Distinctive Features and Definitions of Young,

I Mature, and Old-Growth Douglas-Fir/Hardwood

Forests

Bruce B. Bingham and John 0. Sawyer, Jr.

I 1

Authors

BRUCE B. BINGHAM is a botanist, USDA Forest Service,

Pacific Southwest Forest and Range Experiment Station,

Arcata, California 95521. JOHN 0. SAWYER, JR., is a

professor of botany, Department of Biological Sciences,

Humboldt State University, Arcata, California 95521.

Abstract

We examined the structure of 70 Douglas-fir/hardwood

stands ranging from 40 to 560 years old in northwestern

California and southwestern Oregon for features that best

distinguished among young (40-100 year), mature (100-200

year), and old-growth (>200 year) forests. Separate analyses

of 11 groups of variables representing six components of

forest structure identified features of trees, understory cover,

snags, and logs as important discriminators. Independently,

features of understory and dead-wood components were

weak-to-moderate discriminators among forest age-classes.

The strongest discriminators were features of tree height and

diameter components. A discriminant model using features

from several components proved to have the greatest overall

discriminating power among forest age-classes. This multiple

components model included seven variables: the basal area

of conifers 240 m tall, the basal area of conifers 290 cm in

d.b.h., the basal area of conifers <45 cm in d.b.h., the basal

area of hardwoods 45 to 90 cm in d.b.h., the cover of hardwoods 18 m tall, the density of hardwood saplings 1 to 2 m

tall, and the density of hardwood snags 220 cm in d.b.h. and

22 m tall. We present definitions for young, mature, and

old-growth Douglas-fir/hardwood forests based on a wide

range of structural features. In addition, we revise the interim

old-growth definition for this forest type introducing new

minimum standards along with average values + 95-percent

confidence limits for certain key features.

Introduction

Old-growth Douglas-fir forests display several distinctive

structural features (Franklin and others 1981). These features,

which include large live trees, large snags, and large logs,

play numerous important roles in forest ecosystems. Controversies surrounding the loss of old-growth from the forested

landscapes of Washington, Oregon, and California demand

the immediate attention of managers and researchers. To

assist managers in their planning efforts, the Old-Growth

Definition Task Group (1986) developed interim definitions

for certain kinds of old-growth forests with minimum criteria

for key features. With new data available, their interim

definition for old-growth Douglas-fir/hardwood forest should

363
be expanded. For comparison, quantitative definitions for

mature and young forests are needed. Such comparisons can

help managers develop strategies to recognize and conserve

key features when manipulating forests.

In northwestern California and southwestern Oregon, lowelevation (<l200 m) Douglas-fir forests are mixtures of

Douglas-fir and hardwood trees. The hardwoods typically

account for major percentages of stand basal areas and

include tanoak, Pacific madrone, canyon live oak, golden

chinkapin, California-laurel, California black oak, and Oregon

white oak. Conifers other than Douglas-fir generally account

for minor percentages and include redwood, ponderosa pine,

incense-cedar, Port-Orford-cedar, sugar pine, western redcedar, and Pacific yew.

Franklin and Dyrness (1973) refer to these forests as Douglasfir on mixed evergreen sites. Sawyer and others (1977) used

the name Douglas-fir/hardwood to distinguish these forests

from others in Oregon and Washington, where, at lower

elevations, Douglas-fir dominates with conifer associates

instead of hardwoods.

The structural features of Douglas-fir/hardwood forests

change with age. These changes ultimately lead to a forest

where Douglas-fir and the hardwoods are self-replacing

(Sawyer 1980, Thornburgh 1981). This study identified

major features of young, mature, and old-growth Douglasfir/hardwood forests in northwestern California and southwestern Oregon. We used these features and others from a

wide range of forest components to develop definitions of

young, mature, and old-growth forests. In addition, our results

provided data for revising the interim old-growth definition

for this vegetation type. We present new minimum standards

and average values along with the 95-percent confidence

limits for key features such as large trees, snags, and logs.

Location and Environment

Douglas-fir/hardwood forests extend from southwestern

Oregon to central California. Along the coast, these forests

replace redwood forests on upland and more xeric sites

(Stuart 1987). To the south, Douglas-fir dominance dwindles,

and evergreen oaks, tanoak, and Pacific madrone become the

dominant trees (Sawyer and others 1977, Wainwright and

Barbour 1984). In the mountains, the forest type’s upper

elevational limit occurs when the hardwoods are replaced

by conifers, particularly white fir (Sawyer and Thomburgh

1977).

We conducted this study in the Northern California Coast

Range and Klamath Mountains provinces (Irwin 1960).

The Northern California Coast Range province extends

from Oregon as a narrow band of low coastal mountains.

The province widens in southern Humboldt and Mendocino

364

counties and continues south to the San Francisco Bay

region. The Klamath Mountains province is adjacent to the

northern Coast Ranges and includes the Siskiyou Mountains

of southwestern Oregon and extends to the Yolla Bolly

Mountains of California. Soils supporting Douglas-fir/hardwood forests in both provinces are derived predominantly

from metasedimentary rocks.

The climate of northwestern California is generally mild with

wet winters and dry summers. Average annual precipitation

at coastal and low-elevation mountain locations ranges from

650 to >2000 mm. High summer and low winter temperatures are typically near 38 and -7 oC, respectively.

The Northern California Coast Range and Klamath Mountains provinces are characterized by steep and rugged terrain.

Abrupt changes in aspect, slope, soil properties, and localized

disturbance regimes result in rapid shifts in tree-species dominance and changes in forest structure. In Douglas-fir/hardwood forests, these shifts are expressed as a forest mosaic

with patches of conifers, hardwoods, or diverse mixtures.

Canopy openings are common and display various stages of

forest regeneration.

Fire has played an important role in the origin and development of Douglas-fir/hardwood forests before and since the

arrival of European settlers (Agee, this volume; Johnson

1979). Tree-ring studies in the northern California Coast

Range concluded that fires occurred so frequently that settlers, and Native Americans before them, set fires regularly

(Anonymous 1983). During settlement, original forest lands

were often burned repeatedly after timber harvest as a means

of converting the areas to farm or grazing lands (Show and

Stuart 1932). For these fires to bum out of control and into

adjacent forests was not unusual.

Since the early 1900’s, fire management has intensified. The

suppression of wildfire has resulted in a scarcity of young

stands that originated after naturally ignited fires. Most young

stands <100 years old in northwestern California originated

after logging. In many of these stands, fire was used as a

means of reducing slash accumulations. Large quantities of

dead wood often remained, however. These young managed

stands do not necessarily possess the same structural features

as found in young stands that originated after a catastrophic

event other than logging.

Methods

Study Sites

Local ecologists proposed more than 100 candidate study

stands with tentative age-class assignments. Each candidate

was a forest stand of relatively homogeneous structure, floristic composition, and physiography. We selected 70 Douglasfir/hardwood stands ranging from 40 to 560 years old and
centered in three geographic areas (see frontispiece). Stands

were selected to represent three site moisture categories (dry,

mesic, or wet) characteristic of each area.

Stand sizes ranged from 5 to about 100 ha. Twenty-two

stands were located on the Northern California Coast Range

Preserve, a 3200-ha old-growth reserve managed by The

California Nature Conservancy and the USDI Bureau of

Land Management. Seven stands were located on private

lands. The remaining stands were located on lands administered by either the USDA Forest Service or the USDI

Bureau of Land Management.

Field Measurements

We sampled 285 plots, each of which consisted of a series

of circular plots ranging from 0.002 to 0.2 ha. The smaller

plots were systematically positioned within the 0.2-ha plot.

Stands <20 ha were sampled by using three (0.2-ha) plots.

Larger stands were sampled by using five (0.2-ha) plots. The

first plot in each stand was randomly located. Subsequent

plots were positioned at 150-m intervals. Plots were placed

a minimum of 50 m from the stand edge.

Snags 250 cm in d.b.h. and 215 m tall were sampled in a

0.2-ha plot. Data on live trees 250 cm in d.b.h., snags 210

cm in d.b.h., total canopy cover, and stand physiography

(plot aspect, slope, topographic position, and shape) were

collected in a 0.l-ha plot. Live trees 25 to 50 cm in d.b.h.,

saplings 21 m tall and <5 cm in d.b.h., logs 210 cm in

diameter, and the percentage cover of trees and large shrubs

18 m tall were sampled in a 0.05-ha plot. Percentage cover

estimates of small shrubs, ferns, herbs, and grasses were

made in a 0.02-ha plot. Also estimated in the 0.02-ha plot

was the percentage cover of mosses, lichens, rocks, and total

vegetation cover in three height-classes; 10.5-m, >0.5- to

1.0-m, and >l.0- to 2.0-m. Four 0.002-ha plots were used to

sample seedlings cl m tall, litter depth, and soil texture. The

distance to streams, ponds, and springs was noted.

Tree ages in stands were collected by increment coring a

minimum of three dominant or codominant canopy

Douglas-fir trees from at least three plots. Tree ages in

old-growth stands were determined from ring counts on

stumps along adjacent roads or clearcuts, or from increment

cores, or by a combination of both methods. In plots where

trees were cored, we selected at least one healthy

codominant or dominant tree near plot center. In young

stands, these trees were typically <45 cm in d.b.h. In mature

stands, codominant and dominant Douglas-fir trees were

typically 45 to 90 cm in d.b.h., and 290 cm in d.b.h. in

old-growth stands. Each stand was placed in a forest ageclass using the mean age as determined from a minimum of

three trees. Mean stand ages ranged from 40 to 560 years.

Douglas-fir trees determined to be >560 years old were rarely encountered, even on the most productive sites.

Vascular plants, snags, and logs were identified to species.

Each snag and log was assigned to one of five decay-classes

(Cline and others 1980). Later, classes 1 and 2 were combined into a sound wood category, and classes 3 through 5

were combined into a rotten wood category.

Data Analysis

For the purpose of analyses, we defined young stands as

those dominated, in terms of relative basal area, by Douglasfir trees 40 to 100 years old, mature stands as those dominated by Douglas-fir trees 101 to 200 years old, and oldgrowth stands as those dominated by Douglas-fir trees >200

years old.

Of the 70 stands we selected, we used 56 to determine distinctive features of the three forest age-classes. The 14 young

stands had originated after logging. Of 14 mature stands, 7

had originated after logging, and 28 stands were old growth.

We reserved 14 stands as a validation set. These stands included 10 old-growth and 4 mature stands. We selected the

validation stands to represent the full range of old-growth

and mature stand ages and site conditions. We lacked additional managed young stands to include as validation data.

We were interested in identifying features of forest structure

that were strong discriminators among young, mature, and

old-growth forests. We started our analyses with 60 variables

representing six components of forest structure. Because of

the importance of certain aspects of forest structure to managers, we viewed dividing the forest into various structural

components, such as tree height, tree diameter, snags, and

logs as a reasonable approach. Forest components could then

be studied separately to reveal important features of each

component. Variables were placed into logical groups for

analysis. Certain groups were analyzed in terms of more

than one unit of measurement (for example, basal area and

density). For our analyses, we ended up with 11 groups of

variables partitioned into six structural components (table 1).

Each group of variables was analyzed independently. Graphical assessment of each variable was used to determine departures from normality. Variables that were not distributed

normally were transformed by using a logarithmic or a

square-root transformation.

For each group of variables, we used stepwise discriminant

analysis (SAS 1985) to assess relative separation among the

three age-classes. Each analysis selected a subset of variables

that provided maximum discrimination among age-classes.

Variables were allowed to enter and to stay in each discriminant function model based on a partial F test with a significance level of 0.1. A moderate-to-low significance level

(50.1) helps to ensure that the variables selected are stable

and make a significant contribution to the power of the

model.

365
Table l-Eleven groups of variables (features) representing 6 forest structure components included in the discriminant function

analyses (each group was analyzed independently of all other groups)

I. Component: Tree height

Group: basal area (m2/ha) of trees 25 cm in d.b.h. (1.37 m above Group: ground cover (percent)

ground) 1 * herb, grass, and fern cover

1 * hardwoods <12m tall 2 * moss and lichen cover

2 * hardwoods 12-40 m tall 3 * vegetation cover <0.5 m tall

* conifers <12 m tall

4 * conifers 12-40 m tall

4 * vegetation cover 0.5-2 m tall

5 * vegetation cover 22 m tall

5 * conifers 140 m tall

V. Component: Snags (standing dead trees)

Group: density (stems/ha) of trees > 5 cm in d.b.h. Group: density (snags/ha) of snags > 10 cm in d.b.h. or top diameter

1 hardwoods <12 m tall 1 * sound snags

2 * hardwoods 12-40 m tall 2 * rotten snags

3 * conifers <12 m tall 3 * hardwood snags 14 m tall

4 * conifers 12-40 m tall 4 * hardwood snags 120 cm in d.b.h. and 12 m tall

5 conifers MO m tall 5 * hardwood snags 240 cm in d.b.h. and >4 m tall

* conifer snags 24 m tall

II. Component: Tree diameter 7 *

Group: basal area (m*/ha) of trees 25 cm in d.b.h.

conifer snags 220 cm in d.b.h. and 22 m tall

8 * conifer snags 140 cm in d.b.h. and 24 m tall

1 * hardwoods <45 cm in d.b.h.

2 * hardwoods 45-90 cm in d.b.h.

* conifers <45 cm in d b h

4 * conifers 45-90 cm in d.b.h.

VI. Component: Logs (dead wood on the ground)

Group: density (pieces/ha) of logs 210 cm in diameter at larger end

1 * sound logs

5 conifers 290 cm in d.b.h. 2 * rotten logs

3 * logs >22 cm in diameter and 22 m long

Group: density (stems/ha) of trees ?5 cm in d.b.h. 4 * logs >44 cm in diameter and 24 m long

1 * hardwoods <45 cm in d.b.h. 5 * logs 250 cm in diameter and of any length

2 * hardwoods 45-90 cm in d.b.h. 6 * logs 250 cm in diameter and > 15 m long

3 * conifers <45 cm in d.b.h.

5

* conifers 45-90 cm in d.b.h. Group: biomass (tons/ha) of logs 210 cm in diameter at larger end

conifers 290 cm in d.b.h. 1 * sound logs

2 * rotten logs

III. Component: Small trees 3 * logs >22 cm in diameter and 22 m long

Group: density (stems/ha) of saplings l-8 m tall and <5 cm in d.b.h. 4 * logs >44 cm in diameter and 24 m long

1 * hardwoods l-2 m tall 5 * logs 250 cm in diameter and of any length

2 * hardwoods > 2-4m tall 6 * logs 250 cm in diameter and 115 m long

3 * hardwoods 24 m tall

4 * conifers l-2 m tall * = variables that were transformed to attain normality

5 * conifers > 2-4 m tall

6 * conifers 24 m tall We used Wilks’ lambda, which is an inverse measure of

Group: density (stems/ha) of seedlings <l m tall

class separation ranging from 0 to 1, to assess the discrim1 * hardwoods <25 cm tall

ination among age-classes. Structure coefficients, which are

2 * hardwoods 25-50 cm tall correlations between variables and canonical variate scores,

3 * hardwoods 250 cm tall were used to assess the importance of each variable. Vari4 * conifers <25 cm tall ables with structure coefficients <0.30 were treated as of

5 * conifers 25-50 cm tall minor importance and were not used for interpretation of the

6 * conifers > 50 cm tall canonical variates. Eigenvalues were reported as a measure

of the relative contribution of each canonical variate to the

IV. Component: Understory cover

Group: shrubs and trees 58 m tall (percent)

discriminatory power of each model. The significance of

1 * shrub cover

each canonical variate was tested using a decomposition

2 * cover of hardwoods

approach to Bartlett’s V statistic (Bartlett 1947). Canonical

3 * cover of conifers variates that were not significant at the 0.05 level or better

were disregarded. With equal prior probabilities, the percentage of training stands (the 56 stands used in the discriminant analyses) correctly assigned to their respective

age-classes was used as a measure of classification success.

366
The variables retained by the 11 stepwise discriminant analyses were combined and analyzed in a final stepwise analysis. 

This final analysis selected variables representing several 

structural components. The classification error rate for the 

multiple components model was determined for old-growth 

and mature stands using the validation data set. We further 

tested the model for its ability to correctly assign young, 

mature, and old-growth stands to their respective age-classes 

using a jackknife classification procedure (Lachenbruch and 

Mickey 1968). 

Tree height (basal are&-Most of the discrimination among 

age-classes was due to differences in the basal area of conifers 240 m tall (table 2). Conifers ~12 m tall was of moderate importance The basal area of tall conifers increased with 

forest age-class (fig. 1). The basal area of short conifers was 

highest in the young age-class, which resulted in strong discrimination of young stands from the other two age-classes 

(fig. 1). 

Tree height (density)--Conifers 12 to 40 m tall and conifers 

240 m tall were both important to the discriminant model 

(table 2). The higher density of confers 12 to 40 m tall provided strong discrimination of the young age-class from 

mature and old-growth forests (fig. 2). Young stands were 

characterized bv a dense layer of conifers intermediate in 

height, and a v&y low density of tall conifers. Mature stands 

generally had a moderately dense lower layer of conifers 12 

to 40 m tall and relatively high densities of tall conifers. In 

old-growth forests, the density of conifers 12 to 40 m tall 

was typically low compared to young and mature stands. The 

density of tall conifers in old growth was similar to that of 

mature forests. The analysis of basal area data indicates, 

however, that the diameters of conifers ~40 m tall are, on the 

average, considerably larger than the diameters of tall 

conifers in mature stands. 

Tree diameter (basal area)-The basal area of conifers 290 

cm in d.b.h. and the basal area of conifers ~45 cm in d.b.h. 

were the most important features separating age-classes (table 

2). The basal area of large-diameter conifers increased with 

age-class (fig. 3) and was more than two and a half times 

greater in old-growth than it was in mature stands. Conifers 

with large diameters were rare in young stands that were 

dominated by conifers ~45 cm in d.b.h. 
Table Z-Variables selected by stepwise discrlminant analyses 00 11 groups of variables representIng SIX forest structure 

components. Data reported for forest age-clasm are meam with standard errors Lo pareotheses 

Variable selected Young 

Forest age-class 

Mature Old growth 

smtcttue 

coefficient 

CVl cvza 

I. Component: Tree height 

5r~qb&s” area of trees x5 cm 

conifers 540 m IalI 

conifers <12 m VIII 

3.2 (1.1) 

1.1 C.2) 25:;: (4.0) 62) 

0.97 

-.61 

Wilks’ lambda 

Exact F statistic (df = 4,104) 

(significant at P < 0.001) 

Classification success (%) 

0.227 

“:: (2.4) (.I) 

Eigenvalue 

28.62 

3.31 

99 

100 57 

group: density of trees 25 cm 

in d.b.h. 

conifers 12-40 m tall 

conifers 240 m IdI 

321.2 (54.0) 138.6 (27.3) 38.8 (7.3) .82 

8.4 (3.1) 48.3 (7.7) 43.5 (2.1) -.79 

Woks’ lambda ,297 Eigenvalue 1.69 

Exact Fstatistic(df=4,104) 

(significant at P < 0.001) 

Classification success (%) 

21.74 87 

93 72 

C%) 

68 

II. Component: Tree diameter 

Group: basal area of !zees 25 cm 

in d.b.h. 

Conifers 290 cm in d.b.h. 

Conifers 45.90 cm in d.b.h. 

Conifers <45 cm in d.b.h. 

Hardwoods 45.90 cm in d.b.h. 

1.1 (5) 

7.8 (1.5) 

154:34 I::;; 

12.5 (2.5) 

19.3 (1.9) 

6.7 (1.5) 

2.8 (1.0) 

34.8 (2.4) 

8.4 (1.1) 

2.0 

5.4 

-.94 -.I3 

.09 .94 

.82 -.09 

-.12 -.43 

3.22 JO 

82 18 

Woks’ lambda 

Exact F statistic (df = 8. 100) 

(significant at P < 0.001) 

Classification success (%) 

Group: density of trees 25 cm 

in d.b.h. 

Conifers &‘O cm in d.b.h. 1.1 C.6) 13.5 (2.9) 28.9 (1.7) 

Conifers 45.90 cm in d.b.h, 31.1 6.1) 56.9 (6.3) 21.7 (2.7) 

Conifers <45 cm in d.b.h. 501.3 (80.9) 233.9 (53.0) 74.8 (13.8) 

wws lambda 

Exact Fstatistic(df= 6,102) 

(significant at P < 0.001) 

Classification success (%) 

III. component: Small trees 

Group: density of saplings l-8 m 

tall and d cm in d.bb. 

Hardwoods l-2 m tall 

conifers >4 m tall 

cotlifers l-2 In tall 

Wilks’ lamMa 

Exact F statistic (df = 6, 102) 

(significant at P < 0.001) 

classification success (%) 

,139 

21.00 

,190 

21.97 

,483 

7.46 

lrnl 71 

Eigenvalue 

86 79 

6) 

89 

322.3 (53.4) 274.5 (76.3) 633.0 (70.6) 

67.9 (19.3) 19.0 (9.6) 17.6 (4.9) 

175.7 (68.1) 123.8 (47.8) 114.7 (32.1) 

EigenvaIue 

79 64 

(%) 

61 

-.97 .03 

.29 .94 

.80 -.05 

2.67 .43 

86 14 

.58 .76 

-.78 .45 

-.25 -.03 

.54 .34 

61 39 

368 
Table timtimed 

Variable selected YOUIlg 

Forest age-class 

MHlUe Old growth 

sbucture 

coefficient 

CVl cv2n 

Ill. Component: Small trees (continued) 

Group: density of seedlings 

<l&tall 

Hardwoods 25.50 cm tall 1307.1 (305.0) 1370.4 (314.2) 

Hardwoods SO cm tall 783.1 (150.1) 617.5 (148.4) 

Wilks’ lambda 0.776 

Exact F statistic (df = 4. 104) 

(si@icmt at P < 0.02) 3.52 

Classification success (%) 36 57 

Iv. Component: Understory cover (percent) 

Group: shrubs and trees $8 m tall 

Cover of hardwoods 

cover of conifers 

W&s’ lambda ,684 

Exact F statistic (df = 4, 104) 

(significant at P < 0.002) 5.43 

Classification success (%) 

Group: ground cover 

Moss and lichen cover 

vegetation coyer 0.5-z m tall 

12.5 

5.2 

57 

5.3 

15.9 g:;; 

50 

43.1 (9.1) 

6.7 (2.3) 

46.1 (7.8) 

1.6 (.5) 

50 

16.8 (4.3) 

3.1 (.9) 

36 

13.7 (4.8) 

15.2 (4.2) 

Wilks’ lambda ,750 

Exact Fstadstic(df=4,104) 

(significant atP < 0.01) 4.01 

Classification success (%) 

V. Component: Snags (standing dead mees) 

Grrqxhmity of snags 210 cm 

Sound snags 

Hardwood Z20 cm in d.b.h. 

wdZZmtaU 

Rotten snags 

Conifer 240 cm in d.b.h. md 

24 m tall 

Woks’ lambda ,394 

Exact Fstati&(df= 8,100) 

(significant at P < 0.001) 7.41 

Clsssiticaticm success (%) 

VI. Component: Logs (dead wood on the ground) 

Group: density of logs 210 cm in 

diameter at largest end 

Logs >44 cm in diameter and 

24 m long 29.1 (8.1) 

Logs SO cm in diameter 129.1 (24.8) 

Sound lags 59.2 (12.3) 

Wilks’ lambda 526 

Exact F statistic (df = 6.102) 

(significant at P < 0.001) 6.45 

Classification success (9) 50 

43 

46.4 (8.2) 

20.0 (5.1) 

39.2 (7.4) 

1.4 (.5) 

57 

7.2 (2.4) 

loo.3 (12.1) 

61.8 (16.4) 

57 

1496.7 (144.3) 

1050.3 (102.0) 

Eigenvalue 

(%) 

64 

0.30 

.79 

.28 

97 

25.0 (2.2) 

2.3 (.4) 

E&value 

(9) 

68 

.a2 

-.60 

.43 

95 

18.0 (3.4) 

24.0 (2.5) 

Eigenvalue 

(;I 

.a9 

.69 

.27 

86 

14.2 (1.7) 

5.1 (1.2) 

17.6 (2.0) 

4.9 (1.0) 

Eigmalue 

‘,“I 

.82 .33 

.31 .92 

.79 .05 

-.48 .22 

99 .27 

78 22 

24.7 (3.0) 

81.8 (5.7) 

80.1 (8.8) 

.48 

-.35 

.40 

Eigenvalue .67 

$1 83 

369 
Table 2-wntiowd 

smxture 

Forest age-class coefficient 

Variable selected YClWlg MSUPZ Old growth CVl cv2* 

VI. Component: Logs (continued) 

Group: biomass of logs 210 cm 

in diameter at larger end 

Logs >44 cm in diameter and 

24 m long 16.2 0.1) 3.5 (1.1) 18.2 (3.6) 1.00 

Wilks’ lambda 0.828 Eigenvalue .21 

Exact F statistic (df = 2, 53) 

(significant at P < 0.02) 5.49 100 

Classification success (5%) 21 71 

Lj Structure ccefficicnts not shown where CV2 was not significant (P > 0.05). 

Two other variables, the basal area of conifers 45 to 90 cm 

in d.b.h. and hardwoods 45 to 90 cm in d.b.h. were impatant 

in discriminating mature stands from young and old-growth 

(table 2). Mature stands were further distinguished from 

young and old-growth stands by having higher basal area of 

conifers 45 to 90 cm in d.b.h. and lower basal area of hardwoods 45 to 90 cm in d.b.h. The basal area values of medium-sized conifers ‘and medium-sized hardwoods in the young 

age-class were. very similar to old growth. As stands advance 

to a mature stage, hardwoods become overtopped by taller 

conifers, resulting in an increase in hardwood mortality. 

As mature stands develop further, the upper tier of conifers 

undergoes natural thinning and the hardwoods typically 

return. 

Tree diameter (density)-Three diameter-classes of conifers 

were important to the discriminant model (table 2). Separation of the three ageclasses was strong (fig. 4) and very 

similar to that provided by the basal-area model. The density 

of conifers BO cm in d.b.h. increased with age-class, and the 

density of conifers ~45 cm in d.b.h. decreased. Mature 

forests had the greatest densities of conifers 45 to 90 cm in 

d.b.h., which was important in discriminating mature from 

the other two age-classes. 

Small trees (saplings)-Two sapling variables, the density 

of conifers 24 m tall and the density of hardwoods 1 to 2 m 

tall we.re important to the model (table 2). They provided 

moderate separation of the forest age-classes (fig. 5). The 

young age-class was the most distinct because of the high 

density of conifer saplings Sl m tall. Mature and old-growth 

forests were similar in their densities of conifer saplings. The 

most important feature distinguishing old growth was a high 

density of hardwood saplings 1 to 2 m tall. Discrimination 

of the mahne age-class was due to densities of hardwood 

saplings that were typically lower than the densities found 

in young and old-growth forests. 

Small trees (seedlings)-The analysis of seedling variables 

selected hardwoods SO cm tall and hardwoods 25 to 50 cm 

tall (table 2). Overall. discrimination was weak. Old-growth 

was the most distinct age-class (fig. 6). The separation was 

primarily due to the density of hardwoods 250 cm tall, which 

was typically highest in old growth and lowest in mature 

forests. 

370 
Understory cover (shrubs and small trees)-Two variables, 

the cover of hardwoods S8 m tall and tbe cover of conifers 

<8 m tall, were important to the model (table 2). Discrimination was moderate. The cover of hardwoods increased with 

age-class, and the cover of conifers decreased (fig. 7). Old 

growth was the most distinct age-class. The cover of hardwoods in old growth averaged twice that of youngSands, 

and the cover of conifers in young stands averaged twice that 

of old growth. 

Understory cover (ground cover)-Tne analysis selected 

the combined cover of mosses and lichens, and the total vegetation cover from 0.5 to 2 m tall (table 2). Discrimination 

between young and mature stands was weak. Old growth 

was the most distinct age-class (fig. 8). Old growth showed 

the greatest development of moss and lichen cover, the more 

important variable in the model. Old growth also had the 

highest cover of vegetation 0.5 to 2 m tall. 

371 
Snags (density)-The density of sound snags and the density 

of rotten snags were the two most important variables to the 

model (table 2). The model was strongest in discriminating 

old-growth from young and mature stands. Old growth was 

characterized by having the lowest densities of sound and 

rotten snags (fig. 9). A higher density of large conifer snags 

240 cm in d.b.h. and Z4 m tall also contributed to the discrimination of old growth. Young and mature forests were 

similar to one another in their densities of sound, rotten, and 

large conifer snags. A fourth variable, the density of hardwood snags 220 cm in diameter and X2 m tall was important in discriminating mature stands. In young, mahxe, and 

old-growth forests, hardwoods are typically an important contributor to the snag component. Hardwoods can be especially 

important in mature stands, however, where they often 

account for up to 90 percent of the total snag density. 

Logs (density)--Three variables were selected, all of similar 

importance to the model (table 2). Although separation between the young and mature age-classes was poor, discrimination of old growth was fairly strong (fig. 10). Old growth 

was distinguished from tbe two younger classes by having 

the highest density of sound logs and the lowest density of 

logs 250 cm in diameter. Mature stands typically had low 

densities of large logs >44 cm in diameter and M m long. 

Young and old-growth stands bad similar densities of logs 

>44 cm in diameter and 24 m long. In young stands, the 

abundance of large pieces of wood on the ground was evidently an artifact of the logging methods practiced more than 

40 years ago. 

Logs (biomass)-Only one variable was selected by the analysis of log biomass (table 2). The biomass of logs 244 cm 

and >4 m long provided essentially no power in distingtishing old growth from the young age-class (fig. 11); both of 

which showed similar averages. A low biomass of large logs, 

however, clearly separated matare from the young and 

old-growth classes. 

Dktinctive Features Based on Multiple Components 

Stepwise discriminant analyses of the 11 groups of variables 

representing six forest-structure components (table 1) selected 

28 variables (table 2). A final stepwise analysis selected 8 

of the 28 variables and produced a model that was very 

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