Forage Resource Evaluation 

System for Habitat—Deer: An 

Interactive Deer Habitat Model

Thomas A. Hanley, Donald E. Spalinger, Kenrick J. Mock, 

Oran L. Weaver, and Grant M. Harris

United States 

Department of 

Agriculture

Forest Service

Pacific Northwest 

Research Station

General Technical Report 

PNW-GTR-858

January 2012

DEPAR TMENT OF AGRICULT URE
Authors

Thomas A. Hanley is a research wildlife biologist, U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station, Forestry Sciences Laboratory, 

11305 Glacier Highway, Juneau, AK 99801-8626. Donald E. Spalinger is a professor, Department of Biology, and Kenrick J. Mock is an associate professor, Department of Mathematical Sciences, University of Alaska Anchorage, 3211 Providence 

Drive, Anchorage, AK 99508-4614. Oran L. Weaver is a contract computer programmer who lives in Anchorage, AK. Grant M. Harris was a wildlife ecologist, 

U.S. Department of Agriculture, Forest Service, Chugach National Forest, 3301 

C Street, Suite 300, Anchorage, AK 99503-3998. Harris is currently a chief of the 

biology division with the Southwest Region of the U.S. Fish and Wildlife Service, 

500 Gold Street SW, Albuquerque, NM 87103.

Cover photographs by Miles Hemstrom and Tom Iraci.

The Forest Service of the U.S. Department of Agriculture is dedicated to the principle of 

multiple use management of the Nation’s forest resources for sustained yields of wood, 

water, forage, wildlife, and recreation. Through forestry research, cooperation with the 

States and private forest owners, and management of the National Forests and National 

Grasslands, it strives—as directed by Congress—to provide increasingly greater service 

to a growing Nation.

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activities on the basis of race, color, national origin, age, disability, and where applicable, 

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USDA, Director, Office of Civil Rights, 1400 Independence Avenue, SW, Washington, DC 

20250-9410 or call (800) 795-3272 (voice) or (202) 720-6382 (TDD). USDA is an equal 

opportunity provider and employer.
Abstract

Hanley, Thomas A.; Spalinger, Donald E.; Mock, Kenrick J.; Weaver, Oran L.; 

Harris, Grant M. 2012. Forage resource evaluation system for habitat—deer: 

an interactive deer habitat model. Gen. Tech. Rep. PNW-GTR-858. Portland, 

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

Station. 64 p.

We describe a food-based system for quantitatively evaluating habitat quality for 

deer called the Forage Resource Evaluation System for Habitat and provide its 

rationale and suggestions for use. The system was developed as a tool for wildlife 

biologists and other natural resource managers and planners interested in evaluating habitat quality and, especially, comparing two or more patches of habitat or 

the same patch at different seasons or under different conditions. It is based on the 

quantity (of biomass) and quality (digestible energy and digestible protein) of the 

habitat’s food resources in relation to user-specified metabolic requirements of deer 

(which differ with species, age, sex, season, and reproductive status). It uses a linear 

programming algorithm to determine the suitable forage that can sustain deer at the 

specified requirements. Output includes the number of deer days (1 deer day equals 

one deer for 1 day) per unit area that the available food resources are capable of 

supporting, the species composition of the solution set to the linear programming 

problem, and the relative importance of biomass versus nutritional quality as limiting factors of the habitat for deer. The system is accessed via the Internet (http://

cervid.uaa.alaska.edu/deer/home.aspx) and consists of a Web-based application 

for analysis at the patch (or “stand”) scale and a geographical information system 

(GIS)-based application for analysis at the landscape scale, which includes spatial 

effects of patch sizes and their shapes and locations in relation to deer home ranges. 

Although the system was developed for Sitka black-tailed deer (Odocoileus hemionus sitkensis) in southeastern Alaska and illustrated with examples for them, it also 

can be applied for other species of deer (with the exception of very large species 

such as moose, Alces alces) elsewhere in the world.

Keywords: Black-tailed deer, Odocoilius hemionus, Alaska, habitat evaluation, 

carrying capacity, nutrition, forest planning.
Contents

1 Overview of the Forage Resource Evaluation System for Habitat

1 Introduction

6 Linear Programming Model

7 Factors Affecting Nutrition and Palatability

11 Carrying Capacity Models

14 Two Calculators: Stand-Level Application and Landscape-Level Application

16 Different Deer (Cervidae) Species

16 Current Status and Future Plans

17 FRESH-Deer for Sitka Black-Tailed Deer in Alaska—Example 

18 Focal Species and Habitat

19 Linked Databases

20 Energy and Protein Constraints

22 Other Foraging Constraints, Optional

23 Importance of Variation in Nutritional Values and Habitat Biomass Values

25 Summer to Winter Conversion

28 Snow Submodel

32 Interpretation of Output

34 Home Range

35 User’s Manuals

35 Management Implications

38 Acknowledgments 

39 English Equivalents

40 Literature Cited

48 Appendix 1: Suggested Field and Laboratory Methods for Obtaining 

Original Data for Forage Biomass and Nutritional Quality

53 Appendix 2: Nutritional Data (Mean ± Standard Deviation) in the 

Nutritional Database

58 Appendix 3: Plant Codes of Species in the Current (2011) Database 

and Their Default Values for Percentage Remaining in Winter, and 

Maximum Percentage in the Solution Set

62 Glossary
1

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

Overview of the Forage Resource Evaluation System 

for Habitat

Introduction

The Forage Resource Evaluation System for Habitat (FRESH) is a system for evaluating habitat quality for deer (family Cervidae) on the basis of available food, its 

nutritional quality, and the nutritional requirements of deer. The FRESH system has 

been available to the public on the Internet via a University of Alaska Anchorage 

Web site1

 (http://cervid.uaa.alaska.edu/deer/home.aspx) since 2005. It was designed 

with adult female Sitka black-tailed deer (Odocoileus hemionus sitkensis) in Alaska 

as the prototype, but it can be used for other medium-size deer (Cervidae) species 

elsewhere in the world. We focus on food because it clearly sets the potential upper 

limit on the number of animals a habitat can support. Forage resources (vegetation 

and nutritional quality) can be measured in the field and can be manipulated by land 

management. We focus on digestible energy 2

 and digestible protein, because they 

are the two most common nutritional limiting factors for wild ungulates (Moen 

1973, Short 1981, White 1993), and their requirements are reasonably well known 

for black-tailed deer and other cervids (Robbins 1993). We focus on adult females 

because they are the productive segment of the population, the animals that produce 

young. Nutritional requirements vary seasonally and with reproductive status (e.g., 

body maintenance without young versus gestation or lactation requirements). This 

system is suitable for any habitat and any species of medium-size deer where the 

availability of forage, its nutritional quality, and the nutritional requirements of the 

deer are known.

The analytical system provides a “snapshot” analysis at one user-specified point 

in time. It is assumed that all available vegetation is potential food, and there is no 

accounting for long-term herbivore-plant dynamics (e.g., the effects of overbrowsing). This is not a simulation model. Rather, it is a calculator: given specific values 

of available forage biomass, its nutritional quality, and animal requirements, it 

calculates the maximum number of “animal days” (one adult female for 1 day) that 

can be sustained by the forage resource. The animal day values are best considered in a relative (comparative) sense, not as absolute values, because they are the 

maximum number of animal days (at one point in time) that can be supported by the 

food if all the suitable food were eaten then (no herbivore-plant feedback loops). 

1 Although implemented on the University of Alaska Web site in 2005, the FRESH system 

might eventually move to a Forest Service Web site but remain accessible to the general 

public.

2 See Glossary for terms highlighted in bold at first mention.
2

GENERAL TECHNICAL REPORT PNW-GTR-858

The instantaneous, food-based “carrying capacity” (see box 1) of an actual 

habitat varies continuously (fig. 1) with marked changes in plant phenology and 

diet (fig. 2) and animal metabolic requirements (see box 2) during summer and 

with variation in snowpack during winter. The snapshot values from our model 

are valid for only their point of measurement and analysis; they are not an annual, 

or even seasonal, average. They are best used for comparing two or more habitats, 

or the same habitat(s) in two or more states (times or post-manipulation, succession, 

etc.). Additionally, a large-scale, geographic information system (GIS) application 

of the system is useful for comparing patterns within the same landscape or 

various landscapes—where size and spatial configuration of habitats are 

important considerations. 

Box 1: Carrying Capacity 

We define “carrying capacity” as the number of “animal days”* per unit area that a 

given habitat can support, based on the quantity and quality of its food resources. We 

do not account for dynamic herbivore-plant interactions (e.g., overgrazing, “properuse factors,” feedback loops, subsequent effects on plant growth, etc.). We base our 

calculations on the total supply of plant biomass (“current annual growth”—see box 

3, p. 8) that is “available” (see box 4, p. 8) to the animal species in question. We are 

essentially answering the question, “If one were to harvest all of the available current 

annual growth of plants in a given area and bring it into a captive animal facility, 

how many animal days could the food support while meeting a user-specified level of 

“metabolic requirements” (see box 2, p. 5)?” The animal days are always specified by 

the user’s choice of metabolic requirements. We usually work with adult females as 

the “animal,” and we specify their reproductive status (i.e., maintenance, reproductive, etc.—see metabolic requirements box 2, p 5). Adult females are the productive 

segment of the population of deer, and their nutritional status is a very useful criterion for evaluating range condition or carrying capacity.

We usually work with spatial units of 1 ha. Thus, we work with “animal days 

per hectare.”

Notice that our definition of carrying capacity is a “snap-shot” definition, applied 

to one particular point in time—the time when plant biomass and nutritional quality 

have been measured. However, the user should be aware that plant biomass, nutritional quality, and metabolic requirements change continuously throughout the year.

* One animal day is the food necessary to support one animal for one day at the specified 

level of nutrition. For example, 22 animal days could be one animal for 22 days, or 22 

animals for one day, or any combination thereof. 

The snapshot values 

from our model are 

valid for only their 

point of measurement 

and analysis; they are 

not an annual, or even 

seasonal, average.
3

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

Figure 1—Monthly changes in potential carrying capacity (deer/ha/year) at one study site 

on Admiralty Island, southeastern Alaska; all 

values are for snow-free conditions, except 

those shown for December with a depth of 20 

cm. (Source: Hanley and McKendrick 1985.)

Figure 2a—Monthly changes in plant biomass 

(current annual growth), January–December 

1981, Admiralty Island, Alaska. Values 

for January and December were estimated 

by extrapolating between November and 

February. All values reflect the availability of 

plant biomass in the absence of snow. Total 

aboveground values include conifer seedlings 

and litter, lichens, and mushrooms. (Source: 

Hanley and McKendrick 1985.)

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

Month

Snow

2 fawns

1 fawn

0 fawns

Potential carrying capacity (deer ha )-1

}

c

1 fawn

0 fawns

2 fawns

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

Month

450

400

350

300

250

200

150

100

50

0

Total 

aboveground

Half-shrubs

Shrubs

Forbs 

and ferns

Plant biomass (kg ha ) -1

Forbs and ferns

Shrubs

Total aboveground

Half-shrubs
4

GENERAL TECHNICAL REPORT PNW-GTR-858 Composition of diet (percent)

Nov.

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

Month

90

80

70

60

50

40

30

20

10

0

Herbs

Lichens

Shrubs

Conifers

Figure 2b—Estimated monthly diet composition, November 1980–December 1981, 

Admiralty Island, Alaska. Values are based on fecal composition data adjusted for in 

vitro dry-matter digestibility (Hanley and McKendrick 1983, Rochelle 1980 for lichens). 

Values for January and December 1981 were estimated by extrapolating between 

November and February 1981. Only values for December 1980 reflect the presence 

of snow. "Herbs" is the combined categories of "forbs and ferns" and "half-shrubs." 

(Source: Hanley and McKendrick 1985.)

The FRESH system includes linked databases of understory biomass and 

forage-specific nutritional data for a variety of habitats and forages. The databases 

provide the user with examples or reference points (in the case of habitats) and 

ballpark-level default estimates (in the case of forages) for data that the user might 

not have. Although field data obtained directly from the user’s study area always are 

best, a user can use FRESH to explore habitat relations with estimates of vegetation or habitat variables based on the examples and default values from the linked 

databases stored within FRESH. 
5

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

Box 2: Metabolic Requirements

Our model runs on user-specified requirements for metabolizable energy and 

protein. It also requires a user-specified daily rate of dry-matter intake of food 

(ovendry weight of total food intake in a day). These values are empirically 

determined. We have provided a recommended set of metabolic requirements 

for Sitka black-tailed deer (see table 1, p. 21) based on scientific literature, 

although the user can choose different values if they have a sound basis for 

doing so. Carrying capacity (see box 1 and Glossary) values will differ dramatically, depending on user-specified metabolic requirements. This is a very 

important consideration in any evaluation of habitat, as summer range is very 

different than winter range, and habitat suitable for only “maintenance” levels 

in summer may have much lower carrying capacities at “reproductive” levels.

Metabolic requirements of animals differ greatly seasonally and as a function of age, sex, reproductive status, and body condition. Summer is a time 

of growth, body reserve accumulation, and reproduction (lactation). Winter 

is a time of growth stasis, weight loss, and deterioration of body condition 

(catabolism of reserves). We usually work with adult females as our unit for 

analysis (see box 3, p. 8). Summer metabolic requirements for adult females 

depend on their body condition going into summer, their reproductive status 

(maintenance only, or maintenance plus lactation for one or more fawns), and 

their need to accumulate body reserves (fat and protein) before going into 

winter. Winter metabolic requirements depend on the animal’s body condition (reserves) going into winter and their activity levels (or winter weather) 

influencing the rate at which they draw on their energy reserves—with the 

difference between the two (reserves minus draw-down) needing to be made 

up by food resources.

Our recommended set of metabolic requirements (table 1) for summer 

includes lactation requirements for both single and twin fawns. However, the 

values are for peak lactation requirements, not “average” requirements across 

the entire summer. Peak requirements are relatively high, and peak requirements for twin fawns are especially high. Peak requirements can be met by 

the adult female through a combination of using her own body reserves and 

relying on food intake. Thus, peak requirements, especially for twin fawns, 

might be an unreasonably high standard for evaluating carrying capacity of a 

habitat. Therefore, we usually use the recommended requirements for “single 

fawn” and refer to that as simply “reproduction” (unspecified as to singletons

(continued on next page) 
6

GENERAL TECHNICAL REPORT PNW-GTR-858

Linear Programming Model

The calculation of animal days follows from an earlier, iterative procedure (Hanley 

and Rogers 1989) that expanded on theory first proposed by Hobbs and Swift 

(1985). It is now accomplished with a linear programming model. Linear programming models are optimization algorithms, where an objective is maximized (or 

minimized) within a set of maximum or minimum constraints. They are called 

linear programming models because they consist of a series of equations, each of 

which is written as a linear equation (e.g., Y > a + bc + de + fg), and the solution is 

found by solving all equations simultaneously. In our case, the objective that is to 

or twins). Of course, the user can specify whatever they wish for 

these requirements.

Our recommended set of metabolic requirements (table 1) for winter 

includes two alternatives, depending upon the user-assumed body condition 

of the animals going into winter: one set of requirements is for “fat” animals 

coming off high-quality summer range; the other set is for “lean” animals 

coming off poor-quality summer range. “Fat” animals have greater body 

reserves than do “lean” animals, and therefore have lower requirements 

from their food resources to survive the winter. Our “requirements” are the 

requirements from the food resources. We assume similar activity budgets, 

winter weather conditions, and appetites (dry-matter intake rates) for 

both alternatives.

Metabolic requirements can be specified in terms of digestible energy 

(in kilocalories or kilojoules), dry-matter digestibility of the diet (percentage 

of dry matter), digestible protein (percentage of dry matter), or crude protein 

(percentage of dry matter). The choices must be specified by the user. Use of 

either digestible energy or digestible dry matter (both relate to metabolizable 

energy requirement) includes an assumed constant metabolic energy coefficient; use of dry-matter digestibility of the diet includes a further assumed 

constant gross energy content of all forages. Protein requirements are dealt 

with by digestible protein or crude protein. Digestible protein includes effects 

of plant tannins on reducing protein digestibility. Crude protein does not 

include effects of tannins and should be viewed as highly suspect—although, 

often times, tannin effects are unknown and only data for crude protein 

are available.
7

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

be maximized is the quantity of forage biomass (kilograms per hectare [kg/ha] or 

pounds per acre [lb/ac]) that can be pooled from all available biomass while meeting 

(or exceeding) specified minimum constraints for digestible energy and digestible 

protein. We are essentially answering the question, “If one were to harvest all the 

available current annual growth of plants in a given area and bring it into a 

captive animal facility for feeding, what is the maximum number of animal days 

that could be supported while meeting (or exceeding) user-specified levels of 

metabolic requirements (mean concentration of digestible energy and digestible protein)?” We answer that question by using a linear programming model to determine 

the maximum amount of forage biomass that can be pooled (from the combination 

of all available forages) while meeting the specified constraints.3

 The maximum 

suitable biomass (kg/ha) is then divided by the user-specified daily dry-matter 

intake of an adult female (kg/day), yielding the maximum number of animal days 

(days/ha) that can be supported within the specified constraints.

The data-entry requirements for analyzing any given habitat are the following: 

(1) a list of all available “forages” (See boxes 3 and 4; a forage is a plant part with a 

unique nutritional composition—e.g., different species are different forages; shrub 

leaves are different forages from shrub twigs, even within the same species); (2) 

the available biomass (kg/ha, dry weight) of each forage; (3) the concentration of 

digestible energy (kilojoules per gram [kJ/g]) of each forage; (4) the concentration 

of digestible protein (percentage of dry weight) of each forage; (5) the daily energy 

requirement (kJ/day) of an adult female; the daily digestible protein requirement 

(grams per day [g/day]) of an adult female; and the daily dry-matter intake (g/day) 

of an adult female. We provide guidelines for all the animal data inputs for female 

black-tailed deer; the forage variables (biomass and nutritional quality) are the 

chief data requirements from the user (see app. 1 for suggested field and laboratory 

methods for original data requirements). If the user does not have their own plant 

nutritional data, they can use our plant nutritional database (automatically linked 

database) for estimates.

Factors Affecting Nutrition and Palatability

Animal requirements for digestible energy and digestible protein are well known 

because they are such basic currencies and common limiting factors in animal 

nutrition, and thus have been studied extensively (National Research Council 

3 There also is an optional forage-specific constraint limiting the maximum proportion of 

the solution that can be comprised of the given forage regardless of its digestible energy 

and digestible protein concentrations (see “Factors Affecting Nutrition and Palatability” 

section). This is necessary to deal with forages that have high digestible energy, protein, 

and biomass availability, yet are known to be eaten in only very small quantities.

We are essentially 

answering the 

question, “If one 

were to harvest all 

the available current 

annual growth of 

plants in a given area 

and bring it into a 

captive animal facility 

for feeding, what is the 

maximum number of 

animal days that could 

be supported while 

meeting (or exceeding) 

user-specified 

levels of metabolic 

requirements?”
8

GENERAL TECHNICAL REPORT PNW-GTR-858

Box 3: Forages

We consider “forages” to be only the current annual growth of plant species 

(except for lichens, which are considered whole). Current annual growth is the 

plant tissue produced in the current year, exclusive of radial growth of older stems 

in shrubs and belowground growth of all plants. For herbaceous plants, it is the 

total aboveground biomass. For shrubs and trees, it is the current year’s twigs and 

leaves. Current annual growth is almost always of much higher nutritional quality 

than is older growth (dead, previous year’s herbaceous material and older shrub 

or tree stems); in fact, the older growth is usually of such low nutritional quality 

that we do not consider it “food.” However, the user can choose to include whatever they wish as food. For example, if old stems are included, estimates of their 

energy and protein values must be included, and it is important to consider them 

separately from current year’s twigs because the nutritional values differ greatly.

Notice that we analyze shrub twigs separately from shrub leaves; i.e., we treat 

them as different foods, or forages, even though they are the same plant species. 

This is because the nutritional quality of twigs differs greatly from that of leaves. 

The choice of what constitutes a separate food, or forage, is entirely up to the user. 

What is important is that whatever the user’s choice for “forage,” the biomass and 

nutritional measures must be made accordingly. If two very different forages 

(e.g., shrub leaves and twigs, or twigs and old stems) are treated as just one (combined) forage, then the potential roles of those forages will be significantly diluted 

in the analysis.

Box 4: Available Biomass

“Available biomass” is the plant biomass that is available to the herbivore at 

the time of the analysis. In summer, it is the total standing crop of current 

year’s growth of a given species of plant. In winter, the available biomass for 

a given species can be greatly reduced from its summer value by seasonal 

loss of above-ground tissue (deciduous species) and by burial by snow. Blacktailed deer are not known to paw through snow in search of forage; thus food 

resources buried by snow are unavailable to deer. Other food resources (e.g., 

lichens in trees, twigs of very tall shrubs) might be out of reach because they 

are too high for deer to reach. However, after a snowpack has firmed, especially with a frozen crust, black-tailed deer are able to walk freely on top of the 

snowpack. Moose are known to “walk down” tall shrubs to reach high portions. The “availability” of any forage is something that the user must consider 

when inputting the “available” plant biomass into the analysis.
9

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

2007, Robbins 1993, Van Soest 1982). Energy is needed for maintaining body heat, 

fueling activity and growth, and conducting basal metabolic processes. Protein 

is needed for building and maintaining body tissue. Not all energy and protein 

consumed by an animal, however, are metabolically available to the animal. “Gross 

energy” (kJ/g) is the total energy contained in the food—the amount that would 

be released by combustion. Only the energy in the digestible portion of a food, 

however, is available to the animal (Robbins 1993). Energy in the indigestible 

fraction passes from the animal as excretory products (feces, and, to a lesser extent, 

urine), which are unused. The energy in the digestible fraction is called “digestible 

energy.” Some of that is lost as heat in conversion to “metabolic” energy, but the 

metabolic energy coefficient for most forages is nearly constant (at about 0.85; 

Robbins 1993), so we have focused the analysis on digestible energy rather than 

metabolic energy per se.

Similarly, “crude protein” (which is calculated simply as 6.25 times the total 

nitrogen concentration of a forage) is only a rough index of the available protein 

content of a forage. Importantly, the digestible fraction of the protein in forage 

can vary greatly, especially for wild forages commonly eaten by deer, which may 

contain tannins and other protein digestion-reducing compounds (Hanley et al. 

1992, Robbins et al. 1987a). Thus, the “digestible protein” concentration of forage is 

much more important than its crude protein concentration when evaluating forages 

for deer species. 

Because digestible energy and digestible protein have been so widely studied, 

good laboratory analytical techniques exist for measuring or estimating their values 

in forages, some of them specifically designed for deer (Hanley et al. 1992). For 

similar reasons, energy and protein requirements of animals, including deer, also 

are reasonably well known (Robbins 1993). Thus, digestible energy and digestible 

protein are our most sound currencies for evaluating forage quality for deer (see 

app. 1 for discussion and details of the importance of choice of methodology for 

evaluating forage quality).

The nutritional quality of plant material, however, is far more complex than 

simply digestible energy and digestible protein. Animals also require vitamins, 

minerals, and micronutrients (National Research Council 2007, Robbins 1993). The 

bacterial flora in the ruminant stomach of deer synthesizes vitamins, so vitamins 

are not an important limiting factor (Van Soest 1982). Minerals and micronutrients 

are important, but usually they are in sufficient concentration in forage, so they are 

less likely a limiting factor than is energy or protein (Hudson and White 1985, Moen 

1973, Robbins 1993). Rarely are levels of minerals or micronutrients inadequate or 

so abundant as to be toxic. Therefore, mineral and micronutrient requirements of 
10

GENERAL TECHNICAL REPORT PNW-GTR-858

deer species have received much less scientific investigation than have energy and 

protein, and they are much less understood. We have not included mineral or micronutrient requirements in our analysis, although theoretically they could be added as 

minimal constraints in an expanded linear programming algorithm.

Moreover, wild forages for herbivores like deer also contain many noxious compounds—some affecting palatability, some affecting digestibility, and some even 

toxic. Noxious organic compounds abound in wild plants, especially in forbs, ferns, 

shrubs, and trees—all the forages commonly consumed by deer and other browsers. 

Ecologists have frequently termed these compounds “secondary compounds” or 

secondary chemistry (secondary to basic plant metabolism) and have considered 

them as defensive mechanisms protecting plants from herbivory (Rosenthal and 

Janzen 1979). They are classified in two principal groups differing functionally in 

the herbivore: (1) digestion-reducing compounds, which decrease the value of forage 

by decreasing its energy or protein digestibility; and (2) toxins, which produce acute 

debilitating effects in the herbivore. The two groups are not necessarily exclusive; 

some compounds serve both functions. Deer actually eat many forages with high 

levels of secondary compounds by mixing them in small amounts in a highly 

diverse diet. Plant secondary chemistry (the study of secondary compounds) is an 

enormously complex subject, with thousands of compounds in the environment and 

different compounds in virtually every forage. The effects of one major class of 

compounds (tannins) have been studied well enough to be incorporated into laboratory analytical techniques for estimating digestible energy and digestible protein 

(Robbins et al. 1987a, 1987b, 1991), but the effects of most compounds are largely 

unknown. Plant secondary chemistry is far too complex to model at this time, yet it 

has very real effects on herbivore use of plants—either through learned behavior or 

through innate palatability preferences/avoidances of specific forages. As explained 

below, we deal with this problem by calculating the effects of tannins on digestible 

protein and digestible dry matter (Hanley et al. 1992) and by allowing the user to 

specify limits on the amount of unpalatable foods that are suitable for the solution 

to the linear programming problem.

“Nutritional wisdom” (the ability of animals to select their food on the basis of 

its nutritional value) is an old hypothesis, yet it has never satisfactorily explained 

diet choice by herbivores in natural settings. Palatability of forages and diet selection in large herbivores are far more complex than simply the nutritional value of 

the food (Gillingham et al. 1997; Hanley 1997; Spalinger and Robbins 1992; Spalinger et al. 1986, 1993). Plant secondary chemistry complicates the problem even 

further. We must acknowledge that forage quality is more than simply digestible 

energy and digestible protein (Parker et al. 1996), and we must account for the fact 
11

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

that some forages are avoided (unpalatable) despite high levels of digestible energy 

and digestible protein. But we must do this within a relatively simple model.

We have taken a very pragmatic approach to the solution of “unpalatability”—

the problem of apparently good forages (in terms of digestible energy and protein) 

not being eaten or eaten in only small proportions, despite abundant availability, 

e.g., alder (Alnus spp.). Forages like alder pose a significant problem in the linear 

programming solution, because the solution would include much or all of the 

alder and, therefore, would inflate the apparent value of the habitat beyond its true 

value. Our approach to resolving this problem in a simple, pragmatic way is to add 

additional, forage-specific constraints to the linear programming model, whereby 

an upper limit (maximum constraint) can be specified for each forage known to be 

relatively unpalatable, regardless of its digestible energy and protein concentrations. 

Thus, if 3.0 percent is specified as the maximum constraint for alder, for example, 

then that will be the maximum amount of alder in the linear programming solution. 

Values for the forage-specific constraints are user-specified. However, the user 

can base their choice of such values on the results of diet composition studies (e.g., 

fecal composition or rumen analyses) of deer in similar habitats (e.g., Lewis 1992, 

1994; Hanley et al. 1985; Parker et al. 1999; Pierce 1981). Thus, the forage-specific 

constraint can be based on field data, not just professional opinion (see app. 2 for 

default values for forages in the current database). This provides a relatively simple 

and workable solution to a problem that is far too complex to model biologically. In 

short, the linear programming solution is driven by forage availability, digestible 

energy, and digestible protein, but it can be restricted by empirically determined 

limits on palatability of any given forage. 

Carrying Capacity Models

The FRESH system is not a carrying capacity model; it is far more limited than 

that. “Carrying capacity” (see box 1) is an ambiguous term in ecology. It is usually 

meant to be the maximum number of animals (of a given species) that a given habitat can support indefinitely (Caughley 1977, May 1973, Pielou 1977). The “indefinitely” aspect requires that trophic dynamics (herbivore-plant, predator-prey) be 

considered and that the system maintains a stable equilibrium for a long (indefinite) 

time. Carrying capacity is a useful concept for theoretical system modeling, but it is 

very problematic for practical application. In the real world, virtually no habitat is 

stable indefinitely, even within “dynamic equilibrium bounds.” Seasonal variations 

occur throughout the year; annual variations occur between years (e.g., weather); 

and disturbances, succession, and other ecological changes are present over both 

short and long time scales. “Maximum number of animals” can vary, depending on 
12

GENERAL TECHNICAL REPORT PNW-GTR-858

sex and age composition desired or assumed, as well as expectations for productivity of the population (e.g., “maximum sustained yield” versus “maximum sustained 

density”) (Caughley 1977, Hobbs 1989, McCullough 1979). 

In practical application, carrying capacity is best determined empirically, 

after carefully defining exactly what is meant about location, time scale, reasonable limits of natural variation (e.g., are droughts or extreme winters included?), 

and animal population demographics. This has been done occasionally for closely 

managed deer populations (McCullough 1979), and it has been done extensively 

for managing livestock grazing (Stoddart et al. 1975). It requires much empirical 

experience with the animals and the habitats, including sound data on animal demographics and vegetation dynamics, and usually, careful manipulation of the animal 

population. Yet, extrapolation to other habitats than those studied involves much 

uncertainty. For most large, free-ranging populations of deer species, empirical 

determination of carrying capacity is impossible.

Theoretical calculation or estimation of carrying capacity for large herbivores is 

confounded by two major problems: (1) food quantity and quality are not substitutable for one another (Hobbs 1989, Hobbs and Hanley 1990, Wallmo et al. 1977); and 

(2) the diet selection process, central to predicting diet composition, has remained 

an exceedingly difficult process to model or predict (Hanley 1997), especially for 

a novel vegetation. The combination of these two problems has implications well 

beyond estimating carrying capacity on the basis of food supplies. It also confounds 

the interpretation of results from habitat-use studies (e.g., habitat selection or preferences) and models of habitat quality derived from such data (e.g., “resource utilization functions”) (Hobbs and Hanley 1990, Van Horne 1983). 

The problem of “nonsubstitutability” of quantity and quality of food is that poor 

quality food cannot be substituted for deficiencies of high-quality food—i.e., much 

poor food is not equal to less good food. The reason is that herbivores are limited by 

the amount of food that they can process (i.e., ingest, digest, and pass through their 

digestive tract). When they reach their limit of intake, they cannot consume more; 

they cannot make up for poor quality food by eating more of it. In fact, food intake 

usually decreases with decreasing food quality (e.g., Cook et al. 2004, White 1983). 

The most common cause is bulk-passage limitations through the gastrointestinal 

tract (especially problematic for ruminants, with their four-chambered stomach) 

(Spalinger et al. 1986, 1993; Spalinger and Robbins 1992). Thus, one cannot simply 

multiply the biomass of forages by their digestible energy (or protein) concentrations, sum for all forages, and then divide by the animal’s daily metabolic requirement to determine the number of animal days that the food can sustain (Wallmo 
13

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

et al. 1977). Furthermore, virtually every forage in the habitat is unique—it has 

a unique combination of biomass and nutritional quality. Thus, it is the combination (mix) of forages that must be optimized in order to quantify the quality of a 

habitat’s food resources. Some of the potential foods may or may not be suitable, 

depending on what else and how much of it is in the diet and the nutritional requirements of the animal.

Empirical observations of diet composition of free-ranging herbivores provide 

a way of determining a suitable mix of potential foods. Some models of carrying 

capacity apply empirically observed diet composition to the array of potential foods 

in the habitat to determine the maximum quantity of food that can be mixed in that 

same proportion (Hobbs 1989). The problem with this is that diet composition is not 

a static, fixed attribute of an animal-habitat interaction. It differs with the relative 

availabilities of the various foods, and therefore, differs with animal population 

density as well. As population density increases, for example, the most preferred 

foods decrease in availability, and diet composition shifts, yet the habitat still might 

yield a diet that is well above minimal requirements. This is the similar problem 

with habitat-use studies—habitat preferences (and resource utilization functions), 

should be expected to shift with changes in relative availabilities of habitats in 

a landscape and with population densities of the animal, the latter because food 

quantity and quality are not substitutable for one another (Hobbs and Hanley 1990). 

The best habitats for an individual animal when population densities are low may 

be those that provide low quantities of high-quality food. At higher population 

densities, however, such habitats may not provide enough food, and more animals 

may choose a habitat that provides a higher quantity of lower (but sufficient) 

quality food—i.e., exactly the opposite pattern of habitat preference. An accurate 

diet prediction model (“optimal foraging theory”) would enable us to account for 

the interaction of forage availability, nutritional quality, and herbivore population 

density; but no such model exists as yet for ruminants, as interactive complexities 

abound (Hanley 1997).

We have taken a very pragmatic approach to resolving this dilemma by avoiding the prediction of diet composition altogether; similarly, we do not predict habitat 

preference or use, either. Instead, we move directly to the question, “what is the 

maximum number of animals (or animal days) that can be supported by a given 

food resource at a given level of metabolic requirement?” Our answer is the maximum number of animal days that can be supported at that instant of analysis (the 

time when food supplies were measured), without concern for an “indefinite” or 

“stable” concept of carrying capacity (herbivore-plant interactions) or the composition of diets at anything less than the maximum density of animals. We calculate 

It is the combination 

(mix) of forages that 

must be optimized in 

order to quantify the 

quality of a habitat’s 

food resources. Some 

of the potential foods 

may or may not be 

suitable, depending 

on what else and how 

much of it is in the 

diet and the nutritional 

requirements of the 

animal.
14

GENERAL TECHNICAL REPORT PNW-GTR-858

the dietary mix that solves the optimization problem of the linear program: maximize the quantity of suitable forage biomass within the specified minimum constraints of digestible energy concentration and digestible protein concentration, and 

maximum, forage-specific limits for forages known to be unpalatable. Our solution 

would be the “optimal diet” only at that population density that is the maximum for 

the habitat, and it would be optimal only for the population as a whole, not for the 

individual animal, which is where diet choices are made. This is not a diet prediction model; it is an animal feeding capacity calculator. It is something more akin to 

what a feedlot manager would use than to what an ecologist would find exciting.

Two Calculators: Stand-Level Application and 

Landscape-Level Application

The FRESH system has two levels of application—(1) a Web-based, stand-level 

application; and (2) a GIS-based, landscape-level application. The Web-based application runs on a server. The user imports their stand-level data, or types it in directly, or 

accesses stand-level data from our linked database. Typical uses might be comparison 

of results from silviculture treatments, or before-versus-after treatment, or various 

types of old-growth forests, or various levels of metabolic requirements, etc. Data are 

analyzed for one stand or habitat—one array of forage availability—at a time.

Where landscape pattern of habitats is an important consideration, the GISbased application is needed. For example, elevation of any given habitat patch is 

very important in a landscape analysis of winter range. Also, the juxtaposition of 

two habitats differing in their food limitations (e.g., one with high-quality food but 

a low quantity of it, the other with low-quality food and a lot of it) can provide a 

higher combined carrying capacity than simply the sum of the carrying capacities 

of the two habitats individually, because high-quality forages from the one habitat 

can be combined with lower quality forages of the other habitat, thereby making 

greater overall use of the lower quality foods.

The GIS application is downloaded from the Web site directly onto the user’s 

computer; the user must supply their own GIS data set; and then all calculations 

take place on the user’s computer. The user must have a description of the forage 

resources (species-specific biomass and nutritional quality) and overstory canopy 

cover of each habitat type in the landscape. The habitat types must be mapped in 

the GIS system (ASCII [American Standard Code for Information Interchange] text 

version of a raster-based vegetation cover and Digital Elevation Model), as elevation 

must be mapped to help account for snow in winter analyses. Additionally, the user 

must specify a mean home-range size (hectares [ha]) for the animal (see “Home 

Range” section). This is a stand-alone GIS application that does not run in ArcMap.
15

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

The FRESH system works by analyzing all cells within a moving window 

(or kernel) the size of the mean home range by computing available forage after 

accounting for habitat type, elevation, and burial by snow (if in winter). It compiles 

all available forage within the home range into one composite array of available forage, then uses the same linear programming algorithm as in the stand-level application to calculate an animal-days/ha value for that “home range.” It then moves over, 

by a user-specified amount of space, to another, overlapping area of home range, 

and repeats the process (an iterative kernel, “moving window” analysis). Hence, 

each grid cell of the landscape has an array of animal-days/ha values, with each 

value representing the results of a given iteration for each time that cell occurs in a 

window. For example, imagine that 50 grid cells representing three different habitat 

types (each with a different set of forages) occur in a window. One combined array 

of available forage is calculated as the weighted mean of the habitats in that window, 

and the maximum number of animal-days/ha that the combined forages can support 

(based on the linear programming model) is calculated. That value is then assigned 

to each grid cell within that home range window, regardless of its habitat type. The 

window then moves over by a user-specified amount, and the process is repeated. 

Some of the cells will occur in the new window, so they will receive another animaldays/ha value. After the entire landscape has been analyzed in this manner, the mean 

value of animal-days/ha for each individual grid cell on the landscape is calculated 

across all of the times that cell occurred in a widow. Thus, each cell has a mean 

value in relation to the various “home ranges” (windows) in which it occurred, and 

the mean value of all cells in the full landscape provides an average value for the 

whole landscape relative to the spatial distribution of its habitat patches. This process 

accounts for the size and spatial locations of each habitat within a scale appropriate 

for the animal—its home range. The underlying assumption is that every animal 

knows an area the size of its home range and can mix forages from anywhere within 

that home range. The synergistic effect of combining forages from different habitats 

can be greater than the sum of the values of each habitat considered in isolation. 

However, the combination of habitats must occur at the scale of what an individual 

animal knows (i.e., its home range) because diet composition and nutritional requirements operate at the scale of the individual animal.

Typical uses of the GIS-based application might be comparisons of landscapelevel management areas, or the same area under various potential management 

alternatives, or temporal patterns for a landscape as it changes with plant succession 

or land management treatments. Data are analyzed for all stands in the landscape, 

all within the same run, and all at the same point of time. 

The synergistic 

effect of combining 

forages from different 

habitats can be 

greater than the sum 

of the values of each 

habitat considered in 

isolation. However, the 

combination of habitats 

must occur at the scale 

of what an individual 

animal knows (i.e., its 

home range) because 

diet composition 

and nutritional 

requirements operate 

at the scale of the 

individual animal.
16

GENERAL TECHNICAL REPORT PNW-GTR-858

Different Deer (Cervidae) Species

The FRESH system can be applied to any medium-size (e.g., 30 to 125 kg) species 

of large, generalist herbivore (i.e., herbivores that consume a mixed-species diet 

without specializing on one or a few forages) for which nutritional requirements 

are reasonably well known and food resources can be quantified. It can be applied 

anywhere. If additional constraints are needed (e.g., concerning micronutrient 

limitation in a particular locale), then FRESH cannot be applied directly. Its theoretical basis would still be applicable, but the system would need to be modified 

to incorporate the new constraint(s). Alternatively, the user could use the current 

FRESH system by substituting the new constraint(s) for either digestible energy or 

digestible protein or both.

The current system has been developed with black-tailed deer in Alaska as the 

focal species. It could easily be applied to mule deer (O. h. hemionus) or whitetailed deer (O. virginianus) with only minor adjustments to the metabolic requirements and new, original habitat data for food resources. For some other species of 

deer, especially very large species like moose (Alces alces) or perhaps elk (Cervus 

elaphus), however, FRESH is inadequate without adding an additional constraint 

involving twig size and foraging time (Shipley and Spalinger 1992; Shipley et al. 

1994, 1996). The time constraint and twig-size relations are needed because browse 

forages consumed by moose often occur as very large twigs, and the size of bite 

taken while feeding on such twigs involves a tradeoff between nutritional quality and time costs of harvesting. Large bites are more time efficient (g/min) than 

are small bites (Shipley and Spalinger 1993, Spalinger et al. 1988), but small bites 

(distal ends of twigs) have higher concentrations of digestible energy and digestible 

protein than do large bites. Thus, each twig presents an array of opportunity (availability) to the moose. Part of the optimization problem is choosing the appropriate 

bite size(s) for each twig (browse) species (Hobbs et al. 2003, Spalinger and Hobbs 

1992). Of course, forage resources of deer and moose range, and metabolic requirements of deer and moose also differ substantially among the species and habitats. 

But those are relatively simple matters to adjust through user-specified data input. 

Current Status and Future Plans

FRESH-Deer is fully operational at both the stand-level (Web-based) and landscape-level (GIS-based) scales of application. We are currently working with the 

Tongass National Forest (southeastern Alaska) to increase the range of data in the 

linked databases for both habitat biomass and forage nutritional quality for Sitka 

black-tailed deer. We will add data to both databases as they become available. 

We also anticipate updating and improving documentation and user-guide 

information periodically.
17

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

A FRESH-Moose application remains in the development stage. We anticipate 

both stand-level and landscape-level applications, but database development for 

habitats and forages requires more time than is needed for FRESH-Deer, as suitable 

habitat data for moose ranges have not been collected.

Ultimately, we intend to expand the analytical framework of FRESH to include 

other factors affecting habitat quality at the landscape scale, beyond forage alone. 

We will consider the current FRESH food-based estimate of carrying capacity as 

the maximum potential (upper limit) of the habitat, and then modify that value by 

the “probability of use” of the habitat, determined from a broad “resource utilization function” model (Long et al. 2009, Manly et al. 1993). In other words, the 

current, food-based FRESH estimate is the carrying capacity that could be achieved 

if the habitat were fully acceptable to the animal, whereas the resource utilization 

function model provides an estimate of probability of use of any given patch (GIS 

cell) of habitat. Resource utilization functions are calculated from habitat-use data, 

usually radiotelemetry studies, and are best considered descriptions of observed 

(past) patterns of use rather than predictions of future patterns. However, when 

multiple habitat-use data sets are available over a wide geographic area and time 

period, then a resource utilization function model calculated from a meta-analysis 

of those data sets might be reasonably robust and offer predictive insight. For 

example, during the 1980s and 1990s in southeastern Alaska, there were four major 

radiotelemetry studies of habitat use by black-tailed deer—on Admiralty Island 

(Schoen and Kirchhoff 1985, 1990), Prince of Wales Island (Yeo and Peek 1992), 

Heceta Island (Farmer 2002), and Mitkof Island (Doerr et al. 2005). Together, those 

studies cover a broad range of southeastern Alaska over a nearly 20-year period. 

Resource utilization functions that are consistent across all four studies are likely 

robust for most of southeastern Alaska’s islands and for deer population densities 

and winters similar to those of the past three decades.

The combination of a food-based upper limit and a behavior-based probability 

of use should yield interesting insights into habitat value for deer. Also, the differences between the two should be insightful as well.

FRESH-Deer for Sitka Black-Tailed Deer in Alaska—Example 

Here we provide the details of the FRESH-Deer system within the context of the 

species and habitat for which it was developed. Text refers directly to the FRESHDeer system on the University of Alaska Anchorage Web site (http://cervid.uaa.

alaska.edu/deer/Home.aspx).
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GENERAL TECHNICAL REPORT PNW-GTR-858

Focal Species and Habitat

Our system for deer has been designed for Sitka black-tailed deer in southeastern 

Alaska (Hanley et al. 1989, Parker et al. 1999). The system would work for any 

similar-size deer anywhere, but the data in our linked databases, recommended 

metabolic requirements, and snow submodel are all centered on Sitka black-tails 

and their habitat in Alaska. We focus on the adult female segment of the population, 

but other segments (e.g., males, yearlings, etc.) could be the focal point by simply 

specifying appropriate metabolic requirements as the “animal data” constraints.

We recommend focusing on the month of July for summer analysis and field 

data collection. July is the time of both peak forage biomass and peak nutritional 

requirements of female deer (fig. 3). Although the user can specify lactation requirements for more than one fawn, such requirements are very high at their peak and 

tend to produce relatively restrictive results in the analysis. Lactating can draw on 

some of their own body reserves to get through the peak. Therefore, we usually run 

the analysis with lactation requirements for one fawn and simply call it Kilocalories per day

2000

1800

1600

1400

1200

1000

800

600

400

200

0

Nov.

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Month

Gestation

Two fawns

Lactation

One fawn

Figure 3—Energy costs (kilocalories/day) of gestation and lactation for a black-tailed deer doe 

with one or two fawns. (Source: Hanley 1984, based on data from Moen 1973 and Robbins and 

Moen 1975.)
19

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

“reproduction.” Requirements for twin fawns, however, can yield further insight 

into forage nutritional quality of a habitat.

For winter analyses, we have focused on the date of 1 February in the snow 

submodel of FRESH-Deer (see below). Forage nutritional quality does not change 

much during winter, but forage availability changes greatly depending on snowpack. Users can choose whatever date they want, but we recommend that they 

focus on their assumptions about snowpack if choosing a different date than 

1 February.

Linked Databases

We provide two databases (forage biomass by habitat and forage nutritional values) 

that are linked to each other and to user-specified, habitat data entry. The habitat 

database contains forage-specific biomass data (fig. 4) for forest stands in 

Figure 4—Screen print of Web page where plant biomass database is accessed through drop-down 

menus to the right of the question mark in the center of screen: first drop-down menu is “Region 

Selection” (“Admiralty/POW” has been selected); second is “Stand Selection.”
20

GENERAL TECHNICAL REPORT PNW-GTR-858

southeastern Alaska. We intend to add to this database continuously as more 

biomass data become available. The user can access this database by hitting the 

“Search Database” button on the Deer Page Main Menu. The user then can select 

a “Region” (group of data, each group collected in one study) and then a “Stand” 

(one habitat description) within that region. Alternatively, the user can directly enter 

their own original data rather than use a data set from our database. Whenever 

“plant codes” (app. 3) are included to describe the forage, FRESH will link them to 

the same plant codes in our forage nutrition database to provide estimates of digestible energy and protein. If the user has their own nutritional data, then those data 

can be input directly instead of using our linked database. Our nutritional database 

is a compilation of nutritional data (app. 2) from southeastern Alaska. However, it 

currently is based on only five studies. More data are needed. We intend to add to 

this database periodically as additional data become available.

Access to the databases, entering or importing original data, and data requirements are outlined in the “Web-based Application User’s Manual” accessed via the 

“User’s Manuals” topic on the Deer tab of the Web site. 

Energy and Protein Constraints

The user must specify the units of measurement for the energy and protein constraints, the season of analysis, and whether to include variation in the estimates 

of nutritional quality (see below). There are two options provided for each of 

the energy and protein constraints (fig. 4). For energy, there is either “Digestible 

Energy” or “Digestible Dry Matter.” For protein, there is either “Crude Protein” or 

“Digestible Protein.” We provide the choice because not all data in our database, 

and not all users’ original data, are in the preferred currencies of digestible energy 

and digestible protein (see app. 1 for suggested field and laboratory methods for 

obtaining original data).

Although most “energy” values are expressed in terms of digestible dry matter, 

digestible energy is the preferred energy constraint. Digestible energy is the product 

of dry matter digestibility (percentage) and gross energy (kilojoules/gram [kJ/g] or 

kilocalories/gram [kcal/g]) of the forage. Of these two factors, dry matter digestibility is far more variable than is gross energy. Dry matter digestibility typically 

ranges between about 30 to 90 percent, whereas gross energy typically ranges 

between 17.6 to 20.1 kJ/g (4.2 to 4.8 kcal/g) with most forages close to 18.8 kJ/g 

(4.5 kcal/g) (Robbins 1993). Thus, studies of forage quality frequently emphasize 

measures of dry matter digestibility rather than taking on the additional expense of 

including gross energy. If one assumes a constant gross energy value of 18.8 kJ/g 

across all forages, then a “nutritional requirement” for dry matter digestibility can 
21

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

be calculated for any given daily digestible energy requirement and daily dry matter 

intake (see deer metabolic requirements, table 1). 

The difference between energy units of kJ and kcal is simply a constant 1.0 

kcal = 4.1868 kJ. Kilocalorie is an older preferred unit, while kJ is the currently 

preferred unit. Many older data sets are in units of kcal. Thus, we provide the option 

for either. However, if the user is using our linked nutritional database, then they 

will be limited to whatever units are in the database.

Similarly, digestible protein is the preferred measure of dietary protein content 

and nutritional constraint. However, measures of digestible protein are relatively 

few in the literature, whereas crude protein data (or total nitrogen [N]; crude 

protein = total N times 6.25) are common. We caution the user, however, that the 

Table 1—Suggested input for deer nutritional constraints (for adult female Sitka 

black-tail in early July and mid winter) (dry-matter intake and requirements for 

metabolizable energy, digestible dry matter, and digestible protein)

 Metabolizable 

 energy Dry-matter Digestible Digestible

Season and nutritional status requirement intake dry matter protein

 kcal/day g/day - - - - - Percent - - - - -

Summer:

Maintenance, no fawns 2,350 1,220 50 4.8

With single fawn 3,100 1,340 60 8.0

With twin fawns 3,500 1,470 62 10.0

Winter:

From high-quality summer range 960 525 48 1.8

From low-quality summer range 1,050 525 52 1.8

Metabolizable energy (ME) requirement and dry-matter intake (DMI): Parker et al. 1999 (fig. 12 for ME and 

fig. 8 for DMI), assuming a summer body weight of 42 kg and winter body weight of 35 kg (Parker et al. 1999) 

and that voluntary DMI increases with increasing ME (within reasonable limits).

Minimum concentration of digestible dry matter (DDM) of diet, given the specified values of ME and DMI and 

an assumed gross energy content of 4.5 kcal/g and ME coefficient of 0.85 (Robbins 1993):

DDM = (ME 4 0.85 4 4.5 4 DMI) × 100.

Minimum concentration of digestible protein (DP) of diet, given the specified value of DMI and assumed body 

weights (as above) is calculated as follows:

Dietary crude protein content (percentage) for maintenance = ([[EUN + MFN (DMI) × 6.25] 4 DMI 4 0.74] × 100)

(Robbins 1993: 183) where EUN (endogenous urinary nitrogen) and MFN (metabolic fecal nitrogen) are 

calculated as in Parker et al. (1999) with body weights (as above).

Dietary crude protein content (percentage) for lactation:

Peak protein requirement for a single fawn = 505 g/day of milk, with a protein content of 0.069 g/g and a 

digestibility coefficient of 0.95 (Sadleir 1980): 505 × 0.069 4 0.95 = 36.68 g/day. Requirement for twin fawns 

is 1.67 times that of a single fawn (Robbins 1993: 213): 36.68 × 1.67 = 61.25 g/day.

Total requirement of dietary crude protein content = maintenance plus lactation. 

Conversion of crude protein (CP) to digestible protein (DP) as follows (Hanley et al. 1992): 

DP = -3.87 + 0.9283 (CP).

Sources: Hanley et al. 1992, Parker et al. 1999, Robins 1993, Sadleir 1980. 
22

GENERAL TECHNICAL REPORT PNW-GTR-858

differences between digestible protein and crude protein can be very significant 

biologically—usually far more so than differences between digestible energy and 

digestible dry matter.

Other Foraging Constraints, Optional

Two additional foraging constraints are included for “advanced” users (we recommend not using them for initial analyses): (1) the maximum percentage that any one 

species can contribute to the total biomass of the solution set (“diet”), and (2) the 

minimum amount of total biomass acceptable for a solution.

The first is somewhat similar to the forage-specific upper limit (maximum 

constraint) that the user can specify for each forage (see “Factors Affecting Nutrition and Palatability” in general discussion of FRESH), except that this time, 

the constraint is not forage-specific; it applies for any (unspecified) forage that 

approaches the value of the constraint in the linear program solution. The reasoning 

behind this constraint is that deer are generalist herbivores and require a floristically 

diverse diet. Most of their forages contain noxious compounds, which are not a 

problem when consumed in moderation, but can be toxic when consumed in excess. 

Thus, no one or few forages can completely dominate a diet to the exclusion of 

others. For example, skunk cabbage (Lysichiton americanus Hulten & H. St. John) 

is a very good forage with high values of digestible energy and digestible protein in 

summer, but it also contains oxalic acid crystals, cyanide compounds, and excessive concentration of water. Skunk cabbage often occurs in extensive patches, 

dominating the vegetation of some habitats. However, it could not constitute such 

a predominant proportion of the total diet of deer. The user might want to guard 

against such occurrence by specifying a generic (not forage-specific) constraint 

on the maximum amount that any one forage (like skunk cabbage, or others not 

anticipated beforehand) can contribute to the final solution. We recommend that this 

constraint not be used at the stand level of application, however, because deer have 

access to other forage resources well beyond the stand itself. It can be useful at the 

landscape level of application because that is the scale at which individual deer can 

select their diets (the user specifies the home range size in the landscape level, GIS 

application). A generic constraint of 40 percent might be a reasonable upper limit 

in that case. However, this constraint seems most useful in subsequent gaming or 

exploratory analyses rather than initial analysis. The default value of this constraint 

is 100 percent (i.e., no effect).

The “minimum amount of biomass” constraint is a simplifying consideration 

that there exists a lower limit of total forage biomass beyond which a deer cannot 

forage profitably (in a time-effective manner) in a habitat. Studies of dry matter 
23

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

intake as a function of available forage biomass (the so-called “functional response”) 

indicate an asymptotic, threshold response that varies with animal body size (Spalinger and Hobbs 1992, Wickstrom et al. 1984). For a deer the size of a female Sitka 

black-tail, the threshold is low, about 25 kg/ha (Spalinger et al. 1988, Wickstrom et 

al. 1984). Thus, only in habitats with very sparse forage does the quantity of forage, 

per se, interfere with the foraging process. This constraint enables the user to take 

foraging efficiency into account by discounting the value of very sparse habitat 

accordingly. The way the constraint works is that whatever value is specified by the 

user is subtracted from the total usable forage of the solution set before that value is 

divided by the daily dry-matter intake of a deer. For example, if 25 kg/ha is specified 

as “minimum amount of total biomass,” and the linear program yielded a solution 

of 100 kg/ha “total biomass used,” then the 25 kg/ha would be subtracted from the 

100 kg/ha before dividing into the daily dry-matter intake per deer (yielding deer 

days per hectare). Obviously, the effect of this “constraint” (note that it is not a 

constraint in the linear program itself) is much more pronounced in forage-sparse 

than forage-abundant habitats. Unlike the “maximum percentage of diet” constraint 

(above), this minimum biomass constraint is more appropriately applied at the stand 

level of application than at the landscape level of application, because it becomes 

increasingly problematic with increasing patchiness of vegetation. (An implicit 

assumption of this constraint is that the forage is evenly distributed in the habitat, 

i.e., not patchy.) In practice, we’ve found that this constraint tends to mask interesting output for low-biomass stands, and therefore, we recommend against its use in 

initial analysis. Its default value is 0 kg/ha (i.e., no effect). 

Importance of Variation in Nutritional Values and 

Habitat Biomass Values

Nutritional values— 

The user may choose to include variation in the estimates of nutritional quality 

(mean + standard deviation) when doing the calculations or simply work with mean 

values (only) for each forage. We highly recommend including variation. The standard deviations in our database simply reflect the variation in values reported from 

various studies. However, it is important to recognize that nutritional quality of any 

given forage at any given time can be variable, from site to site, within the same 

site, and even within different leaves or twigs of the same plant, depending on site, 

microenvironment, phenological differences, and within-plant resource allocation. 

Thus, the nutritional values of forages in our database are just best approximations, 

not precise, fixed values. Including variation in the analysis provides consideration 

that the nutritional values are not precise and fixed.

The user may choose 

to include variation 

in the estimates of 

nutritional quality 

(mean + standard 

deviation) when doing 

the calculations or 

simply work with mean 

values (only) for each 

forage. We highly 

recommend including 

variation.
24

GENERAL TECHNICAL REPORT PNW-GTR-858

The way FRESH deals with nutritional variation is as follows: The available 

biomass of each forage is divided into three equal groups of biomass, differing in 

their nutritional values, and each group is then treated as a separate, unique forage 

in the analysis. The nutritional values assigned to the three groups are (1) the mean 

plus one standard deviation, (2) the mean, and (3) the mean minus one standard 

deviation. Both energy and protein values are raised (and lowered) together, because 

digestible energy and digestible protein tend to be correlated within the same forage 

(Hanley and McKendrick 1983, Johnstone et al. 2002, Leslie et al. 1984). Approximately 68 percent of the time, the values of a normally distributed random variable 

will fall within one standard deviation of the mean (i.e., 16 percent on either tail 

of the distribution) (Snedecor and Cochran 1967). Thus, our three biomass groups 

for each forage would be of approximately equal size if the data were distributed 

normally (strictly, the groups would include 32 percent on either end and 36 percent 

in the middle).

When the user includes nutritional variation in the analysis, the forage list 

becomes very large (three times the number of forages). The output from that analysis may be important to the user, to see exactly which of the expanded forages were 

included in the solution. However, we also provide the user an option of viewing 

the analysis and results with only one line per forage (rather than three lines). Most 

users probably will prefer to view only one line per forage, whereas the analysis 

works on all three “lines” (biomass groups). See the Web-based User’s Manual 

(accessed on Web site) for details.

Nutritional variation is important in application of the analysis because the 

nutritional constraints in the linear programming model are precise. Thus, most 

forages tend to be either included or excluded in the solution, with very few partial 

inclusions. When nutritional variation is included in the analysis, partial inclusion of 

forages is common and much more reflective of the variability that exists in nature.

Habitat biomass values—

A similar problem to that of nutritional variation is that estimates of biomass 

within a habitat also are not highly precise, fixed values. There is always variation 

among samples even within the same stand; greater precision can be obtained with 

greater sampling intensity in the field. However, that is probably a lesser problem in 

comparison to variation between stands of the same “habitat type” (a type within a 

classification of types). 

The FRESH system does not currently deal explicitly with either kind of 

variation in habitat biomass values. However, the user can use FRESH to analyze 

both types of variation. For within-stand variation, we recommend a “runningmeans” analysis, where the Deer Days/Ha value (output solution) is plotted against 
25

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

an increasing sample size (e.g., number of sample quadrats) within the stand, and 

the variation in that value is seen graphically. For example, if the stand’s vegetation 

had been sampled with 30 quadrats (e.g., a 0.5- or 1.0-m2 sampling frame), then the 

mean biomass of each forage in the stand could be calculated for various combinations of the 30 quadrats (e.g., for the first 10 only, then for the first 15, then 20, etc., 

or any other combination). The user might proceed by calculating five (for example) 

descriptions of the stand, each time using an increasing number of quadrats until 

finally using all quadrats for the last description. The FRESH analysis could then 

be applied to each of the five descriptions (arrays of available forages), yielding a 

Deer Days/Ha value for each. A graphical plot of the Deer Days/Ha values against 

the increasing number of quadrats would provide a graphical analysis of the effect 

of increasing sample size (quadrats) on the precision of the Deer Days/Ha estimate 

for that stand. This provides one metric of variation within that stand, rather than 

n independent metrics, where n is the number of forages in the stand. And, this one 

metric of variation (in Deer Days/Ha) is precisely the metric of greatest interest in 

terms of overall habitat value to deer. Alternatively, various statistical subsampling 

techniques could be used to estimate the within-stand variation similarly.

For variation among stands of the same habitat type, we simply recommend that 

the FRESH analysis be applied to each stand, separately, yielding a Deer Days/Ha 

value for each stand. The mean Deer Days/Ha value for the habitat type then can be 

calculated as the mean (and variation) of the Deer Days/Ha values across all stands 

within that habitat type. Not only will that approach yield an estimate of amongstand variation in addition to the mean, but the mean Deer Days/Ha value will 

likely differ from that of one analysis applied to a composite food array calculated 

across all stands. The mean of the individual stand values is the true mean, because 

the value from the composite array is based on a much more diverse vegetation than 

actually occurred in any one stand.

Summer to Winter Conversion

The FRESH system can convert any summer habitat description (list of forages and 

their biomass) to an estimated description of forage availability in winter through 

a relatively simple process (box 5). It requires that the user specify the following 

attributes for each forage: (1) “percentage in winter,” and the forage’s height profile 

in terms of (2) minimum and (3) maximum height in centimeters.

“Percentage in winter” is the snow-free availability of the forage in winter 

expressed as a percentage of its availability in summer. “Percentage in winter” of 

most forages is usually either 0 percent (deciduous) or 100 percent (evergreen). For 

example, leaves of deciduous shrubs and herbs are 0 percent, whereas twigs of most 

shrubs are 100 percent; leaves of evergreen species are 100 percent. Some species, 
26

GENERAL TECHNICAL REPORT PNW-GTR-858

Box 5: Converting a Summer Biomass Data Set (array) to Winter Biomass Availability 

Steps:

1. Additional data requirements for each forage (to be entered into the summer data set):

a. “Proportion of Summer Biomass” (e.g., deciduous forages = 0; evergreen forages are >0 but may be <1.0)

b. Minimum height aboveground (cm)

c. Maximum height aboveground (cm)

d. Winter (1 Feb.) nutritional values (e.g., dry matter digestibility or digestible energy, and digestible protein).

2. Multiply summer biomass value by its “Proportion of Summer Biomass” factor for each species. This 

provides each forage’s “Snow-free Winter Biomass” (Bsf).

3. Determine height profile (minimum to maximum heights) for each forage and assume that the forage’s 

biomass is distributed uniformly throughout its height profile.

4. Determine snow depth in the stand on 1 February (see “Snow Depth Equations” and box 6). Snow depth 

is a function of the stand’s elevation, aspect, slope, and overstory canopy coverage.

5. Reduce each forage’s biomass in proportion to its height profile that is “buried” in snow. Use the following equation, where “Dasc” is the snow depth in the stand, “Min” is the forage’s minimum height, 

“Max” is the forage’s maximum height, “Bsf” is the forage’s snow-free winter biomass, and “Bs

” is the 

forage’s biomass adjusted for burial by snow, assuming a simple linear bottom-up burial process: 

 Bs

 = Bsf x (1 – [Dasc – Min]/[Max – Min]), 

where the maximum value of [Dasc – Min]/[Max – Min] cannot exceed 1.0

For blueberry shrubs (Vaccinium ovalifolium, V. parvifolium, V. alaskaense) and salal (Gaultheria shallon), 

Bs

 is further reduced to incorporate a nonlinear logarithmic decay rate between snow depths of 20 cm (no 

nonlinear effect) and 100 cm (complete burial) for blueberry, and between 1 cm (no nonlinear effect) and 50 

cm (complete burial) for salal: 

For blueberry at snow depths between 20 and 100 cm: 

Bs

 = Bs20 (Y /100), where Bs20 is the snow-free value of Bs at 20-cm depth and Y is the percentage remaining at greater depths.

Y = 286.14 – 62.13 (ln X), where X is the snow depth in centimeters.

 Bs

 = 0 for all depths >100 cm.

For salal at snow depths between 1 and 50 cm:

Bs

 = Bs01 (Y / 100), where Bs01 is the snow-free value of Bs at 1-cm depth and Y is the percentage remaining 

at greater depths.

Y = 100.00 – 25.58 (ln X), where X is the snow depth in centimeters.

 Bs

 = 0 for all depths > 50 cm.

Running the model for winter:

6. Use the Bs

 value and the winter nutritional values for each forage; change the model’s user-specified 

metabolic requirements and dry-matter intake values to appropriate winter values; and run the model.
27

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

however, are partially available in winter. For example, the fern Dryopteris dilatata

is deciduous, but it overwinters as a fiddlehead at ground surface in the forest 

floor and is eaten by deer when the ground surface is not frozen (Gillingham et al. 

2000). Thus, its “percentage in winter” is less than 100 percent, but greater than 0 

percent. The height profile of each forage (minimum and maximum heights above 

the ground surface) provides a description of the vertical zone in which the forage 

biomass occurs during snow-free winter conditions.

Given the above information about each forage, FRESH converts its summer 

biomass to winter biomass availability through the following steps. First, the summer biomass is multiplied by its proportional availability in winter (“percentage in 

winter”). This provides a winter estimate under snow-free conditions. When snow 

is present (see “Snow Submodel,” below), the availability of the snow-free biomass 

is further reduced by burial in snow, with two simplifying assumptions: (1) that the 

forage biomass is evenly distributed throughout the height profile of the forage, and 

(2) that burial by snow occurs as a bottom-up process (i.e., forage below the snow 

depth is not available, whereas forage above the snow depth is available). There is 

no accounting for stem bending or entrapment in the snowpack, with two exceptions: blueberry shrubs (Vaccinium ovalifolium, V. parvifolium, and V. alaskaense) 

and salal (Gaultheria shallon). Blueberry and salal are important winter forages 

throughout the range of Sitka black-tails and northern range of Columbian blacktails (O. h. columbianus). Blueberry species are important because their twigs are 

relatively nutritious, and salal because its leaves are evergreen. The snow burial 

process has been studied for both blueberry and salal, and stem bending and entrapment in the snowpack has been found to be important in both, resulting in nonlinear 

decay rates in their availability with increasing depth of snow (Hovey 1987, Hovey 

and Harestad 1992, Jenkins et al. 1990, Vales 1986, White et al. 2009). We have 

incorporated that effect for blueberry twigs and salal by assuming that blueberry 

is unaffected by entrapment in the snowpack at snow depths of 20 cm or less but is 

entrapped at >20 cm, so its availability decreases in a nonlinear, logarithmic fashion 

between 20 and 100 cm with all twigs buried by snow at 100-cm depth and greater, 

regardless of how tall the blueberry is in snow-free conditions. We have assumed a 

similar process for the lower growing, leafy salal at snow depths from 1 to 50 cm, 

with complete burial at all depths of 50 cm and greater (see box 5 for details).

Both blueberry and salal have relatively limber stems and are especially susceptible to bending and entrapment in the snowpack. Other species of shrubs have 

not been studied, so we have incorporated the nonlinear burial process only for 

blueberry and salal; other browse species follow the simple, linear, bottom-up 

burial process.
28

GENERAL TECHNICAL REPORT PNW-GTR-858

In addition, the user also must change the animal constraints (metabolic 

requirements, table 1) to appropriate values for winter and provide values of nutritional quality for each forage in winter. The FRESH nutritional database includes 

winter nutritional values for users who do not have their own and substitutes them 

in place of summer nutritional values for forages that are in the database.

Although it is a very simple procedure to convert a summer habitat description to a winter habitat description (one keystroke in the FRESH system—see the 

Web-based User’s Manual accessed via the Web site), it is not possible to convert 

a winter description to a summer description. The missing (deciduous) forages 

would be unknown.

Snow Submodel

The current snow submodel is best considered a prototype or first approximation 

model. It is based on very few data and needs to be field verified and, quite possibly, adjusted. The data consist of a 33-year record (fig. 5) from USDA NRCS snow 

courses accessed from the Eaglecrest Road near Juneau (USDA NRCS Web site: 

http://www.ak.nrcs.usda.gov/Snow/southeast.html) and corresponding snow depth 

measurements taken by the National Oceanic and Atmospheric Administration 

National Climate Data Center station at the Juneau International Airport (USDC 

Web site: http://hurricane.ncdc.noaa.gov/dly/DLY), all for 1 February, 1977 to 2009, 

and a published regression relationship (Hanley and Rose 1987) between overstory 

160

140

120

100

80

60

40

20

0

0 100 200 300 400 500 600

Elevation (m)

Snow depth (cm)

y = 6.04 + 0.270x

r = 0.993 2

Figure 5—1 February snow depth, mean of 1977–2009 observations (33 years) from USDA 

Natural Resources and Conservation Service (USDA NRCS) snow courses on Eaglecrest Road 

and Eaglecrest (elevations 152, 366, and 503 m) and from National Oceanographic and Atmospheric Administration National Climate Data Center data at Juneau International 

Airport (elevation 4 m), Juneau, Alaska.
29

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

canopy coverage and its effect on snow depth (fig. 6). The Eaglecrest Road and 

airport sites are all in close proximity, differing mainly in elevation (5 to 503 m), 

thus providing the best available long-term data set for an elevation gradient in 

southeastern Alaska. 

The date of 1 February was chosen as representative of mid-winter snow 

conditions. It provides a winter index for the snapshot analysis from FRESH. Users 

should keep in mind that winter snowpacks vary continuously throughout the winter, and that the FRESH analysis is a snapshot at one point in time, not an average 

for the whole winter. 

The snow submodel (see box 6) predicts the depth of a snowpack under the 

forest canopy as a function of elevation, slope, aspect, overstory canopy coverage 

of the stand, and depth of snow in a level, open area at sea level. The snow depth 

at sea level is specified by the user and provides a means of increasing or decreasing the snowpack in relation to more or less snow as a function of weather, time of 

winter, or geographic climate zone (e.g., outer coast versus mainland sites, northern 

versus southern sites). It allows the user to explore how habitat quality changes with 

greater or lesser amounts of snow at a given base area (sea level). The specified 

120

100

80

60

40

20

0

0 20 40 60 80 100

Overstory canopy coverage (percent)

Snow depth (percent)

y = 94.8 + 0.0025e

r = 0.735 2

x 10

Figure 6—Mean snow depth in the forest (as a percentage of depth in the open) as a function of 

overstory canopy coverage. Each point is the mean of five sampling periods. Solid circles represent 

uneven-aged stands; open circles represent even-aged stands. (From Hanley and Rose 1987.)
30

GENERAL TECHNICAL REPORT PNW-GTR-858

Box 6: Snow Submodel Calculations

Units of measurement:

Snow depth (centimeters)

Elevation (meters)

Aspect (degrees, 0 and 360 = true north)

Slope (degrees)

Overstory canopy coverage (percent)

Plant heights (centimeters)

Snow-depth equations:

Snow depth (D) in a level, open area as a function of elevation (E)

D = 10.3 + 0.27(E)

D is in centimeters; E is in meters. 

 It is always the starting point.

Effect of aspect (A), in relation to slope (S), on snow depth (Da

)

 Da = D + 0.33(cos A)(tan S) × D

Da is in centimeters and is a non-negative value. The least it can be is zero. 0.33(cos 

A)(tan S) × D simply modifies the elevation value, but it interacts with slope. The 

maximum value that tan(S) is allowed to take is 1.00, which occurs at a slope of 

45 degrees (100 percent). Slopes greater than 45 degrees are treated the same as 45 

degrees. The 0.33 constant is strictly arbitrary, yielding a 100 percent greater depth 

on due-North than due-South aspects on 45° slopes, and a 27 percent greater depth 

on 20° slopes.

Effect of slope (S) on snow depth (Ds

)

Ds

 = D × cos(S) 

cos(S) is a proportional multiplier ranging between 0.0 and 1.0.

This effect of slope is independent of aspect. It is the gravitational effect (snow moving downhill) and the sublimation effect (exposure to air; greater surface area with 

greater slope).

Effect of overstory canopy coverage (C) on snow depth (Dc

)

Dc = D × (100 – 0.0025eC/10)/100

This equation is from Hanley and Rose (1987), with modification. The parenthetical expression is a percentage multiplier. Note, however, that canopy coverage was 

measured with a spherical densiometer.

Combined snow depth model (Dsac)

Dsac = [D + 0.33(cos A)(tan S) × D] × cos(S) × (100 – 0.0025eC/10)/100 ,

where D is always an elevation-dependent starting point. 

(continued on next page)
31

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

depth at sea level thus provides a standard for comparison of any stands differing in 

their topographic settings or overstory canopy coverage.

The calculations (box 6) involve the following relations: (1) The effect of elevation is predicted by a data-based regression relationship (fig. 5) between elevation 

and depth by using the regression slope and the user-specified sea-level depth as 

the Y-intercept (note that with a negative Y-intercept value [<0 cm], the “snow line” 

[snow depth >0 cm] moves up the mountains in elevation). The elevation-predicted 

depth is then modified by (2) effect of slope (a cosine function where 0 slope has no 

effect, and all the snow slides off at 90 degrees slope), (3) effect of aspect interacting with slope (a cosine function interacting with a tangent function, where snow 

depth is increased on northerly aspects and decreased on southerly aspects), and 

(4) effect of overstory canopy coverage (an exponentially decreasing function with 

increasing canopy coverage, fig. 6). Although overstory mass (e.g., wood volume) 

also may influence snow interception (Kirchhoff and Schoen 1987), best predictive models across wide ranges of young-growth and old-growth stands have been 

obtained with canopy coverage (Hanley and Rose 1987, Harestad and Bunnell 

1981).

An optional, habitat-specific factor, called the “Shrub/Slash-Interaction Multiplier,” is provided to allow the user to account for the effect of dense shrubs and 

logging or thinning slash in holding snow above the ground, thereby increasing the 

effective depth of snow in that particular habitat. Dense shrubs in high-biomass, 

young clearcuts, for example, intercept much snow and hold it above ground level, 

having the snow-burial effect of much deeper snow. The Shrub/Slash-Interaction 

Multiplier is a user-specified constant that simply multiplies the calculated snow 

depth of the given habitat (as described above and in box 6) by the constant. Its 

default value is 1.0 (i.e., no effect).

Thus, for example: a stand at 300 m elevation, 180° aspect (due south), 25° slope 

(= 56 percent), and 90 percent overstory canopy coverage would have a predicted 

snow depth of 55.8 cm (i.e., 61 percent of D for a level, open-canopied area at 

300 m elevation). Individually, the factors would have the following effects:

D = 10.3 + 0.27(E) = 91.3 cm

 Da = 91.3 + 0.33(cos 180)(tan 25) × 91.3 = 77.3 cm

 Ds

 = 91.3 × cos(25) = 82.7 cm

 Dc = 91.3 × (100 – 0.0025e90/10)/100 = 72.8 cm

Obviously, the effect of elevation is overwhelming. That is why it is always the 

starting point.
32

GENERAL TECHNICAL REPORT PNW-GTR-858

The effect of snow depth in FRESH-Deer is on forage availability only, through 

burial (see “Summer to Winter Conversion” above). We have not attempted to 

model energy costs of locomotion through snow, because the effect of snowpack 

on mobility of deer in southeastern Alaska is highly variable. Frozen crusts on the 

snow surface, resulting from wet snow freezing, are common in this region, and 

deer can easily walk on frozen crusts without breaking through (Parker et al. 1999). 

When snow is not crusted (e.g., very fresh or falling), then deer may sink in it and 

experience high costs of locomotion (Parker et al. 1984). Overall, however, energy 

costs for locomotion in snow are a relatively minor variable in the winter energy 

budget of black-tailed deer in southeastern Alaska, especially in comparison with 

basal metabolic energy costs and the snowpack’s effects of reduced energy intake 

(forage quality, especially) (Hanley and McKendrick 1985, Parker et al. 1999). 

Similarly, energy costs for thermoregulation are unimportant for black-tailed deer 

in southeastern Alaska in virtually all but the most open, exposed habitats (Parker 

1988, Parker and Gillingham 1990, Parker and Robbins 1984, Parker et al. 1999). 

Interpretation of Output

The output provides the total number of Deer Days/Ha the habitat can support plus 

the following key information (cross-referenced to fig. 7): A listing of all key data 

used in the analysis—(1) the animal constraints specified by the user (i.e., metabolic 

requirements; immediately below the yellow bar labeled “Animal Constraints”); (2) 

the total biomass of forage available in the habitat (second line from top, 269.8500); 

(3) the complete list of forages (data summary box immediately beneath “Plant Data 

and Amount Used”), their biomass, nutritional values, and forage-specific dietary 

constraints (“Maximum percentage in diet”). And it provides key results from the 

analysis—(4) the total suitable biomass used in the solution (third line from top, 

129.8168); (5) its mean nutritional qualities (fourth and fifth lines from top, 60.000 

and 8.410; (6) the biomass used of each forage (the amount that each forage contributes to the solution; third column from right in data summary box); (7) the amount 

used expressed as a percentage of that forage’s availability in the habitat (second 

column from right in same summary box); and (8) the amount used expressed as a 

percentage of the total suitable biomass used in the solution (furthest right column 

in same summary box). 

Comparison of the total biomass available and the total biomass used in the 

solution tells whether total biomass of forage was the limiting factor (all available 

biomass would have been used) or nutritional/dietary constraints were limiting 

(“Total Biomass Used” would be less than “Total Available Biomass”). Comparisons of the mean nutritional composition (e.g., mean digestible energy or dry 
33

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

matter; mean digestible protein) of the solution set (“Total Biomass Used”) with the 

user-specified animal constraints identify the limiting constraint(s). For example, 

if the mean concentration of digestible energy in the solution equals the digestible 

energy constraint, while the mean concentration of digestible protein in the solution 

exceeds the digestible protein constraint, then digestible energy was the limiting 

factor, and digestible protein was not limiting. The “Percent Used” value for each 

forage provides a measure of that forage’s relative value in the solution set, and 

therefore, relative value to deer. Forages that were 100 percent used (included in 

the solution) were highly valuable forages; forages that were 0 percent used (not 

included in the solution) contributed nothing and may as well not have been there; 

forages used between 0 and 100 percent were of intermediate value. The relative 

contribution of each forage to the total solution (the “Percent of Total” column) 

provides a description of the solution “diet.” It reflects the combined effects of the 

Figure 7—Screen print of the solution to a habitat from the biomass database in summer for a doe 

with one fawn. The full Web page includes the results for all 30 forages (only the first seven are seen 

here).
34

GENERAL TECHNICAL REPORT PNW-GTR-858

relative quality of each forage and its absolute biomass availability. Comparison of 

the “Percent of Total” with the “Maximum Percentage in Diet” constraint for any 

forage tells whether the forage’s contribution to the solution was limited because 

of its abundance (available biomass), nutritional quality (either digestible energy or 

digestible protein or both), or the forage-specific “Maximum Percentage in Diet” 

constraint. Note that these values for any given forage will differ from habitat to 

habitat because of relative differences in forage availabilities.

All of those comparisons provide important insight into understanding the Deer 

Days/Ha solution and the food and nutritional limitations of the habitat. For additional insight, the user might want to game the system by editing values of available 

biomass or nutritional quality of select forages and repeating the analysis, or by 

varying nutritional constraints (or snow depth in winter) and repeating the analysis. 

This begets a sensitivity analysis, which helps evaluate the relative roles and effects 

of various factors in any given habitat-environment situation.

Home Range

The GIS Application for landscape-level analyses requires that the user specify a 

“grid area” (“pixels per grid”), or window size, for the moving-windows analysis 

(fig. 8). Forage resources from all habitats falling within each “grid” or “window” 

are combined into one array of forage resources for that grid. The grid size specified by the user should reflect the scale of habitat use by an individual animal, i.e., 

a typical home range size. Mean home range size for Sitka black-tailed deer has 

been reported to range from about 80 ha (200 ac) (Admiralty Island—Schoen and 

Kirchhoff 1985) to 200 ha (500 ac) (Prince of Wales Island—Yeo and Peek 1992) 

with seasonal differences in “core areas” (zones of high use) for winter being about 

70 percent the size of those for summer (32 ha winter vs. 45 ha summer, Prince of 

Wales Island). 

Users must choose a home range size for their analysis and divide it into the 

size of a pixel in their GIS system to determine the number of pixels for a sampling 

grid. The sampling grid in the FRESH system is square; thus the grid size specification should be the square root of the number of pixels the user wants per grid. For 

example, for a 100-ha home range size and a pixel size of 30 × 30 m (900 m2

), the 

user-specified grid size would be 33 (i.e., square root of [100 × 10,000 / 900] = 33.3).

Given the variation reported for home range sizes of Sitka black-tails, users 

might want to try several runs with FRESH, varying the grid size specification for 

each run, to examine sensitivity to home range assumptions in the analysis of their 

study area. Sensitivity to home range size will vary with habitats and their spatial 

pattern uniquely for any given landscape.
35

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

User’s Manuals 

User’s manuals are available directly from the Web site by clicking on the “User’s 

Manuals” heading under the Deer tab. Users should begin with the Web-based 

Application User’s Manual to understand how the system works and its data requirements. For large-scale, spatial analysis, the user will need to use the GIS-based 

application, which is accessed through the “Export Data” button on the Deer main 

page. The GIS Application Instruction Manual is available there, as well as in the 

User’s Manuals section. 

Management Implications

The most valuable aspects of FRESH may not be the habitat values it provides, but 

rather the informative or educational value and systematic organization of habitat 

relations and data. FRESH-Deer, for example, not only calculates a quantitative 

value of habitat (Deer Days/Ha), but also identifies which forages are most important 

Figure 8—Screen print of the geographic information system application showing the sampling grid 

and how grid size (pixels per grid) and sampling offset are user-specified.
36

GENERAL TECHNICAL REPORT PNW-GTR-858

in providing that value, which nutritional constraints are most limiting, and how 

those relations change with seasonality, metabolic requirements, and snow depths. 

By varying individual factors, one at a time, users can see their relative importance and how their values affect the broader question of habitat value. Outcomes 

will differ with particular circumstances of the habitat patch or landscape. Such 

sensitivity analysis and gaming can yield much insight into “system behavior” 

of the deer-habitat interaction, at least in the way in which it has been modeled. 

Moreover, the modeling itself provides an explicit and entirely data-driven system 

of cause-and-effect relations determining habitat value for deer within habitat 

patches and landscapes. The model identifies key data needs (e.g., biomass and 

nutritional values of forages by habitat and silviculture treatment), future research 

needs (e.g., topography-overstory-snow relations), and ways of incorporating new 

understanding into a broader assessment of habitat quality for deer (e.g., adding 

deer habitat-selection behavior to the current food-based evaluation by incorporating a meta-analysis of resource utilization functions). It can, therefore, provide 

guidance in identifying priority research needs and be modified to incorporate new 

knowledge. The FRESH system is best thought of as a continuously evolving tool 

for organizing our understanding of deer-habitat relations and using that knowledge 

to evaluate habitat.

Immediate practical uses of FRESH-Deer are in evaluating the quality of 

individual patches of habitat (Web-based application) and their landscape pattern 

(GIS-based application). These are best considered as relative comparisons (i.e., 

relative value of one habitat or landscape pattern versus another), because the Deer 

Days/Ha values are, after all, indices of habitat value calculated for the particular 

time (e.g., early July or early February) and scenario (metabolic requirements, 

snow depth). To date, the stand-level analysis has been useful in evaluating differences in silviculture treatments (Cole et al. 2010, Zaborske et al. 2002) and forest 

composition (Hanley et al. 2006). The stand-level analysis currently provides the 

deer habitat analysis of the Tongass-Wide Young-Growth Studies (McClellan and 

DeSanto, in press), which form the basis of the young-growth monitoring program 

for the Tongass National Forest in Alaska. Work is currently underway to use the 

GIS application for timber planning analysis and young-growth silviculture planning by the Tongass National Forest and private lands owned and managed by the 

Sealaska Corporation. Landscape analyses will primarily be used for comparing 

various alternative planning scenarios and optimizing multiple resource objectives.

The FRESH-Deer system also provides the ability to compare the relative values of summer range and winter range for any given habitat or landscape. This has 

important implications for identifying the overall seasonal “bottleneck” of a given 

The FRESH system is 

best thought of as a 

continuously evolving 

tool for organizing our 

understanding of deerhabitat relations and 

using that knowledge 

to evaluate habitat.
37

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

deer range, and it should be an important part of any habitat evaluation. In southeastern Alaska, deer-habitat concerns have frequently focused on “critical winter 

range,” which was the sole focus of earlier unpublished deer-habitat models based 

on professional judgment. That was because winter is the time of greatest mortality 

of Sitka black-tailed deer, and winter survival was the focus of some of the earliest studies for deer management (Klein and Olson 1960, Olson 1952, Olson and 

Klein 1959), even though it was also known at that time that summer range is most 

important for growth and production of deer bodies and herds (Klein 1964, 1965). 

In fact, summer range is essential for black-tailed deer to recover body condition 

from winter, produce young, and build body reserves (fat, muscle) that enable them 

to survive the forage-poor conditions of winter (Parker et al. 1993, 1999). Thus, a 

focus on winter range alone is not only overly simplistic, it could be misleading and, 

especially, inadequate for guiding land management concerned with a landscape’s 

capacity to produce and sustain deer. When evaluating a given landscape with 

FRESH-Deer, the user should evaluate that landscape for scenarios of both winter 

and summer. If the overall value of the landscape (its mean Deer Days/Ha value for 

all GIS pixels) is much lower in one season than the other, then management efforts 

directed at the lower capacity season will yield the greatest benefits for deer. When 

the relative seasonal values are nearly the same, then management treatments 

benefiting only one season without the other will be relatively limited in their 

potential effect.

FRESH-Deer also can provide an analytical basis for evaluating the consequences of new or even hypothetical changes in habitat for deer. For example, 

there is much interest today in global climate change and its possible effects on 

local environments and habitats. Consequences of climate change for deer would 

be manifested through changes in weather patterns (winter snowpack especially 

important for Sitka black-tails) and vegetation (species composition, production, 

maybe nutritional quality, landscape distribution of species and habitats). Analysis 

of the effects of climate change on deer habitat, therefore, should be directed at the 

presumed consequences for vegetation and snowpack, and those effects can be analyzed, in turn, with FRESH-Deer. Similarly, other community-wide or ecosystemlevel changes, such as terrestrial effects of “marine-derived nutrients” from Pacific 

salmon (Oncorhynchus spp.) in riparian forests could be evaluated for deer on the 

basis of their effects on forage quality and quantity.

One important feature of black-tailed deer habitat in southeastern Alaska that 

is difficult to evaluate with FRESH-Deer is the role of gradients in snowmelt for 

the quality of summer range. Elevation gradients prolong the period of snowmelt 
38

GENERAL TECHNICAL REPORT PNW-GTR-858

through the summer, thereby extending the otherwise brief period of phenologically young, highly nutritious forage that becomes available shortly after vegetation emerges from a snowpack (Klein 1965). Similarly, topographically complex 

alpine and subalpine habitats do the same but on a smaller scale. The timing of 

spring and the availability of skunk cabbage (Lysichiton americanus) can be 

another important aspect of habitat for deer (Parker et al. 1999). Such temporal 

and spatial patterns can be analyzed with FRESH-Deer, but doing so requires 

considerably more analyses than simply one for early July (for example). For 

elevation gradients and seasonal timing, the landscape would need to be analyzed 

at several times during the growing season; for complex spatial gradients, the 

habitat patches would need to be described accordingly. Such detailed analyses 

are likely of most interest to users with specific seasonal or phenology questions 

in mind, and they would require much additional data. For users unwilling or 

unable to devote the time and costs necessary for such detailed analysis, these 

effects must be kept in mind when considering the simpler analysis of one time 

in midsummer. 

In all cases, it is especially important that users remember that the habitat 

values calculated by the FRESH system (Deer Days/ha) are indexes that reflect 

the total food supply—an integration of its total quantity and quality relative to 

the specified nutritional requirements of deer at the particular time of analysis. 

Differences among habitats are consistent with their differences in total food 

resources. Although total food resources determine a habitat’s capacity to produce and sustain deer, they are not a useful predictor of animal behavior. Just as 

population density can be a misleading indicator of habitat quality (Van Horne 

1983), so too can food and habitat preferences be misleading about relative carrying capacities (Hobbs and Hanley 1990). Quantity and quality of food resources 

are not substitutable for each other in herbivore diets, especially for ruminants; 

therefore, optimal food and habitat selection should be expected to vary with 

population density (which is not a measurable habitat feature) (Hobbs and Hanley 

1990). The FRESH system does not provide a basis for predicting optimal food 

or habitat choice (behavior) at any level of population density other than a completely saturated landscape exactly at its food capacity at that time.
39

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

English Equivalents

When you know: Multiply by: To get:

Centimeters (cm) 0.394 Inches

Meters (m) 3.28 Feet

Hectares (ha) 2.47 Acres

Square meters (m2) 10.76 Square feet

Grams (g) 0.0352 Ounces

Kilograms (kg) 2.205 Pounds

Kilojoules (kJ) 0.948 British Thermal Units (BTU)

Kilojoules per gram (kJ/g) 26.932 BTU/ounce

Degrees Celsius (°C) 1.8°C + 32 Degrees Fahrenheit

Jigger (j) 1.500 Ounces

Acknowledgments

We thank the following people for their careful reviews of earlier draft manuscripts: 

Elizabeth C. Cole, John G. Cook, Mary A. Friberg, David R. Klein, Lisa A. Shipley, 

and Beatrice Van Horne. We also thank Kenneth J. Raedeke for his continued interest and help with this project throughout its long evolution. Richard Harris and Ron 

Wolfe of Sealaska Corporation were instrumental in identifying the need for this 

project and have been supportive throughout. We also appreciate the interest and 

support from Forrest Cole and Patricia O’Connor of the Tongass National Forest in 

the use of the system.
40

GENERAL TECHNICAL REPORT PNW-GTR-858

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Appendix 1: Suggested Field and Laboratory Methods 

for Obtaining Original Data for Forage Biomass and 

Nutritional Quality

Field data requirements for Forage Resource Evaluation System for Habitat—Deer 

(FRESH-Deer, a food-based system for quantitatively evaluating habitat quality for 

deer) are the following: (1) identification of “habitat types” (plant community types) 

and “forages” (plant species and parts), (2) estimates of available forage biomass 

and overstory canopy coverage of each habitat type, and (3) estimates of nutritional 

values of each forage for each time of year to be analyzed. All values of biomass 

and nutritional data in the current linked databases of FRESH-Deer are for analyses 

focused on early July for summer and early February for winter. Similarly, parameter values in the current snow submodel are for February 1. The user may elect to 

use those same timeframes for analysis, or select different times. If different times 

are chosen, then it will be important to adjust the recommended metabolic requirements (table 1) accordingly, too.

Frequently, the user will want to input their own original habitat type and 

forage availability data but will not want to collect original forage nutritional data. 

In that case, they can use the linked forage nutritional database for nutritional 

estimates (app. 2), as long as they input their forage data identified by plant codes 

(app. 3). Any forages not included in the current list of plant codes will need to be 

identified as “other” forages (see app. 3) or will need original nutritional data.

Identification of Habitat Types

 For most stand-level analyses, the “habitat type” will be obvious. It will be simply 

the plant community being analyzed. Examples are experimental treatments, 

silviculture treatments, and other a priori defined plant community types. Sometimes, questions arise about whether various a priori plant community types differ 

enough to require classification as different habitat types or whether they should 

be combined as the same habitat type. The answer to that question usually depends 

on the needs of the user, but if several replicate stands are available for each community type, then each stand can be evaluated with FRESH-Deer, and the resulting 

Deer Days/Ha values of each type can be analyzed in an analysis of variance and 

comparison of means, which will identify similar from dissimilar community types 

in relation to deer food resources. Similar community types could be combined into 

one habitat type. Alternatively, similarities in vegetation pattern can be analyzed 

directly with any of various multivariate analytical techniques as described below.

When plant community types or habitat types are not obvious or defined a 

priori, then they must be identified and described by the user. This is a typical plant 
49

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

community ecology problem for which the user can consult various textbooks (e.g., 

Barbour et al. 1987). Choices of habitat types will likely depend on existing plant 

community data sources and maps. Analysis of vegetation gradients of similarity/

dissimilarity will likely be by various multivariate ordination techniques and 

gradient analyses. 

The current geographic information system (GIS) application for landscapelevel analysis requires that habitat types be defined and that each be described in 

terms of its mean forage-specific biomass availabilities (ovendry kilograms per 

hectare [kg/ha] of each forage) and mean overstory canopy coverage (percentage). 

The habitat types will be mapped in GIS space, and all pixels within a given habitat 

type will be assigned the vegetation of that type.

Vegetation Data and Sampling Procedures

The user must identify their “available forages” (see boxes 3 and 4, p. 8 for definitions of “available” and “forages”). Forages are the plant species eaten by deer, 

which usually include virtually all plant species. They are also differentiated 

by plant part when the parts differ substantially in their nutritional quality. For 

example, leaves are considered separately from twigs for shrub species. Similarly, if 

a species occurs in widely different phenological growth stages (e.g., young leaves 

and old, desiccated leaves) simultaneously, it would be best to treat the phenological 

stages as different forages. We consider only current annual growth as “forage,” 

so older stems of shrubs and trees are not included; but that decision is up to the 

user. “Availability” of a forage is influenced by its height profile (minimum and 

maximum heights aboveground, centimeters) specified by the user and its burial 

by snow in the snow submodel. Some users prefer to limit the maximum available 

height to what can be reached by a foraging deer, whereas others prefer not to do 

that, assuming that greater heights can be accessed when deer walk on top of firm 

snowpacks. The user also must identify how much of each forage remains available 

in snow-free winter, as a percentage of its summer biomass (e.g., shrub twigs and 

evergreen leaves may be given values of 100 percent, whereas deciduous leaves are 

0 percent). That value is used in converting summer data sets to winter data sets. 

The user also must specify a “maximum percentage in the solution set” for each 

forage they choose to limit in that way (see “Factors Affecting Nutrition and Palatability” in general discussion of FRESH); that forage-specific constraint should be 

based on known diet composition data from free-ranging deer.

Biomass (ovendry kg/ha) of each available forage must be determined for each 

stand (stand-level analysis) to be analyzed or each habitat type (landscape-level 

analysis) on the landscape. Biomass sampling is another classic plant community 
50

GENERAL TECHNICAL REPORT PNW-GTR-858

ecology problem (see Barbour et al. 1987 or similar text for details). If sampling is 

conducted in summer (e.g., late June through early August), then biomass availability in winter can be calculated by FRESH-Deer through the “convert summer 

to winter” routine (box 5). Although most statistical analyses assume random 

sampling for determining biomass within a plant community, most plant ecologists 

find efficiency is increased markedly by stratified systematic sampling, whereby the 

community is sampled systematically (e.g., sample quadrats placed at given intervals along a measuring tape or transect) and unique patches of vegetation are stratified (sampled separately). Biomass of each forage within each quadrat is measured 

either directly by clipping and weighing (with representative subsamples ovendried 

at 100 °C for dry-weight correction), or estimated indirectly by using allometric 

regression equations to convert measures of plants (e.g., canopy cover, basal stem 

diameter) to biomass (see Alaback 1986 for examples of allometric regressions for 

a wide range of southeastern Alaska species). Users who opt for allometric regressions should be aware, however, that such relations tend to be site-specific (Alaback 

1986); thus separate regressions for the same species may be needed for different 

silviculture treatments, or types of old-growth forest, for example.

Forage Nutritional Analysis

FRESH-Deer operates on user-specified constraints for two forage nutritional 

factors. We have emphasized minimum requirements for digestible protein (DP) 

and digestible energy (DE) as the two most important constraints. In an environment with a known mineral deficiency for deer (e.g., a phosphorus deficiency), the 

minimum requirement for that mineral could be substituted for the DP constraint by 

the user, and the user would need to provide the mineral concentration for all forages. However, for the usual situation, DP and DE will be the prime factors. Digestible energy is calculated as the product of gross energy concentration (kJ/g) and 

dry matter digestibility (DMD, g/100 g or percentage); of those two, gross energy 

is much less variable than is DMD (Robbins 1993), so oftentimes, sampling effort 

is focused on DMD and a constant value is assumed for gross energy. The focus 

on DMD is usually done to maximize the number of samples that can be analyzed 

within a total budget available for laboratory analyses. FRESH-Deer can work with 

either DMD or DE as the energy constraint.

Forage samples should be collected in the field from plants within the habitats 

being analyzed and at the same time of the year that will be the focus of the FRESH 

analysis. We usually focus on early July for summer and early February for winter 

analyses. Samples must be collected for each forage at both of those times. Samples 

must be collected in replicate, so means and standard deviations can be calculated. 
51

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

Usually, this is done by collecting a composite sample of a given forage at any one 

site and then replicate samples from replicate sites. Some forages, such as leaves of 

Vaccinium spp., differ significantly in their DP and DMD values as a function of the 

light environment (shade) of their habitat (Hanley et al. 1992, McArthur et al. 1993, 

Van Horne et al. 1988), and they should be treated separately.

Samples (5 to 10 g dry weight) should be clipped and bagged as quickly as 

possible and, ideally, frozen immediately on dry ice and kept frozen with dry ice 

until freeze-drying as soon as possible. Once freeze-dried, the sample can be kept 

in the dark in a sealed jar at room temperature more or less indefinitely. Immediate 

freezing at ultra-cold temperatures stops all metabolic activity in the plant tissue; 

freeze-drying dries the frozen sample in a way that prevents chemical interaction 

of compounds mixed by the breaking of frozen vacuoles within the plant cells. 

Although freezing with dry ice and subsequent freeze-drying is the ideal, such 

treatment often is highly impractical in remote field situations. Therefore, the next 

best alternative is to bag the sample, keep it out of direct sun, and ovendry it at 

40 °C as soon as possible. Drying at either cooler (20°) or warmer (60°) temperatures is not advisable (Mould and Robbins 1981). If samples are to be ground before 

shipping to a laboratory for analysis, they should be ground dried in a Wiley Mill 

with 20-mesh screen.

For laboratory analyses, we strongly recommend the procedures and equations described by Robbins et al. (1987a, 1987b) and Hanley et al. 1992, although 

the BSA-precipitation can be conducted by the modified method of McArt et al. 

(2006) to reduce costs. Of interest to users in southeastern Alaska is that Hanley 

et al. tested the equations with in vivo feeding trials using Sitka black-tailed deer 

(Odocoileus hemionus sitkensis Merriam). Other users may be interested to know 

that the Robbins et al. equations were developed from in vivo feeding trials with 

several deer species and have recently been found to be robust even for Alaska 

moose (Alces alces Linnaeus) (Spalinger et al. 2010). Digestible protein is estimated 

as a function of “crude protein” (total nitrogen concentration times 6.25) and 

protein-precipitating capacity of plant tannins. For tannin-free graminoids (most 

graminoids), DP can be estimated as a function of crude protein alone. Digestible 

dry matter is estimated as a function of sequential fiber analyses and DP reduction by tannins. The Robbins et al. and Hanley et al. papers provide all necessary 

equations for calculating DP and DMD and interpreting the results. Sequential 

fiber analysis has the strong advantages of being tied directly to in vivo digestion 

results from deer through strong regression relations and providing insight into 

the calculated DMD value through its component parts. We caution against using 

in vitro digestion results for DMD, because in vitro results are simply an index of 
52

GENERAL TECHNICAL REPORT PNW-GTR-858

DMD (they can vary with source of rumen fluid, for example, and the 48-hour time 

period is arbitrary) and are not tied directly to in vivo results from deer with strong 

predictive equations. In vitro DMD is usually less expensive than the sequential 

fiber method and may be used as an estimate in the absence of sequential fiber data, 

but we believe the greater value of the sequential fiber technique is worth the 

additional cost.

Most users will not have their own laboratories capable of performing these 

analyses, so they will need to contract with a commercial lab. Although we make no 

recommendation, nor do we imply a recommendation, one laboratory that has been 

performing these and similar analyses for more than 30 years is the Wildlife Habitat 

Nutrition Laboratory at Washington State University in Pullman, Washington.
53

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

Appendix 2: Nutritional Data (mean ± standard deviation) in the 

Nutritional Database

Summer Winter

Species/part Source DMD DP DMD DP 

- - - - - -- - - - - - - - - - - - Percent - - - - - - - - - - - - - - - - - - - -

Ferns:

Adiantum pedatus 1 52.7 ± 8.5a 10.3 ± 2.1 — —

Athyrium filix-femina 1 55.7 14.6 — —

2 51.1 10.2 — —

Mean 53.4 ± 3.3 12.4 ± 3.1 — —

Blechnum spicant 1 50.5 7.4 (50.5)b (7.4)

2 46.9 5.7 64.6 5.6

Mean 48.7 ± 2.5 6.6 ± 1.2 57.6 ± 10.0 6.5 ± 1.3

Dryopteris expansa 1 55.2 9.6 — —

 dilatata 2 49.4 7.8 66.2 10.4

 dilatata 3 25.1 6.7 48.2 7.3

Mean 43.2 ± 16.0 8.0 ± 1.5 57.2 ± 12.7 8.9 ± 2.2 

Gymnocarpium dryopteris 1 55.9 ± 9.0 8.0 ± 1.7 — —

Polystichum spp. 1 47.5 ± 7.6 6.0 ± 1.2 (47.5) ± 9.4 (6.0) ± 1.3

Mean “ferns” 50.2 ± 4.6 8.6 ± 2.4 54.1 ± 5.7 7.1 ± 1.6

Forbs:

Coptis asplenifolia 1 53.1 6.1 (53.1) (6.1)

2 71.9 6.1 78.3 5.9

3 63.6 5.9 72.4 4.1

Mean 62.2 ± 9.4 6.0 ± 0.1 67.9 ± 13.2 5.4 ± 1.1

Cornus canadensis 1 62.3 6.3 (62.3) (6.3)

2 59.3 7.2 72.2 7.2

3 58.4 8.4 75.6 6.0

Mean 60.0 ± 2.0 7.3 ± 1.1 70.0 ± 6.9 6.5 ± 0.6

Epilobium angustifolium 4 48.2 0.5 — —

5 65.4 5.4 — —

Mean 56.8 ± 12.2 3.0 ± 3.5 — —

Fauria crista-galli 3 78.6 ± 12.3 11.3 ± 3.3 — —

Listera spp. 3 72.7 ± 11.4 9.0 ± 2.6 — —

Lysichiton americanus 1 58.4 17.8 — —

2 50.4 24.8 — —

3 75.5 26.0 — —

Mean 61.4 ± 12.8 22.9 ± 4.4 — —

Maianthemum dilatatum 1 63.5 8.4 — —

2 72.3 8.9 — —

3 77.4 4.5 — —

Mean 71.1 ± 7.0 7.3 ± 2.4 — —
54

GENERAL TECHNICAL REPORT PNW-GTR-858

Appendix 2: Nutritional Data (mean ± standard deviation) in the 

Nutritional Database (continued)

Summer Winter

Species/part Source DMD DP DMD DP 

- - - - - -- - - - - - - - - - - - Percent - - - - - - - - - - - - - - - - - - - -

Monesis uniflora 1 67.5 10.1 (67.5) (10.1)

3 66.1 6.1 62.2 6.6

Mean 66.8 ± 1.0 8.1 ± 2.8 64.9 ± 3.7 8.4 ± 2.5

Potentilla spp. 2 55.7 ± 8.7 4.5 ± 1.3 — —

Prenanthes alata 2 69.4 ± 10.9 11.8 ± 3.4 — —

Rubus pedatus 1 62.6 6.2 (62.6) (6.2)

2 74.3 8.6 75.0 8.0

3 46.3 6.4 54.3 5.7

Mean 61.1 ± 14.1 7.1 ± 1.3 64.0 ± 10.4 6.6 ± 1.2

Streptopus spp. 3 69.8 10.0 — —

 amplexifolius 1 56.1 13.4 — —

2 86.2 9.2 — —

 roseus 1 57.4 11.5 — —

 streptopoides 1 64.8 9.4 — —

2 73.6 9.6 — —

Mean 68.0 ± 11.2 10.5 ± 1.6 — —

Tiarella trifoliata 2 66.3 8.4 65.7 6.9

3 43.5 7.6 61.3 5.1

Mean 54.9 ± 16.1 8.0 ± 0.6 63.5 ± 3.1 6.0 ± 1.3

Mean “forbs” 64.5 ± 7.2 9.0 ± 4.9 66.1 ± 2.8 6.6 ± 1.1

Graminoids:

Luzula parviflora 1 62.1 ± 8.4c 3.8 ± 2.3 — —

Bromus sitchensis 1 65.3 ± 8.9 8.8 ± 5.2 — —

Carex lyngbyaei 3 42.0 ± 5.7 0.1 ± 0.1 — —

Carex mertensii 1 63.4 + 8.6 9.2 ± 5.5 — —

Carex spp. 2 55.7 ± 7.6 9.2 ± 5.5 — —

Deschampsia caespitosa 3 63.3 ± 8.6 3.4 ± 2.0 — —

Elymus arenarius 2 60.3 ± 8.2 6.7 ± 4.0 — —

Mean “graminoids” 58.9 ± 8.0 5.9 ± 3.5 — —

Shrubs:

Alnus sinuata leaves 2 73.4 ± 7.2 13.0 ± 3.7 — —

(eaten only very sparingly)

Empetrum nigrum leaves 3 (29.9) ± 2.9 (1.4) ± 0.4 29.9 ± 2.9 1.4 ± 0.4

Ledum palustra 3 (39.9) ± 3.9 (2.5) ± 0.7 39.9 ± 3.9 2.5 ± 0.7
55

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

Appendix 2: Nutritional Data (mean ± standard deviation) in the 

Nutritional Database (continued)

Summer Winter

Species/part Source DMD DP DMD DP 

- - - - - -- - - - - - - - - - - - Percent - - - - - - - - - - - - - - - - - - - -

Menziesia ferruginea leaves 1 57.8 10.0 — —

2 56.7 7.5 — —

3 46.6 7.7 — —

Mean 53.7 ± 6.2 8.4 ± 1.4 — —

Menziesia ferruginea twigs 1 45.1 2.1 — —

2 54.5 9.1 34.1 1.9

3 21.2 0.3 27.1 2.6

Mean 40.3 ± 17.2 3.8 ± 4.6 30.6 ± 4.9 2.3 ± 0.5

Oplopanax horridus leaves 1 61.0 13.8 — —

2 70.1 8.5 — —

3 74.0 11.7 — —

Mean 68.4 ± 6.7 11.3 ± 2.7 — —

Ribes bracteosum leaves 1 57.4 ± 5.6 10.0 ± 2.9 — —

Ribes laxiflorum leaves 1 67.4 ± 6.6 10.3 ± 3.0 — —

Rubus spectabilis leaves 1 59.6 14.3 — —

2 63.1 16.0 — —

3 43.3 8.0 — —

4 53.9 6.7 — —

5 64.9 10.1 — —

Mean 56.9 ± 8.7 11.0 ± 4.0 — —

Salix sitchensis leaves 4 55.7 8.1 — —

5 55.0 6.3 — —

Salix spp. leaves 5 63.0 10.0 — —

Mean 57.9 ± 4.4 8.1 ± 1.9 — —

Sambucus racemosa leaves 1 59.3 17.0 — —

4 71.3 23.5 — —

5 71.5 16.0 — —

Mean 67.4 ± 7.0 18.8 ± 4.1 — —

Vaccinium ovalifolium/alaskensis

Leaves, forest 1 44.0 12.9 — —

2 43.0 9.4 — —

3 45.8 10.8 — —

4 54.0 9.1 — —

5 47.7 5.7 — —

Mean 46.9 ± 4.4 9.6 ± 2.6 — —

Leaves, sunny habitat 1 54.5 10.8 — —

4 52.5 5.0 — —

5 55.2 5.0 — —

Mean 54.1 ± 1.4 6.9 ± 3.3 — —
56

GENERAL TECHNICAL REPORT PNW-GTR-858

Appendix 2: Nutritional Data (mean ± standard deviation) in the 

Nutritional Database (continued)

Summer Winter

Species/part Source DMD DP DMD DP 

- - - - - -- - - - - - - - - - - - Percent - - - - - - - - - - - - - - - - - - - -

Twigs 1 30.8 2.7 (30.8) (2.7)

2 — — 51.9 4.4

3 27.8 2.5 36.1 4.8

Mean 29.3 ± 2.1 2.6 ± 0.1 39.6 ± 11.0 4.0 ± 1.1

Vaccinium parvifolium

Leaves, forest 1 53.9 12.1 — —

2 63.8 7.6 — —

Mean 58.9 ± 7.0 9.9 ± 3.2 — —

Twigs 1 32.9 2.5 (32.9) (2.5)

2 — — 58.1 5.1

Mean 32.9 ± 8.2 2.5 ± 1.6 45.5 ± 17.8 3.8 ± 1.8

Mean “non-Vacc. shrubs” leaves 55.4 ± 13.1 9.1 ± 5.1 34.9 ± 7.1 2.0 ± 0.8

(does not include Alnus)

Mean “non-Vacc. Shrubs” twigs 40.3 ± 17.2 3.8 ± 4.6 30.6 ± 4.9 2.3 ± 0.5

 (does not include Alnus)

Conifers (current annual growth):

Chamaecyparis nootkatensis 3 (47.3) ± 14.0 (1.9) ± 1.0 47.3 ± 14.0 1.9 ± 1.0

Picea sitchensis 1 39.0 4.0 — —

3 28.7 2.2 30.1 1.6

Mean 33.9 ± 7.3 3.1 ± 1.3 30.1 ± 8.9 1.6 ± 0.8

Tsuga heterophylla 1 53.3 6.9 — —

3 31.0 2.6 26.2 1.0

Mean 42.2 ± 15.8 4.8 ± 3.0 26.2 ± 7.7 1.0 ± 0.5

Lichens (arboreal):

Usnea spp. 2 (71.9) ± 3.8d (-2.2) ± 0.6 71.9 ± 3.8 -2.2 ± 0.6

a Means and standard deviations are calculated from all values for a forage reported in multiple studies. Mean and 

standard deviation for class mean (e.g., “Mean Ferns”) is calculated from mean (or single) values reported for each 

forage within the class. Standard deviations for forages with only one value (i.e., reported in only one study) are 

estimated by calculating the mean coefficient of variation (100 × s/) for all forages with multiple values in the 

class and then applying that coefficient of variation to the single value to estimate its standard deviation.

b Data in parentheses are estimated as same values in winter as measured in summer (or vice versa). 

c All estimates of standard deviations in the graminoids class are based on the variance between forages within 

that class. The coefficient of variation for the class mean (“Mean graminoids”) is applied to each of the individual 

forages to estimate standard deviation of each forage.

d Standard deviations for Usnea spp. are calculated from all values reported for Usnea in Parker et al. 1999. Also 

see Robbins (1987) for digestibility of Alectoria sarmentosa. 

Calculations of digestible dry matter from Parker et al. (1999) make assumption that gross energy of dry matter is 

a constant of 18.83 kJ/g (= 4.5 kcal/g).

Calculations of digestible protein (DP) from McClellan et al. (n.d.), and Hanley and McKendrick (1983), both of 

which give values of nitrogen concentration (% Nitrogen [N]) rather than DP, use the following equation, converting N to Crude Protein and then Crude Protein to DP (Hanley et al. 1992) assuming no effects of tannins:

 DP (%) = -3.87 + 0.9283 (N × 6.25) 
57

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

Appendix 2: Nutritional Data (mean ± standard deviation) 

in the Nutritional Database (continued)

Sources:

1. McClellan et al. (n.d.): Michael McClellan, Thomas Hanley, and others at Pacific Northwest Research Station, 

Juneau—data from samples collected during July and August 1998, composited from several replicate 

sites on Prince of Wales Island as part of followup to “Second Growth Management Study.” All samples, 

except Vaccinium leaves “from sunny habitat” were collected from shaded forest understories. Chemical 

analyses by Habitat Analysis Laboratory, Washington State University, Pullman. Dry Matter Digestibility

estimated from fiber analysis and equation in Hanley et al. 1992. Digestible Protein estimated from crude 

protein (N × 6.25) and equation in Hanley et al. 1992. No values account for effects of tannins. Lower DP 

of Vaccinium “from sunny habitat” reflects only lower N concentration, not the combined effect of lower N 

 and higher tannins.

2. Parker et al. 1999. 

3. Hanley and McKendrick 1983. 

4. McArthur et al. 1993.

5. Hanley et al. 1992.
58

GENERAL TECHNICAL REPORT PNW-GTR-858

Appendix 3: Plant Codes of Species in the Current (2011) Database and 

Their Default Values for Percentage Remaining in Winter, and Maximum 

Percentage in the Solution Set

 Percentage Maximum 

 remaining percentage in

Species Common name Plant code Type in winter solution set

Actaea rubra (Aiton) Willd. red baneberry ACRU2 Forb 0 100

Adiantum pedatum L. northern maidenhair ADPE Fern 0 20

Alectoria Ach. spp. witch’s hair lichen ALECT3 Lichen 100 100

Alnus spp. Mill. leaf alder ALNUS Shrub 0 3

Angelica genuflexa Nutt. kneeling angelica ANGE2 Forb 0 100

Andromeda polifolia L. leaf bog rosemary ANPO_L Shrub 0 2

Andromeda polifolia L. twig bog rosemary ANPO_T Shrub 100 2

Aquilegia formosa Fisch ex DC. western columbine AQFO Forb 0 100

Aruncus sylvester Kostel. ex bride’s feathers ARSY2 Forb 0 100

Maxim.

Athyrium filix-femina L. Roth common ladyfern ATFI Fern 0 10

Blechnum spicant (L.) Sm. deer fern BLSP Fern 100 20

Bromus sitchensis Trin. Alaska brome BRSI Graminoid 0 100

Carex deweyana Schwein. Dewey sedge CADE9 Graminoid 0 100

Carex lyngbyei Hornem. Lyngbye’s sedge CALY3 Graminoid 0 100

Carex mertensii Prescott ex Bong. Merten’s sedge CAME6 Graminoid 0 100

Carex L. spp. sedge CAREX Graminoid 0 100

Chamaecyparis nootkatensis Alaska cedar 100 100

(D. Don) Spach CAGa CHNO Tree

Circaea alpina L. small enchanter’s CIAL Forb 0 100

 nightshade

Claytonia sibirica L. Siberian springbeauty CLSI2 Forb 0 100

Coptis aspleniifolia Salisb. fernleaf goldthread COAS Forb 100 100

Cornus canadensis L. bunchberry dogwood COCA13 Forb 100 100

Coptis trifolia (L.) Salisb. threeleaf goldthread COTR2 Forb 100 100

Deschampsia cespitosa (L.). tufted hairgrass DECA18 Graminoid 0 100

P. Beauv.

Dryopteris dilatata auct. non spreading woodfern DREX2 Fern 10 20

(Hoffm.) Gray

Dryopteris expansa (C. Presl.) spreading woodfern DREX2 Fern 10 20

Fraser

Drosera rotundifolia L. roundleaf sundew DRRO Forb 0 100

Elymus arenarius L. sand ryegrass LEAR11 Graminoid 0 100

Empetrum nigrum L. leaf black crowberry EMNI_L Shrub 100 3

Empetrum nigrum L. twig black crowberry EMNI_T Shrub 100 1

Epilobium angustifolium L. fireweed CHANA2 Forb 0 100

Epilobium ciliatum Raf. fringed willowherb EPCI Forb 0 100

Equisetum arvense L. field horsetail EQAR Forb 0 100

Equisetum pratense Ehrh. meadow horsetail EQPR Forb 0 100

Equisetum L. spp. horsetail EQUIS Forb 0 100

Fauria crista-galli (Menzies) deercabbage NECR2 Forb 0 100

Makino
59

Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

Appendix 3: Plant Codes of Species in the Current (2011) Database and 

Their Default Values for Percentage Remaining in Winter, and Maximum 

Percentage in the Solution Set (continued)

 Percentage Maximum 

 remaining percentage in

Species Common name Plant code Type in winter solution set

Gallium kamtschaticum Steller boreal bedstraw GAKA Forb 0 100

ex Schult.

Galium trifidum L. threepetal bedstraw GATR2 Forb 0 100

Galium L. spp. bedstraw GALIU Forb 0 100

Gaultheria shallon Pursh leaf salal GASH Shrub 100 100

Gaultheria shallon Pursh twig salal GASH Shrub 100 100

Gentiana douglasiana Bong. swamp gentian GEDO Forb 0 100

Geocaulon lividum (Richardson) false toadflax GELI2 Forb 0 100

Fernald

Geranium erianthum DC. woolly geranium GEER2 Forb 0 100

Geum macrophyllum Willd. largeleaf avens GEMA4 Forb 0 100

Goodyera oblongifolia Raf. western rattlesnake GOOB2 Forb 0 100

 plantain

Gymnocarpium dryopteris (L.) western oakfern GYDR Fern 0 20

Newman

Heracleum lanatum Michx. common cowparsnip HEMA80 Forb 0 2

Impatiens noli-tangere L. western touch-me-not IMNO Forb 0 10

Kalmia polifolia Wangenh. leaf bog laurel KAPO_L Shrub 0 3

Kalmia polifolia Wangenh. twig bog laurel KAPO_T Shrub 100 3

Ledum palustre L. leaf marsh Labrador tea LEPA11_L Shrub 100 3

Ledum palustre L. twig marsh Labrador tea LEPA11_T Shrub 100 3

Listera cordata (L.) R. Br. heartleaf twayblade LICO6 Forb 0 100

Listera R. Br. spp. twayblade LISTE Forb 0 100

Luzula parviflora (Ehrh.) Desv. smallflowered woodrush LUPA4 Graminoid 0 100

Lysichiton americanus Hulten American skunkcabbage LYAM3 Forb 0 100

& H. St. John

Maianthemum dilatatum (Alph. false lily of the valley MADI Forb 0 100

Wood) Mac.

Menziesia ferruginea Sm. leaf rusty menziesia MEFE_L Shrub 0 100

Menziesia ferruginea Sm. twig rusty menziesia MEFE_T Shrub 100 100

Mitella pentandra Hook. fivestamen miterwort MIPE Forb 0 100

Moehringia L. spp. sandwort MOEHR Forb 0 100

Moneses uniflora (L.) A. Gray single delight MOUN2 Forb 100 100

Oplopanax horridum (Sm.) devilsclub OPHO Shrub 0 100

Miq. leaf

Osmorhiza purpurea (Coult. purple sweetroot OSPU Forb 0 100

& Rose) Suksd.

Osmorhiza Raf. spp. sweetroot OSMOR Forb 0 100

Oxycoccus microcarpos Turcz. small cranberry VAOX Shrub 0 100

ex Rupr.

Parnassia fimbriata K. D. Koenig fringed grass of PAFI3 Forb 0 100

 Parnassus
60

GENERAL TECHNICAL REPORT PNW-GTR-858

Appendix 3: Plant Codes of Species in the Current (2011) Database and 

Their Default Values for Percentage Remaining in Winter, and Maximum 

Percentage in the Solution Set (continued)

 Percentage Maximum 

 remaining percentage in

Species Common name Plant code Type in winter solution set

Picea sitchensis (Bong.) Carriere Sitka spruce PISI Tree 100 1

CAGa

Platanthera dilatata (Pursh.) scentbottle PLDI3 Forb 0 100

Lindl. ex Beck

Polystichum braunii (Spenner) Braun’s hollyfern POBR4 Fern 100 20

Fee

Potentilla L. spp. cinquefoil POTEN Forb 0 100

Prenanthes alata (Hook.) D. Dietr. western rattlesnakeroot PRAL Forb 0 100

Renunculus L. spp. buttercup RANUN Forb 0 100

Ranunculus uncinatus D. Don woodland buttercup RAUN Forb 0 100

ex G. Don

Ribes bracteosum Douglas ex stink currant RIBR_L Shrub 0 10

Hook. leaf

Ribes bracteosum Douglas ex stink currant RIBR_T Shrub 100 10

Hook. twig

Ribes laxiflorum Pursh. leaf trailing black currant RILA3_L Shrub 0 100

Ribes laxiflorum Pursh. twig trailing black currant RILA3_T Shrub 100 100

Ribes L. spp. leaf current RIBES_L Shrub 0 100

Ribes L. spp. twig currant RIBES_T Shrub 100 100

Rubus chamaemorus L. leaf cloudberry RUCH_L Shrub 0 100

Rubus chamaemorus L. twig cloudberry RUCH_T Shrub 100 100

Rubus parviflorus Nutt. leaf thimbleberry RUPA_L Shrub 0 100

Rubus parviflorus Nutt. twig thimbleberry RUPA_T Shrub 100 100

Rubus pedatus Sm. strawberryleaf raspberry RUPE Forb 100 100

Rubus spectabilis Pursh. leaf salmonberry RUSP_L Shrub 0 100

Rubus spectabilis Pursh twig salmonberry RUSP_T Shrub 0 100

Salix L. spp. leaf willow SALIX_L Shrub 0 100

Salix L. spp. twig willow SALIX_T Shrub 100 100

Sambucus racemosa L. leaf red elderberry SARA2_L Shrub 0 100

Sambucus racemosa L. twig red elderberry SARA2_T Shrub 0 100

Stellaria crispa Cham. & Schltdl. curled starwort STCR2 Forb 0 100

Streptopus amplexifolius (L.) DC. claspleaf twistedstalk STAM2 Forb 0 100

Streptopus Michx. spp. twistedstalk STREP3 Forb 0 100

Streptopus roseus Michx. twistedstalk STRO4 Forb 0 100

Streptopus streptopoides (Ledeb.) small twistedstalk STST3 Forb 0 100

Frye & Rigg

Thelypteris phegopteris (L.) long beechfern PHCO24 Fern 0 100

Slosson

Thuja plicata Donn ex D. Don western redcedar THPL Tree 100 100

 CAGa

Tiarella trifoliata L. threeleaf foamflower TITR Forb 100 100
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Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

Appendix 3: Plant Codes of Species in the Current (2011) Database and 

Their Default Values for Percentage Remaining in Winter, and Maximum 

Percentage in the Solution Set (continued)

 Percentage Maximum 

 remaining percentage in

Species Common name Plant code Type in winter solution set

Tolmiea menziesii (Pursh.) Torr. youth on age TOME Forb 0 100

& A. Gray

Trientalis latifolia Hook. broadleaf starflower TRBOL Forb 0 100

Trisetum cernuum Trin. tall trisetum TRCA21 Graminoid 0 100

Trisetum Pers. spp. oatgrass TRISE Graminoid 0 100

Tsuga heterophylla (Raf.) Sarg. western hemlock TSHE Tree 100 15

CAGa

Tsuga mertensiana (Bong.) mountain hemlock TSME Tree 100 10

Carriere CAGa

Urtica dioica L. stinging nettle URDI Forb 0 1

Vaccinium alaskaense Howell leaf Alaska blueberry VAOV_L Shrub 0 100

Vaccinium alaskaense Howell twig Alaska blueberry VAOV_T Shrub 100 100

Vaccinium cespitosum Michx. leaf dwarf bilberry VACA13_L Shrub 0 100

Vaccinium cespitosum Michx. twig dwarf bilberry VACA13_T Shrub 100 100

Vaccinium L. spp. evergreen leaf blueberry VACCI_L Shrub 100 100

Vaccinium L. spp. evergreen twig blueberry VACCI_T Shrub 100 100

Vaccinium ovalifolium Sm. leaf oval-leaf blueberry VAOV_L Shrub 0 100

Vaccinium ovalifolium Sm. twig oval-leaf blueberry VAOV_T Shrub 100 100

Vaccinium parvifolium Sm. leaf red huckleberry VAPA_L Shrub 0 100

Vaccinium parvifolium Sm. twig red huckleberry VAPA_T Shrub 100 100

Vaccinium uliginosum L. leaf bog blueberry VAUL_L Shrub 0 100

Vaccinium uliginosum L. twig bog blueberry VAUL_T Shrub 100 100

Veratrum viride Aiton green false hellebore VEVI Forb 0 2

Viola glabella Nutt. pioneer violet VIGL Forb 0 100

Viola L. spp. violet VIOLA Forb 0 100

Other fern other fern XFERN Fern 0 20

Other forb other forb XFORB Forb 0 100

Other graminoid other graminoid XGRAM Graminoid 0 100

Other shrub leaf other shrub leaf XSHRUB_L Shrub 0 100

Other shrub twig other shrub twig XSHRUB_T Shrub 100 100

a CAG = current annual growth.

Source: PLANTS Database http://plants.usda.gov/ by genus; plant code = “symbol.” 
62

GENERAL TECHNICAL REPORT PNW-GTR-858

Glossary

animal day (or deer day)—One animal of specified species, age, sex, and body 

weight for 1 day; used in Forage Resource Evaluation System for Habitat (FRESH) 

to quantify the food resources necessary to meet the nutritional requirements, 

as specified for the analysis. For deer days, we usually use an adult female as 

the animal; the body weight and nutritional requirements differ with season and 

reproductive status (see table 1). Gestation or lactation (reproductive) requirements 

are included in the adult female’s requirement as specified in the analysis. 

Animal days can be one animal for several days, several animals for 1 day, or any 

combination thereof; however, nutritional requirements and food availability and 

quality are assumed constant at the time of their measurement (i.e., do not change 

with number of days). 

carrying capacity—The capacity of a habitat’s food resources to meet the 

requirements of a given animal species, measured in units of animal days. Note that 

our definition does not involve any consideration of overgrazing, plant-herbivore 

feedback loops, and long-term sustainability. It is an instantaneous measure of 

the relation between available food resources and specified animal nutritional 

requirements. It is best considered an index of the combination of food quantity and 

quality relative to the animal in question. It provides a quantitative comparison of 

different habitats (or landscapes) at the same time or the same habitat at different 

times. It is not intended as an estimate of the actual number of animals that a habitat 

can support on a long-term basis. 

current annual growth—Leaves, twigs (shrubs), stems (herbs), and flowers and 

fruits produced by plants during the current growing season; the standing crop of 

aboveground net primary productivity exclusive of radial wood increment to the 

older (greater than current year) stems of woody plants. Measures of current annual 

growth are in terms of biomass (see below) and may include an unmeasured effect 

of herbivory. Current annual growth of plants in winter is the biomass produced in 

the immediately preceding growing season.

digestible energy—The energy in a food that becomes available to an animal upon 

digestion, measured in terms of kilojoules (or kilocalories) per gram ovendry weight 

(see below). Gross energy is the total energy in a food, as measured by combustion; 

digestible energy is that fraction of the gross energy that becomes available to the 

animal by the process of digestion. Further losses of energy include urinary and 

gaseous losses (the conversion of digestible energy to metabolic energy) and heat 

losses in chemical and mechanical work (metabolic energy to net energy), which 

are assumed as constant coefficients in our recommended metabolic requirements. 
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Forage Resource Evaluation System for Habitat—Deer: An Interactive Deer Habitat Model

Digestible energy can be measured by multiplying gross energy (determined by 

combustion) by the dry matter digestibility (percentage) of the food.

digestible protein—The protein in a food that becomes available to an animal upon 

digestion, measured in units of grams ovendry weight (see below). The digestible 

protein concentration of a food is the digestible protein expressed as a percentage 

of the ovendry weight of the food. A daily digestible protein requirement can be 

quantified in units of grams per day or percentage of diet (for a given total daily dry 

matter intake). Crude protein (which equals total nitrogen times 6.25) is a measure 

of a food’s protein content exclusive of its digestibility. Although digestible protein 

is highly correlated with crude protein in tannin-free grasses and agricultural 

legumes, it can be reduced greatly in native forages consumed by deer, especially 

those containing tannins (see below). In terms of nutritional requirements and food 

quality, digestible protein is always the better metric than crude protein.

feedback loops—Feedback loops refer to “information” feedback whereby a cause 

generates an effect that in turn affects the original causal factor. Positive feedback 

loops result in the effect increasing with time, whereas negative feedback loops 

result in a dampening of effect with time. The FRESH system does not account 

for herbivore-plant feedback loops because its calculations are based on the total 

standing crop of available plant biomass without limit to how much of it can be 

included in the solution set (other than the limits imposed by the nutritional or 

palatability constraints).

metabolic requirements—Metabolism is the complex of physical and chemical 

processes occurring within a living cell or organism that are necessary for the 

maintenance of life. Although metabolic requirement usually refers to the daily 

minimum requirement for energy (see digestible energy above), we also include 

the daily requirement for digestible protein in our use of the term. It could include 

requirements for other nutritional factors as well.

ovendry weight—Weight (grams or kilograms) of a given material after it has 

been dried to a constant weight in an oven at specified temperature. Plant tissue is 

dried at various temperatures for various reasons (e.g., laboratory analyses usually 

require cooler temperatures than biomass determinations), but a subsample of the 

material is usually dried at 100 °C as a standard. If no temperature is specified with 

the ovendry weight, then drying to constant weight at 100 o

C may be assumed. This 

reduces the effect of variable levels of moisture affecting a given material’s stated 

weight.
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GENERAL TECHNICAL REPORT PNW-GTR-858

biomass—The ovendry weight of plant material (grams or kilograms), often 

expressed on a per-area basis as biomass density (grams per square meter or 

kilograms per hectare). “Available” plant biomass in FRESH refers to the plant 

biomass that is available to deer, either by simply being physically present or above 

snow in winter (plant biomass beneath snow is assumed to be unavailable). Plant 

biomass may refer to a specific forage (species and part) or to the total of all forages, 

depending on context.

plant phenology—Cyclic biological events in plants, such as the seasonal changes 

that occur with new leaf production, maturation, flowering, fruiting, leaf-fall, etc.

plant secondary chemistry (and secondary compounds)—The study of plant 

chemistry dealing with compounds that are not primarily involved in the processes 

of growth and reproduction. Secondary compounds are those compounds that 

are “secondary” to basic plant metabolism and, therefore, are either secondary 

byproducts of metabolism, precursors or regulators of primary metabolism, or 

are produced for other purposes (e.g., protection from ultraviolet radiation). Many 

secondary products are believed to serve as protection against herbivores, either 

as toxic poisons or as digestibility reducing substances. Plant secondary chemistry 

is a very broad and complex science, as secondary compounds are virtually 

innumerable. 

tannins—A highly diverse biochemical group of various soluble, astringent, 

complex, phenolic substances of plant origin. Tannins are widespread in occurrence 

in natural vegetation, especially in woody species commonly eaten by deer. Many 

(but not all) tannins chemically bind with proteins to form insoluble complexes 

resistant to digestion, thereby reducing the digestible protein content of a forage. 

Because of the enormous variety of tannins and their effects, however, it is most 

useful to measure tannin astringency (capacity for binding a standard protein) 

rather than tannin concentration when evaluating forage quality for herbivores. 
Pacific Northwest Research Station

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