SK

361

.U54

no. 82-

10.123
MODEL EVALUATION FORM

Habitat models are designed for a wide variety of planning applications where habitat information is an important consideration in the

decision process. However, it is impossible to develop a model that

performs equally well in all situations. Assistance from users and

researchers is an important part of the model improvement process. Each

model is published individually to facilitate updating and reprinting as

new information becomes available. User feedback on model performance

will assist in improving habitat models for future applications. Please

complete this form following application or review of the model. Feel

free to include additional information that may be of use to either a

model developer or model user. We al so would appreciate information on

model testing, modification, and application, as well as copies of modified

models or test results. Please return this form to:

Habitat Evaluation Procedures Group

U.S. Fish and Wildlife Service

2627 Redwing Road, Creekside One

Fort Collins, CO 80526-2899

Thank you for your assistance.

Species -------

Habitat or Cover Type(s)

Geographic

Location ----------------

Type of Application: Impact Analysis Management Action Analysis ___ Baseline Other

Variables Measured or Evaluated --------------------

Was the species information useful and accurate? Yes No

If not, what corrections or improvements are needed?----------
Were the variables and curves clearly defined and useful? Yes No

If not, how were or could they be improved?

Were the techniques suggested for collection of field data:

Appropriate? Yes No

Clearly defined? Yes No

Easily applied? Yes No

If not, what other data collection techniques are needed?

Were the model equations logical? Yes No

Appropriate? Yes No

How were or could they be improved?

Other suggestions for modification or improvement (attach curves,

equations, graphs, or other appropriate information)

Additional references or information that should be included in the model:

Model Evaluator or Reviewer Date ------------ -------

Agency _

Address -------------------------------

Telephone Number Comm:----------- FTS
Biological Report 82(10.123)

September 1986

HABITAT SUITABILITY INDEX MODELS: WHITE-TAILED DEER

IN THE GULF OF MEXICO AND SOUTH ATLANTIC COASTAL PLAINS

by

Henry L. Short

Habitat Evaluation Procedures Group

National Ecology Center

U.S. Fish and Wildlife Service

2627 Redwing Road

Fort Collins, CO 80526-2899

Performed for

National Ecology Center

Division of Wildlife and Contaminant Research

Fish and Wildlife Service

U.S. Department of the Interior

Washington, DC 20240
This report should be cited as:

Short, H. L. 1986. Habitat suitability index models: White-tailed deer in

the Gulf of Mexico and South Atlantic coastal plains. U.S. Fish Wildl.

Servo Biol. Rep. 82(10.123). 36 pp.
PREFACE

This document is part of the Habitat Suitability Index (HSI) Model Series

[Biological Report 82(10)], which provides habitat information useful for

impact assessment and habitat management. Several types of habitat information

are provided. The Habitat Use Information section is largely constrained to

those data that can be used to derive quantitative relationships between key

environmental variables and habitat suitability. This information provides

the foundation for the HSI model and may be useful in the development of other

models more appropriate to specific assessment or evaluation needs.

The HSI Model section documents the habitat model and includes information

pertinent to its application. The model synthesizes the habitat use information into a framework appropriate for field application and is scaled to

produce an index value between 0.0 (unsuitable habitat) and 1.0 (optimum

habitat). The HSI Model section includes information about the geographic

range and seasonal application of the model, its current verification status,

and a list of the model variables with recommended measurement techniques for

each variable.

The model is a formalized synthesis of biological and habitat information

published in the scientific literature and may include unpublished information

reflecting the opinions of identified experts. Habitat information about

wi 1dl i fe speci es frequently is represented by scattered data sets co11 ected

during different seasons and years and from different sites throughout the

'range of a species. The model presents this broad data base in a formal,

logical, and simplified manner. The assumptions necessary for organizing and

synthesizing the species-habitat information into the model are discussed. The

model should be regarded as a hypothesis of species-habitat relationships and

not as a statement of proven cause and effect relationships. The model may

have merit in planning wildlife habitat research studies about a species, as

well as in providing an estimate of the relative qual ity of habitat for that

species. User feedback concerning model improvements and other suggestions

that may increase the utility and effectiveness of this habitat-based approach

to fish and wildlife planning are encouraged. Please send suggestions to:

Habitat Evaluation Procedures Group

National Ecology Center

U.S. Fish and Wildlife Service

2627 Redwing Road

Ft. Collins, CO 80526-2899

iii
iv
CONTENTS

PREFACE iii

ACKNOWLEDGMENTS vi

HABITAT USE INFORMATION.............................................. 1

Genera 1 ....................................................•....... 1

Food 2

Water 3

Cover 3

Reproduct ion 3

Interspersion 4

HABITAT SUITABILITY INDEX (HSI) MODEL................................ 4

Model Appl icabi 1ity 4

Model Descriptions................................................. 6

Application of the Models.......................................... 27

SOURCES OF OTHER MODE~S 34

REFERENCES 34

v
ACKNOWLEDGMENTS

I gratefully acknowledge Dr. Ernie A. Gluesing, Mississippi State

University; Dr. Larry Marchinton, University of Georgia, Athens; Jean OINeil,

Corps of Engineers, Vicksburg, MS; Richard J. Reiner, Mississippi State

University; and Dr. Dean Stauffer, Virginia Polytechnic Institute and State

University, Blacksburg, for their review of this white-tailed deer model. The

cover of this document was illustrated by Susan Strawn. Word processing was

provided by Carolyn Gulzow, Dora Ibarra, Elizabeth Barstow, and Patricia

Gillis.

vi
WHITE-TAILED DEER (Odocoileus virginianus)

IN THE GULF OF MEXICO AND SOUTH ATLANTIC COASTAL PLAINS

HABITAT USE INFORMATION

General

Thirty subspecies of white-tailed deer have been identified as occurring

throughout Central and North America (Baker 1984), where the species occurs on

habitats varying in soil fertility, nutritional quality, and climatic extremes.

The Habitat Suitability Index (HSI) models developed here are constructed for

one portion of the deer I s range - the Gul f of Mex i co coasta 1 p1a in. The

models should also be relevant to the South Atlantic coastal plain, and

concepts within the models may also be relevant to white-tailed deer habitats

in other portions of their range. The data base from which these models were

developed was largely compiled in east Texas and Louisiana, a subportion of

the gulf coastal plain. Baker (1984) lists several recognizable subspecies of

white-tailed deer occurring within the geographical area for which these

models were developed. I make no distinctions between the requirements of

subspecies of white-tailed deer in these habitat models.

Five major grassland and forest types occur within the South Atlantic and

Gulf of Mexico coastal plains: coastal herbaceous wetlands, longleaf pine/slash

pine (Pinus palustris/E. elliotti) forests, shortleaf pine/oak (E. echinata/

Quercus spp.) forests, loblolly pine (P. taeda)/hardwood forests, and bottomland hardwood forests (Newsom 1984). -Coastal herbaceous wetlands range from

fresh to saline waters. They frequently provide ample cover but only limited

habitat because deer are restricted to higher elevations, such as natural

ridges, dredge spoil deposits, and canal banks. Food abundance is adequate in

freshwater herbaceous wetlands but is limited in saltwater herbaceous wetlands.

Longleaf pine/slash pine habitats frequently are associated with hardwoods

such as oaks. Forestry practices in this habitat type, however, frequently

remove hardwood species that compete with pines - a practice that reduces

carrying capacity for deer. The shortleaf pine/oak and loblolly pine/hardwood

habitat types support a wide variety of plant species in the overstory and

understory layers and consequently provide a relatively high carrying capacity

for white-tailed deer. The bottomland hardwood habitat type provides many

plant species that are food for deer. This is the most productive habitat

type for deer in the coastal plains.

The Gulf of Mexico and South Atlantic coastal plains feature a mild

climate (mean temperature is often from 10 to 20°C), a high precipitation rate

(from 40 cm per year in south Texas to 160 cm in Louisiana), and a long growing

1
season (from 185 to 300 days). The cl imate may cause greater stress to deer

during the hot, humid summer than during the winter season. For example, food

consumption rates of captive deer diminish during summer when ambient temperatures average 27°C and relative humidities average 75% (Short et al. 1969).

Such climatic conditions may also affect the lactation capabilities of deer

and the survival of fawns. The greatest environmental stress to deer in

upland habitats within the coastal plains, however, may be a result of the

highly leached and infertile soils, which are moderately to strongly acid and

low in both organic matter and mineral nutrients. Except during spring growth,

upland vegetation is usually fibrous and low in protein and minerals essential

for good body growth (Short 1969; Short et al. 1969). Thus, white-tailed deer

in the coastal plain have a unique problem, an abundance of forage at all

seasons, but forage that is frequently nutritionally deficient during months

when active plant growth is not occurring. Nutritional deficiencies exist in

the availability to deer of digestible energy, digestible protein, and dietary

phosphorus. The following HSI models are restricted to measures of digestible

energy because more i nformat i on exists about energy requi rements than about

nitrogen and phosphorus requirements of white-tailed deer.

Food

White-tailed deer are small ruminants that require large quantities of

easily digested food in order to satisfy their metabolic requirements for

maintenance, growth, and reproduction. The phenology of herbaceous and woody

plant species changes throughout the year. This variation in the growth form

of plants is accompanied by changes in nutrient composition and in digestibility by deer. Forages that diminish in digestibility and nutrient composition with seasons must be replaced by others of good digestibility in order to

maintain deer in good health. Hence, a goal for deer managers is to provide

foods that are of good digestibility and favorable nutrient composition,

throughout the year.

The diet of white-tailed deer in the coastal plains is varied. During

spring and early summer tender shoots, leaves and twigs of trees, shrubs and

vines, and many broad-leaved herbaceous plants constitute the major portion of

deer diets (Newsom 1984). Woody twigs quickly harden and lose much of their

digestibility when annual growth ceases in late spring and early summer (Short

et al. 1972). Grasses, after they mature in late spring and early summer, are

digested so slowly as to be of little value nutritionally to deer (Short 1975).

Thus, two very common forages in the coastal plain, warm season grasses and

woody browse twigs, are of little nutritional importance to white-tailed deer

by early summer and thereafter. A vari ety of other food is important duri ng

the remainder of the year. Seeds and fruits, especially acorns, represent

palatable and highly digestible foodstuffs from summer into winter (Short and

Epps 1976). Cool season herbages that grow during winter and early spring are

highly digestible foodstuffs until these forages mature during late spring

(Short and Segelquist 1975). Deciduous leaves of woody browse plants retain

high digestibility until leaf fall, and evergreen leaves of woody browse

plants retain their nutritional value throughout the year (Short et al. 1975).

Edible fungi may be similarly useful throughout the year.

2
The HSI models described in this paper emphasize the measurement of food

items such as cool season herbages, leaves of woody browse plants, edible

fungi, and various seeds and fruits as important components in the ~valuation

of the quality of habitat for white-tailed deer during autumn-winter.

Water

Readily available sources of fresh water are important components of

white-tailed deer habitat in the coastal plain (Newsom 1984). The extent and

frequency of water consumption by deer depend on the ambient temperature, the

animal's physical condition, and the kinds of foods consumed (Marchinton and

Hirth 1984). Deer may be able to survive without surface water for some

period of time if rainfall, humidity, and plant succulence are relatively

high.

The HSI models assume that available water for drinking is required by

white-tailed deer and arbitrarily assume that available water must be present

within 1.6 km of the site being evaluated for that location to be considered

potential habitat for white-tailed deer.

Cover

Cover provides more of a refuge for deer from man and dogs than from

harsh winter weather in the coastal plains. Harlow (1984) lists swamps and

dense honeysuckle (Lonicera spp.)-thicketed areas as suitable cover but otherwise offers no definition of what constitutes adequate deer cover or shelter.

I suggest that cover adequate for deer might consist of an 8-ha area (within

each 40 ha of habitat) where cover is sufficiently dense, that a 1.5-m white

pole is not visible at a distance ~50 m.

Cover is usually adequate for white-tailed deer in coastal habitats,

except perhaps in large tracts of recently cleared forest lands, or in areas

where brush has been cleared to favor grass production (Halls 1978).

Reproduction

The reproductive physiology of white-tailed deer is reviewed by Verme and

Ullrey (1984). These physiological processes probably apply equally well to

the different populations of deer that exist within the geographical area of

the coastal plains. Deer from upland sites within the coastal plains are

frequently of small size. This may be due to upland sites usually having

highly leached and infertile soils, which are moderately to strongly acid and

low in both organ i c matter and mi nera 1 content. Except duri ng spri ng growth

the vegetation on these upland sites is frequently fibrous and low in protein

and minerals essential for good body growth. Dietary deficiencies in upland

sites probably limit deer numbers as well as their size. Substantial fawn

losses may occur when deer consume nutritionally deficient diets. High ambient

temperatures can also directly affect the lactation of does, and growth rate

and survival of fawns (Short et a l , 1969). These suggested impacts are not

well substantiated in the literature. They are mentioned here to emphasize

that the nutritional considerations mentioned in the present models should

also be expected to influence the reproductive capabilities of white-tailed

deer on the coastal plains.

3
Interspersion

White-tailed deer require suitable food, cover, and water. Probably the

ideal mix of these three components in the coastal plains would be blocks of

dense cover within forested areas having limited tree canopy cover (to insure

understory food production) and common sources of fresh water. Thi s idea 1

structure for deer habitats probably rarely occurs in nature, where forest

successi on usua lly 1eads to a dense tree canopy with 1imited understory food

reserves. Nor does it occur where intensive forest management or agricultural

management practices, such as the clear-cutting of pine timber, regeneration

of forest stands by planting young pines, thinning of young pine stands, and

removal of competing hardwoods, are prevalent. Early regenerative forest

stages provide abundant forages for deer, and the periodic thinning of established plantations may provide some additional food. These silvicultural

practices need to be coordinated over area and time to provide suitable foods

for deer throughout the lifetime of a forest stand.

The establishment of food plots may be a good compromise between silvicultural practices and deer requirements within the coastal plains. Food

plots, if intensively managed to produce cool season herbages, woody plants

with evergreen leaves, and fruits and seeds, provide a habitat condition that

varies only nominally from the ideal habitat structure described above. The

tree canopy is allowed to close over much of the forest but is kept open over

food plots, which should be abundant (accounting for 2% or more of the total

land area), well distributed, and relatively small (0.4-0.8 ha) (Crawford

1984).

The existence of deer in habitats that are broken mixes of forests and

agricultural lands reflects the great adaptability and tolerance of whitetailed deer, rather than favorable interspersion patterns of habitats. These

habitat mixes vary substantially from the proposed ideal habitat structure

described above. I do not try to represent the variations in the structure of

habitats from the proposed ideal structure of habitats with any particular

variable in the following HSI model s. I do describe an extreme condition

wherein large blocks of habitat with inadequate cover (measured in terms of

area) are considered to provide an inadequate structure and to be inadequate

habitats for deer.

HABITAT SUITABILITY INDEX (HSI) MODELS

Model Applicability

Geographi c area. The mode 1s for the white-tailed deer descri bed here

were developed for the South Atlantic and Gulf of Mexico coastal plains

(Figure 1). The models are restricted to this range because they are based on

results of controlled feeding studies with captive deer conducted at Louisiana

State University, Baton Rouge (Fowler et al. 1968; Short et al. 1969), and on

results from an extensive series of experiments that described the phenology,

composition, and digestibility of forages in the gulf coastal plain.

4
Missouri

,

200 Kilometer.

o

I

o

Texas

/-_.-'-'7 ,

! , Ole lahoma

i

I '".\.

, .~. '.~. ""~'7>.:K.-I~~ I

'-'-._-_.i

Figure 1. These models for the white-tailed deer are considered most

applicable for the area of the South Atlantic/Gulf of Mexico coast

delineated on the above map (after Newsom 1984:368).

Season. The models provide an estimate of the quality of habitat for the

white-tailed deer during autumn-winter. This is a stress period for deer, not

because of the severi ty of the southern wi nter, but because of the 1i mi ted

quality of many forages during these months.

Cover types. The models were developed for application in habitats

described as Forests, Tree Savanna, Forested Wetland, Shrubland, Shrub Savanna,

Scrub-Shrub Wetland, Grassland, Pasture and Hayland, Forbland, Herbaceous

Wetland, and Cropland (U.S. Fish and Wildlife Service 1981).

Minimum habitat area. The minimum habitat area is defined as the mlnlmum

amount of contiguous habitat that is required before a species will live and

reproduce in an area. Average home-range sizes for nonmigratory white-tailed

deer on the coastal plains, summarized from several studies, varied from

59-520 ha (Marchinton and Hirth 1984). I suggest in these models that land

units should be ~40 ha (0.4 km 2 ) in area before they are evaluated as habitats

for deer.

Verification level. The models were developed from quantitative information about body weight, rates of food intake of captive deer during the autumnwinter, and estimated digestibility values for a variety of forages growing in

southern forests. The models also rely on a variety of published information

about the metabolic requirements of mammals, the physiological attributes of

ruminants, and the energy values of different plant parts. The HSI values

5
from these models describe the potential of a habitat to supply food energy to

white-tailed deer. The models are designed to rank the suitability of various

southern habitats as would a biologist with expert knowledge about the habitat

requirements of deer. The models should not be expected to rank habitats in

the same way that population data would, because many nonhabitat-related

criteria can significantly impact populations of wildlife species.

Model Descriptions

Overview. Four related HSI models are described below. Model I is the

most complex and estimates the carrying capacity of habitats during autumnwinter on the basis of the energy requirements of deer during these seasons.

Model II is of lower resolution. It is derived from Model I and is also

driven by intensive field sampling to determine quantities of suitable forage

on an evaluation area. Model III (also derived from Model I), provides a more

general estimate of habitat qual ity because only very general estimates of

forage abundance are utilized. Model IV is of even lower resolution and only

predicts the probable presence or absence of deer on an evaluation area.

Each of these models is driven by estimates of the quantity of suitable

forage on a study area. The selection of the appropriate model for a study

effort is determined by the type of information required, which, in turn,

dictates the level of field effort that must be expended. The following

examples illustrate how the appropriate model might be selected.

An explicit statement is required about

the probable quality of a habitat for

white-tailed deer during autumn-winter.

A general statement is required about

the probable value of a habitat for

white-tailed deer during autumn-winter.

A general statement is required about

the probable presence or absence of

white-tailed deer on a habitat during

autumn-winter.

Mode 1s I and II

Mode 1 III

Model IV

The four models vary in resolution because they are driven by estimates

of forage abundance that vary from highly quantified to descriptive. Sections

within the models document the logic and the assumptions used to translate

habitat information for white-tailed deer into the variables selected for the

models. These sections also describe the assumptions inherent in the models,

identify- the variables used in the models, define and justify the suitability

levels of each variable, and describe the assumed relationships between

variables.

Habitat layers. A variety of forest products important to deer occur in

the different habitat layers of a forest community (Figure 2). The whitetailed deer, in the guilding context of Short (1983), can be considered to

forage in the understory and mi dstory 1ayers and to breed in the understory

layer. Other layers of habitat, however, contribute products useful to deer.

6
Layers

I OVERSTORY I

Twigs and

leaves-fallen---rr--.

and insitu

I TREE BOLE I

Thermal

and physical ...... ......

cover

/ r-,

I MIDSTORY I ...

Hard and

soft mast •

......."

I UNDERSTORY ... / t"'\ r-. r-. "

... ..... r-. ..... r-.

ITERR. SUBSURFACE I

I WATER SURFACE (emergent vegetation)

Products

important

to deer

Browse-twigs

and leaves of

woody vegetation

(food)

Cover

Hard and soft

mast from

woody and

herbaceous

vegetation (food)

Herbaceous

vegetation

(food)

Mushrooms

Water

Figure 2. The products from different layers of habitat that contribute

to the quality of habitat for white-tailed deer.

7
The requirements for physical and thermal cover, for example, can be provided

by the overstory canopy, by the midstory canopy, by emergent herbaceous and

woody vegetation in the surface water 1ayer of temporary wetlands, and by

topographic features and/or woody and herbaceous vegetation in the understory

layer.

Forage items include leaves and twigs dislodged from woody stems in the

overstory and midstory layers and leaves and stems in situ in the midstory, in

the understory, and on emergent vegetation in the water surface 1ayer of

temporary wetlands. Preferred dietary items also include a variety of hard

and soft mast dislodged from the overstory and midstory layers and hard and

soft mast in situ in the midstory, understory, and in suitable emergent vegetat ion in the water surface 1ayer of temporary wetlands. Food components also

include broad- and narrow-leaved herbaceous components from the understory and

from the water surface layer of temporary wetlands, and fungi from the understory layer. Free water for drinking is assumed to be a necessity in these

models.

Metabolic requirements of deer. It is necessary to estimate the metabolic

requi rements of deer to estimate the carryi ng capaci ty of habi tats in these

models. The metabolic requirements of deer in the gulf and South Atlantic

coastal plains have not been measured. A surrogate measure is the interspecies

mean basal metabolic rate (BMR) multiplied by some value that indicates that

usual physical activity requires energy in excess of basal rates. The interspecies mean metabolic rate is estimated by expanding measured or estimated

body weight (kg) to the 0.75 power (kgO. 75) and multiplying by 70 to calculate

requirements in kcal per day. Wallmo et al. (1975) estimated the activity

metabolic rate (AMR), which represents normal physical activity of Colorado

mule deer (Odocoileus hemionus) in winter, to be about 2.0 times the basal

metabolic rate. They also estimated that the activity metabolic rate might be

about 2.3 times the basal metabolic rate for lactating females and about 2.5

times the basal metabolic rate for males during the rut. Mautz (1978) presented a calculation of carrying capacity based on an energy expenditure rate of

150 kcal/kgO. 75/day - a value equal to about 2.1 times the basal metabolic

rate. Moen (1973) lists data estimating the energy expenditure of a deer in

autumn that was bedded 18% of the time, standing 18% of the time, and walking

64% of the time as 1.67 times the basal metabolic rate, and an energy expenditure during winter when the deer was bedded 25% of the time, standing 25% of

the time, and walking 50% of the time as 1.59 times the basal metabolic rate.

I have assumed from these estimates and measurements that a factor of 1.8

times the basal metabolic rate might be a reasonable approximation of the

activity metabolic rate of does, for purposes of this model. I have assumed,

for reasons described below, that the activity metabolic rate of male deer

during the November-December rut is 2.1 times the basal metabolic rate.

The body weights and rates of food consumption listed in Table 1 for

southern deer were determined for captive deer fed a nutritionally adequate

ration ad libitum under controlled conditions (Fowler et al. 1968; Short

1969). The estimated energy requirements (kcal/day) of these deer also varied

duri ng autumn-wi nter. An average energy requi rement of 2,845 kca l/day for

bucks and of 2,098 kcal/day for does was calculated from these data. I

selected these values to represent the average energy requirements of male and

8
Table 1. Measured body weights and food consumption rates (after Fowler et al. 1968) and

estimated energy requirements (kcal/day) for male and female white-tailed deer during

autumn-winter in the coastal plains. The probable gross energy value of wild foodstuffs

during each month is also listed.

Metabo I i c Estimated Gross energy Food in- body vt . Activity ene rgy va Iue 0 f wi Id

Agea Body take ra te

( kgO. 75) met abo t ic requ i rement foodstuffs

Sex Month (months) vt , ( kg ) ( g/kg/day) rate (kca I/day) ( kca I/g )

Male Oct. 16 64 22 22.6 1.8 2848 4.3

Nov. 17 63 15 22.4 2.1 3293 4.8

Dec. 18 60 17 21.6 2.1 3175 4.8

Jan. 19 56 20 20.5 1.8 2583 4.3

Feb. 20 57 20 20.7 1.8 2608 4.3

Ma r , 21 58 21 21.0 1.8 2646 4.3

M..!:..... 22 ---.QL zs.; 22.1 .L.JL 2785 4.3

Mean 60.4 20.5 21.7 1.9 2845

Female Sept. 15 40 28 15.9 1.8 2003 4.3

\0 Oct. 16 42 27 16.5 1.8 2079 4.3

Nov. 17 44 22 17. 1 1.8 2155 4.8

Dec. 18 43 20 16.8 1.8 2117 4.8

Jan. 19 43 17 16.8 1.8 2117 4.3

Feb. 20 42 19 16.5 1.8 2079 4.3

Ma r , 21 42 18 16.5 1.8 2079 4.3

Apr. 22 za.. lL...l J.......l! £l.2.2 4.3

Mean 42.5 21.6 16.6 1.8 2098

aAssumes a June 15 birth date.
female deer during the autumn-winter season. These values will obviously be

in error if the body weights listed in Table 1 do not reflect the body weights

of wild deer, or if the true activity metabolic rates of wild deer differ

significantly from the assumed rates. Actual weights of deer from coastal

plains habitats could be used to refine the estimated metabolic requirements.

Male and female deer differ in their metabolic requirements. I have used the

average metabolic requirements of a deer unit consisting of two does and one

buck to simplify the HSI calculation. Th1Sweighted average of the energy

requirement for a deer unit during autumn-winter in the coastal plains is

about 2,400 kcal/day.

Model!. The HSI model based on energy concepts. This model is similar

in concept to models described by Moen (1973), Wallmo et al. (1977), and Mautz

(1978). Habitat carrying capacity in Model I is estimated for the autumnwinter period (November 15 - February 15), when quality foods available to

deer in' the coastal plain are less common than at other seasons. The

energetics model is quite general to help reduce the amount of field data

required for implementation. Still, intensive sampling is required to use the

model. The energetics model provides both a rationale for developing models

II-IV and a way to assess how those models mimic results from the energetics

model. The logic for the energetics model is shown in Figure 3. I assume in

this model that free water is required by deer within their normal home range,

although Marchinton and Hirth (1984) suggest that white-tailed deer can survive

without free water when rainfall, humidity, and plant succulence are relatively

high. Free water is assumed to be required within 1.6 km of the habitat block

being evaluated, because the home range of white-tailed deer usually does not

exceed 2.6 km 2 [observations of Severinghaus and Cheatum (1956) cited in

Marchinton and Hirth (1984)].

Habitats to be eva1uated for white-tailed deer in the southern forest

type should be relatively large blocks of land that provide thermal and/or

protective cover to deer. Habitat blocks should be at least 40 ha (0.4 km 2 )

in area and provide suitable cover on at least 20% of the area. Cover may be

provided by overstory-midstory layer canopies, by dense (undefined) vegetation

in the understory layer, by dense (undefined) vegetation (e.g., cattails,

Typha spp.) in temporary herbaceous wetlands, or by suitable topographic

features.

The determination of an HSI value for this model involves estimating the

amount of energy metabolically available to deer on a study area and comparing

that estimate to the amount of metabolizable energy (ME) available to deer on

a standard unit of habitat. The HSI determination is actually an estimate of

the relative carrying capacity of the study area. The numerator of the HSI

calculation is the sum of the product of the quantity of each forage class

present on a hectare of habitat (QF), times the digestibility of each forage

class (OF), times values (EV) that will express the product of QF and OF in

terms of metabolizable energy (ME). This numerator is compared to a denominator that represents the metabolizable energy available in a II st andardli habitat

unit. I arbitrarily designated that "st andard" habitat unit to be a hectare

of habitat that wi 11 provi de 100,000 kca1 of metabo1i zab1e energy for deer.

Habitats that provide fractions of that 100,000 kcal of metabolizable energy

receive fractional HSI values. Habitats that provide more than 100,000 kcal

10
Free water occurs within 1.6 km of

habitat block being evaluated during ~No

the time period species is in residency.

I

yes

,

Habitat block being evaluated for NO------~.~I Land area is probably

species is at least 40 ha in area. not useful habitat for

the white-tailed deer.

yes

'f

Thermal or protective cover is

present on at least 20% of habitat block. Cover may be provided

by overstory/midstory layer canopies, dense (undescribed) vegetation in the understory layer, in

emergent vegetation in the water

surface layer of temporary wetlands,

or by topographic features in the

habitat block.

I

yes

,

I-- No

Quantity of each type of forage

physically available to deer

within the habitat block (QF).

f

Estimate of the apparent digestibility of each type of forage

physically available to deer

(OF). If digestibility is <41%

equate this multiplier to O.

f

Calculation of metabolizable

energy (ME) for each type of

forage physically available

to deer. ME =QF x OF x gross

energy value of foodstuff x 0.8.

f

Sum to determine the quantity of Quantity of ME assumed

ME apparently present in the to be present in a ha

habitat block being evaluated of optimum range used

as a denominator in

the HSI proportion. HSI value of habitat block

being evaluated.

Figure 3. Logic used to develop the energetics model (Model I) to provide an

HSI determination of habitat quality for the white-tailed deer.

11
of metabolizable energy receive HSI values rounded to 1.0. For purposes of

discussion, a hectare of habitat that provides 45.5 kg of food that is 64%

digestible and contains 4.3 kcal/g provides 100,000 kcal ME/ha. Such a " standard" habitat unit could provide about 41-42 deer-days use for a deer

unit (2,400 kcal/day). The " standard" habitat unit could provide metabolizable

energy during the autumn-winter season equivalent to that required for 46 deer

for a gO-day autumn-winter season (November 15 to February 15) per square

kilometer of habitat - a high deer density. The desired carrying capacity is,

of course, some fraction of this maximum value so that suitable foodstuffs

will not be overutilized, which might limit their availability in subsequent

years.

The Habitat Suitability Index (HSI) for the energetics model (Model I)

has the following general format:

HSI = carrying capacity of the habitat being evaluated

carrying capacity of a unit of " standard" habitat (1)

The actual equation for calculating HSI is as follows:

n

L (OF.

i=1 1

100,000

x DF. x EV.) 1 1

kcal ME/ha

where =1, ... , n = The classes of suitable forages existing in measureable quantities on a hectare of habitat.

= The quantity (grams) of individual classes of suitable

forage available within 1.5 m of the ground on each

hectare of habitat to be evaluated (a suitable forage

for deer in the gulf and South Atlantic coastal plains

is a type of vegetation whose digestibility in

autumn-winter is estimated or determined to be ~41%).

= The apparent digestibility of each class of suitable

forage. A digestibility of a forage for deer during

the autumn-winter in the gulf and South Atlantic coastal

plains <41% is considered to be a digestibility of O.

EV.

1

100,000 kcal ME/ha

= The energy value of each forage class is equal to the

apparent gross energy (GE) value of each class of

suitable forage times the constant 0.8, which will

provide an estimate of metabolizable energy (ME) after

forage digestible energy (DE) values have been

determined.

= the amount of metabolizable energy (ME) available to

deer on a unit of standard habitat

12
The HSI equation is general and may have relevance throughout the range

of whi te-ta i 1ed deer. The values for DF and EV have been determi ned from

empi ric a 1 stud i e sin the gulf and Sou t hAt 1anticc 0 a stal p1a ins . The HSI

equation may have applicability to other deer ranges in North America if

specific values for forage digestibility and the energy value of each forage

class can be established. The value for quantity of suitable forage needs to

be determined for each range under study.

Estimating guantity of forage (QF). The quantity of forage (g dry matter/

ha) in the energetics model requires the determination of the quantity of

different types of vegetative food available within 1.5 m of the ground in the

habitat blocks to be evaluated. Classes of vegetation are quantified separately because they vary in abundance, phenology, composition, digestibility, and

preference to deer. Quantities of the following seven forage classes are

candidate forage types to be evaluated in the coastal plains. Forage classes

3 to 7, below, are emphasized in these models because, as indicated below,

they provide sufficient digestible energy to deer in the Gulf of Mexico and

South Atlantic coastal plains to maintain deer with only minimum weight losses

during autumn-winter.

1. current year IS twi g growth and needl es from pi nes

2. leaves of current year fallen from perennial woody species

3. leafy browse composed of evergreen or tardily deciduous leaves in

situ on perennial woody species

4. mast from all layers including acorns, fleshy fruits, and seeds from

many agricultural crops

5. 1egumi nous seeds

6. cool season grasses and forbs including growing herbaceous agricultura 1 crops

7. mushrooms

Other foods, such as mature and hardened warm-season grasses, mature and

hardened warm-season forbs, mature and hardened woody twigs, and dried fruits

are common forages whose relative abundance need not be considered in this

evaluation model because they are of limited digestibility to deer.

Estimating digestibility of forage (OF). Forages differ in digestibility

because their nutrient composition is different. Several studies of forages

grown in the coastal p1a ins have descri bed how southern forages di ffer in

apparent digestibility (Short et a l . 1975; Short and Epps 1976; Blair et al.

1977). Estimates of dry matter digestibi 1 ity cited below were determined

using the nylon bag technique, wherein small quantities of individual foodstuffs were inserted into the rumen of cannulated goats used as domestic

animal analogues for deer. The forage digestibil ity estimates have not been

correlated with true digestibility coefficients, although they have been

13
compared with estimates of digestibility calculated with the summative equation

(r 2 = 0.62, Short et al. 1974), and forage digestibility values determined

with the summative equation have been compared with true digestibility coefficients.

All vegetation has a nutrient and caloric content, but not all vegetation

should be considered suitable food for southern deer. This is illustrated in

Tables 2 and 3. These tables contain estimates of the forage digestion rates

required so that deer may survive the autumn-winter period with either minimum

or maximum allowable weight losses. The calculations are based on the energy

requirements of deer at different estimated activity metabol i c rates, the

contribution to energy requirements supplied by metabolizable energy, the rate

of forage intake estimated from feeding trials with captive deer, the estimated

caloric value of individual foodstuffs, and the constant (0.8), which estimates

metabolizable energy (ME) from apparent digestible energy (DE). The values in

Tables 2 and 3 indicate that deer must eat more digestible forages to maintain

the same condition if activity metabolic rates increase, and that bucks may

have an increased activity metabol i c rate while in rut during November and

December, if bucks and does digest the same foodstuffs equally well. The

forage classes that may provide the required levels of metabolizable energy to

deer are also listed in Tables 2 and 3. I suggested earlier that an activity

metabolic rate of 1.8 might be a suitable multiplier of the basal metabolic

rate for female deer duri ng autumn-winter. Fema 1e deer may requi re forages

that are 64% digestible to achieve minimum weight loss during autumn-winter

and they may require forages that are ~41% digestible during autumn-winter to

sustain life with the maximum survivable weight loss (Table 2). Male deer may

also require forages that are 64% digestible to achieve minimum weight loss,

but 'may require forages that are ~46% digestible during autumn-winter to

sustain life with the maximum survivable weight loss (Table 3). Very common

forages, such as mature and hardened warm-season grasses (digestible dry

matter (DDM) = 20%), mature and hardened warm-season forbs (DDM = 27%), and

terminal segments of mature and hardened woody twigs (DDM = 28-40%), are

appa rently not suitable food for southern deer duri ng autumn-winter. These

forages will not yield sufficient metabolizable energy to maintain deer within

the arbitrary 1imits of a maximum 30% weight loss. Note that these very

common forages were not i ncl uded in the 1ists of cl asses of forages to be

sampled on evaluation plots. If the correct activity metabolic rate was

actually lower than my estimate of 1.8 then forages with reduced digestibility

coul d be used by deer duri ng autumn-winter to sustain 1i fe. For example, if

the activity metabolic rate during autumn-winter was actually 1.6 then does

could use forages with digestibilities as low as 33% (Table 2), so that forages

like dried fruits and the terminal portions of woody twig tips could also

provide necessary metabolizable energy during autumn-winter. The listing for

forage digestibility (OF) provides an estimate of dry matter digestibility in

major forage classes available during autumn-winter in the coastal plains. I

14
.......

U1

Table 2. Required rates of forage digestion to provide energy for female deer at a minimum

(4.5% weight loss observed for ad libitum fed deer in confinement~ raw data in Table 1) and

maximum survivable weight loss Tassumes 30% weight loss over a gO-day autumn-winter period)

at different activity metabolic rates.

Required forage digestion Forages providing Required forage digestion Forages providing Activity metabol ic rate for minimum autumn- the necessary meta- rate for maximum survivable the necessary meta- ra te winter weight loss# bol izable energy* autumn-winter weight loss# bol izable energy*

1.5 53 h,i,j,k,1 29 d,e,f,g,h, i,j,k, I

1.6 57 h,i,j,k,1 33 d,e,f,g,h, i,j,k, I

1.7 60 h,i,j,k,1 37 d,e,f,g,h, i,j,k, I

1.8 64 h,i,j,k,1 41 f,g,h, i,j,k, I

1.9 68 i,j, k, I 45 g,h,i,j,k,1

#Calculated as: required rate of forage digestion for female deer = 43 kg O•75(average metabolic body weight November to

December, see Table 1) x 70 (interspecies mean metabol ic rate) x activity metabol ic rate (varies in example) - metabol izable

energy contribution (133 kcal/day under conditions of minimum weight lOSS; 860 kcal/day under conditions of maximum survivable weight loss) ~19.5 g/kg (average food consumption rate November to February; see Table 1) x 43 kg (average body weight

November to February; see Table 1) x 4.6 kcal/g (assumed energy value of mixed diet of 67% roughages, 33% seeds) x 0.8

(conversion factor of digestible energy to metabol izable energy).

*Estimated rates of forage digestion of foodstuffs during autumn-winter (from Short et al. 1975; Short and Epps 1976;

Blair et a l , 1977).

a = 20% (mature and hardened warm-season grasses; this common forage does not provide sufficient energy for any of the

activity metabol ic rate scenarios)

b = 27% (mature and hardened warm-season forbs)

c = 28% (mature and hardened woody twigs, terminal 10 cm)

d = 39% (dried fruits: samaras, achenes, carpels, nutlets)

e = 40% (terminal 5 cm portions of twigs with terminal buds from perennial woody species)

f = 44% (current year's twig growth and needles from pines)

g = 45% (leaves of current year fal len from perennial woody stems)

h = 65% (leafy browse composed of evergreen or tardi Iy deciduous leaves in situ on perennial woody stems)

68% (mast from al I layers including acorns, fleshy fruits, and seeds from many agricUltural crops)

j = 87% (leguminous seeds)

k = 94% (cool-season grasses and forbs including growing herbaceous agricUltural crops)

95% (mushrooms)
Table 3. Required rate of forage digestion to provide energy for male deer at a mlnlmum

(12.5% weight loss observed for ad libitum fed deer in confinement, raw data in Table 1) and

maximum survivable weight loss (assumes 30% weight loss over a gO-day autumn-winter period)

at different activity metabolic rates.

Requ ired forage digestion Forages providing Required forage digestion Forages providing Activity metabol ic rate for minimum autumn- the necessary meta- ra te fo r ma x i mum survivable the necessary metarate .... inter ....eight loss# bol izable energy* autumn-.... inter ....eight loss# bol izable energy*

1.5 42 f,g,h,i,j,k,1 24 b,c,d,e,f,g,h, i,j,k, I

1.6 46 h,i,j,k,1 28 c,d,e,f,g,h, i,j,k, I

1.7 50 h,i,j,k,1 32 d, e, f, g, h, i , j, k, I

1.8 53 h,i,j,k,1 35 d,e,f,g,h, i,j,k, I

1.9 57 h,i,j,k,1 39 d,e,f,g,h, i,j,k, I

2.0 61 h,i,j,k,1 43 f,g,h, i,j,k, I

...... 2.1 64 h,i,j,k,1 46 h,i,j,k,1 O'l

#Calculated as: Required rate of forage digestion for male deer = 60.8 kg O.75(average metabol ic body ....eight October-January; see Table 1) x 70 (interspecies mean metabol ic rate) x activity metabol ic rate (varies in example) - catabol izable energy contribution (533 kcal/day under conditions of minimum ....eight lOss; 1280 kcl/day under conditions of maximum survivable ....eight loss) ~18.5 g/kg (average food consumption rate November-February; see Table 1) x 4.6 kcal/g (assumed energy value

of mixed diet of 67% roughages, 33% seeds) x 0.8 (conversion factor of digestible energy to metabol izable energy).

*Forages identified in Table 2.
have assumed that the quantity of forages (OF) can be determined from field

sampl ing efforts and that the estimate of the true digestibil ity of those

forage classes can be approximated from the following listing.

1. current year's twig growth and needles from pines = 44% DDM

2. leaves of current year, fallen from perennial woody stems =45% DDM

3. leafy browse composed of evergreen or tardily deciduous

leaves situ on perennial woody stems =65% DDM

4. mast from all layers including acorns, fleshy fruits,

and seeds from many agricultural crops =68% DDM

5. 1egumi nous seeds = 87% DDM

6. cool season grasses and forbs including growing

herbaceous agricultural crops = 94% DDM

7. mushrooms = 95% DDM

Estimating energy values of forage. The energy values (EV) of forage are

dependent on the presumed caloric value of each class of forage. Few determinations of the caloric values of southern forages have been made. Blair

et al. (1977) list the caloric values for composite samples of grasses, forbs,

browse leaves and twig tips, browse twigs, pine needles and twigs, mushrooms,

and frui ts at the four seasons to be about 4.35 kca1/g dry matter. Go 11 ey

(1961) determi ned that many forages conta i ni ng carbohydrates have a si mil ar

gross energy (GE) value of 4.3 kca1/g dry matter. Seeds, because of thei l"

high lipid content, have a gross energy value of about 5.1 kcal/g dry matter.

The gross energy value of most forages consumed during autumn-winter is

assumed, for this model, to be about 4.3 kcal/g dry matter. The gross energy

value of seeds such as acorns is assumed to be about 5.1 kcal/g dry matter.

The gross energy value of a forage is a 1aboratory measure. Thi s gross

energy value is never realized by deer because dry matter digestion is usually

not complete and there is work associated with the digestion of roughages.

Digestible energy is calculated as the gross energy value of a forage minus

the gross energy value of the feces produced from eating that forage. The

calculation of metabolizable energy also recognizes that energy associated

with urine and eructated gases of fermentation are costs of digestion that

further diminish the quantity of energy available to deer from a food. Moen

(1973) suggested that the metabolizable energy of forages for deer can be

estimated as 80% of the digestible energy of that food. This value (0.8) is

used as a constant multipHer in this model. The energy value (EV) thus

assigns a caloric value to a class of forage and transforms that caloric value

to a measure, which when multiplied by forage digestibility values, provides

estimates of metabolizable energy.

EV of most roughages

EV of most seeds = 4.3 kcal/g x 0.8= 5.1 kcal/g x 0.8

17
Model II. Representing the energetics model with Suitability Index (SI)

curves. The energetics model can be expressed in the more usual HSI format.

The quantity (QF), digestibility (OF), and energy values (EV) of forages are

condensed into two variables (quantity and digestibility of forage) in Model

II. The calibration of the two variables is directly dependent on calculations

developed and assumpti ons vrnade for the energetics model. The logic used in

this second HSI model is shown in Figure 4. The same restrictions about block

size and water and cover availability in habitats apply for Model II as applied

for Model 1. Model II, as was true for the energetics model, is intended to

evaluate the relative quality of habitats during autumn-winter for white-tailed

deer in the Gulf of Mexico and South Atlantic coastal plains.

The quantity of forage in forage classes 1-7, identified in Model I, can

be assigned an SI value by consulting Figure 5. The discussion for Model I

suggested that habitats that provided 100,000 kca1 of metabolizable energy/

hectare should receive an HSI of 1.0. This value would be realized if a

habitat provided ~45.5 kg of forage dry matter that was ~64% digestible. An

SI value of 0.0 is assigned to forage quantity (QF) if no suitable forage is

present, and an SI of 1.0 is assigned to forage quantity (QF) if ~45.5 kg dry

matter of suitable forage is present (Figure 5).

The digestibility of forages in forage classes 1-7, identified in Model I,

can be assigned an SI value by consulting Figure 6. The discussion for Model I

suggested that forages with an apparent digestibility <41% might not provide

sufficient metabo1 izab1e energy to maintain deer during the autumn-winter

stress period. Forages that are <41% digestible are thus assumed to have an

SI value of 0.0 in this model. The data in Tables 2 and 3 also suggest that

forages ~64% in digestibility produce sufficient metabolizable energy to

maintain deer with only minimum weight losses during autumn-winter.

Digestibility values ~64% are thus assumed to have an SI value of 1.0 in this

model.

HSI determination. The Habitat Suitability Index (HSI) for evaluating

the quality of southern forest habitats for white-tailed deer is determined

from the summed product of SI values representing the quantity and

digestibility of each forage class present in a study area. The conceptual

18
Free water occurs within 1.6 km

of habitat block being evaluated --No

during the time period species

is in residence.

I

yes

Habitat block being

•

evaluated

for species is at least 40 ha

in area.

I

yes

t

>--No -----'J.~I Land area is probably

not useful habitat

for the white-tailed deer.

Thermal or protective cover is

present on at least 20% of

habitat block. Cover may be

provided by overstory/midstory

layer canopies, dense (undescribed) >--No

vegetation in the understory layer,

in emergent vegetation in the

water surface layer of temporary

wetlands, or by topographic

features in the habitat block.

I

yes

t

Quantity of each type of

forage physically available to

deer within the habitat block.

Quantity is transformed into

51 value (QF =51V1).

't

Estimate of the apparent

digestibility of each type of

forage physically available to

deer. Estimates of apparent

digestibility are transformed

into 51 values (OF =SIV2).

+

H51 values are calculated as

the geometric mean of 51 values

representing the relative

quantity and the relative

digestibility of forages

occurring on an evaluation

area.

Figure 4. Logic used to develop H51 Model II for white-tailed deer based

on 51 curves representing the quantity of forage on an evaluation area and

the apparent digestibility of those forages.

19
0.8

-- 1. 0 -t-....._-~~,.....-...-... r-

:>

......

V)

X

QJ

-g 0.6 ......

'r0.4

:::l

V)

o. 0 .,.........~-........~-...-...

.0

ttl 0.2 +J

o 10 20 30 40 50 60 70

Quantity of suitable forage

(kg/ha; lbs/acre) physically

available to deer within the

habitat block

Figure 5. 51 values for QF increase as the quantity of suitable

forage increases per ha of habitat.

1.0 N

:>

......

V) 0.8

x

QJ

"'0

l:: 0.6 ......

>,

0.4 .J-)

'r-

'r-

.0

ttl 0.2

+J

'r-

:::l

V) 0.0

0 25 50 75 100

Apparent dry matter

digestibil ity of

forages physically

available to deer

Figure 6. 51 values increase as the apparent digestibility of forage

increases from 41% to 64%.

20
approach is comparable to the logic developed in the energetics model. The

equation for estimating the suitability of habitats for white-tailed deer is

given below. HSI's greater than 1.0 are rounded to 1.0.

n

HSI = r (SIV1. x SIV2.)1/2

i =1 1 1

(2)

where i=l, ... , n

SIV2 i

= The classes of suitable forages existing in measurable

quantities on a ha of habitat.

,/

= the quantity (QF) of each type of suitable forage (a

suitable forage is a type of vegetation whose digestibility by deer is estimated or determined to be ~41%)

on each ha of habitat to be evaluated as represented by

the appropriate S1 value.

= the apparent digestibility (OF) of each class of suitable forage as represented by the appropriate SI value.

Model III. The relative abundance of foods in a habitat block. Model

III is applicable when only general information about forage abundance is

available from a habitat block. Resolution is fairly low with this model and

only general statements of habitat quality are possible. The logic used in

developing this model is shown in Figure 7. The same restrictions about block

size and water and cover availability in habitats apply for Model III as

app1i ed for Mode 1 1.

Models I and II, as described above, are driven by measures of the abundance of different types of deer foods within an evaluation area. Quantitative

measures of food abundance include clip-weigh techniques for estimating quantities of grass, forb, and browse tissues; establishing, maintaining, and

monitoring seed traps to measure weights of fruits and seeds; and periodic

sampling along transects to measure mushroom production.

Surrogate variables that are quick and easy to measure are sometimes used

when it is not feasible to measure food production directly or intensively.

It is assumed that the surrogate variables approximate food abundance and that

an HSI developed from them approximates habitat quality. These are frequently

untested assumptions. Surrogate variables may be visual estimates along

transects or subplots. Visually estimating the weight of current annual

growth is rapid, and a large number of plots can be examined in a relatively

short time. With training, most observers can estimate within 10 to 15% of

the actual value. The big disadvantage is that the estimates are subjective,

their relation to actual values is not known, and the results are apt to be

biased (Wenger 1984:711). The count of mast-bearing plants per hectare or of

the basal area of mast trees is a surrogate measure of the production of shrub

21
Free water occurs within 1.6 km

of habitat block being evaluated ~No

during the time period species

is in residence.

I

yes

Habitat block being

•

evaluated for ---No--------~.~ILand area is probably

species is at least 40 ha in area. not useful habitat

for the white-tailed deer.

Thermal or protective cover is

present on at least 20% of

habitat block. Cover may be

provided by overstory/midstory

layer canopies, dense (undescribed)---No

vegetation in the understory layer,

in emergent vegetation in the

water surface layer of temporary

wetlands, or by topographic

features in the habitat block.

I

The Habitat Suitability

T

Index

predicting the value of a

habitat block for deer is

determined from estimates

of the quantity of suitable

forage and mast available

within that habitat block.

Figure 7. Logic used to develop HSI model III for white-tailed deer based

on the relative quantity of suitable forage present on a hectare of habitat.

22
and tree mast. This estimate is imprecise because the number of sound mast

items per shrub or tree varies with weather conditions when shrubs or trees

trees are in flower, with the soundness of t nd l vl dual : shrubs and trees, and

with insect damage to the mast crop.

The model represented in Figure 7 is driven by surrogate values that may

have relevance to measures of forage abundance (QF in Model I). If particular

surrogate measures are meaningful, then model users can make decisions about

habitat quality with some accuracy. Visual estimates of the quantity of

cool-season herbages, evergreen leaves of woody browse plants, and edible

fungi are made on randomly located plots scattered throughout the habitat, and

the quantity of hard and soft mast is based on counts of the number of mastbearing plants per unit area. Estimates of the abundance of highly digestible

foodstuffs during autumn-winter within a habitat provide a basis for estimating

the relative quality of that habitat for deer.

Quantities of ME per ha of habitat are predicted in Model III after

estimating the weight of green cool-season grasses and forbs, evergreen leaves

of woody browse plants, and of mushrooms growing on 1 m2 sample plots located

throughout the habitat block and by counting the stems of mast-producing

shrubs and trees occurring within the habitat block. Plant materials with

apparent digestibilities <41% are not considered in Model III and the mass of

suitable forage on the experimental plots is assumed to have a digestibility

of about 50%. This relatively low value is used because no effort is made in

sampling to identify and distinguish individual forage items or the relative

growth stage of green cool season forages. A total of about 58 kg dry matter/

ha of green grasses, green forbs, evergreen leaves of woody browse plants, and

mushrooms need occur within a ha of habitat to produce 100,000 kcal of ME, if

one conservatively applies an average digestibility of 50% to suitable forages,

assumes 4.3 kcal/g gross energy for these dried forages (above), and a

digestible energy to metabolizable energy conversion factor (above) of 0.8

(58,000 g/ha x 4.3 kcal/g x 50% digestibility x 0.8 = 100,000 kcal ME/ha).

Each 1 m2 wi thi n the ha of habitat needs to provi de an average of 6 g dry

matter of suitable foodstuffs to provide about 100,000 kcal ME/ha.

Mast items also contribute to the metabolizable energy available to deer

within a habitat. Lay (1969) reported that six species of shrubs and trees

that produced fruit eaten by deer during autumn-winter produced about 1.13 kg

mast/woody stem. These data may be very conservative estimates because Lay

did not include oaks (Quercus spp.) in his listing and may not have expressed

his data in terms of oven dry weights. I will use these conservative values

'in the following HSI calculation because I have no better data set. If the

relative digestibility of mast items is 68% (above), the average gross energy

value of mast items is 5.1 kcal/g (above), and if the 1.13 kg mast/stem value

is relevant then about 32 stems/ha are required to produce 100,000 kcal ME/ha

(32 x 1.13 = 36,130 g/ha x 5.1 kcal/g x 68% digestibility x 0.8 conversion

factor = 100,000 kcal ME/ha).

A determination of the relative quality of habitat for deer during autumnwinter can be made by estimating the ME available to deer from both forages

(green grass, green forbs, evergreen leaves of woody browse plants, and

mushrooms) and from mast. The presumed energy content of both food sources

23
has been scaled so that absence provides no ME and maximum values (6 g dry

matter/m2 for forages and 32 stems/ha for mast) each may provide 100,000 kcal

ME to deer. These relationships for SIWF and SIWM are expressed as SI curves

in Figures 8 and 9, respectively. The HSI measure is a sum of the two SI

values with values greater than 1.0 rounded to 1.0.

HSI =SIWF (winter forage) + SIWM (winter mast)

Model IV. Predicting the presence or absence of deer on a land unit. The

concept of the quantity of forage ina habi tat block, whi ch dri ves Models

I-III, also drives Model IV. Here, forage quantity is estimated in the

simplest of terms - presence or absence of major forage classes. The logic

used in developing this model is shown in Figure 10. Survey plots within each

hectare of habitat are examined for the presence of cool-season grasses or

forbs, mast, or leafy browse. The hectare of habitat is presumed to have some

value as habitat for deer if any representatives of the three forage classes

occur on one-third of the sample plots. The additional steps suggesting that

the presence of suitable forages on many or most plots is indicative of highquality habitat is not made for this model, because it is difficult to

translate numbers of plants into forage weights without performing the

appropriate conversion steps. Model IV has some real-world relevance because

some habitats, such as pole-sized pine plantations, may provide water and

protecti ve cover but essenti ally no useful food for deer. Thi s condi t ion

could be predicted with Model IV.

Model IV will generally be of limited utility for evaluating habitats

because it is driven by very superficial information. The model is not discussed further because I am unable to express presence or absence of forage in

terms of a Habitat Suitability Index.

Comparison of models I-IV. The energetics model (Model 1) is based on

the abundance of different forage types occurring on a study area, the assumed

digestibility of each of those forage types, the caloric content of the

forages, a conversion factor that transforms estimates of digestible energy to

estimates of metabolizable energy, and estimates of the energy requirements of

deer. The HSI determination compares the quantity of metabolizable energy on

a study area with the quantity of metabolizable energy on a standard study

area. The comparison seems to represent a relative measure of the potential

carrying capacity of the evaluation area for deer during the autumn-winter

seasons in the Gulf of Mexico and South Atlantic coastal plains. Forage

abundance clearly drives this model. Habitats with some forage, if only of

minimal usefulness (forage <41% in digestibility has no usefulness) still have

some utility to deer, whereas habitats with essentially no forage (even if the

micro amounts present are of high digestibility) have little or no utility to

deer.

Model II is based directly on Model!. The major difference in Model II

is that QF, the abundance of different forage types occurring on a study area,

and OF, the assumed di gesti bi 1i ty of these forage types, are represented as

Suitability Index curves in the more usual HSI format. A critical feature of

24
......... 1.0

u...

3

-

V) 0.8

x

OJ

"'tJ 0.6 s::

-

»

+-> 0.4 'r-

'r-

..0

It:! 0.2

+->

'r-

::::I

V) 0.0

0 2 3 4 5 6 7 8

Average dry matter

yield of suitable

forage per 1 m plots

Figure 8. 51 values for winter forage (WF) increase as the grams dry

weight of suitable forage per 1 m2 plots increases.

X

OJ

"0

s::

......

'ro 5 10 15 20 25 30 35 40

Number of stems/ha of species of

woody shrubs and trees that provide

mast to deer during autumn-winter

Figure 9. 51 values for winter mast (WM) increase as the number

of stems of suitable woody shrubs and trees increase within a

hectare of habitat.

25
Free water occurs within 1.6 km

of habitat block being evaluated

during the time period species

is in residence.

I

yes

Habitat block being evaluated

for species is at least 40 ha

in area.

I

yes

I----no

1---- no -----o~ Land area is probably

not useful habitat

for white-tailed deer.

Thermal or protective cover is

present on at least 20% of

habitat block. Cover may be

provided by overstory/midstory

layer canopies, dense (undescribed) no

vegetation in the understory layer,

in emergent vegetation in the

water surface layer of temporary

wetlands, or by topographic

features in the habitat block.

I

yes

Leafy browse or mast or

cool-season grasses and forbs

are present on habitat

to be evaluated.

I

yes

+

Land unit may be useful

habitat for white-tailed

deer.

f----no

Figure 10. Logic used to develop model IV to determine the possible presence

or absence of white-tailed deer on a land unit.

26
an HSI model, such as Model II, is the assumption that meaningful SI curves

can be developed and then meaningfully combined in some logical manner to

reflect the relative quality of habitats. The two related models provide a

test of this assumption. HSI values are calculated for 16 different habitat

configurations in Table 4 using the formats of Models I and II. HSI determinations for Model II are listed for three different combinations of the two SI

curves (Figures 5 and 6) - a geometric mean, an arithmetric mean, and a simple

product of the two SI curves; the three combinations of SI curves are each

highly correlated with the respective HSI value from Model I (r = 0.85 - 0.86),

but each varies disturbingly from HSI values in Model!. Nine of the HSI

values calculated from the geometric mean, seven from the arithmetric mean,

and nine from the simple product of the SI curves varied from their respective

counterparts in Model I by >0.1 units (Table 4). Four of the values from the

geometri c mean ca 1cul ation, three from the a ri thmetri c mean ca 1cul ation, and

five from the simple product of the two SI curves varied from counterpart

values in Model I by ~0.2 units (Table 4). Especially perplexing are conditions where 75%,50%, and 25% of the maximum forage exists on an evaluation

site and this forage is all highly digestible (SIV2 = 1.0). The Model II HSI

values for these habitat conditions should be 0.75, 0.5, and 0.25, but the

geometric mean and arithmetric mean estimate the HSI values as 87% - 88%,

71% - 75%, and 50% - 63%, respectively. The geometric and arithmetric means

each provided HSI estimates that were in error in 7 and 6 of the 12 instances,

respectively, where only 75%, 50%, or 25% of the maximum forage amounts were

present on an evaluation area (Table 4).

Model II uses SI curves instead of quantitative measures of forage abundance and forage digestibility. Comparisons between estimates of HSI values

using SI curves and HSI values from Model I suggest that rules may need to be

established to define: (1) how SI curves should be structured, and (2) how SI

values from a number of variables should be combined to form HSI estimates.

Model III is more conservative than Model I because estimates of forage

abundance are relative rather than highly quantified. Model III is still a

re 1evant model, however, because it is based on the apparent abundance of

leafy browse, mast, cool-season herbaceous growth, and edible fungi - the

forage classes most likely to contribute large quantities of suitable forage

to deer during autumn-winter.

Model IV, although derived from Model I, cannot be directly compared with

Models I-III because it is driven by scanty and only descriptive information

about the apparent presence or absence of forage classes on sample plots.

Application of the Models

Summary of model variables. There are four models for white-tailed deer

described above. Each is driven by estimates of the quantity of suitable

forage on a study area during autumn-winter. Biologists using these models

must determine the level of resolution that is needed. Level of resolution

determi nes the appropri ate model to use and the model determi nes the fi e1d

effort required to measure model variables. Good quantitative estimates of

habitat quality for white-tailed deer can be made if great effort is expended

27
Table 4. A comparison of HSI values developed from Model (I) and Model (II) for 16

calculated habitat configurations.

Model I Model I I

Quantity Appa rent kcal HSI SI SI HSI HSI HSI

of suitable digestibi I ity ME/ha value value value value value value

fa rage of fa rage for for geomet ric arith- simple

kg/ha forage fa rage mean !:: met ric product quantity digest- (SIV1xSIV2)2 mean ( S IV1 x S IV2)

(QF) t i b i I ity (~+SIV21

(OFI 2

45.5 64.00 100173 1. 00 1.0 1.0 1.0 1.0 1.0

45.5 58.25 91173 0.91 1.0 0.75 0.87 0.88 0.75*

45.5 52.50 82173 0.82 1.0 0.5 0.71* 0.75 0.5**

45.5 46.75 73173 0.73 1.0 0.25 0.50** 0.63* 0.25**

34.13 64.00 75141 0.75 0.75 1.0 0.87* 0.88* 0.75

34.13 58.25 68390 0.68 0.75 0.75 0.75 0.75 0.56* N

CO 34.13 52.50 61639 0.62 0.75 0.5 0.61 0.63 0.38**

34.13 46.75 54888 0.55 0.75 0.25 0.43* 0.50 0.19**

22.75 64.00 50086 0.50 0.5 1.0 0.71** 0.75** 0.50

22.75 58.25 45586 0.46 0.5 0.75 0.61* 0.63* 0.38

22.75 52.50 41087 0.41 0.5 0.5 0.50 0.5 0.25*

22.75 46.75 36587 0.37 0.5 0.25 0.35 0.38 0.'13**

11.38 64.00 25054 0.25 0.25 1.0 0.50** 0.63** 0.25

11.38 58.25 22803 0.23 0.25 0.75 0.43** 0.50** 0.19

11.38 52.50 20552 0.21 0.25 0.5 0.35* 0.38* 0.13

11.38 46.75 18301 0.18 0.25 0.25 0.25 0.25 0.06*

*Estlmated HSI value differs from calculated HSI value for Model I (column 4) by >0.1 units.

**Estimated HSI value differs from calculated HSI value for Model I (column 4) by >0.2 units.
to measure suitable forages on a study area. Relative estimates of habitat

qua 1i ty for white-ta i 1ed deer can be made with some assurance if re 1at i ve

estimates are made of the quantity of suitable forages on a study area. Only

gross estimates, which describe the probable presence or absence of whitetailed deer on a study area, can be made, however, if only superficial surveys

are conducted to determine the presence or absence of suitable forages on a

study area.

The biologist, after selecting the most appropriate model, should then

examine recent, high-resolution, aerial photographs of the study area. Large

blocks of land without overstory or midstory cover and blocks of land that

seem devoid of surface water may be areas of little or no value to deer.

These land blocks may frequently consist of cultivated croplands, haylands,

areas where tree harvest has recently occurred and revegetation with new tree

growth has not yet occurred, and areas subjected to pressures of urbanization.

Those land areas near and under overstory and midstory canopies and near

sources of surface water are candidate areas for evaluation as deer habitat

during autumn-winter. Descriptions of how variables for the different models

are combined to determine the respective HSI values, are shown in Figure 11.

The procedures used to estimate the quantity of suitable foods on a study area

are described in Figure 12.

Model assumptions. These models for the white-tailed deer were developed

from several data sets developed in east Texas and Louisiana. Hopefully, the

models have relevance throughout the gulf and South Atlantic coastal plains.

My descriptions of habitat criteria important to white-tailed deer are based

on generalizations about ruminant nutrition, quantitative measures of deer

physiology (mostly obtained from outside the gulf and South Atlantic coastal

plains), and forage composition and digestibility estimates specific to the

coastal plains. My descriptions of procedures for evaluating habitat quality

will be in error if I have made i ncorrec t judgements about kinds of data that

should be used in these models or if I have misinterpreted the meaning of

particular data sets.

The models are based on concepts of the energy requirements of deer. I

have assumed that habitat quality can be evaluated on the basis of the energy

requirements of deer and the available energy within forages. It can be argued

that any carrying capacity model for deer in the coastal plains should also

consider phosphorus and nitrogen requirements and availability in forages. I

have restri cted these models to a consi derati on of energy factors ma in ly

because nitrogen and phosphorus requirements of deer are poorly known and

availability in forages is expensive to measure.

I made several assumptions about the energy requirements of deer. The

basal metabolic rate of southern deer was estimated from the interspecies mean

metabolic rate. The activity metabolic rate of female deer during autumnwinter was estimated as 1.8 times the basal metabolic rate, and the activity

metabolic rate of male deer during the rut was estimated as 2.1 times the

basal metabolic rate. Values for both does and bucks are guesses. If actual

activity metabolic rate requirements are greater, then the forage digestibility

rates necessary to satisfy metabolic requirements may be higher than values

cited in Tables 2 and 3. If actual activity metabolic rate requirements are

29
Habitat variable

Model I

Life requisite Cover types

Quantity of suitable

forage (kg/ha; lbs/acre)

physically available to

deer within the habitat

bloc k ----------,

Apparent dry matter

digestibility of

forages physically

available to deer

Calculation of the

metabolizable energy (ME)

content of each type

of forage physically

available to deer

Mode 1 II

Forests (F)

Tree Savanna (TS)

Forested Wetland (FW)

autumn-winter ~---l Shrub1and (S)

forage Shrub Savanna (SS)

Shrub-Scrub Wetland (SSW)

Grassland (G)

Pasture and Hayland (P-H)

Forbland (F)

Herbaceous Wetland (HW)

Cropland (C)

HSI

Quantity of suitable

forage (kg/ha; lbs/acre)

physically available to

deer within the habitat

block

Apparent dry matter

digestibility of

forages physically

available to deer

Mode 1 III

HF, TS, FW,

S, SS, SSW,

autumn-winter G, P-H, F,

forage HW, C 1-------------- HS I

Average dry matter

yield of suitable forage

per 1 m2 plots -----,

Number of stems/ha of

species of woody shrubs

and trees that provide

mast to deer during

autumn-winter ----------

autumn-winteMF, TS, FW, S J

forage SS, SSW, G, 1-,------HSI

P-H, F, HW, C

Figure 11. The relationship between habitat variables, life requisites,

and cover types to autumn-winter HSI's for white-tailed deer in the gulf

and South Atlantic coastal plains. Habitat variables are listed only for

Models I-III because no HSI calculation is made for Model IV.

30
Variable (definition)

Quantity of suitable

forage (kg/ha; lbs/acre

physically available

to deer within the

habitat block (Models I

and II).

Average dry matter yield

of suitable forage per

1 m2 plots. (Model III).

Cover type

F, TS, FW, S,

SS, SSW, G,

P-H, F, HW, C

Suggested techniques

Establish transects and

1 m2 sample plots along

transects. Provide about

100 plots/ha. Estimate

for each sample plot the

weight of forage in each

of the forage classes

(listed for QF in Model I)

that occurs from the ground

surface to 1.5 m above the

ground surface. Clip

vegetation by forage classes

in every tenth plot, ovendry, weigh forage by forage

class, and correct visual

estimates of forage abundance. Obtain estimates of

mast weights by establishing

traps under mast producing

plants and weighing collected mast or by counting and

weighing a subsample of

mast items.

Establish transects and

1 m2 sample plots along

transects. Provide about

100 plots/ha. Make visual

estimates during autumnwinter of the dry weight of

evergreen leaves of woody

browse plants, mushrooms, and

cool-season grasses and forbs

within 1.5 m of the ground on

each sample plot. Determine

the average dry matter yield

for 1 m2 plots and obtain

relative SI values by consulting Figure 8.

Figure 12. Definitions of variables and suggested measurement techniques.

31
Variable (definitionj

Number of stems/ha of

species of woody shrubs

and trees that provide

mast to deer during autumnwinter (Model III).

Leafy browse, or mast, or

cool-season grasses and

forbs are present on the

habitat block (Model IV).

Apparent dry matter

digestibility of

forages physically

available to deer

(Models I and II).

Calculation of the

metabolizable energy (ME)

content of each type of

forage physically available

to deer (Model I).

Cover type Suggested techniques

Obtain estimates of relative

mast production by counting

the number of stems/ha of

species of woody shrubs and

trees that provide mast to

deer during autumn-winter.

Determine the relative SI

value for the stem count per

ha by consulting Figure 9.

Establish transects and

sample plots along transects.

Provide about 100 plots/ha.

Determine if leafy browse,

edible fungi, cool-season

grasses and forbs, or mast

producing plants occur on

one-third of the sample

plots. If one of the four

forage types does occur on

one-third of the plots consider suitable deer forage

present.

Consult values for the dry

matter digestibility of

different classes of

forages (OF) listed for

Mode 1 I.

Determine if forage type

is a roughage or a seed

and use estimated gross

energy and metabolizable

energy values cited for

energy va1ues (EV) in Model!.

Figure 12.

32

(Concluded).
lower, then forages of lesser digestibilities can be used to satisfy nutritional demands. Different activity metabolic rate values might change the

classes of foods required to satisfy the metabolic energy requirements of

deer. In any case, the very abundant foods, such as warm-season grasses,

warm-season forbs, and woody browse twigs are likely to remain as nutritionally

inadequate foods for deer in the coastal plains during autumn and winter.

Estimates of the digestible dry matter of forages were made in in vivo

microdigestion experiments. None of the forages evaluated with themicrodigestion technique was compared to true digestibility coefficients determined

by digestion trials with white-tailed deer. Consequently, I have assumed that

the microdigestion values do reflect the relative digestibility of selected

foods. The only assurance that this assumption is correct is that the in vivo

microdigestion values bear a significant relationship to values estimated from

the summative equation (Short et al. 1974). Estimates of forage digestion

suggested by the summative equation are based on relationships formulated for

the way that domestic ruminants digest forages of different composition. I

have also assumed that the estimated gross energy values are reasonable and

that the conversion value translating digestible energy to metabolizable

energy is meaningful.

Cycles of changes in body weights and food consumption rates over the

annual period were developed from a limited series of feeding trials performed

under controlled conditions with deer in captivity. I have assumed that results

from these feeding trials reflect cycles that would also occur in wild deer.

I have assumed that the autumn-winter period is critical to the whitetailed deer in the Gulf of Mexico and South Atlantic coastal plains because

the quantity of life-sustaining forage is limited at this time even though

roughages of low digestibility may be in great abundance. I have further

assumed that free water must be available within a radius of 1.6 km of a study

site and that a block of land should be at least 40 ha to be considered potential habitat for deer. These restrictions are intended to help a biologist

quickly and legitimately exclude large blocks of land that have insufficient

cover and no water from consider?tion as deer habitat.

I assumed that I could estimate the carrying capacity of a habitat block

(Model I) by estimating metabolizable energy on that habitat block and dividing

that value by an arbitrary standard for metabol izable energy. I further

assumed that I could express values for forage quantity (QF) and forage digestibility (OF) in Model I as standard SI curves in Model II, and that an HSI

value could be determined from the geometric mean of these indices. Comparisons in Table 4 suggest that this assumption may not be entirely correct.

I have assumed that explicit measures of forage abundance like clip-weigh

techniques, fruit and seed traps, and periodic mushroom surveys are useful

processes for measuri ng the abundance of forages on a study area. I also

assumed that certain surrogate measures of food abundance, such as vi sua 1

estimates of forage abundance along transects and counts of mast producing

plants, might bear some relationship to more quantitative measures of forage

abundance, so that models with some or limited precision can be built using

the surrogate measures.

33
SOURCES OF OTHER MODELS

Severa1 ca rryi ng capac ity mode1s have been deve loped for deer and the i r

habitats (Moen 1973; Wallmo et al. 1977; and Mautz 1978). The model developed

by Wallmo et al. (1977) for mule deer in Colorado has been applied to whitetailed deer ~ccupying the Savannah River Plant in South Carolina (Harlow 1984).

Concepts in that model are similar to those in Model I, although values for

individual components vary in the two models. Harlow (1984) also reviewed

estimates of carrying capacity for the Savannah River Plant based on kilograms

of forage available/kilograms of forage required by deer. Presumably, only

useable forage (some level of digestibility) is used in making the calculation,

although the level of digestibility that constitutes usefulness is not described. Harlow (1984) also described a model that estimated the carrying capacity

of a habitat in Virginia on the basis of the energy available in habitats

during different seasons of the year and the calculated energy requirements

for a deer herd of known size. Many assumptions about the seasonal energy

requirements of deer must be made to utilize the Virginia model. Other models

exist that seem similar in resolution to Model IV. For example, Armbruster

and Porath (1980) developed a scorecard for ranking habitat variables in a

variety of deer habitat types.

REFERENCES

Armbruster, M. J., and W. R. Porath. 1980. White-tailed deer (Odocoileus

virginianus). Pages 12-26 in T. S. Baskett et al., eds., A handbook for

terrestrial habitat evaluation in central Missouri. U.S. Fish Wildl. Servo

Res.Publ.133.

Baker, R. H. 1984. Origin, classification and distribution. Pages 1-18 in

L. K. Halls, ed. White-tailed deer: ecology and management. Stac kpo[e

Books, Harrisburg, PA.

Blair, R. M., H. L. Short, and E. A. Epps, Jr. 1977. Seasonal nutrient yield

and digestibility of deer forage from a young pine plantation. J. Wildl.

Manage. 41(4):667-676.

Crawford, H. S. 1984. Habitat management. Pages 629-646 in L. K. Halls, ed.

White-tailed deer: ecology and management. Stackpole Books--,Harrisburg, PA.

Fowler, J. F., J. D. Newsom, and H. L. Short. 1968. Seasonal variation in

food consumption and weight gain in male and female white-tailed deer.

Proc. Southeastern Assoc. Game Fish Comm. 21:24-32.

Golley, F. B. 1961. Energy values of ecological materials. Ecology 42(3):

581-584.

Halls, L. K. 1978. White-tailed deer. Pages 43-65 in J. L. Schmidt and

D. L. Gilbert, eds. Big game of North America. StackpOle Books, Harrisburg,

PA.

34
Harlow, R. F. 1984. Habitat evaluation. Pages 601-628 in L. K. Halls, ed.

White-tailed deer: ecology and management. Stackpole BookS, Harrisburg, PA.

Lay, D. W. 1969. Foods and feeding habitats of white-tailed deer. Pages

8-13 in White-ta il ed deer in the southern forest habitat. Proc. Syrnp .

Nacogdoches, TX. U.S. For. Serv., South. For. Exp. Stn., New Orleans, LA.

Marchinton, R. L., and D. H. Hirth. 1984. Behavior. Pages 129-168 in L. K.

Halls, ed. White-tailed deer: ecology and management. Stackpole-Books,

Harrisburg, PA.

Mautz, W. W. 1978. Nutrition and carrying capacity. Pages 321-348 in J. L.

Schmidt and D. L. Gilbert, eds. Big game of North America. Stackpole-Books,

Harrisburg, PA.

Moen, A. N. 1973. Wildlife ecology. W. H. Freeman Co., San Francisco, CA.

458 pp.

Newsom, J. D. 1984. Coastal plain. Pages 367-380 in L. K. Halls, ed.

White-tailed deer: ecology and management. Stackpole Books, Harrisburg, PA.

Severinghaus, C. W., and E. L. Cheatum. 1956. Life and times of the whitetailed deer. Pages 57-186 "in W.P. Taylor, ed. The deer of North America.

The Stackpole Co., Harrisbur~ PA.

Short, H. L. 1969. Physiology and nutrition of deer in southern upland

forests. Pages 14-18 in White-tailed deer in the southern forest habitat.

Proc. Symp. Nacoqdoches, TX. U.S. For. Serv., South. For. Exp. Stn., New

Orleans, LA .

. 1975. Nutrition of southern deer in different seasons. J. Wildl. -----,,...,------

Manage. 39(2):321-329.

1983. Wildlife guilds in Arizona desert habitats. U.S. Bur. Land

Manage. Tech. Note 362. 258 pp.

Short, H. L., and C. A. Segelguist. 1975. Elbon rye as quality forage to

Ozark deer during winter. Agron. J. 67(1):92-93.

Short, H. L., and E. A. Epps, Jr. 1976. Nutrient quality and digestibility

of seeds and fruits from southern forests. J. Wild. Manage 40(2):283-289.

Short, H. L., J. D. Newsom, G. L. McCoy, and J. F. Fowler. 1969. Effects of

nutrition and climate on southern deer. Trans. N. Am. Wildl. Nat. Resour.

Conf. 34:137-146.

Short, H. L., R. M. Blair, and L. Burkart. 1972. Factors affecting nutritive

values. Pages 311-318 in Wildland shrubs -- their biology and utilization.

U.S. For. Serv., Gen. Tech. Rep. Int-l., Int. For. Range Exp. Stn., Ogden,

UT.

35
Short, H. L., R. M. Blair, and C. A. Segelquist. 1974. Fiber composition and

forage digestibility by small ruminants. J. Wildl. Manage. 38(2):197-209.

Short, H. L., R. M. Blair, and E. A. Epps, Jr. 1975. Composition and digestibility of deer browse in southern forests. U.S. For. Servo Res. Pap.

SO-Ill. South. For. Exp. Stn., New Orleans, LA.

U.S. Fish and Wildlife Service. 1981. Standards for the development of

habitat suitability index models. 103 ESM. U.S. Fish Wildl. Serv., Div.

Eco1. Serv. n. p.

Verme, L. J., and D. E. Ullrey. 1984. Physiology and nutrition. Pages 91-118

in L. K. Halls, ed. White-tailed deer: ecology and management. Stackpole

Books, Harrisburg, PA.

Wallmo, O. C., L. H. Carpenter, W. L. Regelin, R. B. Gill, and D. L. Baker.

1977. Evaluation of deer habitat on a nutritional basis. J. Range Manage.

30(2):122-127.

Wenger, K. F. 1984. Forestry handbook, 2nd ed. John Wiley and Sons, New York.

1335 pp.

36
50272-101

REPORT DOCUMENTATION 11. REPORT NO. 2- 3. Recipient'. Acce..lon No.

PAGE Biological Report 82(10. 123

•• Title end Subtitle 5. Report Oete

Habitat Suitability Index Models: White-tailed Deer in the September 1986

Gulf of Mexico and South Atlantic Coasta 1 Plains l5.

7. Author(.) 8. Performln. O,..enlzetlon Rept. No.

Henry L. Short

9. Performln. O,..enlzetlon Neme end Addr... National Ecology Center 10. Project/Teak/Work Unit No.

U.S. Fish and Wildlife Service

Drake Creekside Building One 11. Contrect(C) Of" Grent(G) No.

2627 Redwing Road (C)

Fort Collins. CO 80526-2899 (G)

12. Sponsorln. O,...nizatlon Neme end Addre.. Division of Wildlife Contaminant "a. Type of Report & Period Covered

Research

Fish and Wildlife Service

Department of the Interior 1••

Wflshinaton DC 20240

15. Supplementery Note.

IS. Abstract (Limit: 200 wordS)

A review and synthesis of existing information were used to develop a Habitat

Suitabil ity Index (HSI) model for the white-tailed deer (Odocoileus virginianus).

The model consolidates habitat use information into a framework appropriate for

field application. and is scaled to produce an index between 0.0 (unsuitable

habita t) and 1.0 (optimum habitat). HSI models are designed to be used with Habitat

Evaluation Procedures previously deveroped by the U.S. Fish and Wildlife Service.

I

17. Oocurnent Anelysl. e. Descriptors

Ungul ata

Wi 1dl ife

Habitability

Ma thema ti ca1 models

II. Identlfiers/Open·Ended Term.

White-tailed deer

Odocoileus virginianus

Habitat suitability

Co COSATI Field/Group

~1" Avellebility Stetement 19. security Cle •• (Thi. Report) 21. No. of Pe.e.

Release unlimited Unclassified 36

20. securl~ cre•••<ti. 'dBe) 22. Price Unc ass i i e

S..ANSI-Z39.18) S.. InstructIons on Reverse OPTIONAL FORM 272 (4-77)

(Formerly NTIS-35)

Oepertment of Commerce

-:. u.s.GOVERNMENT PRINTING OFFICE: 198~72-932/45,303 REGION NO.8
•

••• ..__ .... J~.

' . '· 0 ,,-

Hawaiian Islands (>

-(:( Headq uarters . Division of Biological

Services, Wasnington, DC

x Eastern Energy anO Land Use Team

Leetown , WV

* Nationa l Coastal Ecosystems Team

Slidell, LA

• Western Energy and Land Use Team

Ft. Coll ins . CO

• Locat ions of Regional Off ices

REGION 1

Regional Director

U,S. Fish and Wildlife Service

Lloyd Five Hundred Building, Suite 1692

500 N.E. Multnomah Street

Portland , Oregon 97232

REGION 4

Regional Director

U.S. Fish and Wildlife Service

Richard B. Russell Building

75 Spring Street , S.W.

Atlanta, Georgia 30303

I

J- - - - ---6 1

r-- - L, J

I : --

I I

1- - -'

I I

l,_ I 2 ,, -~-

-_.J

REGION 2

Regional Director

U.S. Fish and Wildlife Service

P.O. Box 1306

Albuquerque, New Mexico 87 103

REGION 5

Regional Director

U.S. Fish and Wildlife Service

One Gateway Cente r

Newton Corner, Massachusetts 02158

REGION 7

Regional Director

U.S. Fish and Wildlife Service

lOll E. Tudor Road

Anchorage, Alaska 99503

- ..~,.

REGION 3

Regional Director

U.S. Fish and WildlifeService

Federal Building, Fort Snelling

Twin Cities, Minnesota 551 JI

REGION 6

Regional Director

U.S. Fish and Wildlife Service

P.O. Box 25486

Denver Federal Center

Denver, Colorado 8022 5
u.s. FISH ..WILDLIFE f>ERVICE

DEPARTMENT OF THE INTERIOR hi?]

u.s.FISH ANDWILDLIFESERVICE "'-r rw T " "

As the Nation's pri ncipal conservation agency, the Department of the Interior has responsibility for most of our ,nationally owned public lands and natural resources . This includes

fostering the wisest use of our land and water resources, protecting our fish and wildlife,

preserving th & environmental and cultural values of our national parks and historical places,

and providing for the enjoyment of life through outdoor recreation. The Department assesses our energy and mineral resources and works to assure that t heir development is in

the best interests of all our people. The Department also has a major respons ibility for

American Indian reservation communities and for people who live in island territories under

U.S. adm inist ration.