Variation in Nutritional Quality 

of Plants for Deer in Relation 

to Sunny Versus Shady 

Environments

Thomas A. Hanley and Jeffrey C. Barnard

United States Department of Agriculture

Forest 

Service

Pacifi c Northwest 

Research Station

Research Paper

PNW-RP-602 September 2014
Authors

Thomas A. Hanley is a research wildlife biologist, and Jeffrey C. Barnard is a fi sh 

biologist, Forestry Sciences Laboratory, 11175 Auke Lake Way, Juneau, AK 99801.

Cover photos: Bunchberry dogwood (upper) and oval-leaf blueberry (lower) plants, 

growing in shady forest understory (left) and open sunny habitats (right).

All photos by Thomas Hanley.

Non-Discrimination Policy

The U.S. Department of Agriculture (USDA) prohibits discrimination against its customers, 

employees, and applicants for employment on the bases of race, color, national origin, age, 

disability, sex, gender identity, religion, reprisal, and where applicable, political beliefs, 

marital status, familial or parental status, sexual orientation, or all or part of an individual’s 

income is derived from any public assistance program, or protected genetic information in 

employment or in any program or activity conducted or funded by the Department. (Not all 

prohibited bases will apply to all programs and/or employment activities.) 

To File an Employment Complaint

If you wish to fi le an employment complaint, you must contact your agency’s EEO Counselor 

(PDF) within 45 days of the date of the alleged discriminatory act, event, or in the case of a 

personnel action. Additional information can be found online at http://www.ascr.usda.gov/

complaint_fi ling_fi le.html. 

To File a Program Complaint

If you wish to fi le a Civil Rights program complaint of discrimination, complete the USDA 

Program Discrimination Complaint Form (PDF), found online at http://www.ascr.usda.gov/

complaint_fi ling_cust.html, or at any USDA offi ce, or call (866) 632-9992 to request the 

form. You may also write a letter containing all of the information requested in the form. 

Send your completed complaint form or letter to us by mail at U.S. Department of Agriculture, Director, Offi ce of Adjudication, 1400 Independence Avenue, S.W., Washington, D.C. 

20250-9410, by fax (202) 690-7442 or email at program.intake@usda.gov.

Persons with Disabilities

Individuals who are deaf, hard of hearing or have speech disabilities and you wish to fi le 

either an EEO or program complaint please contact USDA through the Federal Relay 

Service at (800) 877-8339 or (800) 845-6136 (in Spanish). 

Persons with disabilities, who wish to fi le a program complaint, please see information 

above on how to contact us by mail directly or by email. If you require alternative means 

of communication for program information (e.g., Braille, large print, audiotape, etc.) please 

contact USDA’s TARGET Center at (202) 720-2600 (voice and TDD).

Supplemental Nutrition Assistance Program

For any other information dealing with Supplemental Nutrition Assistance Program (SNAP) 

issues, persons should either contact the USDA SNAP Hotline Number at (800) 221-5689, 

which is also in Spanish or call the State Information/Hotline Numbers. 

All Other Inquiries

For any other information not pertaining to civil rights, please refer to the listing of the USDA 

Agencies and Offi ces for specifi c agency information.
Abstract 

Hanley, Thomas A.; Barnard, Jeffrey C. 2014. Variation in nutritional quality of 

plants for deer in relation to sunny versus shady environments. Res. Pap. PNWRP-602. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacifi c 

Northwest Research Station. 30 p.

Variation in nutritional quality of natural forages for black-tailed deer (Odocoileus 

hemionus) was studied in summer and winter in southeast Alaska. Freeze-dried 

samples of 17 summer forages collected in early July and 10 winter forages collected in February from three replicate sites each of shady forest understory and 

open, sunny habitat were analyzed and compared for their concentrations of digestible protein, digestible dry matter, and digestible energy. Data from each forage, 

ranging from forb and shrub leaves to woody twigs, were analyzed separately in 

a single-factor analysis of variance experimental design. Only fi ve to seven of the 

summer forages differed signifi cantly (P < 0.05) in values from sun and shade 

treatments: digestible protein was lower while digestible dry matter and digestible 

energy were higher in sun-grown than shade-grown forages. Differences in winter 

forages were fewer and inconsistent. However, despite general trends in patterns 

across all forages, variation both among and within forages was high at all scales 

of sampling: subsamples within a site, replicate sites of habitat types, and habitat 

types. Light intensity plays a major role in determining plant chemistry of some 

species, but the high degree of variation in plant response makes underlying patterns of variation especially important. 

Keywords: Odocoileus hemionus sitkensis, Vaccinium, southeast Alaska, 

tannins, protein, energy, digestible, digestibility, plant defense, forage, forest.
ii

Summary

Major forages for black-tailed deer were studied in summer and winter to compare 

differences in nutritional value related to their light environment: shady forest 

understory vs. open, sunny habitat in coastal forests of southeast Alaska. Nutritional value was quantifi ed in terms of digestible protein, digestible dry matter, and 

digestible energy. Sun-grown forages in summer tended to have lower concentrations of digestible protein and higher concentrations of digestible dry matter and 

digestible energy than did shade-grown forages, while differences in winter were 

few, and responses differed among forages in both seasons. Variation in plant 

response was high among forages and at all scales of sampling. Principal implications of the results were the following:

Light plays an important role in the nutritional quality of some forages for deer 

and the diversity of their chemical and nutritional environment. However, understanding the variability in plant response to light, both among and within forages, 

is at least equally important as understanding the average, overall responses. 

Summer nutritional values observed in this study tended to be higher than those 

reported in other studies within the region, principally because of slightly earlier 

phenology at the time of sampling and also because of explicitly accounting for 

sunny versus shady habitats.

The earlier sampling time in this study better corresponds with peak lactation 

requirements for deer in the region. The data will be important additions to current 

nutritional databases and also provide a basis for expanding databases to begin 

accounting for differences in sunny and shady habitats.
i

Variation in Nutritional Quality of Plants for Deer in Relation to Sunny Versus Shady Environments

Contents

1 Introduction

4 Methods

 4 Field Sampling

 5 Statistical Analyses

6 Results and Discussion

 6 Laboratory Precision

 7 Patterns of Variation

 11 Individual Forages

 13 Across Forages

 14 Comparison With Other Data From Southeast Alaska

19 Implications

 21 Acknowledgments

 22 English Equivalents

 22 Literature Cited

 27 Appendix 1

 28 Appendix 2
2

RESEARCH PAPER PNW-RP-602
1

Variation in Nutritional Quality of Plants for Deer in Relation to Sunny Versus Shady Environments

Introduction

Knowledge of nutritional quality of foods for animals is central to an understanding 

of habitat quality for animals because food is fundamental to life and its processes. 

Nutritional quality of foods for herbivores varies considerably as forages differ 

in tissues, stage of growth, and nutrient and light environments. Large, generalist herbivores that consume a wide variety of plants have a continuous supply of 

potentially thousands of bites of food, yet relatively little of what is available for 

deer to consume may be of suffi cient quality to meet their metabolic needs. Indeed, 

animal science for domestic herbivores has long been centered on understanding 

animal nutritional requirements and the nutritional quality of their forage resources. 

Wildlife biologists, too, have sought an understanding of the nutritional requirements of focal species and principal determinants of food quality (Moen 1973, 

Robbins 1983), although for species like black-tailed deer (Odocoileus hemionus) 

in natural forest environments, the diversity of their potential forage resources far 

exceeds that of domestics in cultivated pastures. 

The enormous variation in chemical composition of wild plants has been of 

keen interest to ecologists for many decades, especially those interested in herbivore-plant interactions, because chemical variation has been seen as both affecting 

the nutritional quality of the plant for the herbivore and acting as a chemical defense 

against herbivory by the plant. The latter consideration was fi rst heightened in 

importance when certain chemicals commonly occurring within plants were recognized as not being necessary for primary metabolic pathways involving growth and 

reproduction yet were detrimental to pathogens and herbivores. Those compounds 

were labeled “secondary” compounds or secondary metabolites and were considered chemical adaptations of plants to protect themselves from herbivory (Dethier 

1954, Fraenkel 1959, Whittaker and Feeny 1971). Since then, much ecological 

theory has developed around the idea of plant defenses against herbivory, especially 

from an evolutionary point of view. The role of environmental variation in affecting 

or determining plant defense is central to all of that theory.

Plant defense theory has primarily evolved around four major hypotheses: 

(1) the “optimal defense” hypothesis, (2) the “carbon-nutrient balance” hypothesis, 

(3) the “growth rate” hypothesis, and (4) the “growth-differentiation balance” 

hypothesis (Stamp 2003). The optimal defense hypothesis (McKey 1974, 1979; 

Rhoades 1979; Rhoades and Cates 1976) identifi ed a production cost with secondary compounds and viewed tradeoffs between growth, reproduction, and defense 

as an optimization problem within an evolutionary context. Environments differ 

in their soil nutrient availability, light, and intensity of herbivory; plants evolve to 

maximize their genetic fi tness within those constraints. The carbon-nutrient balance 
2

RESEARCH PAPER PNW-RP-602

hypothesis (Bryant et al. 1983, Tuomi et al. 1988) explained adaptive plant defenses 

explicitly on the basis of relative availability of carbon and nutrients in the plant’s 

environment: where soil nutrients are limiting to plant growth, surplus energy from 

light is invested in the production of carbon-rich (photosynthetic energy-expensive) 

defensive compounds, such as phenolics including tannins; but where the availability of light is limiting to plant growth, surplus nutrients (especially nitrogen) are 

invested in nitrogen-based defenses, such as alkaloids and other N-based toxins. 

The carbon-nutrient balance hypothesis has been especially useful in studying the 

within-species plasticity of plant responses to their environment. The growth rate 

hypothesis (Coley 1987a, 1987b; Coley et al. 1985) elaborated upon the carbonnutrient balance hypothesis but in the context of among-species differences in plant 

defense. The growth-differentiation balance hypothesis (Herms and Mattson 1992), 

on the other hand, built upon the ideas of Loomis (1932, 1953) in viewing plant 

growth and differentiation of various tissues as an allocation process in relation to 

resource availability. Herms and Mattson (1992) put those ideas within the context 

of ecological and evolutionary tradeoffs involving resource availability, competition, and herbivory in the environment. For example, in a light-limited environment, production of additional leaf tissue should take priority over production of 

carbon-rich defensive compounds.

Although all four hypotheses have provided very useful constructs for viewing 

plant-herbivore-environment interactions, all have had only mixed success in being 

predictive instead of explanatory (Stamp 2003). The carbon-nutrient balance and 

growth-differentiation balance hypotheses are the most mechanistic and applicable 

to within-species response to environmental variation, but both have been found 

to work well for only certain species and kinds of secondary compounds (such as 

phenolics) and not at all well for others, leading some to argue their failure (Hamilton et al. 2001) and others to argue their need for modifi cation (Massad et al. 2012) 

or more judicious application (Lerdau and Coley 2002).

Our interest in this study is in the nutritional quality of major forages commonly available to Sitka black-tailed deer (O. h. sitkensis) in natural forest environments of southeast Alaska. We were interested in plant species and tissues (leaf, 

twig) most commonly consumed in early summer (early July) at the time of peak 

nutritional requirements for lactating females and in mid winter (February) when 

deciduous forages are no longer available. Summer is a time of reproduction (fawnrearing), growth, and accumulation of body reserves in deer, while winter is a time 

of depletion of body reserves and potential starvation (Moen 1973, Robbins 1993). 

Summer forages change rapidly in both nutritional quality and standing biomass 

(ovendry weight per unit area) with advancing plant phenology, while winter 
3

Variation in Nutritional Quality of Plants for Deer in Relation to Sunny Versus Shady Environments

forages change little in nutritional quality but greatly in their availability owing to 

burial by snow (Hanley and McKendrick 1983, 1985; Parker et al. 1999). Earlier 

investigations of nutritional quality of deer forages in forests of southeast Alaska 

indicated signifi cant differences within species in relation to the light environment 

of their habitat, whether they were grown in the shade of forest understory or the 

open, “sunny” environment of young clearcuts, especially for nitrogen, phenolics 

(both tannin and nontannin), and digestible protein concentrations, tannin-binding 

capacity, and even relative palatability (Hanley et al. 1987, 1991; McArthur et al. 

1993; Rose 1990; Van Horne et al. 1988). Those results were consistent with the 

carbon-nutrient balance and growth-differentiation balance hypotheses. However, 

they were heavily infl uenced by oval-leaf blueberry (Vaccinium ovalifolium1

), 

which was the principal species studied and which also yielded very consistent 

results. Despite the high conformance of oval-leaf blueberry with theory, its closely 

related congeneric red huckleberry (V. parvifolium) and another major understory 

species, salmonberry (Rubus spectabilis), did not respond similarly in garden 

experiments in Washington state (Svendsen 1992), thereby casting some doubt 

on the generality of the hypotheses. As other tests of both hypotheses fl ooded the 

scientifi c literature, it became apparent that the results were highly species specifi c 

(Hamilton et al. 2001, Stamp 2003). 

Therefore, we recognized that the nutritional quality of major forages consumed 

by deer in southeast Alaskan forests differs greatly among species and season and 

probably by the light environment of the habitat. However, we lacked confi dence in 

plant defense theory to predict, a priori, species-specifi c differences for any species 

other than the well-studied oval-leaf blueberry. With deer as our focal herbivore 

species, we focused our measures of nutritional quality on digestible protein, digestible dry matter, and digestible energy concentrations because they are the most 

important and well-understood factors in the nutritional ecology of deer (Barboza et 

al. 2009, National Research Council 2007, Robbins 1993, Van Soest 1994). Mineral 

defi ciencies in deer may be important locally but are not common, and secondary 

plant chemistry for nonphenolics is poorly understood for deer. All three measures 

of forage quality we used are affected by tannins in the digestive process: tannins 

binding with proteins reduce protein digestion and dry-matter digestion, which in 

turn reduces digestible energy (calculated as the product of gross energy and drymatter digestibility).

1 We include the very closely related V. alaskaense with V. ovalifolium because they hybridize and are mostly indistinguishable in the fi eld. See appendix 1 for common and scientifi c 

nomenclature and authorities for all plants in this report.
4

RESEARCH PAPER PNW-RP-602

Methods 

Field Sampling

All forage samples were collected from fi eld sites accessible from the Juneau, 

Alaska, road system. Because we needed suffi cient sample material for multiple 

subsamples at each site, multiple sites for each type of habitat, and two types of 

habitat (shady and open), we were limited to only the most common major forages consumed by deer. By “forage” we mean both the plant species and its part 

(leaf, twig). Leaves and twigs (current year’s woody growth) of the same species 

were considered as separate forages, because they differ so much in their physical 

and chemical composition and nutritional value. We studied 17 summer forages: 

leaves of fernleaf goldthread (Coptis aspleniifolia), bunchberry dogwood (Cornus 

canadensis), spreading woodfern (Dryopteris expansa), skunkcabbage (Lysichiton 

americanus), devilsclub (Oplopanax horridus), rusty menziesia (Menziesia ferruginea), fi ve-leaved bramble (Rubus pedatus), salmonberry, threeleaf foamfl ower 

(Tiarella trifoliata), oval-leaf blueberry, red huckleberry, and the evergreen, sexually immature growth form of both blueberry and huckleberry (Vaccinium sp., 

species indistinguishable); twigs of salmonberry, oval-leaf blueberry, red huckleberry, and evergreen, sexually immature Vaccinium species; and current annual 

growth (both leaves and twigs together) of western hemlock (Tsuga heterophylla). 

In winter, we studied 10 forages: leaves of deer fern (Blechnum spicant), fernleaf 

goldthread, bunchberry dogwood, fi ve-leaved bramble, threeleaf foamfl ower, and 

evergreen, sexually immature Vaccinium species; twigs of oval-leaf blueberry, red 

huckleberry, and sexually immature Vaccinium species; and current annual growth 

of western hemlock. Additionally, we collected samples of two other important 

forages for which we could obtain suffi cient samples in only the open habitat and 

not from the shady forest: leaves of fi reweed (Chamerion angustifolium) and current 

annual growth of Alaska cedar (Callitropsis nootkatensis). Because they came from 

only one habitat type, they were not included in our experimental design. Similarly, 

we collected leaf samples of deer fern in summer, too, but from only one open site, 

which excluded it from the experimental design. 

The sampling design for each forage was to collect three subsamples (each 

being a composite collection of many leaves or twigs, usually from several to many 

different plants, suffi cient to yield an ovendry weight of at least 8.0 g) from each of 

three independent sites representative of two distinctly different types of habitat—

shady understory beneath a forest canopy and open, “sunny” habitat of a young 

clearcut (within 5 to 10 years of logging the forest overstory). The shady and sunny 

study sites were not paired or geographically related to one another; they were 
5

Variation in Nutritional Quality of Plants for Deer in Relation to Sunny Versus Shady Environments

simply six independent stands of forest vegetation (three shady, three sunny) scattered on the Juneau road system, although shady and sunny sites were within a few 

kilometers of each other. The three subsamples at each site were not stratifi ed in 

any spatial context, either; they were three bags of the forage, collected from plants 

occurring more or less throughout the site. Therefore, each forage was to have a 

total of 18 subsamples of ≥8 g dry weight each (three subsamples × three sites × two 

habitat types). Sampling times were targeted at early July and February but actually ranged from 26 June through 19 July and from 17 January through 26 March 

(because of interference from snow), with the most intense effort in the middle of 

those ranges. Although both periods of sampling extended longer than we wished, 

we took care to obtain all three of a forage’s subsamples from a given site within the 

same day and to obtain samples from shady and sunny sites within a couple of days 

of each other (i.e., there was no temporal clumping of samples for any forage during 

either season). 

All samples were immediately stored on ice in a cooler during fi eld collection, 

frozen at -18 °C at the end of the day, and freeze-dried and mill-ground within a 

week of harvest. Freeze-dried samples were sent to the Wildlife Habitat and Nutrition Laboratory at Washington State University, Pullman, Washington, for ovendry 

weight correction and analyses: total nitrogen by auto-analyzer, fi ber composition 

by sequential detergent fi ber analysis (Goering and Van Soest 1970, as modifi ed by 

Mould and Robbins 1981 and Robbins et al. 1987b) without sodium sulfi te, tannin 

astringency (protein-precipitating capacity) by the bovine serum albumin technique 

(Martin and Martin 1982, Robbins et al. 1987a), and gross energy by bomb calorimeter. Digestible protein (grams per 100 grams) and digestible dry matter (percentage) were calculated with the equations of Robbins et al. (1987a,1987b) and Hanley 

et al. (1992). Digestible energy (kilojoules per gram) was calculated as the product 

of gross energy and dry-matter digestibility. 

Statistical Analyses 

We quantifi ed laboratory precision by calculating average coeffi cients of variation 

(CV = standard deviation divided by mean) across 13 blind-replicated samples 

(unidentifi ed duplicates sent to the lab) for each of the laboratory analyses. Average 

CV, therefore, was the mean of 13 CV values, each being calculated from a pair of 

the replicated samples. 

We examined patterns of variation in the data by calculating coeffi cients of 

variation for sampling at each of the three levels of stratifi cation: among subsamples 

within study sites, among study sites within shady/sunny habitat types, and between 

the two habitat types. Average subsample CV for each forage was calculated by 
6

RESEARCH PAPER PNW-RP-602

averaging the six CV values calculated within each of the six sampling sites for the 

three subsamples per site. Average study site CV for each forage was calculated by 

averaging the two CV values calculated within each of the two habitat types for 

the three replicate sites per habitat type. The habitat-type CV for each forage was 

calculated from the mean and standard deviation of the two overall habitat-type 

means, one for shady habitats and the other for sunny habitats. We did no statistical 

testing of these data. 

We were interested in statistically testing the shady versus sunny comparison 

of nutritional values of each of our forages, so each forage within each season was 

statistically analyzed independently. The experimental design was a standard completely randomized single factor analysis of variance with two treatments (“shade” 

and “sun” habitats), each replicated three times (sites) with three subsamples at each 

site. We used the SAS general linear model (GLM) procedure (SAS 2004) for the 

calculations and an alpha level of 0.05 (two-tailed test) for statistical signifi cance. 

Preliminary examinations of the data indicated no need for transformation. 

The analysis of variance tests (above) were used for each forage independently 

and should not be used for across-forage inferences, because multiple testing is 

likely to result in some “signifi cant” differences simply by chance (e.g., at an alpha 

level of 0.05, we should expect 1 in 20 tests to indicate a signifi cant difference 

when, in fact, no difference truly occurred). This must be kept in mind when considering the independent forage analyses as well. Therefore, in across-forage comparisons (e.g., results from shady versus sunny habitats with all forages considered 

together), we used the paired-sample t-test and an alpha level of 0.05 (two-tailed). 

Results and Discussion 

Laboratory Precision 

The 13 blind samples analyzed in duplicate to provide estimates of laboratory error 

(variation) were selected to provide a wide range in values for digestible protein 

(1.7 to 21.1 g/100 g), digestible dry matter (36.1 to 79.1 percent), and digestible 

energy (7.9 to 16.1 kJ/g). Their coeffi cients of variation across all 13 samples averaged 0.143 (± 0.042 SE) for digestible protein, 0.018 (± 0.003) for digestible dry 

matter, and 0.019 (± 0.006) for digestible energy. The lower precision in digestible 

protein was primarily the result of relatively high variation in estimates of proteinprecipitating capacity (mean CV = 0.269) rather than total nitrogen (mean CV = 

0.072), which is commonly the case, as the protein-precipitation technique requires 

calculating a mean slope from fi ve or more regression equations rather than just one 

analysis. Laboratory error was within the normal range of precision for digestible 

protein and was exceptionally low for digestible dry matter and digestible energy. 
7

Variation in Nutritional Quality of Plants for Deer in Relation to Sunny Versus Shady Environments

Patterns of Variation

Our sampling design of three subsamples per site, three sites per habitat type, and 

two habitat types enabled us to explore patterns of variation within our data by 

calculating the coeffi cient of variation for each forage at each of those three levels 

of sampling (table 1). Variation is important because high variation means that 

being selective among potential bites of the same forage (at the subsample level), 

among potential stands of similar vegetation (at the site level), and among types 

of environment (at the habitat-type level) can be rewarding to a forager. It also is 

important to forage-sampling researchers because it provides an indication of the 

relative sampling efforts required for estimating true population means at each of 

the three levels. 

As would be expected, the variation among all the different forages (“Column 

CV” in table 1) was greater than the average variation among subsamples, replicate 

sites, and habitat types for all three nutritional variables and in both seasons. This 

simply means that different forages differ greatly in their nutritional value. However, there also are more interesting patterns evident in the data of table 1: 

1. Among-sample variation (“Column mean” row) was substantially greater (up 

 to about an order of magnitude greater) in digestible protein than in either 

 digestible dry matter or digestible energy across all levels of sampling (sub-

 samples, replicate sites, habitat types); and this was true in both summer and

 winter seasons;

2. Variation among forages (“Column CV”) was almost twice as great in summer 

 as in winter for digestible protein, but was similar in summer and winter for 

 digestible dry matter and digestible energy.

3. Average sampling variation (“Column mean” row) in digestible protein and 

 digestible energy was more or less similar across all three levels of stratifi ca-

 tion (subsamples, replicate sites, habitat types) in both seasons.2

 This last point 

 is especially important in indicating that deer (and forage-sampling research-

 ers) experience about the same amount of variation (and choices) in their for-

 aging within a patch of vegetation (i.e., the subsamples within a site) as they 

 do among various patches on the landscape, including even sunny versus shady

 patches for digestible protein and digestible energy. That is true both in sum-

 mer and in winter, although it would be true in summer only for patches that 

 are at about the same phenological stage of seasonal maturity. Within-site 

 variation in nutritional quality, even within the same forage, is therefore very 

2 Average variation in digestible dry matter, on the other hand, was greater between habitat 

types than among subsamples and replicates in both seasons.
8

RESEARCH PAPER PNW-RP-602

ll n Mean Coeffi cients of variation /g)

SubsamplesReplicatesHabitat 

types

 Digestible protein (g/100 g) Digestible dry matter (%) Digestible energy (kJ/g) Season Overall Mean coeffi cients Overall Mean coeffi cients Overall Mean coeffi cientsand mean of variation mean of variation mean of variationforage DP Sub- Repli- Habitat DDM Sub- Repli- Habitat DE Sub- Repli- Habitat 

Table 1—Patterns of variation among samples at three levels of sampling and across all forages in two seasons (summer and winte

code (g/100 g) sample cates types (%) sample cates types (kJ/g) sample cates types

r) 

for digestible protein (g/100 g), digestible dry matter (percent), and digestible energy (kJ/g)a

Seasonandforagecode

Summer: COAS 9.0 0.126 0.166 0.034 55.1 0.029 0.022 0.042 11.6 0.031 0.026 0.031  COCA 7.5 0.153 0.154 0.294 67.1 0.025 0.018 0.037 12.4 0.023 0.015 0.028  DREX 9.7 0.264 0.347 0.311 56.8 0.074 0.046 0.059 11.8 0.080 0.045 0.033  LYAM 22.3 0.070 0.121 0.149 71.5 0.036 0.038 0.015 14.3 0.066 0.069 0.008  MEFE_L 9.2 0.096 0.159 0.310 63.6 0.027 0.037 0.053 13.0 0.031 0.027 0.056  OPHO_L 15.2 0.111 0.071 0.230 73.1 0.037 0.030 0.076 15.1 0.032 0.030 0.076  RUPE 9.6 0.133 0.089 0.182 61.9 0.337 0.026 0.029 13.2 0.055 0.066 0.017  RUSP_L 13.2 0.216 0.118 0.256 64.0 0.048 0.052 0.092 12.6 0.046 0.066 0.084  RUSP_T 4.7 0.437 0.390 0.178 41.0 0.035 0.013 0.066 7.9 0.044 0.000 0.061  TITR 8.5 0.179 0.116 0.368 58.2 0.055 0.036 0.003 11.2 0.075 0.029 0.001  TSHE 5.7 0.236 0.331 0.189 60.3 0.032 0.050 0.045 13.5 0.034 0.068 0.026  VACCI_L 8.8 0.295 0.160 0.190 62.3 ISM ISM 1.530 12.9 ISM ISM 0.009  VAOV_L 10.4 0.233 0.168 0.320 63.2 0.026 0.017 0.064 13.5 0.022 0.029 0.054  VAOV_T 3.1 0.302 0.272 0.290 39.6 0.030 0.031 0.050 8.5 0.034 0.039 0.034  VAPA_L 11.8 0.109 0.132 0.315 60.1 0.063 0.082 0.168 13.2 0.070 0.088 0.157  VAPA_T 4.1 0.314 0.150 0.002 38.7 0.043 0.056 0.127 8.5 0.045 0.055 0.109 Column  mean: 9.5 0.204 0.187 0.226 58.5 0.056 0.034 0.154 12.1 0.043 0.041 0.049 Column  CV: 0.491 0.178 0.174 
9

Variation in Nutritional Quality of Plants for Deer in Relation to Sunny Versus Shady Environments

Table 1—Patterns of variation among samples at three levels of sampling and across all forages in two seasons (summer and winter) for digestible protein (g/100 g), digestible dry matter (percent), and digestible energy (kJ/g)

a (continued)

 Digestible protein (g/100 g) Digestible dry matter (%) Digestible energy (kJ/g) Season Overall Mean coeffi cients Overall Mean coeffi cients Overall Mean coeffi cientsand mean of variation mean of variation mean of variationforage DP Sub- Repli- Habitat DDM Sub- Repli- Habitat DE Sub- Repli- Habitat code (g/100 g) sample cates types (%) sample cates types (kJ/g) sample cates typesWinter: BLSP 6.9 0.166 0.314 0.078 58.4 0.043 0.031 0.171 10.9 0.037 0.054 0.216  COAS 6.2 0.078 0.184 0.077 56.7 0.020 0.019 0.004 11.5 0.015 0.014 0.003  COCA 5.3 0.147 0.151 0.009 69.4 0.014 0.012 0.021 12.5 0.016 0.009 0.015  RUPE 7.3 0.169 0.087 0.071 63.9 0.043 0.030 0.021 12.3 0.036 0.028 0.005  TITR 7.8 0.215 0.245 0.371 64.0 0.026 0.019 0.101 11.9 0.014 0.009 0.090  TSHE 3.0 0.565 0.393 0.325 49.3 0.015 0.059 0.023 10.8 0.023 0.058 0.040  VACCI_L 7.7 0.201 0.128 0.048 63.3 0.016 0.027 0.710 13.0 0.023 0.015 0.015  VACCI_T 5.8 0.178 0.073 0.044 36.9 0.067 ISM 0.029 8.0 0.027 ISM 0.027  VAOV_T 3.1 0.220 0.187 0.076 39.8 0.047 0.029 0.048 8.4 0.041 0.037 0.028  VAPA_T 5.2 0.445 0.059 0.279 39.3 0.026 0.031 0.085 8.6 0.038 0.052 0.099 Column  mean: 5.8 0.238 0.182 0.138 54.1 0.031 0.028 0.121 10.8 0.027 0.031 0.054 Column  CV: 0.297 0.220 0.170ISM = insuffi cient sample material for calculating standard deviations. DP = digestible protein. DE = digestible energy. DDM = digestible dry matter.a Average (mean) coeffi cients of variation (CV) were calculated at the level of subsamples within a study site (three subsamples per site), replicate study sites (target three study sites per habitat type), 

and habitat type (two types–sunny and shady). Mean CV across all forages within a season (“Column CV”) was calculated for overall mean forage value aft er averaging all subsamples, sites, and 

both habitat types. See appendix 1 for common and scientifi c names of forage codes. See appendix 2 for actual numbers of replicate sites for each forage by habitat and nutritional variable. 

Seasonandforagecode
10

RESEARCH PAPER PNW-RP-602

 important, regardless of what statistically signifi cant differences might exist in 

 overall mean values at broader spatial and environmental scales (the main focus 

 of this study). Overall mean values provide information about general differences 

 in patterns of plant response to environmental variation (e.g., sunny versus shady 

 habitats) and a forager’s mean encounter rates with forages of varying quality, but 

 a forager can encounter substantial variation and opportunities for nutritional 

 choice while foraging within even one patch of habitat.

The possible reasons for high variability, especially within subsamples, cannot 

be discerned from this study, but they might include at least four factors affecting 

plant-to-plant variation: 

• Differences in age of tissues sampled during summer (i.e., slight differences 

in phenology among individual plants).

• Inherent variation among plants in their response to light, especially at the 

species level. 

• Micro-environmental variation in light regimes at the level of individual 

leaves and soil variability at the level of individual plants. 

• Micro-environmental variation in susceptibility to frost damage in winter.

However, regardless of reasons, what’s important is that variation was so 

high at all levels of sampling, despite careful attention to collecting only what 

appeared to be similar material (i.e., same phenological stage in summer, lack of 

frost-damaged tissues in winter). 

Moreover, keep in mind that our subsample variation is actually an underestimate of the variation encountered by a forager the size of a deer (or smaller), 

because each subsample was itself a composite collection of leaves (or twigs) from 

several to many plants rather than individual bites. These generalizations are being 

made at the level of all forages studied, however; they do not apply to each and 

every forage individually. Some forages exhibited signifi cant departures from the 

overall generalizations, as evidenced by the values for individual forages within the 

body of the table, especially those forages that differed signifi cantly in their values 

from sunny versus shady habitats (below).

One practical implication of these results is that it is reasonable to apply the 

same estimates of variation in forage quality (standard deviations) in the FRESHDeer habitat evaluation model (Hanley et al. 2012) to both its stand-level and 

landscape-level applications. 
11

Variation in Nutritional Quality of Plants for Deer in Relation to Sunny Versus Shady Environments

Individual Forages

We were able to obtain adequate sample material for most forages in both seasons, 

but we were a little short with a few (see app. 2 for actual sample sizes), most 

notably for the diminutive, evergreen, sexually immature form of Vaccinium species in summer. Overall, results indicated 5 to 7 of the 17 summer forages differed 

in their nutritional values between shade versus sun habitat types, and 3 to 4 of 

the 10 winter forages did so (table 2). Three forages differed in all three nutritional 

variables in summer: leaves of bunchberry, devilsclub, and blueberry; none differed 

in digestible protein in winter.

Table 2—Forage-specifi c laboratory results for 17 summer forages and 10 winter forages from replicated 

shady forest understory (shade) and open sunny (sun) sites for digestible protein (g/100 g), digestible dry 

matter (percent), and digestible energy (kJ/g)a

Season and Digestible protein (g/100 g) Digestible dry matter (percent) Digestible energy (kJ/g) 

forage code Shade Sun Shade Sun Shade Sun

Summer: 

 COAS 9.3 8.8 53.5* 56.8* 11.4 11.9 

 COCA 9.1* 5.9* 65.4* 68.9* 12.2* 12.7*

 DREX 11.8 9.8 59.1 54.4 12.1 11.5 

 LYAM 24.7 19.9 70.9 72.3 14.4 14.2 

 MEFE_L 11.2* 7.2* 61.2 66.0 12.4* 13.5*

 OPHO_L 17.6* 12.8* 69.2* 76.9* 14.3* 15.9*

 RUPE 10.9* 8.3* 60.6 63.2 13.1 13.4 

 RUSP_L 14.5 10.9 59.9 68.4 11.9 13.4 

 RUSP_T 5.3 3.8 39.1 43.0 7.6 8.3 

 TITR 10.9* 6.3* 58.0 58.3 11.2 11.2 

 TSHE 6.4 4.9 58.3 62.2 13.2 13.7 

 VACCI_L 10.7 7.7 ISM ISM ISM ISM 

 VACCI_T 4.6 3.9 ISM ISM ISM ISM 

 VAOV_L 12.7* 8.0* 60.3* 66.1* 13.0* 14.0*

 VAOV_T 3.7 2.5 38.2 41.0 8.3 8.7 

 VAPA_L 14.4* 9.1* 53.0* 67.3* 11.7 14.6 

 VAPA_T 4.1 4.1 35.2* 42.2* 7.9* 9.2* 

Winter: 

 BLSP 6.6 7.3 52.1* 65.4* 9.3 12.6 

 COAS 6.5 5.9 56.9 56.6 11.4 11.5 

 COCA 5.2 5.4 70.4* 68.4* 12.6* 12.3*

 RUPE 7.8 7.0 64.9 63.1 12.4 12.1 

 TITR 9.8 5.6 68.1* 59.7* 12.6* 11.1* 

 TSHE 2.4 3.6 47.1 50.3 10.5 11.1 

 VACCI_L 8.0 7.4 63.6 62.7 12.9 13.2 

 VACCI_T 5.6 6.0 36.2 37.7 7.9 8.2 

 VAOV_T 3.3 2.9 37.8 41.1 8.1 8.7 

 VAPA_T 6.2 4.3 36.9* 41.2* 7.9* 9.0*

ISM = insuffi cient sample material for means testing. a Values are means calculated across replicate sites for each forage. Paired means highlighted in bold and asterisk diff er signifi cantly (P < 0.05, 

single factor analysis of variance). See appendix 1 for common and scientifi c names of forage codes. See appendix 2 for number of replicate sites, 

standard errors, and additional forages with insuffi cient sampling for means testing. 
12

RESEARCH PAPER PNW-RP-602

In summer, all forages that differed signifi cantly between habitat types 

responded the same way: sun values were lower than shade values for digestible 

protein and greater than shade values for digestible dry matter and digestible 

energy. The reasons were the same for all species exhibiting that pattern. Digestible 

protein concentration was lowest in sun habitat because total nitrogen concentration was low and protein-precipitating capacity (refl ecting tannin astringency) 

was high, thereby reducing digestible protein concentration in both its principal 

factors. Digestible dry matter concentration, on the other hand, was highest in sun 

habitat primarily because neutral detergent solubles concentration (mostly cellular 

cytoplasm) was high, and, to a lesser degree, because neutral detergent fi ber digestibility (mostly cell wall) also was high, and the combined effects were easily high 

enough to outweigh the negative dry-matter digestibility effect of tannin-binding 

of proteins. Our data from oval-leaf blueberry provide a clear example. The following are the mean values calculated across all nine subsamples of blueberry from 

each habitat type, sun value fi rst followed by shade value: 2.2 vs. 2.9 percent total 

nitrogen; 0.09 vs. 0.02 mg/mg protein-precipitating capacity; 72.8 vs. 64.9 percent 

neutral detergent solubles; 39.6 vs. 30.7 percent neutral detergent fi ber digestibility; 

and 2.9 vs. 0.7 percent dry-matter loss to tannin binding. Although we did not 

measure specifi c leaf weight and readily digestible carbohydrates, Rose (1990) 

studied them in oval-leaf blueberry in both the fi eld (Juneau, Alaska) and laboratory 

controlled-environment growth chambers: she found that the reduced total nitrogen 

concentration in sun-grown leaves (compared with shade grown) was primarily 

the result of a dilution effect from increased leaf thickness (specifi c leaf weight) 

and increased neutral detergent solubles largely consisting of sugars and starches 

in the leaf. The net effect in our study was the difference of 8.0 vs. 12.7 percent 

digestible protein and 66.1 vs. 60.3 percent digestible dry matter concentrations 

(sun vs. shade, respectively) (table 2). The corresponding difference in digestible 

energy concentration (14.0 vs. 13.0 kJ/g) simply follows from the large difference 

in dry-matter digestibility outweighing the insignifi cant difference in gross energy 

concentration (21.2 vs. 21.6 kJ/g, respectively).

The observed differences in digestible protein concentrations of summer leaves 

are consistent with both the carbon-nutrient balance and growth-differentiation 

balance hypotheses of plant chemical defense theory. However, the elevated drymatter and energy digestibilities in the sun-grown leaves are not consistent with 

any sort of “defense” against a ruminant herbivore like deer, even though herbivory 

by deer can exert a profound infl uence on the physical structure, survival, and 

reproduction of plants and the composition and structure of plant communities in 

southeast Alaska (Gillingham et al. 2000, Hanley 1987, Klein 1965, Lewis 1992). 

The observed differences in plant responses to variation in their light environment 
13

Variation in Nutritional Quality of Plants for Deer in Relation to Sunny Versus Shady Environments

are more consistent with the early ideas of Loomis (1932, 1953) regarding growth 

and differentiation in relation to resource availability but without an emphasis on 

defense against herbivores, per se. Carbon-rich compounds of varied kinds (tannins, 

sugars, starches) increased with increased light and photosynthetic activity (Rose 

1990). Moreover, it is important to recognize that 8 of the 17 summer forages did 

not differ in any of the three nutritional variables in this study (table 2). 

In winter, none of the forages differed in digestible protein concentration in 

relation to sun versus shade habitats, and only 3 of the 10 differed in digestible 

energy concentrations, but in a mixed fashion: digestible energy concentration 

was greater in shade leaves than sun leaves (the opposite pattern from summer) 

of bunchberry and foamfl ower, but greater in sun twigs than shade twigs in red 

huckleberry (table 2). The difference in huckleberry twigs probably just carried 

across from the same pattern in the same twigs of the preceding summer. The 

opposite pattern in the evergreen forbs bunchberry and foamfl ower, however, may 

have refl ected frost damage in the open-grown plants, as some winter mortality 

in open-grown plants of those species is commonly evident as dead leaves in the 

open but seldom under a forest canopy. High variability among replicate sites was 

responsible for the lack of statistical signifi cance in the apparent large difference in 

mean digestible protein for foamfl ower.

Across Forages

The overall pattern of sun versus shade is evident when the forage-specifi c results 

are plotted graphically in comparison with a 1:1 relation (fi gs. 1 through 3). The 

basic trends identifi ed for the statistically signifi cant differences within individual 

forages (above) tend to apply broadly across summer forages despite the lack of statistical signifi cance in many individual comparisons. Most summer values for sungrown forages were lower in digestible protein and higher in digestible dry matter 

and digestible energy than their corresponding shade-grown values (fi gs. 1a, 2a, and 

3a). Winter values, on the other hand, were not consistently over or under the 1:1 

relation (fi gs. 1b, 2b, and 3b). Paired-sample t-tests of sun versus shade values across 

all summer forages indicated that the mean differences (sun minus shade) of -2.8, 

4.3, and 0.8 percent for digestible protein, dry matter, and energy, respectively, were 

all highly signifi cant (P < 0.01), whereas none of those for winter (-0.6, 1.2, and 0.4 

percent, respectively), were statistically signifi cant (P > 0.20 for all). 

We suspect the patterns evident in fi gures 1 through 3 probably apply broadly 

across many, if not most, forages of southeast Alaskan forests, largely consistent 

with the Loomis (1932, 1953) ideas of growth-differentiation balance in relation to 

light availability. Light availability is a strongly controlling factor for growth and 

survival of plants in this region of dark forests and wet, coastal climate (Hanley and 
14

RESEARCH PAPER PNW-RP-602

Barnard 2014, Hanley et al. 2014a). The chemical effects are most evident in leaf 

tissue during the growing season, less evident in twig tissue, and least evident in 

winter when other canopy-infl uenced environmental factors may be more important 

than light. However, the effects of light, even in summer, are highly variable at all 

levels of scale (table 1), resulting in many statistically nonsignifi cant differences in 

overall means for individual forages (table 2). If we are correct in these conclusions, 

then light availability plays a major role in the nutritional quality of forages for deer 

and the diversity of their chemical and nutritional environment.

Comparison With Other Data From Southeast Alaska

The nutritional database for the FRESH-Deer habitat evaluation model (app. 2 of 

Hanley et al. 2012) provides a basis for comparison of our results for digestible 

protein and digestible dry matter with others from southeast Alaska; it is currently 

35

30

25

20

15

10

5

0

Sun value (g/100 g)

Shade value (g/100 g)

Digestible protein, summer forages

0 5 10 15 20 25 30 35

A

12

10

8

6

4

2

0

0 2 4 6 8 10 12

Sun value (g/100 g)

Shade value (g/100 g)

Digestible protein, winter forages B

Figure 1—Digestible protein values (g/100 g) of forages from shady forest understories (shade value) and open, sunny habitats (sun value) for 17 forages in summer (A) 

and 10 forages in winter (B). Forages and values are those listed in table 2. The solid 

line in each fi gure indicates a 1:1 relation between shade and sun values. 
15

Variation in Nutritional Quality of Plants for Deer in Relation to Sunny Versus Shady Environments

90

80

70

60

50

40

30

30 40 50 60 70 80 90

Sun value (percent)

Shade value (percent)

A Digestible dry matter, summer forages

Digestible dry matter, winter forages

90

80

70

60

50

40

30

30 40 50 60 70 80 90

Sun value (percent)

Shade value (percent)

B

Figure 2—Digestible dry matter values (percentage) of forages from shady forest 

understories (shade value) and open, sunny habitats (sun value) for 15 forages in 

summer (A) and 10 forages in winter (B). Forages and values are those listed in 

table 2. The solid line in each fi gure indicates a 1:1 relation between shade and sun 

values. 

based on data from four published studies (Hanley and McKendrick 1983, Hanley 

et al. 1992, McArthur et al. 1993, Parker et al. 1999) and unpublished data. In 

comparison with the values in the database, our summer results are signifi cantly 

higher (P < 0.05, paired-sample t-test) for digestible dry matter in both sun- and 

shade-grown forages and digestible protein in shade-grown forages (fi gs. 4b, 5a, 

and 5b, respectively); they did not differ from the database values for digestible 

protein in sun-grown forages (fi g. 4a). Our winter results did not differ from the 

database values for either sun- or shade-grown forages in either digestible protein 

or digestible dry matter. However, such overall comparison with mean values in the 

database overlooks the variation within individual forages (differences among studies) in the database. On a forage-specifi c basis including that variation, few values 

differed signifi cantly (P < 0.05, two-tailed t-test) from database values: for summer 
16

RESEARCH PAPER PNW-RP-602

forages, only leaves of fernleaf goldthread (both sun grown and shade grown) and 

fi ve-leaved bramble (shade grown only) were higher in digestible protein, and only 

leaves of bunchberry and blueberry (both sun and shade grown) were higher for 

digestible dry matter. None of our summer values were signifi cantly lower than 

database values, and none of our winter values differed signifi cantly from database 

values in either direction.

There are two most likely explanations for this pattern of generally higher 

nutritional value (though highly variable) in our summer data compared with 

database values: (1) our sun-grown samples were all from open, “sunny” habitat, 

whereas most of the database samples were from forest understories, and (2) our 

samples were, on average, collected a couple of weeks earlier than most samples 

in the FRESH-Deer database. Blueberry leaves are the only forage in the current 

FRESH-Deer database that has unique values for shade-grown and sun-grown 

6.30

8.30

10.30

12.30

14.30

16.30

18.30

6.30 7.30 8.30 9.30 10.30 11.30 12.30 13.30 14.30 15.30

Sun value (kJ/g)

Shade value (kJ/g)

A Digestible energy, summer forages

6.30

7.30

8.30

9.30

10.30

11.30

12.30

13.30

14.30

15.30

6.30 7.30 8.30 9.30 10.30 11.30 12.30 13.30 14.30 15.30

Sun value (kJ/g)

Shade value (kJ/g)

B Digestible energy, winter forages

Figure 3—Digestible energy values (kJ/g) of forages from shady forest understories 

(shade value) and open, sunny habitats (sun value) for 15 forages in summer (A) and 

10 forages in winter (B). Forages and values are those listed in table 2. Th e solid line 

in each fi gure indicates a 1:1 relation between shade and sun values. 
17

Variation in Nutritional Quality of Plants for Deer in Relation to Sunny Versus Shady Environments

Digestible protein (g/100 g), sun

0

5

10

15

20

25

30

0 5 10 15 20 25 30

This study’s values (g/100 g)

FRESH-Deer database values (g/100 g)

A

25

35

45

55

65

75

85

25 35 45 55 65 75 85

This study’s values (percent)

FRESH-Deer database values (percent)

Digestible dry matter (percent), sun B

Figure 4—Forage-specifi c comparisons of mean values in current FRESH-Deer 

nutritional database with the values found in this study for individual forages in 

summer in terms of (A) digestible protein (g/100 g) and (B) digestible dry matter 

(percentage). The 17 forages are from those listed in appendix 2 that also have corresponding original values in the database, with this study’s plotted value either that 

of the sun habitat (when signifi cantly different from shade) or the pooled value (for 

no signifi cant difference between sun and shade). Solid line indicates a 1:1 relation. 

leaves. The relative lack of sun-grown forages in the database could largely account 

for the differences in digestible dry matter in our sun-grown samples. However, the 

difference in timing of sampling is very important, too, and would affect shadegrown forages as well as sun grown. We intentionally targeted early July for the 

peak of our sampling effort, because that is the time of peak lactation for deer in the 

region (data from Sadleir 1980, applied to southeast Alaska), whereas the samples in 

the database were simply “July” samples from anytime during that month. Nutritional quality changes greatly and rapidly with changes in plant phenology during 

the growing season (Hanley and McKendrick 1983, Parker et al. 1999), so the 

slightly earlier collection of our samples could account for much of the differences. 
18

RESEARCH PAPER PNW-RP-602

Furthermore, if plants are responding to their environment in a Loomis (1932, 1953) 

growth-differentiation fashion, carbon allocations within leaves might shift from 

highly mobile and digestible sugars and starches early in the growing season to 

digestion-reducing tannins later in the season. Indeed, intake of blueberry leaves by 

Sitka black-tailed deer shifts during the growing season, from relatively high levels 

of intake in early summer to very little intake in later summer (Hanley and McKendrick 1985, Hanley et al. 2014b), and palatability of blueberry leaves is greater for 

tannin-poor shade-grown leaves than for tannin-rich sun-grown leaves (Hanley et 

al. 1987, McArthur et al. 1993).

On the other hand, another factor could have played a role in slightly underestimating the dry-matter digestibility of our sun-grown leaves: we did not include 

0

5

10

15

20

25

30

0 5 10 15 20 25 30

This study’s values (g/100 g)

FRESH-Deer database values (g/100 g)

A Digestible protein (g/100 g), shade

25

35

45

55

65

75

85

25 35 45 55 65 75 85

This study's values (percent)

FRESH-Deer database values (percent)

B Digestible dry matter (percent), shade

Figure 5—Forage-specifi c comparisons of mean values in current FRESH-Deer 

nutritional database with the values found in this study for individual forages 

in summer in terms of digestible protein (g/100 g) (A) and digestible dry matter 

(percentage) (B). The 14 forages are from those listed in appendix 2 that also have 

corresponding original values in the database, with this study’s plotted value either 

that of the shade habitat (when signifi cantly different from sun) or the pooled value 

(for no signifi cant difference between sun and shade). Solid line indicates a 1:1 

relation. 
19

Variation in Nutritional Quality of Plants for Deer in Relation to Sunny Versus Shady Environments

sodium sulfi te in the detergent analysis. Omitting sodium sulfi te tends to underestimate the digestibility of the neutral detergent fi ber (NDF) in tannin-rich forages, 

but including it provides more accurate results for tannin-poor forages (Hanley et 

al. 1992). However, we did not know a priori which forages would be tannin-rich, 

and we wanted to apply the same laboratory methods to all forages. In the blueberry 

leaves used as an example earlier, the mean NDF concentration of the nine sungrown (tannin-rich) subsamples was 27.2 percent with a mean NDF digestibility 

of 39.6 percent; thus, digestible NDF would have contributed about 10.8 percent 

dry matter of the total digestible dry matter of 66.1 percent for the forage. With an 

average lignin-cutin content of 18.8 percent of NDF, the omission of sodium sulfi te 

probably underestimated the NDF digestibility by only a few percentage points at 

most (fi g. 3 in Hanley et al. 1992). If the underestimate were 5 percent too little (i.e., 

true NDF digestibility of 44.6 percent), the net effect would have been a difference 

of 1.4 percent digestible dry matter (i.e., 67.5 instead of 66.1 percent). 

Our winter values did not differ from database values because plants are not 

changing phenologically during winter, and differences between sun- and shadegrown forages are few in winter (above).

Implications

Our results from this study help to quantify the importance of light in affecting the 

chemical and nutritional environment, and therefore, habitat quality for black-tailed 

deer in Alaska. By studying a broad range of forages in both summer and winter, 

rather than just one or a few forages in one season, general patterns are evident 

in light’s infl uence on plant chemistry, especially tannins and neutral detergent 

solubles, in determining digestible protein, digestible dry matter, and digestible 

energy concentrations of forages.

A large and important part of those patterns, however, is the high degree of 

variability inherent in them. Not only do different forages differ in their chemical composition, but they also differ in their response to light, some predictably 

according to resource allocation theory, but others apparently not at all. That 

within-species variation may be as great within a given patch of habitat as among 

patches and even among different kinds of habitat has important implications for 

foraging theory and habitat modeling. Within-habitat diversity should be especially 

important at low population densities of deer, because selectivity of bites could 

result in harvest of forage much better than average; however, that advantage would 

decrease with increasing population density. Forage researchers, too, need to be 

aware of both the overall importance of light and also the high degree of variability 

in plant response, because both factors strongly affect the results of their sampling. 
20

RESEARCH PAPER PNW-RP-602

In studies sharply constrained by the cost of laboratory analyses, compositing fi eld 

samples from a variety of scales will be important for estimating true population 

means but will come at a cost of not understanding their underlying patterns of 

variation. 

The higher dry matter digestibility in many summer forages observed in this 

study, compared with previous work, has direct implications for quantitative habitat 

evaluations for deer. The role of blueberry, especially, is signifi cant because it is 

such a widespread and dominant species in forest understories of the region. The 

pattern of low nitrogen concentration combined with high tannin concentration 

in sun-grown leaves, observed decades ago, led to the idea that the vegetation 

of young, open clearcuts (1 to 15 years after logging) would likely be limiting in 

digestible protein for lactating deer because blueberry is such a dominant forage 

in the available biomass (Hanley et al. 1989, 1991). However, when extensive fi eld 

data for such stands fi nally became available, nutritionally based analyses with 

the FRESH-Deer habitat evaluation model indicated that for all 51 stands of 9- to 

13-year-old clearcuts, digestible dry matter, not digestible protein, was the limiting 

factor for lactating deer (Hanley et al. 2013). The principal reason why digestible 

protein was not limiting was because the vegetation was much more diverse with 

other species than just the dominant blueberry. However, substituting our new 

results for sun-grown forages into the nutritional database of the FRESH-Deer 

model (using sun values for forages differing signifi cantly between sun and shade 

and pooled values for forages not differing signifi cantly between sun and shade), the 

same 51 stands yield a different pattern in nutritional limitations for lactating deer: 

45 stands limited by digestible dry matter, 4 stands limited by digestible protein, 

and 2 stands limited by total biomass (no nutritional limitation). The reason for the 

difference is because the digestible dry matter constraint is more easily satisfi ed 

with the higher digestible dry matter values from the current study; total capacity of 

the habitat (deer days per hectare) increases throughout, but nutritional limitations 

also shift relative to one another. Furthermore, specifi cation of only a slightly higher 

requirement for digestible protein (e.g., for twins instead of singleton fawns) results 

in digestible protein being the limiting factor in many more of the stands. 

Thus, the results from this study should be important additions to the nutritional database of the FRESH-Deer model, both in better matching the database’s 

values with timing of peak lactation demands for deer and in providing a basis for 

expanding the database to account for differences in sunny and shady habitats. 

Although the difference between sunny and shady is likely a gradient response with 
21

Variation in Nutritional Quality of Plants for Deer in Relation to Sunny Versus Shady Environments

increasing light, the gradient may be nonlinear and may exhibit a sharp infl ection 

point similar to that exhibited by major understory species in their growth rates in 

relation to light (Hanley et al. 2013a and unpublished data3

). Rose, in her Alaska 

fi eldwork with carbon-nutrient balance in oval-leaf blueberry (Rose 1990) found 

a threshold shift from “sun” to “shade” chemical composition occurring at about 

60 to 75 percent overstory canopy coverage within low-volume noncommercial 

forests of the region; leaves in stands more open than that were characteristic of 

sunny habitat, while leaves in stands more closed than that were characteristic of 

shady habitat. She did not publish those results, but that idea could be fi eld-tested 

easily, especially using oval-leaf blueberry as a model species. If fi eld-testing, then 

it would be desirable to collect samples at least twice during the growing season, in 

both early summer (e.g., early July, like ours) and later summer (e.g., late August) to 

see if carbon stores shift from labile sugars and starches to tannins as the growing 

season progresses. Specifi c leaf weight (ovendry grams per square centimeter) also 

should be measured.

With better understanding of patterns of nutritional quality of plants in relation 

to environmental variation, combined with advances in quantifying nutritional 

value of forages in terms most directly relevant to deer and better tools for integrating measures of food quantity, quality, and deer nutritional requirements, we are 

now at a point where much progress can be made in better understanding habitat 

quality for deer in quantitatively meaningful terms. With better quantitative 

understanding of light in determining plant chemical composition, growth rate, and 

survival, we will be able to more effectively manage forests for desired outcomes.

Acknowledgments

We thank P. O’Connor, of the Tongass National Forest, for her interest and support 

of this study, funding from the Tongass National Forest for all laboratory analyses, 

B. Davitt and the Wildlife Habitat and Nutrition Laboratory at Washington State 

University for doing the laboratory analyses, and B.T. Bormann, W.B. Collins, P. 

Cunningham, and D.E. Spalinger for their helpful reviews of an earlier draft of the 

manuscript.

3 Preliminary, unpublished data from greenhouse light-gradient trials with four species 

of understory plants indicate that concentrations of total nitrogen and digestible protein 

respond in a highly nonlinear, threshold-type relation to increasing intensity of light, while 

dry-matter digestibility and digestible energy responses are not so consistent (Hanley, T.A.; 

Barnard, J.C.; Bormann, B.T.; Nay, S.M.; Spalinger, D.E. On fi le atUSDA Forest Service 

Forestry Sciences Laboratories, Juneau, Alaska, and Corvallis, Oregon). 3 Preliminary, unpublished data from greenhouse light-gradient trials with four species of understory plants indicate that concentrations of total nitrogen and digestible protein respond in a highly 

nonlinear, threshold-type relation to increasing intensity of light, while dry-matter digestibility and 

digestible energy responses are not so consistent (Hanley, T.A.; Barnard, J.C.; Bormann, B.T.; Nay, 

S.M.; Spalinger, D.E. On fi le at: USDA Forest Service, Forestry Sciences Laboratories, Juneau, 

Alaska, and Corvallis, Oregon).
22

RESEARCH PAPER PNW-RP-602

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) 0.2388 Kilocalories (kcal)

Kilojoules (kJ) 0.948 British thermal units (BTU)

Kilojoules per gram (kJ/g) 26.932 BTU/ounce

Literature Cited

Barboza, P.S.; Parker, K.L.; Hume, I.D. 2009. Integrative wildlife nutrition. New 

York: Springer. 342 p.

Bryant, J.P.; Chapin, F.S., III; Klein, D.R. 1983. Carbon/nutrient balance of 

boreal plants in relation to vertebrate herbivory. Oikos. 40: 357–368.

Coley, P.D. 1987a. Effects of plant growth rate and leaf lifetime on the amount and 

type of anti-herbivore defense. Oecologia. 74: 531–536.

Coley, P.D. 1987b. Interspecifi c variation in plant anti-herbivore properties: the role 

of habitat quality and rate of disturbance. New Phytologist. 106 (Supplement): 

251–263.

Coley, P.D.; Bryant, J.P.; Chapin, F.S., III. 1985. Resource availability and plant 

antiherbivore defense. Science. 230: 895–899.

Dethier, V.G. 1954. Evolution of feeding preferences in phytophagous insects. 

Evolution. 8: 33–54.

Fraenkel, G.S. 1959. The raison d’être of secondary plant substances. Science. 

129: 1466–1470.

Gillingham, M.P.; Parker, K.L.; Hanley, T.A. 2000. Partial consumption of 

shield fern, Dryopteris dilatata, rhizomes by black-tailed deer, Odocoileus 

hemionus sitkensis, and its potential implications. Canadian Field-Naturalist. 

114: 21–25.
23

Variation in Nutritional Quality of Plants for Deer in Relation to Sunny Versus Shady Environments

Goering, H.K.; Van Soest, P.J. 1970. Forage analyses (apparatus, reagents, 

procedures and some applications). Agric. Handb. 379. Washington, DC: U.S. 

Department of Agriculture. 20 p. 

Hamilton, J.G.; Zangeri, A.R.; DeLucia, E.H.; Berenbaum, M.R. 2001. The 

carbon-nutrient balance hypothesis: its rise and fall. Ecology Letters. 4: 86–95.

Hanley, T.A. 1987. Physical and chemical response of understory vegetation to deer 

use in southeastern Alaska. Canadian Journal of Forest Research. 17: 195–199.

Hanley, T.A.; Barnard, J.C. 2014. Responses of southeast Alaska understory 

species to variation in light and simulated herbivory. Res. Pap. PNW-RP-599. 

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacifi c Northwest 

Research Station. 22 p.

Hanley, T.A.; Bormann, B.T.; Barnard, J.C.; Nay, S.M. 2014a. Responses of 

southeast Alaska understory species to variation in light and soil environments. 

Res. Pap. PNW-RP-598. Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacifi c Northwest Research Station. 17 p.

Hanley, T.A.; Gillingham, M.P.; Parker, K.A. 2014b. Composition of diets 

selected by Sitka black-tailed deer on Channel Island, central southeast Alaska. 

Res. Note PNW-RN-570. Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacifi c Northwest Research Station. 21 p.

Hanley, T.A.; McClellan, M.H.; Barnard, J.C.; Friberg, M.A. 2013

Precommercial thinning: implications of early results from the Tongass-Wide 

Young-Growth Studies experiments for deer habitat in southeast Alaska. Res. 

Pap. PNW-RP-593. Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacifi c Northwest Research Station. 64 p.

Hanley, T.A.; Cates, R.G.; Van Horne, B.; McKendrick, J.D. 1987. Forest 

stand-age-related differences in apparent nutritional quality of forage for deer in 

southeastern Alaska. In: Provenza, F.D.; Flinders, J.T.; McArthur, E.D., comps. 

Proceedings—symposium on plant-herbivore interactions. Gen. Tech. Rep. 

INT-222. Ogden, UT: U.S. Department of Agriculture, Forest Service, 

Intermountain Research Station: 9–17.

Hanley, T.A.; McKendrick, J.D. 1983. Seasonal changes in chemical composition 

and nutritive value of native forages in a spruce-hemlock forest, southeastern 

Alaska. Res. Pap. PNW-312. Portland, OR: U.S. Department of Agriculture, 

Forest Service, Pacifi c Northwest Forest and Range Experiment Station. 41 p.
24

RESEARCH PAPER PNW-RP-602

Hanley, T.A.; McKendrick, J.D. 1985. Potential nutritional limitations for blacktailed deer in a spruce-hemlock forest, southeastern Alaska. Journal of Wildlife 

Management. 49: 103–114.

Hanley, T.A.; Robbins, C.T.; Hagerman, A.E.; McArthur, C. 1992. Predicting 

digestible protein and digestible dry matter in tannin-containing forages 

consumed by ruminants. Ecology. 73: 537–541.

Hanley, T.A.; Robbins, C.T.; Spalinger, D.E. 1989. Forest habitats and the 

nutritional ecology of Sitka black-tailed deer: a research synthesis with 

implications for forest management. Gen. Tech. Rep. PNW-GTR-230. Portland, 

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

Station. 52 p. 

Hanley, T.A.; Robbins, C.T.; Spalinger, D.E. 1991. Infl uence of the forest 

environment on nutritional ecology of black-tailed deer in Alaska. In: Bobek, 

B.; Perzanowski, K.; Reglan, W., eds. Global trends in wildlife management. 

Transactions of the 18th International Union of Game Biologists Congress. 

Krakow-Warsaw, Poland: Swiat Press: 357–361.

Hanley, T.A.; Spalinger, D.E.; Mock, K.J.; Weaver, O.L.; Harris, G.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, Pacifi c Northwest Research Station. 64 p.

Herms, D.A.; Mattson, W.J. 1992. The dilemma of plants: to grow or defend. 

Quarterly Review of Biology. 67: 283–335.

Klein, D.R. 1965. Ecology of deer range in Alaska. Ecological Monographs. 

35: 259–284.

Lerdau, M.; Coley, P.D. 2002. Benefi ts of the carbon-nutrient balance hypothesis. 

Oikos. 98: 534–536.

Lewis, S.W. 1992. Relationships between deer and vegetation on Coronation Island, 

southeastern Alaska. Fairbanks, AK: University of Alaska Fairbanks. 103 p. 

M.S. thesis.

Loomis, W.E. 1932. Growth-differentiation balance vs. carbohydrate-nitrogen 

ratio. Proceedings of the American Society for Horticultural Science. 

29: 240–245.

Loomis, W.E. 1953. Growth and differentiation—an introduction and summary. 

In: Loomis, W.E., ed. Growth and differentiation in plants. Ames, IA: Iowa State 

College Press: 1–17.
25

Variation in Nutritional Quality of Plants for Deer in Relation to Sunny Versus Shady Environments

Martin, J.S.; Martin, M.M. 1982. Tannin assays in ecological studies: lack of 

correlation between phenolics, proanthocyanidins and protein-precipitating 

constituents in mature foliage of six oak species. Oecologia (Berlin). 

54: 205–211.

Massad, T.J.; Dyer, L.A.; Vega C.G. 2012. Costs of defense and a test of the 

carbon-nutrient balance and growth-differentiation balance hypotheses for two 

co-occurring classes of plant defense. PLoS ONE. 7(10): e47554. doi: 10.1371/

journal.pone.0047554.

McArthur, C.; Robbins, C.T.; Hagerman, A.E.; Hanley, T.A. 1993. Diet 

selection in a ruminant generalist browser in relation to plant chemistry. 

Canadian Journal of Zoology. 71: 2236–2243.

McKey, D. 1974. Adaptive patterns in alkaloid physiology. American Naturalist. 

108: 305–320.

McKey, D. 1979. The distribution of secondary compounds within plants. In: 

Rosenthal, G.A.; Janzen, D.H., eds. Herbivores: their interactions with secondary 

plant metabolites. New York: Academic Press: 55–133.

Moen, A.N. 1973. Wildlife ecology: an analytical approach. San Francisco, CA: 

W.H. Freeman and Company. 458 p.

Mould, E.D.; Robbins, C.T. 1981. Evaluation of detergent analysis in estimating 

nutritional value of browse. Journal of Wildlife Management. 45: 937–947.

National Research Council. 2007. Nutrient requirements of small ruminants: 

sheep, goats, cervids, and New World camelids. Washington, DC: National 

Academies Press. 384 p.

Parker, K.L.; Gillingham, M.P.; Hanley, T.A.; Robbins, C.T. 1999. Energy and 

protein balance of free-ranging black-tailed deer in a natural forest environment. 

Wildlife Monographs. 143: 1–48.

Rhoades, D.F. 1979. Evolution of plant chemical defense against herbivores. In: 

Rosenthal, G.A.; Janzen, D.H., eds. Herbivores: their interactions with secondary 

plant metabolites. New York: Academic Press: 1–55.

Rhoades, D.F.; Cates, R.G. 1976. Toward a general theory of plant antiherbivore 

chemistry. In: Wallace, J.W.; Mansell, R.L., eds. Recent advances in 

phytochemistry. New York: Plenum Press: 10: 168–213.
26

RESEARCH PAPER PNW-RP-602

Robbins, C.T. 1983. Wildlife feeding and nutrition. New York: Academic Press. 

343 p.

Robbins, C.T. 1993. Wildlife feeding and nutrition. 2nd ed. New York: Academic 

Press. 352 p.

Robbins, C.T.; Hanley, T.A.; Hagerman, A.E.; Hjeljord, O.; Baker, D.L.; 

Schwartz, C.C.; Mautz, W.W. 1987a. Role of tannins in defending plants 

against ruminants: reduction in protein availability. Ecology. 68: 98–107.

Robbins, C.T.; Mole, S.; Hagerman, A.E.; Hanley, T.A. 1987b. Role of tannins 

in defending plants against ruminants: reduction in dry matter digestion? 

Ecology. 68: 1606–1615.

Rose, C.L. 1990. Application of the carbon/nitrogen balance concept to predicting 

the nutritional quality of blueberry foliage to deer in southeastern Alaska. 

Corvallis, OR: Oregon State University. 150 p. Ph.D. thesis.

Sadleir, R.M.F.S. 1980. Energy and protein in relation to growth of suckling blacktailed deer fawns. Canadian Journal of Zoology. 58: 1347–1354.

SAS Institute Inc. 2004. SAS/STAT 9.1 user’s guide. Carey, NC: SAS Institute Inc. 

5121 p.

Stamp, N. 2003. Out of the quagmire of plant defense hypotheses. Quarterly 

Review of Biology. 78: 23–55.

Svendsen, C.R. 1992. Ecological relationships between elk and deer: behavior 

and plant-herbivore interactions. Seattle, WA: University of Washington. 

171 p. Ph.D. dissertation.

Tuomi, J.; Niemelä, P.; Chapin, F.S., III; Bryant, J.P.; Sirén, S. 1988. 

Defensive responses of trees in relation to their carbon/nutrient balance. 

In: Mattson, W.J., ed. Mechanisms of woody plant defenses against insects: 

search for pattern. New York: Springer: 57–72.

Van Horne, B.; Hanley, T.A.; Cates, R.G.; McKendrick, J.D.; Horner, J.D. 

1988. Infl uence of seral stage and season on leaf chemistry of southeastern 

Alaska deer forage. Canadian Journal of Forest Research. 18: 90–99.

Van Soest, P.J. 1994. Nutritional ecology of the ruminant. 2nd ed. Ithaca, NY: 

Comstock Publishing Associates. 488 p. 

Whittaker, R.H.; Feeny, P.P. 1971. Allelochemics: chemical interactions 

between species. Science. 171: 757–770.
27

Variation in Nutritional Quality of Plants for Deer in Relation to Sunny Versus Shady Environments

Appendix 1: 

Table 3—Scientifi c and common names of plants and their forage codesa

Forage code Scientifi c name Common name 

BLSP Blechnum spicant (L.) Sm. Deer fern fronds 

CANO Callitropsis nootkatensis (D. Don) Alaska cedar CAGb

 Oerst. ex D.P. Little

COAS Coptis aspleniifolia Salisb. Fernleaf goldthread leaves 

COCA Cornus canadensis L. Bunchberry dogwood leaves 

CHAN Chamerion angustifolium (L.) Fireweed leaves 

 Holub ssp. angustifolium

DREX Dryopteris expansa (C. Presl) Spreading woodfern fronds 

 Fraser-Jenkins & Jermy

LYAM Lysichiton americanus Hultén & Skunkcabbage leaves 

 H. St. John

MEFE_L Menziesia ferruginea Sm. Rusty menziesia leaves 

OPHO_L Oplopanax horridus (Sm.) Miq. Devilsclub leaves 

RUPE Rubus pedatus Sm. Five-leaved bramble leaves 

RUSP_L Rubus spectabilis Pursh Salmonberry leaves 

RUSP_T Rubus spectabilis Pursh Salmonberry twigs 

TITR Tiarella trifoliata L. Th reeleaf foamfl ower leaves 

TSHE Tsuga heterophylla (Raf.) Sarg. Western hemlock CAG 

VACCI_L Vaccinium L. spp.c

 Immature blueberry leaves 

VACCI_T Vaccinium L. spp.c

 Immature blueberry twigs 

VAOV_L Vaccinium ovalifolium Sm.d Oval-leaf blueberry leaves 

VAOV_T Vaccinium ovalifolium Sm.d Oval-leaf blueberry twigs 

VAPA_L Vaccinium parvifolium Sm. Red huckleberry leaves 

VAPA_T Vaccinium parvifolium Sm. Red huckleberry twigs 

a Source of nomenclature is PLANTS database, http://plants.usda.gov/java/. 

b CAG = current annual growth (both leaves and twigs). 

c Evergreen, decumbent, sexually immature form of several Vaccinium spp. (indistinguishable species 

when immature) including V. ovalifolium, V. alaskaense, and V. parvifolium. d Includes Alaska blueberry, V. alaskaense Howell, which hybridizes with this species. 
28

RESEARCH PAPER PNW-RP-602

Appendix 2: 

Table 4—Results from all species analyzed in this study, including those that were not 

replicated suffi ciently for statistical analysis in the main reporta

 Number of Digestible protein (g/100 g)

Season and replicate sites Shade Sun Pooled

forage code Shade Sun Mean SE Mean SE Mean SE

Summer: 

 BLSP 0 1 11.6 3.3 

 CANO 0 2 1.7 0.2 

 COAS 3 3 9.3 0.9 8.8 0.8 9.1 0.4 

 COCA 3 3 9.1* 0.6 5.9* 0.6 

 CHAN 0 3 8.6 0.6 

 DREX 3 3 11.8 0.8 9.8 0.8 10.8 0.6 

 LYAM 3 3 24.7 1.7 19.9 1.6 22.0 1.0 

 MEFE_L 3 3 11.2* 0.8 7.2* 0.8 

 OPHO_L 3 3 17.6* 0.9 12.8* 1.0 

 RUPE 3 3 10.9* 0.6 8.3* 0.5 

 RUSP_L 2 3 14.5 1.5 10.9 1.3 12.5 1.1 

 RUSP_T 2 3 5.26 1.51 3.75 1.19 3.94 0.67 

 TITR 3 3 10.9* 0.7 6.3* 0.7 

 TSHE 3 3 6.4 1.1 4.9 1.1 5.7 0.5 

 VACCI_L 3 2 10.7 0.9 7.7 1.8 10.1 0.9 

 VACCI_T 3 1 4.6 0.9 3.9 2.3 4.6 0.8 

 VAOV_L 3 3 12.7* 1.0 8.0* 1.0 

 VAOV_T 3 3 3.7 0.5 2.5 0.5 3.1 0.3 

 VAPA_L 2 3 14.4* 1.1 9.1* 0.9 

 VAPA_T 2 3 4.1 0.6 4.1 0.5 4.1 0.4 

Winter: 

 BLSP 2 2 6.6 2.5 7.3 2.3 7.1 1.1 

 CANO 0 1 1.8 0.1 

 COAS 3 3 6.5 0.8 5.9 0.8 6.3 0.3 

 COCA 3 3 5.2 0.6 5.4 0.6 5.2 0.3 

 RUPE 3 3 7.8 0.5 7.0 0.5 7.4 0.4 

 TITR 3 2 9.8 1.0 5.6 1.2 7.7 1.0 

 TSHE 3 3 2.4 0.5 3.6 0.5 3.0 0.4 

 VACCI_L 2 2 8.0 1.0 7.4 1.2 7.8 0.5 

 VACCI_T 2 2 5.6 0.5 6.0 0.6 5.7 0.4 

 VAOV_T 3 3 3.3 0.4 2.9 0.4 3.0 0.2 

 VAPA_T 3 3 6.2 0.8 4.3 0.9 5.4 0.6 
29

Variation in Nutritional Quality of Plants for Deer in Relation to Sunny Versus Shady Environments

Table 4—Results from all species analyzed in this study, including those that were not 

replicated suffi ciently for statistical analysis in the main reporta (continued)

 Number of Digestible dry matter (percent)

Season and replicate sites Shade Sun Pooled

forage code Shade Sun Mean SE Mean SE Mean SE

Summer: 

 BLSP 0 1 62.7 3.9 

 CANO 0 2 54.2 0.5 

 COAS 3 3 53.5* 0.8 56.8* 0.8 

 COCA 3 3 65.4** 0.7 68.9** 0.7 

 CHAN 0 3 69.9 1.0 

 DREX 3 3 59.1 1.7 54.4 1.7 56.8 1.3 

 LYAM 3 3 70.9 1.6 72.3 1.5 71.8 0.8 

 MEFE_L 3 3 61.2 1.5 66.0 1.5 63.7 0.9 

 OPHO_L 3 3 69.2* 1.3 76.9* 1.4 

 RUPE 3 3 60.6 1.2 63.2 1.1 62.0 0.8 

 RUSP_L 2 3 59.9 2.2 68.4 1.8 64.9 1.6 

 RUSP_T 1 2 39.1 1.7 43.0 0.8 42.4 0.9 

 TITR 3 3 58.0 1.3 58.3 1.2 58.1 0.9 

 TSHE 3 3 58.3 1.9 62.2 1.9 60.3 0.9 

 VACCI_L 1 1 62.3 1.1 

 VACCI_T 1 0 33.7 ISM 

 VAOV_L 3 3 60.3* 0.7 66.1* 0.7 

 VAOV_T 3 3 38.2 0.7 41.0 0.7 39.6 0.5 

 VAPA_L 2 3 53.0* 3.3 67.3* 2.7 

 VAPA_T 2 3 35.2* 1.5 42.2* 1.2 

Winter: 

 BLSP 2 2 52.1* 1.6 65.4* 1.1 

 CANO 0 1 50.8 0.4 

 COAS 3 3 56.9 0.7 56.6 0.7 56.7 0.4 

 COCA 3 3 70.4* 0.5 68.4* 0.4 

 RUPE 3 3 64.9 1.3 63.1 1.3 64.0 0.8 

 TITR 2 2 68.1* 0.9 59.7* 0.8 

 TSHE 3 3 47.1 1.7 50.3 1.7 49.5 0.8 

 VACCI_L 2 1 63.6 1.2 62.7 1.8 63.1 0.6 

 VACCI_T 1 1 36.2 0.8 37.7 0.6 37.1 0.6 

 VAOV_T 3 3 37.8 0.9 41.1 0.8 39.6 0.7 

 VAPA_T 3 3 36.9* 0.6 41.2* 0.7 
30

RESEARCH PAPER PNW-RP-602

Table 4—Results from all species analyzed in this study, including those that were not 

replicated suffi ciently for statistical analysis in the main reporta (continued)

 Number of Digestible energy (KJ/g)

Season and replicate sites Shade Sun Pooled

forage code Shade Sun Mean SE Mean SE Mean SE

Summer: 

 BLSP 0 1 12.4 0.4 

 CANO 0 2 12.3 0.1 

 COAS 3 3 11.4 0.2 11.9 0.2 11.6 0.1 

 COCA 3 3 12.2* 0.1 12.7* 0.1 

 CHAN 0 3 13.5 0.2 

 DREX 3 3 12.1 0.3 11.5 0.3 11.8 0.3 

 LYAM 3 3 14.4 0.6 14.2 0.6 14.3 0.3 

 MEFE_L 3 3 12.4* 0.2 13.5* 0.2 

 OPHO_L 3 3 14.3* 0.3 15.9* 0.3 

 RUPE 3 3 13.1 0.6 13.4 0.6 13.3 0.3 

 RUSP_L 2 3 11.9 0.6 13.4 0.5 12.8 0.3 

 RUSP_T 1 2 7.6 0.4 8.3 0.2 8.2 0.2 

 TITR 3 3 11.2 0.3 11.2 0.3 11.2 0.2 

 TSHE 3 3 13.2 0.5 13.7 0.5 13.5 0.2 

 VACCI_L 1 1 12.9 0.1 

 VACCI_T 1 0 7.5 ISM 

 VAOV_L 3 3 13.0* 0.2 14.0* 0.2 

 VAOV_T 3 3 8.3 0.2 8.7 0.2 8.5 0.1 

 VAPA_L 2 3 11.7 0.8 14.6 0.6 13.4 0.5 

 VAPA_T 2 3 7.9* 0.3 9.2* 0.3 

Winter: 

 BLSP 2 2 9.3 0.6 12.6 0.5 11.5 0.6 

 CANO 0 1 11.3 0.9 

 COAS 3 3 11.4 0.1 11.5 0.1 11.4 0.1 

 COCA 3 3 12.6* 0.1 12.3* 0.1 

 RUPE 3 3 12.4 0.3 12.1 0.3 12.3 0.2 

 TITR 2 2 12.6* 0.1 11.1* 0.1 

 TSHE 3 3 10.5 0.4 11.1 0.4 11.0 0.2 

 VACCI_L 2 1 12.9 0.1 13.2 0.3 12.9 0.1 

 VACCI_T 1 1 7.9 0.2 8.2 0.2 8.0 0.1 

 VAOV_T 3 3 8.1 0.2 8.7 0.2 8.4 0.1 

 VAPA_T 3 3 7.9* 0.2 9.0* 0.3 

ISM = insuffi cient sample material. a Results are means and standard errors (SE) for digestible protein (g/100 g), digestible dry matter (percent), and digestible energy 

(kJ/g) for summer and winter forages collected in shady understory forest (Shade) or open, sunny habitats (Sun) as multiple 

subsamples obtained at each of several replicated sites (number of replicate sites shown). Means that differed statistically (single 

factor analysis of variance with alpha of 0.05) are indicated with an asterisk; their SE values are from the analysis of variance 

(reported in LS Means statement of SAS GLM procedure). When means did not differ signifi cantly, all subsamples were pooled 

and their pooled mean and SE were calculated from the subsamples. For forages with insuffi cient material for statistical analysis, 

their reported mean and SE are from the pooling of all their subsamples. See appendix 1 for common and scientifi c names of forage 

codes. 
Pacifi c Northwest Research Station

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

Telephone (503) 808–2592

Publication requests (503) 808–2138

FAX (503) 808–2130

E-mail pnw_pnwpubs@fs.fed.us

Mailing address Publications Distribution

 Pacifi c Northwest Research Station

 P.O. Box 3890

 Portland, OR 97208–3890
U.S. Department of Agriculture

Pacifi c Northwest Research Station

1220 SW 3rd Ave., Suite 1400

P.O. Box 3890

Portland, OR 97208–3890

Offi cial Business

Penalty for Private Use, $300