Landscape-level Habitat

Suitability Models For

Twelve Wildlife Species

In Southern Missouri

Michael A. Larson, William D. Dijak, Frank R. Thompson,

III, and Joshua J. Millspaugh

United States

Department of

Agriculture

Forest

Service

North Central

Research Station

General Technical

Report NC-233

HSI
Published by:

North Central Research Station

Forest Service U.S. Department of Agriculture

1992 Folwell Avenue

St. Paul, MN 55108

2003

Web site: www.ncrs.fs.fed.us

The use of trade, firm, or corporation names in this publication is for the information and convenience of

the reader. Such use does not sonstitute an official endorsement or approval by the U.S. Department of

Agriculture, Forest Service of any product or service to the exclusion of others that may be suitable. Statements of the contributors from outside the U.S. Department of Agriculture, Forest Service may not necessarily reflect the policy of the Department.

Front cover photo of the prairie warbler: copyright © Doug Wechsler/VIREO.
CONTENTS

Page

METHODS ............................................................................................................................................ 3

Geographic Area of Applicability.................................................................................................... 5

Study Area and Primary Input Data ............................................................................................... 6

Examples of GIS Methods and Common Model Components ....................................................... 8

Land Type Adjustments........................................................................................................... 8

Hard Mast Production ........................................................................................................... 11

Patch and Distance Algorithms ............................................................................................. 12

Moving Window Analysis ..................................................................................................... 16

Software Availability ..................................................................................................................... 16

MODEL DESCRIPTION AND APPLICATION ..................................................................................... 19

Ovenbird ...................................................................................................................................... 19

Prairie Warbler ............................................................................................................................. 19

Hooded Warbler........................................................................................................................... 23

Pine Warbler ................................................................................................................................ 25

Wild Turkey ................................................................................................................................. 26

Ruffed Grouse .............................................................................................................................. 28

Gray Squirrel ................................................................................................................................ 31

Black Bear .................................................................................................................................... 34

Bobcat .......................................................................................................................................... 37

Red Bat ......................................................................................................................................... 40

Northern Long-eared Bat.............................................................................................................. 42

Southern Redback Salamander..................................................................................................... 43

ACKNOWLEDGMENTS...................................................................................................................... 46

LITERATURE CITED ........................................................................................................................... 47

APPENDIX—Landscape Simulation .................................................................................................... 51

LANDSCAPE-LEVEL HABITAT SUITABILITY

MODELS FOR TWELVE WILDLIFE SPECIES

IN SOUTHERN MISSOURI

Michael A. Larson, William D. Dijak, Frank R. Thompson, III, and Joshua

J. Millspaugh

ABSTRACT.—Geographic information systems (GIS) and abundant landscape-level data, often from

remote sensing sources, provide new opportunities for biologists to model and evaluate wildlife

habitat quality. Models of habitat quality have not been developed for some species, and many

existing models could be improved by incorporating updated knowledge of wildlife–habitat relationships and landscape variables such as the spatial distribution of habitat components. Furthermore,

landscape analyses and wildlife population priorities are common features of land management

decision processes. We developed GIS-based habitat suitability index (HSI) models for 12 terrestrial

vertebrate species that represent a range of potential conservation concerns and have diverse habitat

requirements. We developed the models for a large, mostly forested area in southern Missouri and

similar landscapes. The models are based primarily on tree species, tree age, and ecological land

type—variables available in many GIS and vegetation databases. After describing and justifying the

models, we applied them to maps of a study area that contained a wide range of tree ages and forest

patch sizes. We believe application of the habitat models in this landscape demonstrated that they

satisfactorily predict habitat suitability. Readers can download a Windows-based software program

from the Internet (www.ncrs.fs.fed.us/hsi/) to use in implementing the models in other landscapes.

About The Authors:

Michael A. Larson is a

Postdoctoral Fellow, present

address: USGS Patuxent Wildlife

Research Center, 11510 American

Holly Drive, Laurel, MD 20708;

e-mail: mlarson@usgs.gov; and

Joshua J. Millspaugh is an

Assistant Professor, Department of

Fisheries, University of Missouri,

Columbia, MO.

William D. Dijak is a GIS

Specialist and Frank R. Thompson,

III is a Project Leader with the

USDA Forest Service, North Central

Research Station, Columbia, MO.

Quantifying habitat quality is important for

management of wildlife populations and

conservation planning. Habitat suitability index

(HSI) models have been used to evaluate wildlife

habitat and the effects of management activities

and development since the early 1980s (U.S.

Fish and Wildlife Service 1980, 1981). These

models are based on functional relationships

between wildlife and habitat variables. Values of

habitat variables (e.g., herbaceous canopy cover,

tree canopy cover, tree height) are related to

habitat quality on a suitability index (SI) scale

from 0 = “not habitat” to 1 = “habitat of maximum suitability.” Habitat suitability index

scores, also on a 0–1 scale, are usually calculated

using a mathematical formula representing

hypothesized relationships among the individual

SIs. Wildlife–habitat relationships may be

supported by empirical data, expert opinion, or

both (U.S. Fish and Wildlife Service 1980,

1981). Traditionally, HSI models are applied to a

sample of locations within land cover types or

dominant overstory vegetation types. Habitat

quality in an area is typically summarized in

terms of habitat units, which represent the

product of the mean HSI score in each vegetation type and the area of land in that vegetation

type, summed across the study area.

Now that geographic information system (GIS)

software and high-speed computer hardware are

widely available, their use among biologists is

increasing. In addition to providing a new,

powerful analytical tool, GIS technology allows

land and wildlife managers to utilize novel

sources of land cover, vegetation, and other

habitat data, namely remote imagery from aerial

photographs and satellite sensors and GIS

databases of elevation, surface water, climate

data, and ecological land types. Concurrent with

GIS developments have been advances in our

understanding of wildlife–habitat relationships,

especially at landscape scales.
Using GIS for HSI-type habitat evaluations has

several advantages over traditional HSI modeling. It is easier and faster to apply GIS-based

habitat models to large geographic areas because

time- and labor-intensive collection of field data

is not necessary. Spatial structure and landscape

patterns are often important aspects of habitat

quality (Donovan et al. 1987, Rickers et al.

1995, Robinson et al. 1995) and are much easier

to incorporate in GIS models. Furthermore, GISbased habitat models can be used to evaluate

landscapes simulated by spatially explicit forest

landscape models [e.g., LANDIS (He et al. 1996,

1999; Mladenoff and He 1999)], which are

useful for comparing alternative land management scenarios over time (e.g., Marzluff et al.

2002, Shifley et al. 2000).

The full use of GIS in habitat modeling, however, requires the revision of existing HSI models

or the development of new ones. Whereas most

existing HSI models are based on relatively

small-scale habitat variables measured by

biologists in the field, GIS-based HSI models

have the capability to more readily focus on

larger scale habitat variables that can be quantified without going afield. In addition to substituting large- for small-scale variables that may

characterize similar habitat attributes (e.g.,

percent forest and spherical densiometer

measurements both quantify canopy cover), new

landscape variables (e.g., patch size and edge

density) may be incorporated into the HSI

model and habitat suitability may be analyzed at

multiple spatial scales.

Our objective was to develop habitat suitability

index models that could be used in a GIS to

evaluate wildlife habitat quality in large (i.e.,

thousands of hectares) forested landscapes. We

developed a series of single-species habitat models

that satisfied three criteria: they represented a

variety of terrestrial vertebrates, were applicable to

landscapes in southern Missouri, and could be

implemented in raster GIS using data available for

large spatial extents. We described the models and

demonstrated their effectiveness by applying them

to landscape maps with diverse patch sizes and tree

ages that were the result of forest management

simulated by LANDIS. Readers can download a

Windows-based software program from the

Internet (www.ncrs.fs.fed.us/hsi/) to modify the

models and apply them in other landscapes.

2
We selected 12 species representing a variety of

terrestrial vertebrates ranging from endangered

species to game species, ranging from mastdependent to disturbance-dependent species,

and including three classes of vertebrates (table

1). We developed a GIS-based habitat model for

each species. The models for the two bat species

and salamander, which are dormant during

winter, and the four neotropical migrant

songbirds consider only habitat used during

summer. Therefore, we did not address potential

winter-habitat limitation for these species. The

remaining five species are nonmigratory, and

their models reflect year-round habitat requirements.

Habitat models existed for most species we

selected, so we revised those models according

to several needs: to reconcile conflicts among

models if more than one model existed, to revise

or delete variables for which field sampling

would be necessary, to incorporate spatially

explicit variables (i.e., interspersion of life

requisites), to adapt models to our geographic

and ecological context, and to incorporate

advances in our understanding of wildlife–

habitat relationships since the original models

were developed. We developed new models for

the two bat species because no previous models

existed. Model revisions and newly developed

models were based on published empirical data

and expert opinion. These models represent our

synthesis of the best information available, but

potential users should recognize that the models

have not been validated with independent data.

While we encourage model validation efforts,

our assumption is that habitat suitability

models, even if not validated, are a useful

method to synthesize and apply current knowledge of habitat relationships to management or

conservation questions. Furthermore, because

habitat suitability models can be developed from

a broad base of existing knowledge, they may

have more general applicability than statistical

models based on single data sets that are narrow

in scope. Methods that have recently been

reported for setting confidence bounds on

habitat suitability indices (Bender et al. 1996,

Burgman et al. 2001) could potentially be

applied to these models.

Mathematical and logical relationships used to

calculate HSI scores varied depending upon the

number and types of SI variables included in the

model. We used arithmetic or geometric means

(i.e., [x1

 + x2 +...+ xn]/n and [x1 x x2 x ... x xn]

1/n

,

respectively) to combine variables representing

life requisites, or tangible resources. We used a

geometric mean when habitat quality was zero if

the value of any single SI variable was zero,

indicating that habitat characteristics were all

necessary and therefore not substitutable.

Otherwise, we used an arithmetic mean. Some

variables (e.g., edge sensitivity), however, were

used to adjust the value of a life requisite

variable because they did not represent resources themselves. We incorporated such

variables, which typically had two or three

discrete values, using simple multiplication

rather than a mean. Occasionally, life requisites

for a species cannot be expected to occur in a

single site or raster cell (i.e., map pixel). For

example, cover and winter food requisites for

ruffed grouse (see table 1 for scientific names of

modeled species) are satisfied by young and

mature forest, respectively. In such cases, the

HSI score in a cell represented predominantly

one life requisite rather than both because we

used a maximum function to choose between

the contrasting life requisite variables. In such

cases we also included variables to account for

the proximity of the life requisites and the

potential for rare instances when both usually

contrasting requisites were satisfied within a

single cell (see Composition and Interspersion

below). We used a minimum function when we

wanted an HSI score to represent a single

limiting factor (e.g., food or cover for gray

squirrels). Exceptions to these rules were rare,

simplified calculations, and resulted in HSI

scores identical to those based on the methods

described here.

METHODS

3
Table 1.—Species selected for habitat modeling in a southern Missouri landscape, their scientific names, the group or management concern

they represent, and the assumed size of their smallest home range in Missouri

 Size of Citations for

 Group or high quality home range

 Species Scientific name management concern home range size

Ovenbird Seiurus aurocapillus Late-successional, area- 0.8 ha Wenny 1989, Porneluzi

 sensitive songbird and Faaborg 1999

Prairie warbler Dendroica discolor Early-successional, area- 0.5 ha Nolan et al. 1999

 sensitive songbird

Hooded warbler Wilsonia citrina Area-insensitive songbird 0.8 ha Evans Ogden and

 Stutchbury 1994,

 Norris et al. 2000

Pine warbler Dendroica pinus Pine-dependent songbird 1 ha Haney and Lydic 1999,

 Rodewald et al. 1999

Eastern wild turkey Meleagris gallopavo Mast-dependent game bird 102 ha Speake et al. 1975,

 silvestris Wigley et al. 1986,

 Badyaev et al. 1996

Ruffed grouse Bonasa umbellus Disturbance-dependent 5 ha McDonald et al. 1998,

 game bird Fearer 1999

Gray squirrel Sciurus carolinensis Mast-dependent mammal 0.6 ha Flyger 1960, Cordes

 and Barkalow 1972,

 Schwartz and

 Schwartz 1981:149

Black bear Ursus americanus Wide-ranging mammal 20 km2 Clark 1991, van Manen

 and Pelton 1997

Bobcat Lynx rufus Wide-ranging, disturbance- 9.8 km2 Hamilton 1982, Rucker

 dependent mammal et al. 1989

Northern long-eared Myotis septentrionalis Snag-roosting bat and ~13 km2 Foster and Kurta 1999

 bat endangered species

Red bat Lasiurus borealis Live-tree roosting bat ~13 km2 Foster and Kurta 1999

Southern redback Plethodon serratus Terrestrial amphibian Unknown

 salamander

4
Geographic Area of Applicability

Our models were designed for large landscapes

(thousands of hectares) in Missouri that contain

mostly central hardwood forests. Users applying

them to other geographic areas or landscapes,

especially where the dominant land cover is not

forest, should consider modifying and validating

the models.

Study Area and Primary

Input Data

After developing each model, we applied it to

GIS maps of our study area. The study area was

a 3,261-ha, nearly 100 percent forested, oakdominated landscape in the Mark Twain

National Forest in southern Missouri (fig. 1). It

Figure 1.—Geographic

location of the 3,261-ha

(approximately 5 x 7 km)

study area relative to the state

of Missouri and the mostly

forested Ozark region.

5
occurs in a 1.8-million-hectare landscape that

also contains contiguous (92%) hardwood forest

(Porneluzi and Faaborg 1999).

Our models utilize digital, raster-based maps of

ecological land types and the age and species

group of dominant overstory trees, which are

available from a variety of sources such as forest

inventories, interpreted aerial photos, and

classified satellite imagery. For our demonstration we used output from the 100th year of

simulated forest management on the study area

(see appendix). The simulation resulted in a

landscape with a wide range of tree ages and

patch sizes (table 2, figs. 2 and 3), which is

helpful for evaluation of model performance. We

used a raster resolution of 30 m2

 (0.09 ha) and

retained that resolution in our HSI models.

Therefore, SI values were based on attributes of

a single raster cell rather than a forest stand or

animal home range whenever possible. A high

resolution is desired because it results in the loss of

less information about the real landscape than

lower resolutions, and if desired, the data or

associated SI values can be averaged at a coarser

scale that may better represent how an animal

perceives its environment.

Dominant overstory tree groups were white oak

(Quercus alba, Q. stellata), red oak (Q. rubra, Q.

coccinea, Q. velutina, Q. marilandica), maple (Acer

spp.), and short leaf pine (Pinus echinata). We

assumed that differences in the age and type of

dominant trees and land type classifications (table

3, fig. 2) among pixels adequately represented

variation in understory characteristics that may

affect habitat quality for some species (e.g., sapling

density and shrub cover) (McKenzie et al. 2000).

The only openings in our study area were cells

containing trees ≤10 years old, indicating a recent

Table 2.—Percentage of area in the southern Missouri study area in categories of tree age and

species group after 100 years of simulated forest management. The sum of reported percentages

may not equal sums in the last column and row due to rounding.

Tree species group

Tree age

 (years) Red oak White oak Maple Pine Sum

 1– 10 0.5 3.7 0.0 0.3 4.4

 11– 20 0.4 3.3 0.0 0.4 4.1

 21– 30 0.1 4.1 0.0 0.4 4.5

 31– 40 0.1 3.5 0.0 0.3 3.9

 41– 50 0.1 3.8 0.0 0.3 4.1

 51– 60 0.1 3.4 0.0 0.2 3.7

 61– 70 0.1 4.0 0.0 0.3 4.4

 71– 80 0.1 4.8 0.0 0.1 5.0

 81– 90 0.3 4.7 0.0 0.1 5.1

 91–100 0.3 3.1 0.0 0.3 3.7

101–110 0.0 0.0 0.0 0.0 0.0

111–120 8.4 9.4 0.1 2.0 20.0

121–130 0.0 0.0 0.0 0.0 0.0

131–140 0.0 0.0 0.0 0.0 0.0

141–150 9.7 8.7 1.5 3.8 23.7

151–160 0.0 0.0 0.0 0.0 0.0

161–170 6.1 5.0 0.7 1.4 13.3

Sum 26.3 61.3 2.3 10.0 100.0

6
7

Figure 2.—GIS coverages of

the southern Missouri study

area (extent = approximately 5

x 7 km; resolution = 0.09 ha)

representing input variables for

the habitat quality models: (a)

age of dominant trees, (b)

species group of dominant

trees, (c) ecological land types,

(d) land type groups, and (e) a

paved road (28.7 ha), private

land (9.5 ha) that appeared as

empty cells, and permanent

ponds (1.5 ha). See the

appendix for more details.
stand-replacing disturbance, and some glades.

The right-of-way of a State highway bisecting

the study area (28.7 ha) and a few small patches

of private land (<2 ha each, 9.5 ha total)

appeared as empty cells (fig. 2). Depending

upon the wildlife species, we also used GIS

coverages for the paved road and permanent

ponds in some habitat quality models (fig. 2).

Examples of GIS Methods and

Common Model Components

Land Type Adjustments

Forest composition and dynamics can vary

considerably by ecological land type (Host et al.

1987, Kupfer and Franklin 2000, Miller 1981).

Land type is also a major factor that determines

other forest characteristics such as density and

composition of herbaceous and woody understory vegetation (Hix and Pearcy 1997, Host and

Pregitzer 1992, Shifley and Brookshire 2000).

Much of wildlife habitat in forest ecosystems is

often characterized by successional stage and

variables associated with understory or ground

cover. Therefore, we used the interaction of age of

dominant trees and land type as an SI variable in

many of our models. Usually, we accomplished this

in one step by specifying SI values in tables with

tree ages in rows and land type categories in

columns (e.g., table 4). Specification of such SI

values was based on differences among land type

categories in the rate of succession and the density

of vegetation. Occasionally, an SI was dependent

solely on land type, such as in the hooded warbler

model, and the interaction with other variables

occurred in the HSI calculation. We used similar

methods to adjust SI values for tree species group

(e.g., see the model for ovenbirds, which do not

use areas dominated by conifers).

Figure 3.—Cumulative

distribution of patch sizes by

tree age category in the

southern Missouri study

area. Young = 1–50 years

(distributions for seedling,

sapling, and pole classes

were very similar), mature =

51–100 years, and old

growth = 101+ years. Not

represented are 0.5 percent

of young forest patches (n =

2,163) 10–26 ha in size, 0.6

percent of mature forest

patches (n = 1,296) 10–127

ha in size, and 2 percent of

old-growth forest patches (n

= 544) 10–50, 627, and 798

ha in size. Note that the

origin is not 0,0 and the xaxis is a log10 scale.

8

0.4

0.6

0.8

1.0

0.1 1.0 10.0

Patch size (ha)

Cumulative proportion

Young

Mature

Old growth
Table 3.—Relationships among land type categories

Land types Land types Ecological land typea

 used in used to predict

our models oak mast production Land form Soil and vegetation

 Glade Glade Southwest side slope Glade savanna

Side slope Dolomite/limestone glade

 Dry Southwest side slope Southwest side slope Dry chert forest

Flat Ridge Xeric chert forest

Ridge Dry chert forest

Flat Xeric chert forest

Flat Dry chert forest

Limestone Northeast side slope Dry-mesic limestone forest

Side slope Xeric limestone forest

Side slope Dry limestone forest

 Mesic Northeast side slope Northeast side slope Dry-mesic chert/sand forest

Upland drainage Upland waterway Gravel wash

Upland waterway Dry bottomland forest

Upland waterway Dry-mesic bottomland forest

Mesic Low floodplain Wet-mesic bottomland forest

Floodplain Calcareous wet forest

High floodplain Mesic bottomland forest

Toe slope Mesic forest

Sinkhole Mesic forest

Sinkhole Acid seep forest

 a

 Based on the classification for the Mark Twain National Forest (Miller 1981).

9
Table 4.—Habitat suitability of forest by habitat requisite, land typea, and age of dominant overstory trees for six bird species in southern Missouri

Hooded Wild turkey

 warbler

 Age of Nesting and

dominant Ovenbird Prairie warbler Nest- brood cover Adult cover Ruffed grouse

 trees ing Forag- Pine

 (years) Glade Dry Mesic Glade Dry Mesic cover ing warbler Glade Other Glade Other Glade Dry Mesic

0b 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 1.0 1.0 0.0 0.00 0.0 0.0 0.0

 1–10 0.0 0.0 0.1 1.0 1.0 0.5 1.0 0.0 0.00 1.0 0.5 0.0 0.00 0.0 0.8 1.0

11–20 0.0 0.1 0.2 1.0 0.5 0.1 0.0 0.0 0.00 1.0 1.0 0.0 0.00 0.0 0.8 1.0

21–30 0.0 0.3 0.4 0.8 0.1 0.0 0.0 0.0 0.00 1.0 0.0 0.0 0.25 0.0 0.5 0.3

31–40 0.0 0.6 0.7 0.5 0.0 0.0 0.0 0.0 0.25 1.0 0.0 0.0 0.50 0.0 0.0 0.0

41–50 0.0 0.9 1.0 0.3 0.0 0.0 0.0 0.0 0.50 1.0 0.1 0.0 0.75 0.0 0.0 0.0

51–60 0.0 0.9 1.0 0.3 0.0 0.0 0.0 0.0 0.75 1.0 0.2 0.0 1.00 0.0 0.0 0.0

≥61 0.0 0.9 1.0 0.3 0.0 0.0 0.0 1.0 1.00 1.0 0.3 0.0 1.00 0.0 0.0 0.0

a Land type categories are described in table 3.

b Trees not present (e.g., failed regeneration after disturbance).

10
Hard Mast Production

Much of the Missouri Ozarks is dominated by

mast-producing hardwoods, especially oaks.

Hard mast provides an important fall and winter

food resource for many wildlife species. Sullivan

(2001) developed a mast production model to

complement other subroutines in LANDIS. The

main input variables were age of dominant trees

and land type, and the main output was an

index of mast production. The model accounted

for varying ratios and densities of red and white

oaks within cells and incorporated spatial and

temporal stochasticity. For the mast-dependent

wildlife species we selected, we used output

from Sullivan’s (2001) model to develop habitat

suitability relationships for mast production. We

used mean mast index values from a single

application of the model in our simulated study

area to determine relative mast production by

tree species group and age of dominant trees on

a cell. Assuming no mast production on cells

dominated by maples or pines, we developed

separate relationships for red and white oaks

only. We rescaled the raw index values to a 0–1

SI interval so that SI = 1 approximately coincided with the maximum mean production of

mast on most land types for both red and white

oaks (fig. 4). This resulted in SI values greater

than one for 20 percent of tree age x land type x

tree species group combinations. We think this

is appropriate, given that the relationship is

based on mean mast production and that

maximum mast production could be greater

than five times higher (Sullivan 2001). We fit

fourth-order polynomial regression functions

forced through the origin to the rescaled index

values over tree age for each tree species group x

land type combination (table 5, fig. 4). We used

Figure 4.—Suitability index

values of cells dominated by

red oaks (top) and white oaks

(bottom) for hard mast

production based on an index

developed by Sullivan (2001)

for forests in southern Missouri. Production varies by

land type, and index values >1

are rounded down to 1 in the

HSI models.

11

0.0

0.5

1.0

1.5

0 50 100 150 200

Tree age (years)

Suitability index

Flat NE slope SW slope

Limestone Mesic Upland drainage

Glade

0.0

0.5

1.0

1.5

0 50 100 150 200

Tree age (years)

Suitability index

Red oak

White oak
the regression functions in the HSI models and

assigned SI = 0 if the function resulted in values

<0 and SI = 1 if the function resulted in values

>1 (fig. 5). Hard mast production is a variable in

models for wild turkey, ruffed grouse, gray

squirrel, and black bear.

Patch and Distance Algorithms

Many species are sensitive to the size of habitat

patches. In models for prairie warblers and

ruffed grouse, we used a patch-definition

algorithm to aggregate cells containing suitable

habitat as defined earlier in the model by SI

relationships with tree age and land type. A

habitat cell was aggregated into a patch if it was

immediately adjacent to another habitat cell,

either by a shared side (i.e., horizontally or

vertically) or corner (i.e., diagonally). Once

habitat patches were defined, we assigned SI

values based on the size of patches (fig. 6).

Table 5.—Polynomial regression coefficients for the mast production suitability index. The y-intercept is zero for all

regression equations.

 Regression parameter

 Oak

group Land typea Tree age (Tree age)2 (Tree age)3 (Tree age)4

Red Flat -7.2535 x 10-3 7.4473 x 10-4 -7.6811 x 10-6 2.0813 x 10-8

NE side slope -1.7311 x 10-2 1.0451 x 10-3 -1.0006 x 10-5 2.6178 x 10-8

SW side slope -1.1024 x 10-2 7.2305 x 10-4 -7.0058 x 10-6 1.8413 x 10-8

Limestone -1.4904 x 10-2 7.2273 x 10-4 -6.5360 x 10-6 1.6524 x 10-8

Mesic -1.4810 x 10-2 6.9970 x 10-4 -6.3497 x 10-6 1.6164 x 10-8

Upland drainage -7.5360 x 10-3 4.4903 x 10-4 -4.2507 x 10-6 1.1012 x 10-8

Glade -6.9367 x 10-3 3.0603 x 10-4 -2.6928 x 10-6 6.6967 x 10-9

White Flat -9.2984 x 10-3 4.1544 x 10-4 -1.7617 x 10-6 5.1103 x 10-10

NE side slope 2.0223 x 10-3 -7.0890 x 10-5 1.8744 x 10-6 -7.2031 x 10-9

SW side slope -3.5297 x 10-3 1.2540 x 10-4 1.5010 x 10-7 -2.8681 x 10-9

Limestone -7.5926 x 10-3 2.5998 x 10-4 -1.1299 x 10-6 6.1906 x 10-10

Mesic -3.3179 x 10-3 1.1667 x 10-4 -3.8002 x 10-8 -1.9354 x 10-9

Upland drainage -2.6792 x 10-3 8.8265 x 10-5 1.8711 x 10-7 -2.4772 x 10-9

Glade -4.2093 x 10-3 1.5445 x 10-4 -6.1443 x 10-7 1.6130 x 10-11

 a Land type categories are described in table 3.

We used different algorithms to assign SI values

based on a cell’s distance from a habitat requisite or

landscape features that individuals avoid. For

example, our two bat species require access to

surface water and black bears avoid paved roads.

The distance algorithm assigned a value to each cell

in the landscape; the value was equal to the

distance between the cell and the nearest cell

representing either water or a road. The SI value

was based on that distance (fig. 7).

Moving Window Analysis

A vector-based GIS is based on lines and points, so

boundaries between land units are explicit features

of the landscape, and metrics such as distance from

a point to a boundary can be measured easily. One

limitation of vector-based GIS is that landscape

characteristics are considered uniform within

defined polygons. We developed our habitat

models for raster-based GIS, in which landscape

12
Figure 5.—Hard mast

suitability index values of cells

dominated by red oaks and

white oaks applied to the study

area in southern Missouri.

13
Figure 6.—Example

application of a patch

definition algorithm. Cells of

varying suitability (top) are

aggregated into habitat

patches (bottom), which

vary in quality based on

size. Extent = approximately 660 m wide;

resolution = 0.09 ha.

14
Figure 7.—Example application of a distance algorithm. A

paved road through the center

of the study area (top) and four

ponds (bottom) affect habitat

suitability based on distance.

Private lands appear as empty

cells.

15
characteristics occupy square grid cells. Therefore, characteristics are uniform only at the

resolution of the raster cells. Another benefit is

that boundaries between groups of cells representing, for example, forest stands are not fixed.

A main limitation of raster-based GIS is that

boundaries are not explicitly defined, so

measuring distances to them is difficult. We

used moving window analyses to overcome that

limitation.

Edge Sensitivity.—In models for ovenbirds and

prairie warblers, we used moving window

analyses to evaluate habitat quality near edges

between suitable habitat and non-habitat. First,

we identified cells containing habitat as those

containing a nonzero value for ≥1 SI relationship; all other cells were defined as non-habitat.

Then, assuming habitat near an edge with nonhabitat was of lower quality than habitat farther

from an edge, we used a moving window

approximately twice as wide as presumed edge

effects to assign an SI value for edge sensitivity.

The central cell of the window received an SI

value ≤0.5 for edge sensitivity if the window

contained non-habitat (i.e., the minimum value

in the window was zero). Otherwise, the central

cell of the window received an SI value of 1.0

for edge sensitivity because habitat edges would

be farther from the central cell than the distance

of presumed edge effects.

After the SI for edge sensitivity is multiplied by

other SI values, non-habitat would appear as

non-habitat (even though it was assigned SI = 1

for edge sensitivity), and habitat suitability near

non-habitat edges would be half as high relative

to similar cells farther than the threshold

distance from edges (fig. 8). We also used this

method in models for red bats and northern

long-eared bats, both of which prefer foraging

near edges and forest openings.

Composition and Interspersion.—In models for

hooded warblers, wild turkeys, ruffed grouse,

and black bears, we used moving window

analyses to assign SI values based on the

composition of habitat requisites. First, we recoded

habitat suitability based on requisites such as

nesting cover, SI1

, and foraging areas, SI2, in the

same GIS map according to the following rules:

code=0 if SI1 < 0.5 and SI2 < 0.5, code=1 if SI1 ≥

0.5, code=2 if SI2 ≥ 0.5, and code=3 if SI1 ≥ 0.5

and SI2 ≥ 0.5 (fig. 9). If both habitat requisites were

present in ideal proportions in a moving window

the approximate size of a high quality home range,

suitability for composition was greatest (i.e., SI3 =

1; fig. 9). The observed proportion of a habitat

requisite was based on the number of cells containing the corresponding codes:

Proportionobserved =

( )( )

( )( ) No of cellsin thewindow No of s

No of s No of s

. . 3

. 1 . 3

+

+

We used a similar approach to evaluate the proximity of habitat components in a moving window

analysis for northern long-eared bats. Recoding of

SIs using unique, generic numbers in a single GIS

map was the same, but the new SI was based

simply on the presence of cells of each type within

the window, not on their proportions.

Software Availability

Readers can download a Windows-based software

program from the Internet (www.ncrs.fs.fed.us/hsi/)

to modify the models and apply them in other

landscapes.

16
Figure 8.—Example using a moving window to evaluate habitat suitability near edges with non-habitat. SI1 defines habitat (1) and non-habitat (0). SI2 based on a 3- x 3-

cell moving window analysis. For the central cell of the moving window, SI2 = 0.5 if the minimum value in the window is SI1 = 0; otherwise SI2 = 1. Extent = approximately

690 m wide; resolution = 0.09 ha.

17
Figure 9.—Example using a moving window to evaluate composition of habitat requisites. Nesting cover, SI1, and foraging areas, SI2, are recoded in the same GIS map (1 if SI1 ≥

0.5, 2 if SI2 ≥ 0.5, 0 if SI1 < 0.5 and SI2 < 0.5). Suitability based on composition (SI3) is 1 if half of a 4-cell-radius moving window contains high quality nesting cover and the

other half contains high quality foraging habitat. SI3 = 0 if high quality habitat for either requisite is absent from the window. Extent = 1.32 km wide; resolution = 0.09 ha.

18
Ovenbird

We chose ovenbirds because they are areasensitive songbirds that select late-successional

hardwood forests (Crawford et al. 1981, Stauffer

and Best 1980) (table 1). Ovenbirds require a

closed overstory canopy and a relatively open

understory (Neimi and Hanowski 1984) because

they forage and nest on the ground (Van Horn

and Donovan 1994) (table 6). Although their

territories are <3 ha (Porneluzi and Faaborg

1999, Van Horn and Donovan 1994, Wenny

1989), ovenbirds usually do not occur in small

forest patches and can experience reduced

pairing success in forest patches <500 ha in size

(Hayden et al. 1985, Robbins et al. 1989, Van

Horn et al. 1995, Villard et al. 1993). Furthermore, ovenbirds avoid pines (Collins 1983; c.f.,

Penhollow and Stauffer 2000) and the edges of

even large forest tracts (Flaspohler et al. 2001a,

b; Ortega and Capen 1999; c.f., Sabine et al.

1996). A pattern recognition model of ovenbird

habitat in the Mark Twain National Forest

indicated higher probabilities of use in forest

stands associated with oak overstory, greater

basal area (≥7.43 m2

/ha), intermediate crown

closure (60–70%), and less ground cover (≤10%) (Sweeney and Dijak 1985).

Our ovenbird habitat model contained three

suitability indices. The first SI related high

habitat quality with trees >50 years old on mesic

sites (Thompson et al. 1992, Yahner 1986)

(table 4, fig. 2). Suitability of forest of a given

age was lower on dry sites because succession to

a relatively closed canopy and open understory

occurs more slowly there (table 4). The second

suitability index addressed ovenbird preference

for broadleaf forest; SI

2

 = 0 if the dominant trees

on the cell were in the pine group, and SI

2

 = 1

otherwise. Therefore, oak-pine stands would receive intermediate values for SI2

 and stands

covered predominantly by pines would appear

as unsuitable habitat. The third SI reduced

habitat suitability by half within 30 m of an edge

between mature forest and either permanent or

temporary openings. Negative edge effects on

nesting success may extend further into a forest

(Flaspohler et al. 2001b), but we chose to be

conservative because they have not been well

documented in southern Missouri. We assumed

that habitat patches in our landscape would be

sufficiently large to preclude other spatial effects

that make ovenbirds area-sensitive. We implemented SI3

 using a moving window analysis. We

moved a square, 3- x 3-cell (0.81-ha) analysis

window across a GIS layer containing output

from SI

1

. We assigned a value for SI

3

 to the

central cell of the window. The value was SI

3

 =

0.5 if the value of SI

1

 in any of the cells within

the window was 0 (or “no data” due to presence

of a road). Otherwise, the value of SI

3

 for the

central cell was 1. We moved the window

systematically one cell at a time.

The HSI score for the ovenbird model was the

product of the three suitability indices because

tree species group and edge effects modified the

suitability of appropriate-aged forest (table 6).

These relationships were evident in the HSI map

of the study area. The first suitability index was

the main component of habitat suitability for

ovenbirds, and SI

2

 and SI

3

 reduced suitability in

small areas of pines and near openings (fig. 10).

Prairie Warbler

Prairie warbler habitat consists of early-successional woody vegetation (Nolan et al. 1999). In

our study area this occurred in recently disturbed forest and glades (table 4). Ideally,

patches of suitable habitat are >3.5 ha, but

patches as small as 0.4 ha are used (Nolan

1978:331–337, Robinson and Robinson 1999).

Annand and Thompson (1997) did not detect

prairie warblers in areas subject to single-tree

and group-selection harvests in southeastern

Missouri. Although prairie warbler habitat may

be associated with edge density in some areas

(Penhollow and Stauffer 2000), habitat quality

may be lower near edges between suitable

habitat and other land cover types than farther

MODEL DESCRIPTION AND APPLICATION

19
Table 6.—Relevant life and habitat requisites and their corresponding HSI model parameters from LANDIS for 12 species in southern Missouri

 Model parameters

 Species Life requisite Habitat requisite and implementation HSI equation

Ovenbird Nesting cover Mature hardwood forest SI1: Tree age by land typea

and food SI2: Tree species group

Nesting cover Edge avoidance SI3

: Moving window

 analysis on SI1 SI1 x SI2 x SI3

Prairie Nesting cover Early-successional woody SI1: Tree age by land type

warbler and food vegetation

Large habitat patches SI2: Patch size algorithm

Nesting cover Edge avoidance SI3: Moving window

 analysis on SI1 (SI1

 x SI2)0.5 x SI3

Hooded Nesting cover Early-successional hard- SI1: Tree species group

warbler wood vegetation Tree age (see SI3 below)

Food Mature hardwood forest SI1: Tree species group

Tree age (see SI3 below)

Nesting cover Site productivity SI2

: Land type

and food Interspersion of nesting SI3: Moving window

and foraging habitat analysis on tree age SI1

 x (SI2 x SI3)0.5

Pine Nesting cover Mature coniferous forest SI1: Tree age

warbler and food SI2

: Tree species group SI1 x SI2

Wild Nesting and Forest openings SI1: Tree age by land type

turkey brooding cover

Adult cover Mature forest SI2: Tree age by land type

Fall and winter Hard mast SI3

: Model of tree age,

food tree species group,

 and land typeb

Cover and food Interspersion of life SI4: Moving window

requisites analysis on SI1 and (max{SI1, [(SI2 + SI3) /

 mean of SI2

 and SI3 2]} x SI4)0.5

Ruffed Fall and winter Hard mast SI1: Model of tree age,

grouse food tree species group,

 and land type

Cover Dense forest regeneration SI2: Tree age by land type

Large habitat patches SI3: Patch size algorithm

Food and cover Interspersion of life SI4

: Moving window

requisites analysis on SI1 and {max[SI1, (SI2 x SI3)0.5]

 mean of SI2 and SI3 x SI4

}0.5

Gray Winter food Hard mast SI1: Model of tree age,

squirrel tree species group,

 and land type

Cover Mature forest SI2: Tree age by land type min(SI1, SI2)

Black bear Fall and winter Hard mast SI1: Model of tree age,

food tree species group,

 and land type

Summer and Soft mast SI2: Tree age by land type

fall food

Food Interspersion of SI3: Moving window

seasonal foods analysis on SI1

 and SI2

Cover Road avoidance SI4: Distance-to-road [max(SI1, SI2) x SI3]

0.5

 algorithm x SI4

(table 6 continued on next page)

20
from edges. Woodward et al. (2001) documented that prairie warblers in southern

Missouri avoided nesting ≤20 m from a forest

edge.

Lancia and Adams (1985) observed a high

correlation between point counts of prairie

warblers and HSI values from Sheffield’s (1981)

HSI model, which was based on stand age and

stocking densities in the shrub and overstory

layers. Our HSI model for prairie warblers was

similar but included two new spatial factors.

Our first SI defined suitable habitat according to

tree age and land type category (Robinson and

Robinson 1999, Thompson et al. 1992) (table 4,

fig. 2). The second SI scored suitable habitat

patches (as defined by SI1

) based on their size

using a patch-definition algorithm. Habitat

patches ≤4 raster cells (0.36 ha) in size received

SI2 = 0, those ≥39 cells (3.51 ha) in size received

SI2 = 1, and those between 5 and 38 cells (0.45

and 3.42 ha) in size received SI2

 = (0.32 x patch

size in ha) – 0.13 (fig. 11). The third SI defined

areas within 30 m of an edge between suitable

and unsuitable habitat (as defined by SI1) using

a square, 3- x 3-cell moving window (Woodward et al. 2001). If the minimum value in the

window was SI1

 = 0, the central window cell

received SI3 = 0.5 because it was near an edge;

otherwise, SI3 = 1.

The HSI score for a raster cell was the geometric

mean of SI1 and SI2 multiplied by SI3 (table 6).

Habitat suitability in the study area was greatest

in the interior of large patches, and most young

forest patches resulting from tree mortality or

group-selection cutting were too small to

provide habitat for prairie warblers (fig. 12).

Bobcat Prey habitat Proportion of early SI1: Moving window

successional forest and analysis on tree age

openings in home range by land type

Cover Road avoidance SI2

: Distance-to-road

 algorithm SI1 x SI2

Red bat Roost sites Crown of large, live trees SI1: Tree age

Land type preferences SI2

: Land type

Water Proximity to roost sites SI3: Distance-to-water

 algorithm

Food Edge between forest SI4

 and SI5: Moving window max[(SI1 x SI2 x SI3)0.33,

canopy and openings analyses on SI1 ({SI1

 x [max(SI4, SI5)]2}0.33)]

Northern Roost sites Dead branches on SI1

: Tree age

long-eared large, live trees

bat Large snags SI2: Tree age

Water Proximity to roost sites SI3: Distance-to-water

 algorithm

Food Forest canopy and gaps SI4

: Moving window

 analysis on SI1

Roost sites Interspersion of life SI5: Moving window

and food requisites analysis on mean of

 SI1, SI2, and SI3 and {max[(SI1 x SI2 x SI3)0.33,

 product of SI1

 and SI4 (SI1 x SI4)] x SI5} 0.5

Southern Food and cover Mature forest SI1: Tree age

redback Moist substrate SI2

: Land type

salamander (SI1 x SI2)0.5

 a See table 3 for definitions of glade, dry, and mesic land types.

 b

 The hard mast production model was developed by Sullivan (2001). See also figs. 2 and 5.

(table 6 continued)

 Model parameters

 Species Life requisite Habitat requisite and implementation HSI equation

21
Figure 10.—

Application of

the ovenbird

habitat quality

model to a

3,261-ha unit of

the Mark Twain

National Forest

in southern

Missouri. There

are separate

images for

suitability as

mature forest

(SI1

), suitability

as deciduous

forest (SI2

),

suitability as

forest interior

(SI3), and HSI =

SI1

 x SI2 x SI3.

22
Figure 11.—Suitability of

habitat patches for prairie

warblers is positively related to

patch size.

Hooded Warbler

Hooded warblers nest in openings and areas

with young hardwood regeneration (Evans

Ogden and Stutchbury 1994). Dense, shrubby

vegetation provides good nesting cover. Hooded

warblers also require mature hardwood stands

for foraging (Evans Ogden and Stutchbury

1994). These two different vegetation types need

to occur in proximity of each other to provide

good hooded warbler habitat. The highest

quality hooded warbler habitat contains both

vegetation types within the size of a typical

territory (0.5–1.1 ha; Evans Ogden and

Stutchbury 1994, Norris et al. 2000). Such

conditions may occur due to single-tree or

group-selection forest disturbances (Annand and

Thompson 1997, Baker and Lacki 1997,

Robinson and Robinson 1999). The only

previously published habitat model for hooded

warblers was a GIS model developed and tested

by Dettmers and Bart (1999). Their model

predicted hooded warbler presence from

topography metrics and the presence of forest. It

was based on their characterization of hooded

warblers in southeastern Ohio as a “hilltop

species associated with the dry moisture

conditions and convex land forms of ridges”

(Dettmers and Bart 1999:157). That description

conflicts with data indicating that in southern

Missouri and northern Arkansas hooded

warblers are associated with mesic sites (Smith

1977, Thompson et al. 1992). Evans Ogden and

Stutchbury (1994:4) also noted that hooded

warblers are “often associated with moist

woodlands and ravines.”

Our HSI model for hooded warblers was a

function of three variables. The first indicated

that cells dominated by pines provided no

habitat (i.e., SI1

 = 0; fig. 2). All other cells were

assigned SI1 = 1. The second SI reflected the

higher densities of hooded warblers on mesic

sites (SI2 = 1) than on dry sites (SI2 = 0.4) and

glades (SI2 = 0) in Missouri (Thompson et al.

1992) (table 3). The third SI specified a relationship between habitat suitability and the proportions of nesting and foraging habitat in a

territory-sized moving window (3 x 3 cells = 0.8

ha; table 1). Raster cells with dominant trees 1

to 10 years old were assigned a generic code of 1

for providing nesting habitat, cells with dominant trees >60 years old were assigned a generic

code of 2 for providing foraging habitat, and all

other cells were assigned a generic code of 0.

The ideal proportions of generic codes in the

analysis window were 22 percent (2 cells) 1s

and 78 percent (7 cells) 2s (Annand and

Thompson 1997). When observed proportions

were ideal, the central window cell received SI3

= 1. Values of SI3 declined as proportions

23

0.0

0.2

0.4

0.6

0.8

1.0

0.0 1.0 2.0 3.0

Patch size (ha)

Suitability index
deviated from the ideal according to the following equation:

SI3

 = (1 – |proportion of 1sobserved – 0.22|) x (1 –

|proportion of 2sobserved – 0.78|).

If either habitat requisite was absent in the

window, SI3 = 0 (table 7).

The HSI score was the product of SI1

 and the

geometric mean of SI2

 and SI3 (table 6). Large

amounts of optimal nesting cover and food existed

in the study area, but juxtaposition of those life

requisites, as identified by SI3, drastically limited

overall habitat suitability for hooded warblers (fig.

13). As expected, suitable habitat appeared at the

interface between nesting and foraging habitats,

along the edges of large openings, and near recent

group-selection cuts and gaps created by wind.

Figure 12.—

Application of

the prairie

warbler HSI

model to a

3,261-ha unit of

the Mark Twain

National Forest

in southern

Missouri. There

are separate

images for

suitability as

early-successional forest

(SI1

), suitability

as large habitat

patches (SI2

),

suitability as

habitat interior

(SI3), and HSI =

(SI1

 x SI2)

0.5 x

SI3.

24
Table 7.—Values of SI3

 based on the proportion of cells providing good nesting cover (hardwoods 1–10 years

old) and foraging habitat (hardwoods >60 years old) for hooded warblers in a home range-sized moving window

(0.8 ha = 9 cells) in a southern Missouri landscape. Optimal proportions are 0.22 and 0.78, respectivelya

.

Proportion Proportion in foraging habitat

in nesting

 cover 0.00 0.11 0.22 0.33 0.44 0.56 0.67 0.78 0.89 1.00

 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

 0.11 0.00 0.29 0.39 0.49 0.59 0.69 0.79 0.89 0.79

 0.22 0.00 0.33 0.44 0.55 0.66 0.78 0.89 1.00

 0.33 0.00 0.29 0.39 0.49 0.59 0.69 0.79

 0.44 0.00 0.26 0.34 0.43 0.51 0.61

 0.56 0.00 0.22 0.29 0.36 0.44

 0.67 0.00 0.18 0.24 0.30

 0.78 0.00 0.15 0.19

 0.89 0.00 0.11

 1.00 0.00

 a SI3 = (1 – |[proportion of cells with dominant trees 1–10 years old] – 0.22|) x (1 – |[proportion of cells

with dominant trees >60 years old] – 0.78|).

Pine Warbler

Pine warblers were the only pine-dependent

species we selected (table 1), and they do not

occur in areas of pure hardwood forest

(Johnston and Odum 1956, Neimi et al. 1997,

Rodewald et al. 1999). Densities of pine warblers generally increase with stand age (Conner

et al. 1979, Evans 1978, Haney and Lydic 1999,

Thompson et al. 1992) and may also be positively correlated with distance from streams

(Murray and Stauffer 1995). Lancia and Adams

(1985) observed a high correlation between

point counts of pine warblers and values from

HSI models (Sheffield 1981, Schroeder 1982).

Schroeder’s (1982) HSI scores were the geometric mean of three variables. Suitability was

positively related to canopy closure of pines and

seral stage of the stand but was negatively

related to presence of a tall deciduous understory. Sheffield’s (1981) model described stands

<15 years old and not predominantly pine or

oak-pine as unsuitable and reserved the highest

suitability for stands with high basal area of

pines, high stocking density of pines >6 m tall,

and low stocking density in the understory.

We developed a model similar to the two

previous ones (Schroeder 1982, Sheffield 1981).

Our first SI1

 was based on tree age, with trees

>60 years old being of the highest quality (table

4, fig. 2). We assumed that in pine stands tree

age was highly correlated with canopy closure

and basal area of pines. Our second variable was

SI2 = 1 if the dominant tree species group was

pine or SI2 = 0 if it was not. The HSI score was

the product of SI1

 and SI2 (table 6). Pine forest

limited the location of pine warbler habitat in

the study area, but the tree age variable (SI1

)

identified less suitable and unsuitable pine

forests in the HSI map as well (fig. 14).

Although it would have been feasible to determine the presence and approximate dominance

status of a deciduous understory in cells

dominated by pines (Schroeder’s V3

), we chose

not to include it as a variable in our model

because it would be difficult or impossible to

incorporate in applications of our model that are

not based on LANDIS. Therefore, we had to

assume that a high density or tall deciduous

understory was absent in all areas dominated by

25
pines >30 years old. Violation of that assumption would result in our model overestimating

the quantity and quality of pine warbler habitat.

Wild Turkey

The wild turkey is a popular game bird throughout its range. Schroeder (1985) developed an

HSI model for wild turkeys as a function of

summer–brood habitat and seasonal foods.

Winter home ranges of turkeys in Arkansas were

smaller when acorns, a significant food source,

were more abundant (Badyaev et al. 1996).

Relative abundance of wild turkeys in a predominantly forested area of southwestern New York was

related positively with proportion of open land,

edge density, and interspersion determined using

satellite imagery of landscape cover types (Glennon

and Porter 1999). Badyaev (1995) and Thogmartin

(1999), however, found that nesting females in

Arkansas selected large patches of habitat and

avoided areas with high edge density. They selected

nest sites in clearcuts and other forest openings and

in pine stands with a hardwood understory

(Badyaev 1995). The age of forests also affects their

use by wild turkeys. Roost sites were associated

with older pine and pine–hardwood stands in

Figure 13.—

Application of the

hooded warbler HSI

model to a 3,261-ha

unit of the Mark

Twain National

Forest in southern

Missouri. There are

separate images for

suitability as

deciduous forest

(SI1), suitability of

land types (SI2

),

suitability of

composition of young

and mature forest in

adjacent cells (SI3),

and HSI = SI1 x (SI2

x SI3)

0.5.

26
Mississippi (Chamberlain et al. 2000). Adult

males in the Ouachita Mountains of Arkansas

selected forest stands >40 years old during

spring, and adult females selected them during

all seasons (Wigley et al. 1986). In central

Mississippi, turkeys of both sexes selected pines

≥30 years old and hardwoods >40 years old

(Miller et al. 1999). Radio-tracking locations

also indicated an avoidance of stands approximately 15 to 35 years old. Miller et al. (2000)

built statistical models of sex- and seasonspecific habitat selection from much of the same

radio-tracking data.

Our HSI model was based largely on the

interspersion of habitat for nesting and broodrearing and habitat for adults. We defined glades

and areas with young trees as being the highest

quality nesting and brooding habitat (SI1; table

4, fig. 2). More mature forest also provided

nesting and brooding habitat, but its quality was

less than young forest. In our model, adult

habitat was the arithmetic mean of variables for

cover provided by mature trees (SI2

; table 4) and

hard mast production for fall and winter food

(SI3; fig. 5). The fourth SI was a composition

index to account for the interspersion of high

Figure 14.—

Application of the

pine warbler HSI

model to a 3,261-ha

unit of the Mark

Twain National

Forest in southern

Missouri. There are

separate images for

suitability as mature

forest (SI1

), suitability as coniferous

forest (SI2), and HSI

= SI1 x SI2.

27
quality nesting and brood-rearing habitat and

adult habitat within a home range-sized moving

window (102 ha [19-cell radius circle]; table 1).

Presence of the requisites was designated as

code=1 if SI1 ≥ 0.5, code=2 if [(SI2 + SI3) / 2] ≥

0.25, code=3 if inequalities for both requisites

were satisfied, and code=0 otherwise. The

suitability threshold for adult cover was 0.25

because in terms of interspersion, a cell was

considered good adult habitat if either SI2 ≥ 0.5

or SI3 ≥ 0.5. Values of SI4 declined as proportions of window area deviated from an ideal of

15 percent nesting and brood cover and 85

percent adult food and cover (i.e., SI4 = {[1 –

|(proportion of cells with SI1 ≥ 0.5) – 0.15|] x [1

– |(proportion of cells with [SI2

 + SI3] / 2 ≥ 0.25)

– 0.85|]}2) (Schroeder 1985). If values of SI1 ≥

0.5 or [SI2

 + SI3] / 2 ≥ 0.25 were absent in the

window, SI4

 = 0 (table 8).

The HSI score was the geometric mean of

composition (SI4) and the maximum of nesting

and brood cover (SI1) and adult habitat ([SI2 +

SI3] / 2) (table 6). The presence of large patches

of high quality nesting and brood cover in the

eastern half of the study area resulted in relatively higher interspersion–composition values

(SI4

), whereas high quality nesting and brood

rearing patches in the west were too small to

constitute a sufficient proportion of ideal home

range areas (fig. 15). The map of HSI scores

revealed high quality habitat in areas with high

interspersion–composition values, especially in the

northeast and south-central portions of the study

area, and the influence of the individual life

requisites elsewhere in the landscape.

Ruffed Grouse

The ruffed grouse is another popular game bird.

Descriptions of its habitat differ between northern

and southern portions of its range. In the north

where aspens (Populus spp.) are a common component of forests, ruffed grouse rely on them as a

primary winter food source. In the south where

winters are less severe, ruffed grouse use a variety

of winter foods, including acorns, multiflora rose

(Rosa multiflora), and hophornbeam (Ostrya

virginiana) (Korschgen 1966, Thompson and

Fritzell 1986). The U.S. Fish and Wildlife Service

HSI model for ruffed grouse, as originally developed, was heavily dependent on aspen as a winter

food source, so it is not directly applicable to our

Table 8.—Values of SI4

 based on the proportion of cells providing good nesting and brood-rearing habitat and adult habitat for wild turkeys in

a home range-sized moving window (102 ha) in a southern Missouri landscape. Optimal proportions are 0.15 and 0.85, respectivelya.

Proportion

 in nesting Proportion in adult habitat

 and brood

 habitat 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.1 0.00 0.06 0.11 0.18 0.27 0.38 0.51 0.65 0.81 0.81

0.2 0.00 0.06 0.11 0.18 0.27 0.38 0.51 0.65 0.81

0.3 0.00 0.05 0.09 0.15 0.22 0.31 0.41 0.52

0.4 0.00 0.04 0.07 0.11 0.17 0.24 0.32

0.5 0.00 0.03 0.05 0.09 0.13 0.18

0.6 0.00 0.02 0.04 0.06 0.09

0.7 0.00 0.01 0.02 0.04

0.8 0.00 0.01 0.02

0.9 0.00 0.00

1.0 0.00

 a

 SI4 = {[1 – |(proportion of cells with SI1 ≥ 0.5) – 0.15|] x [1 – |(proportion of cells with (SI2 + SI3 / 2) ≥ 0.25) – 0.85|]}2.

28
Figure 15.—Application of the

wild turkey HSI model to a

3,261-ha unit of the Mark

Twain National Forest in

southern Missouri. There are

separate images for suitability

as nesting and brood-rearing

cover (SI1

), suitability as

mature forest for adult cover

(SI2), suitability of hard mast

production (SI3

), suitability of

composition of previous SIs

(SI4

), and HSI = (max{SI1, [(SI2

+ SI3) / 2]} x SI4)

0.5.

29
study area (Cade and Sousa 1985). Habitat

quality for the other component of the model,

fall-to-spring cover, was based on high stem

densities. Hammill and Moran (1986) revised

the model by Cade and Sousa (1985), and

despite assuming winter food requirements

would be met in the study area, they included a

variable for interspersion of fall-to-spring cover

and winter food. The high stem densities that

provide cover for ruffed grouse are found in

recently disturbed sites. Wiggers et al. (1992)

found that 7- to 15-year-old hardwood regeneration was correlated with ruffed grouse density in

Missouri. McDonald et al. (1998) found that

ruffed grouse in central Pennsylvania used

mixed oak sapling stands (i.e., trees <12.6 cm

d.b.h.) more than expected and avoided mixed

oak sawtimber stands (i.e., trees >28 cm d.b.h.).

Gullion (1977, 1990) and Gullion and Svoboda

(1972) recommended that both mature forest for

winter foods and young forest for cover occur in

a 4-ha area. Thompson and Dessecker (1997),

however, indicated that habitat quality in the

central hardwood region is also related to the

size of cover patches, with larger being better.

McDonald et al. (1998) observed seasonal

grouse home ranges of 5.0–9.4 ha and suggested

that 1-ha clearcuts in southern mixed oak forests

could provide good grouse habitat. Observations

in Missouri indicated home ranges of 16–100 ha

(Thompson and Fritzell 1989, Wiggers et al. 1992),

but presumably habitat of high quality would result

in substantially smaller home ranges.

Our habitat model for ruffed grouse was similar to

other models for this species in that winter food,

provided by hard mast (SI1; fig. 5), and cover,

provided by forest <31 years old (SI2

; table 4, fig.

2), were main variables. Two additional variables

defined spatial aspects of the model. Following the

recommendation of Thompson and Dessecker

(1997), we included a variable whose value was

positively associated with the size of cover patches.

We defined contiguous patches of cover (i.e., SI2 >

0), and assigned a value of SI3

 = 1 for patches >1 ha

in size and a value equal to the fraction of a hectare

for patches <1 ha in size (fig. 16). The cover

component of the model was a combination of tree

age, land type, and patch size ([SI2

 x SI3]0.5). The

second spatial variable (SI4) was a composition

index to account for the interspersion of high

quality food and cover in a moving window

analysis. The window was the size of an ideal home

range (17.7 ha = an 8-cell radius circle; table 1),

and we assumed the ideal composition was 50

percent food and 50 percent cover (i.e., [proportion of cells with SI1 ≥ 0.5] x [proportion of cells

with (SI2 x SI3)0.5 ≥ 0.5] x 4; table 9).

Figure 16.—Suitability of

habitat patches for ruffed

grouse is positively related

to patch size.

30

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.5 1.0 1.5

Patch size (ha)

Suitability index
The HSI score was the geometric mean of

composition and the maximum of either the

hard mast or cover component (table 6).

Locations providing interspersion of both life

requisites in home range-sized areas limited

overall habitat suitability in the study area (fig.

17). Furthermore, SI3 appeared to adequately

discount the quality of small patches of cover,

reserving the highest HSI scores for areas near

the edges of large cover patches that were

adjacent to mature oak forest.

Gray Squirrel

Gray squirrels are dependent on hard mast for

food more than any other species in our study

(table 1). The link between hard mast availability and habitat quality for gray squirrels is well

established (Gorman and Roth 1989, Gurnell

1983, McShea 2000, Nixon et al. 1975). Allen

(1987) developed an HSI model for gray

squirrels, and the main components were hard

mast production and mature trees, which

provide cavities for cover and reproductive

habitat. Our HSI model for gray squirrels had

the same structure. Rather than predict potential

mast production from canopy cover of mast

producing trees and number of hard mast

species, we used our hard mast index (SI1; fig.

5). We used the age of dominant trees in a cell as

a surrogate variable for canopy cover and tree

cavity availability (SI2). The minimum age of

trees that provided cover with suitability > 0 was

30 years (table 10, fig. 2). That was the approximate age at which trees in our study area

achieved the cover-tree criteria explained in the

U.S. Fish and Wildlife Service HSI model (i.e.,

≥6 m in height and ≥12.7 cm d.b.h., Allen

1987). The minimum age of trees that provided

cover with suitability = 1 was 101 years, and the

tree age function between ages 30 and 100 was

SI2

 = 0.011 x age – 0.222 (table 10). Glades

never provided cover or appropriate cavities,

regardless of tree age, because we assumed trees

would always be too sparse on glades.

The HSI score for gray squirrels was the minimum of the food and cover components (table

6). Hard mast production limited habitat quality

throughout much of the study area (fig. 18).

However, cover suitability was lower than food

suitability in a few places on the flat and

northeast slope land types containing white oaks

≤100 years old.

Table 9.—Values of SI4

 based on the proportion of cells providing hard mast and cover for ruffed grouse in a home

range-sized moving window (4.4 ha) in a southern Missouri landscape. Optimal proportions are 0.5 for each requisitea.

Proportion Proportion providing cover

 providing

hard mast 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.1 0.00 0.04 0.08 0.12 0.16 0.20 0.24 0.28 0.32 0.36

0.2 0.00 0.08 0.16 0.24 0.32 0.40 0.48 0.56 0.64

0.3 0.00 0.12 0.24 0.36 0.48 0.60 0.72 0.84

0.4 0.00 0.16 0.32 0.48 0.64 0.80 0.96

0.5 0.00 0.20 0.40 0.60 0.80 1.00

0.6 0.00 0.24 0.48 0.72 0.96

0.7 0.00 0.28 0.56 0.84

0.8 0.00 0.32 0.64

0.9 0.00 0.36

1.0 0.00

 a

 SI4 = [proportion of cells with SI1 ≥ 0.5] x [proportion of cells with (SI2 x SI3)0.5 ≥ 0.5] x 4.

31
Figure 17.—Application of

the ruffed grouse HSI model

to a 3,261-ha unit of the

Mark Twain National

Forest in southern Missouri.

There are separate images

for suitability of hard mast

production (SI1

), suitability

as early-successional forest

(SI2), suitability as large

habitat patches (SI3),

suitability of composition of

previous SIs (SI4), and HSI

= {max[SI1, (SI2 x SI3)

0.5] x

SI4

}

0.5.

32
Table 10.—Habitat suitability of forest by habitat requisite, land typea, and age of dominant overstory trees for five mammal and one amphibian species in southern

Missouri

Northern

 long-eared bat

 Age of Gray squirrel Black bear Bobcat Southern

dominant Red Foraging Snags for redback

trees (yr) Glade Other Glade Other Glade Dry Mesic bat and roosting roosting salamander

 0b 0.0 0.00 1.0 1.0 1.0 1.0 1.0 0.00 0.00 0.00 0.00

 1–10 0.0 0.00 1.0 1.0 1.0 1.0 0.5 0.00 0.00 0.80 0.00

 11–20 0.0 0.00 0.2 0.1 1.0 0.5 0.1 0.00 0.00 0.40 0.00

 21–30 0.0 0.11 0.1 0.0 0.8 0.1 0.0 0.00 0.00 0.00 0.00

 31–40 0.0 0.22 0.0 0.0 0.5 0.0 0.0 0.14 0.14 0.00 0.00

 41–50 0.0 0.33 0.0 0.0 0.3 0.0 0.0 0.29 0.29 0.14 0.05

 51–60 0.0 0.44 0.0 0.0 0.3 0.0 0.0 0.43 0.43 0.29 0.10

 61–70 0.0 0.56 0.0 0.0 0.3 0.0 0.0 0.57 0.57 0.43 0.15

 71–80 0.0 0.67 0.0 0.0 0.3 0.0 0.0 0.71 0.71 0.57 0.20

 81–90 0.0 0.78 0.0 0.0 0.3 0.0 0.0 0.86 0.86 0.71 0.40

 91–100 0.0 0.89 0.0 0.0 0.3 0.0 0.0 1.00 1.00 0.86 0.60

101–110 0.0 1.00 0.0 0.0 0.3 0.0 0.0 1.00 1.00 1.00 0.80

111–120 0.0 1.00 0.0 0.0 0.3 0.0 0.0 1.00 1.00 1.00 1.00

121–130 0.0 1.00 0.0 0.0 0.3 0.0 0.0 1.00 1.00 1.00 1.00

≥131 0.0 1.00 0.0 0.0 0.3 0.0 0.0 1.00 1.00 1.00 1.00

a Land type categories are described in table 3.

b Trees not present (e.g., failed regeneration after disturbance).

33
Black Bear

We selected black bears because they are the

largest, widest ranging mammals in southern

Missouri (table 1). Rogers and Allen (1987)

developed an HSI model for black bears in the

upper Great Lakes region. Their model incorporated wetland cover types for spring food, soft

mast for summer food, hard mast for fall food,

and avoidance of human influence. Although

Rogers and Allen (1987) acknowledged that

interspersion of food and cover resources

contributed to habitat quality for bears, they did

not include it in the model because data to

establish a relationship were lacking and bears

are highly mobile. Van Manen and Pelton (1997)

developed logistic regression models of habitat use

from radio-tracking locations of bears in Tennessee

and randomly selected points. Models for males

and females had the following GIS-based variables

in common: overstory vegetation type, proximity to

streams, aspect, proximity to improved roads, and

proximity to human activity sites. Avoidance of

pines (Pinus spp.) in favor of oak and oak-pine

forests and avoidance of human influence appeared

to have the strongest influence on habitat use of

bears (Van Manen and Pelton 1997). Clark et al.

(1993) also developed statistical models of black

bear habitat use from GIS-based habitat variables.

Figure 18.—Application of

the gray squirrel HSI model

to a 3,261-ha unit of the

Mark Twain National

Forest in southern Missouri.

There are separate images

for suitability of hard mast

production (SI1

), suitability

as mature forest for cover

(SI2

), and HSI = min(SI1,

SI2

).

34
Their radio-tracking data from Arkansas

indicated that bears used poletimber classes of

hardwood and hardwood-pine more than

expected and northeastern aspects, flat terrain,

and areas <240 m from roads and >600 m from

streams less than expected.

We chose to use hard and soft mast foods,

interspersion of areas producing them, and

avoidance of roads as variables in our HSI

model. Perennial streams were absent from our

study site, and we considered slope aspect much

less important to habitat quality than resource

needs (Rogers 1987) and intolerance of human

activity (Beringer 1986, Kerley et al. 2002,

Linnell et al. 2000). Hard mast production based

on tree species group, tree age, and land type

was SI1

 (fig. 5). Our variable for soft mast

production was based mainly on tree age (SI2

;

table 10, fig. 2). Fantz and Hamilton (1997)

documented high soft mast production in young

clearcuts in southern Missouri, substantially

lower production in stands 7 to 10 years old,

and minimal production in stands without

disturbance for >40 years. Others have found

similar relationships (Perry et al. 1999, Stransky

and Roese 1984). The third SI was a composition index that accounted for the presence of

potential hard and soft mast production within a

home range-sized area (20 km2

 = 84-cell radius

circle; table 1). The values of SI1 and SI2 had to

be ≥0.5 to be included in the moving window

analysis. The ideal proportions were 70 percent

hard mast and 30 percent soft mast because fall

foods are more critical to reproductive success

(Elowe and Dodge 1990) and more alternative

foods are available during summer. Values of SI3

declined as proportions deviated from that ideal

according to the following equation:

SI3

 = ([1 – |(proportion of cells with SI1 ≥ 0.5) –

0.7|] x

[1 – |(proportion of cells with SI2 ≥ 0.5) –

0.3|])2.

If values of SI1 ≥ 0.5 or SI2 ≥ 0.5 were absent in

the window, SI3

 = 0 (table 8). Research on black

bear dens indicated that they are ≥0.3 – 0.8 km

from roads and ≥1.0 – 3.4 km from human

activity (Linnell et al. 2000, Tietje and Ruff

1983). Therefore, the value of SI4

 increased

linearly from 0 when a cell was ≤0.2 km from a

road to 1 when a cell was ≥1 km from a road

(fig. 19).

The HSI value for a cell was the geometric mean

of the greater of summer or fall food and

interspersion multiplied by the road avoidance

variable (table 6). Large patches conducive to

soft mast production resulted in slightly greater

35

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Distance from road (km)

Suitability index

Figure 19.—Habitat suitability for black bears is positively

related to distance from roads.
values for interspersion in the eastern quarter of

the study area (fig. 20). The HSI scores, however, were slightly greater in the western part of

the study area where more forest patches had high

mast production.

Figure 20.—Application of

the black bear HSI model to

a 3,261-ha unit of the Mark

Twain National Forest in

southern Missouri. There

are separate images for

suitability of hard mast

production (SI1

), suitability

of soft mast production (SI2),

suitability of composition of

hard and soft mast production (SI3

), suitability as

distance from roads (SI4

),

and HSI = [max(SI1, SI2) x

SI3]

0.5 x SI4

.

36
Bobcat

The bobcat is a furbearer whose distribution and

abundance are stable or increasing in Missouri

(D. Hamilton, Missouri Department of Conservation, personal communication). The most

important aspect of bobcat habitat is prey

availability (Boyle and Fendley 1987). Primary

prey are small mammals, such as lagomorphs

(e.g., Sylvilagus spp.) and rodents (e.g., Sciurus

spp., Sigmodon hispidus, Microtus spp.) (Erickson

1981, Fritts and Sealander 1978, Korschgen

1957). Boyle and Fendley (1987) provided

many citations indicating that grassy and

shrubby openings, including forest clearcuts, are

the best prey producing areas in the Southeastern United States. Bobcats in southeastern

Missouri preferred brushy fields and forest

regeneration most and agricultural crops, cool

season grasslands, and oak–pine forest least

(Hamilton 1982). Bobcats in southeastern

Oklahoma also preferred grassy and brushy

cover types (Rolley and Warde 1985). Preferred

forest ages in Arkansas were 0- to 20-year-old

regeneration and mature hardwood stands

(Rucker et al. 1989). Bobcats in Illinois (Nielsen

and Woolf 2002, Woolf et al. 2002) and Wisconsin (Lovallo and Anderson 1996a, b) either

showed no preference for or avoided unforested

areas and selected forested areas with low road

densities.

The U.S. Fish and Wildlife Service HSI model

for bobcats in the Southeastern United States

was simply a function of equivalent food value,

calculated as the weighted mean of an SI based

on grass and forb-shrub vegetation across land

cover types in a home range-sized area (Boyle

and Fendley 1987). Another habitat quality

model for bobcats in the Southeast was based on

food, cover, and reproductive requirements

within a land cover type and the distance

between land cover types (Lancia et al. 1982).

Burch and Nichols (1997) developed a GISbased HSI model for bobcats in a unit of the

Wayne National Forest in Ohio. Their model

was a function of SIs for food, using specific

habitat preferences of prey species, and cover/

reproduction based on the presence of forest

and brush.

Our HSI model for bobcats included a food

variable similar to the one used by Boyle and

Fendley (1987) and a road disturbance variable.

We assumed that escape, thermal, and reproductive cover requirements would be met throughout our study area. SI1

 was calculated in two

Table 11.—Values of SI3

 based on the proportion of cells providing good hard mast and soft mast foods for black bears

in a home range-sized moving window (20 km2) in a southern Missouri landscape. Optimal proportions are 0.7 and 0.3,

respectivelya.

Proportion

 providing Proportion providing soft mast

 fall and

winter food 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

 0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

 0.1 0.00 0.10 0.13 0.16 0.13 0.10 0.08 0.06 0.04 0.03

 0.2 0.00 0.16 0.20 0.25 0.20 0.16 0.12 0.09 0.06

 0.3 0.00 0.23 0.29 0.36 0.29 0.23 0.18 0.13

 0.4 0.00 0.31 0.40 0.49 0.40 0.31 0.24

 0.5 0.00 0.41 0.52 0.64 0.52 0.41

 0.6 0.00 0.52 0.66 0.81 0.66

 0.7 0.00 0.64 0.81 1.00

 0.8 0.00 0.52 0.66

 0.9 0.00 0.41

 1.0 0.00

 a

 SI3 = ([1 – |(proportion of cells with SI1 ≥ 0.5) – 0.7|] x [1 – |(proportion of cells with SI2 ≥ 0.5) – 0.3|])2.

37
steps. First, we assigned cells a value between 0

and 1 based on tree age and land type (table 10,

fig. 2) to indicate high prey production in forests

<20 years old and glades. Then we conducted a

moving window analysis to assign an SI1

 value

to the central window cell based on the mean

within the window of cell values from the

previous step. The window was a 59-cell radius

circle (9.8 km2; table 1) based on observed

home ranges of 1–60 km2

 (Conner et al. 1999,

Hamilton 1982, Kitchings and Story 1979,

Miller 1980, Rucker et al. 1989). Ideally, 40 percent

of an area should be in optimal prey habitat (Boyle

and Fendley 1987), so our SI1

 relationship increased linearly from the origin to a value of 1

when the mean in the window was 0.4 (fig. 21).

Lovallo and Anderson (1996b:71) suggested that

“in general, areas ≤100 m from roads contained less

preferred bobcat habitat than roadless areas,” so SI2

increased from 0 at a road edge to 1 at distances

>90 m (fig. 22).

Figure 21.—Habitat

suitability for bobcats is

positively related to

proportion of home range

area consisting of preyproducing habitat.

Figure 22.—Habitat

suitability for bobcats is

positively related to distance

from roads.

38

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

Proportion of home range

Suitability index

0.0

0.2

0.4

0.6

0.8

1.0

0-30 31-60 61-90 >90

Distance from road (m)

Suitability index
The HSI score was a product of the two SIs

(table 6), rather than a geometric mean, because

road disturbance reduced habitat quality, but

roads did not constitute a resource needed by

bobcats. Large patches of young forest (fig. 2)

resulted in slightly greater prey habitat quality in

the eastern part of the study area (fig. 23). Even

there, however, optimal prey habitat constituted

<10 percent of home range-sized areas, which

severely limited overall habitat suitability for

bobcats.

Figure 23.—Application of the

bobcat HSI model to a 3,261-

ha unit of the Mark Twain

National Forest in southern

Missouri. There are separate

images for suitability as high

proportion of early-successional forest or openings (SI1

),

suitability as distance from

roads (SI2), and HSI = SI1 x SI2.

39
Red Bat

The red bat is a solitary, foliage-dwelling bat that

is common in Missouri (LaVal and LaVal 1979).

It is migratory and therefore rare in Missouri

during winter (Schwartz and Schwartz 1981,

Shump and Shump 1982). Primary non-winter

habitat components are roosting sites, foraging

areas, and water (Kurta 2001). Red bat day

roosts in eastern Kentucky and two sites in

South Carolina occurred in trees that averaged

18.5–25 m tall and 38–41 cm d.b.h. in stands

with relatively low tree densities (Hutchinson

and Lacki 2000, Menzel et al. 1998), which are

characteristics consistent with mature forests.

Roost trees in Kentucky were on upper slopes

<500 m from water (Hutchinson and Lacki

2000). Red bats forage for a variety of insects

around and above the canopy of trees (LaVal and

LaVal 1979, Shump and Shump 1982). Much is

still unknown about the habitat needs of red

bats.

Our model of non-winter habitat quality for red

bats consisted of a roosting component and a

foraging component. The roosting component

was a geometric mean of three variables. The

quality of roost trees increases with age (SI1

;

table 10, fig. 2). It is highest on sloping terrain

(SI2 = 1 on southwest and northeast side slopes),

and red bats are almost never observed roosting

on ridge tops (SI2 = 0 on flats, and SI2 = 0.5

otherwise). Roost quality declines >0.75 km

from surface water (SI3

, figs. 2 and 24) because red

bats often travel directly from their roosts to drink

(S.K. Amelon, USDA Forest Service, personal

communication). The foraging component of the

model consists of trees of an appropriate age (SI1;

table 10), presumably near an opening, where the

forest edge provides a travel lane and flight around

the canopies of trees is less restricted than in

contiguous forest. We defined high quality foraging

areas using moving windows of two different

sizes—0.8 ha (3 x 3 cells) and 13.8 ha (7-cell

radius circle)—representing preferred distance to

edge (30 m) and the size of the largest clearcut

used for foraging, respectively (S. K. Amelon,

USDA Forest Service, personal communication).

Using the smaller window, we assigned the central

cell SI4 = 1 if the minimum value in the window

was SI1

 = 0 and the maximum value was SI1 ≥0.5,

indicating an edge between an opening and trees

>60 years old; the central cell was SI4 = 0 otherwise. Using the larger window, we assigned the

central cell SI5 = 0.7 if the minimum value in the

window was SI1 = 0 and the maximum value was

SI1 ≥0.5; the central cell was SI5 = 0 otherwise. The

geometric mean for the foraging component

weighted edge quality (i.e., maximum of SI4 and

SI5) twice as much as tree age (table 6).

The HSI score was the maximum of the roosting

and foraging components (table 6). We did not

include a variable for interspersion between roost

sites and foraging areas because we assumed

Figure 24.—Habitat

suitability for red bats is

negatively related to

distance from water.

40

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.5 1.0 1.5 2.0 2.5

Distance from water (km)

Suitability index
suitable roosts were available ≤8 km from all

foraging areas. The highest quality roost sites

near ponds on the west side of the study area

were evident in the HSI map (fig. 25). The

quality of foraging habitat, however, was

generally higher than the quality of roosting

habitat throughout the study area, especially

around the edges of both small and large forest

openings.

Figure 25.—Application of

the red bat HSI model to a

3,261-ha unit of the Mark

Twain National Forest in

southern Missouri. There are

separate images for suitability as mature forest (SI1

),

suitability of land types (SI2),

suitability as distance from

water (SI3), suitability as

forest edge (max[SI4, SI5]),

and HSI = max[(SI1 x SI2 x

SI3

)

0.33, ({SI1

 x [max(SI4,

SI5)]2}

0.33)].

41
Northern Long-Eared Bat

The northern long-eared bat, also called the

northern bat and formerly considered an eastern

subspecies of Keen’s bat (Schwartz and Schwartz

1981), is a year-round resident of Missouri but

hibernates in caves during winter. It is relatively

rare and of conservation concern in Missouri

(ranked S3 on The Nature Conservancy’s [1992]

5-point endangerment scale [Missouri Natural

Heritage Program 2001]), and it is considered to

have some habitat requirements in common

with the endangered Indiana bat (Myotis sodalis)

(Callahan et al. 1997, Foster and Kurta 1999,

Kurta et al. 1993). Primary non-winter habitat

components are roosting sites, foraging areas,

and water (Kurta 2001). Roost sites in Michigan

were predominantly in large trees, approximately half of which were dead (Foster and

Kurta 1999). Also, half of the roosts were under

exfoliating bark, whereas the other half were

inside hollows or crevices. Day roosts in

Kentucky were in cavities and under bark of live

and dead trees in forest stands with a higher

mean d.b.h. than randomly selected stands

(Lacki and Schwierjohann 2001). Northern bats

forage in mature deciduous forest with small

gaps (Cowan and Guiguet 1965 cited by

Barbour and Davis 1969). Much is still unknown about the habitat needs of northern bats.

Our model of non-winter habitat quality for

northern long-eared bats consisted of roosting,

foraging, and interspersion components. The

roosting component was a geometric mean of three

variables. We modeled cavity availability in live

trees as a function of tree age (SI1, table 10, fig. 2).

Snag availability, the second variable, increased

with stand age (SI2; table 10), but data from the

USDA Forest Service Forest Inventory and Analysis

program collected in Missouri in 1989 (Hansen et

al. 1992, Miles et al. 2001) indicated that large

snags are also available during the first two decades

after stand-replacing disturbance. Third, we

assumed that roosts needed to be within 2 km of

water to be suitable because that was the longest

distance observed between two roost trees used by

the same bat (Foster and Kurta 1999) (SI3, figs. 2

and 26). The foraging component was defined by

tree age (SI1

) adjusted downward if the forest

canopy was not near an edge (S.K. Amelon, USDA

Forest Service, personal communication). We

identified edges of mature forest using a moving

window analysis on a GIS theme containing SI1

values. The window was a 0.8-ha square (3 x 3

cells), assuming the bats prefer to forage within 30

m of a forest edge, and the central cell was assigned

SI4 = 1 if the minimum value in the window was 0;

the value was SI4

 = 0.8 otherwise. We considered

areas in which roost sites and foraging areas did

Figure 26.—Habitat

suitability for northern longeared bats is negatively

related to distance from

water.

42

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.5 1.0 1.5 2.0 2.5

Distance from water (km)

Suitability index
not occur within a distance typically traveled by

a bat during 1 night to be of low habitat quality.

Our interspersion component was calculated

using a moving window 2 km (67 cells) in

radius (12.7 km2; table 1) in which cells were

coded 0 for the roosting component (i.e., if [SI1

x SI2 x SI3]0.33 > 0), 3 for the foraging component

(i.e., SI1 x SI4 > 0), 2 if the inequalities for both

requisites were satisfied, and 1 otherwise. The

central cell of the moving window was SI5 = 1 if

both roosting and foraging components were

present within the window (i.e., if the minimum

coded value in the window was 0 and the

maximum coded value was 3 or if any coded

value in the window was 2). Otherwise, the

central cell of the moving window was SI5 = 0.

The HSI score was the geometric mean of the

interspersion component and the maximum of

the roosting and foraging components (table 6).

The interspersion of roosting and foraging

habitats was limited only on the extreme eastern

edge of the study area by a lack of a nearby

water source (fig. 27). Overall habitat quality

was most influenced by foraging habitat except

in the west where the quality of roosting habitat

was high due to proximity of water.

Southern Redback Salamander

Species richness and abundance of salamanders

vary with microhabitat characteristics that often

correlate with forest age (Petranka et al. 1994).

A literature review by deMaynadier and Hunter

(1995) indicated that Plethodontid salamanders

are more sensitive to clearcutting than other

groups of amphibians. Characteristics such as

their sensitivity to disturbance, longevity, and

site fidelity make them good candidates for

indicators of biodiversity and ecosystem

integrity (Welsh and Droege 2001).

Gustafson et al. (2001) developed a GIS model

to predict salamander abundance from terrestrial

habitat information. Their model was based on

site moisture (as a function of slope position and

aspect) and stand age. We modified their model

by simplifying the calculations and calibrating

our tree age variable to salamander abundance

data from southern Missouri (Herbeck and

Larsen 1999). Habitat quality defined by tree age

increased slowly from SI1

 = 0 between 40 and 80

years and then increased more rapidly to SI1 = 1

by 111 years (table 10, fig. 2). We incorporated

site moisture using land type categories (table

3), where mesic sites were SI2 = 1, dry sites were

SI2

 = 0.5, and glade sites were SI2 = 0.

The HSI score was the geometric mean of the

two variables because they interacted to describe

combined food and cover requirements (table

6). Habitat suitability maps of the study area

indicated broad patterns based on land types

and patterns at a higher resolution based on tree

age (fig. 28).

43
Figure 27.—Application of

the northern long-eared bat

HSI model to a 3,261-ha

unit of the Mark Twain

National Forest in southern

Missouri. There are

separate images for

suitability as mature forest

for roosting and foraging

(SI1), suitability of density of

large snags for roosting

(SI2

), suitability as distance

from water (SI3), suitability

as forest gaps (SI4

),

suitability of interspersion of

roost sites and foraging

habitat (SI5), and HSI =

{max[(SI1 x SI2 x SI3)

0.33,

(SI1 x SI4)] x SI5}

0.5.

44
Figure 28.—Application of

the southern redback

salamander HSI model to a

3,261-ha unit of the Mark

Twain National Forest in

southern Missouri. There

are separate images for

suitability as mature forest

(SI1), suitability of land

types (SI2), and HSI = (SI1 x

SI2

)

0.5.

45
We appreciate the critical review of the entire manuscript by M.W. Hubbard, B.J. Kernohan, G.J. Roloff,

and M.A. Rumble. We also thank the following colleagues for their input and discussions about specific

models: M.W. Hubbard, wild turkey and ruffed grouse models; D.A. Hamilton, black bear and bobcat

models; S.K. Amelon, red bat and northern long-eared bat models; and T.A. Green, salamander model.

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Program LANDIS

For demonstration purposes, we applied our

HSI models to output from a LANDIS simulation of our study area. We simulated forest

growth and disturbance for 100 years under a

sustained long rotation harvest scenario (5

percent of land area affected each decade) with

an equal mixture of even-aged and uneven-aged

management. This resulted in a diverse landscape with large clearcuts, small forest gaps

(~0.2 ha), and contiguous old growth.

LANDIS is a spatially explicit model of forest

growth, succession, and disturbance in potentially large landscapes with a range of potential

spatial resolutions (e.g., 300,000 map pixels,

each of which is 0.01–100 ha in size) (He et al.

2000). We used a raster resolution of 30 m2

(0.09 ha). LANDIS requires user specification of

many parameters, including rates of tree

establishment, growth, and mortality and

sources, frequency, and intensity of disturbances. This provides flexibility in its application to different forest types in different geographic areas. We used the same model specifications that Shifley et al. (2000) used.

The presence of four tree species groups (i.e.,

white oak, red oak, maple, and pine) in decadewide age classes was determined by several

dynamic processes. LANDIS simulated tree

establishment, growth, senescence, and disturbance due to wind, fire (He and Mladenoff

1999), and harvest. All simulated processes were

at least partially dependent upon seven land

type classifications (glade = glade; southwest

side slope, flat, and limestone = dry; northeast

side slope, upland drainage, and mesic = mesic;

table 3). Initial conditions for the LANDIS

simulations were specified based on forest

inventory data from the Mark Twain National

Forest.

APPENDIX—Landscape Simulation

Although >1 age class of >1 tree type could

occur on a raster cell at any given time, we

assumed forest characteristics were best represented by the oldest age class of the dominant

tree type. Dominance was calculated as the age

of the oldest trees present within a tree type

divided by the longevity of that tree type. For

example, 50-year-old pines with a longevity of

250 years were dominant to 60-year-old white

oaks with a longevity of 400 years.

51
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Larson Michael A.; Dijak, William D.; Thompson, Frank R., III;

Millspaugh, Joshua J.

2003. Landscape-level habitat suitability models for twelve

species in southern Missouri. Gen. Tech. Rep. NC-233. St. Paul,

MN: U.S. Department of Agriculture, Forest Service, North Central

Research Station. 51 p.

Describes GIS-based habitat suitability index models for twelve

terrestrial vertebrate species in southern Missouri. Demonstrates

model application to a simulated forest landscape.

KEY WORDS: Landscape, habitat suitability index model, GIS,

vertebrates, Missouri.
MISSION STATEMENT

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a diverse living landscape. Maintaining the good life for generations to come begins with everyday

choices about natural resources. The North Central Research Station provides the knowledge and

the tools to help people make informed choices. That’s how the science we do enhances the

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