Table S-Results (P-values) of cbl-square analyses comparing logs where salamanders WEE captured to the distributions of logs in 

different size- and decay-classes0 

Hcrrington (1988) reported the Del Norte salamander as 

essentially restricted to talus habitat. We concur that rocky 

habitats are important for this species (see macrohabitat 

section, above), but we believe some clarilication is warranted. All of our captures for the Del Norte salamander 

occurred on sites with talus, rock outcrops, or rocky soils. 

Within these areas, however, we also found (his species 

using downed woody debris for cover. Sixty-two percent 

of our captures were associated with such material (fig. 3), 

and 5 percent were within decayed logs. 

We have almost no knowledge of the importance of belowsurface habitat use for forest salamanders (N. Scott, pers. 

comm.): however, our data indicate that large numbers of 

salamanders use the subsurface, surface, and above-surface 

habitats provided by downed woody debris. Similar results 

from other studies in comparable habitats (Aubry and others 

1988; Bury and Corn 1988~~ Corn and Bury, this volume b) 

support the conclusion that these habitat features are probably essential for many species of forest salamanders. 

Size of woody debris cover--The highest capture rates of all 

species combined were in the smallest and the next two lager size-classes of both conifer and hardwood logs (fig. 4). 

Captures were significantly lower than expected in several of 

the intermediate size-classes and did not vary from expected 

captures in others (table 8). The most abundant downed 

woody materials on our sites were. pieces in the 0.5. to 1-m’ 

size-class (fig. 4), but the frequency of captures of herpetofauna for this size-class was significantly less than expected 

(table 8). Although we captured salamanders in the largest 

size-classes of both conifers and hardwoods, the independent 

data set from which we derived our expected values lacked 

sufficient numbers of logs in these classes to allow for 

statistical tests (fig. 4, table 8). 

The slender salamander and ensatina occurred in patterns similar to that described for the total herpetofauna (table 8). The 

clouded salamander, however, apparently chooses the larger 

classes (table S), particularly of conifer logs (fig. 4), consistent with the findings of Corn and Bury (this volume b). The 

Del Norte salamander occurred more often than expected

under the smallest size-class of both hardwood and conifer

debris, less than expected in intermediate conifer debris, and

showed no deviation from expected in large conifer and all

other hardwood classes (table 8).

Decay-class of woody debris-Our estimates for the amount

of downed woody material (logs and branches-snags were

not included) by decay-class indicated a nearly normal

distribution from sound to rotten, with more conifer than

hardwood pieces (fig. 5). Decay-class 3, characterized by

logs with bark exfoliating and heartwood beginning to rot,

contained the most pieces. In contrast, captures of the total

herpetofauna by decay-class indicated significantly more

captures than expected for class 4 conifer and class 4 and 5

hardwood logs, and fewer captures than expected in class 1,

2,3, and 5 conifer logs, and class 1,2, and 3 hardwood logs

(table 8).

The slender salamander and ensatina occurred in patterns similar to those of the total herpetofauna with two differences.

Both occurred as expected in class 5 conifer logs, and the

slender salamander also occurred as expected in class 3 and

5 hardwood logs (table 8). The clouded salamander showed a

strong preference for conifer logs (82 percent of all captures;

fig. 5), and was captured more often than expected in decay

class 3 (table 8). The Del Norte salamander was captured

more often than expected under class 4 and 5 hardwood and

class 4 conifer logs, and significantly less often in association with many of the sound-log classes (table 8).

Conclusions

Although we found 31 species of amphibians and reptiles

over the course of this study, with 1 to 13 species per site,

the herpetofauna of the region demonstrated low equitability,

being dominated by a few species of salamanders. Species

richness did not differ by forest age- or moisture-class, but

species composition did. Several species of amphibian were

rare or absent from young sites, while reptiles were relatively

more abundant on these sites and on the old-growth dry sites.

We captured over 6400 individuals, of which 97.6 percent

were amphibians, with salamanders comprising 88.1 percent.

Terrestrial salamanders were more plentiful on old-growth

sites, with four of eight species sampled at greater than incidental levels, significantly more abundant on these sites.

Concurrently, we found greater relative biomass of amphibians on old-growth mesic sites and greater relative

biomass of reptiles on young and dry sites.

We found that structural components (multiple regression

analysis; macrohabitat variables) associated with older

forests (Franklin and Spies, this volume) were the best

predictors of increased numbers of salamanders.

Our analysis of microhabitat associations indicated that large,

well-decayed logs, both hardwood and conifer, were the most

heavily used downed woody debris. We found that the pattern of use of woody debris varied considerably, both in sizeand decay-class, by species of salamander.

Nineteen of the 31 species we detected were captured at

incidental levels (table 2). Some of these species may be

considered secondary elements of the forest herpetofaunafor example, the sagebrush lizard that invades open areas

subsequent to harvesting. Others, however, were captured

rarely, either because they were uncommon or because we

lacked adequate methods to sample them-for example,

snakes. We know little about these species’ life histories,

their relation with the forest environment, what constitutes a

viable population, or how these populations respond to the

conversion of old-growth forests to young, managed stands.

Management Strategies To Maintain a Viable

Herpetofauna

An increasing body of evidence from research in forested

habitats indicates a change in herpetofaunal species composition, reduction of numbers of species, and reduced abundance within species after logging (Bennett and others 1980;

Blymer and McGinnes 1977; Buhlmann and others 1988;

Bury 1983; Bury and Corn 1988a, 1988b; Bury and Martin

1973; Corn and Bury 1989; Enge: and Marion 1986; Gordon

and others 1962; Herrington and Larsen 1985; Pough and

others 1987; Ramotnik and Scott 1988; Welsh and Lind

1988). Forests cannot be harvested without affecting the

herpetofauna, but we believe that it is possible to reduce

long-term adverse effects. Area-specific strategies can be

developed that take into account the natural history of local

species of reptiles and amphibians and the particular habitat

requirements of these species. With this knowledge, the

timing and potential effects of harvest activities can be

modified to ensure minimal impact and thus maintain the

long-term viability of the herpetofauna.

Based on data reported here and previously (Welsh and Lind

1988) for northwestern California and southwestern Oregon

and summarizing ideas presented by other authors cited

above, we propose a four-part strategy to ensure the longterm viability of all elements of the local herpetofauna in

areas subjected to logging.

Avoid riparian zones-Riparian zones are often the most

species-rich (for all wildlife, not just herpetofauna), and their

protection with no-cut buffer zones could maintain source

populations and corridors for gene flow that would promote

rapid repopulation once regeneration has created suitable

habitat in harvested upland areas (see Bury and Corn 1988b

Bury and others, this volume b; Gilbert and Allwine, this

volume c).

409

Protect special habitats-Protecting special habitats includes springs, seeps, and other headwater habitats; large

rock outcrops; talus slopes; ponds, and unique patches of

habitat that are critical to certain highly sensitive species

(for example, the red-legged frog, Olympic salamander,

tailed frog, and Del Norte salamander) (see also Bury 1988;

Bury and Corn 1988b; Bury and others, this volume a, b;

Corn and Bury 1989; Gilbert and Allwine, this volume c;

Herrington 1988).

Provide microhabitat-For forest herpetofauna, providing

microhabitat means managing for downed woody debris.

Almost all species of forest herpetofauna use woody debris

at some stage, and many species use it throughout their

existence, for cover, foraging, or nesting. After logging,

an abundant amount of woody debris should be left, in all

sizes and decay-classes, including both hardwoods and

conifers when present, to facilitate the recovery of the terrestrial herpetofauna and other small vertebrates (see also

Aubry and others 1988; Bury and others, this volume a;

Corn and Bury, this volume b). The simplest approach is to

leave all downed woody debris that was present before

harvesting.

Minimize direct Bogging effects--Restricting tractor logging would minimize compaction of the soil, and cable logging in winter would have far less effect on hibernating

herpetofauna (Ramotnik and Scott 1988). As much canopy

as possible should be left to prevent drying of the soil and

alteration of understory vegetation.

Acknowledgments

We thank the members of the field crews of the Pacific

Southwest Forest and Range Experiment Station’s Timber/

Wildlife Research Unit for their help in collecting data:

C. Barrows, W. Block, L. Brennan, K. Calabrese, K. Clough,

D. Carle, S. Edwards, S. Goldwasser, P. Hall, M. Hamady,

L. LaClaire, D. Lausche, P. Manley, W. McCleod, K. Milne,

C. Ogan, P. Paton, K. Purcell, T. Quinn, M. Rassmussen,

R. Reynolds, H. Sakai, A. Schaffer, K. Schmidt, J. Sterling,

C. Taylor, D. Taylor, and G. Therien. We also thank James

A. Baldwin and Barry R. Noon for advising on statistical

methods; Paul S. Corn, F. Harvey Pough, C. John Ralph,

and R. Chad Roberts for their reviews of the manuscript

(though all reviewer comments were not incorporated);

and Dana L. Waters for his help with the figures. 0

411
Appendix

Table 9-Study sites in mixed evergreen forests of northwestern California and southwestern Oregon (sites marked with

an asterisk were included for total capture counts but omitted from further analvse-ee text)

Sites

AgeClaSSO

Moispclass ’ c Location

countly,

state

Elevation

(4

Terrestrial:

JnlandLittle Greyback

Tunnel

Waterman Ridge

*Buck Peak

Althouse

Brush Mountain

Gray

Hennessey Peak

Oregon Caves

Shelterwood

*Left Hand

*Upper Horse

Beartooth Mountain

Brannon East

Cedar Creek

*California Greyback

*Frog Pond

*Holcomb Peak

*Serpentine Wet

Big Creek-East

Clouded

Ladder Rock

spotted Owl

Tish Tang

Sharber

*Galleghy Grove

*Page Mountain

*Serpentine Mesic

*Skag Hope

Ham Camp

South Fork

Waters Creek

*Serpentine Dry

coastalBald Mountain

Barnes Thirty

Fawn Prairie

Homestead

Lord Ellis

Mud Springs

Seed Tree

Shelterwood Leave

Elder Creek

Elkhom Xeric

Fanny’s Place

Harwood’s 90

White House

Old Growth

Skunk Creek

Standley Grove

Alpine

Elkhom Hydric

Fox Creek

Ten Mile Creek

Darby’s Place

412

Y

Y

Y

li

iti

M

M

M

M

E

0

0

0

:

0

:

0

0

:

:

0

iii

0

0

0

Y

Y

Y

y’

::

Y

M

M

M”

M

:

0

0

0

0

0

0

M

M

M

ii

M

M

M

ii

M

“w

W

:

W

W

W

M

ii

M

M

M”

M

M

M

D

D

D

D

M

M

z

M

iti

M

E

M”

M

r

W

M

M

M

M

D

T39S,R6W,S8

T18N,R4E,S3&4

T7N,RSE,S 15

T39S,R6W,S33

T40S,R8W,S3

T6N,RSE,S 18

T5N,R6E,S4&5

T5N,R6E,S8&9

T39S,R6W,S31

TlN,R4E,S34

T40S,R6W,S23

T40S,R6W,S16

T6N,R7E,S34

T6N,R4E,S 13&24

T7N,R5E,S30

T19N.R6E.S32

T41S,‘R7W.S14

T39S,R6W.S9

T41S,R9W,S4

T6N,R7E,S36

TlS,R4E,S2

T8N,R6E,S32

TlS,R4E,S2&3

T8N,R5E,S35

T6N,R6E,S7

T41S.R7W,S8.9,17,18

T4lS,R7W,S8

T38S,R9W,S25

T41S,R7W,S7

T40S,R7W,S29&30

T6N,R6E,S3 1

T36S,R7W,S28.29,32,33

T38S,R8W,S29

T6N,R3E,S 17&20 Hum&IA

T22N,R16W,S29&32 Me&A

T7N,RZE,S25 Hum,CA

TZlN,R16W,S13&24 Men,CA

T6N,R3E,S8&17 Hum,CA

T21N,R15W,S13&24 Men,CA

Tl4N,RlE,S21&22 Del,CA

T14N,RlE,S27 De&CA

T22N,R16W,S28 Men,CA

T22N,R16W.S17 Men,CA

T22N,R16W,SZl Men,CA

T21N,Rl5W,S23 Men,C A

T22N,R16W,SZl Men,CA

T14N,RlE,S21&28 Del,CA

T22N,R16W,S29 Men,CA

T2lN,R16W,S26 Men,CA

T22N,R16W,S20&21 Men,CA

T22N,R16W,S17 Me& A

T22N,R16W,S21&22 Men,CA

T22N,R16W,S15&16 Men,CA

T18N,R13W,SlKrZ Me&A

Jos,OR 1205

Del,CA 624

Hum,CA 633

Jos,OR 1366

Jos,OR 427

Hum,CA 1050

Tri,CA 972

Tri,CA 949

Jos,OR 872

Hum,CA 1022

Jos,OR 1362

Jos,OR 1502

Tri,CA 1144

Hum,CA 750

Hum,CA 665

Del,CA 1556

Jos,Or 1539

Jos,OR 1411

Jos,OR 549

Tri,CA 1134

Hum,CA 961

Hum,CA 805

Hum,CA 981

Hum,CA 680

Tri,CA 747

Jos,OR 1334

Jos,OR 1519

Jos,OR 527

Jos,OR 1305

Jos,OR 1053

Tri,CA 659

Jos,OR 614

Jos,OR 544

846

467

750

726

778

689

120

120

555

518

445

695

427

5;;

481

zz

543

445

587
Table 9-continued

Sites

AgeclassaMoistyeclass ’ c Location

coung,

State

Elevation

(4

Aquatic:

Annnon-North

Barnwell Creek

Bybee Creek

Coon Creek

Four Mile Creek

Hawkins Tributary

Kenny Creek

Sixteen Gulch

Sugar Creek

AlthouseMain

Ahhouse-West

Bolan Creek

Chamberlin Creek

Days Gully

Lefthand Creek

Limestone Creek

McKinley Creek-Upper

Page Creek

Barney Creek

Big Creek-East

Big Creek-Main

Brannon Creek

Butte Creek

Campbell Creek

Cedar Creek

Elder Creek

Elk Creek

Elkhorn Creek-Upper

Elkhom Creek-Lower

Fox Creek

Frog Pond Creek

Happy Camp Creek

High Prairie

Horse Liito-East

Little Creek

Little Greyback Crk.

McKinley Creek-Lower

Paralyze-Canyon

Skunk Creek

Y

;

Y

Y

::

Y

iti

iti

M

M

M

M

iti

0

0

:

0

0

0

0

0

0

TSN,RSE,Sll Hum,CA

T22N,R16W,S21 Men,CA

T19N.R5E,S33 Del,CA

T7N,RSE,S15&16 Hurn,CA

T6N,R5E,S20 Hum,CA

T6N,R6E,S15&16 Tri,CA

T21N,R16W.S14&15 Me&A

T18N,R15W,S31 Men.CA

T22N,R16W,S29&32 Me@ A

T41S,R7W,Sll Jos,OR

T41S.R7W,S16 Jos,OR

T40S.R6W,S3 1 Jos,OR

T18N,R15W,S27&28 Men,CA

T38S,R9W.S25&36 Jos,OR

T40S,R6W,S23 Jos,OR

T40S,R6W,S21 Jos,OR

T22N,R16W,S21 Men,CA

T41S,R8W,S2 Jos,OR

T5N,R5E,S32 Hum,CA

T6N,R7E,S36 Tri,CA

T6N.R7E,S24 Tri,CA

T6NvR4E.S 13&24 Hum.CA

TlS,R4E,S2&3 Hum,CA

T7N,R4E,S23 Hum,CA

l7N,R6E,S26 Hum,CA

T22N.R16W,S34 Men,CA

T40S,R5W.S12 Jos,OR

T22N.R16W,S17 Men,CA

T22N,R16W,S17 Men,CA

T22N,R16W,S21&22 Men,CA

T41S.R7W,S14 Jos,OR

T40S,R7W,S29&30 Jos,OR

T14N,RlE.S21&28 Del,CA

T7N.R7E,S7 Hum,CA

T40S.R5W,S6 Jos.OR

T39S.R6W,S8 Jos;OR

T22N,R16W,S21 Me&A

T22N,R16W.S34 Men,CA

T22N.R16W.S29 Men,CA

549

427

671

213

671

488

472

171

466

1341

1158

975

244

526

1361

1067

445

561

823

1133

1067

750

980

884

1006

533

1463

518

518

543

1539

1053

53

1280

1402

1205

ttz

529

l Ageclass: Y - young, M - mature, 0 - old.

Moisture-class: W - wet. M - mesic, D - dry.

i Aquatic sites were not assigned a moistureclass.

County, State: Del - Del None, CA, Hum - Humboldt, CA; Jos - Josephine, OR, Men - Mendocmo, CA, Tn - Trinity, CA.

413

Vertebrate Species Richness Within and Among

Seral Stages of Douglas-Fir/Hardwood Forest in

Northwestern California

Martin G. Raphael

Author

MARTIN G. RAPHAEL was a research ecologist, USDA

Forest Service, Rocky Mountain Forest and Range Experiment Station, Laramie, Wyoming 82070; he is now at the

Pacific Northwest Research Station, Olympia, Washington

98502.

Abstract

One objective of wildlife management programs in National

.Forests is maintaining diversity, often thought of as the number of species or “species richness.” Regional (multistand or

landscape) species diversity varies in relation to species richness within stands and by similarity of species composition

among stands. To evaluate numbers of species of terrestrial

vertebrates in Douglas-fir forests of various ages in northwestern California, I systematically sampled at 166 sites

representing clearcut (<20 years old) through old-growth

(>250 years) forest. Mean numbers of mammalian species

differed among seral stages and was lowest in mid-aged

stands. Reptilian species richness declined with stand age.

Amphibian and avian species richness did not differ among

stages. Mean similarity values of all taxa did not differ

significantly when sites within stages or sites among stages

were compared. Thus, landscape-wide species richness was

independent of the arrangement of seral stages. These results

suggest that maximizing population sizes of selected species

is a more useful management objective than maximizing

species diversity.

Introduction

The National Forest Management Act of 1976 made maintaining biological diversity a prime consideration in multipleuse management of National Forests. Thus, what has been

termed “biodiversity” is now a major national issue, as evidenced by the National Biological Diversity Conservation

and Environmental Research Act (H.R. 4335) brought before

the 100th Congress in 1988. An important, but by no means

the only component of biological diversity is species richness,

the total number of species in a given system. Conservation

strategies for maintaining biodiversity have ranged between

two extremes: managing only for threatened or endangered

species and managing whole ecosystems or communities

(Scott and others 1987, 1988). Managing to maintain species

richness falls under the latter strategy; it is now one of the

major goals of USDA Forest Service wildlife management

programs (Salwasser and Tappeiner 1981, Siderits and Radke

1977, Thomas 1982).

415
Managing Douglas-fir forests of the Pacific Northwest is

under intense scrutiny because of conflicts between logging

and preservation of remaining old-growth stands (The Wilderness Society 1988, Thomas and others 1988). Part of this

concern centers on the importance of old-growth forest to

biological diversity (Harris 1984, Norse and others 1986).

This paper reports the results of a study designed, in part, to

investigate the contribution of old-growth forest to local and

regional species diversity in northwestern California.

Methods

Study Area

Sites were within a 3900-km2 area encompassing parts of the

Klamath, Six-Rivers, and Shasta-Trinity National Forests in

northwestern California. Forest cover-classified as Douglasfir (Mayer and Laudenslayer 1988)-is described elsewhere

(Raphael 1988a). Criteria for site selection included elevation

from 400 to 1300 m, road access, freedom from scheduled

timber harvest over the 3 years of sampling, location on

National Forest land, relatively homogeneous forest cover,

and absence of major confounding features such as streams,

meadows >l ha, and rock outcrops. In addition, sites were

selected to fill an array of stand sizes and age-classes

(Raphael 1984).

Stand Classification

Sites were classified into four seral stages based on years

since cutting or an index of stand age. Clearcut sites (n = 20)

were <20 years old, young sites (n = 37) were 50 to 150

years old, mature sites (n = 56) were 150 to 250 years old,

and old-growth sites (n = 53) were >250 years old. The age

index was calculated with a weighted average of estimated

ages of all softwood trees <90 cm in diameter at breast

height (d.b.h.) counted on four 0.04-ha circular plots and

softwoods > 90 cm counted on one 0.50-ha plot on each site.

The average was weighted by multiplying the estimated age

of each tree by its basal area, summing the products over all

trees, and dividing the sum by total basal area. In this way,

the numerous small trees had less influence on stand age

than if each tree was counted regardless of diameter. Tree

age was estimated from diameter at breast height, based on

extensive tree-ring counts on file in District offices. Details

of vegetation sampling methods and vegetation characteristics

of each stage are reported elsewhere (Raphael 1988c).

Vertebrate Sampling

Vertebrate sampling methods are described elsewhere

(Raphael 1984, 1988c; Raphael and Barrett 1984; Raphael

and Mamot 1986; Raphael and Rosenberg 1983; Raphael

and others 1986; Rosenberg and Raphael 1986; Taylor and

Raphael 1988). Field sampling took place from September

1981 through September 1983. A standardized series of

techniques was used on each site. Sampling stations were

located at or around the center of a site and were separated

from adjacent sites by at least 360 m. Birds were counted

at each site (only in the three oldest seral stages) in variableradius circular plots (12 lo-minute counts in each site, season, and year), insectivores and other small mammals were

trapped at each site in pitfalls (10 2-gallon buckets per site,

2 x 5 grid, 20-m spacing, run continuously), and larger mammals were detected at each site by using a single smokedaluminum station deployed in August and September 1981-

83. In addition, subsets of sites in each seral stage were

more intensively sampled by area- and time-constrained

searches for reptiles and amphibians (0.12 ha per site in

1981,4 person-hours per site in 1982, 1983), livetrap grids

for small mammals (usually 100 25-cm Sherman traps per

site, 10 x 10 grid, 20-m spacing, operated 5 nights in July

1981-83), and drift fence arrays for snakes (two 5-gallon

buckets, connected by a 7.6-m x 50-cm fence with two

20- x 76-cm cylindrical funnel traps on each side of the

fence center).

Data Analysis

Species richness-All observations were reduced to the

presence or absence of each vertebrate species at each site,

pooled over all years of study (1981-83). A matrix of 166

rows (= sites) and 218 columns (= species, including separate entries for birds in spring and winter) was constructed

so that each cell (combination of site and species) contained

a 1 if the species had been detected at that site or a 0 if it

had not. Based on stand age, each site was also classified

into one of four seral stages for calculating species richness

in each seral stage. Total species richness in a stage was

calculated by summing numbers of species detected at any

site within each stage. Mean species richness by stage was

calculated by counting all species occurrences for each site,

then calculating mean counts per site within each stage.

Species accumulation curves were computed to evaluate the

effects of sample size (numbers of sites) on species richness.

A bootstrap procedure (Efron 1982) was used to generate

pseudosamples of increasing numbers of sites from 1 to 50.

At any particular sample size y, a site (row) was randomly

drawn from the site-by-species matrix. Data were stored,

then replaced, and a new row was randomly drawn and data

were accumulated. This process was repeated y times, accumulated cell values > 1 were recoded to 1, and cells were

summed across all species to calculate species richness. The

entire cycle was repeated for a desired number of pseudoreplications, usually 500. The mean and standard deviation

were calculated over all pseudoreplicates and a standard

error of the mean was calculated (which equals standard

deviation under bootstrap theory).

416
Mean species richness was plotted against number of sites

for various combinations of taxa and seral stages. Nonlinear

regression (SPSS/PC+, Version 3.0, Norusis 1988) was used

to estimate parameters of the species-area curve S = CA’,

where S was species richness, A was the number of sites

sampled, and C and z were regression parameters (Connor

and McCoy 1979, Higgs and Usher 1980, Usher 1985).

Equality of regression parameters was tested among curves

for each age-class using Extra Sum of Squares (Draper and

Smith 1966: 67).

Species similarity-To compare occurrences of species between sites or groups of sites, I computed Jaccard’s index of

similarity,

a

RI= 100 9

a+b+c

where a was the number of species present at both i and j, b

was the number present at site i only, and c was the number

at site j only (Pielou 1984: 57). This index has many desirable properties (see Bloom 1981, Huhta 1979, Wolda 1981):

it is easy to interpret (the index value is the percentage of

species common to both sites), it takes no account of species absent from both sites (presence data are unambiguous,

whereas absence could mean the species could not survive at

the site or it was absent merely by chance), and it is metric

(similarity values behave like distances in Euclidian space).

Similarities were computed between all possible pairs of

plots and then sorted into site-by-site comparisons within

each age-class and comparisons across age-classes. Means

and standard errors were calculated for each grouping. I also

computed similarities for each within and between age-class

comparison using a bootstrap procedure in which a site was

randomly drawn, replaced, and compared with another randomly drawn site. Similarity was computed, and a mean and

standard error were computed over the total number of

pseudoreplications used in an analysis (usually 200). To

evaluate the effects of sample size (number of sites) or

similarity, I used bootstrap procedures based on increasing

numbers of subsets of sites from two to the maximum

number of sites in an age-class.

All bootstrap analyses were conducted with a PC-based

FORTRAN program, available on request (Raphael and

Brink, documentation on file ).

Table l-Total numbers of species detected in 4 age-classes of

Douglas-fir forest. northwestern California, 1981-83

Age-class”

Group

Amphibians

Reptiles

Breeding Winteringbirds birds

Mammals

Clearcut

9

12

FE

26

Young Mature. Old-growth Total

12 11 10 12

13 13 11 16

7744 4583 7947 z:

28 33 29 38

NA = not available (see Marcot 1984).

,a Age-classes and number of sites sampled were clearcut stands (<20 years,

n = 20); young stands (~150 years, R = 37 for amphibians, reptiles, and

mammals; n = 27 for birds); mature stands (~250 years, n = 56); and

old-growth stands (>250, n = 53).

Results

Species Richness

Observers detected 167 vertebrate species over all sites and

years. Recorded numbers of species varied within species

groups among age-classes (table l), but these values are

difficult to interpret because of differences in sampling effort

(number of sites) among age-classes. A more meaningful

measure is mean species richness (fig. l), the average number

of species detected per site in each age-class.

Number of amphibian species averaged 2.7 species per site;

means did not differ among age-classes (Welch’s F = 0.22,

P= 0.89, fig. 1). Numbers of reptilian species averaged 2.6

species per site and varied among age-classes (Welch’s F =

25.6, P c 0.01, fig. l), declining from youngest to oldest

sites. Multiple comparisons of means (Dunnett’s (1980) Ts,

P c 0.05) showed significantly more species in clearcut,

fewer in old growth, and no difference between young and

mature sites. An average of 10.8 mammalian species were

detected at each site, and mean numbers of species varied

among age-classes (Welch’s F = 9.1, P c 0.01). Lowest

mean numbers were detected in clearcuts and young stands;

means were significantly greater in mature and old-growth

stands (Dunnett’s 13, P= 0.05). Numbers of breeding avian

species averaged 32.3 species per site; means did not differ

among age-classes (F =0.13, P= 0.88). Avian species richness averaged 15.0 species per site in winter, and means also

did not differ among age-classes (F = 0.96, P= 0.39).

In comparing differences between age-classes, I found large

numbers of species that were infrequently detected. Of 218

vertebrates (including birds in two seasons), 88 (40 percent)

were observed on 5 percent or fewer sites (fig. 2). Thus, estimated species richness at any particular site was influenced

by chance occurrence of rare species. To alleviate this problem, I calculated species accumulation curves across successively larger sets of sites within each age-class (fig. 3).

417
aa I Amphibians 

cc YG MA OG 

Mammals 

cc YG MA OG 

CC = Clearcut YG = Young 

1 T Reotiles 

cc YG MA OG 

“V 

Wintering birds Breeding birds 

40 

30 

20 

IO 

n 

” 

YG MA OG YG MA OG 

MA = Mature OG = Old-growth 

Age -class 

For each species group, curves differed among age-classes 

(table 2). Extra sums-of-squares analyses for each species 

group rejected hypotheses of similar regression curves among 

age-classes (table 3). These analyses showed that, for each 

species group, C, z, or both differed significantly among two 

or more age-classes. Thus, rates that species accumulated as 

numbers of sites increased differed among at least two age 

C1aS.W. 

A more direct comparison, however, was to test differences 

in mean species richness at larger numbers of sites, illustrated 

for n = 10 sites (fig. 4). Means and standard errors were derived from a sample of 500 pseudoreplications of sites representing each age-class. For each species group, 95-percent 

confidence intervals around the mean for any age-class overlapped the means of each other age-class. This pattern held 

for each sample size tested up to the maximum number of 

sites. 

418 
30 

20 

10 

tlo _- 

0 40 

Q, 

% 30 

6 

YG MA OG 

- . . . . . . . . . ,111, ,..,.,.,, - - - 

_ . . . . . _ . . . . . . “...- 

10 ----- 

ii 5 

.- 

iii 0 

- ” . . . . . . . , , , , , , , , , ,.. 

Frequency (percent) 

60 

40 1 

20 

0 

0 IO 20 30 40 50 60 

CC = Clearcut MA = Mature 

YG = Young OG = Old.growth 

Number of sites 

419 
Table 2-Regression parameters from nonlinear model relating

species richnessa to numbers of sites among age-classes of

Douglas-fir forest, northwestern California

Group

Ageclassb

Parameter (SE)a

C Z r

2

Amphibians Clearcut 3.31 (0.15) 0.31 (0.02) 0.96

Young 3.76 (0.12) 0.29 (0.01) 0.97

Mature 4.70 (O.19)

4.79 (0.20)

0.21 (O.01) 0.90

Old growth 0.20 (0.01) 0.91

Reptiles Clearcut 4.32 (0.05) 0.30 (0.00) 1.00

Young 3.97 (0.07) 0.29 (0.01) 0.99

Mature 3.69 (0.08) 0.29 (0.01) 0.98

Old growth 2.80 (0.10) 0.34 (0.01) 0.97

Breeding birdsc Young 34.69 (1.20) 0.22 (0.01) 0.98

Mature 35.33 (0.81) 0.20 (0.01) 0.99

Old growth 34.01 (0.53) 0.19 (0.01) 0.99

Wintering birdsc

Young 17.70 (0.55) 0.25 (0.01) 0.99

Mature 18.72 (0.81) 0.23 (0.01) 0.97

Old growth 18.61 (0.71) 0.22 (0.0l) 0.97

Mammals Clearcut 11.82 (0.42) 0.24 (0.01) 0.95

Young 12.93 (0.33) 0.20 (0.01) 0.96

Mature 15.10 (0.27) 0.18 (0.01) 0.97

Old growth 14.14 (0.16) 0.16 (0.01) 0.96

a

 S = CAz, where S was number of species and A is number of sites in

sample. A varied from 1 to 20, 37, 56, or 53 in clearcuts, young, mature,

or old growth, respectively.

See table 1 for description of age-classes.

c

 Birds were not sampled in clearcut stands.

Table J-Results of analyses testing equality of nonlinear regression lines relating species richness to numbers of sites in different

age-classes of Douglas-fir forests, northwestern California (see

fig. 3)

Group F-ratio

Amphibians 25.46

Reptiles 223.56

Breeding birds 96.48

Wintering birds 3.21

Mammals 103.91

d f P

6,156 <O.OOl

6,156 <O.OOl

4,130 <O.OOl

4,130 <O.OlO

6,156 <O.OOl

Inspection of the species-area curves (fig. 3) showed that 20

to 25 sites were required to include at least 90 percent of all

species sampled in any age-class. Given a z-value (equivalent

to slope coefficient) of about 0.25 (average from table 2), 10

sites would be expected to include about 79 percent and 67

percent of the species that would be expected in 25 and 50

sites, respectively.

Species Similarity

How did species composition differ among age-classes? Species similarity (Jaccard coefficients) averaged 42 percent for

amphibians, 37 percent for reptiles, 60 percent for breeding

birds, 54 percent for wintering birds, and 44 percent for mammals between randomly drawn pairs of sites, regardless of

age-class. Mean similarity between sites within age-classes

and between age-classes was strikingly similar within species

groups, especially considering the wide confidence intervals

about each mean (fig. 5). Pairs of sites within age-classes

were just as likely to share species as pairs of sites between

age-classes.

As in analyses of species richness, Jaccard’s measure of

species similarity was sensitive to the presence of rare

species. Therefore, examining similarity based on larger

subsets of sites to offset the influence of rare species had

more meaning. As larger numbers of sites were included

in samples, between-age-class similarity increased for each

species group (fig. 6). At larger sample sizes, regression

curves predicted nearly equal similarity values among age

comparisons for breeding birds, wintering birds, and amphibians. Similarity of reptilian faunas between age-classes

tended to converge at sample sizes >lO sites. Similarity of

mammalian fauna was consistently lower in clearcut vs.

older age-class comparisons than in other comparisons

(fig. 6). Bootstrapped estimates of mean similarity, however, had large standard errors, and none of the betweenclass comparisons could be considered statistically significant, as is evident when 95-percent confidence intervals

are plotted around mean values, illustrated for samples of

10 sites (fig. 7).

Discussion

Species richness (alpha diversity) of birds and amphibians

did not vary significantly among age-classes of Douglas-fir

forest, especially when the effects of rare species were reduced by pooling sites. Reptilian species richness did vary;

more species occurred in younger than in older stands.

Slightly more mammalian species were found in older than

in younger stands.

Total species diversity did not vary much between ageclasses. Species similarity (beta diversity) did not vary

significantly for any species group, whether comparing

sites within age-classes or sites among age-classes. These

observations of similar alpha and beta diversities among

age-classes led to a conclusion that as area (number of sites)

increased, species accumulated at nearly the same rate

regardless of age-class. In other words, landscapes composed entirely of clearcuts, old-growth, young forest, or

any combination of ages would support similar numbers

of vertebrate species. I demonstrated this for amphibians,

reptiles, and mammals that I sampled in all four age-classes

by comparing species accumulation curves derived for each

age-class with the curve derived by randomly drawing sites

without regard to age-class (fig. 8). At n = 10 sites, mean

species richness (+ 95-percent C.I., experimentwise) varied

420
4 I 

Amphibians 

0 

12 

Breeding birds 

4ol +++I 

20 

Wintering birds I 

I 

cc YG MA OG 

CC = Clearcut MA = Mature 

YG = Young OG = Old-growth 

Age -class 

Amphibians 

40 

0 1 ! 

cc MA CC-YG CC-OG YG-OG 

YG OG CC-MA YG-MA MA-OG 

cc = Clearcut MA = Mature 

YG = Young OG = Old.growlh 

Comparison 

421 
g 80 & 

a 40 

. . . . . . . . . . . . . . . . 

SO 

40 

J YG_,, . . . . ~.k.E . . . +?G ,,,.., 1 

5 10 15 20 

cc = Clearcut MA = Mature 

YG = Young OG = Old.growth 

Number of sites 

from 32 k 8.2 (age-class 1) to 40 k 9.2 (all age-classes). 

Thus, confidence intervals overlapped means for all comparisons, indicating insufficient evidence that means differed 

significantly. 

Do these results suggest that all old growth could be eliminated without negative effects on biodiversity? The answer 

is yes, but only if the number of species is all that counts. As 

shown elsewhere in these proceedings and by other analyses 

of these data (Raphael 1988~. Raphael and others 1988), a 

substantial proportion of species are significantly more abundant in certain age-classes. Loss of preferred age-classes may 

20 Amphibians I 

CC-YG CC-MA CC-OG YG-MA YG-OG MA-OG 

Wintering birds Breeding birds 

I 

YG-MA YG-OG MA-OG YG-MA YG-OG MA-OG 

cc = Clearcut MA = Mature 

YG = Young OG = Old.growth 

Comparison 

reduce the population viability of these species. Species richness is only one component of biodiversity and, for terresnial 

vertebrates of Douglas-fir forests in northwestern California, 

species richness is not a sensitive indicator of habitat value. 

Results of my analyses indicate that species richness is independent of age-class, yet other analyses reported in this 

volume indicate that the abundance of certain species is quite 

dependent on age-class. For this reason, I agree with conclusions of Samson and Knopf (1982) and suggest that species richness should not be used as a basis for allocating 

distributions of land base among age-classes. Rather, I suggest that such decisions should be based on more sensitive, 
0

hmbdro of sik

20

Figure 8-Accumulation of amphibian, reptilian, and mammalian species

with increasing numbers of sites for each of four age-classes and for all

age-classes combined from Douglas-fir forests of northwestern California.

abundance-based measures. Raphael and Hof (unpubl. data),

for example, suggest a MAXMIN approach by use of linearprogramming techniques to compute the optimum mix of

age-classes to maximize the minimum population sixes of

all species of interest. If probabilities of population viability

can be maximized, then probabilities of extinction can be

minimized and species richness will be maintained.

Acknowledgments

I thank Glen Brink and Marilyn Whitfield for writing bootstrap programs and Rudy King for statistical advice. I also

thank my field assistants whose hard work permitted these

analyses. Field studies and subsequent analyses were supported by the Pacific Southwest Region and Pacific Southwest Forest and Range Experiment Station of the USDA

Forest Service and by the University of California Agricultural Experiment Station, Project 3501 MS. I am indebted to

Barry R. Noon and C. John Ralph for comments on earlier

drafts.

This paper is contribution 109 of the Wildlife Habitat Relationships in Western Washington and Oregon Research

Project, Pacific Northwest Research Station, USDA Forest

Service. 0

423
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