray bars, n ¼ 31) spatially explicit (A) and spatially stationary (B)
calibrations of our top performing multiscale resource selection function
for prairie-chicken nesting habitat in Kansas during 2006–2009.
Regression coefficients of determination (R2) close to 1.0 indicate that the
top model accurately classified nest sites in the holdout data set used for
model validation.
797
coefficients for 6 of 8 parameters from the most parsimonious
resource selection model (R2 > 0.50; Table 4), suggesting
that much of the spatial variation in our parameters could be
explained by differences among study sites. Model parameters at the Smoky area exhibited more spatial variation than
either of the Flint Hills areas. For example, slope coefficients
for the effect of visual obstruction on nest site selection
ranged from a large positive effect at the South area
(b ¼ 8.21 to 8.53) to a variable but positive effect at the
Smoky area (b ¼ 1.68 to 7.47; Table 4).
We retrospectively regressed the 8 spatially explicit parameters of the most parsimonious model onto the raw values of
the 13 uncorrelated habitat variables making up the starting
multiscale model. Only 2 of the 104 pairwise correlations
were significant. The effect of visual obstruction was positively related to proportion grassland in the home range area
(r2 ¼ 0.5, P < 0.01). Similarly, the slope coefficient for
distance to the nearest state highway tended to increase
with increasing grassland cover (r2 ¼ 0.51, P < 0.01).
DISCUSSION
Biological Interpretations
Spatial dependence in ecological phenomena has received
growing recognition as being of critical importance for
understanding the dynamics of natural systems (Borcard
and Legendre 2002, Lichstein 2007). Recent studies suggest
that incorporating spatial relationships improves inferences
and applications of ecological models (Thogmartin et al.
2004, Thogmartin and Knutson 2007, van Teeffelen and
Ovaskainen 2007, Latimer et al. 2009). However, wildlife
ecologists studying habitat selection have lacked the analytic
tools to address spatial variation in ecological relationships.
Consistent with previous work, we found that habitat components at multiple spatial scales influenced habitat selection
by grouse (Zimmerman and Gutiérrez 2008, Doherty et al.
2010). In addition, we found strong evidence that selection
processes were local or spatially explicit at 3 different spatial
scales. Spatially explicit models consistently outperformed
spatially stationary models when evaluating nest site selection by female prairie-chickens. Our results provide some of
the first analytical evidence that habitat selection can be
spatially dynamic and the relative importance of different
habitat components on behavioral decisions during nest site
selection depends upon the local environment experienced by
breeding females.
Females are likely weighing benefits and costs associated
with different habitat conditions in the context of other
habitat conditions when selecting nest sites, and no 2 points
in space have exactly the same combination of habitat components at all spatial scales. When grassland patches were
sufficiently large, females selected nest sites with greater
vertical nesting cover. Selection for greater vertical cover
and nest concealment is consistent with previous studies
of prairie-chickens in grazed and ungrazed landscapes
(Svedarsky et al. 2003, Matthews 2009). The influence of
quality nesting habitat on productivity is well documented
for grouse (Bergerud 1988, Holloran et al. 2005, Pitman
Table 4. Mean and variation in spatially explicit effects (slope coefficients) on greater prairie-chicken nest site selection within and across study sites in Kansas,
2006–2009. R2 ¼ coefficient of determination for analysis of variance (ANOVA) of effect by study site. High R2 values indicate variation in spatially explicit
effects is explained by study site and all tests were significant at the a < 0.001 level. Unless otherwise noted, we measured variables at the nest site scale
(<0.01 ha).
Study site
South
Mean
SD
Min.
Max.
Range
North
Mean
SD
Min.
Max.
Range
Smoky
Mean
SD
Min.
Max.
Range
All
Mean
SD
Min.
Max.
Range
R2
a
Distance to
local road
Distance
to lek
Distance
to edge
% Grass in core
area (13 ha)
4.48
0.04
4.43
4.60
0.17
1.68
0.01
1.69
1.66
0.03
2.19
0.05
2.33
2.12
0.21
0.77
0.08
1.00
0.67
0.33
10.21
0.03
10.17
10.25
0.08
1.53
0.22
1.24
2.16
0.92
3.55
0.53
2.81
8.37
5.56
1.44
0.39
0.85
4.51
3.66
1.88
0.46
2.77
1.14
1.63
2.53
1.53
4.61
0.07
4.54
0.40
0.49
0.83
1.29
2.12
1.38
0.83
0.33
10.19
9.86
5.47
4.25
12.02
1.70
13.72
1.88
1.19
6.51
0.40
6.11
0.72
1.43
1.68
7.47
9.15
0.46
0.66
2.73
1.69
4.42
0.36
1.34
2.64
2.43
5.07
7.76
3.63
16.59
3.54
13.05
0.17
1.15
1.41
4.36
5.78
3.08
3.19
2.16
9.41
11.57
1.39
2.97
10.12
12.91
23.03
0.30
2.50
6.51
3.83
10.34
0.85
3.40
3.27
1.68
8.53
10.21
0.90
1.30
2.08
2.73
4.60
7.33
0.94
1.10
1.19
2.77
2.43
5.20
0.36
4.96
3.78
16.59
0.07
16.52
0.51
0.01
0.95
1.41
4.36
5.78
0.23
4.45
4.12
2.16
10.25
12.41
0.70
0.39
4.27
12.02
12.91
24.93
0.51
% Bare
VORa
3.61
0.08
3.51
3.83
0.32
8.31
0.07
8.21
8.53
0.31
1.20
0.93
2.97
3.67
6.64
Distance to
state road
% Grass in home
range (310 ha)
Visual obstruction reading.
798
The Journal of Wildlife Management 77(4)
et al. 2005). However, tradeoffs between nest survival and
female survival can be mediated by concealment of nesting
cover in ground-nesting grouse (Wiebe and Martin 1998),
and we hypothesized that female selection for visual obstruction would exhibit a quadratic pattern with greater selection
for sites with intermediate levels of cover (Westemeier 1973,
Svedarsky et al. 2003). Nest site selection of prairie-chickens
was best described as a linear function of vertical cover,
possibly because quality nesting habitats with heavy cover
were rare in managed rangelands at our study sites. Less than
2%, 9%, and 19% of available nesting locations at our South,
North, and Smoky areas had visual obstruction readings
>50 cm suggested as optimal cover for nesting prairiechickens (Kirsch 1974, Svedarsky 1979). Nest survival has
also been found to be influenced by grassland composition
and landscape pattern for many grassland birds (Johnson
2001, Herkert et al. 2003). Therefore, grassland birds should
select grasslands with high contagion and large patch sizes
to maximize nest survival. In our study, prairie-chickens
tended to nest in areas dominated by grassland habitats at
larger spatial scales, especially in areas where grassland
habitat was limited by fragmentation. The effect of grassland
composition on nest site selection decreased with grassland
fragmentation, whereas local-scale habitat conditions like
vertical nesting cover became increasingly influential. We
found similar spatial heterogeneity in selection for other
habitat variables (Appendix A, available online at www.
onlinelibrary.wiley.com).
Our post hoc analyses indicated that much of the spatial
variation in selection processes could be collapsed to differences among study areas. Certainly, spatially explicit habitat
selection in the relatively unfragmented grasslands of the
Flint Hills showed little variability when pooled within each
study area. However, high spatial variability in selection
processes at the Smoky Hills area was likely due to highly
heterogeneous habitat conditions in this ecoregion. Grassland
patches were smaller and more variable, landscape composition was more diverse with more than 40% agriculture and
fallow areas, and patterns of land ownership with smaller
parcels of land resulted in more diverse grassland management than the 2 Flint Hills study areas. Variable slope
coefficients of habitat selection processes that indicated negative relationships in some parts of the study area and positive
relationships in others make recommendations for habitat
improvement difficult. To interpret spatial variation in
factors affecting nest site selection, we conducted a post
hoc analysis to assess whether local selection coefficients
were related to other local habitat variables. Females demonstrated greater selection for vertical nesting cover as the
amount of grassland habitat within a home range increased.
The habitat measurements themselves were not correlated so
a positive relationship between the selection coefficient of
vertical obstruction and the proportion of home range area in
grassland suggested females may be assessing conditions at
multiple spatial scales simultaneously when selecting nest
sites. A caveat to our inference is that only 2 of 104 post hoc
correlations were significant and the experiment-wise error
rate was high at 1 (1 0.05)104 ¼ 0.65.
Mcnew et al. Spatially Explicit Habitat Selection
If habitats and habitat selection are hierarchically organized
(Johnson 1980, Wiens 1989), environmental variables should
exhibit cross-scale correlations. Failure to consider the potential effects of cross-scale correlations in measured habitat
variables can result in faulty interpretations of habitat associations (Battin and Lawler 2006, Lawler and Edwards
2006). We addressed potential autocorrelation by decomposing pure and shared variation between habitat features at
the 3 separate spatial scales. Shared variation between habitat
variables at multiple scales could be the result of either the
joint effect of both variables or, alternately, the effect of 1
variable that is correlated with a second (Lawler and Edwards
2006). Habitat components at the nest site explained roughly
40% of the variation in nest site selection, whereas components at the core area and home range scale explained 4% and
1% of variation. Thus, habitat components at the nest site
were more influential than components at broader spatial
scales when simply evaluating habitat suitability at a single
spatial scale. However, more than half of the variation was
shared by habitat components across 2 or more spatial scales.
Thus, habitat management focused at a single spatial scale,
the nest site, may be less effective than a comprehensive
strategy addressing habitat deficiencies at multiple spatial
scales (Zimmerman and Gutiérrez 2008, Doherty et al.
2010). A broader perspective is especially needed for the
fragmented Smoky Hills study area where a greater degree of
grassland fragmentation at coarse spatial scales corresponded
with variability in the influence of local habitat variables
like vertical nesting cover. In contrast, nest site selection
in the large unfragmented grasslands of the Flint Hills
appears to be driven primarily by habitat components at
the local nest site.
Methodological Considerations
Habitat selection models can be spatially calibrated and used
to investigate hypotheses about relationships between habitat
patterns and selection responses when habitat conditions are
heterogeneous across space. Nest site selection by female
prairie-chickens was variable across the spatial extent of
our 3 study areas, suggesting that the relative importance
of habitat components vary depending upon the spatial
location of nests in the context of other habitat conditions.
Other potential applications of GWR could include evaluations of the appropriate spatial scale for a field study or
determining the spatial extent at which ecological phenomena occur. The GWR method uses an adjustable spatial
kernel-weighting scheme to fit potentially different coefficient values for each observation (Fotheringham et al. 2002).
Thus, the spatial extent at which habitat selection processes
become stationary can be determined by testing a series of
models with varying fixed bandwidth distances (Miller and
Hanham 2011). If evaluating remotely sensed habitat data
in a GIS, interpolated maps of slope coefficients from
spatially explicit GWR models (see Appendix A, available
online at www.onlinelibrary.wiley.com) can be digitized and
used to map RSFs to identify hot spots of habitat use that
could be the focus of further study or conservation.
799
A few limitations of GWRs should be considered. They are
spatially calibrated generalized linear models (GLMs) and
cannot evaluate nonlinear ecological relationships. Evidence
of a spatially explicit response may result from the misspecification of the predictor as a linear function when the
relationships is actually nonlinear (Miller and Hanham
2011). Thus, researchers should properly explore whether
relationships are nonlinear in a spatially stationary modeling
framework with generalized additive models before proceeding with GWR. Similar to other general linear models, a
limited suite of nonlinear responses may be evaluated in
GWR by including polynomial effects of environmental
variables. Another limitation is that mixed logistic models,
in which some coefficients are allowed to vary spatially and
others are fixed across space, are not possible with any of the
GWR packages currently available (GWR 3.0, R package
spgwr, or GWR tool in ArcInfo 10). Last, formal statistical
tests are not currently available for examining the significance
of the spatial variability in parameter estimates for logistic
calibrations of GWR, and an ad hoc approach of comparing
interquartile ranges of parameter estimates of spatially explicit and stationary calibrations of the same model must be
used to evaluate whether selection coefficients are better
described as spatially explicit or stationary.
MANAGEMENT IMPLICATIONS
Habitat features at 3 hierarchical spatial scales were correlated with nest site selection by prairie-chickens, although
our results suggest that habitat conditions at the nest site
were generally more influential than environmental factors at
broader spatial scales in Kansas. High spatial variability in
nest site selection suggested that the relative importance of
habitat components vary depending upon the local environmental conditions of potential nest sites. Thus, management
of habitats at small spatial scales, such as individual nest sites,
remains impractical. Because much of the spatial variation in
nest site selection by females was explained by regional
differences among study areas, we recommend that habitat
management aimed at improving nesting habitat occur at the
regional scale by increasing the availability of nesting sites
with standing litter or new growth to at least 25 cm during
the nesting period (Apr–Jul). Contrary to previous work, we
did not find declining selection for vertical nest cover that
exceeded a certain level because almost none of the available
sites in the Flint Hills had visual obstruction >40 cm.
However, we agree with other researchers that an optimum
range of vertical nest cover likely exists for prairie grouse,
which maximizes survival of both nests and incubating
females (Hamerstrom and Hamerstrom 1973, Westemeier
1973, Wiebe and Martin 1998). We never observed females
initiating nests in grasslands with standing litter >1 m despite limited availability at our Smoky site, suggesting that
the optimal range of vertical cover is somewhere between
25 cm and 100 cm. Spatial variability in nest site selection
was much greater at the fragmented Smoky area, likely
because habitat conditions at the site were spatially
heterogeneous. Therefore, managers should focus efforts
800
on restoring connectivity of grasslands by restoring unproductive agricultural fields to native grassland and by reducing
encroachment of trees by either mechanical treatment or
rotational prescribed burning (Briggs et al. 2005, McNew
et al. 2012b).
ACKNOWLEDGMENTS
We thank the late Dr. Ron E. VanNimwegen for discussions
that inspired this study. Many field technicians assisted with
collection of field data. Funding and equipment were provided by a consortium of federal and state wildlife agencies,
conservation groups, and wind energy partners under the
National Wind Coordinating Collaborative, including the
National Renewable Energy Laboratory (DOE), U.S. Fish
and Wildlife Service, Kansas Department of Wildlife and
Parks, Kansas Cooperative Fish and Wildlife Research
Unit, National Fish and Wildlife Foundation, Kansas and
Oklahoma chapters of The Nature Conservancy, BP
Alternative Energy, FPL Energy, Horizon Wind Energy,
and Iberdrola Renewables. B. K. Sandercock was supported
by the Division of Biology at Kansas State University. J.
Pitman, R. Gutiérrez, and anonymous reviewers provided
comments that improved the manuscript.
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