DEPAR TMENT OF AGRICULT URE

United States 

Department of 

Agriculture

Forest Service

Pacific Northwest 

Research Station

Research Paper

PNW-RP-593

May 2013

Precommercial Thinning: 

Implications of Early Results 

From the Tongass-Wide 

Young-Growth Studies 

Experiments for Deer 

Habitat in Southeast Alaska

Thomas A. Hanley, Michael H. McClellan, 

Jeffrey C. Barnard, and Mary A. Friberg
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Authors

Thomas A. Hanley is a research wildlife biologist, Michael H. McClellan is a 

research ecologist, and Jeffrey C. Barnard is a fish biologist, Pacific Northwest 

Research Station, Forestry Sciences Laboratory, 11175 Auke Lake Way, Juneau, 

AK 99801; Mary A. Friberg is a wildlife biologist, Tongass National Forest, 8510 

Mendenhall Loop Road, Juneau, AK 99801.

Cover photographs by Michael McClellan.
Abstract

Hanley, Thomas A.; McClellan, Michael H.; Barnard, Jeffrey C.; Friberg, 

Mary 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, Pacific Northwest Research Station. 64 p.

This report documents the results from the first “5-year” round of understory 

responses to the Tongass-Wide Young-Growth Studies (TWYGS) treatments, especially in relation to their effects on food resources for black-tailed deer (Odocoileus 

hemionus sitkensis). Responses of understory vegetation to precommercial silviculture experiments after their first 4 to 8 years posttreatment were analyzed with the 

Forage Resource Evaluation System for Habitat (FRESH)-Deer model. The studies 

were conducted in western hemlock (Tsuga heterophylla)-Sitka spruce (Picea 

sitchensis) young-growth forests in southeast Alaska. All four TWYGS experiments 

were studied: (I) planting of red alder (Alnus rubra) within 1- to 5-year-old stands; 

(II) precommercial thinning at narrow and wide spacings (549 and 331 trees per 

hectare, respectively) in 15- to 25-year-old stands; (III) precommercial thinning at 

medium spacing (420 trees per hectare) with and without pruning in 25- to 35-yearold stands; and (IV) precommercial thinning at wide spacing (203 trees per hectare) 

with and without slash treatment versus thinning by girdling in >35-year-old stands. 

All experiments also included untreated control stands of identical age. FRESHDeer was used to evaluate the implications for deer habitat in terms of forage 

resources (species-specific biomass, digestible protein, and digestible dry matter) 

relative to deer metabolic requirements in summer (at two levels of requirements—

maintenance only vs. lactation) and in winter (at six levels of snow depth). Analyses 

for both summer and winter indicated that in all cases except for Experiment I (red 

alder planting in 1- to 5-year-old stands), habitat values of all treatments exceeded 

untreated controls (P < 0.05), and earlier treatments yielded greater benefits than 

did later treatments (i.e., treating at 15 to 25 years of age was more effective than 

at 25 to 35 years, and at >35 years was least effective). When compared to a wide 

range of old-growth stands from throughout the region, it was apparent that in summer and winter with low snow depths (<20 cm) early treatments (15- to 25-year-old 

stands) yielded better food resources than did old-growth forest, while later treatments (25- to 35-, and 35+ year-old stands) yielded poorer habitat than old growth. 

These results, however, are from only the first 4 to 8 years posttreatment. The next 

study of TWYGS responses is scheduled to occur at 9 to 13 years posttreatment.

Keywords: Silviculture, adaptive management, Odocoileus hemionus, habitat 

model, nutrition, understory vegetation, snow.
Summary

The Tongass-Wide Young-Growth Studies (TWYGS) is a large-scale, long-term 

experimental study of precommercial thinning implemented in 2002 through 2006 

as an adaptive management program conducted by the Tongass National Forest 

in collaboration with the Pacific Northwest Research Station. It was designed 

as a series of four independent experiments, each involving a different age class 

of stands with the expectation of monitoring the results at approximately 5-year 

intervals. This Research Paper reports the results for understory vegetation from 

the first 5-year round of study, especially in relation to their implications for blacktailed deer (Odocoileus hemionus) habitat.

The design of all experiments was that of a randomized complete block analysis 

of variance with a target of 20 replicates (blocks, widely scattered throughout southeast Alaska) in each. Experiment I compared the planting of red alder (Alnus rubra) 

seedlings at two different densities in recent clearcuts approximately 1 to 5 years 

old. Experiment II compared precommercial thinning at narrow versus wide spacing in stands 15 to 25 years old. Experiment III compared moderate precommercial 

thinning alone and in combination with two intensities of pruning in stands 25 to 

35 years old. Experiment IV compared wide spacing conventional thinning (with 

and without slash treatment) with girdling in stands >35 years old. Each TWYGS 

experiment also included a treatment of untreated control young-growth forest of 

corresponding age in each experimental block.

Vegetation response was evaluated in terms of its value as food resources for 

deer using the Forage Resource Evaluation System for Habitat—Deer model to 

quantify habitat value in units of deer days per hectare, where one deer day is the 

food required to maintain one adult female deer for one day at user-specified metabolic requirements. This technique provides one quantitative metric that integrates 

the entire matrix of vegetation biomass and nutritional values into one number 

that is directly comparable among treatments within experiments, among experiments within TWYGS, and among TWYGS results and other studies of vegetation 

response to silviculture treatments in the region.

Results indicated the following:

• Total understory biomass of current annual growth is seldom a sufficient 

measure of habitat quality for deer in southeast Alaska because it most 

likely (about 80 percent probability in the TWYGS experiments) is composed of too much low-digestibility forage. Dry matter digestibility of forage is also of major importance in evaluating habitat for deer in the region.
• There was no effect of alder-planting treatments in Experiment I, but 

the various kinds of thinning treatments in Experiments II through IV 

increased total understory biomass by 3.4 to 5.1 times that of corresponding 

untreated controls. Relative effects for deer varied with season, metabolic 

requirements, and snow depths. 

• Regardless of observed variation among various studies, it is clear that 

young clearcuts provide very high amounts of relatively high-quality food 

for deer in both summer and snow-free winter conditions throughout the 

region. It is with the closing of their young conifer canopies that their value 

as habitat drops sharply.

• Precommercial thinning may extend the high habitat values of young 

clearcuts into an advancing age of young-growth forest, but the stands will 

likely need further treatments if high habitat values are to be maintained. 

Pruning combined with thinning might be quite useful as such a secondary 

treatment.

• For stands that have not been thinned before reaching the relatively large 

tree sizes typical of ages greater than 35 years, thinning by girdling might 

be an effective treatment, but careful contract administration is essential 

when using girdling as a management tool. When girdling is done by chainsaw, too deep a cut leaves the tree with too small an intact bole to sustain 

wind or snow loads.

• The understory response is stronger with earlier treatment (younger stand 

age), mostly because there is more understory vegetation already present to 

serve as nurse stock in younger stands.

• A variety of potential silviculture treatments exists, and they may be 

applied to a variety of stand ages. Given the importance of landscape heterogeneity to deer, such variety in silviculture may be the optimal way to 

proceed.

We caution that our TWYGS results are for only the early response of vegetation to silviculture treatments and do not yet include any measures of stand dynamics through time. Future results will be especially important as red alders gain 

effect (Experiment I), thinned stands begin to close (Experiment II) or not close 

(Experiment III), and older stands have time to respond more fully (Experiment 

IV). Quantification of western hemlock (Tsuga heterophylla) in the understory, and 

slash and its rate of decay, will be important features to monitor and compare in all 

four experiments.
Contents

1 Introduction

3 The TWYGS Experiments

5 Methods

5 Stands (Experimental Units of Treatment)

6 Field Sampling (Data Collection)

9 Quantification of Deer Habitat Value

11 Statistical Analysis

12 Results

12 Cover-to-Biomass Regressions

13 Experiment I (Red Alder Planting in 1- to 5-Year-Old Stands, 8 Years 

Posttreatment)

13 Experiment II (Thinning in 15- to 25-Year-Old Stands, 5 Years Posttreatment)

16 Experiment III (Thinning and Pruning in 25- to 35-Year-Old Stands, 

6 Years Posttreatment)

16 Experiment IV (Thinning by Felling, With or Without Slash Treatment, or

Girdling in >35-Year-Old Stands, 4 Years Posttreatment)

20 Discussion

20 Effects of Logging and Thinning Slash on Habitat Availability

20 Snow Depth and Its Interaction With Shrubs and Slash

21 Limiting Factors

24 Patterns Across Experiments—Vegetation

27 Patterns Across Experiments—Deer Habitat Values

30 Management Implications

36 Conclusions

37 Acknowledgments

38 English Equivalents

38 Tree Spacings and Densities 

38 Literature Cited

44 Appendix 1: Scientific and Common Names and Plant Codes of All Plant

Species in This Report

47 Appendix 2: Canopy Cover-to-Biomass Regression Equations for 

Treatments in Each of the TWYGS Experiments

54 Appendix 3: Species-Specific Results (Oven-Dry Biomass in Kilograms

per Hectare, Mean and Standard Error) From All TWYGS Treatments 

by Experiment
1

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

Introduction 

The Tongass-Wide Young-Growth Studies (TWYGS) is a long-term, adaptive 

management study of silviculture treatments intended to improve habitat for deer 

and other species. It consists of a series of four experiments involving manipulation 

of forest overstory along a gradient of stand ages from 1 through >35 years with 

monitoring intervals of about 5 years each. This report summarizes the results of 

the first cycle of TWYGS experiments as they relate to habitat for Sitka black-tailed 

deer (Odocoileus hemionus sitkensis). 

Even-aged, young-growth forests regenerating from clearcut stands of old 

growth have long been recognized as problematic habitat for Sitka black-tailed deer 

in southeast Alaska because of their characteristically sparse understory vegetation 

(Hanley 1993, Hanley et al. 1989, Schoen et al. 1988, Wallmo and Schoen 1979). 

Without silvicultural intervention, the usual pattern of secondary succession following clearcutting of western hemlock (Tsuga heterophylla)

1

-Sitka spruce (Picea 

sitchensis) forests is one of high biomass and productivity of understory vegetation 

in the early stage of stand regeneration. This is followed by extremely low levels 

of understory after crown closure of the dense, young conifers by about 30 years 

of age through at least the next 100 years (Alaback 1982, 1984). Although departures from that pattern may occur with significant soil disturbance and red alder 

(Alnus rubra) establishment or on poorly productive sites (Hanley 2005), high-lead 

logging on productive sites has been the dominant harvest technique for the past 

four decades (McClellan 2005), resulting in the widespread occurrence of densecanopied young-growth forests throughout the region. As young clearcuts transition 

into closed-canopy, young-growth forest, their value as habitat for black-tailed deer 

decreases in direct proportion to their decreasing understory food resources. This 

habitat decline has social consequences for people of the region because black-tailed 

deer are the principal big game species for sport hunters and the most important terrestrial animal in local subsistence economies (Brinkman et al. 2007, Mazza 2003). 

Furthermore, much of the young-growth forest is concentrated in lower elevation 

areas important as winter range for deer, intensifying the potential impact on local 

deer populations (Hanley 1984).

Even before problems with deer habitat were noted, there was interest in precommercial thinning of young-growth forests of the region because natural regeneration was almost always denser than optimal for timber production (Ruth and 

Harris 1979). Early thinning efforts, however, tended to be conservative (spacings

1

 See appendix 1 for full list of common and scientific names with authorities for all plant 

species identified in this report.
2

research paper pnw-rp-593

of 749 to 1,329 trees per hectare)2 (Ruth and Harris 1979) and were conducted 

early in the successional cycle (often within the first 15 years).3 Furthermore, none 

was conducted in an experimental design in which effects on understory vegetation could be measured and compared reliably. When concern about deer habitat 

emerged as a forest management issue, the best opportunity for assessing the 

effects of precommercial thinning was Wilbur A. Farr’s long-term stand-density 

study of tree growth and yield (DeMars 2000), which involved manipulation of tree 

density within gradients of stand age and site index throughout the region. Farr’s 

experimental plots were small (typically 0.4 ha treated with an interior 0.08-ha 

measurement plot), but treatments were balanced and randomly assigned at each 

site, their histories were well known, and they provided a reliable basis for studying 

understory response to thinning intensity (spacing), stand age when treated, and 

time since treatment.

The initial investigation of understory patterns in the Farr stand-density study 

revealed that within 5 to 7 years of thinning, understory biomass was greater in 

stands thinned at younger stand ages (20 to 30 years) than in older ones (39 to 72 

years) and did not differ with thinned tree density in young stands but increased 

significantly with wider spacing in older stands (Alaback and Tappeiner, as 

reported in fig. 4 of Hanley et al. 1989). However, regardless of stand age or tree 

spacing, all understories were strongly dominated by shrubs (either salmonberry, 

Rubus spectabilis, or blueberry, Vaccinium ovalifolium), while the herb layer 

(especially important for deer) consistently remained sparse and mostly consisted 

of ferns rather than forbs. Wide spacing also tended to result in a dense layer of 

western hemlock seedlings, which, if successful in recruiting into the stand, would 

result in a second, dense layer of hemlock to the exclusion of understory. The western hemlock response was further verified in another, subsequent study of the Farr 

sites (Deal and Farr 1994). The lack of a forb response at all thinning intensities, 

coupled with the western hemlock response at wide spacing, offered poor prospects 

for thinning as a mitigating tool for deer habitat. The most important winter forages 

for deer—the evergreen forbs bunchberry dogwood (Cornus canadensis), trailing 

bramble (Rubus pedatus), fernleaf goldthread (Coptis aspleniifolia), and foamflower 

(Tiarella trifoliata) (Hanley and McKendrick 1983, 1985)—were exactly the species 

that seemed most unlikely to benefit from thinning. The forbs were shaded out by 

both the overstory trees in unthinned stands and the dense shrub response in 

thinned stands. Thus, the challenge of silviculture for deer habitat, and even wildlife habitat more generally, could be primarily focused on growing those four

2

 See table of tree spacings and densities at end of this report for English and metric equivalents. 

3

 Unpublished inventory data. On file with: Ben Case, Tongass National Forest, P.O. Box 309, 

Petersburg, AK, 99833.
3

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

species of evergreen forbs (Hanley 1993), a challenge that, based on results from the 

Farr stand-density study, appeared to be formidable.

Empirical observations of thinned stands, however, indicated a wider range 

of potential understory responses than was suggested by the Farr stand-density 

sites. Variation in soil microsites and patterns of disturbance in both the initial 

logging and subsequent thinning appeared to result in wide variation of understory 

response within and among stands when treatments were applied at the much larger 

operational scale of forest management (e.g., several or more hectares, rather than 

small experimental plots). The need for a large-scale, long-term experimental study 

of precommercial thinning was recognized in 2001 and became the genesis of the 

TWYGS experiments, a regionwide adaptive management program conducted by 

the Tongass National Forest in collaboration with the Pacific Northwest Research 

Station (McClellan 2008). Under this program, silviculture treatments were applied 

within a series of experiments, each involving a different age class of young-growth 

forest, and vegetation responses were monitored at approximately 5-year intervals. 

This report documents the results from the first 5-year round of understory 

responses to the TWYGS treatments, especially in relation to their effects on food 

resources for black-tailed deer. Note, however, that understory plants also are 

important food resources for a wide range of animal species not considered in this 

report. Actual times since treatment varied from 4 to 8 years, but all results analyzed here are “early responses” to silviculture treatment.

The TWYGS Experiments

The TWYGS program was designed as a series of four independent experiments, 

each involving a different age class of stands: 1 to 5, 15 to 25, 25 to 35, and >35 

years old. Each of the four age classes tested silviculture treatments uniquely 

appropriate to that age class. All four experiments shared a common set of guidelines: (1) treatments were practical for application in typical management for timber 

stand improvement throughout the Tongass National Forest; (2) treatments were 

expected to increase deer food resources as well as yield of merchantable timber; 

food resources could be increased by changes in understory plant species composition, biomass, or both; (3) treatments within each experiment differed substantially, 

enough that differences should be readily apparent even without statistical analysis 

for discernment; (4) all treatments were applied at an operational scale typical of 

routine forest management (i.e., several to many hectares rather than small experimental plots); and (5) all treatments were widely replicated throughout the Tongass 

National Forest in a randomized complete block analysis of variance design with 

a target of 20 replications per treatment. The treatments were implemented within 

The TWYGS program 

was designed as a 

series of four 

independent experiments, each involving 

a different age class 

of stands: 1 to 5, 15 to 

25, 25 to 35, and >35 

years old. Each of the 

four age classes tested 

silviculture treatments

uniquely appropriate to 

that age class. All four 

experiments shared 

a common set of 

guidelines.
4

research paper pnw-rp-593

the normal program of timber stand improvement activities by the Tongass National 

Forest, one or two experiments per year, beginning in 2002 and completed in 2006. 

(See McClellan 2008 for a full description of history, rationale, and objectives.) 

The TWYGS experiments were expected to be monitored at 5-year intervals and to 

continue for about 30 years. Results relevant for deer habitat were expected to be 

strongly evident within the first 5 to 10 years after treatment, although treatment 

effects on overstory tree growth would not yet be apparent. 

Each TWYGS experiment included a treatment of untreated control younggrowth forest of corresponding age at each experimental block. The silviculture 

treatments of the four experiments were as follows:

• Experiment I compared the planting of red alder seedlings at two different densities (50 versus 200 trees/ha) in recent clearcuts of approximately 

1 to 5 years in age. Red alder stands have been found to have very different 

understories than those of western hemlock-Sitka spruce forests in southeast Alaska (Deal 1997, Hanley and Barnard 1998, Hanley and Hoel 1996, 

Hanley et al. 2006). Experiment I was designed to determine whether such 

understory differences could be created by planting red alder seedlings into 

young clearcuts without the need of extensive soil disturbance. The effects 

of red alder on understory vegetation, however, were not expected to be evident within the first 5 to 10 years, as more time than that would be needed 

for the alder to grow large enough to exert a significant effect on understory 

vegetation and for untreated conifer crowns to approach closure.

• Experiment II compared precommercial thinning at narrow spacing (549 

trees/ha) versus wide spacing (331 trees/ha) in stands 15 to 25 years old. 

Wider spacing was expected to diminish the effects of conifers on understory vegetation, delay conifer crown closure, and thereby increase total 

understory food resources in both amount and longevity. However, the wide 

spacing might favor heavy recruitment of western hemlock as reported earlier (Deal and Farr 1994).

• Experiment III compared moderate precommercial thinning (420 trees/ha)

alone and in combination with two intensities of pruning4

 (25 versus 50 percent of trees were pruned) in stands 25 to 35 years old. Empirical observations of understory response to experimental pruning treatments (Petruncio 

1994) on the Tongass National Forest indicated a highly favorable understory response for deer habitat: pruning appeared to favor a highly productive and species-diverse understory with a strong and long-lasting response

4

 Pruning involves removal of the lower branches of trees by cutting close to the stem, 

improving wood quality by reducing knots and also increasing light to the understory.

Each TWYGS 

experiment included 

a treatment of 

untreated control 

young-growth forest 

of corresponding age 

at each experimental 

block.
5

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

by blueberry shrubs (a key winter browse) and the evergreen forb species 

so important to deer in southeast Alaska. Pruning is an appropriate silviculture technique for improving wood quality in this age class, although 

subsequent development of epicormic branches may reduce wood quality in 

Sitka spruce (Deal et al. 2003). The heavier pruning was expected to yield a 

stronger and longer lasting understory response.

• Experiment IV compared wide spacing (203 trees/ha) conventional thinning with girdling (killing trees in place by cutting two rings around them, 

severing the phloem) in stands >35 years old. The cutting and girdling 

treatments were designed to leave the same density of live residual trees. 

The conventional thinning also included treatments of the residual slash 

(residual woody debris) by cutting it into lengths of 1.5 m versus 4.6 m or 

not at all. Unthinned stands in this age class are often found in non-timberproduction sites such as beach buffers and old-growth reserves, where the 

main objective of thinning is to increase forage availability, not wood production. Conventional thinning in this age class is especially problematic; 

the large interlocking crowns make it difficult to get cut trees to the ground, 

and once on the ground they create heavy accumulations of large-diameter 

slash. Slash is so large and extensive that it likely reduces access to the 

stand by deer. Suspended slash may directly shade the understory environment, and slash lying on the forest floor occupies potential growing sites, 

both of these factors thereby inhibiting the understory response to overstory 

canopy reduction. The bucking of slash was expected to get the wood to the 

ground and open the understory light environment sooner with pieces cut 

to shorter lengths than longer lengths or not at all. Girdling was expected to 

thin the overstory by killing trees left standing in place, whereby the canopy would open with needle loss, followed by small twig and branch loss, 

and gradually by the falling of semidecomposed trees, resulting in minimal 

slash accumulation at any one time. 

Methods

Stands (Experimental Units of Treatment)

Described in detail by McClellan and DeSanto (N.d)5 are the rationale for selection 

of treatments and age classes, the randomized complete block analysis of variance 

experimental design, and the methods for site selection, treatment layout, and silviculture treatments, including geographic locations, ages, and areas of each stand

5

 McClellan, M.H.; DeSanto, T.L. (No date). Tongass-Wide Young-Growth Studies: study 

plan and establishment report. Manuscript in preparation.
6

research paper pnw-rp-593

in all experiments. Experimental blocks (groups of stands with one treatment per 

stand, each treatment per block) were distributed throughout the Tongass National 

Forest in all ranger districts except the Yakutat Ranger District and Admiralty 

Island National Monument. Stand (experimental unit) selection criteria within each 

block were as follows: (1) site productivity, tree density, and stand composition were 

required to be relatively uniform; (2) stands must not have been previously thinned 

or weeded; (3) desired minimum stand size was at least 4 ha; and (4) all stands 

at each block must be similar and close to one another. In most cases, the experimental units were delineated portions from the same clearcut harvest unit. Each 

treatment was assigned to one stand at each block randomly by someone other than 

the field crew who selected the stands. A minimum buffer of at least 30 to 45 m was 

provided between stands. Although the target number of replicate blocks was 20 for 

each experiment, the actual number varied from 17 to 23 (table 1). 

The experimental treatments were implemented in the following years: Experiment I in 2003, Experiment II in 2002, Experiment III in 2002, and Experiment 

IV in 2006. No pretreatment data were collected for either overstory or understory. 

Data from the first cycle posttreatment (this report) were collected in the following 

years: Experiment I in 2011 (8 years posttreatment), Experiment II in 2007 (5 years 

posttreatment), Experiment III in 2008 (6 years posttreatment), and Experiment IV 

in 2010 (4 years posttreatment). The numbers of years posttreatment differ from 

the idealized 5 because of logistical reasons coupled with a desire to provide a little 

extra time for the red alders to establish and grow in Experiment I.

Field Sampling (Data Collection)

Data were collected throughout the growing seasons (May through September) 

of each year. However, understory biomass data (described in the last paragraph 

of this section) were collected only in mid-June through mid-August to try to 

coincide with the peak of understory phenological development and peak lactation 

nutritional demands for black-tailed deer in early July. Although many data were 

collected for overstory trees, only those for overstory canopy cover are described 

and used in this report. Our focus here is on the understory responses and their 

implications for food resources for deer. Complete details of the field sampling protocols for all vegetation are described in TWYGS Field Protocol Manuals, Versions 

1.3, 1.9, and 2.1 (2008–2011).6

6

 Unpublished documents. On file with: Michael H. McClellan and Jeffrey C. Barnard, 

Forestry Sciences Laboratory, 11175 Auke Lake Way, Juneau, AK 99801.
7

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

Table 1—Replicates (blocks) and areas by experiment 

 Average area 

Experiment Blocks Total area per stand

- - - - - - - Hectares - - - - - - -

I 23 359 15.6

II 20 711 35.6

III 19 718 37.8

IV 17 210 12.4

Source: McClellan and DeSanto (manuscript in preparation).

For Experiments II, III, and IV, the basic sampling design focused on five 

fixed-area plots within each stand.7 Each plot was square and measured 22.36 m on 

each side (for an area of 500 m2), within which all overstory trees were tallied and 

measured. The location of the plots differed with stand size and shape, but the five 

plots were distributed as widely as possible within the following constraints: (1) 

their overall configuration was a square with one plot at each corner and one plot in 

the very center, (2) corner plots were >200 m from one another and >25 m from any 

edge of the stand, and (3) the center plot was >100 m from the corner plots. Total 

overstory canopy coverage was estimated from hemispherical photographs taken at 

1.5 m above ground at the center of each of the five plots per stand (one photo per 

plot, five photos per stand).

Canopy coverage of understory vegetation was estimated within 12 one-squaremeter (100- by 100-cm) quadrats in each of the five plots (total of 60 quadrats per 

stand), with the quadrats systematically located on perpendicular transect lines at 

distances of 4, 5, and 6 m from plot center (three quadrats on each of four lines). 

The percentage of canopy cover (vertical projection of outer perimeter of plant 

canopy to ground) was estimated visually to the following levels of precision: 0 to 

1 percent (estimated to nearest 0.1 percent); 1 to 10 percent (estimated to nearest 

1 percent); 10 to 30 percent (estimated to nearest 5 percent); 30 to 100 percent 

(estimated to nearest 10 percent). Canopy coverage was estimated for each species of vascular plants within 1.3 m of the ground, including all tree seedlings and 

branches, shrubs, and herbs (forbs, grasses, grasslike plants, ferns, clubmosses, and 

horsetails), but not including bryophytes or lichens. 

Sampling for Experiment I involved a different layout, as trees were sampled 

in an entirely different scheme. All understory vegetation sampling was conducted 

systematically in 1-m2

 quadrats along three parallel transects run perpendicular

7

 Sampling details for Experiment IV differed slightly from the ideal, as plot layout 

required modification when treated stands were small, but 60 quadrats for understory 

vegetation were always sampled systematically in each stand.
8

research paper pnw-rp-593

to slope with predetermined start and end points located with handheld global 

positioning system receivers. Transects were of variable length to sample each 

stand as widely as possible. Quadrats (20 per transect, 60 per stand) were spaced at 

3- to 7-m intervals, depending on transect length. Overstory canopy coverage was 

estimated from hemispherical photos taken in relation to understory transects (start 

and end points) and overstory plots (total of 6 to 12 photos per stand, depending on 

size of stand). 

Although canopy coverage can be estimated relatively quickly and easily, it 

is not sufficient for analyzing deer habitat with a food-based model (described 

below), because deer eat plant biomass, not canopy coverage. Canopy coverage data 

were obtained for all plant species in all stands at all blocks throughout TWYGS. 

Understory biomass data, however, could be obtained at only a subset of blocks 

where electricity, drying ovens, and balances were available. Allometric regression 

equations were developed and used for converting estimates of canopy coverage 

(percentage) to estimates of biomass (ovendry weight) of current annual growth 

for each species, and in the case of woody species, leaves separate from twigs. 

Biomass of only current annual growth was measured—total aboveground biomass 

of herbs, only leaves and current year twigs of woody species. Canopy cover-tobiomass equations are known to vary with site environmental conditions, however, 

especially with stand history and the amount of sunlight (Alaback 1986). Therefore, 

separate sets of equations were developed for each treatment (and untreated control) 

within each TWYGS experiment. 

In the first two field seasons, 2007 and 2008 (Experiments II and III), biomass 

regressions were calculated from quadrats that were sampled for canopy coverage 

as part of the design described above–every middle quadrat of the three quadrats 

along each transect line at each plot was sampled for biomass (clipped by species, 

leaves separate from twigs in woody species, and weighed) after canopy coverage 

was estimated for each species (i.e., one third of the 60 quadrats were doublesampled for both species-specific canopy coverage and biomass). Because of inefficiencies in the double-sampling design, however, a different biomass sampling 

protocol for the regression data was used for Experiments I and IV: separate 100-m 

transects were run through the stand, with quadrats spaced at 5-m intervals, and 

specific species were targeted for each quadrat; the target species was then sampled 

(both canopy coverage and fresh weight) in quadrats in which it occurred until 

sufficient samples were obtained throughout the full range of its potential canopy 

coverage. In other words, systematic sampling targeted species individually to 

provide a sufficiently wide range of values to calculate a meaningful regression 

equation for each. In all years, the species within each quadrat were clipped and 
9

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

weighed (fresh weight) with Pesola spring scales, and several samples of each 

species were retained each day for oven-drying (24 hours at 100 °C) for dry-weight 

correction. Sampling for canopy cover-to-biomass regressions was limited to only 

those experimental blocks accessible from the Prince of Wales Island road system 

(about one-third of all blocks) each year, and all sampling was restricted to the 

period mid-June through mid-August. Training and calibration of field crews was 

conducted for at least 1 month before data collection. The resulting canopy coverto-biomass regressions were used to convert all estimates of canopy coverage from 

all stands in the experiment to species-specific estimates of biomass in terms of 

kilograms per hectare, separately for each treatment.

Quantification of Deer Habitat Value

Vegetation results were analyzed in relation to their potential value as food 

for black-tailed deer with the Forage Resource Evaluation System for Habitat 

(FRESH)-Deer model (Hanley et al. 2012); http://cervid.uaa.alaska.edu/deer/Home.

aspx. The FRESH model is based on the quantity and quality of available food 

resources in relation to specified nutritional requirements for deer. It transforms 

an array of species-specific biomass values (kg/ha) with a matrix of corresponding 

forage-specific nutritional values (dry matter digestibility and digestible protein8) 

into one measure of habitat value for deer: deer days/ha, where one deer day is the 

food required to support one animal for one day, with the nutritional requirements 

of the animal specified by the user on the basis of species, age, sex, body weight, 

reproductive status, and season. The deer days/ha result is derived from a linear 

programming model that maximizes the combined biomass of all potential foods 

while meeting specified minimal constraints for digestible energy and digestible 

protein concentrations of the combined biomass. The maximum biomass solution 

(kg/ha) is then divided into the specified daily dry matter intake (g/day) of an 

adult female Sitka black-tailed deer (our focal animal unit), yielding the number 

of deer days/ha that the food could support at that specified level of nutritional 

requirements. 

8

 Protein is needed for body growth and muscle and tissue maintenance; digestible protein 

is that fraction of food protein that is digested and assimilated by the animal. Energy is 

needed to fuel the animal’s heat and tissue production, metabolic processes, and activity. 

Digestible energy is that fraction of a food’s gross energy that is digestible and available to 

the animal. It is the product of a food’s gross energy (total energy released upon combustion) and its dry matter digestibility (the fraction of food dry matter that is digestible). 

Forages vary much more in their dry matter digestibility than in their gross energy, so dry 

matter digestibility is frequently used as a proxy for digestible energy. Requirements for 

dry matter digestibility are calculated directly from requirements for digestible energy, 

assuming a constant value for gross energy and a given daily dry matter intake of food. 

See Hanley et al. (2012) for more explanation of this and the FRESH-Deer model.

The FRESH model 

transforms an array 

of species-specific 

biomass values (kg/

ha) with a matrix of 

corresponding foragespecific nutritional 

values (dry matter 

digestibility and 

digestible protein) into 

one measure of habitat 

value for deer. 
10

research paper pnw-rp-593

The deer days/ha values provided a quantitative basis for direct comparison 

of experimental treatments combining effects of all species-specific biomass and 

nutritional quality. Our summer analysis centered on early July, the time of peak 

vegetation phenological development and peak lactation requirements for deer in 

southeast Alaska (Hanley and McKendrick 1985, Parker et al. 1999). For lactation 

requirements, we used values for one fawn and called that “lactation” (see Hanley 

et al. 2012 for rationale). Our winter analysis centered on mid-winter (February 1). 

Our estimates of food biomass in winter were the summer food biomass values 

minus all deciduous species or plant parts. Our estimates of the effects of snow 

on food availability assumed (1) a simple burying effect from ground up for all 

species except blueberry and salal (Gaultheria shallon), which had logarithmic 

burial functions, (2) an estimate of the plant height profile of each species, and (3) 

an assumption of equal distribution of biomass throughout the height profile. Our 

seasonal estimates of digestible energy and digestible protein concentrations in 

the food were specific to each species and plant part (leaf, twig) and came from an 

unpublished database (http://cervid.uaa.alaska.edu/deer/Home.aspx) based on the 

following studies plus other unpublished studies from southeast Alaska: Hanley and 

McKendrick (1983), Hanley et al. (1992), McArthur et al. (1993), Parker et al. (1999). 

All plant species, but only their current annual growth, were considered potential 

food resources.

We analyzed the food resources under eight different scenarios: two for summer when metabolic requirements differ (for maintenance only, and for maintenance plus lactation), and six in winter when forage availability differs greatly (for 

snow-free conditions and snow depths of 20, 40, 60, 80, and 100 cm). Snow depth

of 20 cm is enough to bury the ground-layer evergreen forbs but not enough to 

affect the availability of shrubs, while the deeper snow depths progressively bury 

more shrubs. Stated snow depths are for snow depth in a treeless open area; depth 

in each stand was reduced as an exponentially decreasing function of overstory 

canopy cover (Hanley and Rose 1987; see Hanley et al. 2012 for details). Metabolic 

requirements were the following: for summer maintenance, metabolizable energy 

(ME) 9839 kJ/day, dry matter digestibility (DMD) 50.0 percent, digestible protein 

(DP) 4.8 percent, and dry matter intake (DMI) 1220 g/day; for summer maintenance plus lactation, ME 12979 kJ/day, DMD 60.0 percent, DP 8.0 percent, and 

DMI 1340 g/day; for winter, ME 4019 kJ/day, DMD 48.0 percent, DP 1.8 percent, 

and DMI 525 g/day (see table 1 of Hanley et al. 2012 for rationale and sources). 
11

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

Statistical Analysis

Cover-to-biomass regression equations— 

All canopy cover-to-biomass regressions were approached as being of the form Y = 

aX, where Y is the biomass (ovendry g/m2), X is the canopy coverage (percentage), 

and a is the regression coefficient (slope) with the Y-intercept forced through the 

origin (0, 0). That is the most simple equation possible for this particular problem. 

After calculating the equation, residuals were examined for pattern, and adequacy 

of the equation was judged on the basis of r (correlation coefficient) and r

2

 (coefficient of determination). Possible transformations (e.g., arcsine for percentage data), 

Y-intercepts other than zero, and multiple regressions (e.g., Y = b1 + b2X + b3X2) 

were considered and used only if they improved the fit sufficiently (determined subjectively).

Silviculture treatment effects— 

The treatment effects in each experiment were analyzed in terms of deer habitat 

value (deer days per hectare) in a randomized complete block analysis of variance 

with blocked groups of stands as blocks, and silviculture treatments (including 

untreated control) as treatments. Our analysis focused on deer habitat values rather 

than any single or group of vegetation variables because the deer habitat values integrate the entire matrix of vegetation biomass and nutritional values into one number 

that is entirely relevant to deer. No single forage or group of forages is sufficient in 

itself; it is the interactive effect of all potential forages, their nutritional values, and 

deer nutritional requirements that determines the food resource value for deer. An 

alpha level of 0.05 was used throughout as the criterion of statistical significance. 

All analyses were performed with SAS software (SAS 2004).

Although silviculture treatments were designed to improve food resources 

for deer and to differ among treatments (as outlined in the “Introduction”), prior 

scientific investigation of silviculture effects in this region were too limited, and 

other empirical observations were too equivocal for us to have strong convictions 

about expected results. Therefore, all hypothesis testing was two-tailed and of null 

differences among treatments. Because blocks were widely distributed throughout 

the Tongass National Forest, we considered our scope of statistical inference to be 

Tongass-wide, recognizing that local patterns of variation may result from environmental factors such as extreme levels of herbivory in areas where deer densities 

might be exceptionally high.

Our analysis focused 

on deer habitat values 

rather than any single 

or group of vegetation 

variables because the 

deer habitat values 

integrate the entire 

matrix of vegetation 

biomass and nutritional 

values into one number 

that is entirely relevant 

to deer. 
12

research paper pnw-rp-593

Results

Cover-to-Biomass Regressions

Understory plant canopy coverage was a strong predictor of biomass, accounting 

for about 86 percent of the variation in biomass for the species and treatments 

throughout the four experiments (overall mean r2 = 0.857; see appendix 2 for 

species- and treatment-specific results). Regression r2 values ranged from 1.000 

to 0.175 (for Cornus canadensis and Rubus spectabilis twigs, respectively, both in 

Experiment II). As is common in cover-to-biomass regression relations, cover (a 

two-dimensional variable) tended to account for variation in biomass much better in 

strongly two-dimensional species (such as C. canadensis) than in strongly threedimensional species (such as R. spectabilis). Regressions for all major species were 

obtained for all treatments, but lesser species could not be sampled sufficiently for 

their own equations. Equations from surrogate species (species of similar growth 

form and appearance, for which we had sufficient data) were used for the lesser 

species, but in aggregate, total biomass of the lesser species was a relatively small 

proportion of the total understory. Virtually all regressions took the form Y = aX, 

with only a few including a non-zero Y-intercept. In none of the cases did residuals 

justify using either data transformations or more complex regressions than simple 

linear regression.

Sampling efficiency with the double-sampling technique of the first two field 

seasons (Experiments II and III) suffered from relatively small sample sizes (low 

frequency of occurrence within the 20 double-sampled quadrats per stand), but 

efficiency was increased greatly with the switch to targeting specific species in 

the third and fourth field seasons (Experiments IV and I). All treatments of 

Experiment I (red alder planting in 1 to 5-year-old stands) were considered similar 

environments, as the red alder was still a minor part of the vegetation in even the 

200 trees/ha treatment, so treatment-specific regressions were not calculated for 

Experiment 1.

One source of error that we did not account for was variation in degree of 

browsing by deer. With all regressions based on data from Prince of Wales Island 

and a moderate degree of browsing, our equations likely overestimated the biomass 

of shrub twigs and leaves per unit of canopy cover on heavily browsed shrubs at a 

few sites (blocks) subject to heavy browsing. That bias would have applied equally 

across all treatments within a block, however, so its effect on the statistical analysis 

would have been minimal. 
13

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

Experiment I (Red Alder Planting in 1- to 5-Year-Old Stands, 

8 Years Posttreatment)

Stands in Experiment I ranged in age from 2 to 8 years (mean = 4.2) when treated 

(McClellan and DeSanto, N.d.) so were about 12 years old when sampled posttreatment. The planted red alder trees were only about 5 to 6 m tall and fairly widely 

scattered in even the densest treatment. The understory of all three treatments was 

dominated by woody species (shrubs and trees); forbs and ferns were about equally 

dominant in the herbaceous layer, and graminoids were minor (table 2; woody 

species also have substantial biomass from prior years’ growth, although we did 

not measure that). No significant differences among treatments were found in deer 

habitat values (deer days/ha) for either of the two summer or six winter scenarios 

(table 3). Snow-free winter habitat values exceeded summer values for reproduction 

requirements (lactation) and were about equal to those for maintenance only. High 

snow-free values were the result of reduced dry-matter intake rates of deer in winter 

combined with high amounts of the key evergreen forb species. Habitat value 

dropped rapidly with increasing snow depth but remained above zero at depths 

of even 100 cm because of Alaska yellow-cedar (Chamaecyparis nootkatensis) 

remaining above the snowpack. Nutritional quality of Alaska yellow-cedar marginally exceeds deer nutritional requirements in winter (Hanley et al. 2012), so in 

combination with western hemlock (also available at snow depths of 100 cm), those 

two conifers provided some suitable food at even our deepest snow depth.

Experiment II (Thinning in 15- to 25-Year-Old Stands, 

5 Years Posttreatment)

Stands in Experiment II ranged in age from 17 to 26 years (mean = 21.9) when 

treated (McClellan and DeSanto, N.d.) so were about 27 years old when sampled 

posttreatment. Understories were strongly dominated by shrubs, even more so than 

in Experiment I and especially so in both thinning treatments (table 4). Thinning 

treatments resulted in shrub biomass levels similar to those of young clearcuts or 

even greater (compare with table 2), although herbaceous vegetation response was 

much less. Understory in untreated controls of Experiment II was much less than 

that of the younger clearcuts of Experiment I.

Both thinning treatments resulted in significantly higher deer habitat value 

(deer days/ha) than the untreated controls in all scenarios except the deepest snow 

conditions, where values were very low across all treatments (table 5). In summer, 

the wider spaced thinning (331 trees/ha) yielded higher habitat value than did the 

less intense thinning, at both sets of deer nutritional requirements. However, for 

winter scenarios, no significant differences were found between the two thinning 
14

research paper pnw-rp-593

Table 2—Mean (and standard error) biomass (ovendry current annual growth in kg/ha) of 

major vegetation groups and five key winter forages, 8 years posttreatment in Experiment 

I: untreated control and two levels of red alder planting in 1- to 5-year-old stands at time of 

treatment

Untreated control 50 trees per hectare 200 trees per hectare

 Kilograms per hectare

Total forbs 190.04 (28.54) 192.36 (26.59) 163.78 (23.03)

Total ferns 233.55 (26.03) 254.62 (30.82) 240.75 (31.47)

Total graminoids 23.90 (14.04) 30.46 (10.86) 6.34 (2.76)

Total shrubs 375.47 (22.12) 404.74 (26.00) 460.70 (35.66)

Total trees 198.88 (21.25) 224.12 (32.42) 206.54 (25.32)

Key winter forages:

Coptis aspleniifolia 16.78 (5.07) 10.91 (3.38) 15.12 (5.96)

Cornus canadensis 73.75 (13.35) 58.87 (12.39) 60.30 (12.11)

Rubus pedatus 8.06 (2.02) 10.20 (2.35) 10.95 (3.43)

Tiarella trifoliata 5.40 (1.11) 6.18 (2.15) 3.81 (1.20)

Vaccinium ovalifolium twigs 120.43 (11.67) 124.96 (12.49) 141.62 (17.69)

Note: See appendix 3 for all species-specific data.

Table 3—Mean (and standard error) deer habitat values (deer days/ha) of three treatments 

in Experiment I, 8 years posttreatment: untreated control and two levels of red alder 

planting in 1- to 5-year-old stands at time of treatment

Untreated control 50 trees per hectare 200 trees per hectare

Summer:

Maintenance only 703.1a

 (41.1) 758.4a (53.4) 718.0a (58.1)

Maintenance plus lactation 327.7a (39.2) 377.6a (40.7) 335.1a (39.4)

Winter, at snow depths in open:

0 cm 779.7a (65.4) 668.1a (76.2) 705.0a (90.4)

20 cm 192.9a (21.4) 166.6a (17.5) 209.1a (27.9)

40 cm 129.0a (17.3) 107.2a (13.4) 133.7a (19.6)

60 cm 88.7a (14.4) 70.6a (10.7) 87.2a (14.7)

80 cm 58.0a (12.0) 43.1a (8.6) 52.5a (11.0)

100 cm 29.6a (10.1) 20.3a (6.9) 22.7a (8.4)

Note: Values are for two levels of deer nutritional requirements in summer and six levels of snow depth 

in winter (snow depth in a treeless open area); means with same superscript within rows do not differ 

significantly at α of 0.05.
15

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

Table 4—Mean (and standard error) biomass (ovendry current annual growth in kg/ha) of 

major vegetation groups and five key winter forages, 5 years posttreatment in Experiment II: 

untreated control and two levels of thinning in 15- to 25-year-old stands at time of treatment

Untreated control 549 trees per hectare 331 trees per hectare

Kilograms per hectare

Total forbs 35.77 (10.06) 55.01 (8.58) 108.45 (26.73)

Total ferns 42.53 (15.01) 75.87 (10.54) 131.84 (22.12)

Total graminoids 2.18 (1.96) 1.61 (0.56) 5.62 (1.73)

Total shrubs 171.90 (37.92) 560.60 (42.56) 726.12 (59.41)

Total trees 13.18 (4.10) 35.16 (4.89) 73.84 (17.55)

Key winter forages:

Coptis aspleniifolia 2.38 (0.90) 4.01 (1.23) 7.04 (4.37)

Cornus canadensis 10.05 (3.45) 24.83 (4.30) 47.26 (15.49)

Rubus pedatus 1.99 (0.64) 8.47 (1.59) 8.99 (2.36)

Tiarella trifoliata 1.69 (0.45) 4.11 (2.54) 3.88 (1.09)

Vaccinium ovalifolium twigs 26.26 (6.87) 69.12 (11.00) 110.57 (20.30)

Note: See appendix 3 for all species-specific data.

Table 5—Mean (and standard error) deer habitat values (deer days/ha) of three treatments in 

Experiment II, 5 years posttreatment: untreated control and two levels of thinning in 15- to 

25-year-old stands at time of treatment 

Untreated control 549 trees per hectare 331 trees per hectare

Summer:

Maintenance only 194.0a

 (42.2) 587.8b (43.7) 812.5c (51.2)

Maintenance plus lactation 88.1a (19.4) 232.4b (24.8) 364.3c (31.5)

Winter, at snow depths in open:

0 cm 103.0a (27.5) 302.2b (37.8) 406.5b (57.3)

20 cm 29.4a (7.70) 92.0b (15.9) 132.4b (25.9)

40 cm 19.6a (4.9) 56.7b (10.1) 77.1b (14.9)

60 cm 12.3a (3.0) 33.7b (6.2) 44.0b (8.4)

80 cm 7.2a (1.7) 17.3b (3.5) 20.5b (1.0)

100 cm 3.1a (3.6) 4.5a (1.9) 2.0a (0.4)

Note: Values are for two levels of deer nutritional requirements in summer and six levels of snow depth in winter (snow depth in 

a treeless open area); means with same superscript within rows do not differ significantly at α of 0.05.
16

research paper pnw-rp-593

intensities. Snow-free winter habitat values were similar to those of summer for 

reproduction (lactation) requirements, and as in Experiment I, all winter values 

dropped rapidly with increasing snow depth.

Experiment III (Thinning and Pruning in 25- to 35-Year-Old 

Stands, 6 Years Posttreatment)

Stands in Experiment III ranged in age from 25 to 37 years (mean = 29.0) when 

treated (McClellan and DeSanto, N.d.) so were about 35 years old when sampled 

posttreatment. Again, a strong shrub response occurred in all three thinning treatments, with shrubs strongly dominating all understories (table 6), and biomass of 

untreated controls continued to decrease from levels observed in younger stands 

(compare with tables 2 and 4). Although the thinning with 25 percent pruning treatment appeared to result in the greatest biomass response, including that of the key 

winter forages for deer (table 6), differences in deer habitat values among thinning 

treatments were very few, and none in winter (table 7). All thinning treatments had 

higher deer habitat values than did the untreated controls in all scenarios except the 

two deepest snow depths, but only in the summer maintenance-only scenario did 

a difference among thinning treatments occur—thinning with 25 percent pruning 

yielding the highest values. As in Experiment II, snow-free winter habitat values 

were very similar to summer values for reproduction requirements, and all values 

dropped rapidly with increasing snow depth.

Experiment IV (Thinning by Felling, With or Without Slash 

Treatment, or Girdling in >35-Year-Old Stands, 4 Years 

Posttreatment)

Stands in Experiment IV ranged in age from 33 to 60 years (mean = 43.2) when 

treated (McClellan and DeSanto, N.d.) so were about 47 years old when sampled 

posttreatment. This experiment included four thinning treatments, and shrubs 

dominated the understories of each (table 8), while biomass in the untreated 

controls continued to decline below that of untreated controls in the other three 

experiments (compare with tables 2, 4, and 6). All thinning treatments resulted in 

greater summer habitat values for deer than did the untreated controls, but none of 

the thinning treatments differed from one another (table 9). Results from winter 

scenarios for deer habitat value were more complicated in Experiment IV than in 

the other three experiments, with much overlap of nonsignificant differences among 

treatments (and controls) and with patterns shifting in relation to snow depth. For 

snow-free conditions, all thinning treatments yielded higher values than did the 

untreated controls, with thinning by girdling yielding significantly highest values. 

Thinning by girdling consistently yielded the highest values throughout all winter 
17

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

Table 6—Mean (and standard error) biomass (ovendry current annual growth in kg/ha) of major 

vegetation groups and five key winter forages, 6 years posttreatment in Experiment III: untreated 

control, thinning (to 420 trees/ha) alone, and thinning with two levels of pruning (25 percent and 50 

percent of trees) in 25- to 35-year-old stands at time of treatment 

 Thinning with 25 Thinning with 50

Untreated control Thinning alone percent pruning percent pruning

 Kilograms per hectare

Total forbs 7.78 (3.49) 29.98 (8.65) 54.57 (15.88) 26.75 (7.34)

Total ferns 10.56 (4.22) 51.43 (9.23) 80.20 (17.63) 90.48 (21.65)

Total graminoids 0.06 (0.04) 4.23 (2.35) 3.04 (1.15) 2.68 (0.94)

Total shrubs 49.82 (19.36) 221.02 (41.18) 368.00 (71.04) 186.53 (36.41)

Total trees 7.26 (3.89) 11.03 (2.50) 17.46 (3.58) 8.08 (3.44)

Key winter forages:

Coptis aspleniifolia 0.44 (0.24) 1.98 (1.43) 2.10 (0.85) 2.84 (1.43)

Cornus canadensis 1.78 (1.31) 12.20 (4.01) 23.25 (7.61) 9.43 (2.52)

Rubus pedatus 0.31 (0.21) 2.58 (1.07) 5.33 (1.52) 6.16 (2.21)

Tiarella trifoliata 1.58 (0.81) 0.03 (0.03) 4.46 (1.65) 2.99 (0.98)

Vaccinium ovalifolium twigs 5.62 (2.05) 31.96 (8.10) 46.80 (12.44) 17.86 (4.41)

Note: See appendix 3 for all species-specific data.

Table 7—Mean (and standard error) deer habitat values (deer days/ha) of four treatments in 

Experiment III, 6 years posttreatment: untreated control, thinning (to 420 trees/ha) alone, and 

thinning with two levels of pruning (25 percent and 50 percent of trees) in 25- to 35-year-old stands at 

time of treatment

 Thinning with 25 Thinning with 50

Untreated control Thinning alone percent pruning percent pruning

Summer:

Maintenance only 60.3a

 (22.0) 257.4b (43.5) 415.5c (79.1) 235.8b (49.7)

Maintenance plus lactation 29.4a (11.3) 118.6b (19.4) 167.7b (40.6) 103.9b (26.1)

Winter, at snow depths in open:

0 cm 26.6a (10.0) 125.3b (32.8) 207.5b (51.0) 114.1b (29.8)

20 cm 6.6a (2.3) 31.0b (7.5) 44.3b (11.2) 22.6b (5.7)

40 cm 4.5a (1.5) 19.1b (4.5) 27.1b (6.7) 14.9b (4.0)

60 cm 3.0a (1.0) 11.5b (2.7) 16.2b (3.9) 9.7b (2.8)

80 cm 2.5a (1.0) 6.0a (1.4) 8.5a (2.0) 5.9a (3.6)

100 cm 1.0a (0.5) 1.7a (0.5) 2.3a (0.5) 2.7a (1.2)

Note: Values are for two levels of deer nutritional requirements in summer and six levels of snow depth in winter (snow depth in a treeless 

open area); means with same superscript within rows do not differ significantly at α of 0.05.
18

research paper pnw-rp-593

Table 8—Mean (and standard error) biomass (ovendry current annual growth in kg/ha) of major vegetation 

groups and five key winter forages, 4 years posttreatment in Experiment IV: untreated control, conventional 

thinning (to 203 trees/ha) alone, thinning with two levels of bucking the slash (to 1.5-m and 4.6-m lengths), 

and thinning by girdling in >35-year-old stands at time of treatment

Untreated Conventional Thinning with Thinning with Thinning by

control thinning alone 1.5-m bucking 4.6-m bucking girdling

Kilograms per hectare

Total forbs 9.62 (3.75) 20.32 (4.95) 16.52 (4.57) 32.29 (8.88) 26.46 (8.77)

Total ferns 11.53 (5.09) 85.70 (16.86) 29.68 (4.59) 96.44 (17.00) 44.69 (8.41)

Total graminoids 0.00 (0.00) 6.14 (2.83) 2.23 (1.06) 6.43 (4.38) 3.12 (2.12)

Total shrubs 22.28 (9.77) 110.90 (20.67) 104.15 (24.25) 97.20 (19.97) 138.77 (24.09)

Total trees 2.91 (2.04) 33.91 (6.99) 16.73 (3.57) 31.98 (5.92) 17.76 (4.01)

Key winter forages:

Coptis aspleniifolia 0.11 (0.07) 0 (0) 0.01 (0.01) 0.02 (0.02) 0.15 (0.13)

Cornus canadensis 1.10 (0.81) 3.58 (1.53) 4.80 (3.05) 1.56 (0.55) 3.54 (1.26)

Rubus pedatus 0.79 (0.69) 1.19 (0.56) 1.32 (0.77) 0.74 (0.31) 1.53 (0.68)

Tiarella trifoliata 2.68 (2.13) 6.96 (2.89) 1.44 (0.71) 2.03 (1.50) 11.91 (8.12)

Vaccinium ovalifolium twigs 7.65 (3.66) 10.56 (3.92) 16.15 (7.62) 21.08 (6.24) 29.84 (8.03)

Note: See appendix 3 for all species-specific data.

Table 9—Mean (and standard error) deer habitat values (deer days/ha) of five treatments in Experiment IV, 4 

years posttreatment: untreated control, conventional thinning (to 203 trees/ha) alone, thinning with two levels 

of bucking the slash (to 1.5-m and 4.6-m lengths), and thinning by girdling in >35-year-old stands at time of 

treatment

 Conventional Thinning Thinning

Untreated Thinning with 1.5-m with 4.6-m Thinning by

control alone bucking bucking girdling

Summer:

Maintenance only 33.3a (12.2) 188.5b (32.3) 127.0b (21.0) 178.2b (23.8) 172.3b (28.2)

Maintenance plus lactation 15.7a (5.8) 98.7b (16.3) 58.2b (7.7) 94.4b (17.1) 80.1b (15.2)

Winter, at snow depths in open:

0 cm 30.4a (12.7) 96.1b (24.7) 69.5b (25.5) 68.0b (13.1) 119.8c (26.1)

20 cm 7.1a (3.3) 10.5a (3.4) 14.2a (6.3) 20.1b (5.8) 28.0b (7.8)

40 cm 6.6a (3.1) 9.3a (3.0) 12.5a (5.6) 17.8b (3.0) 24.9b (6.9)

60 cm 5.6a (2.6) 7.7a (2.5) 10.3a (4.6) 14.7a (4.2) 20.5b (5.7)

80 cm 4.7a (2.1) 6.0a (1.9) 8.1a (3.6) 11.5a (3.3) 16.2b 4.5)

100 cm 3.7a (1.7) 4.4a (1.4) 5.8a (2.6) 8.4a (2.4) 11.8b (3.2)

Note: Values are for two levels of deer nutritional requirements in summer and six levels of snow depth in winter (snow depth in a treeless open area); 

means with same superscript within rows do not differ significantly at α of 0.05.
19

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

scenarios, but the results for other thinning treatments did not differ from those 

of the controls in any of the snow depths >0 cm (except for thinning with 4.6-m 

bucking at 20- and 40-cm snow depths, which were similar to thinning by girdling). 

Although total shrub response was strong in all thinning treatments (table 8), most 

of that response was from salmonberry, which is a good summer forage but poor 

winter forage (Hanley and McKendrick 1983), while the blueberry response was 

greatest in the thinning by girdling treatment (table 8 and app. 3).

The strong response of thinning by girdling is especially surprising in that 

failure of the girdling was common among the replicates, making the treatment 

more similar to conventional thinning alone. The boles of many trees that had 

been girdled subsequently broke at the girdle, with the tree falling as though it had 

been felled. Such failure of girdling was widespread through this experiment and 

widespread through many of its stands, with almost an even distribution of failure 

ranging from 3 to 96 percent of the girdled trees in a stand (table 10). We did not 

know when the failures occurred, however, only that they had occurred by the time 

Table 10—Effect of girdling failure (percentage of girdled trees found snapped) 

on deer habitat values (deer days/ha) for two summer scenarios differing in 

nutritional requirements (maintenance only and maintenance plus lactation) and 

two winter scenarios differing in open-area snow depth (0 and 20 cm)

Percentage of Summer Winter

trees snapped Maintenance Lactation 0 cm snow 20 cm snow

3 186.9 104.4 63.3 10.1

11 40.9 15.1 49.0 7.4

20 8.4 3.4 0.0 0.0

22 382.7 209.2 360.0 0.3

27 140.6 44.8 85.2 15.4

29 422.6 204.6 338.6 87.2

32 112.8 46.9 48.7 16.4

35 165.6 44.2 235.0 92.8

42 24.0 9.2 22.9 4.8

56 194.4 92.3 138.3 53.9

61 151.9 56.9 177.9 62.9

66 101.6 52.7 19.2 1.7

71 168.3 101.1 57.9 6.3

74 70.4 25.7 43.9 18.9

85 230.4 127.8 98.9 0.1

95 299.8 143.5 110.1 27.5

96 228.1 79.8 187.1 70.6

Correlation coefficient 0.139 0.098 -0.069 0.137

Note: The correlation coefficient between percentage of trees snapped and deer days/ha is also shown for each 

scenario.
20

research paper pnw-rp-593

that we sampled the stands 4 years after their treatment. The correlation between 

girdling failure (percentage of trees found snapped) and deer habitat values (deer 

days/ha) was nil (P > 0.50 in all cases; table 10; r0.50(2),16 = 0.170).

Discussion

Effects of Logging and Thinning Slash on Habitat Availability

Abundance of logging and thinning slash (residual woody stems and branches from 

felled trees) is highly variable within and among treated stands and can be great 

enough to restrict movement of deer and limit their access to vegetation within a 

stand. The FRESH model does not include consideration of such effects, as the 

FRESH analysis is based on just the vegetation alone. If we could predict how a 

given amount of slash would reduce access by deer, then we could reduce the deer 

days/ha result accordingly (e.g., a 15-percent reduction in access would result in a 

15-percent reduction in the deer days/ha value because the area available to deer 

would be 15 percent less), but quantitative relations between slash and access by 

deer are unknown. Therefore, although effects of slash have not been included in 

our analyses, one should be aware that slash can be an important variable affecting 

habitat use by deer. 

Snow Depth and Its Interaction With Shrubs and Slash

Similarly, modeling the effects of snow with the FRESH model is somewhat problematic for all treatments except the untreated controls of Experiments II through 

IV because all other treatments had very shrubby understories and large amounts 

of thinning or logging slash on the ground. The FRESH snow burial process works 

from the ground up (e.g., a snow depth of 35 cm buries all plant material within 35 

cm of the ground surface), which is reasonable within relatively open understories 

typical of forests. However, in open-grown understories strongly dominated by 

shrubs, the shrubs may grow so densely that they intercept the snow and hold it 

above the ground surface, increasing the effective burial depth significantly. Similarly, the same may happen with large amounts of thinning or logging slash lying on 

the ground. Shrubs and slash together are even more likely to produce that effect. 

However, we know of no data to quantify the effect. The FRESH snow submodel 

includes an optional “shrub/slash interaction” coefficient that effectively increases 

the snow depth when it is assigned a value greater than 1.0 (Hanley et al. 2012), but 

because the choice of any such value would be subjective in the absence of data, we 

did not use that option (i.e., we used its default value of 1.0 for all our analyses). 

Snow depths of 0 and 20 cm are the most meaningful and important depths 

to consider. Snow-free conditions occur frequently at lower elevations throughout 

Although effects 

of slash have not 

been included in our 

analyses, one should 

be aware that slash 

can be an important 

variable affecting 

habitat use by deer.
21

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

winters in southeast Alaska, and the snow-free analysis makes use of the entire 

understory vegetation. Low-growing evergreen forbs are particularly important 

contributors to deer habitat in snow-free conditions (Hanley and McKendrick 1984, 

Hanley et al. 1989, Parker et al. 1999). Snow depth of 20 cm (actually, it is less than 

that in the stand after being reduced by the effect of overstory canopy) is also an 

important depth, as it is deep enough to bury the evergreen forbs but unlikely to 

affect the availability of the shrub layer. Thus, the contrast between snow depths of 

0 and 20 cm can be thought in terms of the complete understory vegetation (0 cm) 

versus only the shrub and tree layers (20 cm). We have analyzed effects of snow 

at progressively greater depths (40 through 100 cm) to provide perspective of the 

relative availability and quality of the shrub and tree layers at progressively greater 

heights above the ground, but the reliability of those results is less than those for the 

0- and 20-cm depths. The values for deeper snow are most meaningful for relative 

(not absolute) comparisons of increasing depth within a treatment and for treatments and experiments at a given depth.

Limiting Factors

The FRESH analysis not only provides an estimate of the value of a given habitat 

in deer days/ha, it also identifies the most important factor limiting that value. The 

habitat value may be limited by either (or both) of the nutritional constraints or the 

total amount of vegetation biomass available (Hanley et al. 2012). An understanding of potential limiting factors provides insight into the relative tradeoffs between 

quantity and quality of the forage resources, as quantity and quality are not substitutable for one another (Hobbs and Hanley 1990), and habitat value cannot be 

determined by simply multiplying the two together (Wallmo et al. 1977).

Examination of limiting factors (digestible protein vs. dry matter digestibility 

vs. total available biomass) for the four most important nutritional/snow scenarios 

(summer with two levels of nutritional requirements and winter at snow depths of 0 

and 20 cm) across all treatments in each of the four experiments (table 11) reveals 

several important results:

1. Digestible protein was virtually never the limiting factor; it was limiting in 

only 1 of the 938 cases analyzed. That result seems common for forb- and 

shrub-rich forest understories of southeast Alaska (Hanley and McKendrick 

1984, Hanley et al. 2006, Parker et al. 1999) even though it contrasts 

sharply with bunchgrass-dominated habitat of eastern Washington for 

black-tailed deer (Wagoner 2011, Wagoner et al. 2013) and willowdominated ranges of south central Alaska for moose (Alces americanus) 

(McArt et al. 2009). 

The contrast between 

snow depths of 0 and 

20 cm can be thought in 

terms of the complete 

understory vegetation 

(0 cm) versus only the 

shrub and tree layers 

(20 cm). We have 

analyzed effects of 

snow at progressively 

greater depths (40 

through 100 cm) to 

provide perspective of 

the relative availability 

and quality of the 

shrub and tree layers 

at progressively 

greater heights above 

the ground, but the 

reliability of those 

results is less than 

those for the 0- and 

20-cm depths.
22

research paper pnw-rp-593

2. Dry matter digestibility (and by implication, digestible energy) was by 

far the most common limiting factor (762 of 938 cases, or 81 percent of

the cases). 

3. In the 19 percent of the cases in which total available biomass (no nutritional limitation) was limiting, it was in summer for maintenance only 

(low nutritional requirements) and snow-free winter (minimal nutritional 

requirements combined with high forage quality of evergreen forbs), which 

are the most relaxed nutritional settings. At higher nutritional requirements 

in summer (lactation) or poorer quality food resources (only shrubs) in winter, dry matter digestibility was the limiting factor in virtually all cases.

The overwhelming importance of dry matter digestibility (calculated on 

requirements for digestible energy) as a nutritional limitation emphasizes its importance in evaluating deer habitat and understory response to silviculture in southeast 

Table 11—Limiting factors

Number of Limiting factor (number of cases)

Experiment/scenario cases DP DMD Biomass

Experiment I:

Summer, maintenance 51 0 34 17

Summer, lactation 51 0 51 0

Winter, snow-free 51 0 36 15

Winter, 20 cm snow 51 0 51 0

Experiment II:

Summer, maintenance 48 0 22 26

Summer, lactation 48 0 48 0

Winter, snow-free 48 0 33 15

Winter, 20 cm snow 48 0 48 0

Experiment III:

Summer, maintenance 52 0 23 29

Summer, lactation 52 0 51 1

Winter, snow-free 52 0 35 17

Winter, 20 cm snow 52 0 52 0

Experiment IV:

Summer, maintenance 85 0 52 33

Summer, lactation 85 1 84 0

Winter, snow-free 83 0 61 22

Winter, 20 cm snow 81 0 81 0

Note: The limiting factor is that which most limited the total amount of biomass in the solution set, whether it 

was a nutritional constraint or total biomass availability. Values in the table are the number of cases where the 

FRESH solution was most limited by digestible protein (DP), dry matter digestibility (DMD), or total available 

biomass (biomass; i.e., neither DP nor DMD was limiting), and the total number of cases analyzed in each 

experiment for each of four nutritional/snow scenarios. Number of cases analyzed differed for scenarios within 

Experiment IV when there was no solution for a case (i.e., 0 deer days/ha).
23

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

Alaska. It does not mean that digestible protein is not important; it means only that 

digestible energy tends to be the most limiting factor in these forest environments, 

even in summer when protein limitations are most likely. That is why leaves of 

preferred shrubs (e.g., blueberry, salmonberry, devilsclub [Oplopanax horridus] in 

summer) and especially forbs, which as a group have high dry matter digestibility, 

are so important to deer. Total understory biomass of current annual growth is 

seldom a sufficient measure of habitat quality for deer because it most likely (about 

80 percent probability in the TWYGS experiments) is composed of too much lowdigestibility forage. 

Oval-leaf blueberry (V. ovalifolium), a very common and often dominant shrub, 

illustrates the important tradeoffs among limiting factors. High nutritional requirements for lactation make summer a potentially stressful time in terms of both 

protein and energy requirements (Parker et al. 1999). As an understory dominant, 

oval-leaf blueberry often composes much of the available food resources, both 

within stands and across landscapes. The nutritional quality of its leaves, however, 

is very different in sunny habitats versus shady forest understories: digestible 

protein concentrations are lower and dry matter digestibility is greater in sun-grown 

than shade-grown habitats (Hanley et al. 1992, McArthur et al. 1993, Rose 1989, 

Van Horne et al. 1988), primarily because of high concentrations of both tannins 

(reducing protein digestion) and starches (increasing dry matter digestibility) in the 

sun-grown leaves (Rose 1989, Van Horne et al. 1988). Therefore, although oval-leaf 

blueberry contributes significantly to the digestible protein component of deer 

diets in shady forests and very little to the digestible protein component in sunny 

habitats, its more important role in the overall landscape during summer is its 

contribution to the digestible energy of diets from sunny habitats. Summer protein 

requirements in sunny habitats and summer energy requirements in shady forests 

are met by forages other than oval-leaf blueberry. Thus, oval-leaf blueberry fulfills 

different dietary roles in different habitats but is not sufficient without other forages. This is an example of the importance of variety both in forages and in habitats 

for deer. 

Protein requirements for deer are relatively low in winter and unlikely to be 

limiting then, while dry matter digestibility of winter forages (and, therefore, 

digestible energy) is low and very likely to be limiting in winter, especially when 

snow is on the ground (Hanley and McKendrick 1985, Parker et al. 1999). Blueberry 

twigs are the most nutritious common browse forage for deer in winter, yet they 

only marginally meet the digestible energy requirement then (e.g., see table 1 and 

appendix 2 in Hanley et al. 2012). That is why the evergreen forbs, with digestible 

energy concentrations well above required levels, are so important in winter; by 

mixing evergreen forbs with blueberry twigs in the diet, a much greater proportion 

Total understory 

biomass of current 

annual growth is 

seldom a sufficient 

measure of habitat 

quality for deer 

because it most likely 

(about 80 percent 

probability in the 

TWYGS experiments) is 

composed of too much 

low-digestibility forage.
24

research paper pnw-rp-593

of the available twigs can be included in the FRESH solution set (suitable food 

supply). The relatively high availability of evergreen forbs in snow-free winter 

habitats was the driving force behind total forage biomass being the limiting factor 

in all 29 percent of the snow-free cases that were biomass limited in the TWYGS 

experiments (all 69 of the 234 cases for snow depth of 0 cm, table 11). However, 

our analyses assumed that evergreen forbs maintained 100 percent of their summer 

availability in winter. If that is not true, then our winter snow-free habitat values 

would be lower, and dry matter digestibility would be the dominant limiting factor 

even more strongly than we have concluded. 

Patterns Across Experiments—Vegetation

The experimental design of the TWYGS study was not intended for making statistical comparisons across experiments; however, differences in responses among 

experiments were large, and general patterns are clearly evident. Understory 

vegetation of the untreated controls decreased sharply with advancing stand age, 

with total understory biomass of Experiment IV (>35 years old) being only about 

5 percent of that of Experiment I (1 to 5 years old) (47 versus 1022 kg/ha; fig. 1), 

which is exactly the pattern to be expected for postlogging secondary succession 

in this region (Alaback 1982). There was no effect of alder-planting treatments 

in Experiment I, but the various kinds of thinning treatments in Experiments II 

through IV increased total understory biomass by 3.4 to 5.1 times that of corresponding untreated controls. Although the relative response to silviculture 

treatment (treatment:control) was greater in the older age classes (3.4:1 versus 5.1:1 

versus 4.9:1 for Experiments II, III, and IV, respectively), the decreasing total biomass with advancing stand age resulted in absolute responses being much greater in 

younger stands than in older stands (887, 385, 231 kg/ha for Experiments II, III, and 

IV, respectively) (fig. 1). Across all experiments and treatments, shrubs dominated 

the understory vegetation, and understory composition (proportion of biomass 

comprised of forbs, ferns, shrubs, etc.) did not differ much between silviculture 

treatments and untreated controls. Importantly for deer habitat, forbs remained a 

significant component of all understory plant communities, whether stands had 

been treated or not–they comprised about 9 to 21 percent of the total current annual 

growth across all treatments (fig. 1), although ferns tended to be more abundant 

than forbs throughout. 

For perspective, the total understory biomass of current annual growth and its 

proportional distribution within 31 stands of the most common community types 

(“Vaal-Coca” and “Vaal-Lyam”) of old-growth forest on Admiralty and Prince of 

Wales Islands, southeast Alaska, was 353 kg/ha, of which 31 percent was forbs, 9 

There was no effect 

of alder-planting 

treatments in 

Experiment I, but 

the various kinds of 

thinning treatments in 

Experiments II through 

IV increased total 

understory biomass 

by 3.4 to 5.1 times 

that of corresponding 

untreated controls.
25

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

Figure 1—Total biomass (ovendry kg/ha) of current annual growth of understory vegetation and its proportional 

distribution among forbs, ferns, graminoids, shrubs, and trees in each of the four experiments, shown separately for 

untreated controls and pooled across all silviculture treatments within each experiment. Circles are proportional to 

total biomass.

Experiment I

Experiment II

Experiment III

Experiment IV

Forbs

Ferns

Graminoids

Shrubs

Trees

CONTROLS TREATMENTS

1022 kg/ha 1094 kg/ha

266 kg/ha 887 kg/ha

74 kg/ha 385 kg/ha

47 kg/ha 231 kg/ha
26

research paper pnw-rp-593

percent ferns, <1 percent graminoids, 49 percent shrubs, and 11 percent tree seedlings (Hanley and Brady 1997). Depending on stand age and treatment, our younggrowth stands tended to have more ferns (12 to 28 percent) and fewer forbs (9 to 21 

percent) than those old-growth stands.

A comparison of biomass of western hemlock and Sitka spruce within the 

understory vegetation provides an indication of evidence for or against the potential 

“hemlock flush” that might be expected in response to thinning young-growth 

stands (Deal and Farr 1994, Tappeiner and Alaback 1989), as western hemlock 

was the species reported to exhibit the response. Stands in Experiment I were too 

young and were not thinned, but the high biomass of western hemlock relative to 

Sitka spruce occurred in virtually all of the thinning treatments of Experiments II 

through IV, whereas that pattern did not occur in the untreated controls (table 12). 

Table 12—Biomass (ovendry, in kilograms per hectare) of current 

annual growth of western hemlock (TSHE) and Sitka spruce (PISI) 

within the understory vegetation in each treatment of Experiments 

I through IV when sampled posttreatment

Ovendry biomass 

Experiment/treatment TSHE PISI

 Kilograms per hectare

Experiment I (1 to 5 years old)

Untreated controls 62.0 55.5

Red alder at 50 trees per hectare 63.4 70.6

Red alder at 200 trees per hectare 63.4 85.5

Experiment II (15 to 25 years old)

Untreated controls 2.3 8.9

Thinned to 549 trees per hectare 29.5 3.2

Thinned to 331 trees per hectare 44.9 20.1

Experiment III (25 to 35 years old)

Untreated controls 4.8 2.0

Thinning alone 10.1 0.4

Thinning with 25 percent pruning 11.2 5.1

Thinning with 50 percent pruning 3.9 2.4

Experiment IV (>35 years old)

Untreated controls 0.5 2.3

Conventional thinning 28.4 4.4

Thinning with 1.5-m bucking 12.7 2.7

Thinning with 4.6-m bucking 26.6 3.7

Thinning by girdling 14.6 2.8
27

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

Figure 2—Summer deer habitat values (deer days/ha) for two levels of nutritional requirements 

(maintenance only and maintenance plus lactation) for all treatments within all four Tongass-Wide 

Young-Growth Studies (TWYGS) experiments and two data sets for old-growth forests (data set 1 = 

unpublished Tongass National Forest data for 100 stands of the Size Density Model; data set 2 = 31 

stands from Hanley and Brady 1997 for “Vaal-Coca” and “Vaal-Lyam” community types). “Maintenance” values are the full height of the bar; “Lactation” values are the height of only the lower bar. 

Values of treatments that did not differ (P > 0.05) within an experiment have been pooled. TWYGS 

treatments are as follows: Experiment I (1 = untreated controls; 2 = red alder at 50 trees/ha; 3 = red 

alder at 200 trees/ha); Experiment II (1 = untreated controls; 2 = thinning to 549 trees/ha; 3 = thinning to 331 trees/ha); Experiment III (1 = untreated controls; 2 = thinning alone; 3 = thinning with 

25 percent pruning; 4 = thinning with 50 percent pruning); Experiment IV (1 = untreated controls; 

2 = conventional thinning; 3 = thinning with 1.5-m bucking; 4 = thinning with 4.6-m bucking; 5 = 

thinning by girdling).

Although four to five years posttreatment is too early to judge the fate of understory 

hemlock, results from Experiments II through IV will likely be telling in years 

ahead.

Patterns Across Experiments—Deer Habitat Values

Patterns in deer habitat values (deer days/ha) closely paralleled the patterns in 

understory biomass, with both summer and winter values greatest in the youngest 

stands (Experiment I) and decreasing through Experiment IV (figs. 2 and 3). 

Similarly, all silviculture treatments in Experiments II through IV significantly 

exceeded their corresponding untreated controls in summer and in all but two 

treatments of Experiment IV in winter. Although among-treatment differences were 

apparent in Experiments II and III in summer, very few differences (other than 

with controls) were evident in winter. For perspective, we also calculated summer 

and winter habitat values for two sets of old-growth forest understory data: (1) 100 

stands from the Accuracy Assessment Study of the Tongass Size-Density Model 

(Caouette and DeGayner 2005, 2008) from randomly selected stands throughout 

southeast Alaska (Tongass National Forest, unpublished data on file, Ketchikan and 

Juneau, Alaska) for the five mid-volume classes (classes 4N, 4S, 5H, 5N, 5S), and 
28

research paper pnw-rp-593

(2) 31 stands of the two most common community types (“Vaal-Coca” and “VaalLyam”) of old-growth forest on Admiralty and Prince of Wales Islands, southeast 

Alaska (Hanley and Brady 1997). Summer and winter values of old-growth forests 

exceeded those of untreated controls in all experiments except the young clearcuts 

of Experiment I, as expected, but thinning treatments exceeded or approached the 

values for old growth in summer for Experiments II and III (fig. 2) and in winter for 

Experiment II (fig. 3). The young clearcuts of Experiment I exceeded values for old 

growth in all of the summer and low-snow winter scenarios analyzed. Although the 

heaviest thinning treatment of Experiment II (331 trees/ha) yielded exceptionally 

high values for both summer scenarios (fig. 2), there was nothing exceptional about 

it in either winter scenario (fig. 3).

The drop in habitat values with increasing snow depth was common to all 

treatments of all experiments (fig. 4, for Experiments II through IV) and is to be 

expected with increased burial of forage by snow. The immediate effect of losing 

the ground-layer evergreen forbs within the first 10 cm of depth is always most 

pronounced, however, because loss of the forb component of the diet results in a 

sharp drop in digestible energy as only shrubs and trees remain available. Although 

Figure 3—Winter deer habitat values (deer days/ha) for two levels of snow depths (0 cm and 20 cm) 

for all treatments within all four Tongass-Wide Young-Growth Studies (TWYGS) experiments and 

two data sets for old-growth forests (data set 1 = unpublished Tongass National Forest data for 100 

stands of the Size Density Model; data set 2 = 31 stands from Hanley and Brady 1997 for “VaalCoca” and “Vaal-Lyam” community types). “Snow-free” values are the full height of the bar; “20 

cm snow” values are the height of only the lower bar. Values of treatments that did not differ (P > 

0.05) within an experiment have been pooled. TWYGS treatments are as follows: Experiment I (1 

= untreated controls; 2 = red alder at 50 trees/ha; 3 = red alder at 200 trees/ha); Experiment II (1 = 

untreated controls; 2 = thinning to 549 trees/ha; 3 = thinning to 331 trees/ha); Experiment III (1 = 

untreated controls; 2 = thinning alone; 3 = thinning with 25 percent pruning; 4 = thinning with 50 

percent pruning); Experiment IV (1 = untreated controls; 2 = conventional thinning; 3 = thinning 

with 1.5-m bucking; 4 = thinning with 4.6-m bucking; 5 = thinning by girdling).

The immediate effect 

of losing the groundlayer evergreen forbs 

within the first 10 cm of 

depth is always most 

pronounced, however, 

because loss of the 

forb component of the 

diet results in a sharp 

drop in digestible 

energy as only shrubs 

and trees remain 

available.
29

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

Figure 4—Deer habitat values (deer days/ha) of Tongass-Wide Young-Growth Studies (TWYGS) treatments in relation to increasing 

snow depth (centimeters in an open, treeless area): (a) Experiment II (untreated controls, thinning to 549 trees/ha, and thinning to 

331 trees/ha), (b) Experiment III (untreated controls, thinning only, thinning with 25 percent pruning, and thinning with 50 percent 

pruning), (c) Experiment IV (untreated controls, conventional thinning, thinning and bucking slash to 1.5-m lengths, thinning and 

bucking slash to 4.6-m lengths, and thinning by girdling), and (d) all silviculture treatments (exclusive of controls) combined for each 

experiment. In (a) through (c), values that did not differ between treatments at a given snow depth have been pooled.

the general pattern of silviculture treatments being significantly greater than the 

untreated controls, and mostly similar to one another within each experiment, 

repeated in all of thinning Experiments II through IV (figs. 4a through 4c), the 

magnitudes of the responses differed greatly among experiments, declining with 

advancing age of the stands (fig. 4d). In both summer (fig. 2) and winter at all snow 

depths less than 60 to 80 cm (fig. 4), silviculture treatments increased deer habitat 

value, and the increases were greater with younger stand ages, but treatment effects 

decreased relative to controls with increasing snow depth. Actual values of dense 

shrubby and slash-filled habitats might be even lower than indicated by their forages alone, especially with increasing snow depth (see discussion above of “Snow 

Depth and Its Interaction With Shrubs and Slash”).
30

research paper pnw-rp-593

Management Implications

The TWYGS experiments are the most widespread, comprehensive, and strongly 

replicated study of young-growth forest management and its effects on black-tailed 

deer habitat ever conducted in southeast Alaska, but they are not the first. Studies 

of precommercial thinning (Cole et al. 2010), “commercial” thinning (Zaborske et 

al. 2002), canopy-gap thinning (Alaback, unpublished report),9 and even red alder 

as an alternative pathway of secondary succession (Hanley 2005, Hanley et al. 

2006) have been conducted and evaluated with the FRESH model for their implications for deer habitat. Although a long-term study of the effects of pruning on tree 

growth and wood quality was initiated in the early 1990s (Petruncio 1994), its 

effects on understory vegetation have not been quantified, so the TWYGS Experiment III is the first analysis of the effects on deer habitat of pruning in the region, 

and the TWYGS Experiment IV is the first of any kind of study of the effects 

of thinning by girdling in southeast Alaska. Taken together, all these studies are 

beginning to develop a scientific basis for managing young-growth forests for Sitka 

black-tailed deer. 

Surprisingly, quantification of species-specific biomass of current annual 

growth (and plant part) in young clearcuts has seldom been done in southeast 

Alaska. Aside from the TWYGS Experiment I, the only other study we know of 

was that by Sealaska Corporation in its Port Frederick lands, northeastern Chichagof Island, as part of an early use of the FRESH model (data collected by R. 

Johnson of ABR, Inc., under contract with Sealaska Corporation, Juneau, Alaska, 

unpublished but available on the FRESH-Deer Web site, http://cervid.uaa.alaska.

edu/deer/Home.aspx, with permission from R. Wolfe, Sealaska Corp.). The Sealaska data are from five stands 3 to 4 years old and seven stands 6 to 10 years old. 

Mean habitat values across all 12 stands for the same two summer and two winter 

scenarios we have analyzed were the following (deer days/ha, mean + standard 

error): summer maintenance only, 2,766 + 505; summer lactation, 1,094 + 195; winter with 0 cm snow, 1,483 + 278; winter with 20 cm snow, 275 + 75. Those values 

are much greater than ours from Experiment I (table 3)—about 3.5 times greater in 

summer but only about 2 times greater in snow-free winter and 1.5 times greater in 

winter with 20 cm of snow. Sealaska vegetation included more fireweed (Epilobium 

angustifolium), bunchberry dogwood, and oval-leaf blueberry. Both fireweed 

9

 Paul Alaback. An evaluation of canopy gaps in restoring wildlife habitat in second growth 

forests of southeastern Alaska. Unpublished final report to the Tongass National Forest, 

Craig and Thorne Bay Ranger Districts, Craig and Thorne Bay, Alaska, dated February 20, 

2010. 32 p.
31

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

and blueberry contribute to summer habitat values, and bunchberry dogwood is 

very important in snow-free winter conditions. The relatively high abundance of 

fireweed in the Sealaska data from the Port Frederick area was most likely a local 

site effect, as the geographic distribution of fireweed in southeast Alaska is spotty 

(Hultén 1968). On the other hand, the relatively lower abundance of blueberry and 

bunchberry dogwood in our data may have been a result of overall higher levels of 

herbivory by deer at our study sites, as both species are highly preferred by deer, 

and oval-leaf blueberry is known to decrease in abundance with deer browsing 

pressure (Hanley 1987). Within our study sites, oval-leaf blueberry plants that were 

lightly browsed weighed about 3.0 (leaves) to 3.9 (twigs) times more than heavily 

browsed plants of the same species per unit of canopy coverage (app. 2, Experiment 

I). Regardless of the variation between and within both data sets, however, it is 

clear that young clearcuts provide very high amounts of relatively high-quality food 

for deer in both summer and snow-free winter conditions throughout the region. It 

is with the closing of their young conifer canopies that their value as habitat drops 

sharply.

Effects of precommercial thinning of 16- to 18-year-old western hemlock-Sitka 

spruce stands were studied in seven replicated sites on Prince of Wales and Long 

Islands, southeast Alaska, by Cole et al. (2010). Treatments were the following: 

untreated controls, and thinning to 750, 500, 370, and 250 trees/ha. Cole et al. 

quantified understory vegetation immediately before treatment and 2, 4, and 7 years 

posttreatment, thereby providing an understanding of the dynamics of change in 

response to thinning. The Cole et al. results clearly indicated a peak in understory 

biomass occurring at about 4 years posttreatment, with forbs peaking at about 3 

years, and shrubs slowly declining by 7 years posttreatment. Although the Cole et 

al. treatments included both wider and narrower spacing than did our thinning in 

Experiment II, the among-site variation and fewer replicates resulted in no significant differences among the Cole et al. treatment effects (other than all thinnings 

yielding significantly more understory than the untreated controls). Deer habitat 

values paralleled the pattern in understory dynamics, with all thinnings yielding 

significantly higher values than those of untreated controls (by about twice as much 

at year 7 posttreatment) yet not differing among one another. Habitat values of the 

thinning treatments at 7 years posttreatment for the four seasonal scenarios we have 

analyzed were the following (deer days/ha, mean + standard error): summer maintenance only, 818 + 177; summer lactation, 344 + 85; winter snow-free, 390 + 156; 

winter 20 cm snow, 253 + 118. Those results are similar to the TWYGS Experiment II results for the widest spacing (331 trees/ha, table 5), except that for the 

winter with 20 cm of snow they were about twice as great as those for the TWYGS 
32

research paper pnw-rp-593

treatments. The latter effect is directly related to the Cole et al. vegetation having 

about twice as much biomass of oval-leaf blueberry twigs (204 + 91 kg/ha; Cole 

et al. 2010: table 3) as that of the TWYGS thinnings (table 4). The pattern of stand 

dynamics in the Cole et al. data indicate that the effects of precommercial thinning 

within the age class of our Experiment II (15 to 25 years) are likely to be relatively 

short-lived before they decline with increasing conifer crown closure. Precommercial thinning may extend the high habitat values of young clearcuts into an advancing age of young-growth forest, but the stands will likely need further treatments if 

high habitat values are to be maintained. Results from our Experiment III indicate 

that pruning combined with thinning may be quite useful as such a secondary 

treatment, as Experiment III thinnings increased deer habitat values by about 4 to 

6 times those of untreated controls (table 7), even though treatments were applied 

to stands that had never been treated previously and had already nearly attained 

conifer crown closure by age 25 to 35 years. Their effect should be expected to be 

much greater if applied to stands that have already been precommercially thinned 

earlier. Subsequent rounds of monitoring the Experiment III responses will be especially helpful regarding how long their effects might carry into the young forests’ 

dynamics.

For stands that have not been thinned before reaching the relatively large tree 

sizes typical of ages greater than 35 years, our results from Experiment IV indicate 

that thinning by girdling might be an effective treatment (table 9). Although those 

results are for only 4 years posttreatment, they are encouraging in that girdling 

had already increased deer habitat values by about 4 to 6 times those of untreated 

controls. Conventional thinning with or without bucking of slash was almost as 

effective as girdling, but thinning such large trees produces very large sizes and 

amounts of slash. The girdling results are encouraging, but they also are disconcerting in that we have no idea why they occurred (other than their greater response of 

oval-leaf blueberry), especially given the complete lack of relation between girdling 

failure and deer habitat values (table 10). The relatively high rate of failure in many 

girdled stands emphasizes the importance of careful contract administration when 

using girdling as a management tool. When girdling is done by chainsaw, too deep 

a cut leaves the tree with too small an intact bole to sustain wind or snow loads. 

Experiment IV already has clearly shown the consequences of that.

Land managers have had interest in thinning stands older than those of Experiment IV since at least the mid-1980s when the Tongass National Forest initiated 

a study of “commercial thinning” called the Second-Growth Management Area 

Demonstration Project (Zaborske et al. 2002). Strictly speaking, true commercial 

The relatively high 

rate of failure in 

many girdled stands 

emphasizes the 

importance of careful 

contract administration 

when using girdling 

as a management tool. 

When girdling is done 

by chainsaw, too deep 

a cut leaves the tree 

with too small an intact 

bole to sustain wind or 

snow loads.
33

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

thinning (selling the thinned trees at a profit) is unlikely in southeast Alaska (Crone 

2005), but thinning of such older, large-treed stands might nevertheless be justifiable for other purposes such as wildlife habitat restoration in certain areas. Stands 

in the Second-Growth Management study were 42 to 95 years old when treated in 

1984 and 1985. Treatments included untreated controls, individual tree selection 

(even spacing to from 173 to 269 trees/ha), strip thinning (removing all trees within 

alternating strips 6.1 m wide), and a combination of strip and individual tree selection treatments (thinning included within the leave strips). The study was unevenly 

replicated at only five sites, so results were limited accordingly. Understory vegetation was measured only once, 13 to 14 years posttreatment, but the data strongly 

showed two major results: (1) understories of both strip-thinning treatments were 

strongly dominated by western hemlock seedlings and saplings, which appeared to 

be thriving at the time of sampling, and (2) the individual tree selection treatment 

yielded surprisingly favorable results (compared with untreated controls) in terms 

of deer days/ha when analyzed with the FRESH model.

Although results were discussed by both Zaborske et al. (2002) and Hanley 

(2005), neither included an analysis of winter habitat values. The original, speciesspecific data are unpublished but available in the FRESH-Deer database on the 

FRESH Web site (http://cervid.uaa.alaska.edu/deer/Home.aspx). For the four sites 

with individual tree selection thinning, results for the four seasonal scenarios we’ve 

analyzed are the following (deer days/ha, mean + standard error): summer maintenance only, 249 + 68; summer lactation, 52 + 18; winter 0 cm snow, 95 + 37; winter 

20 cm snow, 2 + 2.10 Values were about 3 to 6 times those of the untreated control 

stands for summer and twice their value in snow-free winter, but the commercial 

thinning treatments yielded virtually nothing for deer when snow buries the herb 

layer, because the shrub response at all four sites was mainly from salmonberry (a 

good summer forage but very poor winter forage) with very little blueberry in the 

shrub layer (three of the four sites had relatively high levels of immature, decumbent blueberry in the forb layer but virtually no mature, larger plants). The SecondGrowth Management study also demonstrated a very strong site-specific response 

of hemlock seedlings within the individual tree selection treatments (Hanley 2005). 

Therefore, much remains uncertain about thinning in this age class, yet strongly 

replicated studies with trees of such size are very expensive. 

10 These summer values differ slightly from those reported in Zaborske et al. (2002) 

and Hanley (2005) because both of these authors used slightly different user-specified 

metabolic requirements for the deer and site-specific plant nutritional data rather than the 

regionwide nutritional database in FRESH-Deer.
34

research paper pnw-rp-593

Forest managers in southeast Alaska have also been interested in alternatives to 

conventional thinning, and in the mid-1980s through 1990s, the Tongass National 

Forest created nearly 600 “canopy gap” treatments throughout Prince of Wales 

Island alone (Alaback, unpublished report—see footnote 9). Canopy gap treatments 

in young-growth forest were intended to simulate environmental conditions of small 

(< 0.25-ha) gaps that occur naturally in old-growth forests as a function of wind 

or disease disturbance (Ott and Juday 2002). Although theoretical considerations 

might suggest that the newly created gaps would simply respond the same as small 

clearcuts (Hanley 2005) or fill rapidly with western hemlock regeneration (Deal 

and Farr 1994), Alaback (unpublished report) found highly favorable understory 

responses 20 years after treatment in his retrospective study of 76 canopy gap 

treatments on Prince of Wales Island. Alaback’s treatment gaps were 12 to 45 m in 

diameter, and 30 were surrounded by thinned young-growth forest, while the other 

46 were surrounded by untreated young growth. Stands were about 20 to 26 years 

old when treated. After 20 years posttreatment, Alaback found no significant differences in understory vegetation of thinned versus untreated young-growth forest, but 

the vegetation within the canopy gaps was significantly greater in species diversity 

and biomass than that of the surrounding forests. On average, biomass of the key 

evergreen forbs (Coptis aspleniifolia, Cornus canadensis, Rubus pedatus, Tiarella 

trifoliata) was about 9 times greater in the gaps than in the surrounding forests (15.9 

versus 1.8 kg/ha), and that of oval-leaf blueberry was about 5 times greater (219.7 

versus 45.4 kg/ha). Alaback used the FRESH model to calculate habitat values for 

black-tailed deer in two seasonal scenarios, summer with lactation requirements 

and winter with no snow. His results were as follows (deer days/ha, estimated from 

figs. 9 and 10 of Alaback, unpublished report): summer lactation, about 65 in gaps 

versus 15 in thinned forests; snow-free winter, about 45 in gaps versus 3 in thinned 

forests. Those are very low values of key species biomass and deer habitat value 

for the surrounding forest (compare with our untreated controls in tables 8 and 9 

for similar age stands at time of sampling), especially for the biomass of evergreen 

forbs, but the significantly higher habitat values of the gaps in both summer and 

winter are important in that these effects are 20 years after treatment. The significantly greater biomass of blueberry in gaps than surrounding forest indicates that 

deer habitat values would be correspondingly greater there in winter with snow, too. 

However, perhaps most surprising of all is that Alaback found no evidence for any 

sort of western hemlock flush, as tree seedling density did not differ with treatment 

(was low throughout) or with gap size. The gaps were persisting in time, not filling 

in with conifers. Thus, he concluded that canopy gap treatments may offer surprisingly long-lasting and effective silviculture treatment for maintaining deer habitat 
35

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

in young-growth forests of the region, and he developed a model for estimating 

long-term consequences for deer habitat relative to gap size and frequency on a 

landscape. The lack of tree response, however, becomes a cost to long-term timber 

production in a landscape filled with long-persisting gaps.

Silviculture treatments of young-growth forest for deer habitat may be able to 

take advantage of favorable summer habitat when red alder comprises substantial 

portions of a forested landscape. Red alder is common in young-growth forests 

of southeast Alaska where substantial areas of mineral soil were exposed during 

logging (Ruth and Harris 1979). Although that seldom occurs anymore with highlead logging, mixed stands of western hemlock-Sitka spruce-red alder, originating 

in the 1950s through early 1970s, are common in the region. The understory of such 

stands has been shown to be strongly related to the alder, with significant correlations between percentage of red alder in the stand basal area and total understory 

biomass (r2 = 0.743), net production of shrubs (r2 = 0.758), and net production of 

herbs (r2 = 0.855) (see Hanley et al. 2006 for a gradient of young-growth stands 

aged 38 to 42 years, ranging from 0 to 86 percent red alder by basal area). However, 

the main understory beneficiaries (or correlates) of the alder in the Hanley et al. 

(2006) study were salmonberry and deciduous forbs rather than blueberry and 

evergreen forbs, so although deer habitat value was positively correlated with the 

red alder in summer (r2 = 0.846), it was not significantly correlated with the alder 

in winter (r2 = 0.246). Similar patterns in understory biomass and composition have 

been reported for mixed alder-conifer young growth elsewhere in the region, too 

(Deal 1997, Hanley and Barnard 1998, Hanley and Hoel 1996). Deer habitat values 

for the two most strongly alder-dominated stands in the Hanley et al. (2006) study 

can be calculated from the original data in their table 3 (for stands with 64 and 86 

percent red alder overstories), which we have done for the four seasonal scenarios 

we have been using for comparison (deer days/ha, mean + standard error): summer 

maintenance, 127 + 53; summer lactation, 91 + 30; winter snow-free, 12 + 2; winter 

20 cm snow, 0 + 0. Thus, the alder can provide much better deer habitat in summer 

than that of untreated pure conifer young growth (compare with our untreated controls in table 9) and somewhat better than that of Alaback’s gap treatments of similar age (above), but the red alder understories are very poor deer habitat in winter, 

even in snow-free conditions. The greatest value of mixed alder-conifer stands for 

deer in southeast Alaska on highly disturbed sites, therefore, is in summer within a 

diverse landscape of other habitats suitable for meeting winter requirements. Future 

observations of Experiment I will assess whether this pattern exists on sites with 

less soil disturbance where alder was planted rather than regenerated naturally.
36

research paper pnw-rp-593

Landscape diversity is a very important feature in any long-term habitat 

management plan for deer, as different forages and habitats provide critical food 

resources in mixed diets and at different times of the year and conditions in winter 

(Hanley 1993, 1996). The treatments of the TWYGS experiments, coupled with 

canopy gap treatments, “commercial” thinning in some cases, and even mixed red 

alder-conifer stands provide a diverse array of potential management practices for 

creating and maintaining a diverse, young-growth forest landscape of various stand 

ages, compositions, and structures. Forest managers already seem to understand 

this, as much interest today is focusing on silviculture treatments that vary in 

their application, from variable-spaced thinning to “skips and gaps” of treatments. 

However, our understanding of the long-term consequences of silvicultural manipulations in southeast Alaska, in terms of stand dynamics through time, is only in 

its infancy today. With the exception of the Cole et al. (2010) study, all studies 

reviewed above, including the TWYGS experiments, are descriptions of results at 

one point in time and need repeated measurements. Empirical results from silviculture studies require experimentation and time. Unfortunately, there is no substitute 

for either. 

Conclusions 

We consider the results presented in this report to be preliminary, representing just 

4 to 8 years posttreatment. Subsequent responses through time and their patterns 

of temporal dynamics will be more valuable for planning and conducting long-term 

young-growth forest management programs. The comprehensive experimental 

approach of the TWYGS experiments, with replicated design and study sites widely 

scattered throughout southeast Alaska, will allow us to make robust conclusions 

about the effects of silviculture treatments on deer habitat quality in southeast 

Alaska. Meanwhile, we have gained several insights from this analysis of shortterm results: 

1. Although young clearcuts may be highly productive and provide suitable habitat in summer and snow-free conditions, they decline in value 

rapidly with conifer canopy closure. Without silvicultural manipulation, 

the resulting young-growth stands become very poor, sparse habitat for 

deer, as evidenced by the strongly decreasing values of the untreated 

control stands with increasing stand age in this study and in the studies 

we reviewed. Once stands have reached the canopy crown closure stage 

of structure, virtually any sort of disturbance to their overstory is better 

Empirical results 

from silviculture 

studies require 

experimentation and 

time. Unfortunately, 

there is no substitute 

for either.
37

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

than nothing (with the exception, perhaps, of strip thinning). All treatments in all TWYGS experiments and all other studies we reviewed 

yielded higher quality habitat for deer than that of their untreated, 

closed-canopy controls. 

2. The understory response is stronger with earlier treatment (younger 

stand age), mostly because there is more understory vegetation already 

present to serve as nurse stock in younger stands.

3. Precommercial thinning may maintain the favorable conditions of 

young clearcuts for probably an additional decade beyond normal canopy crown closure, possibly much longer if repeated again and coupled 

with pruning. However, girdling of older stands also appears promising 

(very preliminary), as do “commercial thinning” and canopy-gap treatments, and even the red alder pathway of succession. Clearly, a variety 

of potential treatments exists, and they may be applied to a variety of 

stand ages. Given the importance of landscape heterogeneity to deer, 

such variety in silviculture may be the optimal way to proceed.

4. Future results will be especially insightful as red alders gain effect 

(Experiment 1), thinned stands begin to close (Experiment II) or not 

close (Experiment III), and older stands have time to respond more 

fully (Experiment IV). Quantification of western hemlock in the 

understory, and slash and its rate of decay, will be important features to 

monitor and compare.

Acknowledgments

We thank the following individuals for their support and contributions, without 

which TWYGS would not have been possible: Forrest Cole, James Russell, Eugene 

DeGayner, Shiela Spores, and Charles Streuli. We also thank Troy Heithecker, 

Satish Serchan, Tongass National Forest silviculturists and foresters who laid out 

the treatments, and the scores of field technicians who collected the data in challenging conditions. Ashley Steel, Robert Deal, Sheila Spores, Brian Kleinhenz, and 

Bea Van Horne provided reviews of an earlier draft manuscript of this report; we 

greatly appreciate their helpful suggestions and improvements.

A variety of potential 

treatments exists, and 

they may be applied to 

a variety of stand ages. 

Given the importance 

of landscape 

heterogeneity to 

deer, such variety in 

silviculture may be the 

optimal way to proceed.
38

research paper pnw-rp-593

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

Kilojoules (kJ) 0.2388 Kilocalories 

Degrees Celsius (°C) 1.8 °C + 32 Degrees Fahrenheit

Tree Spacings and Densities 

Distance Distance Number of trees Number of trees

between trees between trees per acre per hectare

Feet Meters

9 2.74 538 1329

12 3.66 303 749

14 4.27 222 549

15 4.57 194 479

16 4.88 170 420

17 5.18 151 373

18 5.49 134 331

20 6.10 109 269

23 7.01 82 203

25 7.62 70 173

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Ruth, R.H.; Harris, A.S. 1979. Management of western hemlock-Sitka spruce 

forests for timber production. Gen. Tech. Rep. PNW-88. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacific Northwest Forest and Range 

Experiment Station. 197 p.

SAS Institute Inc. 2004. SAS/STAT 9.1 User’s Guide. Cary, NC: SAS Institute 

Inc. 5121 p.

Schoen, J.W.; Kirchoff, M.D.; Hughes, J.H. 1988. Wildlife and old-growth forests 

in southeastern Alaska. Natural Areas Journal. 8: 138–145.

Tappeiner, J.C.; Alaback, P.B. 1989. Early establishment and vegetative growth 

of understory species in the western hemlock–Sitka spruce forests in southeast 

Alaska. Canadian Journal of Botany. 67: 318–326.

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

1988. Influence of seral stage and season on leaf chemistry of southeastern 

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

Wagoner, S.J. 2011. The effects of spring cattle grazing on the nutritional ecology 

of mule deer (Odocoileus hemionus) in eastern Washington. Pullman, WA: 

Washington State University. 84 p. M.S. thesis.

Wagoner, S. J., Shipley, L.A., Cook, R.C., Hardesty, L. [In press]. Spring cattle 

grazing and mule deer nutrition in a bluebunch wheatgrass community. Journal 

of Wildlife Management. 

Wallmo, O.C.; Carpenter, L.H.; Regelin, W.L.; Gill, R.B.; Baker, D.L. 1977.

Evaluation of deer habitat on a nutritional basis. Journal of Range Management. 

30: 122–127.
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Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

Wallmo, O.C.; Schoen, J.C. 1980. Response of deer to secondary forest succession 

in southeast Alaska. Forest Science. 26: 448–462.

Zaborske, R.R.; Hauver, R.N.; McClellan, M.H.; Hanley, T.A. 2002. Understory 

vegetation development following commercial thinning in southeast Alaska: 

preliminary results from the second-growth management area demonstration 

project. In: Parker, S.; Hummel, S.S., eds. Beyond 2001: a silvicultural odyssey of 

sustaining terrestrial and aquatic ecosystems. Proceedings of the 2001 national 

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Department of Agriculture, Forest Service, Pacific Northwest Research Station: 

74–82.
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Appendix 1: Scientific and Common Names and Plant Codes of All Plant 

Species in This Report1

Species Common name Plant code

Actaea rubra (Aiton) Willd. Red baneberry ACRU2

Adiantum pedatum L. Northern maidenhair ADPE

Alectoria Ach. spp. Witch’s hair lichen ALECT3

Alnus spp. Mill. Alder ALNUS

Angelica genuflexa Nutt. Kneeling angelica ANGE2

Andromeda polifolia L. Bog rosemary ANPO

Aquilegia formosa Fisch. ex DC. Western columbine AQFO

Aruncus sylvester Kostel. ex Maxim. Bride’s feathers ARSY2 

Athyrium filix-femina L. Roth Common ladyfern ATFI

Blechnum spicant (L.) Sm. Deer fern BLSP

Bromus sitchensis Trin. Alaska brome BRSI

Carex deweyana Schwein. Dewey sedge CADE9

Carex lyngbyei Hornem. Lyngbye’s sedge CALY3

Carex mertensii Prescott ex Bong. Merten’s sedge CAME6

Carex L. spp. Sedge CAREX

Chamaecyparis nootkatensis (D. Don) Spach Alaska cedar CHNO

Circaea alpina L. Small enchanter’s nightshade CIAL

Claytonia sibirica L. Siberian springbeauty CLSI2

Coptis aspleniifolia Salisb. Fernleaf goldthread COAS

Coptis trifolia (L.) Salisb. Threeleaf goldthread COTR2

Cornus canadensis L. Bunchberry dogwood COCA13

Deschampsia cespitosa (L.) P. Beauv. Tufted hairgrass DECA18

Dryopteris dilatata auct. non (Hoffm.) A. Gray Spreading woodfern DREX2

Dryopteris expansa (C. Presl.) Fraser-Jenkins & Jermy Spreading woodfern DREX2

Drosera rotundifolia L. Roundleaf sundew DRRO

Elymus arenarius L. Sand ryegrass ELAR

Empetrum nigrum L. Black crowberry EMNI

Epilobium angustifolium L. Fireweed CHANA2

Epilobium ciliatum Raf. Fringed willowherb EPCI

Equisetum arvense L. Field horsetail EQAR

Equisetum pratense Ehrh. Meadow horsetail EQPR

Equisetum L. spp. Horsetail EQUIS

Fauria crista-galli (Menzies ex Hook.) Makino Deercabbage NECR2

Galium kamtschaticum Steller ex Schult. & Schult. f. Boreal bedstraw GAKA

Galium trifidum L. Threepetal bedstraw GATR2

Galium L. spp. Bedstraw GALIU

Gaultheria shallon Pursh Salal GASH

Gentiana douglasiana Bong. Swamp gentian GEDO

Geocaulon lividum (Richardson) Fernald False toadflax GELI2

Geranium erianthum DC. Woolly geranium GEER2

Geum macrophyllum Willd. Largeleaf avens GEMA4

Goodyera oblongifolia Raf. Western rattlesnake plantain GOOB2
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Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

Gymnocarpium dryopteris (L.) Newman Western oakfern GYDR

Heracleum lanatum Michx. Common cowparsnip HEMA80

Impatiens noli-tangere L. Western touch-me-not IMNO

Kalmia polifolia Wangenh. Bog laurel KAPO

Ledum palustre L. Marsh Labrador tea LEPA11

Listera cordata (L.) R. Br. Heartleaf twayblade LICO6

Listera R. Br. spp. Twayblade LISTE

Luzula parviflora (Ehrh.) Desv. Smallflowered woodrush LUPA4

Lysichiton americanus Hultén & H. St. John American skunkcabbage LYAM3

Maianthemum dilatatum (Alph. Wood) A. Nelson & J.F. Macbr. False lily of the valley MADI

Menziesia ferruginea Sm. Rusty menziesia MEFE

Mitella pentandra Hook. Fivestamen miterwort MIPE

Moehringia L. spp. Sandwort MOEHR

Moneses uniflora (L.) A. Gray Single delight MOUN2

Oplopanax horridus (Sm.) Miq. Devilsclub OPHO

Osmorhiza purpurea (J.M. Coult. & Rose) Suksd. Purple sweetroot OSPU

Osmorhiza Raf. spp. Sweetroot OSMOR

Oxycoccus microcarpos Turcz. ex Rupr. Small cranberry VAOX

Parnassia fimbriata K.D. Koenig Fringed grass of Parnassus PAFI3

Picea sitchensis (Bong.) Carrière Sitka spruce PISI

Platanthera dilatata (Pursh.) Lindl. ex Beck Scentbottle PLDI3

Polystichum braunii (Spenner) Fèe Braun’s hollyfern POBR4

Potentilla L. spp. Cinquefoil POTEN

Prenanthes alata (Hook.) D. Dietr. Western rattlesnakeroot PRAL

Ranunculus L. spp. Buttercup RANUN

Ranunculus uncinatus D. Don ex G. Don Woodland buttercup RAUN

Ribes bracteosum Douglas ex Hook. Stink currant RIBR

Ribes laxiflorum Pursh. Trailing black currant RILA3

Ribes L. spp. Currant RIBES

Rubus chamaemorus L. Cloudberry RUCH

Rubus parviflorus Nutt Thimbleberry RUPA

Rubus pedatus Sm. Strawberryleaf raspberry RUPE

Rubus spectabilis Pursh. Salmonberry RUSP

Salix L. spp. Willow SALIX

Sambucus racemosa L. Red elderberry SARA2

Stellaria crispa Cham. & Schltdl. Curled starwort STCR2

Streptopus amplexifolius (L.) DC. Claspleaf twistedstalk STAM2

Streptopus Michx. spp. Twistedstalk STREP3

Streptopus roseus Michx. Twistedstalk STRO4

Streptopus streptopoides (Ledeb.) Frye & Rigg Small twistedstalk STST3

Thelypteris phegopteris (L.) Slosson Long beechfern PHCO24

Thuja plicata Donn ex D. Don Western redcedar THPL

Tiarella trifoliata L. Threeleaf foamflower TITR

Tolmiea menziesii (Pursh.) Torr. & A. Gray Youth on age TOME

Trientalis latifolia Hook. Broadleaf starflower TRBOL

Trisetum cernuum Trin. Tall trisetum TRCA21
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Trisetum Pers. spp. Oatgrass TRISE

Tsuga heterophylla (Raf.) Sarg. Western hemlock TSHE

Tsuga mertensiana (Bong.) Carrière Mountain hemlock TSME

Urtica dioica L. Stinging nettle URDI

Vaccinium alaskaense Howell Alaska blueberry VAOV

Vaccinium cespitosum Michx. Dwarf bilberry VACA13

Vaccinium L. spp. evergreen Blueberry VACCI

Vaccinium ovalifolium Sm. Oval-leaf blueberry VAOV

Vaccinium parvifolium Sm. Red huckleberry VAPA

Vaccinium uliginosum L. Bog blueberry VAUL

Veratrum viride Aiton Green false hellebore VEVI

Viola glabella Nutt. Pioneer violet VIGL

Viola L. spp. Violet VIOLA

Other fern Other fern XFERN

Other forb Other forb XFORB

Other graminoid Other graminoid XGRAM

Other shrub Other shrub leaf XSHRUB

1 Source of nomenclature is PLANTS Database, http://plants.usda.gov/ by genus; plant code = “symbol.”
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Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

Appendix 2: Canopy Cover-to-Biomass 

Regression Equations for Treatments in Each

of the TWYGS Experiments1

Experiment 1 (1 to 5 years old, plus 8 years posttreatment)

Species code Plant All treatments

(see app. 1) part Y-intercept Slope N P r

2

ATFI Whole 0 2.294 10 <0.001 0.927

ALRU2 Leaf 0 1.650 8 <0.001 0.869

ALRU2 Twig 0 1.087 8 <0.001 0.921

BLSP Whole 0 1.608 13 <0.001 0.894

CHANA2 Whole 0 3.797 11 <0.001 0.795

CHNOa CAGb 0 1.365 12 <0.001 0.855

COAS Whole 0 0.469 11 <0.001 0.909

COCA13 Whole 0 0.579 11 <0.001 0.920

DREX2 Whole 0 0.583 10 <0.001 0.821

GASH Leaf 0 0.951 10 <0.001 0.739

GASH Twig 0 0.228 10 <0.001 0.764

GYDR Whole 0 0.666 12 <0.001 0.756

LYAM3 Whole 0 1.721 12 <0.001 0.855

MADI Whole 0 0.476 6 0.007 0.798

MEFE Leaf 0 0.470 11 <0.001 0.762

MEFE Twig 0 0.160 11 0.001 0.662

NECR2 Whole 0 0.905 12 <0.001 0.946

OPHO Leaf 0 1.020 10 0.003 0.643

PHCO24 Whole 0 1.631 10 <0.001 0.940

PICOC CAG 0 1.289 13 <0.001 0.863

PISI CAG 0 1.447 11 <0.001 0.778

PTAQ Whole 0 1.761 13 <0.001 0.871

RIBR Leaf 0 0.544 9 0.002 0.738

RIBR Twig 0 0.329 9 0.004 0.669

RILA Leaf 0 0.355 11 <0.001 0.705

RILA Twig 0 0.045 11 <0.001 0.765

RUPA Leaf 0 0.753 11 <0.001 0.961

RUPA Twig 0 0.454 11 <0.001 0.959

RUPE Whole 0 0.400 12 <0.001 0.863

RUSP Leaf 0 0.449 11 <0.001 0.931

RUSP Twig 0 0.200 11 <0.001 0.865

SARA2 Leaf 0 0.818 9 <0.001 0.855

SARA2 Twig 0 0.529 9 0.001 0.763

TITR Whole 0 0.502 9 <0.001 0.873

TSHE CAG 0 0.303 11 0.005 0.563

VAOVc Leaf 0 0.245 8 0.002 0.828

VAOVc Twig 0 0.181 8 <0.001 0.858

VAOVd Leaf 0 0.725 9 <0.001 0.859
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VAOVd Twig 0 0.699 9 0.002 0.732

VAPA Leaf 0 0.678 10 <0.001 0.756

VAPA Twig 0 1.056 10 <0.001 0.802

1

All equations are of the form (biomass, ovendry g/m2) = Y-intercept + slope x (percentage 

canopy cover).

a CHNO, Chamaecyparis nootkatensis, current USDA PLANTS name is 

Cupressus nootkatensis, CUNO. b CAG = Current Annual Growth.

c

Heavily browsed oval-leaf blueberry.

d Lightly browsed oval-leaf blueberry.

Experiment II (15 to 25 years old, plus 5 years posttreatment)

Species code Plant Untreated control

(see app. 1) part Y-intercept Slope N P r2

ATFI Whole 0 1.171 5 <0.001 0.954

BLSP Whole 0 0.521 3 <0.001 0.998

COAS Whole 0 1.015 3 0.002 0.996

COCA13 Whole 0 0.912 3 <0.001 1.000

DREX2 Whole 0 0.338 6 <0.001 0.995

GYDR Whole 0 0.309 5 0.002 0.938

MEFE Leaf 0 0.677 3 0.001 0.997

RUPE Whole 0 0.626 3 0.003 0.995

RUSP Twig 0.137 0.435 5 0.266 0.382

RUSP Leaf 0.072 0.545 5 0.325 0.315

TITR Whole 0 0.379 5 <0.001 0.996

TSHE CAG 0.113 0.059 7 0.462 0.560

VAOV Twig 0 0.689 5 <0.001 0.992

VAOV Leaf 0 0.689 5 <0.001 0.992

Experiment II (Continued)

Species code Plant 549 trees/ha

(see app. 1) part Y-intercept Slope N P r2

ATFI Whole 0 0.778 5 <0.001 0.984

BLSP Whole 0 1.679 4 0.002 0.974

COAS Whole 0.113 0.641 3 0.068 0.989

COCA13 Whole 0.873 0.403 5 0.061 0.740

DREX2 Whole 0.158 0.657 6 0.065 0.614

grass Whole 0 1.131 5 0.007 0.868

GYDR Whole 0.345 0.401 6 0.004 0.902

MEFE Twig 0 0.233 6 0.002 0.871

MEFE Leaf 0.184 1.325 6 0.002 0.933

OPHO Leaf 0.819 0.156 3 0.797 0.098

PISI CAG 0.111 0.235 6 0.132 0.472

RUPE Whole 0 1.110 5 <0.001 0.970

RUSP Twig 2.501 0.201 7 0.351 0.175

RUSP Leaf 1.681 0.847 7 0.078 0.495

TITR Whole -0.004 0.656 3 0.035 0.931

TSHE CAG 1.796 0.333 7 0.141 0.380
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Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

VAOV Twig -0.605 0.478 7 0.032 0.634

VAOV Leaf 4.257 0.688 7 0.273 0.233

Experiment II (Continued)

Species code Plant 331 trees/ha

(see app. 1) part Y-intercept Slope N P r2

ATFI Whole 0 1.379 6 <0.001 0.991

BLSP Whole 0.262 1.377 6 0.001 0.943

COCA13 Whole 0 0.849 6 <0.001 0.998

DREX2 Whole 0 1.020 6 <0.001 0.985

EPAN Whole 0.001 0.561 3 0.042 0.996

grass Whole 0 1.067 6 <0.001 0.932

GYDR Whole 0.358 0.235 6 0.069 0.605

LYAM3 Leaf 0 1.932 4 0.001 0.982

MADI Whole 0 1.014 4 0.006 0.941

MEFE Twig 0.218 0.122 7 0.023 0.677

MEFE Leaf 0 1.040 7 <0.001 0.911

PISI CAG 0.056 1.155 5 <0.001 0.984

RIBES Twig 0 0.372 3 0.015 0.971

RIBES Leaf 0 0.774 4 0.043 0.793

RUPE Whole 0.267 0.549 6 0.001 0.942

RUSP Twig 4.489 0.151 7 0.580 0.065

RUSP Leaf 3.331 1.160 7 0.116 0.418

TITR Whole 0.129 0.495 4 0.268 0.537

TSHE CAG 0.476 0.922 7 0.111 0.428

VAOV Twig -1.561 0.796 7 0.012 0.748

VAOV Leaf 4.804 0.944 7 0.153 0.361

VAPA Twig 0 1.950 3 0.048 0.906

VAPA Leaf 0 3.706 3 0.079 0.848

Experiment III (25 to 35 years old, plus 6 years posttreatment)

Species code Plant Untreated control

(see app. 1) part Y-intercept Slope N P r2

ATFI Whole 0 0.594 4 <0.001 0.988

BLSP Whole 0 0.412 5 <0.001 0.955

COAS Whole 0 0.439 4 <0.001 0.996

COCA13 Whole 0 1.033 4 <0.001 0.991

DREX2 Whole 0 0.368 5 <0.001 0.964

GYDR Whole 0 0.300 4 0.001 0.982

MEFE Twig 0 0.128 2 0.167 0.933

MEFE Leaf 0 0.274 2 0.032 0.998

RUPE Whole 0 0.257 4 0.005 0.950

RUSP Twig 0 0.238 4 0.091 0.668

RUSP Leaf 0 0.642 4 0.161 0.535

TITR Whole 0 0.424 3 0.009 0.983

TSHE CAG 0 0.254 3 0.009 0.983

VAOV Twig 0 0.117 5 0.061 0.625

VAOV Leaf 0 0.461 5 0.001 0.939
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research paper pnw-rp-593

Experiment III (Continued)

Species code Plant Thinning only

(see app. 1) part Y-intercept Slope N P r2

ATFI Whole 0 0.704 5 <0.001 0.958

BLSP Whole 0 1.041 5 0.008 0.854

COAS Whole 0 0.430 5 0.011 0.838

COCA13 Whole 0 0.709 5 0.003 0.911

DREX2 Whole 0 0.462 5 <0.001 0.954

grass Whole 0 1.753 3 0.022 0.956

GYDR Whole 0 0.597 5 0.001 0.940

MEFE Twig 0 0.059 4 0.063 0.735

MEFE Leaf 0 0.317 4 0.039 0.804

OPHO Leaf 0 0.830 3 0.070 0.865

PHCO24 Whole 0 0.638 3 0.035 0.931

PISI CAG 0 0.336 4 0.063 0.737

RUPE Whole 0 0.374 5 <0.001 0.974

RUSP Twig 0 0.317 5 <0.001 0.972

RUSP Leaf 0 0.703 5 0.001 0.944

TITR Whole 0 0.593 5 0.004 0.904

TSHE CAG 0 0.427 5 0.006 0.875

VAOV Twig 0 0.243 5 0.031 0.727

VAOV Leaf 0 0.604 5 0.003 0.918

Experiment III (continued)

Species code Plant Thin and prune 25 percent of trees

(see app. 1) part Y-intercept Slope N P r2

ATFI Whole 0 0.875 5 <0.001 0.977

BLSP Whole 0 1.268 4 0.003 0.966

COAS Whole 0 0.665 4 0.011 0.913

COCA13 Whole 0 0.768 5 <0.001 0.992

DREX2 Whole 0 0.912 5 0.009 0.848

grass Whole 0 1.112 3 0.100 0.810

GYDR Whole 0 0.676 5 <0.001 0.969

LYAM3 Leaf 0 1.630 3 0.015 0.970

MEFE Twig 0 0.116 5 0.005 0.893

MEFE Leaf 0 0.685 5 0.001 0.946

PISI CAG 0 1.369 5 <0.001 0.973

RUPE Whole 0 0.731 5 <0.001 0.974

RUSP Twig 0 0.288 5 0.004 0.894

RUSP Leaf 0 0.984 5 <0.001 0.954

TITR Whole 0 0.683 4 <0.001 0.998

TSHE CAG 0 0.485 5 0.015 0.809

VAOV Twig 0 0.431 5 0.002 0.922

VAOV Leaf 0 1.448 5 0.005 0.884
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Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

Experiment III (Continued)

Species code Plant Thin and prune 50 percent of trees

(see app. 1) part Y-intercept Slope N P r2

ATFI Whole 0 1.488 4 0.002 0.971

BLSP Whole 0 1.867 4 <0.001 0.992

COAS Whole 0 0.543 2 0.158 0.940

COCA13 Whole 0 0.615 5 <0.001 0.979

DREX2 Whole 0 0.549 5 <0.001 0.966

GYDR Whole 0 0.608 5 <0.001 0.988

MEFE Twig 0 0.104 2 0.106 0.973

MEFE Leaf 0 0.750 2 0.069 0.988

PISI CAG 0 0.635 3 0.070 0.865

RUPE Whole 0 0.860 5 0.001 0.939

RUSP Twig 0 0.252 5 <0.001 0.999

RUSP Leaf 0 0.786 5 <0.001 0.971

TITR Whole 0 0.745 4 0.015 0.896

TSHE CAG 0 0.128 4 0.041 0.798

VAOV Twig 0 0.197 5 <0.001 0.960

VAOV Leaf 0 0.682 5 <0.001 0.994

VAPA Twig 0 0.312 2 0.148 0.947

VAPA Leaf 0 0.345 3 0.090 0.828

Experiment IV (>35 years old, plus 4 years posttreatment)

Species code Plant Untreated control

(see app. 1) part Y-intercept Slope N P r2

ATFI Whole 0 0.263 12 <0.001 0.946

COCA13 Whole 0 0.279 10 <0.001 0.904

DREX2 Whole 0 0.157 11 <0.001 0.750

GYDR Whole 0 0.135 11 <0.001 0.848

LYAM3 Leaf 0 0.512 7 0.002 0.817

MEFE Twig 0 0.017 9 0.008 0.606

MEFE Leaf 0 0.135 9 <0.001 0.939

OPHO Leaf 0 0.240 12 <0.001 0.918

PHCO24 Whole 0 0.484 11 <0.001 0.885

RIBR Twig 0 0.196 9 0.001 0.756

RIBR Leaf 0 0.301 9 <0.001 0.794

RILA Twig 0 0.060 4 0.005 0.950

RILA Leaf 0 0.399 4 0.006 0.943

RUSP Twig 0 0.250 11 <0.001 0.817

RUSP Leaf 0 0.407 11 <0.001 0.821

SARA2 Twig 0 0.234 9 <0.001 0.809

SARA2 Leaf 0 0.366 9 0.001 0.748

TITR Whole 0 0.161 8 <0.001 0.848

TSHE CAG 0 0.161 11 <0.001 0.831

VAOV Twig 0 0.278 11 <0.001 0.716

VAOV Leaf 0 0.262 11 <0.001 0.852
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research paper pnw-rp-593

Experiment IV (Continued)

Species code Plant Thin by felling only

(see app. 1) part Y-intercept Slope N P r2

ATFI Whole 0 0.557 10 <0.001 0.800

COCA13 Whole 0 0.363 7 <0.001 0.938

DREX2 Whole 0 0.602 11 <0.001 0.837

GYDR Whole 0 0.423 10 <0.001 0.862

LYAM3 Leaf 0 0.475 3 0.002 0.996

MEFE Twig 0 0.152 9 <0.001 0.871

MEFE Leaf 0 0.330 9 <0.001 0.909

OPHO Leaf 0 0.492 9 <0.001 0.890

PHCO24 Whole 0 0.641 11 <0.001 0.784

RIBR Twig 0 0.257 7 0.003 0.792

RIBR Leaf 0 0.432 7 0.003 0.795

RILA Twig 0 0.227 11 <0.001 0.902

RILA Leaf 0 0.609 11 <0.001 0.887

RUSP Twig 0 0.470 9 <0.001 0.779

RUSP Leaf 0 0.637 9 <0.001 0.859

SARA2 Twig 0 0.814 11 <0.001 0.877

SARA2 Leaf 0 1.022 11 <0.001 0.878

TITR Whole 0 0.414 10 <0.001 0.961

TSHE CAG 0 0.667 11 <0.001 0.804

VAOV Twig 0 0.460 10 <0.001 0.776

VAOV Leaf 0 0.395 10 <0.001 0.820

Experiment IV (Continued)

Species code Plant Thin and buck slash to 1.5 m

(see app. 1) part Y-intercept Slope N P r2

ATFI Whole 0 0.462 7 <0.001 0.871

COCA13 Whole 0 0.490 8 <0.001 0.935

DREX2 Whole 0 0.169 11 <0.001 0.989

GYDR Whole 0 0.528 11 <0.001 0.736

LYAM3 Leaf 0 1.056 10 <0.001 0.921

MEFE Twig 0 0.790 8 <0.001 0.817

MEFE Leaf 0 1.615 8 <0.001 0.874

OPHO Leaf 0 0.680 8 <0.001 0.929

PHCO24 Whole 0 0.464 10 <0.001 0.918

RIBR Twig 0 0.274 6 0.003 0.862

RIBR Leaf 0 0.730 6 <0.001 0.947

RILA Twig 0 0.372 4 0.040 0.801

RILA Leaf 0 0.682 4 0.010 0.920

RUSP Twig 0 0.260 9 0.018 0.525

RUSP Leaf 0 0.640 9 <0.001 0.798

SARA2 Twig 0 0.287 7 <0.001 0.946

SARA2 Leaf 0 0.439 7 <0.001 0.860

TITR Whole 0 0.246 7 0.005 0.765

TSHE CAG 0 0.416 10 <0.001 0.769

VAOV Twig 0 0.340 9 0.003 0.700

VAOV Leaf 0 0.363 9 0.012 0.569
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Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

Experiment IV (Continued)

Species code Plant Thin and buck slash to 4.6 m

(see app. 1) part Y-intercept Slope N P r2

ATFI Whole 0 1.006 9 <0.001 0.767

COCA13 Whole 0 0.309 8 <0.001 0.915

DREX2 Whole 0 0.730 11 <0.001 0.819

GYDR Whole 0 0.552 8 0.001 0.803

LYAM3 Leaf 0 0.717 6 <0.001 0.908

MEFE Twig 0 0.273 9 0.132 0.261

MEFE Leaf 0 0.421 7 0.002 0.833

OPHO Leaf 0 0.386 10 <0.001 0.899

PHCO24 Whole 0 0.621 9 <0.001 0.804

RILA Twig 0 0.211 10 0.001 0.720

RILA Leaf 0 0.456 10 <0.001 0.978

RUSP Twig 0 0.308 7 <0.001 0.898

RUSP Leaf 0 0.574 7 0.003 0.799

SARA2 Twig 0 0.472 8 0.020 0.564

SARA2 Leaf 0 0.414 8 <0.001 0.863

TITR Whole 0 0.236 8 <0.001 0.875

TSHE CAG 0 0.651 9 <0.001 0.915

VAOV Twig 0 0.701 9 0.017 0.530

VAOV Leaf 0 0.569 9 <0.001 0.943

Experiment IV (Continued)

Species code Plant Thin by girdling

(see app. 1) part Y-intercept Slope N P r2

ATFI Whole 0 0.300 10 <0.001 0.886

COCA13 Whole 0 0.320 7 <0.001 0.963

DREX2 Whole 0 0.192 11 <0.001 0.767

GYDR Whole 0 0.414 10 <0.001 0.981

LYAM3 Leaf 0 1.050 5 0.034 0.715

MEFE Twig 0 0.259 8 0.008 0.662

MEFE Leaf 0 0.662 8 <0.001 0.885

OPHO Leaf 0 0.386 10 <0.001 0.899

PHCO24 Whole 0 0.472 9 <0.001 0.887

RIBR Twig 0 0.252 9 <0.001 0.957

RIBR Leaf 0 0.915 9 <0.001 0.868

RILA Twig 0 0.233 8 <0.001 0.818

RILA Leaf 0 0.532 8 0.001 0.797

RUSP Twig 0 0.729 8 0.015 0.592

RUSP Leaf 0 0.864 8 <0.001 0.841

SARA2 Twig 0 0.418 12 <0.001 0.698

SARA2 Leaf 0 0.369 12 <0.001 0.916

TITR Whole 0 0.545 13 <0.001 0.865

TSHE CAG 0 0.525 9 <0.001 0.827

VAOV Twig 0 0.658 7 0.010 0.693

VAOV Leaf 0 0.581 7 0.002 0.829

1

All equations are of the form (biomass, ovendry g/m2) = Y-intercept + slope x (percentage 

canopy cover).
54

research paper pnw-rp-593

Appendix 3: Species-Specific Results (Oven-dry Biomass in Kilograms 

Per Hectare, Mean and Standard Error) From All TWYGS Treatments by 

Experiment

Experiment I (1 to 5 years old, plus 8 years posttreatment)

 Alder at 50 Alder at 200 

Plant Untreated control trees/ha trees/ha

Species parta Mean SE Mean SE Mean SE

Forbs:

Aconitum delphiniifolium W 2.08 1.86 <0.01 <0.01 — —

Actaea rubra W 1.86 1.86 0.11 0.11 — —

Aruncus dioicus W — — 7.39 6.35 6.44 6.44

Chamerion angustifoliumb W 8.92 4.23 7.62 4.60 13.93 7.57

Circaea alpina W <0.01 <0.01 0.01 0.01 0.08 0.07

Claytonia siberica W 0.13 0.12 0.01 0.01 <0.01 <0.01

Coptis aspleniifolia W 16.78 5.07 10.91 3.38 15.12 5.96

Cornus canadensis W 73.75 13.35 58.87 12.39 60.30 12.11

Cornus suecica W 0.50 0.26 0.15 0.10 0.44 0.24

Epilobium ciliatum W — — 0.04 0.03 0.01 0.01

Equisetum arvense W — — 0.42 0.38 0.01 0.01

Equisetum spp. W 0.58 0.54 0.69 0.47 0.04 0.04

Galium spp. W 0.05 0.04 0.01 0.01 0.02 0.01

Heracleum maximum W — — 2.91 2.65 — —

Lysichiton americanus W 41.77 10.16 55.27 15.37 25.39 6.67

Maiathemum dilatatum W 7.79 2.67 6.86 2.40 7.28 3.10

Nephrophyllidium crista-galli W 6.19 4.17 4.37 1.94 2.49 1.64

Platanthera spp. W — — 0.47 0.47 — —

Prenanthes alba W 0.10 0.07 0.12 0.07 0.12 0.08

Rubus pedatus W 8.06 2.02 10.20 2.35 10.95 3.43

Sanguisorba canadensis W — — 0.04 0.03 — —

Streptopus amplexifolius W 16.14 8.88 15.69 5.78 15.71 8.45

Streptopus spp. W 1.58 0.77 2.19 1.44 1.59 0.96

Streptopus streptopoides W <0.01 <0.01 0.02 0.02 <.01 <.01

Tiarella trifoliata W 5.40 1.11 6.18 2.15 3.81 1.20

Tolmiea menziesii W — — — — 0.01 0.01

Trientalis europaea arctica W <0.01 <0.01 0.20 0.20 0.03 0.02

Viola glabella W 0.03 0.02 <0.01 <0.01 <0.01 <0.01

Viola spp. W 0.09 0.08 0.05 0.02 <0.01 <0.01

Total forbs 190.04 28.54 192.36 26.59 163.78 23.03

Ferns:

Adiantum pedatum W 0.05 0.04 0.67 0.51 1.59 0.94

Athyrium filix-femina W 103.80 21.38 121.04 14.83 116.04 23.08

Blechnum spicant W 73.83 14.80 59.41 15.56 62.55 15.26

Dryopteris expansa W 12.67 3.88 10.21 3.75 11.46 4.52
55

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

Gymnocarpium drypoterisW 23.42 3.33 25.49 5.23 21.32 4.28

Phegopteris connectilisW 9.77 4.75 24.47 14.40 8.70 4.74

Pteridium aquilinum W 8.53 7.54 15.38 10.22 19.09 15.03

Total ferns 233.55 26.03 254.62 30.82 240.75 31.47

Graminoids:

Carex laeviculmisW—— 1.89 1.89——

Carex spp.W 2.63 1.96 5.48 4.18 1.31 0.95

Juncus spp.W———— 0.12 0.12

Luzula spp.W—— 0.25 0.19——

Unknown grassW 21.26 13.97 22.85 9.31 4.91 2.27

Total graminoids 23.90 14.04 30.46 10.86 6.34 2.76

Shrubs:

Gaultheria shallon

L 8.98 8.98 6.71 6.71 15.41 12.76

Gaultheria shallonT 2.15 2.15 1.51 1.51 3.69 3.06

Ledum groenlandicumL—— 4.04 3.59 4.38 3.08

Ledum groenlandicumT—— 4.30 3.82 4.66 3.28

Linnaea borealisW 0.13 0.1

1 0.31 0.27 2.87 2.12

Menziesia ferruginea

L 34.39 6.48 33.87 7.53 32.70 5.94

Menziesia ferrugineaT11.70 2.2011.53 2.5611.13 2.02

Oplopanax horridiumL 9.65 3.18 26.95 10.10 25.21 8.86

Ribes spp.L 0.94 0.62 0.71 0.49 0.73 0.49

Ribes spp.T 0.12 0.08 0.09 0.06 0.09 0.06

Ribes bracteosumL 0.56 0.34 0.19 0.14 0.81 0.57

Ribes bracteosumT 0.34 0.21 0.12 0.09 0.49 0.35

Ribes lacustreL 1.03 0.71 1.66 0.86 1.10 0.78

Ribes lacustreT 0.13 0.09 0.21 0.1

1 1.10 0.78

Ribes laxiflorum 

L 0.15 0.15 0.35 0.24 0.1

1 0.08

Ribes laxiflorum T 0.02 0.02 0.04 0.03 0.01 0.01

Rubus parviflorusL 2.04 1.07 2.06 1.44 1.86 1.10

Rubus parviflorusT 1.23 0.64 1.24 0.87 1.12 0.66

Rubus spectabilis

L 21.44 6.04 19.97 7.13 22.04 4.31

Rubus spectabilisT 9.56 2.69 8.91 3.18 9.83 1.92

Sambucus racemosaL 1.02 0.42 2.71 1.07 0.55 0.25

Sambucus racemosaT 0.66 0.27 1.76 0.69 0.36 0.16

Vaccinium caespitosumW———— 0.20 0.20

Vaccinium ovalifolium

L 124.77 12.09 129.46 12.94 146.72 18.33

Vaccinium ovalifoliumT 120.4311.67 124.96 12.49 141.62 17.69

Vaccinium spp. immatureW 2.06 1.19 3.21 1.33 5.62 2.05

Vaccinium oxycoccosW 0.06 0.05 0.02 0.02 0.03 0.02

Vaccinium parvifolium

L 8.46 2.76 6.42 2.08 12.58 3.78

Vaccinium parvifoliumT 13.18 4.30 10.01 3.25 19.61 5.90

Vaccinium vitis-idaeaW 0.27 0.27 1.82 1.82 0.25 0.25

Total shrubs 375.47 22.12 404.74 26.00 460.70 35.66
56

research paper pnw-rp-593

Trees:

Alnus rubra L 5.47 5.47 4.63 2.28 16.38 7.15

Alnus rubra T 3.60 3.60 3.05 1.50 10.79 4.71

Alnus viridis sinuata L — — 0.30 0.30 — —

Alnus viridis sinuata T — — 0.20 0.20 — —

Chamaecyparis nootkatensis CAG 2.36 1.74 1.57 1.53 3.83 2.28

C. nootkatensis seedlings W 5.25 2.64 2.83 1.86 2.06 1.30

Malus fusca L 0.97 0.97 — — — —

Malus fusca T 0.64 0.64 — — — —

Pinus contorta contorta CAG 0.24 0.24 3.57 3.51 2.10 1.85

Pinus contorta contorta seedlings W 0.27 0.27 1.92 1.75 2.87 2.81

Picea sitchensis CAG 17.17 16.19 26.99 26.04 62.22 19.61

Picea sitchensis seedlings W 38.34 11.24 43.57 10.83 23.23 6.75

Thuja plicata CAG 9.15 6.24 6.42 3.92 8.96 4.36

Thuja plicata seedlings W 16.97 6.54 10.30 3.80 10.28 5.04

Tsuga heterophylla CAG 31.66 7.36 35.60 6.70 37.88 6.26

Tsuga heterophylla seedlings W 30.36 3.93 27.80 4.16 25.56 3.56

Tsuga mertensiana CAG 0.93 0.64 1.58 1.16 0.36 0.20

Tsuga mertensiana seedlings W 1.26 0.77 1.00 0.67 0.02 0.02

Total trees 198.88 21.25 224.12 32.42 206.54 25.32

Total biomass (all species) 1021.96 40.26 1108.00 48.45 1080.05 63.32

a

 Plant parts: W = whole plant; L = leaf; T = twig; CAG = current annual growth (twigs and needles together).

b Chamerion angustifolium is Epilobium angustifolium.

Experiment II (15 to 25 years old, plus 5 years posttreatment)

 Thin to 549 Thin to 331

Plant Untreated control trees/ha trees/ha

Species parta Mean SE Mean SE Mean SE

Forbs:

Caltha leptosepala W — — — — 0.02 0.02

Chamerion angustifolium W — — — — 0.09 0.08

Circaea alpina W 0.73 0.73 0.02 0.02

Clintonia uniflora W — — — — 0.01 0.01

Coptis aspleniifolia W 2.38 0.90 4.01 1.23 7.04 4.37

Cornus canadensis W 10.05 3.45 24.83 4.30 47.26 15.49

Drosera rotundifolia W — — — — <0.01 <0.01

Galium spp. W — — — — 0.01 0.01

Galium trifidum W 0.02 0.02 — — 0.06 0.04

Galium triflorum W — — — — <0.01 <0.01

Heracleum maximum W 1.62 1.62 — — 0.62 0.62

Linneaea borealis W — — 0.12 0.12 0.07 0.05

Listera convallarioides W 0.02 0.02 0.01 0.01 0.04 0.04

Listera cordata W <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

Lysichiton americanum W 14.85 7.21 7.49 4.46 30.34 13.43
57

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

Maianthemum dilatatumW 1.18 0.64 4.95 3.58 8.74 5.58

Moneses unifloraW 0.01 0.01 0.05 0.04 0.16 0.15

Prenanthes albaW 0.03 0.03—— 0.06 0.06

Ranunculus uncinatusW—— <0.01 <0.01——

Rubus pedatusW 1.99 0.64 8.47 1.59 8.99 2.36

Saxifragia mertensiaW 0.06 0.06————

Stellaria calycanthaW———— <0.01 <0.01

Stellaria crispaW———— <0.01 <0.01

Streptopus amplexifoliusW 1.02 0.43 0.92 0.67 0.76 0.49

Streptopus lanceolatus W———— 0.01 0.01

Streptopus spp.W <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

Streptopus streptopoidesW———— <0.01 <0.01

Tiarella trifoliata

W 1.69 0.45 4.1

1 2.54 3.88 1.09

Trientalis europa arcticaW———— 0.05 0.05

Vertrum viride

W 0.02 0.02————

Viola glabellaW 0.08 0.08—— 0.19 0.13

Unknown forb

W———— 0.01 0.01

Total forbs 35.77 10.06 55.01 8.58 108.45 26.73

Ferns:

Athyrium filix-feminaW 32.72 13.53 18.95 4.44 65.96 14.55

Blechnum spicantW 1.77 1.1311.73 4.39 10.63 4.43

Dryopteris expansaW 4.04 1.26 30.68 5.67 44.01 12.52

Gymnocarpium drypoterisW 3.67 1.08 12.95 2.80 10.45 1.63

Phegopteris connectilisW 0.14 0.08 0.33 0.31 0.37 0.27

Polypodium glycyrrhiza W 0.20 0.20—— 0.17 0.17

Polystichum munitum 

W—— 1.24 1.24 0.25 0.25

Unknown fernW———— <0.01 <0.01

Total ferns 42.53 15.01 75.87 10.54 131.84 22.12

Graminoids:

Carex spp.W 1.73 1.73 0.31 0.18 1.23 0.78

Cymophyllus fraseri 

W—— 0.01 0.01 0.02 0.02

Unknown grass

W 0.45 0.25 1.29 0.46 4.36 1.61

Total graminoids 2.18 1.96 1.61 0.56 5.62 1.73

Shrubs:

Menziesia ferruginea 

L 22.02 8.20 107.55 26.56 66.56 12.93

Menziesia ferruginea T 7.58 2.82 18.63 4.66 9.88 1.57

Oplopanax horridus L 8.15 2.4311.27 3.71 8.60 2.27

Ribes spp. L 0.03 0.03 1.25 0.85 2.47 1.44

Ribes spp.T 0.01 0.01 0.57 0.38 1.19 0.69

Ribes bracteosum L 2.21 2.02 0.31 0.31 2.41 1.39

Ribes bracteosum T 1.00 0.92 0.14 0.14 1.09 0.63

Ribes lacustre L 0.26 0.15 1.14 0.82 5.85 3.68

Ribes lacustre T 0.12 0.07 0.51 0.37 2.65 1.67
58

research paper pnw-rp-593

Ribes laxiflorum L 1.28 1.28 1.69 1.34 0.19 0.18

Ribes laxiflorum T 0.58 0.58 0.77 0.61 0.09 0.08

Rubus parviflorus L — — 0.18 0.12 0.92 0.90

Rubus parviflorus T — — 0.08 0.06 0.42 0.41

Rubus spectabilis L 20.04 8.89 103.19 16.13 193.43 23.61

Rubus spectabilis T 16.76 7.11 44.19 4.55 65.73 3.07

Salix spp. L — — 0.02 0.02 — —

Salix spp. T — — 0.01 0.01 — —

Sambucus racemosa L 0.33 0.31 0.92 0.65 3.36 2.04

Sambucus racemosa T 0.15 0.14 0.42 0.29 1.52 0.92

Vaccinium caespitosum L 0.26 0.26 — — — —

Vaccinium caespitosum T 0.12 0.12 — — — —

Vaccinium ovalifolium L 55.19 14.43 150.61 15.80 197.72 24.08

Vaccinium ovalifolium T 26.26 6.87 69.12 11.00 110.57 20.30

Vaccinium oxycoccos L — — — — 0.03 0.03

Vaccinium oxycoccos T — — — — 0.03 0.03

Vaccinium parvifolium L 6.25 3.97 31.50 13.72 33.70 11.47

Vaccinium parvifolium T 3.29 2.09 16.58 7.22 17.73 6.04

Total shrubs 171.90 37.92 560.60 42.56 726.12 59.41

Trees:

Alnus spp. L — — — — 1.24 1.24

Alnus spp. T — — — — 0.18 0.18

Alnus rubra L 1.24 1.24 1.58 1.58 3.55 1.91

Alnus rubra T 0.18 0.18 0.36 0.36 0.46 0.26

Alnus sinuata viridis L — — — — 2.31 1.58

Alnus sinuata viridis T — — — — 0.28 0.20

Chamaecyparis nootkatensis CAG 0.08 0.08 — — 0.29 0.17

Picea sitchensis CAG 8.88 3.49 3.23 0.47 20.05 6.86

Thuja plicata CAG 0.38 0.18 0.47 0.16 0.57 0.20

Tsuga heterophylla CAG 2.34 0.60 29.45 3.71 44.91 11.57

Tsuga mertensiana CAG 0.08 0.08 0.07 0.07 — —

Total trees 13.18 4.10 35.16 4.89 73.84 17.55

Total biomass (all species) 265.57 58.89 728.28 53.07 1045.82 67.24

a

 Plant parts: W = whole plant; L = leaf; T = twig; CAG = current annual growth (twigs and needles together).

b Chamerion angustifolium is Epilobium angustifolium.
59

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

Experiment III (25 to 35 years old, plus 6 years posttreatment) 

Thin and 25 Thin and 50

Untreated control Thinning alone percent prune percent prune

Species Plant parta Mean SE Mean SE Mean SE Mean SE

Forbs:

Actaea rubra W 0.01 0.01 0.06 0.06 0.07 0.07 — —Chamerion angustifolium* W — — 0.08 0.05 — — 0.01 0.01Circaea alpina W 0.19 0.18 0.08 0.05 0.17 0.09 <0.01 <0.01

Clintonia uniflora W 0.18 0.13 0.65 0.39 0.19 0.12 0.55 0.35

Coptis aspleniifolia W 0.44 0.24 1.98 1.43 2.10 0.85 2.84 1.43Cornus canadensis W 1.78 1.31 12.20 4.01 23.25 7.61 9.43 2.52Epilobium ciliatum W — — 0.01 0.01 — — 0.03 0.03Equisetum arvense W 0.01 0.01 0.29 0.27 0.13 0.13 0.01 0.01Galium spp. W — — — — 0.05 0.05 <0.01 <0.01

Galium trifidum W — — — — 0.02 0.02 0.02 0.02

Galium triflorum W — — — — 0.03 0.02 0.02 0.02Heracleum maximum W — — — — 0.67 0.67 — —Linneaea borealis W — — — — 0.29 0.29 — —Listera convallarioides W — — — — — — 0.01 0.01Listera cordata W <0.01 <0.01 — — <0.01 <0.01 <0.01 <0.01

Lysichiton americanum W 1.99 1.64 5.55 3.42 15.57 6.15 1.36 0.79

Maianthemum dilatatum W 0.10 0.05 4.04 1.81 0.87 0.46 1.17 0.60Moneses uniflora W 0.16 0.14 0.05 0.04 0.02 0.01 0.09 0.05Orthilia secunda W — — — — 0.03 0.03 0.01 0.01Platanthera stricta W — — 0.03 0.03 0.01 0.01 — —Prenanthes alba W 0.09 0.09 0.21 0.21 0.29 0.24 0.12 0.11

Ranunculus unci0tus W <0.01 <0.01 — — — — <0.01 <0.01

Rubus pedatus W 0.31 0.21 2.58 1.07 5.33 1.52 6.16 2.21

Stellaria crispa W — — <0.01 <0.01 0.01 0.01 — —Streptopus amplexifolius W — — 0.20 0.12 0.12 0.07 0.40 0.26Streptopus lanceolatus W 0.63 0.63 0.99 0.98 0.72 0.71 1.08 1.08Streptopus spp. W 0.07 0.05 0.28 0.28 0.01 0.01 0.43 0.43Streptopus streptopoides W — — — — — — 0.04 0.03Tiarella trifoliata W 1.58 0.81 — — 4.36 1.65 2.95 0.98Tiarella trifoliata unifoliata W — — 0.03 0.03 0.10 0.10 0.04 0.04Viola glabella W — — — — 0.58 0.43 — —
60

research paper pnw-rp-593 Urtica diocia

W

—

—

—

—

—

— 0.01 0.01

Unknown orchid

W

—

—

—

—

—

— <0.01 <0.01

Unknown forb

W 0.02 0.01 0.01 <0.01

—

— <0.01 <0.01

Total forbs 7.78 3.49 29.98 8.65 54.57 15.88 26.75 7.34

Ferns:

Adiantum pedatum

W

—

—

—

—

—

— 0.02 0.02

Athyrium filix-femina

W 2.69 1.33 8.91 1.91 16.69 6.22 18.41 6.04

Blechnum spicant

W 0.49 0.14 17.13 5.29 27.17 11.21 43.48 16.45

Dryopteris expansa

W 2.74 0.91 9.83 2.96 20.87 3.86 9.64 2.12

Gymnocarpium drypoteris

W 2.47 1.22 14.75 3.40 14.97 5.29 17.44 5.68

Phegopteris connectilis

W 0.43 0.25 0.81 0.33 0.60 0.34 1.49 0.79

Polypodium glycyrrhiza 

W 0.03 0.02

—

— 0.03 0.03 0.02 0.02

Polystichum braunii

W 0.45 0.45

—

— 0.26 0.26

—

—

Total ferns 10.56 4.22 51.43 9.23 80.20 17.63 90.48 21.65

Graminoids:

Carex spp.

W <0.01 <0.01 0.45 0.33 0.71 0.47 1.31 0.70

Unknown grass

W 0.06 0.04 3.80 2.33 2.34 0.95 1.40 0.38

Total graminoids 0.06 0.04 4.23 2.35 3.04 1.15 2.68 0.94

Shrubs:

Gaultheria shallon

L

—

—

—

—

—

— 0.07 0.07

Ledum groenlandicum

L

—

—

—

—

—

— <0.01 <0.01

Menziesia ferruginea 

L 3.45 1.47 7.89 2.01 29.90 6.91 23.41 7.40

Menziesia ferruginea 

T 1.60 0.68 1.47 0.38 5.03 1.16 3.24 1.02

Oplopanax horridus 

L 4.41 2.75 15.16 4.68 17.49 8.15 12.66 6.20

Ribes bracteosum 

L 1.58 1.15 0.75 0.51 1.54 1.38 0.08 0.08

Ribes bracteosum 

T 0.76 0.55 0.36 0.24 0.74 0.66 0.04 0.04

Ribes lacustre L 0.03 0.03 0.43 0.25 1.01 0.42 0.15 0.08Ribes lacustre T 0.02 0.02 0.21 0.12 0.48 0.20 0.07 0.04Ribes laxiflorum 

L

—

—

—

— 0.09 0.09 0.02 0.02

Ribes laxiflorum 

T

—

—

—

— 0.04 0.04 0.01 0.01

Rubus parviflorus 

L

—

— 0.29 0.29 1.58 1.04 0.57 0.57

Rubus parviflorus 

T

—

— 0.14 0.14 0.76 0.50 0.27 0.27

Rubus spectabilis 

L 6.15 3.96 52.72 13.21 71.21 19.64 41.65 10.07

Rubus spectabilis 

T 2.27 1.46 23.59 6.01 20.32 5.81 13.31 3.22
61

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

Sambucus racemosa L — — 2.61 2.15 9.02 5.29 5.42 3.72Sambucus racemosa T — — 0.85 0.70 2.93 1.72 1.81 1.20Vaccinium caespitosum 

seedlings W <0.01 <0.01 — — — — — —

Vaccinium caespitosum L 0.70 0.70 — — — — — —Vaccinium caespitosum T 0.20 0.20 — — — — — —Vaccinium spp. immature W 0.05 0.02 0.13 0.03 0.14 0.04 0.09 0.02Vaccinium ovalifolium L 22.13 8.07 79.63 20.18 157.29 41.81 61.85 15.27Vaccinium ovalifolium T 5.62 2.05 31.96 8.10 46.80 12.44 17.86 4.41Vaccinium parvifolium L 0.45 0.37 1.70 0.68 1.68 1.06 1.99 0.60Vaccinium parvifolium T 0.41 0.33 1.54 0.61 1.52 0.96 1.80 0.55Vaccinium vitis-idaea W <0.01 <0.01 — — — — — —Total shrubs 49.82 19.36 221.02 41.18 368.00 71.04 186.53 36.41

Trees:

Alnus rubra L — — — — 0.13 0.09 — —Alnus rubra T — — — — 0.05 0.04 — —Chamaecyparis nootkatensis CAG 0.03 0.03 — — 0.27 0.16 1.29 1.25Picea sitchensis seedlings W 0.02 0.01 0.05 0.02 0.05 0.02 0.05 0.03Picea sitchensis CAG 1.99 1.38 0.35 0.12 5.01 2.09 2.38 1.35Thuja plicata CAG 0.43 0.35 0.51 0.27 0.00 0.00 <0.01 <0.01

Tsuga heterophylla seedlings W 0.04 0.02 0.04 0.01 0.04 0.01 0.06 0.02

Tsuga heterophylla CAG 4.78 2.59 10.08 2.40 11.11 2.31 3.80 1.28Tsuga mertensiana seedlings W — — — — <0.01 <0.01 — —Tsuga mertensiana CAG — — — — 0.12 0.10 — —Total trees 7.26 3.89 11.03 2.50 17.46 3.58 8.08 3.44

Total biomass (all species) 74.16 26.51 317.75 51.87 524.00 94.60 314.56 59.82a Plant parts: W = whole plant; L = leaf; T = twig; CAG = current annual growth (twigs and needles together).b Chamerion angustifolium is Epilobium angustifolium.
62

research paper pnw-rp-593 Experiment IV (>35 years old, plus 4 years posttreatment) Plant Untreated control Conventional thin Thin and 1.5-m buck Thin and 4.6-m buck Thin by girdling Species parta Mean SE Mean SE Mean SE Mean SE Mean SE Forbs: Aruncus dioicus W — — 0.17 0.15 — — 0.27 0.27 0.01 0.01 Chamerion angustifoliumb W — — 0.17 0.10 0.04 0.03 0.05 0.03 0.07 0.04 Circaea alpina W 1.01 0.86 1.85 1.10 0.82 0.62 1.94 1.16 2.51 1.44 Claytonia siberica W <0.01 <0.01 — — <0.01 <0.01 — — — — Clintonia uniflora W 0.31 0.29 0.20 0.20 — — 0.04 0.04 — —

Coptis aspleniifolia W 0.11 0.07 — — 0.01 0.01 0.02 0.02 0.15 0.13

Cornus canadensis W 1.10 0.81 3.58 1.53 4.80 3.05 1.56 0.55 3.54 1.26

Epilobium ciliatum W — — 0.22 0.17 0.01 0.01 0.04 0.04 0.02 0.02Equisetum arvense W — — 0.04 0.04 — — — — — —Galium spp. W — — — — — — <0.01 <0.01 0.05 0.05

Galium trifidum W — — 0.17 0.11 — — — — — —

Galium triflorum W — — — — — — — — 0.06 0.06Listera cordata W <0.01 <0.01 — — — — — — 0.01 0.01

Lysichiton americanus W 1.80 1.38 3.80 2.39 5.84 2.83 10.45 5.52 4.89 2.98

Maiathemum dilatatum W 1.62 1.39 1.56 1.25 1.71 1.54 12.24 7.92 0.68 0.36Mitella pentandra W — — 0.07 0.05 0.02 0.02 0.93 0.84 0.63 0.42Moneses uniflora W 0.05 0.04 0.02 0.02 0.06 0.06 0.02 0.02 0.04 0.03Prenanthes alba W — — — — — — — — 0.01 0.01

Ranunculus uncinatus W — — 0.13 0.13 — — — — 0.02 0.02Rubus pedatus W 0.79 0.69 1.19 0.56 1.32 0.77 0.74 0.31 1.53 0.68

Streptopus amplexifolius W 0.18 0.14 0.15 0.11 — — — — — —Streptopus spp. W — — 0.01 0.01 — — 0.01 0.01 0.01 0.01

Tiarella trifoliata W 2.68 2.13 6.96 2.89 1.44 0.71 2.03 1.50 11.91 8.12

Tolmiea menziesii W — — 0.10 0.07 — — 1.68 1.06 0.32 0.22Viola glabella W — — 0.04 0.04 — — 0.06 0.06 <0.01 <0.01

Viola spp. W 0.03 0.03 — — 0.01 0.01 0.15 0.12 — —

Unknown forb W 0.10 0.06 0.06 0.05 0.44 0.37 0.06 0.04 — —Total forbs 9.62 3.75 20.32 4.95 16.52 4.57 32.29 8.88 26.46 8.77
63

Precommercial Thinning: Implications of Early Results From the Tongass-Wide Young-Growth Studies Experiments

Ferns:

Adiantum pedatum

W <0.01 <0.01 0.31 0.23

—

— 0.06 0.05 0.07 0.07

Athyrium filix-femina

W 2.25 1.39 15.61 3.53 5.62 1.36 31.74 8.90 7.00 2.48

Blechnum spicant

W 1.59 1.01 15.00 7.06 5.02 1.90 5.13 1.99 9.86 2.81

Dryopteris expansa

W 3.22 0.88 34.06 8.29 5.48 1.60 32.50 7.67 7.57 2.27

Gymnocarpium drypoteris

W 2.23 0.80 15.91 3.60 11.84 3.48 20.69 6.53 16.23 3.52

Phegopteris connectilis

W 2.75 2.27 4.10 1.28 1.50 0.72 6.24 2.42 3.84 1.26

Polystichum braunii

W <0.01 <0.01 0.58 0.30

—

— 0.09 0.09 0.12 0.12

Pteridium aquilinum 

W

—

— 0.12 0.12

—

—

—

—

—

—

Total ferns 11.53 5.09 85.70 16.86 29.68 4.59 96.44 17.00 44.69 8.41

Graminoids:

Carex spp. 

W

—

—

—

—

—

— 0.16 0.16

—

—

Unknown grass 

W

—

— 6.14 2.83 2.23 1.06 6.27 4.39 3.12 2.12

Total graminoids 0.00 0.00 6.14 2.83 2.23 1.06 6.43 4.38 3.12 2.12

Shrubs:

Menziesia ferruginea 

L 0.46 0.22 2.08 0.56 21.12 5.67 2.29 0.66 4.89 1.60

Menziesia ferruginea 

T 0.06 0.03 0.96 0.26 10.33 2.77 1.49 0.43 1.91 0.63

Oplopanax horridus 

L 0.29 0.14 3.48 1.20 1.45 0.77 1.49 0.62 3.28 1.55

Ribes spp.

L

—

—

—

— 0.02 0.02

—

—

—

—

Ribes spp.

T

—

—

—

— 0.01 0.01

—

—

—

—

Ribes bracteosum 

L <0.01 <0.01 2.68 2.52 0.33 0.32

—

— 0.23 0.19

Ribes bracteosum 

T <0.01 <0.01 1.59 1.50 0.12 0.12

—

— 0.06 0.05

Ribes lacustre L

—

— 2.87 1.43 1.93 1.18 0.78 0.53 0.90 0.51

Ribes lacustre T

—

— 1.07 0.53 1.05 0.65 0.36 0.25 0.39 0.22

Ribes laxiflorum 

L <0.01 <0.01 0.31 0.19 0.18 0.17 2.79 2.17 0.88 0.65

Ribes laxiflorum 

T <0.01 <0.01 0.12 0.07 0.07 0.06 1.29 1.00 0.24 0.18

Rubus parviflorus 

L

—

—

—

— 0.05 0.05 0.18 0.17 0.79 0.63

Rubus parviflorus 

T

—

—

—

— 0.02 0.02 0.08 0.08 0.22 0.17

Rubus spectabilis 

L 3.29 1.74 27.84 6.82 21.87 7.36 27.55 9.08 32.90 8.71

Rubus spectabilis 

T 2.02 1.07 20.51 5.02 8.87 2.99 14.80 4.88 27.76 7.35

Salix spp. 

L

—

— 0.58 0.58

—

—

—

—

—

—

Salix spp. 

T

—

— 0.46 0.46

—

—

—

—

—

—

Sambucus racemosa 

L 0.11 0.11 13.69 6.17 0.90 0.49 1.62 1.00 1.61 0.65

Sambucus racemosa 

T 0.07 0.07 10.91 4.92 0.59 0.32 1.84 1.14 1.83 0.73

Vaccinium ovalifolium 

L 7.20 3.45 9.06 3.36 17.28 8.15 17.12 5.07 26.36 7.10
64

research paper pnw-rp-593 Vaccinium ovalifolium T 7.65 3.66 10.56 3.92 16.15 7.62 21.08 6.24 29.84 8.03 Vaccinium spp. immature W 0.33 0.14 0.21 0.10 0.28 0.15 0.32 0.18 0.59 0.36 Vaccinium parvifolium L 0.29 0.27 0.74 0.54 0.30 0.20 0.94 0.60 1.16 0.80 Vaccinium parvifolium T 0.30 0.29 0.86 0.63 0.28 0.19 1.16 0.74 1.32 0.90 Total shrubs 22.28 9.77 110.90 20.67 104.15 24.25 97.20 19.97 138.77 24.09 Trees: Alnus rubra L 0.04 0.04 1.15 0.67 1.14 1.03 1.69 0.94 0.26 0.20 Alnus rubra T 0.02 0.02 0.85 0.67 0.46 0.42 0.91 0.50 0.22 0.17 Alnus viridis L 0.10 0.10 — — 0.01 0.01 — — — —

Alnus viridis T 0.06 0.06 — — <0.01 <0.01 — — — —Chamaecyparis nootkatensis CAG — — — — <0.01 <0.01 — — — —Picea sitchensis CAG 2.29 1.85 4.35 1.23 2.73 0.77 3.71 1.54 2.75 0.99Thuja plicata CAG 0.01 0.01 0.01 0.01 0.09 0.07 — — 0.13 0.10Tsuga heterophylla CAG 0.38 0.19 14.76 5.68 5.91 1.39 6.69 2.39 7.03 2.89Tsuga heterophylla seedlings W 0.09 0.04 13.65 3.50 6.79 2.49 19.90 5.25 7.58 2.55Total trees 2.91 2.04 33.91 6.99 16.73 3.57 31.98 5.92 17.76 4.01

Total biomass (all species) 46.87 17.17 258.04 39.52 169.01 27.27 264.92 29.98 230.79 33.86a Plant parts: W = whole plant; L = leaf; T = twig; CAG = current annual growth (twigs and needles together).b Chamerion angustifolium is Epilobium angustifolium.
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