United States Department of Agriculture / Forest Service

Rocky Mountain Research Station

Research Paper RMRS-RP-64

April 2007

A Different Time and Place Test of ArcHSI: 

A Spatially Explicit Habitat Model for 

Elk in the Black Hills

Mark A. Rumble

Lakhdar Benkobi

R. Scott Gamo
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Rumble, Mark A.; Benkobi, Lakhdar; Gamo, R. Scott. 2007. A different time and place test of ArcHSI: A spatially explicit habitat model for elk in the Black Hills. Research Paper RMRS-RP-64. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. 16 p.

Abstract

We tested predictions of the spatially explicit ArcHSI habitat model for elk. The distribution of elk relative 

to proximity of forage and cover differed from that predicted. Elk used areas near primary roads similar to 

that predicted by the model, but elk were farther from secondary roads. Elk used areas categorized as good 

(> 0.7), fair (> 0.42 to 0.7), and poor (≤ 0.42) HSI (habitat suitability index) from the model proportional 

to the distribution of the landscape during summer, but not winter. Evaluation of the cover component of 

the model showed elk used areas disproportional to the predicted value as cover. Foraging elk strongly 

selected areas predicted to have good forage and avoided areas predicted as fair or poor forage; selection 

for better forage was more pronounced during winter than summer. Forage and cover coefficients assigned 

to vegetation structural stages were tested and modified. Forage coefficients generally reflected the biomass 

of herbaceous vegetation available to elk.

Authors

Mark Rumble is a research wildlife biologist with the Rocky Mountain Research Station at the Forest and 

Grassland Research Laboratory in Rapid City, SD. He has a B.S. in Wildlife Biology from Washington State 

University, an M.S. in Wildlife Biology from South Dakota State University, and Ph.D. in Zoology and 

Physiology from the University of Wyoming.

Lahkdar Benkobi is a research scientist with the Department of Forest and Rangeland Stewardship, Colorado 

State University. He has a B.S. from the Institute of Agricultural Technology, and M.S. and Ph.D. in Range 

Science from Colorado State University.

R. Scott Gamo is a regional wildlife biologist with the Idaho Game and Fish Department. He has a B.S. in 

Biology from Black Hills State University and an M.S. in Zoology and Physiology from the University of 

Wyoming.

Acknowledgments

This study was supported by the U.S. Forest Service, Rocky Mountain Research Station in cooperation 

with South Dakota Department of Game, Fish and Parks (Agreement Number RMRS-98113-CC), U.S. 

Department of the Interior, Fish and Wildlife Service, Federal Aid Project W-75-R-121, the Black Hills 

National Forest (Agreement Number ISA-203-01-004), the Rocky Mountain Elk Foundation, and Colorado 

State University (Agreement Number 00-JV-11221609). L. Conroy, C. Holte, L. Flack, and M. Tarby provided 

technical field assistance. J. Jenks, M. Oehler, and B. Wakeling provided helpful review and comments.
Executive Summary

Wildlife habitat models are important tools that help resource managers evaluate and disclose the 

probable effects of prospective land management actions on wildlife. We tested a spatially explicit 

habitat model for elk (ArcHSI) using telemetry data collected in the Black Hills, South Dakota. The 

distribution of elk estimated from telemetry, relative to proximity of forage and cover, differed from 

that predicted by ArcHSI. The distribution of elk relative to primary roads was similar to that predicted 

by the model, but elk were located farther from secondary roads than predicted by the model. After 

modifying the road effects component of ArcHSI, we tested the predicted habitat suitability index 

(HSI) from ArcHSI. Output from ArcHSI was categorized as good (> 0.7), fair (> 0.42 to 0.7), and poor 

(≤ 0.42), and elk selected areas in these categories proportional to the composition of the landscape during 

summer, but not during winter. The predicted value of forested stands for cover and forage for elk was 

also categorized as good, fair, or poor. Elk selected stands for cover different from the model predictions 

during summer and winter. In both summer and winter, foraging elk strongly selected areas predicted to 

have good forage HSI and avoided areas predicted as fair or poor forage. Selection for areas predicted 

as better forage was more pronounced during winter than summer. To test model coefficients for cover 

and forage assigned to vegetation structural stages, we compared bedded elk use with predicted use. Elk 

selected vegetation structural stages for cover different from composition of the area during both summer 

and winter. The correlation between values for cover assigned to vegetation structural stages and selection 

ratios from resource selection analyses was zero. Elk selected vegetation structural stages different from 

proportional availability for forage during summer and winter. The correlations between forage coefficients 

assigned to vegetation structural stages and selection ratios from analyses were significantly positive 

(r ≥ 0.70) during summer and winter. During summer, elk selected grasslands, aspen, and white spruce 

< 40 percent canopy closure for forage; all structural stages of ponderosa pine were selected less than their 

proportional area for forage by elk. During winter, elk preferentially selected grasslands and ponderosa 

pine < 40 percent canopy closure for foraging, but avoided ponderosa pine 40 to 70 percent canopy 

closure, and white spruce. Selection ratios for foraging from resource selection analyses were positively 

correlated (r = 0.61) with herbaceous biomass available in vegetation structural stages, suggesting that 

forage coefficients generally reflected the biomass of herbaceous vegetation available to elk.

We believe that the structure of this model has application for evaluating forest management prescriptions 

for elk habitat in other areas and in particular, ponderosa pine forests. However, forage and cover 

coefficients assigned to vegetation structural stages should be modified to reflect local conditions where 

the model is being applied.
Figure 1. South Dakota and the Black Hills National Forest boundary with cutout of the north and south study 

areas.
USDA Forest Service RMRS-RP-64. 2007 1

Introduction

Habitat models have been used as tools for assessing 

effects of land management alterations on animals since 

the 1970s (Berry 1986). Pros and cons of various habitat 

models have been extensively debated. Application of 

untested models can be unreliable (Laymon and Barrett 

1984), but wildlife models do not need to depict reality, 

rather they can simplify and provide useful results if the 

assumptions are understood and are correctly applied 

(Starfield 1997). The litmus test for a useful model should 

not be how well it predicts truth, but does it improve 

the decision process (Starfield 1997). Consequently, 

continuous testing and validation of wildlife habitat 

models should be encouraged.

Elk (Cervus elaphus) are particularly amenable to the 

use of habitat models for quantifying the effects of land 

alterations (Roloff and others 2001), perhaps because 

of the large research database that exists. Geographic 

information systems (GIS) have allowed results of 

habitat models to be visually displayed. Some models 

include spatially explicit components, such as roads or 

habitat juxtaposition, programmed in the GIS. Benkobi 

and others (2004) used elk locations from Custer State 

Park, South Dakota, to test predictions of a habitat 

effectiveness model (ArcHabcap, unpublished software, 

Black Hills National Forest, Custer, SD) patterned after 

models developed in Oregon and Washington (Thomas 

and others 1979; Thomas and others 1988; Wisdom 

and others 1986). Specifically, Benkobi and others 

(2004) tested the spatially explicit effects of roads and 

forage‐cover proximity (habitat juxtaposition) and the 

mathematical calculations for combining model compo‐

nents of habitat effectiveness for elk. Juntti and Rumble 

(2006) wrote new code for this model (ArcHSI) that 

substantially reduced runtime and expanded the use of 

the model to personal computers with ArcGIS 8.1 (ESRI 

2001) or newer software. Nonetheless, there remained a 

need to test and refine ArcHSI in environments where 

it is used by the Forest Service to evaluate alterna‐

tives for land management. Although expert opinion 

is a valid test of habitat models, validation research is 

a better approach for testing wildlife habitat models 

(Holthausen and others 1994). This paper reports the 

results of a validation test and subsequent modifica‐

tions of a spatially explicit elk habitat suitability model 

in the Black Hills National Forest. This test represents 

a step toward the pragmatic modeling approach that 

builds from application of an initial imperfect model 

(Starfield 1997).

Methods

Study Area

Our study included two areas of the Black Hills in 

western South Dakota and eastern Wyoming (fig. 1). The 

southernmost area (hereafter referred to as the south area) 

was located on the Limestone Plateau. The northern study 

area (hereafter referred to as the north area) was located 

north of U.S. Highway 85 and included portions of the 

Limestone Plateau and steep canyons and drainages that 

occur as elevations drop to the surrounding mixed-grass 

prairies to the north. The north and south areas were 

originally separated. However, a few elk moved between 

these areas (Benkobi and others 2005) and we expanded 

the boundary of the south area to join the north area. 

Average annual precipitation of these areas is similar, 

but the north area receives 7 to 10 cm more precipi‐

tation from snow pack during winter than the south area 

(Orr 1959). Elevation declines rapidly near the northern 

boundary of the north area from approximately 2,040 to 

1,280 m; in the south, elevation declines gradually from 

2,040 to 1,800 m.

Ponderosa pine (Pinus ponderosa) is the dominant 

vegetation type, comprising 79 percent of the north area 

and 78 percent of the south area. White spruce (Picea 

glauca) comprises 2 percent of the north and < 1 percent 

of the south, while quaking aspen (Populus tremuloides) 

comprises 10 percent of the north area, but only 3 percent 

of the south area. White spruce and aspen occur on mesic 

sites and north‐facing slopes in both areas (Hoffman and 

Alexander 1987). Aspen occurs as climax vegetation and 

as a seral community to ponderosa pine or white spruce in 

the Black Hills (Hoffman and Alexander 1987). Meadow 

and grassland vegetation comprised 7 percent of the 

north area and 12 percent of the south area. Remaining 

portions of the study areas were characterized by bur 

oak (Quercus macrocarpa), shrublands dominated 

by mountain mahogany (Cercocarpus montanus) and 

Rocky Mountain juniper (Juniperus scopulorum), and 

non‐forested areas. Common understory shrubs in 

forested areas included snowberry (Symphoricarpus 

occidentalis), common juniper (J. communis), bur oak, 

bearberry (Arctostaphylos uva-ursi), Ribes spp., Oregon 

grape (Mahonia repens), and chokecherry (Prunus 

virginiana). Common graminoids in meadows and 

beneath forested stands include Kentucky bluegrass 

(Poa pratensis), smooth brome (Bromus inermis), 

timothy (Phleum pratense), green needlegrass (Nasella 

viridula), poverty oatgrass (Danthonia spicata), and 

fuzzyspike wild rye (Elymus innovatus).
2 USDA Forest Service RMRS-RP-64. 2007

Vegetation in the Black Hills National Forest is 

mapped and classified in a hierarchal system (Buttery 

and Gillam 1983). The hierarchal classification system 

is a surrogate seral stage model for forest vegetation 

based on vegetation type, structural stages, and overstory 

canopy cover (hereafter referred to as canopy closure to 

avoid confusion with use of habitats for cover by elk). 

Structural stages of vegetation types include grass/forb, 

shrub/seedling, sapling/pole forest (2.5 to 22.9 cm dbh), 

mature forest (> 22.9 cm dbh), and old growth forest 

(unpublished, RMRIS data dictionary, U.S. Forest 

Service, Rocky Mountain Region, Lakewood, Colorado). 

Within the sapling/pole and mature structural stages, 

canopy closure is categorized as 0 to 40 percent, 41 to 

70 percent, and > 70 percent. This vegetation classifi‐

cation was applied to land units of approximately 4 to 32 

ha delineated using topographic features such as ridges, 

valleys, and vegetation type.

We delineated and classified vegetation on private 

lands with the aid of 1:24,000 digital orthorphotograph 

maps and 1:24,000 color aerial photographs. Land units 

on private lands were identified and the boundaries were 

digitized using ArcEdit to be part of the GIS vegetation 

coverage. We used the adjacent classified Forest Service 

lands as references for the vegetation classification 

assignments.

Elk Capture and Telemetry

In August 1998, we captured and collared 22 female 

elk with VHF transmitters in the south area and 13 female 

elk in the north area using a net-gun fired from a helicopter. 

To ensure that these elk were a representative sample of 

the population, we divided the north and south areas into 

approximately equal quarters. Our capture protocol was 

to capture 20 percent of the elk in each of the quarters—

20 percent could be captured as encountered and only 

two animals could be captured from a herd group. In 

late January and early February of 1999, we captured an 

additional 12 female elk in the north area and two female 

elk in the south area and placed VHF telemetry collars 

on them. VHF radio transmitters were equipped with 

mortality and activity sensors. In February 2000, four 

female elk were captured and geographic positioning 

system (GPS) telemetry collars were attached to them. 

Two of these elk were previously collared from earlier 

captures. In March and early April 2001, two female elk 

and two male yearling elk were captured using modified 

collapsible clover traps (McCullough 1975; Thompson 

and others 1989) and GPS collars were attached to them. 

Yearling male elk associate with herds of females with 

calves until they are approximately 2.5 years old, at 

which time they begin exploratory movements (Hurley 

and Sargeant 1991). Activity sensors in GPS collars 

queried the position of the collar every second for 10 

minutes immediately after a GPS location was recorded. 

If the collar was in a head down position > 10 percent of 

the time, but < 99 percent, we considered these locations 

foraging locations (Rumble and others 2001). GPS 

collars were programmed to drop off or quit collecting 

data on 1 December 2000 and 2001.

Elk with VHF telemetry collars were located from 

spring to fall during daylight hours at 1 to 2 week intervals 

from the ground with hand-held yagi antenna, from 

spring to fall or during winter from a fixed-wing aircraft 

or from the ground. We located elk from the time they 

were captured until 1 October 2001. When possible, we 

obtained visual confirmation of the locations. All visual 

locations were recorded with hand‐held or aircraft GPS. 

The GPS telemetry collars recorded locations at 4‐hr 

intervals during 2000 and 2‐hr intervals during 2001.

Evaluation of the Model—Following tests of the 

ArcHabcap for elk, Benkobi and others (2004) recom‐

mended that (1) the distance that primary roads affect 

elk dispersion patterns be extended to 350 m; (2) the 

effects of primitive roads on elk should be eliminated; 

(3) habitat suitability within the area affected by roads 

should be reduced by 50 percent; and (4) the calculation 

of habitat effectiveness should combine model compo‐

nents using an arithmetic average that weighted forage 

values three times greater than cover or habitat juxtapo‐

sition. These recommendations were incorporated in the 

model we tested. New code that reduced run times for 

a large vegetation coverage was written in Arc Macro 

Language (ESRI 2001) and calculated habitat suitability 

(HSI) for elk (Juntti and Rumble 2006). Previous 

versions of this model estimated habitat effectiveness, 

which is the percent of area or time that is usable by elk 

excluding hunting season periods (Lyon and Christensen 

1992). Habitat suitability is the ability of the habitat 

in its current condition to provide life requisites of a 

species (Ministry of Environment, Lands and Parks 

Resources Inventory 1999). Spatial components in the 

models included forage and cover juxtaposition and road 

effects (Juntti and Rumble 2006). The model used the PC 

version of ArcInfo 8.1 for a personal computer (ESRI 

2001). The GIS vegetation coverage and road coverage 

were the inputs for the model. We ran the ArcHSI model 

for summer and winter for the north and south areas. We 

used k-means cluster analysis (SPSS 2001) to classify 

groups of HSI scores from the model into categories of 

> 70, > 0.42 to 0.70, and 0 to 0.42, which we assigned as 

good, fair, and poor, respectively.

Because distributions of elk are altered by distur‐

bances during hunting seasons (Conner and others 

2001; Millspaugh and others 2000; Rumble and others, 
USDA Forest Service RMRS-RP-64. 2007 3

2005), we excluded elk locations from 1 September 

to 30 November from our tests of the model. We also 

excluded elk locations between 11 and 25 April because 

that was the period during which elk migrated to summer 

range (Benkobi and others 2005). To integrate daytime 

GPS locations with radio telemetry locations of elk, we 

randomly selected one GPS location between sunrise and 

sunset from each animal at 16‐day intervals and included 

these with locations obtained from radio telemetry. The 

attributes of the GIS coverage output from ArcHSI were 

assigned to elk locations using a spatial join in ArcMap 

8.1 (ESRI 2001).

We evaluated coefficients for forage and cover 

areas by creating 100‐m intervals in both directions 

from forage‐cover edges using proximity analysis in 

grid‐cell modeling (ESRI 1999). Because these elk 

use open habitats more at night (Rumble and others 

2001), we evaluated the elk distribution into openings 

from cover using the GPS nighttime locations. Mature 

ponderosa pine with 40 to 70 percent overstory closure 

was classified as both forage and cover in the model 

and a juxtaposition coefficient of 1.0 was assigned to 

this vegetation structural stage. Using the Design I 

resource selection analysis (Manly and others 2002), 

we evaluated the distribution of elk locations relative to 

forage‐cover edges by comparing expected use by elk in 

100‐m intervals with observed use.

To test the effects of roads on the distribution of elk, 

we used proximity grid‐cell analysis to create distance 

intervals from roads. For primary roads, the interval 

width was 50 m; for secondary roads, the interval width 

was 20 m; and for primitive roads, the interval width 

was 5 m. These interval widths were based on earlier 

test (Benkobi and others 2004) that we assumed would 

be sensitive to the response by elk. Predicted use by elk 

of each interval away from roads was tested using the 

Design I analysis (Manly and others 2002). We used 

linear regression (trend line) to estimate the distance 

from primary and secondary roads that elk use, inter‐

sected the expected use, and this was our estimate of 

the distance from roads that elk demonstrated avoidance 

(for example, Rowland and others 2000).

Following evaluation of roads and juxtaposition 

coefficients, we changed the distance values in ArcHSI 

regarding how roads influenced the distribution of elk and 

then applied the model to our study areas. Elk occurring in 

each of the study areas were distinct herds (Benkobi and 

others 2006), so independent tests of the ArcHSI model 

were made for the north and south study areas. We used 

the Design I analysis (Manly and others 2002) to test the 

hypothesis that elk selected areas of good, fair, and poor 

HSI categories proportional to their areal extent.

We used different data sets to test hypotheses of elk 

selection for predicted forage and cover HSI categories. 

We used daytime locations of bedded elk to test the 

hypothesis that elk selected HSI cover categories propor‐

tional to areal extent during summer and winter. To test 

the hypothesis that foraging elk selected HSI forage 

categories proportional to available, we used nighttime 

locations of elk equipped with GPS collars indicating 

elk foraging activity (Rumble and others 2001). For 

estimates of availability, the analysis of ArcHSI forage 

predictions necessitated the use of a subset of the GIS 

coverage of the study areas encompassing those GPS 

locations. These analyses were also made using the 

Design I analysis.

We then used the Design I analysis comparing elk 

selection of vegetation structural stages to evaluate the 

associated coefficients for forage or cover during summer 

and winter. We combined the diameter structural stage 

categories of quaking aspen and white spruce for these 

analyses to minimize the number of cells with fewer than 

five observations. We also combined ponderosa pine 

dbh categories for analyses comparing elk cover use. We 

used regression analysis to evaluate relations between 

selection ratios (1) resulting from resource selection 

analyses with forage and cover coefficients assigned to 

vegetation structural stages and (2) for forage and herba‐

ceous biomass in vegetation structural stages.

Although we established hypotheses and tested them, 

our goal was not to modify the model to the point that 

elk selection of vegetation structural stages was propor‐

tional to the predicted HSI. This population of elk is 

managed below forage carrying capacity by the South 

Dakota Department of Game, Fish and Parks to reduce 

elk use of private lands and we expected elk to demon‐

strate differential selection for “better” habitats (for 

example, Fretwell and Lucas 1969). We assigned new 

forage coefficients to vegetation structural stages in 

the model based on relative herbaceous biomass and 

selection ratios; new cover coefficients were assigned 

to vegetation structural stages based primarily on the 

selection ratios from the Design I analyses (Manley and 

others 2002). In assigning new coefficients to vegetation 

structural stages, we assumed that a selection ratio of 1.0 

(elk use equaled expected use) represented a coefficient 

of 0.5, selection ratios ≥ 1.0 were assigned coefficients 

0.5 to 1.0, and selection ratios ≤ 1.0 were assigned coeffi‐

cients 0 to 0.5. We placed an additional constraint on 

winter forage coefficients—they could not be larger than 

summer forage coefficients. We placed this constraint 

because during the summer, elk had full access to all 

structural stages so selection of forage habitat probably 

represented choices. During winter, however, elk were 
4 USDA Forest Service RMRS-RP-64. 2007

concentrated and the selection ratios from analyses in 

some structural stages likely reflected the relatively 

higher elk densities.

Results

We obtained 1,235 locations of elk using VHF trans‐

mitters that we observed or were able to confidently 

place within a polygon from the GIS coverage. These 

were distributed as follows: north area summer—182, 

north area winter—222, south area summer—423, and 

south area winter—408. The GPS telemetry provided 

2,676 night locations of foraging elk during summer and 

881 night locations of foraging elk during winter. We 

obtained 465 locations of bedded elk from VHF collars 

that were used to assess elk selection of cover, 264 were 

during summer and 201 were during winter.

Habitat Juxtaposition

Summer and winter use by elk of juxtaposition band 

intervals into forage or cover areas from forage‐cover 

edges were similar, so we combined the seasons for 

subsequent analyses. Interval bands of 100 m extended 

into forage areas from cover ≤ 300 m, while bands into 

cover extended ≤ 1000 m (fig. 2). In the north, elk use of 

band intervals from forage‐cover edges was cover similar 

(P = 0.63) to expected use. However in the south area, elk 

avoided the first 100 m into cover from forage‐cover edges 

(P < 0.01) and used the next interval out to 200 m propor‐

tional to availability. Elk selected intervals from 200 m 

to 400 m from cover (P < 0.01); the interval from 400 to 

500 m was marginally selected (P = 0.08). Eliminating 

mature stands of ponderosa pine with 40 to 70 percent 

overstory closure did not affect the distribution patterns 

of elk relative to forage‐cover edges.

Figure 2. Radio telemetry locations 

(observed) of bedded elk (in cover); 

GPS telemetry locations of foraging elk 

(in forage); and expected elk locations 

in 100 m intervals from forage-cover 

edges.
USDA Forest Service RMRS-RP-64. 2007 5

Figure 3. Elk locations from radio telemetry (observed) and 

expected elk locations of intervals from primary, secondary, 

and primitive roads. For the area of increasing selection by 

elk, a linear regression was plotted to estimate the distance 

that roads affected elk distribution.

Roads

Elk avoided areas near primary roads (P < 0.01). 

The estimate from linear regression of elk distribution 

relative to primary roads showed that avoidance of 

primary roads extended to approximately 350 m (fig. 3). 

Elk also avoided areas near secondary roads to a distance 

of approximately 260 m (P < 0.01). Elk demonstrated 

increased selection (positive slope to regression line) of 

both primary and secondary roads out to approximately 

500 m. Elk did not show any patterns of avoidance 

or selection (P = 0.43) from expected of areas near 

primitive roads.

Habitat Suitability

VHF telemetry locations of elk differed from the 

categories predicted by ArcHSI. In the north area, elk 

selected areas predicted to have good HSI, and against 

areas predicted to have poor HSI during the summer (

X2

2 = 7.5, P = 0.03, fig. 4). During winter, the proportion 

of our study predicted as good HSI declined, and in the 

north, elk selected HSI categories similar to the propor‐

tional area (X2

2 = 0.38, P = 0.8). In the south area, elk 

showed selectivity for areas predicted good HSI and 

avoided areas predicted poor HSI during summer 

(X2

2 = 57.5, P < 0.01, fig. 5). Winter elk distributions 

in the south differed from the proportional areas of HSI 

categories predicted by ArcHSI (X2

2 = 8.33, P = 0.02). 

Specifically, elk selected habitats predicted to have poor 

HSI greater than expected (P = 0.05).

Cover—During the summer, bedded elk selected HSI 

cover categories different (X2

2 = 22.4, P < 0.01) from 

model predictions (fig. 6). Elk selected areas predicted 

as good cover (P = 0.10), and avoided areas predicted 

as fair or poor cover (P ≤ 0.04). During winter, bedded 

elk selected HSI cover categories different from those 

predicted by the model (X2

2 = 6.9, P = 0.03). However, 

when multiple range tests were evaluated and no 

patterns of selection were evident among the categories 

(P > 0.11).

Elk avoided grasslands as cover (P = 0.02) during 

summer (table 1). Expected elk use was < 5 observa‐

tions for several vegetation structural stages of aspen. 

Nonetheless, elk seemed to select for the shrub/seedling 

and > 70 percent overstory canopy closure stages of 

aspen for cover during summer (P < 0.01). For cover, 
6 USDA Forest Service RMRS-RP-64. 2007 Figure 5. Daytime radio telemetry locations of elk (observed) and expected elk locations relative to habitat suitability predicted by the ArcHSI model during summer and winter for the south study area in the Black Hills, SD. See figure 4 for explanation of data. Figure 4. Daytime radio telemetry locations of elk (observed) and expected elk locations relative to habitat suitability predicted by the ArcHSI model during summer and winter for the north study area in the Black Hills, SD. Habitat suitability was calculated as of each polygon. Categories of HSI were poor: 0 to 0.42, fair: > 0.42 to 0.70, and good: > 0.7.
USDA Forest Service RMRS-RP-64. 2007 7

Table 1. Observed elk use (number of individuals) compared with expected use (number of individuals) of vegetation structural stages 

for cover during summer in the Black Hills, SD1.

Cover coefficient Observed Expected Bonferroni

in ArcHSI elk use elk use Selection adjusted

Vegetation structural stage model (# indiv.) (# indiv.) ratio p‐value2

Grassland 0 13 30 0.4 0.02

Mountain shrub 0 4 2 1.7

Aspen shrub/seedling 0.5 13 2 6.1 < 0.01

Aspen 0 to 40 percent overstory closure 0.5 11 5 2.1

Aspen > 40 to 70 percent overstory closure 1.0 7 8 0.9

Aspen > 70 percent overstory closure 1.0 7 2 3.2 0.02

Ponderosa pine shrub/seedling 0 3 3 1.1

Ponderosa pine 0 to 40 percent overstory closure 0.5 84 87 1.0

Ponderosa pine > 40 to 70 percent overstory closure 0.5 82 92 0.9

Ponderosa pine > 70 percent overstory closure 1.0 5 22 0.2 < 0.01

White spruce shrub/seedling 0 1 0 2.7

White spruce 0 to 40 percent overstory closure 0.5 17 4 4.6 < 0.01

White spruce > 40 to 70 percent overstory closure 0.5 14 4 3.6 < 0.01

White spruce > 70 percent overstory closure 1.0 2 1 1.5

1

 Elk use determined from daytime VHF 3 telemetry locations of bedded elk and expected use determined from proportional area.

2

 Probability that the selection ratio was different from 1.0 or that selection differed from random use.

Figure 6. Daytime radio telemetry locations 

of bedded elk (observed) and expected 

elk locations relative to cover prediction 

by the ArcHSI model during summer 

and winter the Black Hills, SD. Habitat 

suitability cover values were reduced by 

50 percent for areas ≤ 300 m from primary roads and ≤ 260 m from secondary 

roads.
8 USDA Forest Service RMRS-RP-64. 2007

ponderosa pine ≤ 70 percent overstory canopy closure 

was used by elk similar to availability, but ponderosa 

pine > 70 percent canopy closure was avoided by 

bedded elk (P < 0.01). Expected use of white spruce 

≤ 70 percent overstory canopy closure was low, but 

bedded elk selected this vegetation structural stage for 

cover during the summer (P < 0.01). During summer, 

selection ratios from resource selection analyses were 

not correlated (r < 0.01) with cover coefficients assigned 

to vegetation structural stages in the model.

During winter, bedded elk avoided grasslands (P

< 0.01), but selected ponderosa pine < 40 percent canopy 

closure (P < 0.01, table 2). Coefficients for cover during 

winter were not correlated (r < 0.01) with selection ratios 

from the resource analysis of elk in vegetation structural 

stages. This was particularly evident in structural stages 

of ponderosa pine.

Forage—Elk showed strong selection for areas 

predicted as good forage HSI and avoided areas rated fair 

or poor forage HSI during summer and winter (P < 0.01, 

fig. 7). The selection ratio from analyses of predicted to 

be good HSI for forage during winter (5.86 ± 0.15) was 

stronger than during summer (2.17 ± 0.06).

During summer, night GPS locations of foraging elk 

showed clear selection for meadows, aspen with ≤ 70 

percent overstory closure, white spruce shrub/seedling, 

and white spruce with ≤ 40 percent overstory closure 

(P < 0.01, table 3). All structural stages of ponderosa 

pine were used proportional to availability or avoided by 

foraging elk during summer (P ≥ 0.08). Summer forage 

coefficients were correlated (r = 0.85) with selection 

ratios for vegetation structural stages. Selection ratios 

were correlated (r = 0.61, fig. 8) with herbaceous 

biomass in vegetation structural stages. Theoretically, 

this regression should have a 0-intercept, but the 

intercept was influenced by high selection ratios for a few 

uncommon vegetation structural stages with less herba‐

ceous biomass than meadows. During winter, meadows 

and mature ponderosa pine ≤ 40 percent overstory 

closure were selected by foraging elk (P ≤ 0.01, table 

4). During winter, foraging elk avoided mountain shrub, 

aspen > 70 percent overstory closure, all sapling/pole 

structural stages of ponderosa pine, mature ponderosa 

pine > 40 percent overstory canopy closure, and white 

spruce stands (P ≤ 0.07). Winter forage coefficients for 

vegetation structural stages in the ArcHSI model were 

correlated (r = 0.70) with selection ratios exhibited by 

elk for vegetation structural stages. The correlation 

between selection ratios and herbaceous biomass was 

also positive (r = 0.60).

Discussion

Habitat Juxtaposition

Elk distribution in the Black Hills National Forest 

relative to forage‐cover edges differed from patterns 

observed in western Oregon (Wisdom and others 1986), 

the Blue Mountains of eastern Oregon and Washington 

(Thomas and others 1988), and Custer State Park in the 

southeastern Black Hills (Benkobi and others 2004). 

Most noticeable was elk use similar to, or less than 

expected of areas ≤ 100 m into forage areas or greater than 

Table 2. Observed elk use (number of individuals) compared with expected use (number of individuals) of vegetation structural stages 

for cover during winter in the Black Hills, SD1.

Cover coefficient Observed Expected Bonferroni

in ArcHSI elk use elk use Selection adjusted

Vegetation structural stage model (# indiv.) (# indiv.) ratio p‐value2

Grassland 0 3 23 0.1 < 0.01

Mountain shrub 0 0 2 0.0

Aspen shrub/seedling 0 4 2 2.4

Aspen < 40 percent overstory closure 0 6 4 1.5

Aspen > 40 to 70 percent overstory closure 0 5 6 0.8

Aspen > 70 percent overstory closure 0 1 2 0.6

Ponderosa pine shrub/seedling 0 4 2 1.9

Ponderosa pine < 40 percent overstory closure 0.2 98 66 1.5 < 0.01

Ponderosa pine > 40 to 70 percent overstory closure 0.5 65 70 0.9

Ponderosa pine > 70 percent overstory closure 1.0 9 17 0.5

White spruce shrub/seedling 0 0 0 0.0

White spruce < 40 percent overstory closure 0 0 3 0.0

White spruce > 40 to 70 percent overstory closure 0 1 3 0.3

White spruce > 70 percent overstory closure 0 0 1 0.0

1

 Elk use determined from daytime VHF telemetry locations of bedded elk and expected use determined from proportional area.

2

 Probability that the selection ratio was different from 1.0 or that selection differed from random use.
USDA Forest Service RMRS-RP-64. 2007 9

Figure 7. Nighttime GPS locations of foraging elk 

(observed) and expected elk locations relative 

to foraging habitat suitability predicted by the 

ArcHSI model during winter and summer in 

the Black Hills, SD. Habitat suitability foraging 

values were reduced by 50 percent for areas 

≤ 300 m from primary roads and ≤ 260 m from 

secondary roads.

Table 3. Observed elk use (number of individuals)compared with expected use (number of individuals) of vegetation structural stages 

for foraging during summer in the Black Hills, SD1.

Forage coefficient Observed Expected Bonferroni

in ArcHSI elk use elk use Selection adjusted

Vegetation structural stage model (# indiv.) (# indiv.) ratio p‐value2

Grassland 1.0 370 178 2.1 < 0.01

Mountain shrub 0.5 1 15 .1 < 0.01

Aspen shrub/seedling 1.0 35 12 3.0 < 0.01

Aspen 0 to 40 percent overstory closure 1.0 74 27 2.7 < 0.01

Aspen > 40 to 70 percent overstory closure 1.0 89 38 2.3 < 0.01

Aspen > 70 percent overstory closure 0.5 1 7 0.1

Ponderosa pine shrub/seedling 1.0 18 15 1.2

Ponderosa pine 2.5 to 23 cm dbh

0 to 40 percent overstory closure 1.0 30 46 0.7

> 40 to 70 percent overstory closure 0.5 32 85 0.4 < 0.01

> 70 percent overstory closure 0.2 1 22 0.0 < 0.01

Ponderosa pine > 23 cm dbh

0 to 40 percent overstory closure 0.5 406 456 0.9 0.08

> 40 to 70 percent overstory closure 0.5 257 403 0.6 < 0.01

> 70 percent overstory closure 0.2 17 91 0.2 < 0.01

White spruce shrub/seedling 1.0 9 3 3.6 < 0.01

White spruce 0 to 40 percent overstory closure 1.0 91 25 3.6 < 0.01

White spruce > 40 to 70 percent overstory closure 0.5 26 27 1.0

White spruce > 70 percent overstory closure 0.2 3 9 0.3

1

 Elk use determined from nighttime GPS telemetry locations of foraging elk and expected use determined from proportional area.

2

 Probability that the selection ratio was different from 1.0 or that selection differed from random use.
10 USDA Forest Service RMRS-RP-64. 2007

Figure 8. Linear regression of selection ratio from 

analysis of habitat selection by foraging elk with 

GPS collars in summer and winter on herbaceous 

biomass in vegetation structural stages of the 

Black Hills, SD.

Table 4. Observed elk use (number of individuals) compared with expected use (number of individuals) of vegetation structural stages 

for foraging during winter in the Black Hills, SD1.

Forage coefficient Observed Expected Bonferroni

in ArcHSI elk use elk use Selection adjusted

Vegetation structural stage model (# indiv.) (# indiv.) ratio p‐value2

Grassland 1.0 200 119 1.7 < 0.01

Mountain shrub 0.5 1 13 .1 0.02

Aspen shrub/seedling 1.0 10 6 1.8

Aspen 0 to 40 percent overstory closure 0.5 4 11 0.4

Aspen > 40 to 70 percent overstory closure 0.5 0 12 0.0 < 0.01

Aspen > 70 percent overstory canopy closure 0.2 0 1 0.0

Ponderosa pine shrub/seedling 1.0 5 10 0.5

Ponderosa pine 2.5 to 23 cm dbh

0 to 40 percent overstory closure 1.0 19 36 0.5 0.07

> 40 to 70 percent overstory closure 0.5 43 74 0.6 < 0.01

> 70 percent overstory closure 0.2 3 16 0.2 < 0.02

Ponderosa pine > 23 cm dbh

0 to 40 percent overstory closure 1.0 416 284 1.5 < 0.01

> 40 to 70 percent overstory closure 0.5 166 211 0.8 < 0.01

> 70 percent overstory closure 0.2 9 47 0.2 < 0.01

White spruce shrub/seedling 0.0 1 2 0.6

White spruce 0 to 40 percent overstory closure 0.0 2 18 0.1 < 0.01

White spruce > 40 to 70 percent overstory closure 0.0 1 17 0.1 < 0.01

White spruce > 70 percent overstory closure 0.0 1 5 0.2

1

 Elk use determined from nighttime GPS telemetry locations of foraging elk and expected use determined from proportional area.

2

 Probability that the selection ratio was different from 1.0 or that selection differed from random use.
USDA Forest Service RMRS-RP-64. 2007 11

expected in areas 200 to 600 m into cover. We believe 

these deviations from patterns observed elsewhere were 

influenced by avoidance of areas adjacent to primary 

roads and secondary roads (see next section). Most large 

meadows in our study area had a primary or secondary 

road resulting in greater use of areas by elk > 200 m 

from forest edges of meadows. We do not believe that 

the patterns observed warrant modifying coefficients for 

elk habitat suitability near forage—cover edges.

Roads

Negative effects of roads on elk are well documented 

(for example, Canfield and others 1999). Road densities 

that are < 1/2 of which occurred in our study reduce 

elk habitat by 40 percent (Lyon 1983). Primary and 

secondary roads in the Black Hills are forest roads with 

a gravel base. The elk distribution that we observed 

relative to primary roads was similar to that reported in 

Custer State Park. Because the trend line of elk locations 

intersected the expected locations curve at approxi‐

mately 350 m, we recommend a 350 m buffer distance 

for primary road effects on elk in the Black Hills National 

Forest. Elk avoidance of areas adjacent to secondary 

roads in Custer State Park extended to 60 m (Benkobi 

and others 2004). However, in the central and northern 

Black Hills, secondary roads negatively affected elk up 

to 260 m from the road. We recommend that the coeffi‐

cient for roads be applied to areas extending 260 m 

from secondary roads. Elk displacement from areas near 

primary and secondary roads resulted in selection for 

areas > 300 m from primary roads and > 260 m from 

secondary roads to approximately 550 m for both road 

classes. In the Blue Mountains of Oregon, elk showed 

increasing selection for areas corresponding to distances 

further from roads. This trend was evident up to 1.8 km 

from roads (Rowland and others 2000). Several studies 

have shown that elk are further from roads during day 

than at night (Ager and others 2003; Millspaugh 1999). 

We believe similar daytime movements by elk away 

from primary and secondary roads and into the forest for 

cover resulted in the increased selection for areas 260 to 

550 m from these roads.

Primitive roads appeared to have little effect on the 

distribution of elk in this study and in Custer State 

Park (Benkobi and others 2004). The lack of a demon‐

strated effect of primitive roads on elk distribution is 

difficult to interpret. Other research has shown negative 

responses by elk to relatively low levels of disturbance 

by vehicles or humans (Cassirer and others 1992; Ward 

and Cupal 1979; Wisdom and others 2004). In Custer 

State Park, primitive roads were closed to public use 

explaining the lack of effect primitive roads had on elk 

distribution. However, we speculate that the distribution 

of elk relative to primitive roads in this study was a 

swamping effect from sporadic disturbance in an area 

with high density of primitive roads. We believe that elk 

response to roads was hierarchal, first avoiding primary 

roads, then secondary roads, and lastly primitive roads. 

When areas adjacent to primary and secondary roads 

were eliminated from the GIS, the average distance from 

primitive roads to random points was 145 m (Rumble 

and others 2005), leaving little opportunity for elk to 

avoid primitive roads during their daily travels of up to 

3 km/day (Clutton-Brock and others 1982; Craighead 

1973). It seems plausible that the conditioned response 

of elk to primitive roads at these densities would depend 

on the frequency of disturbance. During aerial telemetry 

flights, we occasionally observed elk on primitive roads. 

Unimproved access such as seismic or power lines, or 

gas rights‐of‐way, did not deter elk habitat selection in 

Canada (Jones and Hudson 2002). At this time, our data 

do not support inclusion of an effect for primitive roads 

on elk.

ArcHSI Predictions

The distribution of elk in areas predicted as good, fair, 

or poor overall HSI followed similar patterns to those 

reported by Benkobi and others (2004) for Custer State 

Park in the southern Black Hills. During summer, elk 

selected areas predicted by the model as good overall 

HSI and avoided areas predicted as poor overall HSI 

by the model. The overall HSI for both study areas 

decreased during winter, primarily because coefficients 

for aspen and white spruce were zero for both forage and 

cover. Elk winter ranges in the Black Hills occur at lower 

elevations than where white spruce and most of the aspen 

occur. The model did not perform as well during winter. 

In the north, elk did not prefer areas predicted to be good 

overall HIS. In the south, elk avoided areas predicted as 

good overall HSI. Thus, errors in some coefficients for 

model components were probable.

ArcHSI weighs forage habitat three times greater 

than cover or juxtaposition of habitats when computing 

the overall HSI. Nighttime GPS telemetry showed 

strong selection by elk for habitats with good forage 

HSI ratings. Selection for areas predicted to be good for 

forage habitats was even more pronounced during winter. 

Strong selection for forage habitats was supportive 

of the greater weight applied to forage coefficients in 

calculating HSI for an area. Forage digestibility declines 

during fall to winter and additional energy expendi‐

tures are incurred from travel and foraging in snow 

(Wickstrom and others 1984; Parker and others 1984). 

Increased selection by elk for areas predicted to have 

good forage HSI during winter was consistent with the 

ecology of elk.
12 USDA Forest Service RMRS-RP-64. 2007

Vegetation structural stages—Recommended coeffi‐

cients for vegetation structural stages resulting from our 

analyses are in table 5. Coefficients for cover assigned 

to vegetation structural stages in ArcHSI were propor‐

tional to the percent of overstory closure, but elk use 

of vegetation structural stages was poorly predicted by 

the model. The initial coefficients for cover probably 

carried over from previous models when thermal cover 

was thought to be important for elk (see Cook and 

others 1998). Despite some use by bedded elk, the cover 

coefficient for grasslands should remain zero during 

summer. Although there were few observations of elk 

in the mountain shrub vegetation structural stage for 

cover during summer, our data indicates the value of 

this structural stage to elk as cover is greater than in 

previous models. For cover during summer, elk showed 

strong selection for all structural stages of aspen except 

the 40 to 70 percent overstory closure structural stage. 

Consequently, we believe the coefficients for cover in 

aspen during summer should be 1.0. Assuming that the 

criteria for elk hiding cover is obscuring 90 percent of a 

standing elk at 61 m (Thomas and others 1979), all struc‐

tural stages of ponderosa pine in our study qualified as 

hiding cover. Open canopy stands of ponderosa pine had 

medium and tall shrubs, such as bur oak and common 

juniper, that contributed to the horizontal screening of 

elk and even more so for bedded elk. Ponderosa pine 

germinates readily in open stands in the Black Hills 

(Shepperd and Battaglia 2002) and saplings provided 

visual obstruction of elk. Vegetation structural stages of 

ponderosa pine ≤ 70 percent overstory closure averaged 

300 to 400 kg/ha of herbaceous biomass (unpub‐

lished data, Rocky Mountain Research Station, Rapid 

City, South Dakota). We often observed groups of elk 

where some were bedded while others were foraging. 

Vegetation structural stages that provided a combination 

of forage and cover were sought by elk. Although elk 

avoided ponderosa pine with high overstory canopy 

cover for bedding during summer, we believe elk would 

have selected ponderosa pine > 70 percent overstory 

closure (for example, Millspaugh and others 1998) 

if aspen or white spruce had not been well distributed 

and available on elk summer ranges. Consequently, the 

coefficient for cover in ponderosa pine > 70 percent 

overstory cover should be < 1.0, but > 0.5. We assigned 

a cover coefficient of 0.7 to ponderosa pine > 70 percent 

overstory closure. Structural stages of ponderosa pine 

≤ 70 percent overstory cover were used proportionally 

to available, and coefficients of 0.5 for the Black Hills 

are appropriate during summer.

Elk avoided bedding in grasslands and mountain shrub 

vegetation structural stages during winter so cover coeffi‐

cients for these vegetation structural stages should be 

zero. Observations of elk in aspen during winter probably 

reflected occasions where undisturbed elk bedded near 

forage areas. Aspen was not common on elk winter range 

in our study, but it occasionally occurred along the edges 

of meadows on northeast slopes. Thus, despite some use 

by bedded elk, we suggest winter cover coefficients 

in aspen should not be greater than 0.5. We assigned 

a cover coefficient of 0.5 to shrub/seedling ponderosa 

pine because bedded elk demonstrated a selection for it. 

Table 5. Proposed coefficients for forage and cover in vegetation structural stages for ArcHSI evaluated for elk 

use in the Limestone Plateau and northern Black Hills, SD.

Cover coefficient Forage coefficient

Vegetation structural stage Summer Winter Summer Winter

Grassland 0.0 0.0 1.0 1.0

Mountain shrub 0.5 0.0 0.1 0.0

Aspen shrub/seedling 1.0 0.5 1.0 1.0

Aspen 0 to 40 percent overstory closure 1.0 0.5 1.0 0.2

Aspen > 40 to 70 percent overstory closure 1.0 0.5 1.0 0.2

Aspen > 70 percent overstory closure 1.0 0.0 0.2 0.1

Ponderosa pine shrub/seedling 0.5 0.5 0.5 0.2

Ponderosa pine 2.5 to 23 cm dbh

0 to 40 percent overstory closure 0.5 0.7 0.3 0.2

> 40 to 70 percent overstory closure 0.5 0.5 0.2 0.2

> 70 percent overstory closure 0.7 0.3 0.1 0.1

Ponderosa pine > 23 cm dbh

0 to 40 percent overstory closure 0.5 0.7 0.5 0.5

> 40 to 70 percent overstory closure 0.5 0.5 0.3 0.3

> 70 percent overstory closure 0.7 0.3 0.1 0.1

White spruce shrub/seedling 1.0 0.0 1.0 0.2

White spruce 0 to 40 percent overstory closure 1.0 0.0 1.0 0.0

White spruce > 40 to 70 percent overstory closure 1.0 0.0 0.5 0.0

White spruce > 70 percent overstory closure 1.0 0.0 0.2 0.0
USDA Forest Service RMRS-RP-64. 2007 13

Winter selection by elk for ponderosa pine with < 40 

percent overstory closure suggests that this vegetation 

structural stage provided forage and cover. Selection by 

elk of vegetation structural stages providing both forage 

and cover are described by Wisdom and others (1986) 

as optimal cover. Consequently we recommend using a 

winter cover coefficient of 0.7 in ponderosa pine < 40 

percent overstory closure. The value of open ponderosa 

pine as cover to elk during winter in other areas will 

depend on the understory vegetation. In more xeric sites 

than occurred in our study, a cover coefficient of 0.5 

for open ponderosa pine would likely be appropriate. 

The selection against ponderosa pine with > 70 percent 

overstory closure is consistent with the hypotheses 

(1) elk do not require thermal cover during winter (Cook 

and others 1998), or (2) forage is of greater impor‐

tance to elk than cover. Given a choice, elk in our study 

selected open canopy pine forest over dense canopy 

forest during winter and the winter cover coefficient for 

ponderosa pine > 70 percent overstory closure should 

be < 0.5. These alterations to the cover coefficients are 

likely study area specific and changes elsewhere should 

be supported by quantitative measurement of elk hiding 

cover described by (Thomas and others 1979).

Foraging elk strongly selected structural stages with 

greater herbaceous vegetation, used vegetation structural 

stages with moderate amounts of herbaceous vegetation 

in random patterns, and avoided those stages with 

the least herbaceous vegetation. Correlation between 

selection ratios and standing biomass confirmed that 

elk selected forage habitats approximately proportional 

to the amount of available forage. Although grasslands 

had the greatest herbaceous biomass, they did not have 

the greatest selection ratio. Nonetheless, grasslands 

typically have the greatest herbaceous biomass and 

we assigned them a forage coefficient of 1.0 because 

elk in the Black Hills are primarily grazers (Wydeven 

and Dahlgren 1983). Standing biomass in the mountain 

shrub vegetation structural stage averaged only 145 ±

40 (x

_

 ± SE) kg/ha. Consequently, the summer forage 

coefficient for mountain shrub should be 0.1. All struc‐

tural stages of aspen had strong selection by foraging elk 

and should be assigned forage coefficients of 1.0 during 

summer. During summer, foraging elk avoided all tree 

stages of ponderosa pine > 40 percent overstory closure, 

suggesting that coefficients in ArcHSI were overesti‐

mated. We recommend forage coefficients of 0.3 to 0.1 

for increasing canopy closure in sapling pole ponderosa 

pine and 0.5 to 0.1 for the mature structural stages of 

ponderosa pine. Herbaceous biomass in sapling/pole 

ponderosa pine < 40 percent overstory canopy closure 

averaged approximately 20 percent less than mature 

ponderosa pine < 40 percent overstory closure. The 

forage coefficient of 0.3 reflects lower available herba‐

ceous vegetation. The selection of white spruce for 

forage was not expected despite high coefficients for 

summer forage in some structural stages of white spruce. 

Examination of the foraging locations in white spruce 

showed that 25 percent were along the interface between 

meadow and spruce stands or resulted from mapping 

errors in the delineation of the stand boundaries that 

included portions of adjacent meadows. Consequently, 

we did not modify the forage coefficients in ArcHSI for 

white spruce.

During winter, foraging elk selected vegetation struc‐

tural stages approximately proportional to estimates of 

herbaceous biomass. Variability in the relation between 

herbaceous biomass and winter selection ratios exhibited 

by foraging elk likely reflected greater density of animals 

on a constricted winter range (Benkobi and others 2005). 

Some vegetation structural stages were rare or absent on 

winter ranges. Meadows received the greatest use during 

winter by foraging elk. During the winter of 2001, we 

observed extensive browsing by elk of shrub/seedling 

stages of aspen following a wildfire that burned approx‐

imately 65 percent of the winter range (Benkobi and 

others 2005). Coefficients in shrub/seedling aspen should 

remain 1.0. Elk avoided foraging in the tree structural 

stages of aspen during winter. We recommend winter 

forage coefficients for tree stages of aspen be 0.2 for 

stages ≤ 70 percent overstory closure and 0.1 for stages 

> 70 percent overstory closure. Avoidance of winter 

foraging in sapling/pole structural stages of ponderosa 

pine > 40 percent overstory cover suggests that forage 

coefficients should be 0.2, 0.2, and 0.1 for the canopy 

closure categories of ≤ 40 percent, > 40 to 70 percent, and 

> 70 percent of sapling pole ponderosa pine, respectively. 

Mature ponderosa pine ≤ 40 percent overstory closure 

was given a forage coefficient of 0.5, which was limited 

by the summer forage coefficient. High density of elk 

on winter range would result in use of marginal habitats 

by elk (Fretwell and Lucas 1969). Winter forage coeffi‐

cients in structural stages of ponderosa pine > 40 percent 

overstory closure reflected the reduced herbaceous 

biomass and selection ratios by foraging elk.

Figure 9 shows the outcome of modifications to 

ArcHSI forage and cover coefficients. While there is 

circularity in using the same data to retest ArcHSI, we 

believe some post hoc review of the model output after 

modifying forage and cover coefficients was warranted. 

The overall HSI for our study area declined from the 

previous version of the model. Winter applications 

showed decreased overall HIS, which was expected 

because aspen and white spruce occur at higher 
14 USDA Forest Service RMRS-RP-64. 2007

elevations than the elk winter ranges in the Black 

Hills and had little value for elk during winter. Finally, 

modifications to coefficients eliminated the selection for 

habitats predicted as poor HSI.

Conclusion

Habitat models provide repeatable quantitative evalu‐

ations and tend to simplify the complicated habitat 

requirements of animals (Starfield 1997). Our test 

confirmed the need for validation of ArcHSI before 

widespread use and commitment of resources (Laymon 

and Barret 1984; O’Neil and others 1988; Shamberger 

and O’Neil 1986). This study represented the second test 

of a spatially explicit elk model in the Black Hills. In 

the Black Hills National Forest, both primary roads and 

secondary roads pushed herds farther back than reported 

by Benkobi and others (2004). Primitive roads did not 

appear to affect elk distributions, which is contrary to 

studies in other areas. Coefficients for the distance that 

primitive roads affect elk habitat could be derived from 

studies in areas with lower densities of roads than occur 

in the Black Hills. Patterns of elk distribution associated 

with juxtaposition of forage‐cover edges were influ‐

enced by primary and secondary roads that traversed 

most of the large meadows (foraging areas). When we 

incorporated road effects from this study into the model 

recommendations of Benkobi and others (2004), results 

during winter suggested that modification to the coeffi‐

cients for forage and cover were necessary. Vegetative 

conditions that meet the cover requirements for elk 

are likely specific to local physical and environmental 

conditions. In our study, forage and cover were available 

in some structural stages of ponderosa pine. Elk did not 

select for high overstory closure for cover during winter. 

Figure 9. Daytime radio telemetry elk 

locations (observed) and expected elk 

locations relative to habitat suitability 

predicted by the ArcHSI model during 

summer and winter after modifying 

forage and cover coefficients to reflect 

habitat selection by elk in the Black 

Hills, SD.
USDA Forest Service RMRS-RP-64. 2007 15

This study and others (Jones and Hudson 2002) demon‐

strated that biomass of herbaceous vegetation largely 

determines foraging habitats of elk. Consequently, HSI 

coefficients for elk foraging habitats can be estimated 

by comparing relative herbaceous standing crop 

among vegetation structural stages. We believe that the 

structure of this model has application for evaluating 

forest management prescriptions for elk habitat in other 

areas and in particular, ponderosa pine forests. However, 

forage and cover coefficients assigned to vegetation 

structural stages should be modified to reflect local 

conditions where the model is being applied.

Literature Cited

Ager, A.A.; Johnson, B.K.; Kern, J.W.; Kie, J.G. 2003. Daily 

and seasonal movements and habitat use by female Rocky 

Mountain elk and mule deer. Journal of Mammalogy 

84:1076‐1088.

Benkobi, L.; Rumble, M.A.; Brundige, G.C.; Millspaugh, 

J.J. 2004. Refinement of the Arc‐Habcap model to predict 

habitat effectiveness for elk. Research Paper RMRS‐RP‐51. 

Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 18 p.

Benkobi, L.; Rumble, M.A.; Stubblefield, C.H.; Gamo, R.S.; 

Millspaugh, J.J. 2005. Seasonal migration by female elk 

in the Black Hills of South Dakota and Wyoming. Prairie 

Naturalist 37:151‐165.

Berry, K.H. 1986. Introduction: Development, testing, 

and application of wildlife-habitat models. In: Verner, 

J.; Morrison, M.L.; Ralph, C.J., editors. Wildlife 2000: 

Modeling habitat relationships of terrestrial vertebrates. 

Madison, WI: University of Wisconsin Press: 3-4.

Buttery, R.F.; Gillam, B.C. 1983. Forested ecosystems. In: 

Hoover, R.L.; Wills, D.L., editors. Managing forested lands 

for wildlife. Denver, CO: Colorado Division of Wildlife in 

cooperation with U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Region: 43‐71.

Canfield, J.E.; Lyon, L.J.; Hillis, J.M.; Thompson, M.J. 1999. 

Ungulates. In: Joslin, G.; Youmans, H., coordinators. 

Montana Chapter of The Wildlife Society: Effect of 

recreation on Rocky Mountain wildlife: A review for 

Montana, Committee on Effects of Recreation on Wildlife, 

http://www.montanatws.org. 6.1‐6.25.

Cassirer, E.F., Freddy, D.J.; Ables, E.D. 1992. Elk responses 

to disturbance by cross-country skiers in Yellowstone 

National Park. Wildlife Society Bulletin 20:375-381.

Clutton-Brock, T.H.; Guinness, F.E.; Albon, S.D. 1982. Red 

Deer: Behavior and ecology of two sexes. Chicago IL: 

University of Chicago Press.

Conner, M.M.; White, G.C.; Freddy, D.J. 2001. Elk movement 

in response to early-season hunting in northwest Colorado. 

Journal of Wildlife Management 65:926-940.

Cook, J.G.; Irwin, L.L.; Bryant, L.D.; Riggs, R.A.; Thomas, 

J.W. 1998. Relations of forest cover and condition of elk: A 

test of the thermal cover hypothesis in summer and winter. 

Wildlife Monographs No. 141.

Craighead, J.J.; Craighead, F.C.; Ruff, R.L.; O’Gara, B.W. 

1973. Home ranges and activity patterns of non-migratory 

elk of the Madison drainage herd as determined by 

bio‐telemetry. Wildlife Monographs No. 33.

ESRI. 1999. ARC/INFO. Redlands, CA: Environmental 

Systems Research Institute, Inc.

ESRI. 2001. ArcMap 8.1. Redlands, CA: Environmental 

Systems Research Institute, Inc.

Fretwell, S.D.; Lucas, H.L., Jr. 1969. On territorial behavior 

and other factors influencing habitat distribution in birds: 

Theoretical development. Acta Biometrica 19:16‐36.

Hoffman, G.R.; Alexander, R.R. 1987. Forest vegetation 

types of the Black Hills National Forest of South Dakota 

and Wyoming: A habitat type classification. Research 

Paper RM‐276. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Forest and 

Range Experiment Station.

Holthausen, R.S.; Wisdom, M.J.; Pierce, J.; Edwards, D.K.; 

and Rowland, M.M. 1994. Using expert opinion to evaluate 

a habitat effectiveness model for elk in western Oregon and 

Washington. Research Paper PNW‐RP‐479. Portland, OR: 

U.S. Department of Agriculture, Forest Service, Pacific 

Northwest Research Station.

Hurley, M.A.; Sargeant, G.A. 1991. Effects of hunting and 

land management on elk habitat use, movement patterns, 

and mortality in western Montana. In: Christensen, A.G.; 

Lyon, L.J.; Lonner, T.N., compilers. Bozeman, MT: 

Proceedings of elk vulnerability symposium. Montana 

State University.

Jones, P.F.; Hudson, R.J. 2002. Winter habitat selection at 

three spatial scales by American elk, Cervus elaphus,

in west‐central Alberta. Canadian Field Naturalist 

116:183‐191.

Juntti, T.M.; Rumble, M.A. 2006. Arc habitat suitability 

index computer software. General Technical Report 

RMRS‐GTR‐180WWW. Fort Collins, CO: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research 

Station. 31 p.

Laymon, S.A.; Barrett, R.H. 1984. Developing and testing 

habitat-capability models: Pitfalls and recommendations. 

In: Verner, J.; Morrison, M.L.; Ralph, C.J., editors. 

Wildlife 2000: Modeling habitat relationships of terrestrial 

vertebrates. Madison WI: University of Wisconsin Press: 

87‐91.

Lyon, L.J. 1983. Road density models describing habitat 

effectiveness for elk. Journal of Forestry 81:592-595.

Lyon, L.J.; Christensen, A.G. 1992. A partial glossary of elk 

management terms. General Technical Report INT‐288. 

Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Intermountain Research Station.

Manly, B.F.J.; McDonald, L.L.; Thomas, D.L.; McDonald, 

T.L.; Erickson, W.P. 2002. Resource selection by animals: 

statistical design and analysis for field studies. Dordrecht, 

Netherlands: Kluwer Academic Publishers.

McCullough, D.R. 1975. Modifications of the clover deer 

trap. California Fish and Game 61:242‐244.

Millspaugh, J.J.; Raedeke, K.J.; Brundige, G.C.; Willmott, 

C.C. 1998. Summer bed sites of elk (Cervus elaphus) 

in the Black Hills, SD: consideration for thermal cover 

management. American Midland Naturalist 139:133-140.

Millspaugh, J.J.; Brundige, G.C.; Gitzen, R.A.; Raedeke, K.J. 

2000. Elk and hunter space‐use sharing in South Dakota. 

Journal of Wildlife Management 64:994‐1003.

Ministry of Environment, Lands and Parks Resources 

Inventory. 1999. British Columbia Wildlife Habitat 
16 USDA Forest Service RMRS-RP-64. 2007

Rating Standards. British Columbia, Canada: Terrestrial 

Ecosystems Task Force Resources Inventory Committee, 

http://srmwww.gov.bc.ca/risc/pubs/teecolo/whrs/.

O’Neil, L.; Roberts, T.H.; Wakeley, J.S.; Teaford, J.W. 1988. 

A procedure to modify habitat suitability index models. 

Wildlife Society Bulletin: 16:33-36.

Orr, H.K. 1959. Precipitation and streamflow in the Black Hills. 

Station Paper No. 44. Fort Collins, CO. U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Forest and 

Range Experiment Station.

Parker, K.L.; Robbins, C.T.; Hanley, T.A. 1984. Energy 

expenditures for locomotion by mule deer and elk. Journal 

of Wildlife Management 48:474‐488.

Roloff, G.J.; Millspaugh, J.J.; Gitzen, R.A.; Brundige, 

G.C. 2001. Validation tests of a spatially explicit habitat 

effectiveness model for Rocky Mountain Elk. Journal of 

Wildlife Management 65:899‐914.

Rowland, M.M; Wisdom, M.J.; Johnson, B.K.; Kie, J.G.. 

2000. Elk distribution and modeling in relation to roads. 

Journal of Wildlife Management 64:672‐684.

Rumble, MA.; Benkobi, L.; Lindzey, F.; Gamo, R.S. 2001. 

Evaluating elk habitat interactions with GPS collars. In: 

Sibbald, A.M.; Gordon, I.J., editors. Aberdeen, Scotland, 

UK: Tracking animals with GPS. Macaulay Land Use 

Research Institute: 11‐17.

Rumble, M.A.; Benkobi, L.; Gamo, R.S. 2005. Elk responses 

to humans in a densely roaded area. Intermountain Journal 

of Science (1-2):10‐24.

Shamberger, M.L.; O’Neil, L.J. 1986. Concepts and Constraints 

of Habitat-model testing. In: Verner, J.; Morrison, M.L.; 

Ralph, C.J., editors. Wildlife 2000: Modeling habitat 

relationships of terrestrial vertebrates. Madison, WI: 

University of Wisconsin Press: 5-10.

Shepperd, W.D.; Battaglia, M.A. 2002. Ecology, silviculture 

and management of Black Hills, ponderosa pine. General 

Technical Report RMRS‐GTR‐97. Fort Collins, CO: 

U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 112 p.

SPSS. 2001. SPSS 11.3. Chicago, IL: SPSS, Inc.

Starfield, A.M. 1997. A pragmatic approach to modeling 

wildlife management. Journal of Wildlife Management 

61:261-270.

Thomas, J.W.; Black, H., Jr.; Scherzinger, R.J.; Pedersen, 

R.J. 1979. Chapter 8: Deer and elk. In: Thomas, J.W., ed. 

Wildlife habitats in managed forests, the Blue Mountains 

of Oregon and Washington. Agriculture Handbook No. 

553. Washington, DC: U.S. Department of Agriculture, 

Forest Service.

Thomas, J.W.; Leckenby, D.A.; Henjum, M.; Pederson, R.J.; 

Bryant, L.D. 1988. Habitat effectiveness index for elk on 

Blue Mountain winter ranges. General Technical Report 

PNW‐218. Portland, OR: U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station.

Thompson, M.J.; Henderson, R.E.; Lemke, T.H.; Sterling, 

B.A. 1989. Evaluation of a collapsible clover trap for elk. 

Wildlife Society Bulletin 17:287-290.

Ward, A.L.; Cupal, J.J. 1979. Telemetered heart rate of three 

elk as affected by activity and human disturbance. In: Shaw, 

J., ed. Symposium on dispersed recreation and natural 

resource management. Logan, UT: Utah State University, 

College of Natural Resources: 47‐56.

Wickstrom, M.L.; Robbins, C.T.; Hanley, T.A.; Spalinger, D.E.; 

Parish, S.M. 1984. Food intake and foraging energetics 

of elk and mule deer. Journal of Wildlife Management 

48:1285‐1301.

Wisdom, M.J.; Bright, L.R.; Carey, C.G.; Hines, W.W.; 

Peterson, R.J.; Smithey, D.A.; Thomas, J.W.; Witmer, G.W. 

1986. A model to evaluate elk habitat in western Oregon. 

Publication No. R6‐F&WL­216‐1986. Portland, OR: 

U.S. Department of Agriculture, Forest Service, Pacific 

Northwest Region.

Wisdom, M.J., Preisler, H.K.; Cimon, N.J.; Johnson, B.K. 

2004. Effects of off‐road recreation on mule deer and elk. 

Transactions of the North American Wildlife and Natural 

Resources Conference 69:531-550.

Wydeven, A.P; Dahlgren, R.B. 1983. Food habits of elk in 

the northern Great Plains. Journal of Wildlife Management 

47:916-923.
RMRS

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