(Cervus elaphus), mule

Appendix S4: Detailed description of the methods and results for models of the relative abundance of elk (Cervus elaphus), mule deer (Odocoileus hemionus) and white-tailed deer (Odocoileus virginiana), which were used as predictor variables in the Mexican wolf depredation risk model. Methods The primary prey for the Mexican wolf (Canis lupus baileyi) are elk (Cervus elaphus), mule deer (Odocoileus hemionus) and white-tailed deer (Odocoileus virginiana; Reed et al., 2006). Data on density or abundance of these species is not available for the study area. However, several authors have found a correlation between species distribution models (SDMs) and local abundance estimated through independent studies (Bradley, 2016; Nielsen, Johnson, Heard, & Boyce, 2005; Tôrres et al., 2012; VanDerWal et al., 2009; Weber, Stevens, DinizFilho, & Grelle, 2017) and it has been concluded that Maxent’s raw output can be directly interpreted as a model of relative abundance (Phillips, Anderson, Dudík, Schapire, & Blair, 2017). Consequently, we develop species distribution models for the Mexican wolf’s primary prey species using Maxent (Phillips, Aneja, Kang, & Arya, 2006) and used the raw outputs of the model prediction maps as biotic variables in the depredation risk model. We obtained spatially unique presence points for each species (elk n = 1,775; mule deer n = 1,120; white-tailed deer n = 367) from the Global Biodiversity Information Facility database (GBIF, www.gbif.org, accessed May 2015), New Mexico Department of Game and Fish, and Arizona Game and Fish Department. We modeled the relative abundance of natural prey as functions of an initial suite of 19 bioclimatic, 7 biotic, and 8 landscape and human variables (see Table S4.1 for the list of variables, sources and calculation details). The original scale of variables was 30 m except the bioclimatic variables which were 1 km. All rasters were rescaled to 1 km. For the soil suborders, we converted the polygon base map to a raster with 1 km pixel size. We followed the same modeling approach that was used for the depredation risk model with some modifications in components and justifications. Sampling bias Sampling bias is common in data from open access databases like GBIF (Beck, Böller, Erhardt, & Schwanghart, 2014; Hortal, Jiménez-Valverde, Gómez, J., Lobo, & Baselga, 2008). In addition, presence points obtained from Game and Fish agencies are derived from census survey data that are mostly gathered from areas with high natural prey abundance. Hence, our natural prey presence points database was suspected to be affected by sampling bias. Therefore, we used spatial filtering, which functions to decrease randomly the number of presence points in oversampled regions, to reduce the effect of sampling bias (Boria, Olson, Goodman, & Anderson, 2014; Kramer-Schadt et al., 2013; Radosavljevic & Anderson, 2014). To find the optimum filtering scale for each prey species, we randomly removed all but one presence point within three scales: 1 km (covariates’ pixel size); the radius of the species’ average home range size; and the radius of the species’ maximum home range size. We tested these two home range sizes because there was wide variation in reported home range sizes of these species in the western states (Heffelfinger, 2006). We did not use the minimum reported home range sizes because they were smaller than 1 km square for deer. We calculated the average home range sizes by averaging the radius of minimum and maximum reported home range sizes. The minimum and maximum reported home range radii for western populations of the prey species

were: 5.7 - 14.2 km2 for elk; 2.7 - 6.6 km2 for mule deer; and 1.2 - 3.2 km2 for white-tailed deer (DeYoung & Miller, 2011; Mackie, Kie, Pac, & Hamlin, 2003; Wallace, 1991). Background extent We used three different scales for defining the background extent in the natural prey species models. We restricted the background extent to the maximum reported dispersal distance for each species (Anderson & Raza, 2010). For mule deer and white-tailed deer, the maximum reported dispersal distance in western states were 113 km and 224 km, respectively (Hygnstrom et al., 2008). For elk, the maximum reported dispersal distance was 2,800 km (O’Gara, 2002). Because the longest dispersal distance for elk was larger than the dimensions of our study area we did not apply this limitation for it. However, it also has been argued that including inaccessible areas in background might not affect the accuracy of the model because Maxent still can recognize whether these points are similar or dissimilar to presence points (Merow, Smith, & Silander, 2013). Thus, we also made models without background extent limitation. In addition, we considered a smaller scale by limiting the background extent in maximum reported home range size of the species allowing Maxent to model the effects of environmental variables on relative abundance within home range scale. Thus, for each species we created 3 sets of filtered presence points that rarified at different scales and 3 sets of background points based on 3 different scales (Table S4.2). To identify the best combination of presence and background points, we ran Maxent using each combination of filtered presence points and background data. We then compared the accuracy of their predictions to find the best combination by applying spatially independent k-fold cross-validation (see the model evaluation section). Model evaluation Although the threshold-dependent and independent metrics are the most common metrics in evaluating the accuracy of Maxent models, there is no convention on which metric performs better than others (Muscarella et al., 2014). Moreover, there is no certain threshold applicable for all situations to distinguish a good model from a bad model (Muscarella et al., 2014; Yackulic et al., 2013). Thus, to find the most accurate model for each species of natural prey, we also evaluated the accuracy of models’ predictions based on expert knowledge on the distribution and abundance of species across the study area. Moreover, SDMs predict potential distribution of species (Guisan & Zimmermann, 2000), while we needed the relative abundance of natural prey in their actual distribution range as the predictors in the depredation risk model. Thus, we compared the prediction maps with reported natural prey occurrence in Arizona and New Mexico’s game management units to modify predictions (changed to zero) in those game management units without species occurrence (for Arizona we used AZGF 2017a, b and for New Mexico we used expert knowledge). Results Evaluation metrics could not distinguish the best models predicting natural prey abundance in their current distribution range and, therefore, we relied on expert’s evaluations. For elk, the experts selected the model made with 14 km filtered presence points and without background limitation as best (Tables S4.2 –S4.3). However, this model overestimated the relative abundance of elk in some isolated mountains in southern game management units where no elk populations currently exist (since the model predicted potential not actual relative abundance). Therefore, we changed the predicted relative abundance values in these areas to

zero. The prediction map of this model indicated that areas with highest relative abundance of elk (most suitable habitats) existed in major mountain ranges and high plateaus (Figure S4. 1). For mule deer, the experts selected four models as best, but each had its own strengths and weaknesses (Tables S4.2 –S4.3). Since an average prediction map can enhance the accuracy of predictions, we made an average prediction map based on these selected models (Marmion et al 2009). We did not use weighted averaging because none of the metrics could rank models well. The average prediction map showed that the mule deer was the most widespread species among the natural prey. Areas with highest mule deer relative abundance were mostly located in mountainous and rugged areas at moderate and high elevations, whereas areas of lowest relative abundance were mostly in flat and low elevation areas (Figure S4.2). For white-tailed deer, the experts selected two models as best (Tables S4.2 –S4.3). Prediction maps of these models were averaged to produce the prediction maps. According to this map, areas with higher relative white-tailed deer abundance were mostly located in the central and southeastern Arizona and major mountain areas of southern New Mexico (Figure S4. 3).

Table S4.1. Description of covariates for spatial modeling of natural prey abundance in Arizona and New Mexico Variables Abbreviation Variable description/calculation Source BIOTIC VAVRIABLES Land Cover Majority

LCM

Arc GIS 10.3 Focal Statistics tool to identify the major landcover category within each species’ mean home range size. We reduced the number of landcover categories from 50 to 19 by combining similar landcover types (e.g., Madrean Pine-Oak Forest and Woodland and Southern Rocky Mountain Ponderosa Pine Woodland were considered pine woodland).

Southwest Regional Gap Analysis land cover map (USGS National Gap Analysis Program 2007; http://swregap.nmsu. edu/)

Land Cover Majority Variation

LCMV

Arc GIS 10.3 Focal Statistics tool (Spatial Analysis toolbox) to calculate the number of landcover types within each species’ mean home range size.

Southwest Regional Gap Analysis land cover map (USGS National Gap Analysis Program 2007; http://swregap.nmsu. edu/)

Average annual Normalized Difference Vegetation Index of 2010

NDVI

Arc GIS 10.3 Raster Dataset tool (Data management toolbox) to mosaic annual NDVI raster files to create an integrated raster for entire study area

Web-Enabled Landsat data (USGS 2015; https://landsat.usgs.g ov/WELD.php)

Canopy Cover Majority (categorical)

CCM

Arc GIS 10.3 Focal Statistics tool to identify the major landcover category within each species’ mean home range size.

LANDFIRE canopy cover (USGS 2013; http://www.landfire.g ov/)

Tree Canopy Cover (continuous)

TCC

Considering the middle range of tree cover categories.


Vegetation Height

VH

Arc GIS 10.3 Focal Statistics tool to identify the major vegetation height category within each species’ mean home range size.


Vegetation Height Variation

VHV

Arc GIS 10.3 Focal Statistics tool (Spatial Analysis toolbox) to calculate the number of vegetation height categories within each species’ mean home range size.


Soil Suborder Majority

SSM

Arc GIS 10.3 Zonal Statistics tool to identify the major soil suborder of the Integrated soil suborder maps within each species’ mean home range size.

Soil Survey Geographic (SSURGO,USDA 2015; https://websoilsurvey .nrcs.usda.gov/) and General Terrestrial Ecological Units (GTES, USFS 1998; https://www.fs.usda.g ov/detail/r3/landmana gement/gis/?cid=stelp rdb5201889) datasets

LANDSCAPE AND HUMAN FEATURES Elevation

-

National Elevation Dataset

National Elevation Dataset (USGS 2009; https://lta.cr.usgs.gov /NED)

Slope

-

Arc GIS 10.3 slope tool (Spatial analysis toolbox)


Heat Load Index

HLI

Arc GIS 10.3 Heat Load Index tool (Geomorphometry & Gradient Metrics toolbox; Evans et al. 20114)


Terrain Ruggedness Index

TRI

Arc GIS 10.3 Raster Calculator tool to calculate the average absolute difference between each pixel elevation value and each of its eight neighbors (Riley et al. 1999)


Density of Roads

DenR

Arc GIS 10.3 Focal Statistics tool (Spatial Analysis toolbox) to calculate density of major roads in natural prey’s average home range size

TIGER data (https://www.census. gov/)

Distance to Roads

DisR

Arc GIS 10.3 Euclidean Distance tool (Spatial Analysis toolbox) to calculate the minimum distance from each pixel to major roads

TIGER data (https://www.census. gov/)

Distance to Water Resources

DW

Arc GIS 10.3 Euclidean Distance tool (Spatial Analysis toolbox) to calculate the minimum distance from each pixel to perennial water resources including springs, artificial water resources, rivers and lakes

National Hydrologic Dataset (USGS 2015b; https://nhd.usgs.gov/)

BIOCLIMATIC VARIABLES Annual mean temperature

BIO1

Mean of the monthly mean temperature

WorldClim (Hijmans, Cameron, Parra, Jones, & Jarvis, 2005

http://www.worldcli m.org/) Mean diurnal range

BIO2

Mean of difference between mean monthly maximum and mean monthly temperature

WorldClim (Hijmans et al 2005; http://www.worldcli m.org/)

Isothermality

BIO3

(BIO2/BIO7) *100

WorldClim (Hijmans et al. 2005; http://www.worldcli m.org/)

Temperature seasonality

BIO4

Standard deviation of the 12 mean monthly temperature values multiplied by 100


Maximum temperature of warmest month

BIO5

Mean maximum temperature of the warmest month


Minimum Temperature of coldest month

BIO6

Mean minimum temperature of the coldest month


Temperature annual range

BIO7

(BIO5 – BIO6)


Mean temperature of wettest quarter

BIO8

Mean temperature of three consecutive months with highest average monthly precipitation


Mean temperature of driest quarter

BIO9

Mean temperature of three consecutive months with lowest average monthly precipitation


Mean temperature of warmest quarter

BIO10

Mean temperature of three consecutive months with highest mean maximum monthly temperature


Mean temperature of coldest quarter

BIO11

Mean temperature of three consecutive months with lowest mean maximum monthly temperature


Annual precipitation

BIO12

Mean annual precipitation


Precipitation of wettest month

BIO13

Mean monthly precipitation of wettest month


Precipitation of driest month

BIO14

Mean monthly precipitation of driest month


Precipitation seasonality

BIO15

Standard deviation of the means of monthly precipitation


Precipitation of wettest quarter

BIO16

Sum of mean precipitation for three consecutive months with highest mean precipitation

WorldClim (Hijmans et al. 2005;

http://www.worldcli m.org/) Precipitation of driest quarter

BIO17

Sum of mean precipitation for three consecutive months with lowest mean precipitation


Precipitation of warmest quarter

BIO18

Sum of mean precipitation for three consecutive months with highest mean precipitation


Precipitation of coldest quarter

BIO19

Sum of mean precipitation for three consecutive months with lowest mean monthly temperature


Table S4.2. Combinations of background extent and presence points filtering scale used for modeling relative abundance of natural prey for the Mexican wolf (Canis lupus baileyi): elk (Cervus elaphus), mule deer (Odocoileus hemionus), and white-tailed deer (O. virginiana). Background extenta Presence points filtering scaleb No 1 km AHRS (10 km) MHRS (14 km) Elk MHRS (14 km) 1 km AHRS (10 km) MHRS (14 km) Mule deer No 1 km AHRS (4.7 km) MHRS (6.6 km) Mule deer MDD (113 km) 1 km AHRS (4.7 km) MHRS (6.6 km) Mule deer MHRS (6.6 km) 1 km AHRS (4.7 km) MHRS (6.6 km) White-tailed deer No 1 km AHRS (2.2 km) MHRS (3.1 km) White-tailed deer MDD (224 km) 1 km AHRS (2.2 km) MHRS (3.1 km) White-tailed deer MHRS (3.1 km) 1 km AHRS (2.2 km) MHRS (3.1 km) Species Elk

a

Extent in which random background points were selected: No = No background limitation, MDD = maximum dispersal distance, MHRS = maximum home range size. b Scale in which all presence points but one are randomly removed: AHRS = average home range size, MHRS = maximum home range size.

βc

Full.AUCd

Mean.AUCe

Mean. AUC.DIFFf

Mean.OR10g

Mean.ORminh

Number of parameters

Whitetailed deer

Featuresb

Mule Deer

Background extenta

Elk

Presence points filtering scalea

Species

Table S4.3. Settings and evaluation metrics for species distribution models of natural prey for the Mexican wolf (Canis lupus baileyi): elk (Cervus elaphus), mule deer (Odocoileus hemionus), and white-tailed deer (O. virginiana). The spatial models highlighted in gray were selected as best by the experts and used to create species distribution maps.

1 km 1 km 10 km 10 km 14 km 14 km 1 km 1 km 1 km 4.7 km 4.7 km 4.7 km 6.6 km 6.6 km 6.6 km 1 km 1 km 1 km 2.2 km 2.2 km 2.2 km

No 14 km No 14 km No 14 km No 113 km 6.6 km No 113 km 6.6 km No 113 km 6.6 km No 224 km 3.1 km No 224 km 3.1 km

LQTHP HP LQT LQ LQTHP LQ LQTP LQTP LQT LQTP LQT LQT LQTP LQTP LQT LQTP LQT LQTHP LP LQ H

1.5 0.5 2 3.5 2.5 2 1 1 1 1.5 1 1 1 1.5 1.5 3.5 2.5 2 4 1.5 4

0.930 0.714 0.906 0.613 0.898 0.631 0.823 0.811 0.607 0.791 0.791 0.627 0.782 0.787 0.621 0.908 0.910 0.629 0.909 0.900 0.600

0.884 0.643 0.888 0.582 0.882 0.560 0.678 0.670 0.548 0.684 0.653 0.540 0.688 0.683 0.527 0.889 0.901 0.602 0.901 0.886 0.521

0.056 0.144 0.028 0.069 0.038 0.108 0.154 0.167 0.073 0.121 0.149 0.107 0.106 0.114 0.102 0.047 0.036 0.039 0.028 0.050 0.098

0.272 0.258 0.200 0.139 0.242 0.194 0.287 0.310 0.166 0.237 0.309 0.261 0.257 0.240 0.227 0.134 0.128 0.152 0.140 0.140 0.164

0.000 0.002 0.006 0.000 0.008 0.056 0.020 0.040 0.010 0.010 0.028 0.006 0.022 0.002 0.005 0.013 0.020 0.000 0.008 0.031 0.016

39 61 12 14 7 25 112 28 31 51 87 53 62 45 38 18 17 15 13 17 7

3.1 km No LQP 4.5 0.896 0.898 3.1 km 224 km LP 6.5 0.895 0.888 3.1 km 3.1 km LQ 5.5 0.560 0.569 a See the description in Table S4.2. b L = linear, Q = quadratic, P = product, T = Threshold and H = Hinge. c β multipliers. d AUC based on unpartitioned dataset. e AUC based on the testing data (i.e., AUCtest), averaged across four bins. f Difference between AUCtrain and AUCtest. g The 10 % omission rate of the training records. h The lowest presence threshold.

0.019 0.039 0.030

0.085 0.162 0.076

0.008 0.000 0.008

6 8 2

Table S4.4. Uncorrelated variables with more than 5 % contribution in the best species distribution models for natural prey of the Mexican wolf (Canis lupus baileyi): elk (Cervus elaphus), mule deer (Odocoileus hemionus), and white-tailed deer (O. virginiana). Species Elk

Background extenta No No

Mule deer

Presence points filtering scaleb MHRS (14 km)

Uncorrelated variables with more than 5% contributionc BIO10 (59%), BIO19 (24%), BIO3 (17%)

MHRS (6.6 km) BIO7 (34%), Slope (28%), BIO4 (17%), BIO19 (7%), VH (8), CCM (6%)

MHRS (6.6 km)

1 km

MHRS (6.6 km)

AHRS (4.7km)

MHRS (6.6 km)

MHRS (6.6 km) CCM (26%), Slope (23%), SSM (14%), TCC (13%), HLI (13%), DW (11%)

MDD (224 km)

1 km

MHRS (3.1 km)

AHRS (2.2 km)

White-tailed deer

Slope (60%), CCM (40%) VH (35%), Slope (18%), LCM (17%), CC (14%), DR (10%), HLI (6%)

BIO12 (31%), BIO13 (19%), BIO4 (17%), BIO6 (12%), SSM (11%), LCV (5%), BIO19 (5%) SSM (35%), VH (33%), CCM (32%)

a

Extent in which random background points are selected: No = no background limitation, MDD = maximum dispersal distance, MHRS = maximum home range size. b

Scale in which all presence points but one are randomly removed: AHRS = average home range size, MHRS = maximum home range size. c

See Table S4.1 for description of variables.

Figure S4.1. Map of relative abundance of elk (Cervus elaphus) in Arizona and New Mexico, USA. Blue represents areas with lower relative abundance and red represents areas with higher relative abundance. Presence points (black dots) are filtered in 14 km scale (maximum home range size).

Figure S4.2. Map of relative abundance of mule deer (Odocoileus hemionus) in Arizona and New Mexico. This map is the average of prediction maps of four selected models (See Table S4.3). Blue represents areas with lower relative abundance and red represents areas with higher relative abundance. Presence points (black dots) are filtered in 1 km scale.

Figure S4.3. Map of relative abundance of white-tailed deer (Odocoileus virginiana) in Arizona and New Mexico based on Maxent modeling results. This map is the average of two selected models (see Table S4.3). Presence points (black dots) are filtered at 1 km scale.

REFERENCES: Anderson, R. P., & Raza, A. (2010). The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela. Journal of Biogeography, 37, 1378– 1393. AZGF. (2017a). Arizona’s most-valued hunting and fishing locations v1.0. Retrieved July 20, 2017, from http://azgfd.maps.arcgis.com/apps/MapSeries AZGF. (2017b). Where to hunt. Retrieved July 20, 2017, from https://www.azgfd.com/hunting/units/ Beck, J., Böller, M., Erhardt, A., & Schwanghart, W. (2014). Spatial bias in the GBIF database and its effect on modeling species’ geographic distributions. Ecological Informatics, 19, 10–15. Boria, R. A., Olson, L. E., Goodman, S. M., & Anderson, R. P. (2014). Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecological Modelling, 275, 73–77. Bradley, B. A. (2016). Predicting abundance with presence-only models. Landscape Ecology, 31, 19–30. DeYoung, R. W., & Miller, K. V. (2011). White-tailed deer behavior. In D. G. Hewitt (Ed.), Biology and management of white-tailed deer (pp. 311–351). Boca Raton, FL: CRC Press. Guisan, a, & Zimmermann, E. N. (2000). Predictive habitat distribution models in ecology. Ecological Modelling, 135, 147–186. Heffelfinger, J. (2006). Deer of the Southwest: A Complete Guide to the Natural History, Biology, and Management of Southwestern Mule Deer and White-tailed Deer. College Station, Texas: Texas A & M University Press. Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., & Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965–1978. Hijmans, R. J., Cameron, S. S., & Parra, J. (2005). The Museum of Vertebrate Zoology, University of California, Berkeley. http://www.worldclim.org/. Accessed May 2015. Hortal, J., Jiménez-Valverde, A., Gómez, J., Lobo, J., & Baselga, A. (2008). Historical bias in biodiversity inventories affects the observed environmental niche of the species. Oikos, 117, 847–858. Hygnstrom, S. E., Groepper, S. R., VerCauteren, K. C., Frost, C. J., Boner, J. R., Kinsell, T. C., & Clements, G. M. (2008). Literature review of mule deer and white-tailed deer movements in western and midwestern landscapes. Great Plains Research, 18, 219–231. Kramer-Schadt, S., Niedballa, J., Pilgrim, J. D., Schröder, B., Lindenborn, J., Reinfelder, V., … Wilting, A. (2013). The importance of correcting for sampling bias in MaxEnt species distribution models. Diversity and Distributions, 19, 1366–1379.

Mackie, R. J., Kie, J. G., Pac, D. F., & Hamlin, K. L. (2003). Mule deer (Odocoileus hemionus). In G. A. Feldhamer, B. C. Thompson, & J. A. Chapman (Eds.), Wild mammals of North America: Biology, management, and conservation. 2nd ed (pp. 889–905). Baltimore, MD: Johns Hopkins University Press. Merow, C., Smith, M. J., & Silander, J. A. (2013). A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography, 36, 1058–1069. Muscarella, R., Galante, P. J., Soley-Guardia, M., Boria, R. A., Kass, J. M., Uriarte, M., & Anderson, R. P. (2014). ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in Ecology and Evolution, 5, 1198–1205. Nielsen, S. E., Johnson, C. J., Heard, D. C., & Boyce, M. S. (2005). Can models of presenceabsence be used to scale abundance ? Two in life history case studies extremes considering. Ecography, 28, 197–208. O’Gara, B. W. (2002). Taxonomy. In D. E. Toweill & J. W. Thomas (Eds.), North American elk: ecology and management (pp. 3–66). Washington, DC: Smithsonian Institution Press. Phillips, S. B., Aneja, V. P., Kang, D., & Arya, S. P. (2006). Modelling and analysis of the atmospheric nitrogen deposition in North Carolina. International Journal of Global Environmental Issues, 6, 231–252. Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E., & Blair, M. E. (2017). Opening the black box: An open-source release of Maxent. Ecography, 40, 887–893. Radosavljevic, A., & Anderson, R. P. (2014). Making better Maxent models of species distributions: complexity, overfitting and evaluation. Journal of Biogeography, 41, 629– 643. Reed, J. E., Ballard, W. B., Gipson, P. S., Kelly, B. T., Krausman, P. R., Wallace, M. C., & Wester, D. B. (2006). Diets of free-ranging Mexican gray wolves in Arizona and New Mexico. Wildlife Society Bulletin, 34, 1127–1133. Tôrres, N. M., De Marco, P., Santos, T., Silveira, L., de Almeida Jácomo, A. T., & Diniz-Filho, J. A. F. (2012). Can species distribution modelling provide estimates of population densities? A case study with jaguars in the Neotropics. Diversity and Distributions, 18, 615–627. USDA. (2015). Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. Retrieved August 6, 2015, from https://websoilsurvey.nrcs.usda.gov USDA. (1998). General Terrestrial Ecological Survey (Southwest Region). Retrieved September 15, 2015, from https://www.fs.usda.gov USGS. (2009). National Elevation Dataset. 2015. The National Map. Accessed May 2015. Retrieved May 2, 2015, from https://lta.cr.usgs.gov/NED USGS. (2013). LANDFIRE existing vegetation type layer. U.S. Department of Interior,

Geological Survey. Retrieved May 3, 2015, from https://landsat.usgs.gov USGS. (2015a). National Hydrography Dataset. The National Map. Retrieved May 2, 2015, from https://nhd.usgs.gov/ USGS. (2015b). Web-Enabled Landsat data (WELD). Retrieved May 2, 2015, from https://landsat.usgs.gov/web-enabled-landsat-data-weld-projects USGS National Gap Analysis Program. (2007). Digital land stewardship map for the southwestern United States. Version 1.0. New Mexico Cooperative Fish and Wildlife Research Unit, New Mexico State University. Accessed May 2015. VanDerWal, J., Shoo, L., Johnson, C., Williams, S., Mooij, A. E. W. M., & DeAngelis, E. D. L. (2009). Abundance and the environmental niche: Environmental suitability estimated from niche models predicts the upper limit of local abundance. The American Naturalist, 174, 282–291. Wallace, C. M. (1991). Elk habitat use in the White Mountains, Arizona. Tucson, Arizona: University of Arizona.Dissertation. Weber, M. M., Stevens, R. D., Diniz-Filho, J. A. F., & Grelle, C. E. V. (2017). Is there a correlation between abundance and environmental suitability derived from ecological niche modelling? A meta-analysis. Ecography, 40, 817–828. Yackulic, C. B., Chandler, R., Zipkin, E. F., Royle, J. A., Nichols, J. D., Campbell Grant, E. H., & Veran, S. (2013). Presence-only modelling using MAXENT: When can we trust the inferences? Methods in Ecology and Evolution, 4, 236–243.