United States

Department of 

Agriculture

Forest Service

Northern 

Research Station

General Technical

Report NRS-4

Development of 

Landscape-level Habitat 

Suitability Models for 

Ten Wildlife Species in the 

Central Hardwoods Region

Chadwick D. Rittenhouse

William D. Dijak

Frank R. Thompson, III

Joshua J. Millspaugh
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ABSTRACT

Decades of studies on wildlife-habitat relationships have provided important insights into 

the habitat requisites for many game and nongame species. Many species of conservation 

or management importance are area or edge sensitive, or need interspersion of habitat 

requisites to maintain viable populations; however, most habitat suitability models do not 

incorporate spatial relationships or landscape attributes. Our objective was to develop 

landscape-level habitat suitability models for 10 species in the Central Hardwoods Region 

of the Midwestern United States: American woodcock (Scolopax minor), cerulean warbler 

(Dendroica cerulea), Henslow’s sparrow (Ammodramus henslowii), Indiana bat (Myotis 

sodalis), northern bobwhite (Colinus virginianus), ruffed grouse (Bonasa umbellus), timber 

rattlesnake (Crotalus horridus), wood thrush (Hylocichla mustelina), worm-eating warbler 

(Helmitheros vermivorus), and yellow-breasted chat (Icteria virens). All models included 

spatially explicit variables and relationships based on the best available empirical data and 

expert opinion. We provide an overview of habitat characteristics for each species, discuss 

the habitat variables used in each model, and provide supporting reference materials for all 

assumed relationships between quantity of a resource and quality for each species modeled. 

The models are included in a stand-alone software package, Landscape HSImodels version 

2.1, available from the U.S. Forest Service, Northern Research Station (www.nrs.fs.fed.

us/hsi). The HSI maps produced by the Landscape HSImodels software are readily displayed 

within GIS software (e.g., ArcView or ArcGIS). All models may be modified to address sitespecific habitat conditions and then applied to other regions. For example, the models may 

be used to identify priority areas for conservation or management. Additionally, the models 

may be applied to output from forest simulation software (e.g., LANDIS) and used to evaluate 

the effects of forest management alternatives in a planning context. As such, these models 

provide a general approach for evaluating habitat suitability at large spatial scales.

COVER PHOTOS

Top left, timber rattlesnake, courtesy of William E. Peterman, University of Missouri; top right, 

wood thrush, courtesy of Steve Maslowski, U.S. Fish and Wildlife Service; bottom left, ruffed 

grouse, USDA Forest Service “find-a-photo:” website; and bottom right, Indiana bat, courtesy 

of U.S. Fish and Wildlife Service.

Manuscript received for publication 29 December 2006

THE AUTHORS

CHADWICK D. RITTENHOUSE, Department of Fisheries and Wildlife Sciences, University of 

Missouri, 302 Anheuser-Busch Natural Resources Building, Columbia, MO 65211

WILLIAM D. DIJAK, USDA Forest Service, Northern Research Station, 202 Anheuser-Busch 

Natural Resources Building, Columbia, MO 65211

FRANK R. THOMPSON, III, USDA Forest Service, Northern Research Station, 202 

Anheuser-Busch Natural Resources Building, Columbia, MO 65211

JOSHUA J. MILLSPAUGH, Department of Fisheries and Wildlife Sciences, University of 

Missouri, 302 Anheuser-Busch Natural Resources Building, Columbia, MO 65211
Contents

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

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

 Area of Applicability and Test Landscape ..................................................2

 Primary Input Data ....................................................................................3

 GIS Methods and Spatial Relationships....................................................5

Model Development and Application.....................................................6

 Modeling Philosophy .................................................................................6

 American Woodcock .................................................................................6

 Cerulean Warbler ......................................................................................8

 Henslow’s Sparrow ................................................................................. 11

 Indiana Bat ..............................................................................................14

 Northern Bobwhite ..................................................................................17

 Ruffed Grouse .........................................................................................20

 Timber Rattlesnake .................................................................................24

 Wood Thrush ...........................................................................................27

 Worm-eating Warbler ..............................................................................30

 Yellow-breasted Chat ..............................................................................34

Conclusions ..............................................................................................36

Acknowledgments ...................................................................................37

Literature Cited .........................................................................................37
1

INTRODUCTION

Decades of studies on wildlife-habitat relationships have 

provided important insights into the habitat requisites 

for many game and nongame species. Information 

gained from these studies has been used to develop 

wildlife habitat models (e.g., habitat suitability index 

[HSI] models; U.S. Fish and Wildlife Serv. 1980, 

1981), the application of which enables assessment 

of current habitat conditions and predictions of how 

habitat suitability may change under management (e.g., 

habitat evaluation procedures; U.S. Fish and Wildlife 

Serv. 1980, 1981). Radio-telemetry (Rodgers 2001) and 

computing (e.g., geographic information systems; GIS) 

technology have enhanced our understanding of wildlifehabitat relationships, especially with regard to wildlife 

spatial ecology. We now recognize that many species of 

conservation or management importance are area or edge 

sensitive, or need interspersion of habitat requisites to 

maintain viable populations.

Habitat suitability index models (U.S. Fish and Wildlife 

Serv. 1980, 1981) remain a common approach for 

assessing wildlife habitat quality (Gustafson et al. 2001, 

Marzluff et al. 2002, Larson et al. 2003, Larson et al. 

2004). Habitat suitability index models evaluate the 

resource attributes considered important to a species’ 

abundance, survival, or reproduction. Habitat suitability 

is described by an empirical or assumed relationship 

between habitat quality and resource attributes on a 

relative scale that ranges from 0 (not suitable habitat) to 

1 (highly suitable habitat) (U.S. Fish and Wildlife Serv. 

1980, 1981). The HSI values can be visually presented as 

habitat suitability maps, which may then be used to make 

relative comparisons across management alternatives 

(Gustafson et al. 2001, Marzluff et al. 2002, Larson et 

al. 2004). Originally, these maps were summarized in 

terms of habitat units, which is the HSI value multiplied 

by a unit of area. In this way, habitat units became 

the currency for evaluating management alternatives 

in terms of the total amount of habitat lost or gained 

(U.S. Fish and Wildlife Serv. 1980, 1981; Klaus et al. 

2005). However, habitat occupancy depends not only 

on the HSI values but also on the composition and 

confi guration of habitat units. Thus, the spatial context 

of wildlife-habitat relationships should be incorporated 

in HSI models. The use of GIS technology facilitates 

inclusion of spatially explicit landscape attributes in HSI 

models.

Our objective was to develop landscape-level, GIS-based 

HSI models for 10 species in the Central Hardwoods 

Region of the Midwestern United States (Table 1). 

The species selected represent a range of habitat 

requirements (e.g., grassland, forest, disturbancedependent, and disturbance-sensitive) and management 

priorities (e.g., game species, Partners in Flight priority 

species, threatened and endangered species) in the 

Central Hardwoods Region. We developed the HSI 

models to evaluate breeding habitat suitability for 

migratory species and year-round habitat suitability for 

nonmigratory species. We based all models on the best 

available empirical data and expert opinion. All models 

incorporated spatially explicit variables and advances 

in the understanding of wildlife-habitat relationships 

Table 1.—Wildlife species selected for habitat suitability modeling in the Central Hardwoods Region and 

their management or conservation status

Species Scientifi c name Management or conservation description

American woodcock Scolopax minor Disturbance-dependent, migratory game bird

Cerulean warbler Dendroica cerulean Late-successional, area-sensitive songbird

Henslow’s sparrow Ammodramus henslowii Grassland-dependent, area- and edge-sensitive

 songbird

Indiana bat Myotis sodalis Snag- and cave-roosting bat, endangered species

Northern bobwhite Colinus virginianus Disturbance-dependent game bird

Ruffed grouse Bonasa umbellus Early successional forest-dependent game bird

Timber rattlesnake Crotalus horridus Threatened species

Wood thrush Hylocichla mustelina Forest-dependent songbird

Worm-eating warbler Helmitheros vermivorus Late-successional, fi re-sensitive songbird

Yellow-breasted chat Icteria virens Disturbance-dependent songbird
2

since the original models were developed. We provided 

an overview of important habitat characteristics for 

each species and discussed the habitat variables chosen 

for inclusion in the model. We provided supporting 

reference materials for all assumed relationships between 

the quantity of a resource and quality for that species. All 

models developed were included in a stand-alone software 

package, Landscape HSImodels version 2.1, (Dijak et al. 

In press) available from the U.S. Forest Service, Northern 

Research Station (www.nrs.fs.fed.us/hsi).

We purposefully developed HSI relationships for these 

models based on information available from GIS layers. 

Additionally, the HSI maps produced by the Landscape 

HSImodels software are readily displayed within GIS 

software (e.g., ArcView or ArcGIS). Thus, these models 

can be used for the evaluation of habitat suitability at 

large spatial scales. For example we used these models, 

coupled with appropriate GIS layers of future vegetative 

conditions under alternative forest management 

scenarios, to assist land managers and planners in the 

forest management planning process. Additionally, these 

models may be modifi ed to address site-specifi c habitat 

conditions and then applied to other regions. As such, 

these models provide a general approach to evaluating 

habitat quality and may be used to identify priority areas 

for conservation or management in addition to the effects 

of forest management.

METHODS

Area of Applicability and Test Landscape

We developed HSI models for application to the Central 

Hardwoods Region in the Midwestern United States. 

Defi nitions of the Central Hardwoods Region vary; we 

based our defi nition largely on Bailey’s (1996) ecoregional 

classifi cation system. We defi ned the Central Hardwoods 

Region as the Hot Continental Division (220) within the 

Humid Temperate Domain, excluding the mountainous 

portions (M220), and included the eastern portion of the 

Prairie Division (250) (Bailey 1996). The forested areas 

within this region are deciduous and contain primarily 

oak (Quercus spp.) and hickory (Carya spp.) forests, with 

some maple (Acer spp.), beech (Fagus spp.), mixed upland 

hardwoods, bottomland hardwoods, and lesser amounts 

of pine (Pinus spp.) and cedar (Juniperus virginiana). We 

demonstrated the HSI models on a landscape defi ned as 

the Patoka district of the Hoosier National Forest (HNF, 

Fig. 1). The Patoka district contained approximately 

10 0 10 20 30 40 Kilometers

Figure 1.—The Central Hardwoods Region of the Midwestern United States includes the Hoosier 

National Forest in Indiana. The 4,281 ha display area within the Patoka district is outlined.
3

26,868 ha. However, we restricted all fi gures presented 

to a smaller, 4,281 ha portion of the Patoka district 

(hereafter, test landscape) for optimal display resolution 

using a cell size of 10 m.

Primary Input Data

The HSI models required four different raster-based 

maps of information (Fig. 2): tree age, species of the 

dominant overstory trees, ecological land type, and 

land-cover type. Additional raster-based maps required 

for some of the species models will be explained 

within individual species account. Tree age and species 

information for the initial forest conditions may be 

obtained from forest inventories, interpreted from aerial 

photographs, or derived from satellite imagery (e.g., 

remote sensing). We used Forest Inventory Analysis (FIA) 

data, the HNF’s inventory database, land-use and landcover data, and Indiana GAP data to establish current 

forest conditions for the test landscape. We assigned 

tree ages (Fig. 2) for stands located on public lands by 

2 0 2 4 Kilometers

N

W E

S

Age

0

1-10

11-20

21-30

31-40

41-50

51-60

61-70

71-80

81-90

91-100

101-110

111-120

121-130

131-140

Age

0

1-10

11-20

21-30

31-40

41-50

51-60

61-70

71-80

81-90

91-100

101-110

111-120

121-130

131-140

Dominant Species

Non forest

Red Cedar

Pine

Sugar Maple

Red Maple

Hickory

American Beech

White & Green Ash

Yellow Poplar

Black Cherry

White Oak

Chestnut Oak

Red Oak

Shingle & Pin Oak

American Elm

Grassland

Dominant Species

Non forest

Red Cedar

Pine

Sugar Maple

Red Maple

Hickory

American Beech

White & Green Ash

Yellow Poplar

Black Cherry

White Oak

Chestnut Oak

Red Oak

Shingle & Pin Oak

American Elm

Grassland

Elt

Dry Ridges

S & W Slopes

Mesic Ridges

N & E Slopes

Bottomlands

Water

Elt

Dry Ridges

S & W Slopes

Mesic Ridges

N & E Slopes

Bottomlands

Water

Dry Ridges

S & W Slopes

Mesic Ridges

N & E Slopes

Bottomlands

Water

Land Cover

Forest

Cropland

Grassland

Water

Urban

Road

Land Cover

Forest

Cropland

Grassland

Water

Urban

Road

a. b.

c. d.

2 0 2 4 Kilometers

N

W E

S

Age

0

1-10

11-20

21-30

31-40

41-50

51-60

61-70

71-80

81-90

91-100

101-110

111-120

121-130

131-140

Age

0

1-10

11-20

21-30

31-40

41-50

51-60

61-70

71-80

81-90

91-100

101-110

111-120

121-130

131-140

Dominant Species

Non forest

Red Cedar

Pine

Sugar Maple

Red Maple

Hickory

American Beech

White & Green Ash

Yellow Poplar

Black Cherry

White Oak

Chestnut Oak

Red Oak

Shingle & Pin Oak

American Elm

Grassland

Dominant Species

Non forest

Red Cedar

Pine

Sugar Maple

Red Maple

Hickory

American Beech

White & Green Ash

Yellow Poplar

Black Cherry

White Oak

Chestnut Oak

Red Oak

Shingle & Pin Oak

American Elm

Grassland

Elt

Dry Ridges

S & W Slopes

Mesic Ridges

N & E Slopes

Bottomlands

Water

Elt

Dry Ridges

S & W Slopes

Mesic Ridges

N & E Slopes

Bottomlands

Water

Dry Ridges

S & W Slopes

Mesic Ridges

N & E Slopes

Bottomlands

Water

Land Cover

Forest

Cropland

Grassland

Water

Urban

Road

Land Cover

Forest

Cropland

Grassland

Water

Urban

Road

2 0 2 4 Kilometers

N

W E

S

Age

0

1-10

11-20

21-30

31-40

41-50

51-60

61-70

71-80

81-90

91-100

101-110

111-120

121-130

131-140

Age

0

1-10

11-20

21-30

31-40

41-50

51-60

61-70

71-80

81-90

91-100

101-110

111-120

121-130

131-140

Dominant Species

Non forest

Red Cedar

Pine

Sugar Maple

Red Maple

Hickory

American Beech

White & Green Ash

Yellow Poplar

Black Cherry

White Oak

Chestnut Oak

Red Oak

Shingle & Pin Oak

American Elm

Grassland

Dominant Species

Non forest

Red Cedar

Pine

Sugar Maple

Red Maple

Hickory

American Beech

White & Green Ash

Yellow Poplar

Black Cherry

White Oak

Chestnut Oak

Red Oak

Shingle & Pin Oak

American Elm

Grassland

Elt

Dry Ridges

S & W Slopes

Mesic Ridges

N & E Slopes

Bottomlands

Water

Elt

Dry Ridges

S & W Slopes

Mesic Ridges

N & E Slopes

Bottomlands

Water

Dry Ridges

S & W Slopes

Mesic Ridges

N & E Slopes

Bottomlands

Water

Land Cover

Forest

Cropland

Grassland

Water

Urban

Road

Land Cover

Forest

Cropland

Grassland

Water

Urban

Road

a. b.

c. d.

Figure 2.—Primary input data for landscape-level HSI models developed for the Central Hardwoods 

Region. Information included tree age (a), dominant overstory species (b), land-cover type (c), and 

ecological land type (d).
4

subtracting the year of stand origin from the year of 

analysis (2003). We identifi ed 14 different dominant 

overstory tree species (or species groups) and included 

two additional overstory types, nonforest and grassland, 

for a total of 16 different dominant overstory species 

(Table 2, Fig. 2). We used ecological land types (ELT) 

derived from 10-m Digital Elevation Model (DEM) 

layers1

. The ELT coding followed Van Kley et al. (1994) 

and grouped types by slope, aspect, and relative moisture 

(Table 3, Fig. 2).

We classifi ed land-cover type for public lands using the 

HNF forest type codes and for private lands using the 

land-use land-cover data digitized by the School of Public 

and Environmental Affairs, Indiana University, for the 

HNF, cross-referenced with the Indiana GAP data. The 

HNF forest type codes distinguished between 12 types 

of closed and open canopy forests or clearcuts, as well 

as croplands, grasslands (i.e., grassland, pasture, or hay 

fi elds), water, urban areas, and wetlands. We collapsed the 

HNF forest type codes into six general land-cover types: 

1

Created by Guafon Sho, Purdue University

Table 2.—Dominant overstory tree species (or species group) classifi cations for the Central 

Hardwoods Region

Species Code Name Species / Description

1 Nonforest cropland, urban areas, roads, or water

2 n/a species code not used

3 Eastern red cedar Juniperus virginiana

4 Pine Pinus echinata and P. strobus

5 Sugar maple Acer saccharum

6 Red maple Acer rubrum

7 Hickories Carya spp.

8 American beech Fagus grandifolia

9 White and green ash Fraxinus americana and F. pennsylvanica

10 Yellow poplar Liriodendron tulipifera

11 Black cherry Prunus serotina

12 White oak Quercus alba

13 Chestnut oak Q. prinus

14 Red oaks Q. rubra, Q. falcata, Q. velutina, and Q. coccinea

15 Shingle and pin oak Q. imbricaria and Q. palustris

16 American elm Ulmus americana

17 Grassland cool or warm season grassland, pasture, or hay fi elds

Table 3.—Ecological land type codes and descriptions for the Central Hardwoods Region

Code ELT Description

1 Dry ridges Summit or upper shoulder slope positions with ridgetops

 generally narrower than 75 m and slope gradient <15%.

2 South and west slopes Backslope positions with generally south aspect and slope

 gradient >15%.

4 Mesic ridges Summit or upper shoulder slope positions with broad, fl at

 ridgetops generally wider than 75 m and slope gradient <15%.

5 North and east slopes Backslope positions with generally north aspect and slope

 gradient >15%.

6 Bottomlands Bottomland positions along minor stream valleys and fl oodplains

 of minor streams

7 Water Water
5

1) forest; 2) croplands; 3) grasslands; 4) water; 5) urban 

areas; and 6) roads (Fig. 2) for use in the HSI models.

GIS Methods and Spatial Relationships

We modeled some wildlife species considered area or 

edge sensitive, or that use multiple habitat types to 

meet life-history requirements. Because these spatial 

relationships were common to many of the species’ 

models, we present the methodology for them here and 

address other requirements as needed within speciesspecifi c models. Area-sensitive species require a minimum 

area of contiguous habitat (i.e., a minimum patch size) 

for occupancy or breeding. We addressed minimum area 

requirements in two steps. First, we used a suitability 

index (SI) to identify cells containing suitable habitat 

based on tree age, tree species, ELT, or land-cover type. 

We used a patch-defi nition algorithm to aggregate cells 

of suitable habitat that were adjacent (i.e., horizontally, 

vertically or diagonally) to other cells of suitable habitat. 

Once aggregated, we then used a second SI to assign 

values to pixels based on the size of the habitat patch in 

which they occurred.

Edge-sensitive species may experience adverse effects due 

to edges, such as reduced survival, nest success, or nest 

density near habitat edges (Donovan et al. 1997, Winter 

et al. 2000, Woodward et al. 2001). In contrast, species 

such as the northern bobwhite (Colinus virginianus) 

use woody edges adjacent to croplands or grasslands as 

escape cover (Roseberry and Klimstra 1984, Williams 

et al. 2000). Thus, edge effects may be positive or 

negative, depending on the species. We defi ned a habitat 

edge as a change in land-cover type (i.e., grassland to 

cropland) or tree age (i.e., early successional forest to 

mature forest). We addressed edge sensitivity using two 

different approaches: a distance algorithm and a movingwindow analysis. The distance algorithm assigned SI 

values based on the distance of a cell to a habitat or 

landscape feature (i.e., roads) that defi ned a habitat edge. 

Because the distance algorithm assigned an SI value to 

each cell within the landscape, it was often the most 

computationally intensive step in the HSI models. We 

used a moving window for edge sensitivity when the 

effect was limited to adjacent cells; otherwise, we used 

the distance algorithm. The moving-window analysis 

adjusted the suitability of cells adjacent to habitat edges. 

For example, the Henslow’s sparrow (Ammodramus 

henslowii) is a grassland species that is sensitive to 

woody edges (Winter and Faaborg 1999, Winter et 

al. 2000, Bajema and Lima 2001). We applied the 

moving window to a previous SI that identifi ed patches 

of suitable grassland habitat. If the center cell of the 

moving window contained suitable grassland habitat and 

any cell within the radius of the window contained nongrassland habitat (e.g., forest, urban areas, or roads), the 

SI value of the center cell was reduced. In other words, 

a cell containing habitat that was otherwise suitable for 

Henslow’s sparrows had reduced suitability due to the 

cell’s proximity to nonsuitable habitat. We also used the 

moving-window analysis to assign suitability based on 

the composition or interspersion of habitats needed for 

life history requisites.

Some wildlife species have different habitat needs for 

different activities, such as foraging habitat separate 

from nesting habitat or escape cover. We used a 

moving-window analysis to assess the proportion 

of different habitat requisites within a defi ned area, 

typically the average home range size for a species. 

For example, northern bobwhites nest in grasslands, 

forage in cropland, and use woody edges for escape 

cover (Stoddard 1931, Roseberry and Klimstra 1984, 

Roseberry and Sudkamp 1998, Williams et al. 2000). 

We applied the moving window to previous SIs that 

identifi ed suitable grassland, cropland, and woody 

edges, respectively. We recoded each habitat type (e.g., 

grassland = 1, cropland = 2, and woody edges = 3) and 

determined the proportion of each of these three habitat 

requisites contained within the moving window. We 

assigned SI value based on the ideal proportion of these 

three habitat requisites. If all three habitat requisites were 

present within the window in the ideal proportion, the 

SI value of the center cell of the window was greatest 

(SI = 1.00). Otherwise, the SI value was reduced based 

on the difference between the ideal proportion and the 

observed proportion. If the window did not contain 

one of the three habitat requisites, the center cell 

received SI = 0.00. The fi nal HSI value represented the 

composite habitat-specifi c SI values modifi ed by the SI 

for composition.
6

MODEL DEVELOPMENT AND 

APPLICATION

Modeling Philosophy

These models were developed for the explicit purpose 

of assessing habitat suitability of large geographic areas 

(>1000 ha) at relatively high resolution (≤30 m cell 

size). Within the species-specifi c accounts, we defi ned 

suitable habitat as either breeding habitat or year-round 

habitat. The primary sources of information for these 

HSI models were extensive literature reviews and expert 

opinion. When available we used empirical data in the 

development of suitability relationships; however, the 

HSI approach in general is less reliant on empirical data 

for model application than approaches such as resource 

selection functions (Manly et al. 2002). In this way, HSI 

models may be applied to large landscapes without laborintensive fi eld data collection.

Wildlife species experts participated in all stages of model 

development including literature summaries, initial 

model development, model review and refi nement, and 

fi nal model approval. A Species Viability Evaluation 

Panel (SVE Panel) included species experts from state and 

federal agencies, the scientifi c research community and 

nongovernmental organizations. The group convened 

in 2002 as part of the Hoosier National Forest land 

management planning process to summarize relevant 

literature on habitat requirements and population 

status for species of conservation concern within the 

Central Hardwoods Region. Following this meeting 

we conducted additional literature reviews and created 

the initial HSI models. We presented the HSI models 

to the SVE Panel in January 2004. The SVE Panel 

suggested minor revisions to the avian species models 

and extensive revisions to the Indiana bat (Myotis sodalis) 

and the timber rattlesnake (Crotalus horridus) models. We 

incorporated all model revisions suggested by the SVE 

Panel. The SVE Panel approved all models in April 2004.

American Woodcock

Overview

The American woodcock (Scolopax minor) is a migratory 

game species confi ned to North America. Woodcocks 

breed in the eastern United States north to the boreal 

forest of Canada and winter in the southeastern United 

States (Keppie and Whiting 1994). Woodcock habitat 

requirements vary by gender, time of day, and season. 

During the breeding season, young to mid-age forests 

provide feeding and diurnal roosting sites for both sexes 

and nesting sites for females (Keppie and Whiting 1994). 

At night, males use open areas for display habitat and 

both sexes use open areas for nocturnal roosting sites. 

During the nonbreeding season, woodcocks use a variety 

of forests, including bottomland hardwoods and upland 

mixed pine-hardwoods (Keppie and Whiting 1994). 

An existing HSI model used small-shrub cover, largeshrub cover, sapling density, and basal area to identify 

woodcock diurnal habitat (Straw et al. 1986). Other 

studies on American woodcocks indicate that forest and 

mixed forest, agriculture, and developed areas provide 

habitat for nesting and brood rearing, feeding, and 

displaying (Keppie and Whiting 1994). Size of openings 

and the interspersion of forested and open areas are also 

important habitat features (Klute et al. 2000).

HSI model

We developed an American woodcock HSI model 

for breeding and migration habitat in the Central 

Hardwoods Region. The fi rst suitability index (SI1

) 

identifi ed tree species suitable for nest sites and diurnal 

cover. While on the wintering grounds woodcock 

use a variety of forests, including upland mixed pinehardwoods and mature longleaf pine that recently has 

been burned (Keppie and Whiting 1994). During 

breeding and migration, woodcock primarily use young 

deciduous forests for diurnal cover. We set SI1

 = 0.00 

if the dominant tree type was pine, cedar, nonforest, or 

grassland, and SI1

 = 1.00 otherwise. This designation 

zeroed out grasslands and nonforest areas that may 

be used for diurnal or roosting habitat. However, we 

assigned value to grasslands as display and roosting 

habitat in SI

3

. Therefore, the contribution of grasslands 

to the overall habitat suitability was retained in the 

model.

In the second suitability index (SI2

), we assigned 

suitability based on tree age and ELT. American 

woodcock use deciduous forests for nesting, foraging, 

and diurnal roosts (Keppie and Whiting 1994). Young 

to mid-age forests interspersed with openings provide 

nest sites and young brood habitat (Keppie and Whiting 

1994). Woodcock also nest and rear broods in fi eld/forest 
7

edges (Murphy and Thompson 1993). Young hardwoods 

and mixed woods with shrubs adjacent to openings 

provide moist ground for daytime feeding and diurnal 

cover (Hudgins et al. 1985, Keppie and Whiting 1994). 

Hudgins et al. (1985) characterized diurnal sites as having 

lower elevation and slope than random sites, possibly due 

to factors affecting food availability. In upland areas, sites 

used by young broods had greater soil moisture than nest 

sites (Murphy and Thompson 1993). We grouped ELTs 

to account for the infl uence of moisture on sites used 

by woodcocks. Mesic ridges, north and east slopes, and 

bottomlands constituted the mesic ELTs, and dry ridges 

and south and west slopes the dry ELTs. We assigned 

maximum suitability (SI2

 = 1.00) to stands 1-10 years of 

age on mesic sites and SI2

 = 0.00 to stands >40 years of 

age (Fig. 3). We used linear regression to assign suitability 

to stands 11-40 years of age:

2 SI age =− × 1.33 0.033

where age is the dominant tree age for a cell. For stands 

on dry ELTs, we multiplied the age function by 0.5 to 

reduce suitability value.

In the third suitability index (SI3

), we identifi ed open 

areas suitable for display, roosting, and nesting habitat. 

Male woodcock use open areas, including abandoned 

agricultural fi elds, forest gaps and cuts, meadows, 

pastures, orchards, bogs, and other natural clearings for 

aerial courtship displays (Keppie and Whiting 1994). 

Both male and female woodcock also use open areas for 

night roosts, but some woodcock remain in diurnal cover 

(e.g., forested) at dusk (Krohn 1971, Wishart and Bider 

1976) and some females move to different forested cover 

at dusk (Sepik and Derleth 1993). Female woodcock nest 

in hawthorn and crabapple fi elds (Liscinsky 1972) and 

shrubby old fi elds (Murphy and Thompson 1993). We 

assigned value to all cells 0-10 years of age based on ELT. 

For mesic ELTs, we assigned SI3

 = 0.30 and for dry ELTs 

we assigned SI3

 = 0.10. All cells with trees >10 years of 

age received SI3

 = 0.00. Because SI

3

 assigned value to all 

cells 0-10 years of age, including cells containing roads or 

urban areas, we used SI4

 to zero out these nontarget open 

areas. We assigned SI4

 = SI

3

 for grassland, cropland, and 

forest, otherwise SI4

 = 0.00. Therefore, suitability of open 

areas = SI

4

.

In the fi fth suitability index (SI5

), we assigned value 

based on the interspersion of young- to mid-age forest 

(SI2

) and open areas (SI4). Klute et al. (2000) compared 

known woodcock habitat to random areas using buffers 

of multiple spatial scales and found that used sites had 

higher interspersion of water, wetlands, and deciduous 

forest, with less agricultural and developed lands. 

Vegetative structure (e.g., tree density, basal area, edge 

height) and opening size also can be used to characterize 

breeding habitat (Gutzwiller et al. 1983). The median 

Tree age (years)

0 10 20 30 40 50

Suitability index value

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

Mesic Sites

Dry Sites

Figure 3.—American woodcock habitat 

suitability as a function of stand age and 

ecological land type. Suitability value (SI2

) 

on mesic sites (mesic ridges, north and 

east slopes, and bottomlands) = 1.00 at 

maximum (solid line). Suitability value 

(SI2

) on dry sites (dry ridges and south 

and west slopes) = 0.50 at maximum 

(dashed line).
8

distance between diurnal sites and singing grounds 

of singing males was 364 m (range = 50-964 m) in 

Pennsylvania (Hudgins et al. 1985), which is comparable 

to studies in Maine (Dunford and Owen 1973, Sepik 

and Derleth 1993). The quality of singing grounds may 

be determined by surrounding nesting and brood-rearing 

cover (Dwyer et al. 1983) because females do not move 

young broods far from the nest (Sepik et al. 1993) and 

nests often are located near display sites (Murphy and 

Thompson 1993). We used a moving window with a 

200-m radius, which corresponds to the median distance 

between diurnal sites and singing grounds and the average 

total home range size (15 ha, range 0.3-171 ha) for male 

woodcock in Pennsylvania (Hudgins et al. 1985). The 

ideal proportion of nesting, foraging, and display habitat 

is approximately 80 percent nest/forage (forest) to 20 

percent display (open).2

The fi nal habitat suitability value was the geometric mean 

of 1) maximum of SI1 × SI2

 (nesting and foraging) and SI4

(display); and 2) SI5

, the interspersion of these habitats:

2 124 5 HSI SI SI SI SI = ×× (max(( ), ) )

Application to test landscape

The fi rst suitability index identifi ed deciduous forest with 

interspersion of open areas (Fig. 4). The second suitability 

index, which assigned value based on tree age and ELT, 

identifi ed numerous forest patches with stands 1-40 years 

of age. The amount and type of disturbance (e.g., fi re, 

wind, and harvest) greatly infl uenced the suitability of 

deciduous forest. Suitability index 3 assigned value to all 

cells <10 years of age, including roads and urban areas, 

for display habitat. Suitability index 4 reclassifi ed SI

3

 and 

retained only grasslands, croplands, and young forest as 

display habitat. Suitability index 5 revealed areas with 

high interspersion of forest and open areas. The fi nal 

HSI map refl ected the locations of young forest and open 

areas.

Cerulean Warbler

Overview

The cerulean warbler (Dendroica cerulea) is a neotropical 

migratory bird that breeds in eastern North America and 

winters in northern South America. In North America, 

this species is found from April to September in large 

tracts of mature and second-growth forests with tall 

deciduous trees (Hamel 2000a). Because no previous HSI 

model existed for cerulean warblers, we developed a new 

model based on reported ecological relationships gathered 

from an extensive literature review.

HSI model

We developed a cerulean warbler HSI model for breeding 

habitat in the Central Hardwoods Region. The fi rst 

suitability index (SI1

) identifi ed suitable tree species for 

breeding habitat. Cerulean warblers use a variety of tree 

species for nesting throughout their range, including 

maple (Acer spp.), American beech (Fagus grandifolia), 

ash (Fraxinus spp.), black walnut (Juglans nigra), tulip 

poplar (Liriodendron tulipifera), oak (Quercus spp.), and 

elms (Ulmus spp.) (Table 3b in Hamel 2000b). Hamel 

(2000b) noted that G. Vanderah3

 located nests in pine 

trees in southern Illinois. However, Robbins et al. (1989) 

found a negative relationship between relative abundance 

of cerulean warblers and coniferous canopy cover in the 

Middle Atlantic states. Without published information 

on the use of conifers for breeding habitat, we identifi ed 

only deciduous trees as suitable. We believe this 

restriction provided a conservative estimate of breeding 

habitat suitability. We accomplished this by evaluating 

the dominant tree type for each cell and setting SI1

 = 

0.00 if the dominant tree type was pine or cedar, and SI1

= 1.00 otherwise.

In the second suitability index (SI2

), we assigned habitat 

quality based on forest age and ELT. Cerulean warblers 

breed in mature and second-growth forests with tall 

deciduous trees (Hamel 2000a). Habitats include wet 

bottomland, mesic slope, or upland (Hamel 2000b), 

ranging in elevation from 30-1000 m (Hamel 2000a), 

though cerulean warblers may occur in greater densities 

in fl oodplains or other mesic conditions (Lynch 1981, 

Garber et al. 1983, Kahl et al. 1985, Robbins et al. 

1992). Historical accounts indicate that cerulean warblers 

were found in both old-growth bottomland forests 

(Widmann 1895a, 1895b, 1897) and upland forests 

3

Illinois Natural History Survey, pers. comm. with Hamel, 

May 1993

2

S. Backs, Indiana Department of Natural Resources, pers. 

comm.
9

(Todd 1893, Torrey 1896, Schorger 1927). Presently, 

cerulean warblers often are associated with bottomland or 

fl oodplain forests, but this association may be due to the 

current forest distribution patterns and not necessarily 

due to a preference for bottomland over upland forest 

(Hamel 2000b). Recent studies indicate cerulean warblers 

use upland habitats and ridgetops as frequently as 

bottomland habitats (Rosenberg et al. 2000, Weakland 

and Wood 2002, Bosworth and Wood 2003, Nicholson 

2003).

In the U.S. Fish and Wildlife Service (USFWS) Region 

3, which includes the northern edge of the Central 

Hardwoods Region, cerulean warblers were more 

numerous in riparian bottomland forest (40 percent 

of birds detected) than mesic uplands (28 percent) or 

dry upland forest (21 percent) (Rosenberg et al. 2000). 

Within the Central Hardwoods Region, cerulean 

warblers use both upland and bottomland forests. 

Rosenberg et al. (2000) observed cerulean warblers in 

mesic upland, bottomland and lake margin habitats, and 

dry upland forest in Indiana. In southern Indiana, Basile 

and Islam (2001) reported almost exclusive use of ridges, 

but in earlier successional forest stages. In Ohio, cerulean 

warblers are associated with dry oak-hickory woodlots, 

mixed mesophytic forests, wet beech maple woodlands, 

1 0 1 2 3 4 Kilometers N

W E

S

1 0 1 2 3 4 Kilometers 

N

W E

S

0 0.6

0.1

0.2

0.3

0.4

0.5

0.7

0.8

0.9

1.0

SI Legend

SI 1 SI 2

SI 3 SI 4

SI 5 HSI

Figure 4.—American woodcock habitat suitability for a 4,281-ha portion of the Hoosier 

National Forest, Indiana.
10

and extensive fl oodplain forests (Peterjohn and Rice 

1991). In western Kentucky, cerulean warblers are found 

in mature, relatively undisturbed deciduous forests 

(Mengel 1965).

We used two different functions to assign suitability to 

trees on mesic and dry sites (Fig. 5). For trees on mesic 

sites, which included mesic ridges, north and east slopes, 

and bottomlands, we subjectively assigned a suitability 

value based on the equation:

( ) 2 (( 60.1799)/ 8.7242)

1.0104

1 treeage

SI

e − − = +

We developed this equation by fi tting a sigmoid function 

with SI2

 = 0.01 at 20 years of age, SI2 = 0.50 at 60 years 

of age, and SI2

 = 1.00 for ≥100 years of age. For trees on 

dry ridges, we subjectively assigned a suitability value 

based on the equation:

( ) 2 (( 60.2385)/ 9.1812)

0.8105

1 treeage

SI

e − − = +

We developed this equation by fi tting a sigmoid function 

such that SI2

 = 0.01 at 20 years of age, SI2 = 0.40 at 60 

years of age, and SI2

 = 0.80 for ≥100 years of age. All 

trees <20 years of age, as well as trees on south and west 

slopes, received SI2

 = 0.00.

In the third suitability index (SI3

), we established an 

area requirement by assigning a suitability value based 

on deciduous forest area (Fig. 6). The cerulean warbler 

is considered an area-sensitive species. Minimum patch 

sizes used by individuals varies by region, ranging 

from 10 ha in Ontario (Hamel 2000a), 20-30 ha in 

Ohio (Peterjohn and Rice 1991), 700 ha in Middle 

Atlantic states (Robbins et al. 1989), to 1600 ha in the 

Mississippi Alluvial Valley in Tennessee (Robbins et al. 

1992). Cerulean warbler population surveys conducted 

in USFWS Region 3 found 65 percent of the birds in 

patches >400 ha, 25 percent in patches 80-400 ha, and 

10 percent in patches <40 ha in size (Rosenberg et al. 

2000). Distinguishing between minimum patch size 

needed for occupancy and minimum patch size needed 

for breeding is important because the requirements may 

not be synonymous. In the Middle Atlantic states, 50 

percent occupancy occurs at 700 ha, with the maximum 

probability of occurrence at 3000 ha; however, 700 ha is 

the minimum area required for breeding (Robbins et al. 

1989). Hamel (1992) provided a minimum tract size of 

1750 ha, but it is not clear whether the requirement was 

for occupancy or breeding.

We distinguished between area requirements for 

occupancy and suitability for breeding. For breeding 

habitat suitability, we subjectively assigned SI3

 = 0.01 

for 100-ha forest patches, SI3

 = 0.10 for 700-ha patches 

Tree age (years)

0 20 40 60 80 100 120

Suitability index value

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

Mesic sites

Dry ridges

Figure 5.—Cerulean warbler habitat 

suitability for breeding as a function of stand 

age and ecological land type. Suitability 

value (SI2

) on mesic sites (mesic ridges, 

north and east slopes, and bottomlands) 

= 1.00 at maximum (solid line). Suitability 

value (SI2

) on dry ridges = 0.80 at maximum 

(dashed line).
11

(minimum area required for breeding) (Robbins et al. 

1989), and SI3

 = 1.00 for patches ≥3000 ha, and fi t a 

sigmoid function:

( ) 3 (( 1173.6472)/ 215.5805)

1.0002

1 patchsize SI

e − − = +

The fi nal habitat suitability value was the geometric mean 

of the three suitability indices:

3 HSI SI SI SI = ×× 123

Application to test landscape

Suitability index 2, which assigned value based on tree 

age and ELT, contributed greatly to the heterogeneous 

pattern observed in the fi nal habitat suitability map (Fig. 

7). Stand size, homogeneity of tree age within a stand, as 

well as delineation of stand boundaries will infl uence the 

pattern observed when SI2

 is applied to other landscapes. 

Suitability index 3, which assigned value based on 

forest patch size, treated continuous canopy gaps (e.g., 

roads, power lines, and railroads) as patch boundaries. 

We considered the fact that the density or proportion 

of forested cells within a patch may affect patch value. 

For example, nonforested areas contained within large 

forested patches may reduce patch value. Conversely, 

predominantly forested landscapes that contain roads 

may be undervalued if roads create patches. Weakland 

and Wood (2002) found that cerulean warblers did 

not avoid internal edges (such as natural canopy gaps, 

open-canopy or partially open-canopy roads). Thus, we 

assumed patch size was an appropriate and conservative 

measure of forest value and did not include such effects 

in this model.

Henslow’s Sparrow

Overview

The Henslow’s sparrow is a short-distance, migratory bird 

that breeds in east-central North America and winters in 

the southeastern United States. Henslow’s sparrows are a 

ground-nesting, obligate grassland species. Throughout 

their range the amount of habitat has declined from 

historic levels due to conversion of grasslands to intensive 

agricultural production, woody stem invasion (especially 

on abandoned agricultural lands), and fragmentation of 

remaining grasslands (Smith 1992). Henslow’s sparrows 

are considered both area and edge sensitive, which may 

intensify the effects of habitat loss and fragmentation. 

Although no previous HSI model existed for Henslow’s 

sparrows, several studies have described ecological 

relationships, including micro- and macro-habitat 

characteristics. We developed an HSI model based 

on reported ecological relationships and an extensive 

literature review. Primary sources for the literature review 

include Burhans (2002), Herkert et al. (2002), and 

references contained therein (e.g., Pruitt 1996).

Forest patch area (ha)

0 500 1000 1500 2000 2500 3000 3500

Suitability index value

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

Figure 6.—Cerulean warbler habitat suitability 

for breeding as a function of deciduous forest 

area. Suitability value (SI3

) = 0.10 at 700 ha 

(min. area required for breeding, Robbins et al. 

1989). Suitability value is maximum (= 1.00) 

for 3000 ha patches.
12

HSI model

We developed a Henslow’s sparrow HSI model for 

breeding habitat in the Central Hardwoods Region. 

The HSI model contained three suitability indices that 

addressed land-cover type, area sensitivity, and edge 

sensitivity. The fi rst suitability index (SI1

) identifi ed 

grasslands as breeding habitat. Henslow’s sparrows 

nest on the ground in grasslands, but are also found in 

hayfi elds, pastures, and meadows in the northeastern 

United States (Smith 1992). Both habitat structure 

and moisture are associated with Henslow’s sparrow 

occupancy of grassland sites. Key structural characteristics 

include the presence of tall, dense grass with a well 

developed litter layer, standing dead vegetation, and little 

or no woody vegetation (Herkert et al. 2002). Henslow’s 

sparrows may breed in fi elds that are infrequently 

mowed or lightly grazed (Skinner et al. 1984, Smith and 

Smith 1992, Cully and Michaels 2000), but frequent 

disturbance, such as burning, mowing, or haying, can 

render areas inhospitable (Pruitt 1996, Herkert 2001). 

Henslow’s sparrows breed principally in mesic grasslands 

(Hands et al. 1989), but also in dry and wet prairies 

(Swengel 1996). For SI1

 we assumed the disturbance 

interval on the Hoosier National Forest test landscape 

was suffi ciently long for development of the necessary 

structural characteristics. Therefore, we evaluated only 

the land-cover type for each cell and set SI1

 = 1.00 

if the land-cover type was grassland and SI1

 = 0.00 

otherwise. The grassland cover type included warm and 

cool season grasslands, as well as hayfi elds, pastures, and 

prairies. Thus, the model may over-predict suitability in 

landscapes where Henslow’s sparrows only use grasslands.

In the second suitability index (SI2

), we addressed a 

grassland area requirement. The minimum patch size 

used by Henslow’s sparrows varies by region and also 

depends on landscape context. Samson (1980) and 

Harroff (1999) found Henslow’s sparrows in grassland 

fragments as small as 10 ha, but we found no published 

accounts of sparrows in patches <10 ha (but see Mazur 

11 0 1 2 3 4 Kilometers 0 1 2 3 4 Kil t

N

W E

S

0 0.6

0.1

0.2

0.3

0.4

0.5

0.7

0.8

0.9

1.0

SI Legend

SI 1 SI 2

SI 3 HSI

Figure 7.—Cerulean warbler habitat suitability for breeding on a 4,281-ha portion of the 

Hoosier National Forest, Indiana. The fi nal habitat suitability value was the geometric 

mean of three suitability indices.
13

1996). In general, Henslow’s sparrow density increases 

with increasing area (Winter and Faaborg 1999) and 

amount of grassland at the landscape level (Mazur 1996, 

McCoy 2000). We did not fi nd published information 

to quantify a suitability relationship for the percentage 

of grassland at the landscape level. We refrained from 

subjectively assigning a landscape-level relationship and 

instead proceeded with a patch-level relationship. Herkert 

(1994) provided a quantitative estimate of the probability 

of occurrence based on area and estimated at least 55 

ha are required to detect Henslow’s sparrows 50 percent 

of the time. We plotted SI2

 = 0.01 for 10-ha patches, 

SI2

 = 0.50 for 55-ha patches, and SI2 = 1.00 for 100-ha 

patches, and fi t a sigmoid function (Fig. 8):

( ) 2 ( 1*( 55.1692)/ 9.5151))

1.0090

1 patchsize SI

e − − = +

We applied this function to cells where SI1 >0.00 and 

patch size >10 ha. For grassland patches ≤10 ha, SI2

 = 

0.00.

In the third suitability index (SI3

), we reduced the value 

of grassland habitat adjacent to forest and urban edges. 

Henlsow’s sparrows nest in grasslands with little woody 

cover (Herkert 1994, Pruitt 1996) and do not nest 

within woody edges (Winter 1999). Henslow’s sparrows 

exhibit both a demographic response (e.g., nest success) 

and a distributional response (e.g., density) to habitat 

edges. Nest success decreased with increased proximity 

to woody edges (Winter and Faaborg 1999, Winter et al. 

2000), probably due to an increase in predator activity 

near woody edges (Winter et al. 2000). Adult density also 

decreased with increased proximity to edges (Winter et 

al. 2000, Bajema and Lima 2001). We found insuffi cient 

published information to develop a function describing 

the relationship between distance to edge and nest success 

or density because most studies used categorical data. 

Instead, we applied a moving window of 3 × 3 cells to 

pixels with a SI1

 > 0.00. The moving window assessed 

the land-cover type within the window and assigned 

SI

3

 = 0.00 to the center pixel if the window contained 

non-grassland habitat. In this way, grassland immediately 

adjacent to edges received no suitability value. If the 

moving window contained only grassland habitat we 

assigned SI3

 = 1.00 to the center pixel. In other words, 

the center pixel retained the value assigned in SI1

 (= 1.00 

for grassland).

The fi nal habitat suitability value was the geometric mean 

of SI1

 and SI

2, multiplied by SI3

, to impose the edgesensitivity penalty:

( ) 2 HSI SI SI SI = ×× 12 3

Application to test landscape

The fi rst suitability index identifi ed numerous patches of 

grassland; however, few patches exceeded the minimum 

Grassland patch area (ha)

0 10 20 30 40 50 60 70 80 90 100 110 120

Suitability index value

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

Figure 8.—Henslow’s sparrow habitat suitability 

for breeding as a function of grassland area. 

Left arrow indicates 10 ha (minimum area 

requirement, Samson 1980, Harroff 1999) 

and right arrow indicates 55 ha (50 percent 

occurrence, Herkert 1994). Suitability value 

(SI2

) = 0.00 for patches ≤10 ha and SI2 = 1.00 

for patches >100 ha.
14

patch size constraint of 10 ha (Fig. 9). Habitat suitability 

was greatest for large patches of grassland with minimal 

edge habitat.

Indiana Bat

Overview

The Indiana bat is a federally endangered, migratory 

species found in deciduous forests of the eastern United 

States. Indiana bats use different habitats for breeding 

and over-wintering. Indiana bats breed and raise their 

young in forested areas during the summer (Cope et al. 

1974, Humphrey et al. 1977) and migrate to caves or 

abandoned mines to hibernate during the winter (Hall 

1962).

HSI model

We developed an Indiana bat breeding season (summer) 

HSI model for the Central Hardwoods Region. Menzel 

et al. (2001) provided a review of available literature on 

habitat requirements for the Indiana bat, Rommé et al. 

(1995) developed a summer habitat HSI model, and 

Farmer et al. (2002) developed and evaluated an Indiana 

bat HSI. The Farmer et al. (2002) model contained 

suitability indexes for number of land-cover types, roost 

tree density, and percentage of landscape in forest. Of 

these, only roost tree density differed between sites with 

and without Indiana bats. Our model differs from Farmer 

et al.’s model in two ways. First, we used Forest Inventory 

Analysis data and estimates of snag density by tree age 

class (Fan et al. 2003) to identify potential roost trees 

(SI1

), as recommended by Farmer et al. (2002). Second, 

we accounted for solar radiation of roost trees (SI2

 and 

SI

4

). Finally, all of the SIs in our HSI model were based 

on reported ecological relationships for Indiana bats in 

the Central Hardwoods.

The fi rst suitability index (SI1

) addressed maternity roost 

trees and was a function of snag suitability and density. 

Indiana bats form maternity roosts under the loose bark 

of live, dead, or dying trees (Kurta 1995) and in tree 

crevices (Kurta et al. 2002). Among living trees, roosts 

are most commonly found in shagbark hickory (Gardner 

et al. 1991, Callahan et al. 1997); however, the structural 

characteristics of snags may be more important than the 

tree species (Rommé et al. 1995). We derived a roost 

suitability function based on snag diameter at breast 

height (d.b.h.) and snag density as functions of tree age. 

Published estimates for the d.b.h. of roost trees used by 

maternity colonies range from 8-83 cm (Gardner et al. 

1991), with an average d.b.h. of 35.0 cm (Carter et al. 

2000), 36.7 cm (Gardner et al. 1991), 40.9 cm (Kurta 

et al. 1996), or 58.4 cm (Callahan et al. 1997). Based 

11 0 1 2 3 4 Kilometers 0 1 2 3 4 Kil t

N

W E

S

0 0.6

0.1

0.2

0.3

0.4

0.5

0.7

0.8

0.9

1.0

SI Legend

SI 1 SI 2

SI 3 HSI

Figure 9.—Henslow’s sparrow habitat 

suitability for breeding on a 4,281-ha portion 

of the Hoosier National Forest, Indiana.
15

on published estimates and expert opinion4

, we assumed 

snags become suitable (SI = 0.01) at 17 cm d.b.h.; SI 

= 0.50 at 30 cm d.b.h.; and SI = 1.00 at 50 cm d.b.h. 

We fi t a sigmoid function to the values with SigmaPlot 

(Indiana Bat “17-50” Curve, Fig. 10).

We used snag density (number of snags/ha) by size class 

information from Fan et al. (2003) to estimate snag 

density by tree age class (Fig. 11). Fan et al. (2003) 

used data from remnant old-growth tracts and Forest 

Inventory Analysis data from Missouri to predict cavity 

tree and snag density as a function of rotation age. After 

consultation with Fan, we decided that the snag density 

information for age class 90 may be misrepresented 

due to low sample size and removed age class 90 data 

from the following analyses. We determined the average 

suitability by snag size class using the Indiana Bat “17-

50” Curve (Fig. 10). We multiplied snag densities for 

each size class by the average suitability from the Indiana 

Bat “17-50” Curve and summed across each tree age 

class. We scaled the results to 0-1 by dividing each tree 

4

S. Amelon, Northern Research Station, U.S. Forest Service; 

and V. Brack, Jr., Environmental Solutions and Innovations, 

pers. comm.

Snag d.b.h. (cm)

0 5 10 15 20 25 30 35 40 45 50 55 60

Snag suitability

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

Figure 10.—Indiana bat “17–50” curve 

describing snag suitability as a function of 

diameter at breast height (cm).

Tree age (years)

0 10 20 30 40 50 60 70 80 90 100 110 120

Snag density (number/ha)

0

2

4

6

8

10

12

14

16

18

20

17-25 cm dbh

26-48 cm dbh

>48 cm dbh

Figure 11.—Snag density by tree age class for 

three snag size classes. Age class 90 data was not 

included in analyses because of small sample size.
16

age class value by the maximum value (age class 110) and 

plotted these relative values (Fig. 12). We fi t a quadratic 

function for snag suitability by tree age class:

2

1 SI age age = − ×+ × 0.2930 0.0045 0.0001

and used this function to assign suitability value (SI1) to 

trees 1-100 years of age. Suitability was maximum (SI1

 = 

1.00) for trees ≥100 years of age.

In the second suitability index (SI2

), we identifi ed open 

habitat and early successional forest. Indiana bats forage 

in open areas, including pastures and old fi elds (Brack 

1983), over or near water (Jones et al. 1985, Gardner 

et al. 1996), and along borders of cropland (Clark et al. 

1987a, 1987b) or habitat edges (Brack 1983). For SI2

we assigned suitability value based on tree age: stands 

with trees 0-20 years of age, which included open areas, 

croplands, roads, and water as an artifact of our ageassignment process, received the highest suitability value 

(SI2

 = 1.00) and trees >20 years of age received SI2 = 

0.00.

In the third suitability index (SI3

), we constrained 

suitability of roost tree habitat by distance to water 

sources. Indiana bat maternity roosts are commonly 

located in riparian or bottomland areas (Gardner et al. 

1991, Callahan et al. 1997), including wetlands (Kurta 

et al. 2002). In Indiana, Humphrey et al. (1977) found 

two roost trees located less than 200 m from a creek that 

was used for foraging and Brack (1983) found a roost 

tree on the bank of a river. In Missouri, all reported 

colonies were found near a stream or river (Callahan 

et al. 1997). We assumed that all potential roost trees 

located within 1000 m of permanent water sources 

were accessible to Indiana bats and thus had maximum 

suitability (SI3

 = 1.00). We assigned suitability for 

potential roost trees located 1000-4000 m from water 

using the function (Fig. 13):

3 3 1.33

1000

dist SI = −

which declined linearly from SI3 = 1.00 at 1000 m to SI

3

= 0.00 at 4000 m. Potential roost trees located more than 

4000 m from a water source received SI

3

 = 0.00.

In the fourth suitability index (SI4

), we evaluated roost 

exposure to solar radiation. Indiana bat maternity 

roosts often occur in trees exposed to direct sunlight 

(Humphrey et al. 1977, Kurta et al. 1993a, 1993b, 

Callahan et al. 1997). Solar radiation may decrease 

time of fetal development and increase juvenile growth 

(Callahan et al. 1997) or reduce metabolic costs for 

thermal regulation. Roost exposure to sunlight may 

result from gaps in forest canopy or may be due to roost 

location near a habitat edge. We used a 3 cell × 3 cell 

moving window to evaluate the interspersion of potential 

Tree age (years)

0 10 20 30 40 50 60 70 80 90 100 110 120

Suitability index value

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

Figure 12.—Indiana bat habitat 

suitability for roost sites as a function of 

tree age, snag density, and size class.
17

roost trees (SI1

) with open areas and forest gaps (SI2). If 

the center cell of the moving window had SI1

 >0.50 and 

any adjacent cell had SI2

 >0.50, then we assigned SI4 = 

1.00; otherwise we assigned SI4

 = 0.00. This procedure 

considered solar radiation from canopy gaps due to tree 

fall but did not account for canopy gaps created by large 

snags. Thus, it likely underestimated roost exposure to 

solar radiation.

The fi nal habitat suitability value was the maximum 

of the composite roost site suitability or the foraging 

suitability:

HSI Maximum Maximum SI SI SI SI (( ) ( ) 12 3 4 , ,) a = ×× «¬

( ) 12 3 Maximum SI SI SI ( , ) 0.5 o × × »1⁄4

Application to test landscape

This equation identifi ed whether an individual cell 

contained a potential roost tree in forest (SI1

) or in 

open/early successional habitat adjacent to forest (SI2

), 

and then considered the value of that cell based on the 

potential for solar radiation (SI4

) and distance to water 

(SI3

) (Fig. 14). If a cell did not have value as a potential 

roost site (SI1

) or as an open area for foraging (SI2), it still 

had value as foraging habitat or alternative (secondary) 

roost sites. Thus, the remainder of the equation 

captured the value of forests for foraging habitat and 

alternative roost sites (SI1

), or the value of open areas for 

foraging habitat (SI2

), modifi ed by the distance to water. 

Distance to water was not limiting in the test landscape 

and therefore SI

3

 = 1.00. The fi nal HSI map contained 

large areas of forest that may be used for foraging and 

alternative roost sites. The greatest potential for primary 

roost sites was along forest edges.

Northern Bobwhite

Overview

The northern bobwhite is a nonmigratory game species 

that breeds throughout the eastern United States. In the 

northern part of their range, bobwhite are associated 

with heterogeneous, patchy landscapes that contain early 

successional woody cover, grasslands, and row crops 

(Roseberry and Sudkamp 1998). Due to their importance 

as a game species, northern bobwhite are a popular 

research species, perhaps the most intensively studied 

bird in the world (Guthery 1997). Several HSI (or HSItype) models exist for northern bobwhite, including an 

early quantitative method for evaluating habitat from 

aerial photos (Backs 1981), the original HSI developed 

by Schroeder (1985), and several recent models that 

incorporate landscape-level attributes (e.g., Brady et al. 

1993, Roseberry 1993, Roseberry and Sudkamp 1998, 

Burger et al. 2004).

HSI model

We developed a northern bobwhite HSI model for 

the Central Hardwoods Region. The fi rst suitability 

Distance to water (m)

0 1000 2000 3000 4000 5000

Suitability index value

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

Figure 13.—Indiana bat habitat suitability for 

roost sites as a function of distance from roost 

tree to water. Suitability value (SI3

) = 1.00 

for roost trees <1000 m from water and SI3

 = 

0.00 for roost trees >4000 m from water.
18

index (SI1

) identifi ed grasslands used for nest sites, 

cover, and food. Northern bobwhite nest in fi elds 

where plant succession has progressed at least 1 year 

following disturbance (Dimmick 1972). Prescribed fi re 

or mechanical disturbance conducted every 1-5 years 

maintains habitat conditions for bobwhite populations 

(Stoddard 1931, Landers and Mueller 1986). We 

evaluated the land-cover type for each cell and set SI1

 = 

0.50 if the land-cover type was grassland and SI1

 = 0.00 

otherwise.

In the second suitability index (SI2

), we identifi ed food 

sources. Bobwhite eat seeds of agricultural crops and 

weeds, as well as forest, agricultural, and rangeland 

vegetation, especially understory plants and plants 

along fi eld margins (Brennan 1999). Common foods 

include beggarweeds (Bidens spp.), ragweeds (Ambrosia

spp.), Lespedezas spp., corn (Zea spp.), partridge peas 

(Chamaecrista spp.), acorns (Quercus spp.), sumacs (Rhus

spp.), pine seeds (Pinus spp.), soybeans (Glycine spp.), 

and rowpeas (Pisum spp.) (Landers and Johnson 1976). 

Inbapa1y000s2

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 8 1 0 1 2 3 4 Kil t

SI 1 SI 2

SI 3 SI 4

HSI

1 0 1 2 3 4 Kilometers 

N

W E

S

0 0.6

0.1

0.2

0.3

0.4

0.5

0.7

0.8

0.9

1.0

SI Legend

Figure 14.—Indiana bat habitat suitability for a 4,281-ha portion of the Hoosier National 

Forest, Indiana.
19

Seeds from agricultural crops, such as corn, soybeans, 

and wheat (Triticum spp.), predominate fall and winter 

diets (Larimer 1960, Roseberry and Klimstra 1984). 

We assumed grasslands identifi ed in SI1

 provided food 

in addition to nesting habitat and that woody edges 

identifi ed in SI

3

 provided food in addition to escape 

cover. Therefore, we used SI2

 to identify agricultural food 

sources. We evaluated the land-cover type for each cell 

and set SI2

 = 0.40 if the land-cover type was cropland 

and SI2

 = 0.00 otherwise.

In the third suitability index (SI3

), we identifi ed woody 

edge cover. Bobwhite prefer areas where approximately 

50 percent of the ground is exposed and 50 percent 

contains upright growth of herbaceous and woody 

vegetation (Schroeder 1985). Brushy or woody edges 

along crop fi elds and grasslands often meet these 

requirements. In addition to grasslands and croplands, 

bobwhite will also use open canopy (<50 percent) 

pinelands and mixed pine-hardwood forests (DeVos 

and Mueller 1993, Brennan 1999). A landscape-level 

assessment of bobwhite habitat suitability in Illinois 

associated bobwhite abundance with high woody edge 

density (≥30 m/ha) (Roseberry and Sudkamp 1998). 

Woody edge often is used for escape cover (Williams et 

al. 2000), thus the fi rst 30 m of woody cover from a fi eld 

edge appears the most important, with use declining with 

distance from the fi eld edge. We used a 60-m moving 

window to identify forest within 30 m of grassland or 

cropland. If the center pixel contained forest ≥1 year of 

age and the remaining cells contained either grassland 

or cropland, we set SI3

 = 0.30 for the center pixel. 

Otherwise, we set SI3

 = 0.00.

In the fourth suitability index (SI4

), we used a moving 

window to evaluate interspersion of habitat types. 

In agricultural regions, the interspersion of nesting 

(grassland), food (grassland and cropland), and cover 

(woody edge) provide optimum habitat for bobwhite 

(Roseberry and Sudkamp 1998). The proportion of 

each habitat type varies somewhat by study: 30-65 

percent row crops and 15-30 percent grassland in 

Illinois (Roseberry and Sudkamp 1998); 75-90 percent 

open land consisting of 50-60 percent cropland and 

20-30 percent grassland in Missouri (Dailey 1989); 

and 30-40 percent grassland, 40-60 percent cropland, 

5-20 percent brushy cover, and 5-40 percent woodland 

cover (Johnsgard 1973). We evaluated the proportion 

of grassland, cropland, and woody edge using a moving 

window with a 360-m radius. The area within the 

moving window equaled 40.7 ha, which approximated 

the maximum average northern bobwhite home range 

of 38 ha5

 reported within the Central Hardwoods. 

Estimates of bobwhite home range size vary by season 

and location. Home ranges averaged 38 ha in winter in 

northeast Missouri5

, 9 ha (range 6-11 ha) during a latewinter period with prolonged snow cover in southern 

Illinois (Roseberry 1964), and 15 ha (range 12-19 ha) for 

late winter in a different year in Illinois (Bartholomew 

1967). We based the ideal proportion on the midpoints 

of Roseberry and Sudkamp (1998): grassland = 0.22, 

cropland = 0.47, and woody cover = 0.31, and set SI4

 = 

0.50 if the window contained the ideal proportion. The 

suitability value declined toward zero as a function of the 

difference between the observed proportion within the 

moving window and the ideal proportion: 

4 SI PROP PROP PROP =− − − 0.5* ((1 1) * (1 2) * (1 3))

where PROP1, PROP2, and PROP3 equaled the absolute 

value of the observed proportion minus the ideal 

proportion of grassland, cropland, and woody cover, 

respectively. In other words, SI4

 was maximized when the 

observed proportions equaled the ideal proportions.

We used the fi fth suitability index (SI5

) to zero out roads 

and urban areas that received suitability value from 

SI

4

 because we assigned value to all cells based on the 

composition of habitat within the moving window. We 

assigned SI5

 = 1.00 for forest, cropland, and grassland, 

otherwise SI

5

 = 0.00.

The fi nal habitat suitability value was the sum of 1) the 

maximum value of SI1

, SI2, and SI3; and 2) the product 

of SI

4

 and SI

5

 (Fig. 15):

HSI Maximum Maximum SI SI SI SI SI = +× ( ( , ), ) 12 3 4 5 ( )

5

Burger, L.W., Jr., Mississippi State University, pers. comm.
20

We used an additive HSI equation as opposed to a 

multiplicative equation because we recognized that 

grassland, cropland, or woody cover provided bobwhite 

habitat; however, the highest suitability value occurred 

when at least two of the three habitat types were present 

within the moving window.

Application to test landscape

The fi rst, second, and third suitability indices identifi ed 

grasslands, croplands, and woody edges within the 

landscape. The fourth suitability index identifi ed several 

areas where the interspersion of these three habitat 

requirements occurred. After excluding nonusable open 

areas (i.e., roads and urban areas), the fi nal HSI map 

refl ected the cumulative value of each habitat type and 

its interspersion. In general, grasslands adjacent to woody 

edges provided the greatest area of suitable habitat on the 

landscape.

Ruffed Grouse

Overview

The ruffed grouse (Bonasa umbellus) is a nonmigratory 

game species that breeds throughout the boreal forests 

of North America and in portions of deciduous forest in 

the eastern United States. Ruffed grouse are associated 

with early successional forests in all parts of their range, 

SI 1 SI 2

SI 3 SI 4

HSI

1 0 1 2 3 4 Kilometers 

N

W E

S

0 0.6

0.1

0.2

0.3

0.4

0.5

0.7

0.8

0.9

1.0

SI Legend

SI 5 

Figure 15.—Northern bobwhite habitat suitability for a 4,281-ha portion of the Hoosier 

National Forest, Indiana.
21

including aspen and poplar forests in the north and 

oak-hickory or mixed deciduous-coniferous forests 

in the south and east (Rusch et al. 2000). Cade and 

Sousa (1985) developed the fi rst HSI model for grouse; 

however, the model relied on aspen buds for winter food 

and thus has limited application within the Central 

Hardwoods due to the low abundance of aspen.

HSI model

We developed a ruffed grouse HSI model for the 

Central Hardwoods Region. The fi rst suitability index 

(SI1

) identifi ed potential food sources for ruffed grouse. 

Ruffed grouse diets vary seasonally but usually contain 

leaves, buds, and fruits of deciduous forest plants (Rusch 

et al. 2000). During brood rearing, adults and chicks 

eat invertebrates (Bump et al. 1947) in addition to the 

leaves of herbaceous plants (Norman and Kirkpatrick 

1984). In late autumn and winter, hard mast, consisting 

primarily of white oak, red oak, chinkapin oak, and black 

oak acorns, are an important food source (Thompson 

and Fritzell 1986). We assumed hard mast was the most 

limiting of these food sources given its predominance in 

fall and winter diets and used SI1

 to estimate suitability 

based on acorn mast production. Sullivan (2001) 

developed models for acorn production based on tree 

age, species (white oak or red oak) and ELT, which he 

subsequently modifi ed6

 for application to the Hoosier 

National Forest.

In the second suitability index (SI2

), we identifi ed 

early successional forest used for nesting, feeding, and 

roosting. While ruffed grouse in the northern portion of 

their range are associated with aspen-dominated forests 

(Gullion et al. 1962, Kubisiak et al. 1980, DeStefano 

and Rusch 1984, Kubisiak 1984), grouse in the southern 

portion of their range use early successional forests 

containing oaks, hickories, and pines (Rodgers 1980, 

Gudlin and Dimmick 1984, Hunyadi 1984, Wiggers 

et al. 1992). Grouse also will use young cedar stands 

for winter roosts (Thompson and Fritzell 1988). For 

SI2

 we assigned suitability value based on tree age and 

ELT. Stands with trees 1-20 years of age on mesic ridges, 

slopes, and bottomlands received the highest suitability 

value (SI2

 = 1.00). Stands with trees 1-20 years of age 

located on dry ridges and slopes had SI2

 = 0.80. For both 

mesic and dry sites, suitability value declined to zero with 

increasing stand age.

In the third suitability index (SI3

), we addressed known 

early successional forest area requirements. Grouse home 

ranges vary in size depending on region and forest type. 

In central Pennsylvania, male home ranges averaged 5.0-

9.4 ha in the breeding season and 11.0-14.0 ha in the 

summer (McDonald et al. 1998). Home ranges of male 

grouse in Missouri ranged from 45-68 ha in spring and 

summer to 84-109 ha in fall and winter (Thompson and 

Fritzell 1989). In southern Illinois, grouse home ranges 

were 26.9-226.2 ha (Woolf et al. 1984). We assumed 

the minimum year-round early successional forest area 

requirement was 4 ha; however, patches smaller than 4 

ha may be used or defended during the spring (Archibald 

1975, Maxson 1989). Therefore, we developed a 

suitability function to assign value to patches 0.01-4 ha:

3 SI patchsize = / 4

where patchsize was the area of early successional forest. 

Patches of early successional forest >4 ha received 

maximum suitability value (SI3

 = 1.00).

In the fourth suitability index (SI4

), we used a 

moving window with a 180-m radius to evaluate the 

interspersion of early successional forest and acorn 

production. The 10-ha area of the moving window 

corresponded to the average home-range size of ruffed 

grouse (McDonald et al. 1998). We calculated SI4

 in 

three steps. First we assigned a value (M4

) based on the 

proportion of acorn mast within the moving window. 

We plotted M4

 = 0.01 for proportion of mast = 0.00, M4

= 0.50 at 0.05, and M4

 = 1.00 at 0.10, and fi t a sigmoid 

function to these values:

( ) 4 (( 0.0499892)/ 0.00288211)

0.999478

1

M

ppnMast e − − = +

If the proportion of mast exceeded 0.10, the mast value 

was M

4

 = 1.00. Second, we assigned a value (ES4) based 

on the proportion of early successional forest that met 

the minimum area requirement within the moving 

window. We plotted ES4

 = 0.01 for proportion of early 6

N. Sullivan, Northern Research Station, U.S. Forest Service, 

pers. comm.
22

succesional forest = 0.00, ES4

 = 0.50 at 0.20, and ES4 = 

1.00 at 0.40, and fi t a sigmoid function to these values:

( ) 4 (( 0.200233)/ 0.0398903)

1.00329

1 ppnES ES

e − − = +

If the proportion of early successional forest exceeded 

0.40, ES4

 = 1.00. Last, we calculated the suitability 

value as the product of the mast value and the early 

successional forest value (Fig. 16): 

444 SI M ES = ×

In SI1 we assumed that acorn mast was an important 

food source for grouse during late fall and winter 

(Thompson and Fritzell 1986). When applied to SI4

, 

this assumption will produce SI4

 = 0.00 when M

4

 = 0.00. 

The proportion of mast within the moving window may 

equal zero when the proportion of early successional 

forest within the moving window is 1.00 (e.g., the size of 

an early successional forest patch exceeds the size of the 

moving window). Future users may want to adjust the 

function for M

4

 such that M

4

 receives a nonzero value 

(i.e., ≥ 0.01) to retain early successional patches in the SI4

equation.

In the fi fth suitability value (SI5

), we addressed a 

minimum forest area requirement. Although grouse use 

relatively small patches of early successional forest, these 

patches must be imbedded within a larger, contiguous 

forested area. In Missouri, grouse were released in 

early successional patches located within forests of 

109, 259, and 1090 ha (Hunyadi 1984). Backs (1984) 

recommended that release locations in Indiana have a 

minimum of 400 ha of relatively contiguous forested 

area surrounded by 5-8 km2

 of primarily forested cover 

types. We plotted SI5

 = 0.01 for forested area = 100 ha, 

SI

5

 = 0.25 at 200 ha, and SI5 = 1.00 at 400 ha, and fi t a 

sigmoid function to these values (Fig. 17):

( ) 5 (( 277.118)/ 24.6569)

1.0009

1 patchsize SI

e − − = +

The suitability value increased to a maximum of SI5 = 

1.00 for patches ≥400 ha.

The fi nal habitat suitability value was the geometric mean 

of 1) the maximum value of SI1

 or the geometric mean of 

SI2

 and SI

3

; and 2) SI4; multiplied by SI5 (Fig. 18):

( ) 1 23 4 5 HSI Maximum SI SI SI SI SI , § · = ×× ×  ̈  ̧ © 1

Proportion of mast

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

Suitability index value

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

Proportion of early successional habitat

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Figure 16.—Ruffed grouse habitat suitability 

as a function of early successional habitat 

and proportion of mast within a 10-ha moving 

window.
23

Forest area (ha)

0 100 200 300 400 500

Suitability index value

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

Figure 17.—Ruffed grouse habitat suitability 

as a function of forest area. Suitability value 

(SI5

) = 0.00 for patches ≤100 ha and SI5 = 

1.00 for patches >400 ha.

1 0 1 2 3 4 Kilometers 

N

W E

S

0 0.6

0.1

0.2

0.3

0.4

0.5

0.7

0.8

0.9

1.0

SI Legend

SI 1 SI 2

SI 3 SI 4

SI 5 HSI

Figure 18.—Ruffed grouse habitat 

suitability for a 4,281-ha portion of 

the Hoosier National Forest, Indiana.
24

Application to test landscape

We used the maximum of either acorn mast production 

(SI1

) or early successional forest patches ( ) 2 3 SI SI ×

because a single cell cannot provide both habitat 

requirements. The fourth suitability index identifi ed 

several areas where the interspersion of acorn mast 

production and early successional forest patches occurred. 

After imposing the minimum forested area constraint 

(SI5

), the fi nal HSI map consisted of several small patches 

of suitable grouse habitat (e.g., early successional forest 

embedded within mast-producing forest) surrounded by 

predominantly unsuitable habitat.

Timber Rattlesnake

Overview

The timber rattlesnake is the only woodland rattlesnake 

in the eastern United States. During the active season 

(May-September) typical habitat includes rocky, open 

sites in deciduous hardwood forest (Klauber 1997) 

and lightly wooded clearings and oak-hickory knolls 

containing boulders, rock slabs, and outcrop fi ssures 

(Brown 1992). Reinert (1984a) found timber rattlesnakes 

in forested areas with greater surface vegetation and 

less rock density than in other portions of their range. 

Because rattlesnakes hibernate for up to 7 months of 

the year in southern Indiana (Walker 2000), overwintering sites (hibernacula) are critical habitat features. 

We developed our HSI model based on the reported 

ecological relationships from studies conducted in 

Indiana (Walker 2000), Pennsylvania (Reinert 1984a, 

1984b), New York (Brown et al. 1982, Brown 1991), 

New Jersey (Reinert and Zappalorti 1988), South 

Carolina (Andrews and Gibbons 2005), and West 

Virginia (Adams 2005).

HSI model

We developed a timber rattlesnake HSI model for 

breeding habitat in the Central Hardwoods Region. The 

fi rst suitability index (SI1

) identifi ed early successional 

forested habitat used for foraging and basking. 

Rattlesnakes eat a variety of animals but the primary 

prey species are small mammals. Early successional 

habitat, such as canopy gaps and forest edges, affects 

small mammal abundance (Osbourne et al. 2005), 

diversity, and richness (Sekgororoane and Dilworth 

1995). Additionally, canopy gaps may provide rattlesnake 

rookery (i.e., birthing) and basking opportunities (Adams 

2005). While rattlesnakes are typically found in forests 

with large coarse woody debris (e.g., fallen logs) and high 

canopy closure, most rattlesnake relocations in southern 

Indiana were associated with small canopy breaks where 

sunlight reached the ground (Walker 2000). Gravid 

females were particularly associated with forest clearings 

in Indiana (Gibson and Kingsbury 2002) and with road 

edges, log landings, and regenerating hardwood stands in 

West Virginia (Adams 2005).

We grouped ELTs to account for the infl uence of 

moisture on vegetation growth. Mesic ridges, north 

and east slopes, and bottomlands constituted the mesic 

ELTs, and dry ridges and south and west slopes the dry 

ELTs. We assigned SI1

 = 1.00 to stands 1-10 years of 

age on both mesic and dry ELTs. Mesic sites with stands 

>10 years of age had SI1

 = 0.00, but stands on dry sites 

retained higher suitability value due to delayed canopy 

closure. We assigned SI1

 = 0.50 for stands 11-20 years of 

age, SI1

 = 0.30 for stands 21-30 years of age, and SI1 = 

0.10 for stands 31-40 years of age. Dry ELTs with trees 

>40 years of age had SI1

 = 0.00.

In the second suitability index (SI2

), we identifi ed woody 

debris habitat used for cover and foraging. Rattlesnakes 

use mid- to late-successional second growth deciduous 

forest with 62 percent mean canopy closure and 19.3 cm 

mean d.b.h. (Reinert 1984a). Males use predominantly 

forest sites (69 percent canopy cover) with moderate to 

dense forest fl oor vegetation (Reinert 1984b). Nongravid 

females used forested sites with 67 percent mean canopy 

cover but with less surface vegetation than male sites 

(Reinert 1984b). We assumed stands containing trees 

>100 years of age contained coarse woody debris suitable 

for rattlesnake use, and that suitability declined with 

decreasing stand age. We evaluated tree age for each cell 

and set SI2

 = 0.00 for trees 1-30 years of age. For trees 

31-100 years of age, we assigned suitability using the 

function (Fig. 19): 

2 0.42857

70

treeage SI = −
25

We developed this equation by fi tting a linear regression 

equation such that SI2 = 0.00 at 30 years of age and SI2 = 

1.00 for trees ≥100 years of age.

In the third suitability index (SI3

), we evaluated the 

proportion of woody debris and foraging habitat using a 

moving window with an 850-m radius. The area within 

the moving window equaled 227 ha, which approximated 

the maximum average male rattlesnake home range 

size of 207 ha (Reinert and Zappalorti 1988). Home 

range sizes in southern Indiana averaged 174 ha (range 

112-382 ha) for males and 72 ha (range 15-181 ha) for 

females (Walker 2000). We based the ideal proportion 

for foraging and woody debris cover on the average 

percentage canopy cover of rattlesnake locations reported 

by Walker (2000): foraging, basking, and rookery (open 

canopy) = 0.15, and woody debris cover and foraging 

(closed canopy) = 0.85. We set SI3

 = 1.00 if the window 

contained the ideal proportion. The suitability value 

declined toward zero with increasing difference between 

the actual proportion within the moving window and the 

ideal proportion.

In the fourth suitability index (SI4

), we assigned 

value based on proximity to den sites or hibernacula. 

Rattlesnake habitat requirements vary by sex and 

reproductive status. Gravid female rattlesnakes often 

bask on rocks near den sites (Reinert 1984b). The mean 

dispersal distance for female rattlesnakes (gravid and 

nongravid) in New York averaged 280 m and ranged 

from 191-425 m (Brown et al. 1982). In West Virginia 

the mean dispersal distance was 584 m (range 328-

832 m) for gravid females and 872 m (range 618-1121 

m) for nongravid females (Adams 2005). The spatial 

requirements for male rattlesnakes greatly exceed the 

requirements for nongravid females. Male rattlesnakes 

dispersed up to 1.4 km from hibernacula in New York 

(Brown et al. 1982) and up to 3.6 km in West Virginia 

(Adams 2005). Male rattlesnakes also make movements 

during the breeding season of 2 km (Walker 2000). Based 

on the large spatial requirements of rattlesnakes in New 

York, Brown (1993) estimated a population with 50 

adults would require a 2.4 km radius of protected land 

around a den site. We calculated the distance from each 

cell to the nearest den site and assigned SI4

 = 1.00 to cells 

within 500 m of den sites. For cells located 500-2500 m 

from den sites, we assigned suitability using the function 

(Fig. 20):

4 SI distden =− × 1.25 0.0005

where distden is the distance from a cell to the nearest den 

site. Suitability declined with increasing distance from a 

den site. Cells located >2500 m from a den site received 

SI

4

 = 0.00. This suitability index will underestimate 

the suitability of habitat in situations where not all den 

site locations are known. Therefore, comprehensive 

knowledge of den site locations is needed. We recommend 

consulting with local experts to identify den sites.

Tree age (years)

0 10 20 30 40 50 60 70 80 90 100 110 120

Suitability index value

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

Figure 19.—Timber rattlesnake habitat 

suitability as a function of stand age. 

Suitability value (SI2

) = 0.00 for stands ≤30 

years and SI2

 = 1.00 for stands >100 years.
26

In the fi fth suitability index (SI5

), we reduced the 

suitability value of habitat near roads. Roads may present 

barriers to rattlesnake movements (Fitch 1999, Sealy 

2002) or reduce rattlesnake survival (Seigel and Pilgrim 

2002). An experimental study on the Savannah River Site 

in South Carolina revealed that large rattlesnakes had a 

greater tendency to avoid roads than smaller rattlesnakes 

(Andrews and Gibbons 2005). Male rattlesnakes had 

higher road mortality during the breeding season than 

females, presumably due to increased movements 

associated with mate searching (Aldridge and Brown 

1995). We calculated the distance from each cell to the 

nearest road and assigned SI5

 = 1.00 to cells greater than 

100 m from roads. For cells <100 m from roads we 

assigned suitability using the function (Fig. 21):

5 100

distroad SI =

where distroad is the distance from a cell to the nearest 

road. Suitability increased with increasing distance from 

a road.

Distance to den site (m)

0 500 1000 1500 2000 2500 3000

Suitability index value

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

Distance to road (m)

0 10 20 30 40 50 60 70 80 90 100 110 120

Suitability index value

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

Figure 20.—Timber rattlesnake habitat 

suitability as a function of distance from den 

sites. Suitability value (SI4

) = 1.00 for habitat 

≤500m from den sites and SI4

 = 0.00 for habitat 

>2500m from den sites.

Figure 21.—Timber rattlesnake habitat 

suitability as a function of distance from roads.
27

The fi nal habitat suitability value was the geometric mean 

of the maximum value of SI1

 or SI2 multiplied by SI3, 

and reduced by the product of SI4

 and SI

5

 (Fig. 22):

( ) 12 3 4 5 HSI Maximum SI SI SI SI SI = × ×× (, )

Application to test landscape

We used the maximum of either the fi rst or the second 

suitability indices because a single cell could not contain 

both early successional and mid- to late-successional 

habitat. We reduced the value of each cell based 

on its distance from den sites and roads. These two 

requirements greatly reduced the amount of suitable 

habitat; the fi nal HSI map refl ects the importance of 

conserving near-den habitat for rattlesnakes. We used 

pseudo-den locations in the test landscape to demonstrate 

the full capability of the model without revealing actual 

den locations.

Wood Thrush

Overview

The wood thrush (Hylocichla mustelina) is a neotropical 

migratory bird that breeds in eastern North America 

and winters in Central America. Wood thrushes nest in 

1 0 1 2 3 4 Kilometers 

N

W E

S

0 0.6

0.1

0.2

0.3

0.4

0.5

0.7

0.8

0.9

1.0

SI Legend

SI 1 SI 2

SI 3 SI 4

SI 5 HSI

Figure 22.—Timber rattlesnake habitat suitability for a 4,281-ha portion of the Hoosier 

National Forest, Indiana.
28

shrubs and small trees of deciduous, mixed-deciduous 

coniferous, bottomland hardwood, and pine forests with 

deciduous understory (Roth et al. 1996). Existing habitat 

studies for wood thrush indicate forest area (Robbins et 

al. 1989), harvest type and age (Thompson et al. 1992, 

Robinson and Robinson 1999, Pagen et al. 2000, Gram 

et al. 2003), as well as canopy height, tree density, and 

type of canopy cover (e.g., deciduous vs. coniferous) 

(Robbins et al. 1989), are important habitat features. A 

breeding habitat suitability index model for the Gulf of 

Maine watershed used vegetative cover, forest patch size, 

distance from edge, and moisture regime as suitability 

indices (Banner and Schaller 2001).

HSI model

We developed a wood thrush HSI model for breeding 

habitat in the Central Hardwoods Region. The fi rst 

suitability index (SI1

) identifi ed suitable tree species 

for nesting habitat. Wood thrush nest in a variety 

of deciduous trees, and conifer stands are used if a 

deciduous subcanopy is present (Roth et al. 1996). We 

accomplished this by evaluating the dominant tree type 

for each cell and setting SI1

 = 0.20 if the dominant tree 

type was pine or cedar, and SI1

 = 1.00 otherwise.

In the second suitability index (SI2

), we assigned 

suitability value based on tree age and ELT. Wood thrush 

abundance varies by forest age with a peak in early to 

mid-successional forest, a decline, and then an increase 

in mature deciduous or mixed forest (Kahl et al. 1985). 

In Missouri, the density of breeding birds did not vary 

in regeneration (0-10 years of age), sapling (11-20 years 

of age), and pole and sawtimber (>20 years of age) 

within clearcutting sites, but was higher than uncut sites 

(Thompson et al. 1992). Also in Missouri, wood thrush 

density was greater in even-aged treatments compared 

to control sites 2-3 years after harvest, suggesting wood 

thrush use mature forest that includes some disturbance 

(Gram et al. 2003). Wood thrush abundance and density 

also varied with respect to slope. In southern Illinois, the 

number of wood thrush detected in ravines was greater 

than on ridges (Table 1 in Robinson and Robinson 

1999), possibly due to differences in moisture conditions 

and subsequent effects on forage availability and 

understory development.

We ranked ELTs based on slope and moisture and 

multiplied that ranking by an age function to determine 

the suitability value. We ranked ELTs as follows: north 

and east slopes and bottomlands = 1.00, mesic ridges = 

0.75, and dry ridges and south and west slopes = 0.50 

based on Robinson and Robinson (1999). All forested 

stands 11-40 years of age received a suitability value 

corresponding to the ELT rank described above (Fig. 23). 

For stands 41-90 years of age, we multiplied the ELT 

ranking by an age function:

2

2 SI treeage treeage =− × + × 4 0.012 0.001

Tree age (years)

0 10 20 30 40 50 60 70 80 90 100 110

Suitability index value

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

N & E slopes and

 Bottomlands

Mesic ridges

S & W slopes

Dry ridges

Figure 23.—Wood thrush habitat 

suitability as a function of tree age 

and ecological land type.
29

to reduce suitability value of mid-successional forests. 

Forest stands >91 years of age received a suitability value 

corresponding to the ELT rank (Fig. 23).

In the third suitability index (SI3

), we addressed 

known forest area requirements. Wood thrushes have 

been detected in forest fragments as small as 0.2 ha 

(Robbins et al. 1989) and have successfully nested in 

3-ha fragments (Friesen et al. 1999), but nest success 

is typically higher in larger fragments and contiguous 

forests. In rural Pennsylvania, nesting success in forest 

fragments less than 80 ha in size was usually below a 

sustainable level (Hoover et al. 1995). Robbins et al. 

(1989) suggested that the minimum area required for 

breeding was 1.0 ha, with a maximum probability of 

occurrence at 500 ha. Mueller et al. (1999) estimated 

forest patch size requirement for 500 breeding pairs was 

2800 ha. We developed a suitability function based on 

the 1-ha minimum area required for occupancy in the 

Middle Atlantic States (Robbins et al. 1989) (Fig. 24). 

Although percentage forest in the landscape may be a 

better metric of habitat suitability than patch size, we 

lacked data on 1) breeding as a function of percentage 

forest; and 2) effective landscape size (e.g., size of moving 

window) in which to evaluate percentage forest. Instead, 

we fi t a power function such that SI3

 = 0.01 for forest 

patch size = 1 ha, SI3

 = 0.50 for forest patch size = 100 

ha, and SI3

 = 1.00 for forest patches ≥500 ha:

(0.4626)

3 SI patchsize = × 0.0568

In the fourth (SI4) and fi fth suitability indexes (SI5), 

we addressed the interspersion of post-fl edging habitat 

and breeding habitat. Recent studies indicate juvenile 

(Anders et al. 1998, Vega Rivera et al. 1998) and adult 

wood thrush (Thompson et al. 1992, Pagen et al. 2000, 

Gram et al. 2003) used early successional and mature 

forest during breeding and post-breeding. Juvenile 

wood thrush used early successional forest (<30 years of 

age), including abandoned farms, roadsides, and forest 

openings, and use shifted to mature deciduous forest late 

in the post-fl edging period (Vega Rivera et al. 1998). 

Survival of post-fl edging juvenile wood thrush increased 

with an increase in shrub density associated with midsuccessional or edge habitats within forest fragments in 

Missouri (Fink 2003). For juveniles, the distance between 

natal area and fi rst dispersal sites varied, but averaged 1.5 

± 0.3 km in Virginia (Vega Rivera et al. 1998) and 2.2 ± 

0.3 km in Georgia (Lang et al. 2002). In Missouri, the 

mean distance for fi rst dispersal was 824.5 m, and total 

average dispersal distance was 1067 m (Fink 2003). We 

used SI

4

 to assign suitability value to cells with stands 

11-40 years of age. We applied a moving window (SI5

) 

with a 1-km radius to SI

4

 and determined the proportion 

of 11-40 year old habitat in the window. We assumed the 

minimum suitability value was 0.10 irregardless of the 

proportion of post-fl edging habitat within the moving 

window. We also assumed that the maximum suitability 

value was reached when the proportion of post-fl edging 

habitat = 0.20. If the proportion of post-fl edging habitat 

Forest patch area (ha)

0 100 200 300 400 500 600

Suitability index value

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

Figure 24.—Wood thrush habitat suitability as 

a function of deciduous forest area. Suitability 

value (SI3

) = 0.00 at 1.0 ha and SI3 = 1.00 for 

patches >500 ha (from Table 5, Robbins et al. 

1989).
30

ranged from 0.0-0.40, we assigned suitability value using 

the function (Fig. 25):

( ) 2 0.200

0.5

0.0932

5

ppnPFhab

SI e

§ · − − × ̈  ̧ © 1 =

We developed this equation using a Guassman function 

such that SI

5

 = 0.10 if the proportion of post-fl edging 

habitat = 0.00 or 0.40, and SI5

 = 1.00 if the proportion 

of post-fl edging habitat = 0.20. If the proportion of 

post-fl edging habitat exceeded 0.40, then SI5

 = 0.10. 

The 1-km moving window was computationally 

intensive and may be prohibitive when multiple model 

runs are conducted. In forested landscapes that have 

diverse and juxtaposed stand ages, SI5

 might be omitted 

without compromising the general utility of the HSI 

model. However, knowledge of site-specifi c conditions 

and known dispersal distances by wood thrush should 

be considered before omitting SI5

. We recommend 

comparison of HSI results with and without SI5

 to aid in 

the decision process.

The fi nal habitat suitability value was the geometric 

mean of SI2

, SI3, and SI5, multiplied by SI1 to account for 

reduced suitability of pines (Fig. 26):

( ) 3 HSI SI SI SI SI =× ×× 1 235

Application to test landscape

Suitability index 2, which assigned value based on tree 

age and ELT, contributed greatly to the heterogeneous 

pattern observed in the fi nal habitat suitability map. 

Stand size, homogeneity of tree age within a stand, as 

well as delineation of stand boundaries will infl uence the 

pattern observed when SI2

 is applied to other landscapes. 

Suitability index 3, which assigned value based on forest 

patch size, treated continuous canopy gaps (e.g., roads, 

power lines, and railroads) and nonforested areas as 

patch boundaries. Natural disturbance from wind and 

fi re, combined with moderate tree harvest, created postfl edging habitat. While the moving-window analysis 

was computationally intensive due to the large size of 

the window, it identifi ed several large areas with low 

interspersion of breeding and post-fl edging habitat. 

When applied to other landscapes, consideration of the 

known dispersal distances and the level of disturbance 

should be made before committing computer resources 

to calculation of SI

5

. The fi nal HSI map refl ected the 

diversity of stand ages, sizes, and fi re histories.

Worm-eating Warbler

Overview

The worm-eating warbler (Helmitheros vermivorus) is a 

neotropical migratory bird that breeds in eastern North 

America and winters in Central America and the Greater 

Proportion of post-fledging habitat

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Suitability index value

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

Figure 25.—Wood thrush habitat suitability 

based on the proportion of post-fl edging habitat 

within 1 km of natal site. Suitability value (SI5

) 

= 1.00 for landscapes with 20 percent postfl edging habitat. Suitability value (SI5

) = 0.10 for 

landscapes with >40 percent of post-fl edging 

habitat.
31

Antilles. Worm-eating warblers nest on the ground in 

large tracts of mature deciduous and mixed-deciduousconiferous forests with moderate to steep slopes and 

patches of dense understory shrubs (Hanners and Patton 

1998). Existing habitat studies for worm-eating warblers 

indicate forest area (Robbins et al. 1989, Wenny et 

al. 1993, Gale et al. 1997), forest edge (Wenny et al. 

1993), slope (Robbins et al. 1989, Wenny et al. 1993), 

prescribed fi re (Artman et al. 2001), harvest type and age 

(Thompson et al. 1992, Robinson and Robinson 1999, 

Pagen et al. 2000, Gram et al. 2003), as well as canopy 

height, tree density, moisture gradient, and foliage 

density up to 1 m (Robbins et al. 1989), are important 

habitat features.

HSI model

We developed a worm-eating warbler HSI model for 

breeding habitat in the Central Hardwoods Region. The 

fi rst suitability index (SI1

) identifi ed suitable tree species 

for nesting habitat. Worm-eating warblers rarely occur 

in pine forest (James and Neal 1986, McNair and Post 

1993). Without published information on the use of 

conifers for nest sites, we identifi ed only deciduous trees 

as suitable for nesting habitat. We accomplished this 

1 0 1 2 3 4 Kil t 1 0 1 2 3 4 Kilometers 

N

W E

S

0 0.6

0.1

0.2

0.3

0.4

0.5

0.7

0.8

0.9

1.0

SI Legend

SI 1 SI 2

SI 3 SI 4

SI 5 HSI

Figure 26.—Wood thrush habitat suitability for a 4,281-ha portion of the Hoosier National 

Forest, Indiana.
32

by evaluating the dominant tree type for each cell and 

setting SI1 = 0.00 if the dominant tree type was pine or 

cedar, and SI1

 = 1.00 otherwise.

In the second suitability index (SI2

), we assigned 

suitability value based on tree age and ELT. Wormeating warblers inhabit large tracts of mature forest. In 

addition to mature forest, worm-eating warblers use early 

successional forest during the breeding (Robinson and 

Robinson 1999, Pagen et al. 2000) and post-breeding 

periods (Pagen et al. 2000). Several studies associate 

worm-eating warblers with moderate to steep slopes 

(Mengel 1965, Wenny et al. 1993, Gale et al. 1997, 

Faaborg et al. 1998), but they also use ridges (Dunn 

and Garrett 1997, Robinson and Robinson 1999). The 

distribution of worm-eating warblers also is associated 

with a moisture gradient, with increased abundance 

associated with increased moisture (Robbins et al. 

1989). Artman et al. (2001) had a higher proportion of 

territories in mesic than intermediate and xeric sites.

We ranked ELTs based on slope and moisture and 

multiplied that ranking by an age function to determine 

the suitability value. We ranked ELTs as follows: ravines 

(slopes) = 1.00 and ridges = 0.859 (from Table 1 in 

Robinson and Robinson 1999); mesic sites = 1.00, 

intermediate sites = 0.660, and xeric sites = 0.226 (from 

Table 5 in Artman et al. 2001). All forested stands <39 

years of age received a suitability value corresponding to 

the ELT rank described above (Fig. 27). For stands 40-89 

years of age, we multiplied the ELT ranking by an age 

function:

2 SI treeage =−× + 17.125 0.7698

2 3 0.01125 0.000052083 ×− × treeage treeage

to reduce suitability value of mid-successional forests 

(Fig. 27). Forest stands ≥90 years of age received a 

suitability value corresponding to the ELT rank described 

above.

In the third suitability index (SI3

), we addressed known 

forest area requirements. The worm-eating warbler 

is considered an area-sensitive species. Robbins et al. 

(1989) found worm-eating warblers in isolated forest 

fragments as small as 21 ha in the Middle Atlantic States, 

but they suggested that the minimum area required for 

breeding was 150 ha with a maximum probability of 

occurrence at 3000 ha. Nesting success in small forest 

fragments (21-56 ha) located within a 70 percent forested 

landscape (10-km radius) in southern New England was 

similar to nesting success in a large, unfragmented forest 

exceeding 750 ha in size (Gale et al. 1997), suggesting 

that landscape context may ameliorate nesting success in 

small forest fragments. Worm-eating warblers may also be 

an edge-sensitive species, but determining whether edge 

sensitivity occurs in addition to area sensitivity or is an 

artifact of the distribution of forest remnants (e.g., along 

moderate to steep slopes unsuitable for agriculture or 

Tree age (years)

0 10 20 30 40 50 60 70 80 90 100 110

Suitability index value

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

N & E slopes

Mesic ridges 

S & W slopes and

 Bottomlands

Dry ridges 

Figure 27.—Worm-eating warbler 

habitat suitability as a function of tree 

age and ecological land type.
33

development) is an area for further research. In the oakhickory forests of central Missouri, worm-eating warblers 

did not occupy a 300-ha isolated forest with over 40 

percent forest edge, but occupied an isolated forest of 

340 ha with less than 10 percent forest edge (Wenny et 

al. 1993). Worm-eating warblers bred in an 800-ha forest 

with less than 10 percent forest edge that was connected 

to forest along the Missouri River (Wenny et al. 1993).

We developed a suitability function based on the 

minimum area (150 ha) required for occupancy in the 

Middle Atlantic States (Robbins et al. 1989) and in 

Missouri (340 ha) (Wenny et al. 1993) (Fig. 28). For 

forest patches 0-1000 ha, we assigned suitability using 

the following function:

( ) 3 (( 530.1425)/ 81.8617)

1.0032

1 patchsize SI

e − − = +

We developed this equation by fi tting a sigmoid function 

such that SI

3

 = 0.01 for 150-ha patches, SI3 = 0.10 for 

340-ha patches, and SI3

 = 1.00 for patches ≥1000 ha.

In the fourth suitability index (SI4

), we reduced 

suitability value based on fi re history for the previous 

decade. Worm-eating warblers nest on the ground in 

depressions along steep slopes (Gale 1995) and thus 

are susceptible to the effects of fi re on nest habitat. Site 

moisture levels may mitigate the initial effects of fi re, but 

the proportion of breeding territories and the density 

of adults declines in the fi rst breeding season post-burn 

(Artman et al. 2001). Over time, additional fi res result 

in a continued decline in density with no recovery to 

preburn conditions observed 1 year post fi re (Artman et 

al. 2001). Aquilani et al. (2003) reported no difference in 

worm-eating warbler abundance in unburned and burned 

areas 1-4 years post-burn, suggesting that the effects of 

fi re may be relatively short-lived. We accounted for the 

effects of fi re that extend several years post-burn but did 

not completely devalue the habitat for the entire decade 

because our model input consisted of a fi re history for the 

previous decade. We assigned a suitability value of 0.50 

to a cell where fi re occurred during the previous decade 

and 1.00 to unburned cells. In applications with shorter 

time steps (e.g., annual instead of decade), SI4

 should 

be adjusted to increase the fi re penalty. Additionally, 

we assumed fi res of different intensities affected wormeating warblers similarly and assigned a single suitability 

value based on the presence of fi re regardless of intensity. 

However, SI4

 may be adjusted to assign suitability value 

based on fi re intensity.

The fi nal habitat suitability value was the geometric mean 

of SI1

, SI2, and SI3, multiplied by SI4, to account for 

reduced suitability due to fi re (Fig. 29):

( ) 3 HSI SI SI SI SI = ×× × 123 4

Forest patch area (ha)

0 100 200 300 400 500 600 700 800 900 1000 1100 1200

Suitability index value

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

Figure 28.—Worm-eating warbler habitat 

suitability for breeding as a function of 

deciduous forest area. Suitability value (SI3

) 

= 1.00 for patches >1000 ha. Left arrow 

indicates 150 ha (minimum area requirement, 

Robbins et al. 1989) and right arrow indicates 

340 ha (minimum area requirement, Hayden 

et al. 1985, Wenny et al. 1993).
34

Application to test landsape

Suitability index 2, which assigned value based on tree 

age and ELT, contributed greatly to the heterogeneous 

pattern observed in the fi nal habitat suitability map. 

Stand size, homogeneity of tree age within a stand, as 

well as delineation of stand boundaries will infl uence the 

pattern observed when SI2

 is applied to other landscapes. 

Suitability index 3, which assigned value based on forest 

patch size, treated continuous canopy gaps (e.g., roads, 

power lines, and railroads) and nonforested areas as patch 

boundaries. Prescribed burning (SI4

) reduced suitability 

for approximately 5 percent of the landscape. The fi nal 

HSI map refl ects the diversity of stand ages, sizes, and fi re 

histories.

Yellow-breasted Chat

Overview

The yellow-breasted chat (Icteria virens) is a migratory 

bird that breeds in North America and winters in 

Central America. Chats are considered a disturbancedependent shrubland species (Eckerle and Thompson 

2001). In the eastern and Midwestern United States, 

chats are found in early second-growth forest and shrubs 

in abandoned agricultural fi elds, clear-cuts, power-line 

Wewapa1y010s2

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1 0 1 2 3 4 Kil t

SI 1 SI 2

SI 3 SI 4

HSI

1 0 1 2 3 4 Kilometers 

N

W E

S

0 0.6

0.1

0.2

0.3

0.4

0.5

0.7

0.8

0.9

1.0

SI Legend

Figure 29.—Worm-eating warbler habitat suitability for breeding on a 4,281-ha portion of 

the Hoosier National Forest, Indiana.
35

corridors, fencerows, forest edges and openings (Eckerle 

and Thompson 2001). Like other shrubland bird species, 

the yellow-breasted chat is both area and edge sensitive 

(Annand and Thompson 1997, Woodward et al. 2001, 

Rodewald and Vitz 2005). Because no previous HSI 

model existed for yellow-breasted chats, we developed 

a new model based on reported ecological relationships 

gathered from extensive literature review.

HSI model

We developed a yellow-breasted chat HSI model for 

breeding habitat in the Central Hardwoods Region. The 

fi rst suitability index (SI1

) identifi ed early successional 

forest habitat and old fi elds for nesting habitat. Yellowbreasted chats nest in low, dense, deciduous and 

coniferous vegetation (Eckerle and Thompson 2001), 

and use old fi elds when woody plants invade and reach 

peak densities in dense shrub thickets (Shugart and 

James 1973). Chats also colonize clearcuts and powerline corridors (Kroodsma 1982). Use of old fi elds in 

Indiana declined when saplings shaded 50 percent of the 

ground (Kahl et al. 1985). In Virginia, chats were present 

in mixed-oak stands 3-12 years of age (Connor and 

Adkisson 1975). In southeast Missouri, chat numbers 

were higher in clearcut areas than shelterwood, group 

selection, or single-tree selection forest regeneration 

methods (Annand and Thompson 1997).

We assigned a suitability value to each cell based on tree 

age and ELT (Table 4). All forested stands <10 years of 

age received SI1

 = 1.00 for both mesic and dry ELTs. On 

the two driest ELTs, suitability value declined as tree age 

increased to 40 years. Trees ≥41 years of age on dry ELTs 

and trees ≥11 years of age on mesic ELTs had SI1

 = 0.00.

In the second suitability index (SI2

), we addressed an 

early successional forest area requirement. The minimum 

patch size used by yellow-breasted chats varies by region 

and type of opening, but chats are rarely detected in 

patches <0.40 ha (Robinson and Robinson 1999). The 

minimum patch size for breeding may be larger than 

0.40 ha observed by (Robinson and Robinson 1999). For 

example, chats nested in uneven-aged openings of 0.14-

0.58 ha in Missouri, but nested in higher densities in 

even-aged openings of 3-13 ha (Gram et al. 2003). Patch 

size in old fi elds surrounded by oak-hickory forest ranged 

from 2.4-16.3 ha in Missouri (Burhans and Thompson 

1999). We developed a breeding suitability function for 

patches of contiguous cells where SI1

 >0.00 and applied 

the function to patches 0.01–5 ha in size (Fig. 30):

( ) 2 (( 3.3389)/ 0.5965)

1.0442

1 patchsize SI

e − − = +

We developed this equation by fi tting a sigmoid function 

such that SI2

 = 0.01 for 0.4 ha patches, SI2 = 0.10 for 

2.0 ha patches, and SI3

 = 1.00 for patches ≥5 ha. Patches 

0.01 ha had SI2

 = 0.00.

In the third suitability index (SI3

), we reduced the value 

of early successional habitat adjacent to mid- to latesuccesional forest and urban edges. Yellow-breasted chats 

are considered edge sensitive, with reduced captures of 

both hatch-year and after-hatch-year chats near matureforest edges (Rodewald and Vitz 2005). Edge avoidance 

likely occurs for a variety of reasons, including reduction 

in nest predation rates; yellow-breasted chat nests located 

within 20 m of a forested edge had higher daily predation 

rates than nests located ≥20 m from a forested edge 

Table 4.—Nesting habitat suitability by tree age and ecological land type (ELT) for yellow-breasted 

chats. We assigned maximum suitability (SI1

 = 1.00) to stands 1-10 years of age on all ELTs. Suitability 

declined to zero for stands 11-40 years of age on the two driest ELTs. Stands >11 years on mesic ELTs 

and stands >41 years on all ELTs had no suitability (SI1

 = 0.00).

SI1

Stand Age Dry Ridges S and W Slopes Mesic Ridges N and E Slopes Bottomlands

 1 – 10 1.00 1.00 1.00 1.00 1.00

11 – 20 0.50 0.50 0.00 0.00 0.00

21 – 30 0.30 0.30 0.00 0.00 0.00

31 – 40 0.10 0.10 0.00 0.00 0.00

≥41 0.00 0.00 0.00 0.00 0.00
36

(Woodward et al. 2001). We applied a moving window 

with a 20-m radius to cells with SI1 >0.00. The moving 

window reduced the value of cells located <20 m from 

a nonsuitable edge (SI3

 = 0.00), but retained the value 

assigned in SI1

 for cells located >20 m from a nonsuitable 

edge (SI3

 = SI1).

The fi nal habitat suitability value was the geometric mean 

of SI1

 and SI

2, multiplied by SI3

, to impose the edgesensitivity penalty (Fig. 31):

( ) 2 HSI SI SI SI = ×× 12 3

Application to test landscape

The fi rst suitability index identifi ed numerous patches 

of early successional forest ranging in size from 0.10-

7.52 ha (Fig. 31). Imposing the minimum patch size 

constraint removed only patches created by small-scale 

disturbance (e.g., treefall gaps) and identifi ed 10 patches 

greater than 5 ha (SI2

 = 1.00). The edge penalty had 

a large effect on the amount of suitable habitat. Small 

patches and irregularly shaped patches (e.g., linear) had a 

higher proportion of habitat lost to the edge penalty than 

large, regularly shaped patches (e.g., round). In addition 

to patch size and shape, harvest type also will infl uence 

the amount of habitat lost to the edge penalty. Harvest 

techniques that retain trees in older size classes (e.g., from 

selective harvest, shelterwood) will lose a greater amount 

of habitat to the edge penalty than even-aged techniques.

CONCLUSIONS

The landscape-level, GIS-based habitat suitability models 

we developed represent the state of our knowledge, 

given tradeoffs between model complexity and model 

utility. We quantifi ed the suitability relationships using 

information gained from empirical studies and expert 

opinion. Some models had detailed functions describing 

suitability relationships because existing data supported 

the complexity of those relationships, whereas other 

models had sigmoid or linear functions that described 

the same relationship. For example, both the wood 

thrush and the worm-eating models had age × ELTspecifi c functions (Fig. 23, Fig. 27) supported by point 

count, nesting, mist-netting, and radio-telemetry data, 

for both nesting and post-fl edging habitat use. Contrast 

this with the American woodcock age × ELT functions 

(Fig. 3), which were developed from a more limited 

database. The resulting suitability relationship clearly 

lacks the specifi city of the wood thrush or worm-eating 

warbler functions. The importance of data collection 

during different parts of the breeding season cannot be 

overemphasized. Additional research may reveal whether 

the functions used in other models are equally complex 

or as simple as we portrayed them. In this way, the HSI 

models reveal the limitations of existing data and suggest 

areas where additional research efforts may be focused.

Early successional forest patch area (ha)

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0

Suitability index value

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

Figure 30.—Yellow-breasted chat habitat 

suitability for breeding as a function of early 

successional forest area. Suitability value (SI2

) 

= 0.00 for patches ≤0.01 ha and SI3

 = 1.00 for 

patches ≥5 ha. Left arrow indicates 0.4 ha 

(minimum area requirement, Robinson and 

Robinson 1999) and right arrow indicates 2.0 ha 

(minimum area requirement; SVE Panel 2002).
37

ACKNOWLEDGMENTS

The development of these models benefi ted greatly 

from the knowledge and comments freely shared by 

many people. Our gratitude extends to the following: 

all participants of the Species Viability Evaluation 

Panel; Cynthia Basile, Clark McCreedy, Judi Perez, 

Kelle Reynolds and Tom Thake of the Hoosier National 

Forest; Sybill Amelon, John Faaborg and the University 

of Missouri Avian Ecology Lab, Mike Larson, Neil 

Sullivan and Mark Yates. We thank Dirk Burhans, 

John Dunning, D. Todd Farrand, Mark Ryan and John 

Tirpak for comments that improved this manuscript. 

The U.S. Forest Service Northern Research Station and 

the University of Missouri provided fi nancial and logistic 

support for this project.

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N

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Rittenhouse, Chadwidk D.; Dijak, William D.; Thompson, Frank R., III; Millspaugh, 

Joshua J. 2007. Development of Landscape-level Habitat Suitability Models for 

Ten Wildlife Species in the Central Hardwoods Region. Gen. Tech. Rep. NRS-4. 

Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern 

Research Station. 47 p.

Reports landscape-level habitat suitability models for 10 species in the Central 

Hardwoods Region of the Midwestern United States: American woodcock, cerulean 

warbler, Henslow’s sparrow, Indiana bat, northern bobwhite, ruffed grouse, timber 

rattlesnake, wood thrush, worm-eating warbler, and yellow-breasted chat. All models 

included spatially explicit variables and relationships based on the best available 

empirical data and expert opinion. Also includes overview of habitat characteristics for 

each species, a discussion of the habitat variables used in each model, and supporting 

reference materials for all assumed relationships between quantity of a resource and 

quality for each species modeled. The models are included in a stand-alone software 

package, Landscape HSImodels version 2.1, available from the U.S. Forest Service, 

Northern Research Station (www.nrs.fs.fed.us/hsi). These models provide a general 

approach for evaluating habitat suitability at large spatial scales.

KEY WORDS: birds, geographic information systems, habitat relationships, mammals, 

reptiles
www.nrs.fs.fed.us

Capitalizing on the strengths of existing science 

capacity in the Northeast and Midwest to attain a more 

integrated, cohesive, landscape-scale research program