Black-tailed Prairie Dog Habitat Suitability Modeling for the
Southern Great Plains: Cross-scale Analysis of Soils, Topography
and Climate
David J. Augustine, Research Ecologist, USDA-Agricultural Research Service,
1701 Centre Ave, Fort Collins, CO 80526; David.Augustine@ars.usda.gov
Willam E. Armstrong, GIS Specialist, USDA-Agricultural Research Service,
1701 Centre Ave, Fort Collins, CO 80526; Billy.Armstrong@ars.usda.gov
Jack F. Cully, Assoc. Professor of Biology and Assistant Wildlife Unit Leader,
Kansas Coop. Fish and Wildlife Research Unit, 204 Leasure Hall, KSU,
Manhattan, KS 66506, bcully@ksu.edu
Michael F. Antolin, Professor, Department of Biology, Colorado State University,
Fort Collins, CO 80523-1878; Michael.antolin@colostate.edu
1
ABSTRACT
We developed multi-scale habitat suitability models for black-tailed prairie dogs (BTPD)
in the southwestern Great Plains, corresponding to the western region of the Great Plains LCC.
We used long-term (10-yr), high-resolution datasets on BTPD colony boundary locations
collected at 7 study areas distributed across the region to develop resource selection functions
based on colony locations and expansion patterns. Models are based on (1) soil maps and
associated Ecological Sites (NRCS SSURGO database), (2) a topographic wetness index based
upon water runoff and solar insolation patterns (TWIsi) that tests a priori hypotheses for
topographic controls on BTPD, and (3) broad climatic gradients in temperature and mean annual
precipitation. We show that BTPD habitat suitability is positively associated with soil organic
matter, pH, clay content and depth to a restricted layer as well as TWIsi. BTPD habitat
suitability is negatively associated with slope and soil sand content. The negative influence of
slope is stronger on soils with high organic matter content. The positive influence of TWIsi is
greater for soils with low sand content. Habitat suitability is positively associated with soil clay
for areas with mean annual precipitation of 400 – 500 mm, but where mean annual precipitation
declines to 350 mm, habitat suitability becomes negatively associated with soil clay content.
Resulting models and map products provide a basis for land managers to compare and prioritize
areas of conservation importance for BTPD and evaluate habitat for a suite of associated species
of concern at scales from pastures to broad landscapes.
We also provide the first assessment BTPD habitat suitability relative to Ecological Site
Descriptions, which is essential for incorporating BTPD into associated state and transition
models being developed and used by NRCS, USFS and BLM. We present the relative value of
different Ecological Sites for BTPD in each of 3 regions based on Major Land and Resource
Areas (MLRAs): MLRAs 67B/69 (eastern CO), MLRAs 72/77A (southwestern KS), and
MLRAs 77A/B (northeast NM; OK and TX panhandles). Models and maps have immediate
utility for land managers in the GPLCC and provide a tool for evaluation of plague mitigation
strategies and future BTPD and plague management in response to climate change.
2
INTRODUCTION
Because black-tailed prairie dogs (BTPD) function as ecosystem engineers and keystone
species in Great Plains grasslands, their conservation and management lies at the core of many
conservation efforts in the region. BTPD management is challenging and controversial because
they may compete with livestock (Derner et al. 2006) and are severely affected by epizootic
plague outbreaks caused by the bacterium Yersinia pestis (Cully et al. 2010). Furthermore, large
BTPD colony complexes are needed to achieve conservation goals for multiple associated
species including black-footed ferrets (Mustela nigripes; Roelle et al. 2005), mountain plovers
(Charadrius montanus; Dinsmore et al. 2010, Augustine 2011) and burrowing owls (Tipton et al.
2009). Management such as dusting with insecticides to control plague transmission, poisoning
to control prairie dog populations, and translocations to establish new populations are expensive
(Andelt 2006), emphasizing the need to ensure they are applied in a spatially optimized manner
to provide multiple ecosystem goods and services.
Black-tailed prairie dogs are broadly distributed in central North America, and hence
adapted to range of temperature and precipitation regimes and plant communities. Although
many social and economic factors influence where BTPD complexes can be conserved or
expanded, a suite of critical abiotic and biotic factors also controls BTPD habitat suitability. In
particular, climate, soils, topography and vegetation structure vary widely across the GPLCC and
directly influence BTPD persistence and expansion. At the eastern edge of their range, BTPD
can be limited by tall vegetation and increased predation risk, while forage and water limitations
may be constraining in the western portion of their range (Koford 1958, Hoogland 1995). The
influence of precipitation regimes (long-term mean precipitation; seasonal and interannual
variability) on BTPD colony expansion rates has direct relevance to contemporary management
and long-term conservation planning in the face of climate change, but has never been
systematically assessed.
The ability to evaluate and map BTPD habitat within the GPLCC planning area would
provide a valuable tool for optimizing use of scarce BTPD conservation funds. Research on
landscape-scale patterns and controls of plague in BTPD complexes over the past 15 year (Cully
et al. 2006, Antolin et al. 2006, Cully et al. 2010) has highlighted the need for an empiricallybased, landscape-scale habitat suitability model to assist in evaluating plague mitigation
strategies and understanding BTPD and plague responses to climate change. Such an effort
would also improve our understanding of local versus large-scale constraints on BTPD
distribution and abundance. Past efforts to model BTPD habitat suitability focused on the
northern Great Plains, and lacked high-resolution data on BTPD colony locations and expansion
rates (Proctor et al. 2006). Belak (2001) examined high-resolution BTPD colony maps for two
sites in South Dakota, but did not assess the influence of climate. Over the past 15 years,
research on BTPD ecology (Stapp et al. 2004, Antolin et al. 2006, Augustine et al. 2008, Cully et
al. 2010) and USFS monitoring have generated high-resolution, long-term datasets on BTPD
colony boundaries for National Grasslands encompassing more than 1 million acres of the
GPLCC. These data provided a unique opportunity to develop and test a quantitative habitat
suitability model for BTPD because (1) measurements were repeated annually, thus quantifying
colony expansion pattterns during plague-free periods, (2) sites are distributed across a broad
north-south temperature gradient and east-west precipitation gradient (Fig. 1), and (3) analysis of
a subset of these data demonstrated that 10-12 years of measurements provides a substantially
different perspective on BTPD distribution than short-term (1-3 year) surveys (Augustine et al.
2008).
3
We developed and tested quantitative BTPD habitat suitability models for the
southwestern Great Plains that examined the influence of climate, soils, and topography. We
evaluated soils in two ways. First, we examined resource selection functions (RSFs) based upon
quantitative measures of soil texture, organic matter content, pH and depth to a restricted layer.
Second, we examined RSFs that aggregated these soil attributes at the level of Ecological Sites
recently developed by the Natural Resource Conservation Service (NRCS). We controlled for
the influence of land use by focusing on National Grasslands consistently managed with
moderate cattle stocking rates, and thereby independently evaluated the influence of climate, soil
and topography on BTPD habitat. Thus, our models do not incorporate the influence of land use,
but will be essential for future incorporation of land use effects into modeling and conservation
planning efforts.
METHODS
Study Area
We studied BTPD habitat suitability in the
western portion of the Great Plains LCC
(Figure 1). Our analyses focused on BTPD
colonies occurring with 7 study sites
consisting of National Grasslands or
geographically distinct sub-units of National
Grasslands in Colorado, Kansas, Oklahoma,
New Mexico, and Texas. The Kiowa and Rita
Blanca NGs encompass a precipitation
gradient, with colonies in New Mexico
(Kiowa) exhibiting different plague epizootic
patterns than colonies in Texas and Oklahoma
(Rita Blanca, Cully et al. 2010); these
Grasslands were therefore treated as separate
sites. Similarly, the eastern and western units
of the Pawnee National Grassland encompass
a precipitation gradient and colonies in the
two units exhibit different plague epizootic
patterns (Stapp et al. 2004), hence were
treated as separate sites. Within the
administrative boundaries of each study site
(Figure 1, blue boundaries), land ownership
consists of a mosaic of private, state and
federal lands. Analyses of prairie dog colony
locations and surrounding locations lacking
prairie dogs were based on data from
~900,000 ha (2.1 million acres) of federal land
occurring within the administrative
boundaries of the 7 study areas.
Figure 1. Locations of study sites in the southern Great
Plains with long-term, high-resolution datasets on blacktailed prairie dog colonies.
4
We extrapolated habitat suitability models derived from the 7 study sites to the western portion
of the Great Plains LCC consisting of those counties within the shortgrass steppe ecoregion (see
Lauenroth et al. 1999) that encompassed the precipitation and temperature gradients represented
by our study sites (Figures 1-3, Table 1). These models are based upon topographic and soil
attributes and do not address land use change or the influence of grazing management, and hence
are intended to represent variation in habitat in the absence of anthropogenic effects on soils and
vegetation.
Climate data
Given the goal of examining the influence of precipitation and temperature gradients on habitat
suitability, we examined two sources of spatially interpolated annual climate data for the
southern Great Plains. First we focused on precipitation and maximum/minimum daily
temperature data compiled using the TOPS (Terrestrial Observation and Prediction System;
http://ecocast.arc.nasa.gov/topwp/) model as data are available online for any specified region of
the country (www.coasterdata.net), and were developed in coordination with efforts of the Great
Plains LCC (www.greatplainslcc.org/resources/). However, for climate data compiled at the
scale of our 7 National Grassland study areas, our preliminary analyses revealed unusually low
predicted annual precipitation at the easternmost study area (Cimarron National Grassland;
TOPS predicted mean annual precipitation for 1980-2009 = 368 mm), which was similar to
predictions for our westernmost site (Timpas Unit, Comanche National Grassland; predicted
mean annual TOPS precipitation for 1980-2009 = 361). Data from the long-term meterological
station at the Cimarron National Grassland (Elkhart, KS www.ncdc.gov) showed that TOPS
consistently underpredicted actual precipitation for a region of southwestern KS and southeastern
CO, for unknown reasons. We therefore considered a second source of spatially interpolated
long-term weather datasets generated by the PRISM model (Parameter-elevation Regressions on
Independent Slopes Model; http://www.prism.oregonstate.edu). We found these data to be in
much stronger agreement with point data from meterological stations located on or near the
National Grasslands. All subsequent analyses of precipitation and temperature gradients were
therefore based on the PRISM database (Figures 2 and 3; Table 1). We used databases of longterm (1980-2010) mean annual precipitation (mm) and maximum daily temperature (degrees C)
at a resolution of 8 km x 8 km.
BTPD Colony Locations
BTPD colonies have been mapped annually with global positioning systems (GPS) units
on National Grasslands (NGs) encompassing ~900,000 ha of federally managed grassland in the
western portion of the Great Plains LCC. GPS mapping began as early as 1993 on Pawnee NG,
and occurred nearly annually at all study areas during 2001-2010 (Stapp 2004, Augustine et al.
2008, Cully et al 2010; Table 1). Annual datasets for each of the 7 study sites were screened for
mapping errors including datum accuracy and consistency in the resolution of boundary
5
Table 1. Mean annual precipitation and temperature during 1980-2010 (PRISM database), hectares of
National Grassland, and years of black-tailed prairie dog colony mapping for each of 7 study sites in the
western portion of the Great Plains LCC.
Study Site
Carrizo
Cimarron
Kiowa
Pawnee East
Pawnee West
Rita Blanca
Timpas
Hectares
102692
43978
23777
37901
46841
37952
69738
Mean Annual
Precipitation
(mm)
20.1
21.0
20.1
17.6
16.9
20.8
20.6
Mean Daily
Maximum
Temperature
(˚C)
418
440
415
380
348
417
352
6
Years of BTPD Colony
Mapping
2001-2006, 2008-2010
2001-2010
2001-2006,2009-2010
2001-2010
2001-2010
2001-2006,2009-2010
2001-2010
Figure 2. Map of the distribution of study sites in within the western portion of the Great Plains LCC
relative to variation in mean annual precipitation.
7
Figure 3. Map of the distribution of study sites in within the western portion of the Great Plains LCC
relative to variation in mean annual precipitation.
8
mapping. Kiowa and Rita Blanca National Grasslands were not mapped in 2007 or 2008. Data
for the Carrizo Unit of the Comanche National Grassland for 2007 were excluded due to
mapping errors.
Modeling Approach
Traditional habitat suitability models relied on simple functions to relate the distribution of an
organism to limiting factors such as food and cover, based on knowledge of the organism’s
ecology. An index of habitat suitability was derived as an integrated function of these limiting
factors. For black-tailed prairie dogs, Clippinger (1989) and Proctor et al. (2006) focused on
limitations imposed by soil texture, slope, and composition of plant species on a site. Qualitative
relationships between these factors and BTPD distribution are evident throughout their range
(Koford 1955, Clippinger 1989), but quantitative relationships have only been tested for a few
specific locations in the northern Great Plains (Reading and Matchett 1997, Belak 2001). When
evaluating wildlife habitat, a key consideration is the scale of habitat selection being evaluated.
Habitat selection can be categorized into four hierarchical scales of analysis (Johnson 1980):
First-order habitat selection = selection of the geographical range of a species.
Second-order habitat selection = selection of a home range by an individual or social group
within the available area defined by the geographical range.
Third-order habitat selection = selection of habitat components within the immediate vicinity
of an individual or social group’s home range
Fourth-order habitat selection = selection or procurement of resource items (e.g. food items)
from those available at a given location
We evaluated habitat selection using two different metrics: colony presence and colony
expansion pattern. These two metrics correspond to analyses of second-order and third-order
habitat selection respectively. The second-order habitat selection analysis based on colony
presence defined the area of available habitat at a broad spatial scale (allotments in which BTPD
colonies have been mapped; Fig 2.) and examined the influence of soils, topography, and climate
on colony presence. The third-order analysis of habitat selection based on colony expansion
pattern analysis defined available habitat at a finer spatial scale based on the direction and extent
of colony expansion over a plague-free interval of 3 or more years.
For the broad-scale analysis of BTPD colony occupancy, we quantified the maximum
cumulative extent of all colony locations mapped during 2001 – 2010. A screening process
following Augustine et al. (2008) was applied to exclude allotments potentially affected by
incomplete colony mapping. For each allotment, we generated a set of random locations to
quantify availability of habitat attributes, where the number of randomly selected pixels was
equal to the number of pixels encompassed by the colony boundaries (used pixels). Available
pixels were selected at two spatial scales: within a 2 km buffer of colony boundaries, and within
a 0.5 km buffer of colony boundaries. Nearby colonies could potentially overlap in the area from
which available pixels were selected, thereby inducing non-independence among colonies within
the dataset. To address this issue, we implemented an ArcGIS script that identified all colonies
whose boundaries were within the buffer distance (2 km or 0.5 km depending on scale of
available habitat) of one another. Pairs of colonies located less than the buffer distance to one
another were then grouped together into a single colony cluster, and the process repeated until all
colony clusters within the dataset were separated by more than the buffer distance. Colony
clusters, rather than individual colonies, were then used as independent subjects in the logistic
9
regression. This method dramatically reduced but did not eliminate the possibility that available
habitat associated with two different colony clusters could overlap. To prevent an available pixel
from being included in the set of available pixels for two different colonies, we selected available
pixels randomly and without replacement.
Prior to analysis, we excluded all colony clusters that were < 10 ha to reduce influence of
small colonies that may not yet have expanded sufficiently to express selection relative to
topographic position or soil characteristics. Colony cluster polygons were converted to 30-m
resolution rasters, where each used pixel (value = 1) was contained completely within a colony
boundary. Clusters where the amount of surrounding available habitat (i.e. within the buffer
distance) on NSF lands was less than the area of the colony cluster were also removed from
analysis. This was done because most colony clusters meeting this criteria had expanded to the
point where they occupied nearly all of the NSF land in that area, leaving little or no available
habitat for comparison.
Table 2. Number of colony clusters or colonies used in analyses of BTPD habitat suitability at 3 different
spatial scales.
Scale of Analysis
2-km Buffer
0.5-km Buffer
Local Expansion Pattern
Study Site
Area (Ha)
Colony
Clusters
Pixels
Colony
Clusters
Pixels
Colonies
Carrizo
102,692
28
142,275
71
139,097
66
120,153
Pawnee West
46,841
15
60,122
38
58,097
22
45,110
Cimarron
43,978
12
59,267
26
33,876
21
32,703
Rita Blanca
37,952
19
49,953
28
49,052
6
9,857
Timpas
69,738
19
14,738
23
14,088
13
10,725
Pawnee East
37,901
10
24,238
16
12,957
15
14,877
Kiowa
23,777
10
24,243
14
23,243
9
15,047
113
374,836
216
330,410
152
248,490
Study Site
Total
Pixels
We also analyzed patterns of colony expansion relative to soils, topography, and local
climatic conditions. We calculated annual changes in boundaries of 164 colonies across the 7
study sites during 2001 – 2005, when colonies in all or a majority of each site did not experience
plague epizootics (Cully et al. 2010, Cully and Antolin, unpublished). For each colony in each
year, we determined whether the colony was expanding, stable, shifting, or declining based on
the following definitions:
Expanding: colony area increased by more than 20% between years 1 and 2, and area
occupied in year 1 makes up at least 80% of area occupied in year 2.
Stable: colony area changed by less than 20%, and colony area in year 1 makes up at least
80% of area in year 2.
Shifting or declining: colony area increased by less than 20%, and colony area in year 1
makes up less than 80% of area in year 2
10
Decreasing: colony area declined by more than 20%.
We identified those colonies expanding and/or stable for a sequence of at least 3 consecutive
years, and used these colonies to evaluate expansion patterns relative to topoedaphic and climate
variables. Colonies less than 10 ha in size were excluded from analysis. For each colony in the
expansion dataset, we identified the centroid of the colony in the first year of the sequence and
the distance from the centroid to the maximum extent of the colony at the end of the expansion
period. This distance plus 90 m was used to establish a buffered area around the centroid that
defined the area of available habitat (e.g. see Augustine et al. 2007). We added 90 m to the
distance between colony centroid and maximum colony boundary extent in order to be able to
sample available habitat surrounding those colonies with minimal expansion (i.e. consistently
stable colonies) which may not have expanded because they were surrounded by low-quality
habitat. We identified all 30-m resolution pixels that were within the area into which the colony
expanded (used pixels = 1) and randomly selected the same number of pixels from the buffered
area into which the colony did not expand (available pixels = 0). Numbers of colony clusters or
colonies used in habitat suitability model fitting at each of the 3 spatial scales at which we
defined available habitat (2-km buffer, 0.5 km buffer, local expansion pattern) are summarized in
Table 2.
Model Predictors
Vegetation
Most assessments of wildlife habitat suitability are based on vegetation characteristics.
However, this approach is problematic for species that substantially modify vegetation in areas
they inhabit. BTPD are well-known to modify their habitat by burrowing, grazing and clipping
tall vegetation. As a result, variables such as vegetation cover (e.g. Whicker and Detling 1988,
Hartley et al. 2009) and remotely-sensed greenness indicies (e.g. the Normalized Difference
Vegetation Index [NDVI]) differ substantially between grassland on versus off BTPD colonies
for reasons unrelated to habitat selection or suitability. Furthermore, maps of vegetation
characteristics other than remotely-sensed cover and NDVI are typically unavailable for broad
landscapes, or if available have low resolution and accuracy. To derive predictions that are of
greatest utility to land managers and conservation planning, our RSF models did not consider
vegetative predictors. Rather, they are based on climatic, topographic and edaphic variables that
are available across the entire Great Plains LCC. The parameters we used are correlated with
regional variation in grass species (C4 shortgrasses vs. C3 mid-height grasses; Epstein et al. 1997)
and local variation in site potential for different plant communities (including variation in shrub
presence and density) and hence vegetation structure (Dodd et al. 2002; USDA-NRCS
Ecological Site Descriptions), but do not explicitly include vegetation structure or species
composition. At a local scale, we note that vegetation structure can potentially have a strong
influence on colony expansion patterns, but such influences are not incorporated into our habitat
models.
Topography
Traditional habitat suitability models often use slope and aspect, but these parameters
provide an incomplete measure of topography. For example, ridges and swales can have the
same slope and aspect, but differ in value as BTPD habitat. We used 10-m resolution digital
elevation models (DEMs) for each study area to derive a a topographic wetness index (TWI) for
11
each site. TWI was calculated in ArcGIS using the Landscape Connectivity and Pattern (LCaP)
tool (Theobald 2007). We computed TWI in two ways: (1) excluding any effect of aspect on the
index (TWIn), and (2) incorporating aspect using a weighting from 0 (xeric) to 1.0 (mesic) based
on relative solar insolation (TWIsi). TWIsi quantifies differences between ridges, slopes and
swales and north- and south-facing slopes independent of soil texture effects. Initial model
fitting for datasets with varying definitions of available habitat showed that TWIsi consistently
outperformed TWIn, so all subsequent model fitting and selection analyses only considered
TWIsi. We also used the 10-m DEMs to calculate slope for each pixel across each study site
(Table 2). The TWI and Slope rasters were then resampled to a 30-m resolution aligned with the
soil and BTPD rasters and used for subsequent model fitting.
Soils
We used the Soil Survey Geographic (SSURGO) database created by the USDA’s
Natural Resources Conservation Service to quantify a suite of soil attributes. We used the
USDA’s Soil Data Viewer tool to derive quantitative maps of soil properties rather than
categorical maps of soil types or ecological sites, including percent sand to 1 m depth (SAND),
percent clay to 1 m depth, average soil depth to bedrock or a restrictive layer, soil organic matter
content, and soil pH, all at a 30-m pixel resolution (Table 3). Use of these quantitative soil
properties allowed us to model habitat suitability across all 8 study sites, even though specific
soil series may only be found at one or two sites.
Each map unit within the SSURGO database (i.e. each polygon) is typically composed of
one or more “components”, where the components represent the major soil types within a map
unit. Differences in soil properties can exist over short distances between map unit components,
but these are not represented spatially in the SSURGO database. For each map unit, an estimate
of the percent composition of each component is provided in the database. To obtain a single
value for each quantitative soil attribute for each map unit, we used a “dominant condition”
aggregation method where we first grouped together components with like attribute values in a
map unit. For each group, percent composition was set to the sum of the percent composition of
all components participating in that group. Soil horizon attributes were aggregated to 1 m depth
at component level, before components were aggregate to the map unit level. The attribute value
for the group with the highest cumulative percent composition was then assigned to each map
unit. As a result, our analyses are contingent upon the accuracy of the soil mapping process, and
do not reflect the potential influence of fine-scale spatial variation in soil components within map
units.
Our second approach used NRCS Ecological Site Descriptions to assess variation in
BTPD habitat suitability. Ecological Site Descriptions (ESD’s) are becoming a key tool guiding
rangeland management in the Great Plains because they are based on the SSURGO database, and
NRCS has developed detailed descriptions of plant communities, potential site productivity,
models linking livestock management to plant community states and transitions for each ESD.
In 2010, the NRCS, US Forest Service, and Bureau of Land Management signed a MOU
establishing that all three agencies would collectively use the ESD framework to guide rangeland
management. At present, however, most first-round ESD’s do not incorporate prairie dogs. For
models evaluating BTPD habitat selection for Ecological Sites, we did not include quantitative
soil attributes, because these attributes are used to define the ESD boundaries. We used the
dominant ESD within each SSURGO database poloygon in our models, but note that ESD’s do
not necessarily map 1:1 to soil components, as discussed above for quantitative soil attributes.
12
Table 3. Summary of topographic, soil and climate attributes used in modeling
black-tailed prairie dog habitat suitability.
Parameter
Units
Slope
Derived from 10-m digital elevation model, degrees
TWIsi
Index ranging from ~1-30; derived from 10-m digital
elevation model following Theobald ( 2007)
Sand
% by weight to 1 m depth
Clay
% by weight to 1 m depth
Organic Matter
% by weight to 1 m depth
pH
Result of 1:1 soil:water method
Depth
Depth to impermeable layer, cm
Precipitation
Mean annual amount, 1980-2010, mm
Temperature
Mean monthly maximum, 1980-2010, ˚C
Ecological site definitions vary among Major Land and Resource Areas (MLRAs) within
the Great Plains, and hence types of ecological sites varied among some study sites. We
therefore analyzed ESD’s in three clusters of study sites, based on consistency in ESD
definitions: (1) MLRA 067B/69: Pawnee East, Pawnee West, Timpas, and Carrizo, (2) MLRA
77A/B: Rita Blanca and Kiowa, and (3) MLRA 72: Cimarron.
Model Fitting and Selection:
We used general linear mixed models fit with the Laplace approximation method (Bolker
et al. 2009) to assess relative BTPD habitat suitability. With this modeling approach, we
generated population-level resource selection functions (RSFs) across two orders of selection
and 7 BTPD populations based upon the used-available designs of 2nd and 3rd order habitat
selection (Johnson 1980), where the probabilities generated by the RSFs are proportional to the
probability of use by BTPD (Manly et al. 2002). We used logistic regression with a binary
response variable with values of 1 for used pixels and 0 for available pixels. All models included
a random intercept term that treated each colony cluster (clusters defined as a group of colonies
within a 0.5 or 2 km neighborhood of one another) as a subject to account for the nesting of used
and available pixels within colony clusters, and to account for variation in sample sizes among
colony clusters (Gillies et al. 2006). All models were fit using the GLIMMIX procedure in SAS
v9.3.
Models based on quantitative soil and topographic variables considered 8 possible
predictors: slope (SL), topographic wetness index incorporating the effect of solar insolation on
evaporation (TWIsi), mean soil sand content to 1 m depth (SAND), mean soil clay content to 1
m depth (CLAY), soil organic matter content (OM), soil pH (pH), and soil depth to a restricted
layer (DTR). We compared the suite of potential models based on two criteria: minimization of
AIC (Burnham and Anderson 2002), and maximization of the area under the Receiver Operating
Characteristics curve (Area under ROC curve; Hanley and McNeil 1982; Gonen 2006). Our use
of general linear mixed models requires that each candidate model be fit individually without the
aid of automated model comparison procedures available for general linear models in some
statistical packages. We therefore used a 3-stage approach for considering and selecting within
13
sets of candidate models with and without interaction terms. First, we evaluated the set of
candidate models that only included the 7 possible topoedaphic predictors (no interaction terms)
using backward selection and minimization of AIC. Second, we evaluated a set of candidate
models that included all predictors in the best model identified in the first step, but that also
considered interactions between topographic variables (TWIsi and Slope) and those soil
characteristics that could influence soil moisture and hence site productivity (SAND, CLAY,
OM). In this second step, we identified the best models with single interaction terms for TWIsi
and Slope, and then also considered a model with both the TWIsi interaction term that minimized
AIC and the Slope interaction term that minimized AIC.
Third, we evaluated a set of candidate models that included all predictors in the model
identified in the second step, but that also considered interactions between 4 topoedaphic
variables (TWIsi, SAND, CLAY, OM) and the climatic variables that vary across the study
region (PRECIP = mean annual precipitation, and TEMP = mean maximum monthly
temperature). We hypothesized that large-scale variation in temperature and precipitation could
influence BTPD habitat selection via their influence on moisture availability and hence forage
productivity in this water-limited ecosystem. The four topoedaphic variables above were
selected for tests of interactions with climate because they all influence moisture availability at
the local level. TWIsi is a direct measure of topographic effects on moisture, with highest values
in swales and drainages. SAND, CLAY and OM influence moisture availability through water
infiltration and soil water holding capacity. We evaluated all possible TEMP x topoedaphic
interactions (4 models), all possible PRECIP x topoedaphic interactions (3 models; PRECIP x
SAND not considered due to high covariance), and models that included an interaction term for
both TEMP and PRECIP.
Mixed models generate coefficients for prediction at both the colony-specific level
(conditional model) and for prediction at the level of the population of colonies within the study
region (marginal or population model). Because our goal was prediction at the population level,
we examined model fit using a method that included assessing the model’s prediction accuracy at
the population level. When assessing the prediction accuracy of a model, true positive and false
negative rates are two widely used indicies (Wang et al. 2011). For a binary test, a threshold
cutoff can be defined where values above the threshold are assigned a positive outcome, and
values below the threshold are assigned a negative outcome. The receiver operator characteristic
(ROC) curve is the entire collection of true positive and false negatives for varying thresholds
from 0 to 1. A summary index of model performance (i.e. predictive abilility) can then be
defined as the area under the ROC curve (AuROC), which is equivalent to the probability that
model predictions for a randomly selected pair of used and available pixels are correctly ordered.
Wang et al. (2011) note that on the basis of results from Pepe (2005), and Pepe et al. (2006),
“when using a combined linear test as a decision rule, the ROC-based approach may outperform
the likelihood-based approach in terms of prediction performance. On the other hand, it is
possible that when prediction is of interest, allowing some variables with weaker association to
stay in a model may improve prediction accuracy (Pinsky 2005).” For these reasons, when
comparing models with versus without climate variables (and hence comparing models with
different random coefficients), we considered both AuROC (following Gonen 2006) and AIC in
model selection. Specifically, we only considered models including interactions with
precipitation and temperature when they increased AuROC relative to the model lacking
interactions with climate, and then used AIC to compare and select among the set of models that
increased AuROC relative to the best model without climate interactions.
14
Figure 4. Example of colony clusters defined by the 2-km linkage rule and the associated
distribution of pixels representing available habitat for a portion of the Pawnee West study site.
The green background shows the distribution of the National Grassland property. Each colony
cluster is represented by points of a different color. In this example, there are 7 colony clusters.
Within each color, dense concentrations of points show used pixels located on colonies, and
sparsely distributed points are available pixels.
15
Figure 5. Example of colony clusters defined by the 0.5-km linkage rule and the associated
distribution of pixels representing available habitat for each cluster. Area shown is a portion of
the Pawnee West study site. The green background shows the distribution of the National
Grassland property (National Forest System lands). Each colony cluster and its associated
available habitat are represented by points of a different color. In this example, there are 9
colony clusters. The distribution of used pixels is the same as in Figure 5, except that a group of
small colonies in the northern portion of the area of Figure 5 were not included with the 0.5-km
rule because they each became a separate cluster < 10 ha in size, and hence fell below the
colony size cutoff.
16
This model fitting approach was applied to 3 different datasets where available habitat
surrounding colonies was defined at different scales. The first two datasets correspond to an
analysis of second-order habitat selection: (1) used and available pixels defined based on a 2 km
buffer around each colony cluster, (2) used and available pixels defined based on 0.5 km buffer
around each colony cluster. The third dataset corresponds the third-order habitat selection,
where used and available pixels were defined based on the local pattern of colony expansion
over >3 consecutive years. For the first dataset, the model fitting procedure was applied to (a)
the full dataset combining colony clusters from all 7 National Grasslands (referred to as global
models hereafter), and (b) each National Grassland modeled separately (referred to as local
models hereafter).
Finally, to evaluate BTPD habitat selection relative to Ecological Sites, we analyzed the
2-km buffer and the expansion pattern databases for the 3 groups of study sites defined based on
MLRAs. We also included SLOPE and TWIsi in the ecological site models. In these analyses,
we did not consider interactions with climate variables due to limited variation in temperature
and precipitation within the different MLRAs.
Model Mapping:
We used the selected models to generate maps of relative BTPD habitat suitability at the scale of
the 7 National Grassland study sites, and at the scale of the broader shortgrass steppe study
region encompassing 74 counties in Colorado, New Mexico, Oklahoma, Kansas, and Texas. For
ease of reference, raster files are organized by study site and county (Appendix A). Following
Manly et al. (2002), we calculated a relative value for each pixel based on the selected model’s
coefficients and intercept, exponentiated these values, and then used a linear stretch of
exponentiated values to obtain rescaled RSF predicted values between 0 and 1 (see also Johnson
et al. 2006, DeCesare et al. 2012). We refer to these as the RSF probability maps.
Specifying how different probability values correspond to classes of suitable versus
unsuitable habitat depends upon the level and types of error that one is willing to accept. Given
the design of our sampling, where locations of BTPD colonies represent used habitat and
locations lacking BTPD colonies represent available habitat, the “available” habitat is likely to
include both areas of high quality (or potentially suitable) habitat that has not yet been colonized,
and areas of low quality (or unsuitable) habitat that is being avoided by colonizing prairie dogs.
In this view, false negative model predictions (i.e. where pixels occurring within known BTPD
colony locations are predicted to not have BTPD present) are a more egregious error than false
positive model predictions (i.e. where pixels within “available” habitat are predicted to have
BTPD present). We therefore mapped RSF probability categories based on cutoff values
corresponding to low and fixed false negative error rates of 5, 10 and 15%, and then present the
false positive error rates corresponding to each of these cutoff values. In all of the category maps
we present, we use the following categories of probabilities:
Category 1: RSF probability values below the cutoff for a 5% false negative rate
Category 2: RSF probability values below the cutoff for a 10% false negative rate but not
included in category 1,
Category 3: RSF probability values below the cutoff for a 15% false negative rate but not
included in category 1 or 2
Category 4: RSF probability values above the cutoff for a 15% false negative rate.
17
Thus, category 1 depicts areas consistently predicted to represent low quality habitat even
under a stringent false negative error rate and category 4 represents areas consistently predicted
to be high quality habitat, even with considerable relaxation of the false negative error rate (and
correspondingly lower false positive rate). Categories 2 and 3 represent areas of intermediate
habitat value.
We compared the best local models (fit to a specific study site using data only from that
study site) with selected global models (fit using data from all study sites combined) in terms of
the proportion of the landscape predicted to be in each of the 4 categories above (relative value
comparison) and in terms of the proportion of the landscape predicted to be in category 1 by one
model but in category 4 by the other model. We used spatial differences in model predictions as
our primary means of comparing the models, as interpretation of differences in coefficients is
difficult when models contain multiple and differing interaction terms.
Results
Second-order habitat selection
Our assessment of second-order habitat selection measured available habitat at two
scales: a 2 km buffer surrounding the maximum extent of each colony cluster, and a 0.5 km
buffer surrounding the maximum extent of each colony cluster. The 2 km buffer distance was
originally selected as an appropriate compromise between larger distances, which would cause
increasing overlap among nearby colony buffers, and shorter distances, which would sample a
less extensive area of the landscape. However, we also conducted the same analyses using the
0.5 km buffer to assess whether our selection of buffer distance notably affected the habitat
suitability model, in particular the direction of the effect of different topoedaphic parameters.
We first present detailed findings for the 2 km buffer modeling effort, as these findings form the
basis for our final, large-scale mapping of habitat suitability, and then present the comparable
models based on the 0.5 km buffer distance. For the second-order habitat selection analysis, we
first examined global models based on the full dataset (all colonies from all 7 study sites), and
then also fit local models for each of the 7 study sites for comparison.
Global second-order models
For the set of models that did not include interaction terms, the most parsimonious model
included all 7 topoedaphic predictors (TWIsi, Slope, % Sand, % Clay, pH, % Organic matter,
and Depth to a restricted layer; Table 4), which was a substantial improvement of all competing
models with 6 or fewer predictors (Δ AIC > 558). Of the potential models including interactions
between slope and soil parameters, the most parsimonious included a Slope x Organic Matter
interaction (Table 4; Δ AIC relative to no interaction model = 251.3). Of the potential models
including interactions between TWIsi and soil parameters, the most parsimonious model
included a TWIsi x Sand interaction (Table 4; Δ AIC relative to no interaction model = 748.0).
18
Table 4. Summary of second-order RSF model set including direct effects of up to 7 topoedaphic predictors, and
potential interactions between topographic predictors (TWIsi and/or Slope) and soil predictors that influence soil
moisture holding capacity (Clay, Sand, Organic matter). Included in the model set are the best model excluding
interaction terms (a), the best model with a single Slope interaction term (b) the best model with a single TWIsi
interaction term (c) and the selected model with both Slope and TWIsi interaction terms (d). All models included a
random intercept.
No. of
Parameters
AIC
Δ AIC
pH
1
519179.1
31400.5
TWIsi
1
514935.7
27157.1
Slope
1
514527.5
26748.9
Sand
1
497848.3
10069.7
Clay
1
500988.7
13210.1
OM
1
513868.4
26089.8
Restr
1
518314.7
30536.1
TWIsi Slope Clay OM pH DTR
6
492838.6
5060.0
TWIsi Sand Clay OM pH DTR
6
490398.9
2620.3
TWIsi Slope Sand Clay pH DTR
6
490141.3
2362.7
Slope Sand Clay OM pH DTR
6
490133.1
2354.5
TWIsi Slope Sand OM Clay DTR
6
489298.6
1520.0
TWIsi Slope Sand OM pH DTR
6
489287.5
1508.9
TWIsi Slope Sand Clay OM pH
6
489276.1
1497.5
TWIsi Slope Sand Clay OM pH DTR (a)
7
488717.4
938.8
TWIsi Slope Sand Clay OM pH DTR Slope*Clay
8
488693.3
914.7
TWIsi Slope Sand Clay OM pH DTR Slope*Sand
8
488683.5
904.9
TWIsi Slope Sand Clay OM pH DTR Slope*OM (b)
8
488466.1
687.5
TWIsi Slope Sand Clay OM pH DTR TWIsi*Clay
8
488637.7
859.1
TWIsi Slope Sand Clay OM pH DTR TWIsi*OM
8
488583.6
805.0
TWIsi Slope Sand Clay OM pH DTR TWIsi*Sand (c)
TWIsi Slope Sand Clay OM pH DTR TWIsi*Sand
Slope*OM (d)
8
487969.4
190.8
9
487778.6
0.0
Model Parameters
19
Table 5. Summary of models that include interactions with mean annual precipitation and/or mean monthly
maximum temperature. Letters in parentheses show the best model including an interaction with precipitation
(a), the best model including an interaction with temperature (b), and the best model with both temperature and
precipitation (c). The final selected global model for second-order habitat selection is shown in bold.
Best model without climate interactions:
AuROC
AIC
# of Random
Coefficients
TWIsi Slope Sand Clay OM pH DTR TWIsi*Sand Slope*OM
0.6343
487778.6
0
0.6377
487213.8
1
2631.5
0.6392
485862.0
1
1279.7
0.6288
484624.4
1
42.1
0.6418
484582.3
1
0.0
0.6390
486253.5
1
1671.2
0.6359
485629.0
1
1046.7
0.6419
487771.3
1
3189.0
0.6067
482098.0
2
Interactions with Precipitation:
TWIsi Slope Sand Clay OM pH DTR TWIsi*Sand SL*OM Precip
-TWIsi*Precip
TWIsi Slope Sand Clay OM pH DTR TWIsi*Sand SL*OM Precip
Precip OM*Precip
TWIsi Slope Sand Clay OM pH DTR TWIsi*Sand SL*OM Precip
Precip Sand*Precip
TWIsi Slope Sand Clay OM pH DTR TWI*Sand SL*OM Precip
Precip Clay*Precip (a)
Interactions with Temperature:
TWIsi Slope Sand Clay OM pH DTR TWIsixSand SL*OM Temp
Precip OM*Temp
TWIsi Slope Sand Clay OM pH DTR TWIsixSand SL*OM Temp
Precip Clay*Temp
TWIsi Slope Sand Clay OM pH DTR TWIsixSand SL*OM Temp
Precip TWIsi*Temp (b)
Interaction with Precipitation and Temperature:
TWIsi Slope Sand Clay OM pH DTR TWIsixSand SL*OM Precip
Precip Clay*Precip Temp OM*Temp (c)
20
Δ AIC
Table 6 Coefficients and associated standard errors for the best topoedaphic model (see model selection statistics
in Table 4) and the best model including topoedaphic predictors plus interactions with mean annual precipitation
(model selection statistics in Table 5). For a summary of habitat suitability maps based on the Topoedaphic +
Precipitation model, see Table 7.
Topoedaphic + Precipitation
Model
Topedaphic Model
Coefficient
Std Error
Coefficient
Std Error
Intercept
-2.9322
0.09884
5.6411
0.4356
TWIsi
0.08353
0.002025
0.06968
0.002067
Slope
-0.05225
0.008034
-0.08081
0.007802
Sand
-0.01129
0.000487
-0.01085
0.000495
Clay
0.01582
0.000664
-0.2531
0.005908
OM
0.3941
0.01101
0.4315
0.01087
pH
0.2504
0.0105
0.3203
0.01075
0.00198
0.000095
0.000555
0.000099
TWIsi x Sand
-0.00119
0.000046
-0.00083
0.000047
Slope x OM
-0.08895
0.006393
-0.06223
0.006028
Precipitation
-0.02261
0.001121
Precipitation x Clay
0.000675
--
Restr
21
Including both interaction terms further reduced AIC by 190.8 relative to the best model
with a single interaction term (Table 4). The final selected model based on topoedaphic
predictors had an area under the ROC curve of 0.6343, with coefficients presented in Table 6.
Consideration of an expanded model set that allowed for interactions between the
precipitation gradient and topoedaphic predictors showed the most parsimonious model to
include an interaction between precipitation and soil clay content (Table 5). This model both
increased model predictive ability (AuROC = 0.6418) and was substantially more parsimonious
relative to the best topoedaphic-only model (Δ AIC = 3196.3). Including interactions between
the temperature gradient and topoedaphic predictors model increased model predictive ability to
a similar degree (AuROC = 0.6419) but with substantially less parsimony AIC (Δ AIC = 7.3).
The validity of using AIC to compare models with different numbers of random coefficients (e.g.
model with no climate interactions vs. model with temperature interaction) is unclear based upon
the current statistical literature, due to varying approaches in calculating the degrees of freedom
for models with different numbers of random coefficients. However, both the temperature and
precipitation models include a random intercept and one random coefficient (either temperature
or precipitation respectively, analyzed at the study site scale), and thus the same degrees of
freedom regardless of the method of calculation. The precipitation and temperature models had
similar predictive ability, but the model including precipitation was more parsimonious than the
model including temperature. Models including interactions with both precipitation and
temperature yielded lower AIC (reflecting the inclusion of an additional random coefficient), but
had substantially reduced predictive ability and thus were rejected from consideration. Our final
Figure 6. Predicted relative BTPD habitat suitability as a
function of slope for varying levels of soil organic matter
content based on the final selected Topoedaphic +
Precipitation model (Table 6).
Figure 7. Predicted relative BTPD habitat suitability as a
function of Topographic Wetness Index with aspect
correction (TWIsi) for varying levels of soil sand content
based on the final selected Topoedaphic + Precipitation
model (Table 6).
22
selected second-order RSF for prairie dog habitat therefore included 7 topoedaphic predictors,
precipitation, and TWIsi x Sand, Slope x Organic matter, and Precipitation x Clay interactions
(Table 5 and 6).
Coefficients of the selected model including precipitation (Table 6) show that BTPD
habitat suitability increases with increasing soil pH and depth to a restricted layer across all
levels of the other predictors. BTPD habitat suitability declines with increasing slope, but does
so more rapidly on soils with high organic matter content than on soils with low organic
mattercontent (Figure 6).
The TWIsi x Sand interaction shows that BTPD habitat suitability is positively associated
with the topographic wetness index (i.e. greater suitability for swales and draws), but this
positive association is greater for soils with low sand content than for soils with high sand
content (Figure 7). Thus, high-quality
Figure 8. Predicted relative BTPD habitat suitability as a
habitat is associated with lowlands with
function of soil clay content for varying levels of mean
high silt+clay content, whereas sandy
annual precipitation (see model coefficients in Table ?).
lowlands have lower relative habitat
value.
The Precipitation x Clay
interaction shows that BTPD habitat
suitability is positively associated with
soil clay content for regions with 400 –
500 mm precipitation, but the strength of
this association increases with increasing
mean annual precipitation (Figure 8). At
the lowest end of the precipitation
gradient (as precipitation declines from
400 to 350 mm) the association with soil
clay content switches from positive to
negative, i.e. declining habitat quality
with increasing clay content at 350 mm mean annual precipitation (Figure 3).
Habitat suitability maps were generated for each of the 7 study areas where suitability is
measured as a probability (varying from 0 to 1) derived from the best global RSF including
topoedaphic predictors and precipitation. Maps are referenced in Table 7. At some study sites,
in particular the Cimarron National Grassland, a striped pattern is evident in the predictions for
BTPD habitat suitability in areas of relatively low or zero slopes. This striping pattern is an
artifact of the algorithm used to model water flow patterns when calculating the Topographic
Wetness Index. The artifact was most notable at the Cimarron site due to the lower quality of the
DEM for this site, presumably resulting from differences in the method used to create the DEM
for this county. As resolution of DEMs improves and more accurate methods are used to
23
Table 7. Index of maps of BTPD habitat suitability generated based on the final selected global model including topoedaphic predictors, precipitation and an
interaction between precipitation and soil clay content (see Table 6 for coefficients).
Study Site
Map #
Output Type
Title
Carrizo
1
RSF Probability
Map of Global Topoedaphic + Precipitation RSF Model: Carrizo Study Area
Cimarron
2
RSF Probability
Map of Global Topoedaphic + Precipitation RSF Model: Cimarron Study Area
Kiowa
3
RSF Probability
Map of Global Topoedaphic + Precipitation RSF Model: Kiowa Study Area
Pawnee East
4
RSF Probability
Map of Global Topoedaphic + Precipitation RSF Model: Pawnee East Study Area
Pawnee West
5
RSF Probability
Map of Global Topoedaphic + Precipitation RSF Model: Pawnee West Study Area
Rita Blanca
6
RSF Probability
Map of Global Topoedaphic + Precipitation RSF Model: Rita Blanca Study Area
Timpas
7
RSF Probability
Map of Global Topoedaphic + Precipitation RSF Model: Timpas Study Area
Carrizo
8
RSF Category
Map of Global Topoedaphic + Precipitation Model Categories: Carrizo Study Area
Cimarron
9
RSF Category
Map of Global Topoedaphic + Precipitation Model Categories: Cimarron Study Area
Kiowa
10
RSF Category
Map of Global Topoedaphic + Precipitation Model Categories: Kiowa Study Area
Pawnee East
11
RSF Category
Map of Global Topoedaphic + Precipitation Model Categories: Pawnee East Study Area
Pawnee West
12
RSF Category
Map of Global Topoedaphic + Precipitation Model Categories: Pawnee West Study Area
Rita Blanca
13
RSF Category
Map of Global Topoedaphic + Precipitation Model Categories: Rita Blanca Study Area
Timpas
14
RSF Category
Map of Global Topoedaphic + Precipitation Model Categories: Timpas Study Area
Table 8. RSF probability cutoff values that correspond to false negative error rates of 5, 10 and 15%. These probability cutoffs were used to generate the RSF
category maps (Maps 8-14 in Table 7).
False Negative Error Rate
5.0
10.0
15.0
0.06010
0.07695
0.09585
False Positive Rate
36.7
30.4
25.6
Sensitivity
90.0
80.0
70.0
1-Specificity
73.5
61.0
51.4
RSF Probability Cutoff
24
generate them (e.g. high-resolution LiDAR), such artifacts can be removed from habitat models
based upon topographic indicies.
We classified the RSF probabilities into 4 categories based on cutoff probabilities that
correspond to different false negative error rates (Table 8). Category 1 (low habitat suitability)
corresponds to locations where BTPD are predicted to be absent based on a relatively stringent
false negative rate of 5%. Category 4 (high habitat suitability) corresponds to locations where
BTPD are predicted to be present based on a less stringent false negative rate of 15%, which is
associated with a lower false positive rate (Table 8), and hence a lower rate of incorrectly
predicting BTPD presence. Maps depicting the distribution of the 4 probability categories for
each study site are referenced in Table 7.
Local second-order models
Our analysis of local models first evaluated the set of candidate models that included up
to 7 topoedaphic predictors, and then examined an expanded model set that included potential
interactions between topography (TWIsi, Slope) and soil characteristics that influence waterholding capacity (Sand, Clay, or OM), following the same process as the global model analysis.
Because our analysis of interactions with precipitation and temperature in the global models was
based on among-site variation in climate, precipitation and temperature were not considered in
local models.
Selected local models included all 7 topoedaphic predictors at 4 sites, 6 predictors at
Kiowa and Pawnee West, and 4 predictors at Rita Blanca. All selected local models included an
interaction between slope and one soil parameter (either clay or organic matter), and 5 of 7 local
models included an interaction between TWIsi and one soil parameter (either clay or organic
matter).
The magnitude and sign of the best local models were largely consistent with the best
global model, with the exception that the global model included a TWIsi x Sand interaction
rather than with clay or organic matter (Table 16). When the main effect and interaction term
coefficients are considered together, all local models and the global model predict that habitat
suitability increases with increasing TWIsi, soil organic matter content, and soil clay content
(except under low mean annual precipitation in the global model; Fig. 3). All local and the
global models predict that habitat suitability decreases with increasing slope. Most (7 of 8)
models predict that habitat suitability increases with increasing soil depth to a restricted layer,
and with increasing soil pH (Table 16).
Predictions of the global topoedaphic + precipitation model showed a high degree of
consistency with the best models fit to each local dataset (Table 19). Disagreement between the
global versus local models was less than 5% of the landscape for 5 of 7 study sites: Carrizo,
Cimarron, Kiowa, Pawnee West, and Timpas (Table 19). The greatest disagreement occurred at
the Pawnee East study site, where the local model was based on a small sample size (10 colony
25
Table 9. Summary of model set for BTPD colonies on the Carrizo Unit of the Comanche National Grassland
including direct effects of up to 7 topoedaphic predictors, and potential interactions between topographic
predictors (TWIsi and/or Slope) and soil predictors that influence soil moisture holding capacity (Clay, Sand,
Organic matter). Included in the model set are the best model excluding interaction terms (a), the best model with
a single TWIsi interaction term (b) the best model with a single Slope interaction term (c) and a model with both
Slope and TWIsi interaction terms (d). The selected model (c) is highlighted in bold. All models included a random
intercept.
Carrizo
Parameters
Δ AIC
OM
1
11889.6
pH
1
11700.2
TWIsi
1
11634.4
Restr
1
11241.0
Slope
1
9471.2
Sand
1
5615.0
Clay
1
2668.0
TWIsi Slope Sand Clay OM Restr
6
1655.7
TWIsi Sand Clay OM pH Restr
6
1470.1
TWIsi Slope Sand Clay pH Restr
6
712.8
TWIsi Slope Clay OM pH Restr
6
655.1
Slope Sand Clay OM pH Restr
6
575.4
TWIsi Slope Sand Clay OM pH
6
546.6
TWIsi Slope Sand Clay OM pH Restr (a)
7
545.9
Model (a) + TWIsi x OM
8
533.8
Model (a) + TWIsi x Sand
8
486.7
Model (a) + TWIsi x Clay (b)
8
469.6
Model (a) + Slope x OM
8
475.7
Model (a) + Slope x Sand
8
46.5
Model (a) + Slope x Clay (c)
8
0.0
Model (a) + Slope x Clay + TWIsi x Clay
9
69.0
26
Table 10. Summary of model set for BTPD colonies on the Cimarron National Grassland including direct effects of
up to 7 topoedaphic predictors, and potential interactions between topographic predictors (TWIsi and/or Slope)
and soil predictors that influence soil moisture holding capacity (Clay, Sand, Organic Matter). Included in the
model set are the best model excluding interaction terms (a), the best model with a single TWIsi interaction term
(b) the best model with a single Slope interaction term (c) and a model with both Slope and TWIsi interaction
terms (d). The selected model (c) is highlighted in bold. All models included a random intercept.
Cimarron
Number
Δ AIC
Restricted
1
26898.3
Slope
1
26577.5
TWIsi
1
23501.5
pH
1
19329.0
Clay
1
8664.3
OM
1
7123.2
Sand
1
2257.8
TWIsi Slope Sand Clay pH Restr
6
837.4
Slope Sand Clay OM pH Restr
6
831.3
TWIsi Slope Sand OM pH Restr
6
618.3
TWIsi Sand Clay OM pH Restr
6
340.6
TWIsi Slope Clay OM pH Restr
6
308.0
TWIsi Slope Sand Clay OM Restr
6
276.4
TWIsi Slope Sand Clay OM pH
6
274.7
TWIsi Slope Sand Clay OM pH Restr (a)
7
264.8
Model (a) + TWIsi x Clay
8
244.0
Model (a) + TWIsi x Sand
8
214.4
Model (a) + TWIsi x OM (b)
8
1.4
Model (a) + Slope x OM
8
254.6
Model (a) + Slope x Sand
8
249.5
Model (a) + Slope x Clay (c)
8
247.2
Model (a) + Slope x Clay + TWIsi x OM (d)
9
0.0
27
Table 11. Summary of model set for BTPD colonies on the Kiowa National Grassland including direct effects of up
to 7 topoedaphic predictors, and potential interactions between topographic predictors (TWIsi and/or Slope) and
soil predictors that influence soil moisture holding capacity (Clay, Sand, Organic Matter). Included in the model set
are the best model excluding interaction terms (a), the best model with a single TWIsi interaction term (b) the best
model with a single Slope interaction term (c) and a model with both Slope and TWIsi interaction terms (d). The
selected model (c) is highlighted in bold. All models included a random intercept.
Kiowa
Parameters
Δ AIC
pH
1
1863.0
Restricted
1
1776.0
Sand
1
1433.0
Slope
1
1363.9
TWIsi
1
1351.7
OM
1
1161.1
Clay
1
1045.7
TWIsi Slope Clay pH Restr
5
510.3
Slope Clay OM pH Restr
5
281.5
TWIsi Clay OM pH Restr
5
256.0
TWIsi Slope OM pH Restr
5
235.9
TWIsi Slope Clay OM pH
5
138.9
TWIsi Slope Clay OM Restr
5
89.2
TWIsi Slope Sand Clay pH Restr
6
359.6
Slope Sand Clay OM pH Restr
6
282.3
TWIsi Sand Clay OM pH Restr
6
256.8
TWIsi Slope Sand OM pH Restr
6
205.0
TWIsi Slope Sand Clay OM pH
6
127.8
TWIsi Slope Sand OM Clay Restr
6
91.2
TWIsi Slope Clay OM pH Restr (a)
6
88.4
TWIsi Slope Sand Clay OM pH Restr
7
88.6
Model (a) + TWIsi x Clay
7
89.1
Model (a) + TWIsi x OM (b)
7
67.2
Model (a) + Slope x Clay
7
3.3
Model (a) + Slope x OM (c)
7
0.0
Model (a) + Slope x OM + TWIsi x OM (d)
8
1.3
28
Table 12. Summary of model set for BTPD colonies on the Eastern Unit of the Pawnee National Grassland including
direct effects of up to 7 topoedaphic predictors, and potential interactions between topographic predictors (TWIsi
and/or Slope) and soil predictors that influence soil moisture holding capacity (Clay, Sand, Organic Matter).
Included in the model set are the best model excluding interaction terms (a), the best model with a single TWIsi
interaction term (b) the best model with a single Slope interaction term (c) and a model with both Slope and TWIsi
interaction terms (d). The selected model (d) is highlighted in bold. All models included a random intercept.
Pawnee East
Parameters
Δ AIC
pH
1
923.4
TWIsi
1
792.6
Clay
1
903.1
OM
1
841.6
Sand
1
925.7
Restricted
1
579.6
Slope
1
281.7
Slope Sand OM Restr
1
176.2
TWIsi Sand Clay OM pH Restr
6
387.1
TWIsi Slope Clay OM pH Restr
6
173.6
TWIsi Slope Sand OM pH Restr
6
169.9
TWIsi Slope Sand Clay OM pH
6
112.1
TWIsi Slope Sand OM Clay Restr
6
66.3
Slope Sand Clay OM pH Restr
6
64.9
TWIsi Slope Sand Clay pH Restr
6
63.7
TWIsi Slope Sand Clay OM pH Restr (a)
7
61.2
Model (a) + TWIsi x Clay
8
56.2
Model (a) + TWIsi x Sand
8
56.2
Model (a) + TWIsi x OM (b)
8
12.8
Model (a) + Slope x OM
8
58.3
Model (a) + Slope x Sand
8
40.8
Model (a) + Slope x Clay (c)
8
39.0
Model (a) + Slope x Clay + TWIsi x OM (d)
9
0.0
29
Table 13. Summary of model set for BTPD colonies on the Western Unit of the Pawnee National Grassland
including direct effects of up to 7 topoedaphic predictors, and potential interactions between topographic
predictors (TWIsi and/or Slope) and soil predictors that influence soil moisture holding capacity (Clay, Sand,
Organic Matter). Included in the model set are the best model excluding interaction terms (a), the best model with
a single TWIsi interaction term (b) the best model with a single Slope interaction term (c) and a model with both
Slope and TWIsi interaction terms (d). The selected model (d) is highlighted in bold. All models included a random
intercept.
Pawnee West
Parameters
Δ AIC
Restricted
1
1306.4
pH
1
1267.5
TWIsi
1
979.3
Clay
1
913.9
Sand
1
843.7
Slope
1
883.4
OM
1
674.2
TWIsi Sand Clay OM Restr
5
349.0
TWIsi Slope Sand Clay Restr
5
260.7
Slope Sand Clay OM Restr
5
245.4
TWIsi Slope Clay OM Restr
5
211.9
TWIsi Slope Sand Clay OM
5
189.4
TWIsi Slope Sand OM Restr
5
189.1
TWIsi Sand Clay OM pH Restr
6
350.0
Slope Sand Clay OM pH Restr
6
247.1
TWIsi Slope Sand Clay pH Restr
6
243.9
TWIsi Slope Clay OM pH Restr
6
213.7
TWIsi Slope Sand Clay OM pH
6
191.4
TWIsi Slope Sand OM pH Restr
6
190.2
TWIsi Slope Sand Clay OM Restr (a)
6
186.3
TWIsi Slope Sand Clay OM pH Restr
7
186.8
Model (a) + TWIsi x OM
7
187.0
Model (a) + TWIsi x Sand
7
175.0
Model (a) + TWIsi x Clay (b)
7
171.4
Model (a) + Slope x Sand
7
183.4
Model (a) + Slope x Clay
7
158.4
Model (a) + Slope x OM (c)
7
44.3
Model (a) + Slope x OM + TWIsi x Clay (d)
8
0.0
30
Table 14. Summary of model set for BTPD colonies on the Rita Blanca National Grassland including direct effects of
up to 7 topoedaphic predictors, and potential interactions between topographic predictors (TWIsi and/or Slope)
and soil predictors that influence soil moisture holding capacity (Clay, Sand, Organic Matter). Included in the
model set are the best model excluding interaction terms (a), the best model with a single TWIsi interaction term
(b) the best model with a single Slope interaction term (c) and a model with both Slope and TWIsi interaction
terms (d). The selected model (d) is highlighted in bold. All models included a random intercept.
Rita Blanca
Parameters
Δ AIC
Sand
1
1571.48
OM
1
1564.61
pH
1
1229.80
TWIsi
1
1171.54
Restricted
1
1127.85
Clay
1
871.62
Slope
1
558.88
TWIsi Sand Clay
3
506.48
TWIsi Slope Sand
3
449.70
Slope Sand Clay
3
116.28
TWIsi Slope Clay
3
152.02
Slope Sand Clay OM
4
118.02
Slope Sand Clay pH
4
117.90
TWIsi Slope Clay Restr
4
105.88
Slope Sand Clay Restr
4
118.19
TWIsi Slope Sand Clay (a)
4
56.67
TWIsi Slope Sand Clay Restr
5
58.67
TWIsi Slope Sand Clay OM
5
58.61
TWIsi Slope Sand Clay pH
5
58.59
TWIsi Sand Clay OM pH Restr
6
507.03
TWIsi Slope Sand OM pH Restr
6
214.18
Slope Sand Clay OM pH Restr
6
121.24
TWIsi Slope Clay OM pH Restr
6
97.16
TWIsi Slope Sand Clay OM Restr
6
60.60
TWIsi Slope Sand Clay OM pH
6
60.48
TWIsi Slope Sand Clay pH Restr
6
60.43
TWIsi Slope Sand Clay OM pH Restr
7
62.34
Model (a) + TWIsi x Sand
5
52.4
Model (a) + TWIsi x Clay (b)
5
13.1
Model (a) + Slope x Sand
5
50.9
Model (a) + Slope x Clay (c)
5
22.8
Model (a) + Slope x Clay + TWIsi x Clay (d)
6
0.0
31
Table 15. Summary of model set for BTPD colonies on the Timpas Unit of the Comanche National Grassland
including direct effects of up to 7 topoedaphic predictors, and potential interactions between topographic
predictors (TWIsi and/or Slope) and soil predictors that influence soil moisture holding capacity (Clay, Sand,
Organic Matter). Included in the model set are the best model excluding interaction terms (a), the best model with
a single TWIsi interaction term (b) the best model with a single Slope interaction term (c) and a model with both
Slope and TWIsi interaction terms (d). The selected model (d) is highlighted in bold. All models included a random
intercept.
Parameters
Number
Δ AIC
Clay
1
2535.7
OM
1
2454.3
pH
1
2352.7
TWIsi
1
2336.6
Sand
1
1812.8
Restricted
1
1674.7
Slope
1
984.1
TWIsi Sand Clay OM pH Restr
6
827.9
TWIsi Slope Sand Clay OM pH
6
343.6
TWIsi Slope Sand OM pH Restr
6
325.0
TWIsi Slope Clay OM pH Restr
6
180.8
TWIsi Slope Sand Clay pH Restr
6
148.9
TWIsi Slope Sand Clay OM Restr
6
120.8
Slope Sand Clay OM pH Restr
6
55.5
TWIsi Slope Sand Clay OM pH Restr (a)
7
53.6
Model (a) + TWIsi x Sand
8
8
8
8
8
8
55.6
53.1
7.5
44.8
43.3
43.3
9
0.0
Model (a) + TWIsi x OM
Model (a) + TWIsi x Clay (b)
Model (a) + Slope x Sand
Model (a) + Slope x Clay
Model (a) + Slope x OM
(c)
Model (a) + Slope x OM + TWIsi x Clay (d)
32
Table 16. Summary of BTPD habitat suitability models selected for each of 7 study sites on the basis of BTPD
colonies locations monitored at the site during 2001-2010. We also present coefficients of the global model (i.e. fit to
data from all 7 sites combined = topoedaphic model in Table 6) for comparison.
Study Site
AuROC
Carrizo
0.6316
Intercept
-8.9027
TWIsi
-0.00945
Slope
-0.6292
Sand
0.007488
Clay
0.05429
OM
0.223
pH
0.9234
DTR
-0.00034
Slope*Clay 0.01672
Slope*OM
TWIsi*Clay
TWIsi*OM
TWIsi*Sand
Cimarron
0.8384
Kiowa
0.6087
-8.6946
0.1233
-0.2287
-0.01474
0.1092
2.26
-0.3456
0.03213
0.006239
-4.6525
0.0744
0.1981
0.0309
1.1364
0.2156
0.002723
Local Models
Pawnee East
Pawnee West
0.5829
0.5705
-4.0264
0.06736
-0.2961
0.02758
0.03551
0.3721
0.1498
0.002009
0.005261
-0.4113
-0.06504
0.08898
0.0735
0.1085
-0.01575
0.004683
0.4591
Rita Blanca
0.5889
Timpas
0.6694
Global
Model
0.6343
-1.504
0.08225
-0.8128
0.008761
0.04179
-8.8533
0.3212
-0.2114
-0.0340
-0.0326
-0.1724
1.1344
0.0126
-2.9322
0.08353
-0.0523
-0.0113
0.01582
0.3941
0.2504
0.00198
-0.7616
-0.0118
-0.089
0.000475
0.01435
-0.18
-0.00219
-0.00192
-0.03816
-0.0012
33
Table 17. RSF probability cutoff values that correspond to false negative error rates of 5, 10 and 15% for each of
the local models. These probability cutoffs were used to generate the RSF category maps (Maps 22-28 in Table
18).
False Negative Error Rate
Carrizo
5%
10%
15%
0.19620
0.32000
0.40790
35.93
30.55
26.07
0.02858
0.03377
0.04955
22.17
12.21
7.47
0.02368
0.03035
0.03549
42.00
34.66
27.76
0.21770
0.28570
0.33380
False Positive Rate
39.04
33.74
28.69
Probability Cutoff
42.63
37.67
32.40
False Positive Rate
42.63
37.67
32.40
0.21300
0.26594
0.29285
40.18
33.68
28.64
0.10050
0.17333
0.22265
32.81
26.29
22.08
Probability Cutoff:
False Positive Rate:
Cimarron
Probability Cutoff
False Positive Rate
Kiowa
Probability Cutoff
False Positive Rate
Pawnee East
Pawnee West
Rita Blanca
Probability Cutoff
Probability Cutoff
False Positive Rate
Timpas
Probability Cutoff
False Positive Rate
34
Table 18. Index of maps of BTPD habitat suitability generated based on the final selected local model fit to data from each study site separately (see Table 15
for coefficients).
Study Site
Map #
Model
Output Type
Map Title
Carrizo
15
Local (Table 15)
RSF Probability
Map of Local Topoedaphic RSF Model: Carrizo Study Area
Cimarron
16
Local (Table 15)
RSF Probability
Map of Local Topoedaphic RSF Model: Cimarron Study Area
Kiowa
17
Local (Table 15)
RSF Probability
Map of Local Topoedaphic RSF Model: Kiowa Study Area
Pawnee East
18
Local (Table 15)
RSF Probability
Map of Local Topoedaphic RSF Model: Pawnee East Study Area
Pawnee West
19
Local (Table 15)
RSF Probability
Map of Local Topoedaphic RSF Model: Pawnee West Study Area
Rita Blanca
20
Local (Table 15)
RSF Probability
Map of Local Topoedaphic RSF Model: Rita Blanca Study Area
Timpas
21
Local (Table 15)
RSF Probability
Map of Local Topoedaphic RSF Model: Timpas Study Area
Carrizo
22
Local (Table 15)
RSF Category
Map of Local Topoedaphic Model Categories: Carrizo Study Area
Cimarron
23
Local (Table 15)
RSF Category
Map of Local Topoedaphic Model Categories: Cimarron Study Area
Kiowa
24
Local (Table 15)
RSF Category
Map of Local Topoedaphic Model Categories: Kiowa Study Area
Pawnee East
25
Local (Table 15)
RSF Category
Map of Local Topoedaphic Model Categories: Pawnee East Study Area
Pawnee West
26
Local (Table 15)
RSF Category
Map of Local Topoedaphic Model Categories: Pawnee West Study Area
Rita Blanca
27
Local (Table 15)
RSF Category
Map of Local Topoedaphic Model Categories: Rita Blanca Study Area
Timpas
28
Local (Table 15)
RSF Category
Map of Local Topoedaphic Model Categories: Timpas Study Area
35
Table 19. Summary of the percent of the landscape in each of 4 habitat suitability categories based on the best
global model (fit to all colonies at all study sites) versus the best local model (fit only to colonies within each study
site). The magnitude of spatial inconsistency between local versus global models is shown as the percent of land
area predicted to be in category 1 by one model but in category 4 by the other model.
Global Topoedaphic +
Precipitation Model
Local
Topoedaphic Model
Spatial Inconsistency
between Global vs. Local
Models
% of Landscape
in RSF Category
% of Landscape
in RSF Category
% of Landscape
Total
Hectares
102692
1
2
3
4
1
2
3
4
1 Global,
4 Local
1 Local,
4 Global
Sum
47
4
4
45
45
11
8
36
0.04
0.16
0.2
Cimarron
43978
61
5
8
26
79
1
3
17
2.30
0.00
2.3
Kiowa
23777
28
11
18
43
51
19
5
25
0.16
4.00
4.2
Pawnee East
Pawnee
West
37901
31
17
15
37
39
9
8
44
3.60
7.80
11.4
11
17
25
47
14
10
10
66
1.20
0.60
1.8
Rita Blanca
46841
37952
23
14
4
59
19
12
10
59
3.00
5.10
8.1
Timpas
69738
9
5
8
78
20
5
3
72
0.25
4.20
4.5
Study Site
Carrizo
36
clusters). This disagreement was due to the local model including a large positive coefficient for
Sand, which was in contrast to the negative or small positive coefficients for Sand in all other
local and global models (Table 16). The second-highest rate of local versus global model
disagreement was for the Rita Blanca National Grassland (8.1% Table 19). This error is most
likely related to the fact that the Rita Blanca site spans two different counties and states, the
effects of which are addressed in greater detail in the Discussion. Overall, the generally strong
match between global and local models provides strong support for the application of the global
topoedaphic + precipitation model in predicting habitat suitability across the broader project
area.
Ecological Site-based second-order models
An ecological site is defined as “a distinctive kind of land with specific physical
characteristics that differs from other kinds of land in its ability to produce a distinctive kind and
amount of vegetation” (USDA 1997). The quantitative soil parameters considered in the previous
BTPD habitat suitability models (e.g. Sand, Clay, Organic matter, pH and Depth to restricted
layer) are among the soil characteristics that have been used to classify map units in the
SSURGO database into different ecological sites. Types of ecological sites and their definitions
vary across the Great Plains, and have typically been standardized by NRCS across counties at
the level of Major Land and Resource Areas (MLRAs). As noted by NRCS
(http://esis.sc.egov.usda.gov/), MLRA’s are used by the Natural Resources Conservation
Service “in the planning, design, implementation, and evaluation of natural resource
management activities. MLRA boundaries reflect nearly homogenous areas of landuse,
elevation, topography, climate, water resources, potential vegetation, and soils.” In some cases,
a portion of the ecological sites in a given MLRA also occur in adjacent MLRAs, but it is also
possible for adjacent MLRAs to have largely different sets of ecological sites.
We analyzed BTPD habitat suitability relative to ecological sites by first comparing the
types of ecological sites present (i.e. as defined by NRCS) at each of the 7 study sites (Table 18).
We note that in a few cases, we combined two rare ecological sites with strong similarities into a
single category for these analyses (e.g. “Draw” and “Swale” combined into Draw/Swale and
Sandstone and Sandstone Breaks combined in the Sandstone/Sandstone Breaks). Based on this
analysis, study sites were placed into 3 groups corresponding to (1) 4 sites in MLRA 67B and 69
that included a total of 14 ecological sites, of which 7 were present across 3 or all of the 4 study
sites, (2) 2 sites in MLRAs 77A/77B, which included 18 ecological sites, of which 7 occurred in
both study sites, and (3) 1 site occurring at the boundary of MLRAs 72/77A, which had 6
ecological sites, of which 4 were unique to the study site. For each group we considered a model
that only included Ecological Site as a categorical predictor of BTPD habitat quality, and a
second model that included the topographic predictors (TWIsi and Slope) in addition to
Ecological Site. Coefficients for each ecological site provide a measure of that ecological site’s
value as habitat relative to a “reference” ecological site. For each analysis, we identified the
ecological site for which the ratio of used:available pixels was closest to 1.0, and used it as the
37
reference site. It is possible for ecological sites with a negative coefficient to be widely used by
BTPD, as the coefficient’s value represents a ranking relative to the reference group.
For Group 1 (MLRAs 67B/69), the model based on ecological site as the sole predictor
was a substantial improvement over the null model (ΔAIC = 8,617.0; AuROC = 0.5711). Five
ecological sites had neutral (not different from zero) or positive coefficients: Salt Flat, Alkaline
Plains, Overflow, Clayey Plains, and Loamy Plains. Large negative coefficients were observed
for the Shallow Siltstone, Limestone Breaks, Shaly Plains, Sandy Bottomland, Deep Sand, and
Sandstone/Sandstone Breaks. Including TWIsi and Slope in addition to Ecological Site further
improved the model (ΔAIC relative to the Ecological Site-only model = 2263.8; AuROC =
0.5969), but had minimal influence on relative rankings of the ecological sites other than Salt
Flat having a small but significant negative coefficient relative to Alkaline Plains (Table 20).
For Group 2 (MLRAs 77A/B), the model with ecological site as the sole predictor was a
substantial improvement over the null model (ΔAIC = 3,581; AuROC = 0.5866; Table 21).
Seven ecological sites had neutral (not different from zero) or positive coefficients: Sandy Loam,
Loamy Upland, Deep Hardland, Salt Flat, Loamy Bottomland, Gravelly Loam, and Draw/Swale.
Large negative coefficients were found for the Shallow Siltstone, Sandy Plains, Sand Hills,
Hardland Slopes, Sandy Bottomland, and Gravelly ecological sites. Smaller but significantly
negative coefficients were observed for the Very Shallow, Playa, High Lime, and Limy Upland
ecological sites. The Malpais Upland ecological site was too rare in the dataset to be analyzed
effectively. Including TWIsi and Slope in addition to Ecological Site further improved the
model (ΔAIC relative to the Ecological Site-only model = 1412, AuROC = 0.6054), but had
minimal influence on relative rankings of the ecological sites (Table 22).
For Group 3 (Cimarron National Grassland; MLRAs 72/77A), the model based on
ecological site as the sole predictor was a substantial improvement over the null model (ΔAIC =
18,221; AuROC = 0.7574; Table 23). Two ecological sites had neutral or positive coefficients:
Limy Upland and Loamy Upland. Negative coefficients were observed for all ecological sites
with soils of high sand content: Sandy, Sands, Sandy Lowland, and Choppy Sands. Including
TWIsi and Slope in addition to Ecological Site further improved the model (ΔAIC relative to the
Ecological Site-only model = 3518, AuROC = 0.8253, but did not influence relative rankings of
the ecological sites (Table 24).
38
Table 20. Number of BTPD colony clusters in which different ecological sites occurred (either in used and/or
available pixels) for each study site. Based on variation in MLRAs and the types of ecological sites present at each
study site, the sites were grouped into 3 separate datasets for analysis: (1) Pawnee E, Pawnee W, Carrizo and
Timpas (ecological sites in light grey shading), (2) Kiowa and Rita Blanca (ecological sites in bold type), and (3)
Cimarron (ecological sites in dark grey shading).
Pawnee
E
Pawnee
W
Carrizo
Timpas
Kiowa
Rita
Blanca
Cimarron
MLRA(s):
67B
67B
67B
69
77A/B
77A/B
72/77A
Loamy Plains
10
15
28
19
Gravel Breaks
4
9
18
1
Sandstone/Sandstone Breaks
3
5
9
5
Shaly Plains
4
11
Clayey Plains
3
7
Shallow Siltstone
2
1
Overflow
1
8
Site:
Deep Sand
2
Limestone Breaks
8
10
1
1
17
Saline Overflow
17
Alkaline Plains
15
Sandy Bottomland
1
2
Salt Flat
3
4
Sandy Plains
6
14
5
18
6
4
9
8
Deep Hardland
18
4
Sandy Loam
16
9
Very Shallow
14
5
High Lime
5
10
Draw/Swale
3
3
Sand Hills
3
1
Playa
10
Hardland Slopes
5
Loamy Bottomland
3
26
1
Gravelly Loam
8
Shallow Sandstone
3
Gravelly
2
Malpais Upland
1
Limy Upland
17
Loamy Upland
7
7
9
Sandy
9
Sands
8
Choppy Sands
7
Sandy Lowland
4
39
Table 21. Coefficients for a resource selection function based upon ecological sites in eastern Colorado, fit to BTPD
colonies at 4 study sites (Group 1 in Table 20), where available habitat was defined using a 2 km buffer around
colony clusters. Coefficient estimates for each ecological site reflect that sites value as habitat relative to the
Alkaline Plains site. One site (Salt Flat) did not differ significantly in value from Alkaline Plains. Alkaline Plains was
selected as the reference group because this ecological site had a ratio of used:available pixels of 1.04, which was
closest to 1 of all ecological sites.
Ecological Site
Estimate
Standard Error
t Value
Pr > |t|
Shallow Siltstone
-6.6143
Limestone Breaks
-2.9473
2.3315
-2.84
0.0046
0.1522
-19.36
<.0001
Shaly Plains
-2.0058
0.1355
-14.8
<.0001
Sandy Bottomland
-1.6221
0.0863
-18.8
<.0001
Deep Sand
-1.4945
0.113
-13.22
<.0001
Sandstone
-1.3706
0.07744
-17.7
<.0001
Gravel Breaks
-0.8519
0.06799
-12.53
<.0001
Sandy Plains
-0.481
0.06203
-7.76
<.0001
Saline Overflow
-0.3689
0.08973
-4.11
<.0001
Salt Flat
-0.02793
0.07536
-0.37
0.711
Alkaline Plains
Loamy Plains
0
0.2462
Reference Group
0.06091
4.04
<.0001
Overflow
0.6303
0.07562
8.33
<.0001
Clayey Plains
0.7216
0.1002
7.2
<.0001
40
Table 22. Coefficients for a resource selection function based on ecological sites in eastern Colorado (MLRAs
67B/69) plus two topographic parameters (TWIsi and Slope), fit to BTPD colonies at 4 sites (Group 1 in Table 18),
where available habitat was defined using a 2 km buffer around colony clusters. Coefficient estimates for each
ecological site reflect value as habitat relative to the Alkaline Plains ecological site. Alkaline Plains was selected as
the reference group because this ecological site had a ratio of used:available pixels of 1.04, which was closest to 1
of all ecological sites.
Predictor
Estimate
Standard Error
t Value
Pr > |t|
TWIsi
0.01231
0.001748
7.04
<.0001
Slope
-0.1867
0.004618
-40.42
<.0001
Shallow Siltstone
-6.6152
2.2194
-2.98
0.0029
Limestone Breaks
-2.6315
0.1481
-17.77
<.0001
Shaly Plains
-1.9425
0.1362
-14.26
<.0001
Sandy Bottomland
-1.6157
0.08684
-18.6
<.0001
Deep Sand
-1.4349
0.1132
-12.68
<.0001
Sandstone
-0.9952
0.0786
-12.66
<.0001
Gravel Breaks
-0.6851
0.06873
-9.97
<.0001
Sandy Plains
-0.4711
0.06267
-7.52
<.0001
Saline Overflow
-0.4151
0.09068
-4.58
<.0001
Salt Flat
-0.1509
0.07605
-1.98
0.0472
Alkaline Plains
Loamy Plains
0
0.2206
Reference Group
0.0615
3.59
0.0003
Overflow
0.4991
0.07633
6.54
<.0001
Clayey Plains
0.5752
0.1008
5.71
<.0001
41
Table 23. Coefficients for a resource selection function based upon ecological sites occurring in counties of
northeast New Mexico, Oklahoma panhandle, and Texas Panhandle (MLRAs 77A/B; Group 2 in Table 18), where
available habitat was defined using a 2 km buffer around colony clusters. Coefficient estimates for each ecological
site reflect that sites value as habitat relative to the Sandy Loam site. Sandy Loam was used as the reference group
because this ecological site had a ratio of used:available pixels of 1.11, which was closest to 1 of all ecological sites.
Ecological Site Model
Estimate
Standard Error
t Value
Pr > |t|
Shallow Sandstone
-6.7203
1.7911
-3.75
0.0002
Malpais Upland
-6.6749
5.9933
-1.11
0.2654
Sandy Plains
-3.3249
0.1353
-24.58
<.0001
Sand Hills
-2.841
0.7733
-3.67
0.0002
Hardland Slopes
-1.628
0.1497
-10.87
<.0001
Sandy Bottomland
-0.7117
0.2467
-2.89
0.0039
Gravelly
-0.6677
0.06216
-10.74
<.0001
Very Shallow
-0.4296
0.04977
-8.63
<.0001
Playa
-0.3956
0.1101
-3.59
0.0003
High Lime
-0.2988
0.04113
-7.27
<.0001
Limy Upland
-0.19
0.02821
-6.73
<.0001
Sandy Loam
0
.
.
.
Loamy Upland
0.05808
0.08136
0.71
0.4753
Deep Hardland
0.351
0.0308
11.4
<.0001
Salt Flat
0.5965
0.06467
9.22
<.0001
Loamy Bottomland
0.6459
0.1529
4.22
<.0001
Gravelly Loam
0.665
0.0408
16.3
<.0001
Draw and Swale
1.941
0.09697
20.02
<.0001
42
Table 24. Coefficients for a resource selection function based upon ecological sites occurring in counties of
northeast New Mexico, Oklahoma panhandle, and Texas Panhandle (MLRAs 77A/B; Group 2 in Table 18) plus
topographic parameters (TWIsi, Slope), where available habitat was defined using a 2 km buffer around colon
clusters. Coefficient estimates for each ecological site reflect that sites value as habitat relative to the Sandy Loam
site. Sandy Loam was used as the reference group because this ecological site had a ratio of used:available pixels
of 1.11, which was closest to 1 of all ecological sites.
Ecological Site + Topography Model
Estimate
Standard Error
t Value
Pr > |t|
TWIsi
0.04308
0.002666
16.16
<.0001
Slope
-0.4234
0.01654
-25.59
<.0001
Shallow Sandstone
-6.1457
1.4424
-4.26
<.0001
Malpais Upland
-5.8808
5.8672
-1
0.3162
Sandy Plains
-3.3641
0.1362
-24.69
<.0001
Sand Hills
-2.7484
0.7573
-3.63
0.0003
Hardland Slopes
-1.1604
0.1533
-7.57
<.0001
Playa
-1.0369
0.1128
-9.2
<.0001
Sandy Bottomland
-0.856
0.2622
-3.26
0.0011
Gravelly
-0.4357
0.06389
-6.82
<.0001
High Lime
-0.3222
0.04159
-7.75
<.0001
Limy Upland
-0.2623
0.02854
-9.19
<.0001
Very Shallow
-0.2216
0.05063
-4.38
<.0001
Sandy Loam
0
Reference Group
Deep Hardland
0.2051
0.03122
6.57
<.0001
Loamy Upland
0.2123
0.08394
2.53
0.0115
Loamy Bottomland
0.514
0.154
3.34
0.0008
Gravelly Loam
0.6631
0.041
16.17
<.0001
Salt Flat
0.6715
0.06559
10.24
<.0001
Draw and Swale
1.6951
0.09755
17.38
<.0001
43
Table 25. Coefficients for a resource selection function based upon ecological sites in southwestern
Kansas (Cimarron National Grassland), where available habitat was defined using a 2 km buffer around
colony clusters. Coefficient estimates for each ecological site reflect that sites value as habitat relative
to the Limy Upland site. Limy Upland was used as the reference group because this ecological site had a
ratio of used:available pixels of 1.02, which was closest to 1 of all ecological sites.
Ecological Site Model
Choppy Sand
Sandy Lowland
Sands
Sandy
Limy Upland
Loamy Upland
Estimate
-4.9985
-3.9526
-3.0221
-1.5382
0
1.85
Standard Error
0.2108
0.1032
0.08972
0.07884
Reference Group
0.02997
t Value
-23.71
-38.29
-33.68
-19.51
Pr > |t|
<.0001
<.0001
<.0001
<.0001
61.73
<.0001
Table 26. Coefficients for a resource selection function based upon ecological sites in southwestern
Kansas (Cimarron National Grassland), where available habitat was defined using a 2 km buffer around
colony clusters. Coefficient estimates for each ecological site reflect that sites value as habitat relative
to the Limy Upland site. Limy Upland was used as the reference group because this ecological site had a
ratio of used:available pixels of 1.02, which was closest to 1 of all ecological sites.
Ecological Site + TWIsi + Slope Model
TWIsi
Slope
Choppy Sands
Sandy Lowland
Sands
Sandy
Limy Upland
Loamy Upland
Estimate
0.1101
-0.08377
-5.2822
-4.6736
-3.2556
-1.7566
0
1.489
44
Standard Error
0.002011
0.008445
0.2119
0.1055
0.09135
0.08186
Reference Group
0.03106
t Value
54.77
-9.92
-24.92
-44.31
-35.64
-21.46
Pr > |t|
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
47.93
<.0001
Comparison of spatial scales for evaluating second-order models
All previous results are based on models where colony clusters were defined by a 2-km
linkage rule, and then available habitat surrounding the cluster was defined by a 2-km buffer
distance. We evaluated how reducing this distance influenced model results by comparing the 2km model results to the same model evaluation process based on a 0.5-km linkage rule for
colony clusters and an associated 0.5-km buffer distance for available habitat. By reducing this
distance to 0.5 km, the number of different colony clusters in the dataset increases substantially
because colonies separated by 0.5 – 2.0 km are now considered separate (independent) relative to
one another. This can potentially increase the power of our tests of model likelihood and fit. At
the same time, we decrease the area of the landscape from which available pixels are selected
(using 0.5 km buffer distance rather than 2.0 km), thereby potentially reducing our ability to
discriminate features within the landscape which characterize the most suitable BTPD habitat.
Using the 0.5-km linkage and buffer distance, we identified a total of 216 colony clusters
for analysis (Table 2) which was double the number of colony clusters using the 2-km linkage
rule. Model selection based on AIC identified a model with all 7 topoedaphic predictors plus
interactions between TWIsi x Clay and Slope x OM as the most parsimonious model within the
set of models that did not include climate interactions (Table 26). Incorporating climate
variables yielded a model that included interaction terms for Precipitation x Clay (as in the 2-km
model) plus Temperature x OM (not included in the 2-km model; Table 27 and 28). The models
differ in terms of the sign of the Clay coefficient because the 0.5-km model includes an
interaction term for TWIsi x Clay interaction term, while the 2-km model included a TWIsi x
Sand interaction. The models differ in terms of the sign of the OM coefficient because the 0.5km model included a Temperature x OM interaction while the 2-km model does not. Overall,
however, both models show high similarity in that both include an interaction between TWIsi
and soil texture, an interaction between slope and soil organic matter content, and an interaction
between soil clay content and the precipitation gradient (Table 28). Although the Temperature x
OM interaction term was retained in the 0.5-km model based on minimization of AIC, the
magnitude of the effect of this term on habitat suitability was small.
Predictions of the two models showed a high degree of consistency for 6 of the 7 study
sites (Table 29b), and indicating that our models are robust across a range of linkage and buffer
distances. The one notable exception was on the Cimarron National Grassland, where 18% of
the landscape that was mapped as high-quality habitat 0.5-km model was mapped as low-quality
habitat by the 2-km model. Inspection of the map outputs shows that these two models produced
similar predictions for the upland region north of the Cimarron River, but differed in some areas
of sandy soils south of the Cimarron River. Colonies on soils south of the river are more
restricted in extent and show lower expansion rates than colonies on soils north of the river,
which is more in accord with the 2-km model’s prediction that the region north of the river was
largely suitable habitat, while the region south of the river was a more complex mosaic of habitat
in categories 1, 2 and 3. We used the 2-km model as our final selected second-order model
45
because relative to the 0.5-km model, it was based upon available pixels sampled from a larger
proportion of the landscape, had the greater predictive ability (greater AuROC), and included
fewer parameters.
Table 27. Summary of the set of topoedaphic RSF models considered for the dataset based on a 0.5 km linkage
and buffer distance. The selected model is shown in bold.
Model
Parameters
AIC
Δ AIC
pH
1
457721.1
13040.0
DTR
1
456956.6
12275.5
OM
1
456849.5
12168.4
TWIsi
1
456310.1
11629.0
Slope
1
452920.3
8239.2
Sand
1
452745
8063.9
Clay
1
449364.2
4683.1
TWIsi Sand Clay OM pH DTR
6
447405.5
2724.4
TWIsi Slope Sand OM pH DTR
6
447276
2594.9
TWIsi Slope Sand Clay OM DTR
6
445437.3
756.2
TWIsi Slope Sand Clay pH DTR
6
445401.5
720.4
Slope Sand Clay OM pH DTR
6
445260.6
579.5
TWIsi Slope Clay OM pH DTR
6
445206.9
525.8
TWIsi Slope Sand Clay OM pH
6
445171.4
490.3
TWIsi Slope Sand Clay OM pH DTR (a)
7
445142.9
461.8
(a) + TWIsi x OM
8
445128.6
447.5
(a) + TWIsi x Sand
8
445102.5
421.4
(a) + TWIsi x Clay
8
444799.5
118.4
(a) + Slope x Sand
8
445140.8
459.7
(a) + Slope x Clay
8
445102.6
421.5
(a) + Slope x OM
8
445055.2
374.1
(a) + TWIsi x Clay + Slope x OM
9
444681.1
0.0
46
Table 28. Summary of models that include interactions with mean annual precipitation and/or mean monthly
maximum temperature for colony clusters and available habitat defined based on the 0.5-km linkage and buffer
distance. Letters in parentheses show the best model including an interaction with precipitation (a) , the best
model including an interaction with temperature (b), and the best model with both temperature and precipitation
(c). The final selected global model for the 0.5-km linkage/buffer distance is shown in bold.
AuROC
0.5841
AIC
444681.1
# of Random
Coefficients
0
Δ AIC
TWIsi Slope Sand Clay OM pH Restr TWIsi*Clay SL*OM Precip
Precip Precip*TWIsi
0.5851
444521.8
1
148.2
TWIsi Slope Sand Clay OM pH Restr TWIsi*Clay SL*OM Precip
Precip Precip*OM
0.5847
444411.8
1
38.2
TWIsi Slope Sand Clay OM pH Restr TWI*Clay SL*OM Precip
Precip Precip*Clay
0.5863
444377.9
1
4.3
TWIsi Slope Sand Clay OM pH Restr TWIsi*Clay SL*OM Precip
Precip Precip*Sand (a)
0.5858
444373.6
1
0.0
TWIsi Slope Sand Clay OM pH Restr TWIsi*Sand SL*OM Temp
Precip Temp*Clay
0.5848
444560.5
1
186.9
TWIsi Slope Sand Clay OM pH Restr TWIsi*Sand SL*OM Temp
Precip Temp*TWIsi
0.5843
444479.1
1
105.5
TWIsi Slope Sand Clay OM pH Restr TWIsi*Sand SL*OM Temp
Precip Temp*OM (b)
0.5845
444392.5
1
18.9
0.5861
443219.4
2
71.3
0.5869
443148.1
2
0.0
Best model without climate interactions:
TWIsi Slope Sand Clay OM pH Restr TWIsi*Clay Slope*OM
Interactions with Precipitation:
Interactions with Temperature:
Interactions with Precipitation and Temperature:
TWIsi Slope Sand Clay OM pH Restr TWIsi*Sand SL*OM Precip
Precip Precip*Sand Temp Temp*OM
TWIsi Slope Sand Clay OM pH Restr TWIsixSand SL*OM
Precip Precip*Clay Temp Temp*OM (c)
47
Table 29a. Comparison of final selected topoedaphic and topoedaphic + climate models for datasets based on a 2km versus 0.5-km colony linkage rule and buffer distance.
Best Topoedaphic Model
Best Topoedaphic + Climate Model
2 km
0.5 km
2 km
0.5 km
113
216
113
216
AuROC
0.6343
0.5841
0.6418
0.5869
Intercept
-2.9322
-2.7656
5.6411
-4.0769
TWIsi
0.08353
0.08166
0.06968
0.07852
Slope
-0.05225
-0.1242
-0.08081
-0.1089
Sand
-0.01129
-0.00301
-0.01085
-0.00336
Clay
0.01582
0.05127
-0.2531
0.1327
OM
0.3941
0.2258
0.4315
-0.3983
0.3203
0.217
0.000555
0.000728
# of Colony Clusters
pH
0.2504
0.158
Restr
0.00198
0.000738
TWIsi x Sand
-0.00119
-0.00083
-0.00241
TWIsi x Clay
-0.06223
-0.00231
-0.09991
Precipitation
-0.02261
0.007059
Precipitation x Clay
Temperature
0.000675
-0.00022
Slope x OM
-0.08895
-0.08192
-0.06428
Temperature x OM
0.03672
48
Table 29b. Comparison of mapped distribution of BTPD habitat suitability categories based on the final
selected topoedaphic + climate models for datasets derived from a 2-km versus a 0.5-km linkage rule
and buffer distance.
Study Site
Carrizo
Cimarron
Kiowa
Pawnee
East
Pawnee
West
Rita Blanca
Timpas
2km Global Topoedaphic +
Precipitation Model
500 m Global Topoedaphic
+ Precipitation Model
(% of Landscape in RSF
Category)
(% of Landscape in RSF
Category)
Spatial Inconsistency between 2 km
and 0.5 km models
(% of Landscape)
1 for 2km,
1 for 0.5
4 for 0.5
km, 4 for 2
km
km
1.3
0.0
18.5
0.0
0.7
0.0
1
47
61
28
2
4
5
11
3
4
8
18
4
45
26
43
1
24
46
12
2
16
4
14
3
12
1
18
4
48
49
56
31
17
15
37
39
23
15
23
0.0
1.0
1.1
11
23
9
17
14
5
25
4
8
47
59
78
35
8
25
28
10
18
13
12
25
24
70
32
0.0
0.0
0.0
6.7
0.0
6.3
6.7
0.0
6.3
49
Sum
1.3
18.5
0.7
Third-order Habitat Selection
Our assessment of third-order habitat selection defined the habitat available to a colony
locally on the basis of the direction and extent of that colony’s expansion over a series of more
than 3 consecutive years. We view this analysis as being similar to the selection of habitat
within an animal’s home range, where the home range is defined on the basis of the outermost
positions in a set of an animal’s locations over a period of time. Across all 7 study sites, we
identified a total of 152 colonies meeting the criteria of having been mapped for a series of 4 or
more consecutive years where the colony was stable or expanding.
For the set of models that did not include interaction terms, the most parsimonious model
included all 7 topoedaphic predictors (TWIsi, Slope, % Sand, % Clay, pH, % Organic matter,
and Depth To a Restricted layer; Table 30), which was an improvement of all competing models
with 6 or fewer predictors (Δ AIC > 6). Of the potential models including interactions between
slope and soil parameters, the most parsimonious included a Slope x Organic Matter interaction
(Table 4; Δ AIC relative to no interaction model = 251.3). Of the potential models including
interactions between TWIsi and soil parameters, the most parsimonious model included a TWIsi
x Sand interaction (Table 4; Δ AIC relative to no interaction model = 191.0). Including both
interaction terms further reduced AIC by 49.2 relative to the best model with a single interaction
term (Table 4). The selected model based on topoedaphic predictors had an area under the ROC
curve of 0.5928, with coefficients presented in Table 32.
Consideration of an expanded model set that allowed for interactions between climate
(precipitation and temperature) and topoedaphic predictors showed the most parsimonious model
to include an interaction between precipitation and soil clay content plus an interaction between
temperature and soil organic matter (Table 31). This model both increased model predictive
ability (AuROC = 0.5960) and was more parsimonious relative to the best topoedaphic-only
model (Δ AIC = 1033.6). Our final selected third-order RSF for prairie dog habitat therefore
included 7 topoedaphic predictors, Precipitation, Temperature, and TWIsi x Sand, Slope x
Organic matter, Temperature x Organic Matter, and Precipitation x Clay interactions (Tables 31
and 32).
50
Table 30. Summary of third-order RSF model set including direct effects of up to 7 topoedaphic predictors, and
potential interactions between topographic predictors (TWIsi and/or Slope) and soil predictors that influence soil
moisture holding capacity (Clay, Sand, Organic matter). Included in the model set are the best model excluding
interaction terms (a), the best model with a single TWIsi interaction term (b), the best model with a single Slope
interaction term (c) and the selected model with both Slope and TWIsi interaction terms (d). All models included a
random intercept.
Predictors
Parameters
AIC
ΔAIC
pH
1
342943.1
11244.1
DTR
1
342621
10922.0
TWIsi
1
342018.5
10319.5
OM
1
341257.6
9558.6
Slope
1
339409.5
7710.5
Sand
1
336926.4
5227.4
Clay
1
334799.9
3100.9
TWIsi Slope Sand OM pH DTR
6
333460.9
1761.9
TWIsi Sand Clay OM pH DTR
6
333441.3
1742.3
TWIsi Slope Sand Clay pH DTR
6
332221
522.0
TWIsi Slope Clay OM pH DTR
6
332052.9
353.9
TWIsi Slope Sand Clay OM DTR
6
332011.3
312.3
Slope Sand Clay OM pH DTR
6
331964.4
265.4
TWIsi Slope Sand Clay OM pH
6
331945.2
246.2
TWIsi Slope Sand Clay OM pH DTR (a)
7
331939.2
240.2
(a) + TWIsi x OM
8
331930.5
231.5
(a) + TWIsi x Sand
8
331918.1
219.1
(a) + TWIsi x Clay (b)
8
331748.2
49.2
(a) + Slope x Clay
8
331933.2
234.2
(a) + Slope x Sand
8
331941.2
242.2
(a) + Slope x OM (c)
8
331901.3
202.3
(a) + TWIsi x Clay + Slope x OM (d)
9
331699
0.0
51
Table 31. Summary of third-order RSF models that include interactions with mean annual precipitation and/or
mean monthly maximum temperature. The final selected global model for second-order habitat selection is shown
in bold.
Parameters
# of Random
Coefficients
AuROC
AIC
Best model without climate interactions (d)
9
0
0.5928
331699.0
(d) + Temp + Temp x TWIsi
11
1
0.5960
331493.5
674.5
(d) + Temp + Temp x OM
11
1
0.5965
331188.5
369.5
(d) + Temp + Temp x Clay
11
1
0.5949
330990.2
171.2
(d) + Precip + Precip x TWIsi
11
1
0.5943
331545.9
726.9
(d) + Precip + Precip x Sand
11
1
0.5870
331137.6
318.6
(d) + Precip + Precip x OM
11
1
0.5949
331045.9
226.9
(d) + Precip + Precip x Clay
11
1
0.5956
330819.0
0.0
(d) + Temp + Temp x Clay + Precip + Precip x Clay
13
2
0.5965
330806.7
141.3
(d) + Temp + Temp x OM + Precip + Precip x Clay
13
2
0.5960
330665.4
0.0
Predictors
ΔAIC
Table 32. Coefficients and associated standard errors for the best model including topoedaphic predictors plus
interactions with mean annual precipitation and mean maximum monthly temperature (model selection statistics
in Tables 30 and 31). For a summary of habitat suitability maps based on this model, see Table 33.
Coefficient
Intercept
Standard Error
2.8934
2.7608
TWIsi
0.07622
0.005554
Slope
-0.1316
0.02084
Sand
-0.0051
--
Clay
-0.1576
0.00893
OM
-0.4982
0.1529
pH
0.2499
0.01883
DTR
-0.00042
--
Temperature
-0.06196
0.09925
Precipitation
-0.01275
0.003679
TWIsi x Clay
-0.00251
--
Slope x OM
-0.05797
--
Temperature x OM
0.04676
0.007904
Precipitation x Clay
0.000528
--
52
Coefficients of the selected third-order model including climate (Table 32) show that
BTPD habitat suitability increases with increasing soil pH and decreases with increasing soil
sand content across all levels of the other predictors. BTPD habitat suitability declines with
increasing slope, but does so more rapidly on soils with high organic matter content than on soils
with low organic matter content (Figure 8). Note that this relationship between slope, organic
matter, and habitat suitability is nearly identical to that predicted by the global second-order
model (Figure 6).
Figure 9. Predicted relative BTPD habitat suitability as a
function of slope for varying levels of soil organic
matter content based on the final selected third-order
Topoedaphic + Climate model (Table 32).
Figure 10. Predicted relative BTPD habitat suitability as
a function of TWIsi for varying levels of soil organic
matter content based on the final selected third-order
Topoedaphic + Climate model (Table 32).
The TWIsi x Clay interaction (Figure 10) shows that habitat suitability increases with
increasing TWIsi for soils with clay content less than 30%. However, for soils with clay content
greater than 30%, the relationship between habitat suitability and TWIsi becomes negative
(Figure 10), suggesting that on soils with high water holding capacity, the need to select for areas
that receive runon is alleviated. The relationship between habitat suitability, soil clay content,
and TWIsi depicted in Figure 10 is the inverse of the relationship between habitat suitability, soil
sand content, and TWIsi represented in the global second-order model (Figure 7). Predicted
habitat suitability increases with increasing soil clay content, but due to the Precipitation x Clay
interaction, the strength of BTPD selection for high-clay soils increases with increasing mean
annual precipitation. Conversely, the selection for low-clay soils is greatest at the dry end of the
precipitation gradient. This relationship is similar to that predicted by the global second-order
53
model (Figure 8), except that in the third-order model, the slope of the relationship between clay
and habitat suitability remains positive across the full range of mean annual precipitation found
within the study region.
Figure 11. Predicted relative BTPD habitat suitability as
a function of soil clay content for varying levels of mean
annual precipitation based on the final selected thirdorder Topoedaphic + Climate model (Table 32).
Figure 12. Predicted relative BTPD habitat suitability as
a function of soil organic matter content for varying
levels of mean maximum monthly temperature based
on the final selected third-order Topoedaphic + Climate
model (Table 32).
Finally, predicted habitat suitability increases with soil organic matter content, and due to
the temperature x Organic Matter interaction, the slope of this relationship increases with
increasing mean monthly maximum temperature. However, in contrast to the Precipitation x
Clay interaction where a large shift in Clay slope occurs across the precipitation gradient, we
found only a minor shift in the Organic Matter slope across the temperature gradient (Figure 11
vs. 12). Thus, although the Temperature x OM interaction was retained in the final selected
third-order RSF model, its influence on predicted habitat suitability values is minor compared to
the other three interaction terms.
54
Table 33. Summary of the percent of the landscape in each of 4 BTPD habitat suitability
categories based on the final selected second-order RSF including topoedaphic and climate
predictors (Table 6) for 73 counties in the southwestern Great Plains.
State
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
County
Adams
Arapahoe
Baca
Bent
Boulder
Broomfield
Cheyenne
Crowley
Denver
Douglas
El Paso
Elbert
Huerfano
Jefferson
Kiowa
Kit Carson
Larimer
Las Animas
Lincoln
Logan
Morgan
Otero
Phillips
Prowers
Pueblo
Sedgwick
Washington
Weld
Yuma
Cheyenne
Finney
Gove
Grant
Greely
Grey
Haaskell
Hamilton
Kearny
Total
Area
2
(km )
3,062
2,083
6,623
3,989
694
156
4,615
2,070
149
1,461
4,741
4,788
1,979
592
4,627
5,601
1,454
9,519
6,692
4,775
3,361
3,284
1,783
4,259
5,776
1,423
6,534
10,389
6,142
2,644
3,368
2,759
1,489
2,017
2,242
1,496
2,585
2,256
Square km in Habitat Category
1
2
3
4
635
341
424
1,662
787
235
196
866
2,217
368
407
3,631
500
292
324
2,874
226
45
42
381
15
9
12
121
1,144
367
387
2,716
83
223
251
1,512
40
15
18
76
1,112
69
54
226
3,125
590
415
611
2,707
317
296
1,468
970
122
109
778
167
24
29
372
683
185
259
3,499
1,030
403
456
3,712
387
121
169
777
2,885
600
671
5,362
1,362
743
837
3,750
1,869
511
580
1,815
1,234
463
337
1,326
267
91
155
2,772
379
120
60
1,224
822
210
187
3,040
790
368
490
4,127
531
92
55
745
1,832
453
586
3,663
2,391
1,266
1,665
5,066
4,082
436
240
1,383
1,184
206
129
1,126
1,162
140
196
1,871
761
491
512
995
388
52
47
1,002
33
32
78
1,874
724
100
76
1,342
182
21
28
1,265
503
212
534
1,336
804
113
157
1,181
55
% of County in Habitat
Category
1
2
3
4
21
11 14
54
38
11
9
42
33
6
6
55
13
7
8
72
33
6
6
55
9
5
8
77
25
8
8
59
4
11 12
73
27
10 12
51
76
5
4
15
66
12
9
13
57
7
6
31
49
6
6
39
28
4
5
63
15
4
6
76
18
7
8
66
27
8
12
53
30
6
7
56
20
11 13
56
39
11 12
38
37
14 10
39
8
3
5
84
21
7
3
69
19
5
4
71
14
6
8
71
37
6
4
52
28
7
9
56
23
12 16
49
66
7
4
23
45
8
5
43
34
4
6
56
28
18 19
36
26
4
3
67
2
2
4
93
32
4
3
60
12
1
2
85
19
8
21
52
36
5
7
52
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
New
Mexico
New
Mexico
New
Mexico
New
Mexico
Oklahoma
Oklahoma
Oklahoma
Texas
Texas
Texas
Texas
Texas
Wyoming
Total
Lane
Logan
Meade
Morton
Rawlins
Scott
Seward
Sherman
Stanton
Stevens
Thomas
Wallace
Witchita
Chase
Cheyenne
Deuel
Dundy
Hayes
Keith
Kimball
Lincoln
Perkins
1,850
2,779
2,526
1,892
2,756
1,858
1,657
2,736
1,763
1,884
2,777
2,368
1,861
2,325
3,085
1,135
2,379
1,835
2,851
2,460
6,638
2,290
360
474
1,129
963
1,305
225
1,104
336
200
1,064
328
396
117
1,626
2,271
627
2,168
1,573
2,336
1,648
5,852
1,355
128
187
222
77
551
63
22
331
66
222
285
266
68
94
248
209
18
164
118
286
154
100
82
274
121
109
420
72
27
243
52
64
729
263
111
60
144
96
36
62
68
270
144
114
1,280
1,843
1,054
743
481
1,497
505
1,826
1,445
534
1,435
1,444
1,564
545
421
203
157
36
329
256
488
721
19
17
45
51
47
12
67
12
11
56
12
17
6
70
74
55
91
86
82
67
88
59
7
7
9
4
20
3
1
12
4
12
10
11
4
4
8
18
1
9
4
12
2
4
4
10
5
6
15
4
2
9
3
3
26
11
6
3
5
8
2
3
2
11
2
5
69
66
42
39
17
81
30
67
82
28
52
61
84
23
14
18
7
2
12
10
7
31
Colfax
4,443
1,014
324
310
2,795
23
7
7
63
Harding
5,477
2,833
395
500
1,749
52
7
9
32
Mora
3,269
989
360
248
1,673
30
11
8
51
Union
Beaver
Cimarron
Texas
Dallam
Hansford
Lipscomb
Ochiltree
Sherman
Laramie
9,794
4,694
4,768
5,302
3,886
2,377
2,398
2,371
2,386
6,580
3,487
2,867
2,633
2,013
917
485
1,731
417
443
3,147
868
357
389
179
956
46
222
57
108
967
1,252
389
233
216
337
55
93
94
44
897
4,186
1,081
1,513
2,894
1,676
1,790
352
1,803
1,791
1,570
36
61
55
38
24
20
72
18
19
48
9
8
8
3
25
2
9
2
5
15
13
8
5
4
9
2
4
4
2
14
43
23
32
55
43
75
15
76
75
24
244,927
90,448
19,553
19,695
115,230
36.9
8.0
8.0
47.0
56
Figure 12. Map of predicted habitat suitability categories for 73 counties in the southwestern
Great Plains, based on the final selected second-order habitat suitability model.
57
Table 34. Summary of the percent of the landscape in each of 4 BTPD habitat suitability
categories based on the final selected third-order RSF including topoedaphic and climate
predictors (Table 32) for 73 counties in the southwestern Great Plains.
State
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
County
Adams
Arapahoe
Baca
Bent
Boulder
Broomfield
Cheyenne
Crowley
Denver
Douglas
El Paso
Elbert
Huerfano
Jefferson
Kiowa
Kit Carson
Larimer
Las Animas
Lincoln
Logan
Morgan
Otero
Phillips
Prowers
Pueblo
Sedgwick
Washington
Weld
Yuma
Cheyenne
Finney
Gove
Grant
Gray
Greeley
Hamilton
Haskel
Kearny
Lane
Logan
Meade
Morton
Total
Area
2
(km )
3,062
2,083
6,623
3,989
694
149
4,615
2,070
149
1,461
4,741
4,786
1,979
592
4,627
5,601
1,454
9,519
6,692
4,775
3,361
3,285
1,783
4,259
5,776
1,423
6,534
10,389
6,142
2,644
3,368
2,759
1,489
2,242
2,017
2,584
1,496
2,256
1,850
2,779
2,526
1,892
Square km in Habitat Category
1
2
3
4
698
254
258
1,852
624
193
196
1,069
1,931
376
313
4,002
796
377
553
2,263
202
65
49
377
8
3
5
132
777
494
626
2,718
146
338
471
1,115
42
18
15
75
954
86
67
355
3,125
590
415
611
2,395
364
271
1,755
758
169
158
895
148
20
20
403
634
197
494
3,301
773
444
434
3,950
428
160
191
676
2,937
691
660
5,231
1,522
753
787
3,630
1,682
508
417
2,168
1,103
307
319
1,632
443
291
575
1,976
251
53
86
1,392
726
234
288
3,011
1,153
661
952
3,010
411
62
68
882
1,678
548
500
3,807
2,985
1,858
1,831
3,714
3,273
362
277
2,230
956
305
166
1,217
894
82
189
2,203
297
171
214
2,078
327
53
44
1,065
563
30
56
1,592
34
34
72
1,877
517
373
436
1,258
145
9
14
1,328
725
92
66
1,372
151
68
83
1,548
403
169
161
2,047
771
61
85
1,610
902
18
60
912
58
% of County in Habitat
Category
1
2
3
4
23
8
8
60
30
9
9
51
29
6
5
60
20
9
14
57
29
9
7
54
5
2
3
89
17
11
14
59
7
16
23
54
28
12
10
50
65
6
5
24
66
12
9
13
50
8
6
37
38
9
8
45
25
3
3
68
14
4
11
71
14
8
8
71
29
11
13
46
31
7
7
55
23
11
12
54
35
11
9
45
33
9
9
49
13
9
18
60
14
3
5
78
17
5
7
71
20
11
16
52
29
4
5
62
26
8
8
58
29
18
18
36
53
6
5
36
36
12
6
46
27
2
6
65
11
6
8
75
22
4
3
72
25
1
3
71
2
2
4
93
20
14
17
49
10
1
1
89
32
4
3
61
8
4
4
84
14
6
6
74
31
2
3
64
48
1
3
48
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
New Mexico
New Mexico
New Mexico
New Mexico
Oklahoma
Oklahoma
Oklahoma
Wyoming
Texas
Texas
Texas
Texas
Texas
Total
Rawlins
Scott
Seward
Sherman
Stanton
Stevens
Thomas
Wallace
Wichita
Perkins
Lincoln
Kimball
Keith
Hayes
Dundy
Deuel
Cheyenne
Chase
Union
Mora
Harding
Colfax
Texas
Cimarron
Beaver
Laramie
Sherman
Ochiltree
Lipscomb
Hansford
Dallam
2,756
1,858
1,657
2,736
1,763
1,884
2,777
2,368
1,861
2,291
6,638
2,461
2,851
1,835
2,379
1,135
3,085
2,324
9,794
3,269
5,477
4,443
5,301
4,768
4,693
6,580
2,363
2,371
2,250
2,377
3,886
928
153
787
210
831
362
179
288
82
916
5,534
1,438
2,081
1,413
2,043
443
2,044
1,305
2,926
507
2,554
762
1,089
1,938
2,085
3,126
204
203
1,106
295
373
165
36
199
270
10
144
107
273
51
246
158
290
115
52
83
111
394
166
637
126
405
302
610
655
230
930
182
57
96
70
464
177
46
80
337
46
456
135
263
62
100
121
227
128
70
19
220
205
134
868
169
429
329
302
454
172
934
118
50
72
67
973
1,486
1,622
591
1,918
876
921
2,356
1,544
1,666
1,028
825
506
527
300
233
362
442
720
5,363
2,468
2,088
3,051
3,301
1,721
2,207
1,590
1,859
2,062
975
1,945
2,075
34
8
47
8
47
19
6
12
4
40
83
58
73
77
86
39
66
56
30
15
47
17
21
41
44
48
9
9
49
12
10
6
2
12
10
1
8
4
12
3
11
2
12
4
3
3
10
13
7
7
4
7
7
12
14
5
14
8
2
4
3
12
6
2
5
12
3
24
5
11
3
4
2
9
4
4
1
19
7
6
9
5
8
7
6
10
4
14
5
2
3
3
25
54
87
36
70
50
49
85
65
90
45
12
21
19
16
10
32
14
31
55
75
38
69
62
36
47
24
79
87
43
82
53
244,745
77,492
19,578
20,706
126,969
31.7
8.0
8.5
51.9
59
DISCUSSION
Based on our second-order RSF model, BTPD habitat suitability was positively
associated with soil organic matter, pH, clay content and depth to a restricted layer as well as
with increasing values of the topographic wetness index. BTPD habitat suitability was
negatively associated with slope and soil sand content. The negative influence of slope was
stronger on soils with high organic matter content. The positive influence of TWIsi was greater
for soils with low sand content. Habitat suitability was positively associated with soil clay for
areas with mean annual precipitation of 400 – 500 mm, but where mean annual precipitation
declined to 350 mm, habitat suitability became negatively associated with soil clay content.
The third-order RSF model yielded relationships with topoedaphic and climatic
predictors that were strikingly similar to the second-order model. The strong concordance in
predictions across both spatial scales of the second-order analysis (0.5 km and 2 km buffer
distances) as well as across both second and third order analyses indicates that model predictions
are robust. In all of these models, the 7 topoedaphic predictors have a significant influence on
habitat suitability, and the strongest modifying effect of climate was via the influence of mean
annual precipitation on the selection of habitats with varying levels of soil clay content.
Our BTPD habitat suitability models based upon resource selection functions are
consistent with, but incorporate substantially more detail than, previous habitat suitability
indicies developed for the northern Great Plains (Clippinger 1989; Proctor et al. 2006).
Clippinger’s (1989) index was based on slope, soil texture, and vegetation cover and height. Use
of the index requires measures of vegetation height, which are typically not available from
landscape-scale measurements or from remote sensing. Proctor et al. (2006) simply used a broad
classification of “suitable” habitat as having slopes of less than 10% and soil that support
potential vegetation consisting of grassland, salt-desert shrub, dry salt flats, or mixed barren
sites. In contrast, our models provide an empirically-based, quantitative prediction of relative
habitat quality (RSF probability maps) as well as predicted categories of habitat quality (RSF
category maps), and clearly show that within areas classified as “suitable” by previous indicies,
habitat value varies substantially. Rather than relying on broad categories of potential vegetation
and slope, our model shows how 5 soil attributes and 2 topographic variables can collectively
predict relative habitat value for BTPD.
The habitat categories predicted by our models are also directly linked to measures of
model error. Category 1, which we define as “low quality habitat” represents those areas
predicted to be unoccupied by (hence unsuitable for) BTPD given a 5% false negative rate. This
means that such locations are predicted to be unsuitable when only 5% of the area known to be
occupied BTPD during 2001-2010 is incorrectly classified as being unsuitable. While the
selection of this false negative error rate is arbitrary, the 5% value is relatively low and hence
conservative in predicting low-quality habitat. For our second-order model, the probability
cutoff corresponding to a 5% false negative rate is associated with a correspondingly high false
60
positive rate of 36.7% (Table 8), i.e. that 36.7% of “available” pixels in the dataset are classified
as suitable habitat when using this probability cutoff. Thus, areas classified into habitat category
1 in our models represent areas classified as unsuitable based on a model where there is little
chance (5%) that areas known to support BTPD will be classified as unsuitable, and there is a
much higher chance (36.7%) that areas not known to support BTPD in the past decade will be
classified as suitable. Conversely, habitat category 4, which we define as “high quality habitat”
represents areas classified as suitable based on a model where there is a high chance (15%) that
areas known to support BTPD will be classified as unsuitable and there is a correspondingly
smaller chance (25.6%) that unoccupied locations will be classified as suitable. Categories 2 and
3 represent intermediate quality habitat associated with intermediate error rates. Our approach of
defining categories by fixed false negative error rates is based on the fact that false negatives are
a known error, whereas false positives represent the prediction that BTPD could occur in a
particular location where they do not currently occur, hence are not necessarily an error.
Because our models are based in large part on soil attributes derived from the national
Soil Survey Geographic (SSURGO) database, they are limited by the accuracy of the SSURGO
database. These soil maps were developed on a county by county basis by staff that varied
among counties and states, such that methods for delineation of soil polygon boundaries and the
assignment of soil attribute values can vary. In addition, point data collected in the field were
used to populate databases representing much broader areas, and areas with spatially variable soil
attributes are represented in SSURGO by a single value.
An example of the effects of among-county variation in soil mapping is evident in habitat
suitability maps for the Rita Blanca National Grassland (Maps 6, 13, 20 and 27). The northern
half of this study site is located in Cimarron County, Oklahoma, while the southern half is
located in Dallam County, Texas. An abrupt line in habitat suitability probabilities and
categories is evident at the county line in Maps 6 and 13, which are based on the global model.
This line is not evident in Maps 20 and 27, which are based on the local model. The best local
model for Rita Blanca included only four predictors: TWIsi, Slope, Sand and Clay (Table 14),
whereas the global model additionally included OM, pH, and DTR. These differences between
the local versus global model maps suggest that measurement or mapping of OM, pH and DTR
differed across the county/state line, and as a result, they were not valuable in predicting BTPD
colony locations in the local model. Inspection of the input maps for these two counties showed
that OM was consistently mapped at higher values in Dallam County, TX, compared to Cimarron
County, OK, and hence the global model consistently predicted greater habitat suitability in
Dallam County. These county-level differences in methods/measurements of soil organic matter
are the reason for the discrepancies between local and global models for the Rita Blanca National
Grassland (8.1% of the landscape; Table 19). Use of the local model appears to me more
appropriate for Rita Blanca National Grassland.
We also note that the Carrizo study area encompassed portions of two counties (Baca and
Las Animas Counties, Colorado). Unlike Rita Blanca, we did not observe artifacts associated
61
with the county line (i.e. no evident differences in mapping of soil attribute values between the
two counties) in the Carrizo study area map. Further inspection of model predictions across
county and state boundaries showed the following. First, model predictions were spatially
consistent across county boundaries within a given state. Second, model predictions were
spatially consistent across state boundaries between Colorado, Wyoming and Nebraska as well
as between Colorado, New Mexico and Oklahoma. The only notable spatial inconsistencies
occurred between Prowers County, CO vs. Hamilton County, KS, Baca County, CO vs. Morton
County, KS and where Texas (in particular, Dallam County) bordered on Oklahoma and New
Mexico. As noted previously, differences along the Texas border appear to be the result of
methodologies for measuring and mapping soil organic matter. The unusual striping patterns in
habitat suitability predictions for the northern half of Morton County, KS were an artifact of the
lower-quality DEM available for that county, and did not affect counties in the study region. As
DEMs and soil survey maps continue to be updated and improved, including improved
coordination across state lines, habitat suitability models can be refit and improved.
Our models are unique in accounting for regional variation in climate. We found that
precipitation has a large modifying effect on BTPD selection for soils of varying texture, while
temperature has only minor effects on habitat selection. The interaction between the
precipitation gradient and BTPD selection of soils with varying clay content can be understood
on the basis of how both precipitation and clay influence soil moisture availability and hence
potential aboveground plant production. In semiarid regions, the influence of soil texture on the
retention of soil moisture at a site varies as a function of mean annual precipitation, a
relationship known as the ‘inverse texture hypothesis’ (Noy-Meir 1973, Sala et al. 1988). For
our study region in particular, Sala et al. (1988) showed that sandy soils are more productive than
fine-textured soils when mean annual precipitation is less than 370 mm, whereas the opposite is
true when precipitation is greater than 370 mm. Our model predicts that in the more arid,
western portion of their range, BTPD select for low-clay sites (second-order model) or at least do
not avoid high-sand sites (third-order model). This pattern suggests that in these arid regions,
plant production on sites with relatively more sand and less clay may be important for BTPD in
terms of sustaining food production, while not reaching such a high level of production that plant
height affects visibility. As mean annual precipitation increases, BTPD show increasing
avoidance of high-sand sites, where plant production and height may become sufficient to impact
visibility and impair predator detection. In particular, for mean annual precipitation in the range
of 400 – 500 mm, sandy soils (low clay) are increasingly associated with taller-structured,
unpalatable vegetation such as sand sagebrush (Artemisia filifolia), which can be a deterrent to
BTPD colonization.
For the 7 National Grassland units that we studied, the proportion of the landscape
classified as low-quality habitat (category 1) varied from a high of 61% of the Cimarron National
Grassland to a low of 9% of the Timpas Unit of the Comanche National Grassland (Table 19).
Conversely, the largest expanses of National Grassland classified as highly suitable BTPD
62
habitat (category 4) occur on the Timpas Unit (72% of area; 54,396 ha) and Carrizo Unit (36% of
area; 46,211 ha) of the Comanche National Grassland.
In total, we modeled black-tailed prairie dog habitat suitability for >2.4 billion pixels
corresponding to an area larger than 244,000 km2 in the southern Great Plains. Within this area,
the second-order model classified 47.0% as high-quality habitat, 36.9% was classified as lowquality, and 16.1% was in intermediate quality categories (Table 33). Sixteen counties had
>70% of the land base classified as high-quality habitat: Prowers, Pueblo, Bent, Crowley,
Kiowa, Broomfield, and Otero counties in Colorado; Sherman, Hansford, and Ochiltree counties
in Texas; and Scott, Stanton, Witchita, Haskell, Finney, and Greely counties in Kansas.
Conversely, 7 counties had >70% of the land base classified as low-quality habitat: Chase,
Cheyenne, Keith, Hayes, and Lincoln counties in Nebraska, Lipscomb county in Texas, and
Douglas county in Colorado. The most extensive contiguous areas of high-quality BTPD habitat
occur in the region extending from east-central and southeastern Colorado into west-central
Kansas (Figure 12). Large areas of low-quality BTPD habitat were primarily associated with
sandy soils along major drainages (South Platte, Arkansas, Big Sandy, Cimarron and Purgatoire)
as well as regions of sandy soil and/or rugged topography in El Paso, Douglas and Yuma
counties of Colorado, most of southwest Nebraska, and the region extending along the borders
between New Mexico, Colorado, Oklahoma and Kansas (Figure 12). The third-order model
classified 31.7% of the region as low-quality habitat, and 51.9% as high-quality habitat (Table
34). Similar to the second-order model, large expanses of low-quality habitat were found in
southwestern Nebraska, and in El Paso, Douglas and Yuma counties of Colorado. The largest
expanses of high-quality habitat extended from east-central and southeastern Colorado into westcentral Kansas. These maps provide a basis for conservation planning for black-tailed prairie
dogs and associated species of conservation concern at both local and regional spatial scales.
They also provide a basis for assessing how areas that currently exist as native rangeland and
areas that have been converted to cropland or other land uses compare in terms of their value as
habitat for black-tailed prairie dogs. Finally, both the maps and underlying RSF models provide
a basis for assessing how projected changes in precipitation and mean annual temperature within
this region may modify habitat suitability for prairie dogs.
63
LITERATURE CITED
Andelt, W. 2006. Methods and economics of managing prairie dogs. Pages 129-138 in J. L.
Hoogland, editor. Conservation of the Black-tailed Prairie Dog. Island Press, Washington
D.C.
Antlolin, M., L. Savage, and R. Eisen. 2006. Landscape features influence genetic structure of
black-tailed prairie dogs (Cynomys ludovicianus) Landscape Ecology 21:867-875.
Augustine, D. J. 2011. Habitat selection by mountain plovers in shortgrass steppe. Journal of
Wildlife Management 75:297-304.
Augustine, D. J., J. F. Cully Jr, and T. L. Johnson. 2007. Influence of fire on black-tailed prairie
dog colony expansion in shortgrass steppe. Rangeland Ecology and Management 60:538542.
Augustine, D. J., M. R. Matchett, T. P. Toombs, J. F. Cully Jr, T. L. Johnson, and J. G. Sidle.
2008. Spatiotemporal dynamics of black-tailed prairie dog colonies affected by plague.
Landscape Ecology 23:255-267.
Belak, J. R. 2001. Modeling the effects of habitat quality on black-tailed prairie dog habitat
occupancy using spatially correlated data. Colorado State University, Fort Collins, CO.
Bolker, B., M. Brooks, C. Clark, S. Geange, J. Poulsen, M. Stevens, and J. White. 2009.
Generalized linear mixed models: a practical guide for ecology and evolution. Trends in
Ecology and Evolution 24:127-134.
Clippinger, N. W. 1989. Habitat Suitability Index Models: Black-tailed prairie dog. U.S. Fish
and Wildlife, U.S. Department of the Interior Biological Report 82 (10.156).
Cully Jr, J. F., D. E. Biggins, and D. B. Seery. 2006. Conservation of prairie dogs in areas with
plague. Pages 157-168 in J. L. Hoogland, editor. Conservation of the Black-tailed Prairie
Dog. Island Press, Washington D.C.
Cully Jr, J. F., T. L. Johnson, S. K. Collinge, and C. Ray. 2010. Disease limits populations:
Plague and black-tailed prairie dogs. Vector-Borne and Zoonotic Diseases 10:7-15.
DeCesare, N., M. Hebblewhite, F. Schmiegelow, D. Herviex, G. McDermid, L. Neufeld, M.
Bradley, J. Whittington, K. Smith, L. Morgantini, M. Wheatley, and M. Musiani. 2012.
Transcending scale dependence in identifying habitat with resource selection functions.
Ecological Applications 22:1068-1083.
Derner, J., J. Detling, and M. Antolin. 2006. Are livestock weight gains affected by black-tailed
prairie dogs? Frontiers in Ecology and the Environment 4:459-464.
Dinsmore, S. J., M. B. Wunder, V. J. Dreitz, and F. L. Knopf. 2010. An assessment of factors
affecting population growth of the mountain plover. Avian Conservation and Ecology
5:5. [online] URL: http://www.ace-eco.org/vol5/iss1/art5/.
Gillies, C., M. Hebblewhite, S. Nielsen, M. Krawchuk, C. Aldridge, J. Frair, D. Saher, C.
Stevens, and C. Jerde. 2006. Application of random effects to the study of resource
selection by animals. Journal of Animal Ecology 75:887-898.
Gonen, M. 2006. Receiver Operating Characteristic (ROC) Curves.in Proceedings of the Thirdty
First Annual SAS Users Group International Conference. SAS Institute, Inc, Cary, NC.
Hanley, J., and B. McNeil. 1982. The meaning and use of the area under a receiver operating
characterisic (ROC) curve. Radiology 143:29-36.
Johnson, C. J., S. E. Nielsen, E. H. Merrill, T. L. McDonald, and M. S. Boyce. 2006. Resource
selection functions based on use-availability data: theoretical motivation and evaluation
methods. Journal of Wildife Management 70:347-357.
64
Johnson, D. 1980. The comparison of usage and availability measurements for evaluating
resource preference. Ecology 1980:65-71.
Koford, C. B. 1958. Prairie dogs, whitefaces, and blue grama. Wildlife Monographs 3:3-78.
Lauenroth, W. K., I. C. Burke, and M. P. Gutmann. 1999. The structure and function of
ecosystems in the central North American grassland region. Great Plains Research 9:223
- 260.
Manly, B., L. McDonald, D. Thomas, T. McDonald, and W. Erickson. 2002. Resource Selection
by Animals: Statistical Design and Analysis for Field Studies, Second Edition. Kluwer
Academic Publishers, Dordrecht, The Netherlands.
Noy-Meir, I. 1973. Desert ecosystems: environment and producers. Annual Review of Ecology
and Systematics 4:23-51.
Pepe, M. 2005. Evaluating technologies for classification and prediction in medicine. Statistics in
Medicine 24:3687-3696.
Pepe, M., T. Cai, and G. Longton. 2006. Combining predictors for classification using the area
under the receiver operating characteristic curve. Biometrics 62:221-229.
Pinsky, P. 2005. Scaling of true and apparent ROC AUC with number of observations and
number of variables. Communications in Statistics: Simulation and Computation 34:771781.
Proctor, J., B. Haskins, and S. C. Forrest. 2006. Focal areas for conservation of prairie dogs and
the grassland ecosystem. Pages 157-168 in J. L. Hoogland, editor. Conservation of the
Black-tailed Prairie Dog. Island Press, Washington D.C.
Reading, R. P., and R. Matchett. 1997. Attributes of black-tailed prairie dog colonies in
northcentral Montana. Journal of Wildlife Management 61:664-673.
Roelle, J., B. Miller, J. Godbey, and D. Biggins. 2005. Recovery of the black-footed ferret:
Progress and continuing challenges. Page 288 p. in U. G. S. US Department of Interior,
editor. US Geological Survey.
Sala, O., W. Parton, L. Joyce, and W. Lauenroth. 1988. Primary production of the central
grassland region of the United States. Ecology 69:40-45.
Soil Survey Staff, Natural Resources Conservation Service, United States Department of
Agriculture. Soil Survey Geographic (SSURGO) Databases for Colorado, Kansas,
Oklahoma, New Mexico, and Texas. Available online
at http://soildatamart.nrcs.usda.gov .
Stapp, P., M. F. Antolin, and M. Ball. 2004. Patterns of extinction in prairie dog
metapopulations: plague outbreaks follow El Nino events. Frontiers in Ecology
Environment 2:235-240.
Theobald DM. 2007. LCaP v1.0: Landscape Connectivity and Pattern tools for ArcGIS.
Colorado State University, Fort Collins, CO
(http://warnercnr.colostate.edu/~davet/LCaP.html).
Tipton, H., P. Doherty, and V. Dreitz. 2009. Abundance and density of mountain plover
(Charadrius montanus) and burrowing owl (Athene cunicularia) in eastern Colorado.
Auk 126:493-499.
Truett, J., J. Dullum, M. Matchett, E. Owens, and D. Seery. 2001. Translocating prairie dogs: a
review. Wildlife Society Bulletin 29:863-872.
Wang, Y., H. Chen, R. Li, N. Duan, and R. Lewis-Fernandez. 2011. Prediction-based structured
variable selection through the Receiver Operating Characterisic curves. Biometrics
67:896-905.
65