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Landscape-Scale Patterns of Forest

Fragmentation and Wildlife Richness and

Abundance in the Southern Washington

Cascade Range

John F. Lehmkuhl, Leonard F. Ruggiero, and Patricia A. Hall

Authors

JOHN F. LEHMKUHL, is a research wildlife biologist,

LEONARD F. RUGGIERO,’ is a supervisory wildlife

biologist and PATRICIA A. HALL2

 is a wildlife biologist,

USDA Forest Service, Pacific Northwest Research Station,

Olympia, Washington 98502.

Abstract

The richness and abundance of birds, small mammals, and

amphibians in the community-study stands of Douglas-fir

in southern Washington were examined in relation to their

surrounding landscapes. We also compared landscape patterns with the Franklin and Forman (1987) model of forestlandscape dynamics. Forest and nonforest habitats in 2025-ha

landscapes were mapped around the center of old-growth

study stands from current forest-type maps and aerial photographs. Map data were analyzed with geographic information

1

 Now supervisory wildlife biologist, Rocky Mountain Forest and Range

Experiment Station, Laramie, Wyoming 82070.

2

 Now research assistant, School of Forestry, Northern Arizona University,

Flagstaff, Arizona 86011.

systems. Vertebrate richness and abundance in study stands

and their relation to landscape variables were analyzed at

four scales: stand area and configuration, the habitat composition of the stand’s neighborhood, the habitat composition

of the entire 2025-ha landscape, and relative to indices of

landscape pattern. Correlations indicated that landscape

variables were weakly (R2< 0.15) associated with richness

and abundance in young to old stands. Regression models

suggested that bird richness increased from conventional

edge effects associated with clearcut logging in the surrounding landscape. Bird abundance was highest in large old

stands in clearcut landscapes, suggesting that logging

displaced birds, which were then packed into remaining old

stands. Packing also was implicated as one explanation for

amphibian abundance, but elevation proved the strongest

influence. Mammal richness and abundance showed little

relation to landscape variables. A source-sink phenomenon

between young and old forest was suggested by the higher

abundance of cavity-nesting birds and the greater mammal

richness in young stands surrounded by old growth. We

concluded that fragmentation probably has not yet reached

the critical threshold that would initiate a decline in biotic

diversity, and the opportunity remains for pro-active

management.

425
Introduction

Forest fragmentation is the loss of late-successional forest

habitats to timber harvest or other disturbances, and the isolation of remaining forest patches and wildlife populations.

Legal mandates for public land-management agencies, and

popular sentiment favoring the protection of threatened or

endangered species and maintaining populations of other species has generated interest in the effects of forest fragmentation on biotic diversity (Lehmkuhl and Ruggiero, this volume; Nelson and Salwasser 1983; Nelson and others 1983;

Salwasser and others 1984). Management and research on

forest fragmentation have been guided by studies that describe the effects of habitat isolation in the context of island

biogeographic theories of vertebrate extinction and colonization on oceanic and land-bridge islands (Diamond 1972,

1984, Faaborg 1979, MacArthur and Wilson 1967, Simberloff

1974, Terborgh 1974, Wilcox 1978) and on habitat islands

in terrestrial landscapes (Brown 1971,1978, Forman and

others 1976, Freemark and Merriam 1986, Galli and others

1976, Soule and others 1988, Van Dorp and Opdam 1987,

Whitcomb and others 1977, Whitcomb and others 1981; also

see Biological Journal of the Linnean Society 1986, volume

28). Extinction and colonization on habitat islands, however,

may be only one of many biotic or environmental processes

that determine biotic diversity in fragmented forest landscapes

(Haila 1986, Haila and Jarvinen 1981, Simberloff and Abele

1982, Williamson 1981). Population viability theory (see

Soule 1986 and papers in Soule and Wilcox 1980) and landscape ecology, with its emphasis on patch structure, context,

and edge or boundary effects (Forman and Godron 1986,

Urban and others 1987) have expanded the theoretical basis

for studying the effects of fragmentation on biotic diversity

(Lehmkuhl and Ruggiero, this volume).

The loss and fragmentation of old-growth Douglas-fir forest

in the Pacific Northwest that results from clearcut logging

exemplifies forest fragmentation. During the last 40 years,

logging in Oregon and Washington has converted 80 to 90

percent of the original mature and old-growth forest into a

mosaic of early and late-successional forest (Harris 1984,

Meslow and others 1981, Spies and Franklin 1988). Similar

cutting in coastal northern California has accomplished the

same transformation in about 50 percent of the original forest

there (Rosenberg and Raphael 1986). The situation varies

within the region, however. Fragmentation of industrial forest is a minor issue because high harvest rates have resulted

in the near-complete conversion of old forests to plantations

(Spies and Franklin 1988), and the practice of cutting squaremile blocks leaves few forest fragments. Fragmentation, however, is a critical issue on public forest lands where much of

the remaining pristine forest in the region is located because

the more conservative harvest rates and the prevailing practice of staggered-set clearcutting of small patches have

resulted in a high degree of fragmentation in the remaining

forest habitats and wildlife populations (Franklin and Forman

1987; Harris 1984; Lehmkuhl and Ruggiero, this volume).

Research on wildlife in fragmented landscapes has been

conducted largely in areas of relatively simple patch dynamics such as woodlots in agricultural landscapes and mountaintop habitat islands. Managed forests differ from woodlots or

mountaintop islands in several respects, however. Pattern in a

managed forest is spatially dynamic. Undisturbed forest initially forms the matrix (rather than the patch, as with woodlots) until a threshold is reached where disturbed area exceeds

forest area (Franklin and Forman 1987, Verner 1986). Pattern

in a managed forest is also temporally dynamic. Boundaries

between the forest and clearcuts initially are distinct, but

with secondary succession, boundaries later become increasingly ambiguous (Rosenberg and Raphael 1986). Only a few

studies in northern California (Raphael 1984, Rosenberg and

Raphael 1986) have examined the effects of fragmentation in

such dynamic managed-forest landscapes. The need for more

research is critical.

Our research has analyzed the effects that forest fragmentation and spatial patterns of habitat availability have on wildlife diversity and abundance in western Oregon and Washington. The goals of our research were to extend the interpretation of data on the old-growth vertebrate community (this

volume) beyond the stand to include the context of the landscape, to examine pattern in managed-forest landscapes and

advance the theory of forest landscape dynamics, and to

assess the impact of current forest management practices.

Our analysis was guided by four basic questions about the

richness and abundance of birds, small mammals, and amphibians. Each question addressed a succeeding, higher,

landscape-scale of analysis:

Do species richness and abundance decline with decreasing area of community-study stands as predicted by

insular ecology theory? How does the length of stand

edge affect richness and abundance?

How do the proportions of habitats immediately adjacent

to the community-study stands (the neighborhood context) affect species richness and abundance?

How does the habitat composition of the entire landscape

(the landscape context) affect species richness and abundance in the community-study stands?

Can variations in species richness and abundance in the

community-study stands be associated with indices of

landscape pattern that convey properties of dominance,

point diversity, and patch shape (fractal dimension)?

426
Table 1-Cross-tabulation of sample stands by

age-classes assigned by Spies and Franklin (SF)

(this volume) and the types assigned for this study

through aerial photograph interpretation and

ground-truthing

Photo interpreted typesa

Ages Young Mature Old-growth

Young 90 (9)

Mature 20 (2)

Old-growth --

10(1) -

80 (8)

7 (2) 93 (26)

a

SF values are percentages of types correctly typed by

photo interpretation.

b Number of stands are in parenthesis.

Our paper is more of a progress report than a presentation of

final results. We present results for the southern Washington

Cascades province; similar studies in the Oregon Cascade

and Coast Range provinces are still in progress.

Methods

Mapping Procedures

Study landscapes were circular 2025-ha areas around the 45

community-study stands of old growth in the southern Washington Cascade Range. We defined landscapes by drawing a

circle of radius 2.54 km from the center of the stand. Forest

stands 24 ha and nonforest stands >0.4 ha initially were

mapped and typed, from Forest Service Total Resource Inventory (TRI) maps, as homogeneous forest patches with

similar crown diameters and canopy closure. We then updated and corrected the maps by examining color aerial

photographs taken during 1982, by consulting with field

personnel, and by conducting field reconnaissance. We

digitized, managed, and analyzed map data with vector and

raster geographic information systems (GIS) for personal

computers.

Random samples of 14 percent (N = 171) of the mapped

forest polygons and 6 percent (N = 76) of the nonforest

polygons were ground-truthed to assess the accuracy of types

determined from the TRI maps and photo-interpretation.

Field crews visually truthed nonforest sites and examined

forested stands with plotless sampling techniques to estimate

tree frequencies, densities, and basal areas in five diameterclasses (sapling < 13 cm d.b.h, pole 14-25 cm d.b.h, small

sawtimber 26-51 cm d.b.h, large sawtimber 52-81 cm d.b.h,

and old-growth >81 cm d.b.h with a multistory canopy).

They also estimated the extent of multistory canopy, number

of snags, and canopy closure. Ground-truthing determined

that nonforest types concurred with 95 percent of the mapped

types. Misclassification was much greater with the forested

types, however. An initial comparison of mapped and groundtruthed forest types (on the basis of measured basal areas in

Table 2-Habitat types mapped in the southern Washington

Cascade Range for landscape-scale studies of old-growth

communities

Nonforest types Forest types

(1) Nonvegetated

(2) Human habitation

(3) Agriculture

(4) Water

(5) Rock

(6) Wetland

(7) Grassland

(8) Subalpine meadow

(9) Brush

(10) Clearcut

(11) Clearcut-forb

(12) Clearcut-shrub

(13) Sapling-pole, d.b.h. 0-25 cm trees

(13.1) Clearcut-sapling

(13.2) Clearcut-pole

(13.3) Natural pole

(14) Young, d.b.h. 26-51 cm trees

(15) Mature, d.b.h. 52-81 cm trees

(16) Old growth, d.b.h. (>81 cm

and multistory canopy)

(17) Hardwood-conifer

(18) Hardwood

(19) Riparian

(20) Special feature

the five diameter-classes that were used to map the stands)

showed only 68 percent concurrence between mapped types

and ground-truthed types. This percentage was unacceptable,

so we first reclassified mapped stands by grouping the

ground-truth plots with k-means clustering (Wilkinson 1988).

Cluster groups and types were initially assigned to truthed

stands from photo interpretation, and then cross-tabulated to

determine which mapped types characterized each cluster.

This procedure formed the basis for reassigning all of the

original mapped types to one of four new forest types that

were defined (through cluster anlaysis) by the dominant tree

diameter-classes. The new forest types were sapling-pole,

young, mature, and old-growth types. The forest types assigned to the community-study stands by this method corresponded very well (90 percent) to the age-classes assigned

by Spies and Franklin (this volume) on the basis of increment coring (table 1). These were the only stands in the

landscapes for which age data were available. Old-growth

stands showed the highest consistency with Spies and

Franklin’s classification, followed by young stands, and

mature stands.

Map Variables

Community-Study Stand Variables

We determined the elevation, age, area, perimeter, and shoreline edge index (Patton 1975) for each community-study

stand. The proportion of clearcut area, old-growth forest

area, and late-successional forest (types 14, 15, 16, 17, and

19 in table 2) area were calculated for a 400-m buffer zone

around the periphery of the stand. Four-hundred meters was

chosen as a likely limit of edge effects (Wilcove and others

1986). Habitat patches within the study stand, such as

clearcuts, were included in the buffer zone.

427
Landscape Variables

We characterized the composition of each 2025ha landscape by the area of clearcuts, old-growth forest, and latesuccessional forest. Landscape pattern was measured with

several indices proposed by O’Neill and others (1988) as

standard measures of landscape pattern. We measured

dominance of habitat types as the deviation from maximum

evenness according to the equation,

D = loge (n) + E (Pi loge Pi)

where Pi equals the proportion of the ith habitat type and n is

the total number of types in a particular landscape. The highest possible value was slightly less than loge

(20) = 3.

We measured point diversity not as contagion per O’Neill

and others (1988), but with a raster GIS algorithm for measuring map diversity. The GIS scanned the map and determined for each 0.25-ha grid cell the total number of cells of

different habitat types within a 150-m radius or “window.”

The mean of this frequency distribution was used as an index

of point diversity, an inverse measure of contagion. High

point diversity indicated a complex landscape pattern.

We calculated the fractal dimension as two times the slope

of log(perimeter) over log(area) of all the habitat polygons in

a landscape (Lovejoy 1982, O’Neill and others 1988). This

index measured the complexity of polygon shapes within a

range of 1 to 2. An index of 1 indicated very regular polygon

shapes, such as a circle or square, whereas an index of 2 indicated highly complex polygon boundaries. A disturbance

index was calculated as the ratio of clearcut area to latesuccessional forest area (O’Neill and others 1988). Indices

less than one indicated relatively more forest than clearcut.

Vertebrate Data

We analyzed the common data set used for the cross-province

analyses of bird (Huff and Raley, this volume), small mammal (Aubry and others, this volume), and amphibian (Bury

and others, this volume a) communities. Bird occurrence was

based on those individuals observed within a 50-m variablecircular-plot radius and observed on 2 or more of an average

6 sample-days per stand. Only smaIl mammal and amphibian

pitfall-data taken during autumn were analyzed. Average

daily detections rather than density estimates were used to

measure wildlife use. Sampling effort was the same for each

stand, regardless of area, and between years.

Richness and abundance were analyzed separately for each

taxon. We calculated species richness as the total number of

species detected in the community-study sample-stand at

each landscape’s center. Total abundance in a taxon or functional group was calculated as the sum of average daily

detections for all species in that group. Species richness and

abundance from 1984 and 1985 were averaged for the

analysis. Associations of individual species’ abundance

with landscape variables were examined for those species

occurring in > 10 percent of the stands in either year.

Analytical Procedure

We visually screened the dependent variables of vertebrate

species richness and total abundance for normality by examining the frequency distributions with fitted normal curves

and testing with the Kolmogorov-Smimov test (Wilkinson

1988). The log of total abundance was used to approximate a

normal frequency distribution. We examined the linearity of

dependent-variable responses to independent map variables

by fitting smoothed curves to scatterplots (Wilkinson 1988).

We did not observe nonlinear responses requiring data

transformations.

The univariate associations of species richness and abundance with stand, stand-buffer, landscape-composition, and

landscape-pattern variables were analyzed by partial correlation holding stand elevation and age constant to separate natural from human-caused fragmentation effects as was done

by Rosenberg and Raphael (1986). We used stepwise regression with an enter and removal P-value of 0.15 (Wilkinson

1988) as a multivariate exploratory technique to select a subset of variables that explained the most variation (highest R2

)

in species richness or abundance. Stepwise regression is

often criticized as too dependent on the correlation structure

of the independent variables, but we found few strong correlations between dissimilar variables that would complicate

the interpretation of stepwise regressions. Moreover, selected

variables were not later removed from any of the models, so

little ambiguity existed in the selection of the “best” set of

variables.

The significance of stepwise regression models was tested

with probabilities adjusted for stepwise regression (Wilkinson

1979 in Tabachnick and Fidel1 1983). Assumptions for regression were valid for all models: we found residuals to be

normally distributed and homoscedastic from an examination

of the probability plots of residuals and by plotting residuals

against expected values (Wilkinson 1988). No outliers for

regression models were detected from an examination of

leverage statistics and standardized residuals (Sokal and

Rohlf 1981). We were liberal in noting the significance of

statistical relationships at P < 0.10 in tables, but generally

were conservative in interpreting and discussing relationships

that were significant only at P < 0.05.

Lines were fitted to data in scatterplot figures by the Lowess

algorithm. Lowess smoothing is useful for examining relations between variables because it does not presuppose the

shape of the function, but calculates predicted Y values from

a weighted average of nearby Y values (Wilkinson 1988).

428
Table 3-Summary statistics for sample stand and landscape

variables used in landscape-scale analysis of vertebrate community patterns in the southern Washington Cascade Range

Variable

summary statistics

Std

Min Max Mean error

Sample stand

Area (ha)

Perimeter (km)

Edge index

Elevation (m)

Age

Stand buffer proportions

Clearcut

Old-growth

Late-successional

forest

51.0 1,689.0

3.0 65.6

121.0 550.0

404.0 1,218.0

55.0 730.0

0 0.61

.006 .55

.16 .94

Landscape composition (ha)

Clearcut 0 .48

Old-growth .03 .86

Late-successional

forest .39 .99

Landscape pattern indices

Fractal

Dominance

Point diversity

Disturbance

1.09 1.27

.33 1.66

1.14 2.06

0 1.22

488.0 49.6

25.0 1.9

322.0 11.8

762.0 31.4

297.0 30.0

0.15 0.026

.19 .020

.47 .025

.13 .018

.31 .025

.77 .017

1.20 .0056

.93 .036

1.72 .026

.20 ,035

Species-area curves, however, were fitted with straight lines

to conform to the theoretical distribution (MacArthur and

Wilson 1967, Wilcox 1980).

Results

Landscape Characteristics

Community-study stands-Community-study stand-areas

ranged from 51 ha to 1690 ha, with a mean of 488 ha

(table 3). Old-growth stands were generally larger (mean of

546 ha) than young (mean of 486 ha) or mature (mean of

352 ha) stands (table 3), but the differences were not significant by analysis of variance (P < 0.05). The mean proportion

of clearcut in stand buffers was 15 percent, ranging from 0 to

60 percent. Young and mature sample stands averaged no

more than 9 percent clearcut area in their buffers, but oldgrowth stands averaged 20 percent clearcut.

Composition of Landscapes

Clearcut area ranged from 0 to 48 percent of the total landscape area (table 3), but most landscapes had relatively small

amounts of clcarcut area (<15 percent) (fig, 1A). Old-growth

area was more normally distributed (fig. lB), with an average of 31 percent old-growth (table 3). Late-successional forest area was generally less variable (fig. 1C) than old-growth

with most landscapes comprised of 70 to 90 percent latesuccessional forest. Late-successional forest and clearcut

areas showed a strong reciprocal relationship (fig. 2), which

conforms to Franklin and Forman’s (1987) checkerboard

model of forest-landscape dynamics under a clearcutting

regime. The relation indicated a 50-percent cutover threshold

where dominance of the landscape matrix switches from forest to clearcut habitats. Most landscapes remained well under

this 50 percent threshold, where forest-clearcut edge reaches

an apex. The relation was much different with respect to

old growth. The regression of old-growth area against the

clearcut proportion was not significantly different than zero

(fig. 2). Residual variation around the regression line was

consistently high, within the observed range of 0 to 50 percent clearcut.

Patch Sizes

Over 90 percent of the clearcut patches were <30 ha, with a

few very large cuts on private lands of up to 150 ha. Oldgrowth patch size was extremely skewed toward small stands

(fig. 3). Despite their greater number, the cumulative area of

patches < 10 ha accounted for only 4 percent of the summed

old-growth area for all landscapes, and patches < 20 ha totaled

about 10 percent of the old-growth area. Most old-growth

patches were < 50 ha, but the maximum patch-size was 1741

ha. Late-successional forest generally occurred as larger

patches (fig. 3). Late-successional patches < 10 ha accounted

for only 0.5 percent of the summed area for all landscapes,

and patches < 20 ha totaled 0.8 percent.

As the proportion of clearcut area in a landscape increased,

clearcut patch-size initially increased slightly, but then remained nearly constant (fig. 4A). Old-growth patch-size

increased rapidly up to about 10 percent cutover, then decreased steadily to the 50 percent clearcut point, the maximum observed in our landscapes (fig. 4B). The linear

trend was not significant by regression (P = 0.33). Latesuccessional forest patch-sizes showed a small initial increase

up to about 8 percent cutover, then a steady decline in patch

size as with old growth (fig. 4C). The linear trend was significant by regression (P = 0.009), but with low explained

variation (R2 = 0.14).

Pattern Indices

Patch shapes were not complex as measured by the fractal

index (table 3). Within the possible range of 1 to 2, our maximum index value was 1.27. Fractal dimension showed a

strong negative correlation (r = -0.72) with clearcut area: the

complexity of patch boundaries decreased as the number of

regular-shaped clearcuts increased. Dominance indices

suggested low habitat dominance in most landscapes, with a

mean of 0.93 and a maximum value little more than half the

429
Old-growth forest 

Late-successional forest 

b 400 800 ,200 1600 2000 

Area (hectares) 

theoretical maximum of 3 (table 3). Landscapes showed little 

habitat point diversity at the scale of 12 ha patches: the maximum possible index was 20 habitat-type transitions, whereas 

our landscapes had a maximum of 2.1 transitions and an 

average of 1.7 transitions in a 12-ha area. The disturbance 

index, the ratio of clearcut to late-successional forest, showed 

a relatively low amount of disturbance (table 3). Clearcuts in 

the majority of the landscapes were less than 40 percent the 

area of late-successional forest, with a mean of 20 percent. 

Landscape-Vertebrate Relationships 

Species Richness 

All sample stands-Partial correlations, holding stand elcvation and age constant, revealed few significant or suong 

relations between species richness and landscape variables 

a Old-growth forest 

0.0 0.2 0.4 0.6 0.8 1.0 

Clearcut (proportion) 

(table 4). Correlations were mostly weak (R2 S 0.37), indicating <I4 percent shared variation. The strongest relations were with bid and amphibian species richness. Bird 

richness was positively associated with the proportion of 

cleaxut area within the buffer, and negatively correlated with 

the amount of late-successional forest in the buffer. Arnphibian richness was most strongly correlated with the dominance 

index, indicating association with relatively homogeneous 

landscapes. Small mammal richness was not even weakly 

associated with any landscape variable. 

We graphically examined four questions about the relation of 

species richness to landscape variables: 

l Does richness increase with the size of the sample stand? 

l Is richness in young stands greater when surrounded by 

old growth (may richness in young stands be “subsidized” 

by adjacent old forest)? 

l Does richness increase from local edge effects in stands 

that have a clearcut buffer area? 

l Does richness increase from landscape edge effects in 

stands located in clearcut landscapes? 

Island biogeographic theory predicts a positive relation between species richness and stand area, but species-area 

curves revealed no significant relation between stand area 

and richness (fig. 5). Richness in young stands increased, as 

hypothesized, with the area of old growth in the neighbor430 
,2 _~, *~,~,~1 I 

II Late-successional forest 

10 

4 

2 

0 400 800 1 zoo 1600 2000 

Patch size (hectares) 

hood, in a significant linear relation (P = 0.085; R2 = 0.36) 

only for small mammals (fig. 6B). An opposite relation was 

evident for birds and amphibians (fig. 6A, 6C). A linear 

regressiqn model for birds was significant (P = 0.006). but 

weak (R = 0.16). The line fitted to the amphibian data 

indicated a slight negative relation up to a threshold of 30 

percent old growth, beyond which richness declined rapidly. 

A linear model was not significant. 

Clearcut area in the buffer of sample stands of all ages was 

positively but weakly associated with bird richness (fig. 7A) 

The linear regression model was si@ficant (P = 0.006), 

but with low explained variation (R = 0.162). A similar 

relation was indicated for bird richness and the total area of 

431 
Table 4-Partial mrrelations (stand age and elevation constant) 

of bird, small mammal, and amphibian species richness and 

abundance with landscape variables 

clearcut in the landscape (fig. SA), but it was not significant 

(P = 0.107). Both relations showed little change in bird richness up to about 18 percent clearcut, then a rising trend in 

richness with increasing clearcut area. The similarity of 

responses to clearcut area at both scales may be due to the 

moderate correlation between clearcut in the buffer and landscape (R’ = 0.78). No associations between small mammal 

richness and clearcuts in the buffer (fig. 7B) or landscape 

(fig. 8B) were observed. Amphibian richness initially decreased as clearcut area in the buffer (fig. 7C) and landscape 

(fig. SC) approached 15 percent, but beyond that percentage, 

richness increased steadily. The relations were weak, however, as suggested by the large residual variation. Linear 

regression models were not significant. 

Stepwise regression models including stand age and elevation were consistent with the results of partial correlation and 

were significant for birds and amphibians (table 5). Bird 

richness was best predicted as a positive function of stand 

area and clearcut area in the buffer zone, and a negative 

function of elevation and old-growth area in the landscape. 

These variables accounted for 37 percent (R’) of the vtiation in bird richness. The model for amphibian richness was 

most-strongly influenced by a negative elevation gradient, 

but positively influenced by habitat dominance (table 5). 

The amphibian model was the strongest of the three taxa 

(R2 = 0.56). 

432 

2.4 

2.2 

zl 

. 

v 

g 2.0 I 

c 

0 

'C 

3.0 B 

: 

.- 2.5 

ii 

% 2.0 

3 4 5 6 7 ; 

Log, of stand area 
J-----d 

1°rrB 

7 C 

6 . 

4 . 

. 

3 

:-\~ 

. . 

2 n i 

. 

1 I i 

0.0 0.1 0.2 0.3 0.4 0.5 

Old-growth (proportion) 

Old-growth sample stands-The associations between landscape variables and bird richness in the sample stands classified as old growth were consistent with stepwise regression 

models for all stands (table 6). Bird richness increased with 

landscape disturbance at neighborhood and landscape scales 

as indicated by a positive correlation with clearcut area in 

buffer zones and landscapes, and a negative correlation with 

fractal dimension. Bird richness in old-growth stands also 

showed a negative relation to elevation. In contrast to previous analyses with data from all stands, small mammal richness in old-growth stands was associated with complex patch 

shapes (fractal dimension). As with the regression model for 

all stands, amphibian richness in old-growth stands had a 

strong negative correlation with elevation. 

Species Abundance 

All sample staqds-Vertebrate abundance was weakly associated through partial correlation with few landscape variables 

(table 4). As with bird richness in the regression model, bird 

abundance was negatively associated with old growth in the 

landscape. Small mammal abundance was correlated with 

habitat dominance. Amphibian abundance was not significantly correlated with any variable. 

Stepwise regression suggested a model of bird abundance 

increasing with age and size of stands, with old growth in the 

immediate bulfer, and with increasing clearcut area and decreasing old-growth area in the landscape (table 5). Stepwise 

regression was not able to select a statistically significant 

model of small mammal abundance. The amphibian-abundance model indicated a strong positive influence of habitat 

dominance, and negative influences of complex patch shape 

(fractal index), old-growth area in the landscape, and 

elevation. 

Old-growth sample stands-Spearman correlations of bird 

abundance in old-growth sample stands contradicted the 

regression model for all stand ages by suggesting a negative 

relation with stand area (table 6). The negative associations 

of bird abundance with old-growth area in the landscape and 

with habitat dominance, however, were consistent with the 

regression model. Small mammal abundance in old-growth 

stands surprisingly showed some relatively strong COTrelations not evident in previous statistics: abundance was 

negatively associated with clearcut area in the landscape (and 

its related disturbance index), and was positively associated 

with habitat dominance. Amphibian abundance in old stands 

declined with elevation as in the regression model, but also 

with complex patch shapes in the landscape (fractal index); 

abundance also increased with clearcut area in the stand 

buffer. 
15 B 

2 I 1 

0.0 0.1 0.2 0.3 0.4 0.5 

Clearcut (proportion) 

15 B 

OL I 

i.000 0.175 0.350 0.525 0.700 

Clearcut (proportion) 

434