United States

Department

of Agriculture

Forest Service

Rocky	Mountain

Research	Station

Research Paper

RMRS-RP-51

September 2004

Refi nement of the Arc-Habcap 

Model to Predict Habitat 

Effectiveness for Elk

Lakhdar Benkobi, Mark A. Rumble, 

Gary C. Brundige, Joshua J. Millspaugh
You may order additional copies of this publication by sending your 

mailing information in label form through one of the following media. 

Please specify the publication title and series number.

Fort Collins Service Center

Telephone (970) 498-1392

FAX (970) 498-1396

E-mail rschneider@fs.fed.us

Web site http://www.fs.fed.us/rm

Mailing address Publications Distribution

Rocky Mountain Research Station

240 West Prospect Road

Fort Collins, CO 80526

Benkobi, Lakhdar; Rumble, Mark A.; Brundige, Gary C.; Millspaugh, Joshua J. 2004. Refinement of the ArcHabcap model to predict habitat effectiveness for elk. Res. Pap. RMRS-RP-51. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. 18 p.

Abstract

Wildlife habitat modeling is increasingly important for managers who need to assess the effects of land management activities. We evaluated the performance of a spatially explicit deterministic habitat model (Arc-Habcap) 

that predicts habitat effectiveness for elk. We used five years of radio-telemetry locations of elk from Custer State 

Park (CSP), South Dakota, to test predicted habitat effectiveness by the model. Arc-Habcap forage and coverforage proximity components predicted elk distribution in CSP. However, the cover component failed to predict 

elk distribution in CSP. Habitat effectiveness calculated as the geometric mean of the model components failed 

to predict elk distribution and resulted in under-utilization of habitats predicted to be good and over-utilization of 

habitats predicted to be poor. We developed a new formula to calculate habitat effectiveness as an arithmetic 

average of the model components that weighted forage more than cover or cover-forage proximity. The new formula predicted actual elk distribution across categories of habitat effectiveness. Elk selected cover and forage 

areas ≤100 m from cover-forage edges. Arc-Habcap predicted that areas adjacent to roads were not usable by 

elk. Elk used areas adjacent to primary roads, but use was less than the proportional area comprised for primary roads, and about equal to proportional area adjacent to secondary roads and primitive roads. All sapling/pole 

and mature structural stages of ponderosa pine (Pinus ponderosa) were considered as both forage and cover 

by Arc-Habcap and consequently considered optimal in the cover-forage model component. We suggested revisions for both the cover-forage proximity component and areas adjacent to roads.

The Authors

Lakhdar Benkobi is a Range Scientist with the Department of Rangeland Ecosystem Sciences at Colorado 

State University, Fort Collins, CO.

Mark A. Rumble is a Research Wildlife Biologist with the USDA Forest Service, Rocky Mountain Research 

Station, Center for Great Plains Ecosystem Research in Rapid City, SD.

Gary C. Brundige is a Senior Wildlife Biologist with the South Dakota Department of Game, Fish, and Parks, 

Division of Custer State Parks in Custer, SD.

Joshua J. Millspaugh is an Assistant Professor with the Department of Fisheries and Wildlife at the University 

of Missouri in Columbia, MO.

Acknowledgments

This study was supported by the USDA Forest Service, Rocky Mountain Research Station in cooperation with 

Colorado State University (Agreement No 00-JV-11221609), Custer State Park, South Dakota State University, 

the University of Washington, and the Rocky Mountain Elk Foundation. Authors wish to thank Dr. Dennis Child 

(Head of Rangeland Ecosystem Science Department, Colorado State University) for help and support and Todd 

Mills, interdisciplinary GIS specialist at the Black Hills National Forest, for assistance with GIS.
Contents

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Arc-Habcap Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Study Area. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Forage effectiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Cover effectiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Cover-forage proximity effectiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Habitat effectiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Road effects on elk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Refinement of the Arc-Habcap 

Model to Predict Habitat 

Effectiveness for Elk

Lakhdar Benkobi

Mark A. Rumble

Gary C. Brundige

Joshua J. Millspaugh 

USDA Forest Service RMRS-RP-51. 2004 1

Introduction

Assessing the effects of land management activities, 

such as timber harvest on federal lands, is required by the 

National Environmental Policy Act (1969). It requires an 

assessment of changes in habitat conditions for certain species, which can be complex and expensive. Consequently, 

wildlife habitat models are often preferred. Several wildlifehabitat relationships models have been developed for forest 

managers to predict future distribution and abundance of a 

species given information on existing or future habitat conditions (Black and Scherzinger 1976; Schamberger et al. 1986; 

Lyon 1983; Wisdom et al. 1986; Morrison et al. 1992; Roloff 

1998; Rowland et al. 2000; Roloff et al. 2001). Some of these 

models are currently used in a wide range of applications. A 

comprehensive review of these habitat models and their concepts is provided in Holthausen et al. (1994).

A species of high interest in the Black Hills is Rocky 

Mountain elk (Cervus elaphus nelsoni). Because of the size, 

adaptability, and mobility of elk, there is substantial potential 

for conflicts between elk and other multiple-uses of public 

lands. Public interest in elk is high for recreational hunting, 

which can result in important contributions to local economies. To fulfill the requirements for assessing the impacts of 

silvicultural managements on elk and other wildlife species, 

the Black Hills National Forest (BHNF) developed a spreadsheet-based habitat capability (Habcap) model. This model 

was structured following concepts outlined by Hoover and 

Wills (1984) for managing forested lands for wildlife and 

by Wisdom et al. (1986) for predicting habitat effectiveness 

for elk. Habcap was then converted to a spatially explicit 

GIS-based model (Arc-Habcap) by Utah State University (unpublished). Since wildlife habitat models are simplifications 

of complex reality (Starfield 1997), their predictions should 

be validated using research studies designed to test and improve their reliability before making long-term commitments 

of resources (Laymon and Barret 1984; Shamberger and 

O’Neil 1986; O’Neil et al. 1988). Our objectives were to: (1) 

test the ability of Arc-Habcap to predict and display habitat effectiveness for elk, (2) identify bias or shortcomings in model 

outputs, and (3) make recommendations to improve predictions of habitat effectiveness for elk. Habitat effectiveness in 

this study does not equate to population densities that may 

be manipulated by hunting regulations; it is elk use of vegetation communities relative to the optimum local conditions 

(Wisdom et al. 1986) that provide food and cover for elk.

Arc-Habcap Model

Arc-Habcap is a deterministic GIS-based wildlife habitat 

model developed for USDA Forest Service, Rocky Mountain 

Region. The foundation for Arc-Habcap came from habitat capability models adapted to the Rocky Mountains (Hoover and 

Wills 1984) and the elk habitat effectiveness model developed 

by Wisdom et al. (1986) for western Oregon. Arc-Habcap assigns habitat condition quality indices (Wisdom et al. 1986) 

from 0 to 1.0 (0 = low condition and 1 = optimum condition) to land management units (stands) for forage and 

cover. Vegetation and roads GIS coverages are the input for 

Arc-Habcap. The model then predicts effectiveness of forage (FV), cover (CV), and cover-forage proximity (HDV), as 

well as effects of roads, on elk distributions. Calculations for 

predicting the effectiveness of forage-cover proximity are as 

follows. Each stand is classified as cover if CV ≥ 0.5; forage 

if FV ≥ 0.2; and both cover and forage if CV ≥ 0.5 and FV 

≥ 0.2. The model then creates buffer regions based on proximity to cover-forage edges. Areas of forage or cover occurring 

within 90 m from a cover-forage edge are assigned 1.0 for 

HDV. Forage areas 90 - 274 m from cover-forage edges are 

assigned HDV = 0.54. Cover areas 90 - 274 m from cover-forage edges are assigned HDV = 0.14. Forage or cover beyond 

274 m from cover-forage edges are considered unusable by 

elk and are assigned HDV = 0. Stands classified as both forage 

and cover are assigned HDV = 1.0.

Arc-Habcap model outputs include GIS coverage of the 

effectiveness of vegetative conditions for each component, 

FV, CV, and HDV. The effects of roads on the effectiveness 

of these vegetative conditions are also displayed in the GIS 

outputs (see below). The model outputs also include relational 

attribute tables displaying effectiveness values for FV, CV, 

and HDV. Habitat effectiveness (HE) for elk is calculated as 

a geometric mean of the three model components (FV x CV 

x HDV)1/3 (U.S. Fish and Wildlife Service 1981; Wisdom et 

al. 1986) and is also displayed in the GIS output coverage. 

Areas adjacent to roads are modeled as unsuitable for elk and 

are removed from the land base. The size of area considered 

ineffective habitat due to roads depends on road classes. Road 

classes are determined from estimates of vehicle traffic (primary roads have 30-35 vehicles/week; secondary roads have 

10-35 vehicles/week; and primitive roads have < 7 vehicles/

week). Effects of roads in Arc-Habcap extend 180 m from 

primary roads, 60 m from secondary roads, and 30 m from 

primitive roads.

Refinement of the Arc-Habcap Model to 

Predict Habitat Effectiveness for Elk

Lakhdar Benkobi, Mark A. Rumble, 

Gary C. Brundige, Joshua J. Millspaugh
2 USDA Forest Service RMRS-RP-51. 2004 

This habitat model assumes that: (1) habitat effectiveness for elk can be evaluated based on the amount, quality, and 

distribution of forage and cover; (2) cover and forage are optimally distributed when they are close to a cover-forage edge; 

and (3) roads have a negative effect on elk rendering a portion 

of the habitat unsuitable. The effects of land management on 

elk are evaluated by altering vegetation conditions of stands to 

reflect future conditions after management and comparing to 

model outputs.

Study Area

The area included in this evaluation of Arc-Habcap was 

Custer State Park (CSP), South Dakota. CSP is in the southeastern portion of the Black Hills. It includes 29,150 ha under 

the management jurisdiction of the Division of Custer State 

Park of South Dakota Department of Game, Fish, and Parks. 

CSP is bordered by private lands on the east, Black Hills 

National Forest on the west, Wind Cave National Park on 

the south, and Peter Norbeck Wildlife Preserve on the north. 

Elevations range from 1,137 to 2,083 m. Average annual 

precipitation is approximately 47 cm. Average monthly temperatures, during the coldest and warmest months (February 

and August), are - 4° C and 24° C respectively (NOAA 

1994).

Coniferous forests in CSP are mostly ponderosa pine 

(Pinus ponderosa Laws.). White spruce (Picea glauca

Moench) occurs on some north-facing slopes. Deciduous 

woodlands in the park usually occur in drainages and 

include quaking aspen (Populus tremuloides Michx.), paper birch (Betula papyrifera Marsh.), bur oak (Quercus 

macrocarpa Michx.), and green ash (Fraxinus pennsylvanica Marsh.). Common shrubs include common juniper 

(Juniperus communis L.), mountain mahogany (Cercocarpus 

montanus Raf.), and western snowberry (Symphoricarpos 

occidentalis). Grasslands are dominated by western wheatgrass (Agropyron smithii Rydb.), blue grama (Bouteloua 

gracilis H.B.K.), buffalo grass (Buchloë dactyloides Nutt.), 

sideoats grama (Bouteloua curtipendula Michx.), little 

bluestem (Schizachyrium scoparius Michx.), big bluestem 

(Andropogon gerardi Vitman), and Kentucky bluegrass (Poa 

pratensis). A large wildfire burned approximately 6,500 ha 

of CSP during the summer of 1988. Elk population in the 

park ranged from 750 to 1,000 and the population is regulated through permitted elk hunting (Millspaugh 1999). The 

park receives 1.7 million visitors/year, mostly during the 

summer. There are about 78 km of primary roads, 93 km of 

secondary roads, and 140 km of primitive roads in CSP.

Methods

To test the model, we used 12,067 locations of 21 female and 

15 male elk obtained by Millspaugh (1999) using standard VHF 

radio-telemetry techniques from 1993 to 1997. Information on 

animal trapping and radio transmitters is in Millspaugh et al. 

(1994). We created geographic coverages for the vegetation, 

roads, and radio-telemetry elk locations using ArcInfo 7.2.1 

(ESRI Inc. 1998). We reclassified a vegetation GIS coverage 

of CSP according to Buttery and Gillam (1984). The vegetation 

classification was based on vegetation types, structural stages 

(based on diameter-at-breast hight, DBH), and overstory canopy cover categories. Vegetation types were represented based 

on the dominant plant species of a stand. Structural stages 

for forest vegetation include grass-forb, shrub-seedling, sapling-pole (2.5 – 22.9 cm DBH), and mature (>22.9 cm DBH). 

There is an old growth structural stage, but it was not included 

in our study. Sapling-pole and mature stages include categories 

of overstory canopy cover: 0-40%, 41-70%, and >70%. Table 1 

shows the area in CSP for vegetation types and structural stages. Since Arc-Habcap does not include a category for fire-killed 

forest, we included these areas with the grass-forb structural 

stage of ponderosa pine. Tables 2 – 5 include summer and winter coefficients for vegetation structural stages that occurred 

in CSP. Vegetation and road coverages were used as input for 

Table 1—Area (ha) of vegetation types and structural stages used in the validation of Arc-Habcap predictions of elk use of habitats 

in Custer State Park, South Dakota.

Structural stages

Sapling pole (2.54 - 22.9 cm dbh) Mature (>22.9 cm dbh)

Grass/ Seedling/

Vegetation types forb shrub 0-40% 41-70% >70% 0-40% 41-70% >70%

Aspen 26

Bur oak 53

Grasslands 5,035

Other hardwoods 143

Rocky Mtn. juniper 19

Ponderosa pine 3,988 189 765 1,275 386 2,321 2,605 2,719

White spruce 101

Mountain mahogany 29

Other shrublands 161

Total 9,023 398 765 1,497 386 2,321 2,706 2,719
USDA Forest Service RMRS-RP-51. 2004 3

Table 2—Coefficients used as input in Arc-Habcap during summer for forage areas.

Structural stages

Sapling pole (2.54 - 22.9 cm dbh) Mature (>22.9 cm dbh)

Grass/ Seedling/

Vegetation types forb shrub 0-40% 41-70% >70% 0-40% 41-70% >70%

Aspen 1.0 1.0 1.0 1.0 0.5 1.0 1.0 0.5

Bur oak 1.0 1.0 1.0 0.5 0.2 1.0 0.5 0.2

Other hardwoods 1.0 1.0 1.0 0.5 0.2 1.0 0.5 0.2

Grasslands 1.0 NA NA NA NA NA NA NA

Rocky Mtn. juniper 0.5 0.5 0.5 0.2 0.2 0.5 0.2 0.2

Ponderosa pine 1.0 1.0 1.0 0.5 0.2 0.5 0.5 0.2

White spruce 1.0 1.0 1.0 0.5 0.2 0.5 0.2 0.2

Mountain mahogany 0.5 0.0 NA NA NA NA NA NA

Other shrubs 0.5 0.0 NA NA NA NA NA NA

Table 3—Coefficients used as input in Arc-Habcap during summer for cover areas.

Structural stages

Sapling pole (2.54 - 22.9 cm dbh) Mature (>22.9 cm dbh)

Grass/ Seedling/

Vegetation types forb shrub 0-40% 41-70% >70% 0-40% 41-70% >70%

Aspen 0.0 0.5 0.5 1.0 1.0 1.0 1.0 1.0

Bur oak 0.0 0.5 0.5 0.5 1.0 0.5 0.5 1.0

Other hardwoods 0.0 0.5 0.5 0.5 1.0 0.5 0.5 1.0

Grasslands 0.0 NA NA NA NA NA NA NA

Rocky Mtn. juniper 0.0 0.5 0.5 0.5 1.0 0.5 0.5 1.0

Ponderosa pine 0.0 0.0 0.5 0.5 1.0 0.5 0.5 1.0

White spruce 0.0 0.0 0.5 1.0 1.0 0.5 1.0 1.0

Mountain mahogany 0.0 0.0 NA NA NA NA NA NA

Other shrubs 0.0 0.0 NA NA NA NA NA NA

Arc-Habcap to obtain and display habitat effectiveness FV and 

CV, to calculate HDV for each stand, and to calculate HE. Then, 

we reclassified habitat effectiveness of stands as “good” if values were > 0.7; “fair” if the values were between 0.3 and 0.7; 

and “poor” if the values were < 0.3. Categories of HE were defined from frequency plots of HE values output by Arc-Habcap. 

There were three distinct groups (categories) of values (<0.3, 

0.3-0.7, and >0.7) in these plots.

GIS coverages of winter and summer elk locations were 

overlaid separately on the coverage of predicted effectiveness resulting from Arc-Habcap for FV, CV, HDV, and HE. 

Although Arc-Habcap removed the spatial components of 

areas adjacent to roads from the GIS coverages, we created 

a GIS-coverage for those areas and for corresponding elk 

locations. We also created 100-m band intervals from coverforage edges using ArcView 3.2 (ESRI Inc. 1999). Predicted 

use for each habitat category was calculated using the proportion of area in each habitat category multiplied times elk 

locations during summer or winter (Aebischer and Robertson 

1993).

Table 4—Coefficients used as input in Arc-Habcap during winter for forage areas.

Structural stages

Sapling pole (2.54 - 22.9 cm dbh) Mature (>22.9 cm dbh)

Grass/ Seedling/

Vegetation types forb shrub 0-40% 41-70% >70% 0-40% 41-70% >70%

Aspen 1.0 1.0 0.5 0.2 1.0 0.5 0.5 0.2

Bur oak 1.0 1.0 1.0 0.5 0.2 1.0 0.5 0.2

Other hardwoods 1.0 1.0 1.0 0.5 0.2 1.0 0.5 0.2

Grasslands 1.0 NA NA NA NA NA NA NA

Rocky Mtn. juniper 0.0 0.0 0.5 0.5 1.0 0.5 0.5 1.0

Ponderosa pine 1.0 1.0 1.0 0.5 0.2 1.0 0.5 0.2

White spruce 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Mountain mahogany 0.5 0.0 NA NA NA NA NA NA

Other shrubs 0.5 0.0 NA NA NA NA NA NA
4 USDA Forest Service RMRS-RP-51. 2004 

We used the Design 1 resource analysis selection (Manly 

et al. 1993) to test hypotheses that Arc-Habcap predicted 

observed elk distribution relative to habitat effectiveness 

categories for FV, CV, HDV, HE, and distance intervals 

from cover-forage edges. We tested for differences between 

predicted and observed use by elk of individual habitat effectiveness categories using Bonferroni’s correction to the 

probability of a 1 df χ2 test that the estimated selection ratio w 

= 1.0 (H0: w = 1.0, Manly et al. 1993).

Results

Ponderosa pine was the most common vegetation type in 

CSP. Grassland and the grass-forb structural stage of ponderosa pine resulted in classifying most of CSP as a herbaceous 

vegetation structural stage. There were 5,775 elk locations in 

CSP during summer (June-November) and 5,413 elk locations 

in CSP during the winter (December-May). Elk that occurred 

in areas adjacent to roads considered as ineffective elk habitat 

were not included in the analyses for FV, CV, HDV, and HE. The 

resulting sample sizes of elk locations for testing Arc-Habcap 

during summer and winter were 4,545 and 4,107, respectively.

Forage effectiveness

During summer, Arc-Habcap predicted that about 50% of 

CSP had good effectiveness for forage; 38% was classified 

as fair and 18% was classified as poor. Summer elk dispersion patterns differed from patterns predicted by the forage 

component in Arc-Habcap (χ2 = 20.65, P < 0.01) (figure 1, 

Table 6—Elk use (no. of locations) compared with expected use in areas classified by Arc-Habcap as good, fair, and poor 

habitats for elk in Custer State Park, South Dakota.

Predicted Area Observed Expected Selection Bonferroni adjusted

 effectiveness (ha) elk use elk use ratio p-value (H0: w = 1.0)

Summer

Forage effectiveness (χ2 = 20.91, P < 0.001)

Good 10,020 2,280 2,268 1.005 1.000

Fair 6,397 1,543 1,448 1.067 0.006

Poor 3,663 722 829 0.871 0.001

Cover effectiveness (χ2 = 5.25, P = 0.113)

Good 3,232 676 731 0.924 0.075

Fair 7,162 1,655 1,621 1.021 0.881

Poor 9,686 2,214 2,192 1.010 1.000

Winter

Forage effectiveness (χ2 = 285.14, P < 0.001)

Good 11,520 2,811 2,356 1.193 0.001

Fair 4,034 764 825 0.926 0.052

Poor 4,525 532 925 0.575 0.001

Cover effectiveness (χ2 = 66.42, P < 0.001)

Good 3,096 474 633 0.748 0.001

Fair 4,035 764 825 0.926 0.052

Poor 12,949 2,869 2,648 1.083 0.001

Table 5—Coefficients used as input in Arc-Habcap during winter for cover areas.

Structural stages

Sapling pole (2.54 - 22.9 cm dbh) Mature (>22.9 cm dbh)

Grass/ Seedling/

Vegetation types forb shrub 0-40% 41-70% >70% 0-40% 41-70% >70%

Aspen 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Bur oak 0.0 0.2 0.2 0.5 0.5 0.2 0.5 0.5

Other hardwoods 0.0 0.2 0.2 0.5 0.5 0.2 0.5 0.5

Grasslands 0.0 NA NA NA NA NA NA NA

Rocky Mtn. juniper 0.0 0.5 0.5 0.5 1.0 0.5 0.5 1.0

Ponderosa pine 0.0 0.0 0.2 0.5 1.0 0.2 0.5 1.0

White spruce 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Mountain mahogany 0.0 0.0 NA NA NA NA NA NA

Other shrubs 0.0 0.0 NA NA NA NA NA NA
USDA Forest Service RMRS-RP-51. 20045

Figure 1—Arc-Habcap model predictions of summer forage effectiveness 

(FV) for elk in Custer State Park, South Dakota.

Figure 2—Arc-Habcap model predictions of winter forage effectiveness 

(FV) for elk in Custer State Park, South Dakota.
6 USDA Forest Service RMRS-RP-51. 2004 

table 6). Selection ratios indicated elk used forage areas classified good as expected (w = 1.01, P = 1.0), areas classified 

fair more than expected (w = 1.07, P < 0.01), and areas classified poor less than expected (w = 0.87, P < 0.01). These 

selection ratios were all significantly different from each other 

(P < 0.01).

During winter, the area of CSP predicted to be good effectiveness for forage increased slightly relative to summer. 

Fifty-seven percent of CSP was good forage effectiveness for 

elk, while 20% and 23% were fair or poor, respectively (figure 

2, table 6). Dispersion patterns of elk among areas of winter 

forage categories differed (χ2 = 285.14, P < 0.01). Elk used 

areas predicted to be good forage effectiveness more than 

expected (w = 1.19, P < 0.05), used areas predicted to be fair 

forage effectiveness less than expected (w = 0.93, P > 0.05), 

and avoided areas predicted to be poor forage effectiveness 

(w = 0.58, P < 0.05). These selection ratios also differed from 

each other (P < 0.01).

Cover effectiveness

During summer, the model predicted 48% of CSP had 

poor cover effectiveness, 36% as fair cover, and only 16% as 

good cover effectiveness. Dispersion of elk during summer 

relative to cover was similar to that predicted by Arc-Habcap 

(χ2 = 5.25, P = 0.073) (figure 3, table 6). Elk may have selected areas of good cover effectiveness less than expected 

(w = 0.92, P = 0.08), but elk use of areas of fair or poor cover effectiveness did not differ from 1.0 (w = 1.02, P = 0.9; 

w = 1.01, P = 1.0). Selection ratios for fair and poor cover 

were similar (P = 1.0) but differed from the selection ratio for 

good cover (P < 0.01).

During winter, most of CSP was predicted as poor cover 

effectiveness for elk by Arc-Habcap. About 15% of CSP was 

predicted good cover effectiveness during winter, 20% fair, 

and 64% poor cover effectiveness for elk (figure 4, table 6). 

Dispersion patterns of elk among cover effectiveness categories during winter differed from those predicted by Arc-Habcap 

(χ2 = 66.42, P < 0.01). Elk avoided areas predicted to be good 

cover (w = 0.75, P < 0.01) and areas predicted to be fair also 

were used less than expected (w = 0.93, P = 0.05), but areas 

predicted to be poor cover were selected more than expected 

(w = 1.08, P < 0.01). Selection ratios for these cover effectiveness categories all differed from each other (P < 0.01).

Cover-forage proximity effectiveness

During summer, elk dispersion among coverforage proximity categories differed from that predicted by 

Arc-Habcap (χ2 = 281.67, P < 0.01). Forage and cover areas in CSP were well interspersed (figure 5). More than 70% of 

the park was classified as good effectiveness for cover-forage 

proximity (table 7). All sapling-pole and mature stages of ponderosa pine meet the criteria for being cover and forage (e.g., 

CV ≥ 0.5 and FV ≥ 0.2). Consequently, HDV for these stands 

was predicted to be 1.0. Elk used areas predicted as good and 

fair effectiveness for cover-forage proximity more than 

expected (w = 1.07, P < 0.01; and w = 1.20, P < 0.01). 

However, elk selected against areas predicted as poor 

effectiveness of cover-forage proximity (w = 0.46, P < 0.05) during summer (table 6). These selection ratios differed from each 

other (P < 0.01).

During winter, elk dispersion among cover-forage proximity categories differed significantly from that predicted by 

Arc-Habcap (χ2 = 162.82, P < 0.01). Arc-Habcap predicted 

that 56% of the park consisted of good effectiveness for cover-forage proximity, 20% was fair, and 24% was poor (figure 

6, table 7). Areas predicted to be good or fair effectiveness 

of forage-cover proximity were selected more than expected 

(w = 1.04 and 1.27, respectively, P < 0.05), while areas predicted to be poor effectiveness for cover-forage proximity 

were selected less than expected (w = 0.69, P < 0.01). All 

winter selection ratios for cover-forage proximity differed 

from each other (P < 0.01).

During summer (figure 7), forage areas within 400 m 

from cover are likely to be selected as expected (w = 0.9-1.1, 

P > 0.05). Selection ratios associated with forage areas >400 

m from cover suggested a non-significant avoidance (w < 0.73, 

P > 0.05). The significant difference between observed and expected use by elk of areas beyond 400 m from cover appeared 

to be precluded by the small sample size. Elk use of forage areas relative to cover-forage proximity was not random for the 

first 200 m (P < 0.05), but it became random beyond 200 m 

(P > 0.05). During summer, elk selected for cover within the 

first 100 m from forage more than expected (w = 1.21, P < 0.01). 

Elk generally used cover areas 200-400 m from forage equal to 

availability (w = 0.9 - 1.1, P > 0.14), and used cover >400 m 

from forage less than expected (w < 0.70, P < 0.08).

During winter elk generally stayed close to cover and forage edges (figure 8). Elk selected forage areas ≤ 100 m from 

cover more than expected (w = 1.1, P < 0.01). There appeared 

to be an overall avoidance of forage areas ≥ 200 m from cover, but not all were significant (w = 0.9 - 1.0, P = 0.03 – 1.0). 

Elk selected cover areas ≤ 100 m from forage more than expected (w = 1.38, P < 0.01). Beyond 100 m from forage, elk 

used cover areas less than expected, but not all selection ratios 

were significantly different (w ≤ 0.9, P = <0.01 – 0.3).

Habitat effectiveness

During summer, Arc-Habcap predicted that habitat effectiveness (HE) for elk was fair or poor for most of CSP. Only 

4% of CSP was classified as good HE for elk (figure 9a and 

9b, table 7). Elk dispersion patterns differed from predicted 

HE by the model (χ2 = 24.45, P < 0.01). Elk selected areas 

classified as good HE less than expected (w = 0.66, P < 0.01), 

but selected areas predicted as fair and poor HE as expected 

(w = 1.0, P > 0.7). Selection ratios for fair and poor HE were 

similar (P > 0.05), but both differed from that of good HE 

(P < 0.05). During winter, Arc-Habcap classified 20% of 

the park as good HE for elk, 27% as fair, and 53% as poor 

(figure 10a and 10b, table 7). Elk dispersion patterns among 

categories of HE differed from predicted use (χ2 = 113.49, 

P < 0.01). Elk used areas predicted to be good and fair HE 

less than expected (w = 0.77 - 0.93, P = 0.05), but used areas of poor HE more than expected (w = 1.15, P < 0.01). 

Winter selection ratios for HE all differed from each other 

(P < 0.05).
USDA Forest Service RMRS-RP-51. 20047

Figure 3—Arc-Habcap model predictions of summer cover effectiveness 

(CV) for elk in Custer State Park, South Dakota.

Figure 4—Arc-Habcap model predictions of winter cover effectiveness 

(CV) for elk in Custer State Park, South Dakota.
8 USDA Forest Service RMRS-RP-51. 2004 

Figure 5—Arc-Habcap model predictions of cover-forage distribution effectiveness (HDV) for elk during summer in Custer State Park, South 

Dakota.

Figure 6—Arc-Habcap model predictions of cover-forage distribution effectiveness (HDV) for elk during winter in Custer State Park, South Dakota.
USDA Forest Service RMRS-RP-51. 2004 9

�

���

���

���

���

����

����

����

��� ��� ��� ��� ��� ��� ��� ��� ��� ��� ��� ��� ��� ��� ���

������� ������������

100-m distance intervals from cover-forage edges

Elk observations

Distance into forage Distance into cover

� � � � �

Figure 7—Elk use of areas 

within 100-m intervals 

from cover-forage edges during the summer 

as compared to expected use in Custer State 

Park, South Dakota. 

Bars in each pair with an 

asterisk are significantly 

different (P < 0.05).

�

���

���

���

���

����

����

����

��� ��� ��� ��� ��� ��� ��� ��� ��� ��� ��� ��� ��� ��� ���

Number of winter elk locations

���������������� ���������������������

� � � � � � � � � �

Distance into forage Distance into cover

100-m distance intervals from cover-forage edges

Figure 8—Elk use of areas 

within 100-m intervals 

from cover-forage edges during the winter as 

compared to expected 

use in Custer State 

Park, South Dakota. 

Bars in each pair with an 

asterisk are significantly 

different (P < 0.05).
1

0 USDA Forest Service RMRS-RP-51. 2004 

Figure 9a—Arc-Habcap model predictions of summer habitat effectiveness (HE) for elk in Custer State Park, South Dakota. Figure 9b—Arc-Habcap model predictions of summer habitat effectiveness (HE) for elk along with radio-telemetry elk locations in Custer 

State Park, South Dakota.
USDA Forest Service RMRS-RP-51. 200411

Figure 10a—Arc-Habcap model predictions of winter habitat effectiveness 

(HE) for elk in Custer State Park, South Dakota.

Figure 10b—Arc-Habcap model predictions of winter habitat effectiveness 

(HE) for elk along with radio-telemetry elk locations in Custer State Park, South Dakota.
12 USDA Forest Service RMRS-RP-51. 2004 

Table 7—Elk use (no. of locations) compared with expected use in areas classified by Arc-Habcap as good, fair, and poor 

habitats for elk in Custer State Park, South Dakota.

Predicted Area Observed Expected Selection Bonferroni adjusted

 effectiveness (ha) elk use elk use ratio p-value (H0: w = 1.0)

Summer

Forage-cover distribution (χ2 = 281.67, P < 0.001)

Good 14,152 3,426 3,203 1.069 0.001

Fair 3,013 816 682 1.196 0.001

Poor 2,914 303 660 0.459 0.001

Habitat effectiveness (χ2 = 24.45, P < 0.001)

Good 790 118 179 0.661 0.001

Fair 9,603 2,213 2,174 1.018 0.729

Poor 9,686 2,214 2,192 1.015 1.000

Winter

Forage-cover distribution (χ2 = 162.82, P < 0.001)

Good 11,256 2,391 2,302 1.038 0.016

Fair 3,963 1,028 810 1.268 0.001

Poor 4,860 688 994 0.692 0.001

Habitat effectiveness (χ2 = 113.49, P < 0.001)

Good 4,035 764 825 0.926 0.051

Fair 5,414 851 1,107 0.769 0.001

Poor 10,631 2,492 2,174 1.146 0.001

Road effects on elk

Approximately 24% of CSP was predicted by Arc-Habcap 

as ineffective habitat for elk due to the proximity of roads. 

Elk used these areas of ineffective habitat in nearly the 

same proportion as the area they comprised in CSP. Areas 

adjacent to roads comprised 21% of elk locations during 

summer (figure 11) and 24% of elk locations during winter (figure 12). During summer, elk used areas adjacent to 

primary roads less than expected (w = 0.77, P < 0.01) and 

used areas adjacent to primitive roads more than expected 

(w = 1.41, P < 0.01, table 8). Selection ratios for primary 

and secondary roads during summer were similar, but differed from the selection ratio for primitive roads (P < 0.01). 

During winter, elk used areas adjacent to primary and 

secondary roads less than expected (P < 0.01) and areas 

adjacent to primitive roads more than expected (P < 0.01). 

All selection ratios associated with road categories during 

winter differed (P < 0.01). Negative effects of primary roads 

on elk extended to 350 m during summer and 60 m for secondary roads; primitive roads did not appear to negatively 

affect elk during winter (figure 13, table 8). Negative effects 

of primary roads on elk extended to 300 m during winter. 

The negative effect of secondary roads during winter was 

constrained to 60 m and primitive roads had no real effects 

on elk dispersion patterns (figure 14, table 8).

Discussion

The primary improvement of Arc-Habcap over previous Habcap models was the ability to spatially evaluate the 

proximity of cover and forage and incorporate their juxtaposition effects into the calculation of habitat quality. Another 

improvement is the ability to display the model output in a 

GIS map. Based on Arc-Habcap model predictions, CSP was 

better foraging habitat than cover habitat. We did not know 

the actual activity of elk so we assumed feeding and bedding (cover) were equally distributed among radio-telemetry 

locations. Correctly assigning elk activity was not critical to 

our hypothesis that predictions of habitat effectiveness with 

Arc-Habcap would reflect dispersion patterns of elk across 

the landscape in CSP. Because the elk population in CSP was 

managed below elk carrying capacity, elk should preferentially select good habitats or avoid poor habitats (Fretwell 1973), 

thus significant deviations from the proportional use would 

be expected from a model that accurately depicts elk distributions (e.g., elk would use areas predicted as good more than 

expected and poor less than expected).

Predictions of Arc-Habcap suggested that some components of the model reflected elk responses to habitat conditions. 

Predicted proportional use for FV and HDV generally reflected elk dispersion patterns we expected in CSP. However, the 

model performed poorly for CV and HE. The greater selection ratio for forage habitat during winter versus summer was 

consistent with our expectations as quality and quantity of forage are more likely to limit elk during the winter dormancy of 

grasses than during summer (Thomas et al. 1988).

Arc-Habcap restructured spatial land units into smaller 

units and assigned HDV values based on the proximity of 

cover and forage. Our data support a slightly different decline 

in effectiveness of the HDV coefficients than were reported by 

Wisdom et al. (1986) and Thomas et al. (1988). By examining 

elk locations in 100-m intervals from cover-forage edges, new 
USDA Forest Service RMRS-RP-51. 2004 13

Figure 11—Summer elk locations relative 

to open roads and ineffective areas due 

to roads in Custer State Park, South 

Dakota.

Figure 12—Winter elk locations relative 

to open roads and ineffective areas 

due to roads in Custer State Park, 

South Dakota.
14 USDA Forest Service RMRS-RP-51. 2004 

HDV coefficients were calculated from derivative estimates at 

the midpoint between two distance intervals. We recommend 

an HDV of 1.0 for areas 100 m or less from cover-forage 

edges, 0.5 for areas between 100 and 200 m, 0.1 for areas 

between 200 and 500 m, and 0.01 for areas beyond 500 m 

from forage-cover edges. Elk use some habitats as both forage and cover (Wisdom et al. 1986), but the characteristics of 

these habitats need quantification such as kind and amount of 

the herbaceous or shrub vegetation. A problem associated with 

Arc-Habcap was that all structural stages of ponderosa pine > 

2.5 cm dbh qualified as forage and cover (HDV = 1.0) except 

“old-growth” during summer and those structural stages with 

over-story canopy cover ≤ 40% during winter. As a result, 

some forest stands fell under the category described above 

despite the lack of adequate amount of forage. Consequently, 

we recommend redefining the vegetation conditions for stands 

that provide both forage and cover for elk.

Predictions of HE by Arc-Habcap did not depict elk dispersion patterns in CSP. Wisdom et al. (1986) advocated 

the geometric mean as a method that integrates all components of the habitat model with equal weight, and the 

geometric mean appeared to best represent their expectations of elk responses to habitat conditions. The geometric 

mean method of calculating HE resulted in most of CSP 

predicted to be poor for elk. Areas where vegetation conditions resulted in coefficients of 0 for FV, CV, or HDV, 

resulted in HE = 0. Approximately 40% of elk locations 

occurred in areas predicted as poor HE. A potential solution would be not to allow HE coefficients of 0. We believe 

there is value in allowing some coefficients to be 0 without 

driving HE to 0. Therefore, we recommend an alternative 

method for calculating the HE. The formula we propose was 

based on elk use of areas of good foraging habitat from the 

data and calculates a weighted average using forage, cover, 

and cover-forage proximity [HE = (3FV+CV+HDV)/5]. We 

selected a weight of 3 for FV because we found 3-6 times 

more use of good forage habitats than good cover habitats, 

and we believe that elk can compensate better for lack of 

cover with good forage than the reverse. Some biologists 

suggest that thermal cover is important to elk during winter 

(e.g., Hoover and Wills 1984; Wisdom et al. 1986), but others have questioned its importance (Hobbs 1989; Cook et al. 

1998). The original occupation of grassland biomes by elk 

in western North America (Guthrie 1966; Bryant and Maser 

1982) tends to support our contention that forage is more 

important than cover. Calculations of HE using the weighted 

average in CSP resulted in model predictions that met our 

expectations of how elk used good and fair habitat categories 

better than when we used geometric mean to calculate HE. 

Using the geometric mean method, good and fair HE habitats were under utilized and poor HE habitats were heavily 

used (figure15a and 15b). The weighted average formula allows inclusion of land units with coefficients of 0 for any of 

the 3 components and also allows for compensation among 

components of the model. The rationale for using a geometric average by Wisdom et al. (1986) was that it allowed for 

compensation among model components.

Roads might be one of the best predictors of elk dispersion (Lyon 1984). Areas immediately adjacent to primary and 

secondary roads had relatively less elk use of habitats than areas farther away from roads, in CSP. The number of observed 

elk next to roads tends to be lower than expected up to 300 m 

from roads. This avoidance of areas adjacent to primary and 

secondary roads was evident despite the apparent attraction of 

elk at night during late summer to roadside management areas 

for feeding (e.g., Millspaugh 1999). The negative influence 

on elk noted around primary roads extended beyond the 180 

m (current buffer distance in Arc-Habcap) to approximately 

300 - 350 m. The negative influence of secondary roads extended to 60 m, the current buffer distance in Arc-Habcap. 

Primitive roads had very little effect on elk dispersion patterns in CSP. Eliminating areas adjacent to roads as ineffective 

habitat as predicted by Arc-Habcap was not supported in our 

data or the literature. Lyon (1979) suggested that roads alone 

would not reduce habitat effectiveness for elk below 10-15%. 

Elk use within 50 m of primary roads was approximately 60% 

Table 8—Elk use (no. of locations) of areas adjacent to roads as compared with expected use. These areas were ineffective 

habitats for elk by Arc-Habcap due to proximity to roads in Custer State Park, South Dakota1.

Road Area Observed Expected Selection Bonferroni adjusted

 category (ha) elk use elk use ratio p-value (H0: w = 1.0)

Summer

(χ2 = 104.58, P < 0.001)

Primary 3,780 524 681 0.770 0.001

Secondary 734 116 132 0.878 0.410

Primitive 2,317 590 417 1.413 0.001

Winter

(χ2 = 237.13, P < 0.001)

Primary 3,780 521 723 0.721 0.001

Secondary 734 74 140 0.527 0.001

Primitive 2,317 711 433 1.605 0.001

1

 Areas adjacent to primary roads extended 180 m, areas adjacent to secondary roads extended 60 m, and areas adjacent to primitive roads 

extended 30 m.
USDA Forest Service RMRS-RP-51. 2004 15

of that expected. Negative effects of roads on elk were not evident beyond 300-350 m in CSP. However, elk in parks may not 

perceive roads the same as outside park boundaries. Rowland 

et al. (2000) reported increasing habitat selection ratios as distance from roads increased at least as far as 1.6 km. Future 

investigations on the effects of roads on elk can be studied in 

areas with high density of roads (> 2 km/square mile), using 

research designs that consider areas with road closures, open 

roads, and no roads, so that effects of traffic volume and noise 

can be measured and studied at several periods of the year.

Conclusion

The ability to develop spatial displays of elk habitat in 

GIS is an attractive feature. However, there are substantial 

modifications necessary for the Arc-Habcap model to reflect 

elk dispersion patterns in CSP. A simple modification of the 

formula calculating habitat effectiveness resulted in a better habitat classification that met our expectation of how 

elk should use habitats. Good and fair habitat categories received most of the use as compared to the geometric mean 

�� ��� ��� ��� ��� ��� ��� ��� ��� ���

�

��

��

��

��

�� �� �� �� �� �� �� �� �� ���

�������������

��

���

���

���

���

���

� �� �� �� �� �� �� �� �� ��

�������������

�������������������������������

�������� ��������

���

���

���

���

���

���

���

���������������������������������

���

���������������������������������

�������������

Figure 13—Elk summer use plotted 

against expected use along distance 

intervals from roads primary (a), secondary (b), and primitive (c).
16 USDA Forest Service RMRS-RP-51. 2004 

Figure 14—Elk winter use plotted against 

expected use along distance intervals 

from roads (primary (a), secondary (b), 

and primitive (c).

classification where the poor categories received most of the 

use by elk. With recommended modifications, Arc-Habcap 

will be a useful deterministic tool for biologists and managers.

Literature Cited

Aebischer, N. J.; Robertson, P. A.; and Kenward, R. A. 1993. 

Compositional analysis of habitat use from animal radio-tracking 

data. Ecology. 74: 1313-1325.

Black, H.; Scherzinger, R. J.; Thomas, J. W. 1976. Relationships 

of Rocky Mountain elk and Rocky Mountain mule deer habitat 

to timber management in the Blue Mountains of Oregon and 

Washington. In: Heib, S.R., ed. Elk-logging-roads symposium 

proceedings. December 16-17, 1975, Moscow, ID. University of 

Idaho. 11-31.

Bryant, L. D.; Maser, C. 1982. Classification and distribution. In: 

Thomas, J. W.; Toweill, D. E., eds. Elk of North America: ecology 

and management. Harrisburg, PA: Stackpole Books.

Buttery, R. F.; Gillam, B. C. 1983. Ecosystem descriptions. In: 

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

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

cooperation with U.S. Department of Agriculture Forest Service, 

Rocky Mountain Region. 34-71.

100

150

200

250

300

50 150 250 350 450 550 650 750 850 950

�

��

��

��

��

�� �� �� �� �� �� �� �� �� ���

��

���

���

���

���

���

� �� �� �� �� �� �� �� �� ��

�������� ��������

�������������

���

�������������������������������

�������������

���

���������������������������������

�������������

���

���������������������������������
USDA Forest Service RMRS-RP-51. 2004 17

Byers, C. R.; Steinhorst, R. K. 1984. Clarification of a technique 

for analysis of utilization-availability data. Journal of Wildlife 

Management. 48: 1050-1053.

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. 141.

ESRI, Environmental Systems Research Institute, Inc. 380 New York 

St., Redlands, CA 92373.

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.

Guthrie, R. D. 1966. The extent of wapiti in Alaska and the Yukon 

territory. Canadian Journal of Zoology. 44: 47-57.

Hobbs, N. T. 1989. Linking energy balance to survival in mule 

deer: development and test of a simulation model. Wildlife 

Monographs. 101.

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. Res. 

Pap. PNW-RP-479. Portland, OR: U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station. 16 p.

Hoover, R. L.; Wills, D. L., eds. 1984. Managing forested lands 

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

cooperation with U.S. Department of Agriculture Forest Service, 

Rocky Mountain Region. 459 p.

Layman, S. A.; Barrett, R. H. 1984. Developing and testing habitatcapability models: pitfalls and recommendations. In: Verner, 

J.; Morrison, M. L.; Ralph, C. J., eds. 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. 1984. Field tests of elk/timber coordination guidelines. 

Res. Pap. INT-325. Odgen, UT: U.S. Department of Agriculture, 

Intermountain Forest and Range Experiment Station. 10 p.

Manly, B.; McDonald, L.; Thomas, D. 1993. Resource selection by 

animals, statistical design and analysis for field studies. London, 

UK: Chapman and Hall. 177 p.

Millspaugh, J. J. 1999. Behavioral and physiological responses of elk 

to human disturbances in the southern Black Hills, South Dakota. 

Seattle, WA: University of Washington, Ph.D. dissertation. 260 p.

Millspaugh, J. J.; Brundige, G. C.; Jenks, J. A. 1994. Summer elk 

trapping in South Dakota. Prairie Naturalist. 26: 125-129.

NOAA. 1994. Climatological data, annual summary. Asheville, NC: 

National Oceanic and Atmospheric Climatic Data Center.

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.

Towry, R. K., Jr. 1984. Wildlife habitat requirements. In: Hoover, 

R. L; Wills, D. L., eds. Managing forested lands for wildlife. 

Denver, CO: Colorado Division of Wildlife in cooperation with 

Figure 15b—Comparison of geometric mean habitat effectiveness 

(HE) with a weighted average HE 

during winter in Custer State Park, 

South Dakota. Bars of elk use and 

expected use of habitats with an 

asterisk below are significantly different (P < 0.05).

Figure 15a—Comparison of geometric mean habitat effectiveness (HE) 

with a weighted average HE during summer in Custer State Park, 

South Dakota. Bars of elk use and 

expected use of habitats with an asterisk below are significantly different 

(P < 0.05).

�

���

����

����

����

����

����

����

���� ���� ���� ���� ���� ����

�������� ��������

�

���

����

����

����

����

����

����

���� ���� ���� ���� ���� ���� � �

� � � �

���

���

����������������� �������������������
18 USDA Forest Service RMRS-RP-51. 2004

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

Region. 73-209.

Roloff, G. J. 1998. Habitat potential model for Rocky Mountain elk. 

In: Devos J. C., Jr., ed. Proceedings of a deer/elk workshop 1997. 

Phoenix, AZ: Arizona Game and Fish Department. 158-175.

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.

Shamberger, M.; Farmer, A. H.; Terrell, J. W. 1982. Habitat suitability 

index models: introduction. FSW/OBS-82/10. Washington, D.C.: 

U.S. Department of Interior, Fish and Wildlife Service.

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

habitat-model testing. In: Verner, J.; Morrison, M. L.; Ralph, C. 

J., eds. Wildlife 2000: modeling habitat relationships of terrestrial 

vertebrates. Madison, WI: University of Wisconsin Press. 5-10.

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

management. Journal of Wildlife Management. 61: 261-270.

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. Gen. Tech. Rep. PNW-218. Portland, 

OR: U.S. Department of Agriculture, Pacific Northwest Research 

Station.

U.S. Department of Agriculture, Forest Service, Black Hills National 

Forest. Modeling philosophy: the purpose of Arc-Habcap. 

Unpublished report. Custer, SD: Black Hills National Forest. 

10 p.

U.S. Department of Interior, Fish and Wildlife Service. 1981. 

Standards for the development of habitat suitability index models, 

Washington, D.C.: 103 ESM Release 1-81. U.S. Department of 

Interior, Fish and Wildlife Service. 150 p.

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&WL-216-1986. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Region. 36 p.
Rocky Mountain Research Station

Natural Resources Research Center

2150 Centre Avenue, Building A

Fort Collins, CO 80526
The U.S. Department of Agriculture (USDA) prohibits discrimination 

in all its programs and activities on the basis of race, color, national 

origin, sex, religion, age, disability, political beliefs, sexual orientation, 

or marital or family status. (Not all prohibited bases apply to all 

programs.) Persons with disabilities who require alternative means for 

communication of program information (Braille, large print, audiotape, 

etc.) should contact USDA’s TARGET Center at (202) 720-2600 (voice 

and TDD).

To file a complaint of discrimination, write USDA, Director, Office 

of Civil Rights, Room 326 W, Whitten Building, 1400 Independence 

Avenue, SW, Washington, D.C. 20250-9410 or call (202) 720-5964 

(voice and TDD). USDA is an equal opportunity provider and employer.

The Rocky Mountain Research Station develops scientific 

information and technology to improve management, protection, 

and use of the forests and rangelands. Research is designed to 

meet the needs of the National Forest managers, Federal and 

State agencies, public and private organizations, academic 

institutions, industry, and individuals.

Studies accelerate solutions to problems involving 

ecosystems, range, forests, water, recreation, fire, resource 

inventory, land reclamation, community sustainability, forest 

engineering technology, multiple use economics, wildlife and fish 

habitat, and forest insects and diseases. Studies are conducted 

cooperatively, and applications may be found worldwide.

Research Locations

Flagstaff, Arizona Reno, Nevada

Fort Collins, Colorado* Albuquerque, New Mexico

Boise, Idaho Rapid City, South Dakota

Moscow, Idaho Logan, Utah

Bozeman, Montana Ogden, Utah

Missoula, Montana Provo, Utah

Lincoln, Nebraska Laramie, Wyoming

*Station Headquarters, Natural Resources Research Center, 

2150 Centre Avenue, Building A, Fort Collins, CO 80526.

RMRS

ROCKY MOUNTAIN RESEARCH STATION

Federal Recycling Program Printed on Recycled Paper