Applying Results of Old-Growth Research to

Management: Information Needs, Development

of Technical Tools, and Future Research

Richard S. Holthausen and Bruce G. Marcot

Authors

RICHARD S. HOLTHAUSEN is regional wildlife ecologist,

Pacific Northwest Region, and BRUCE G. MARCOT is technical coordinator, Spotted Owl Research, Development, and

Application Program, Pacific Northwest Research Station;

U.S. Department of Agriculture, Forest Service, 319 S.W.

Pine Street, Portland, Oregon 97208.

Introduction

The objectives of this paper are to describe the application of

results from the Old-Growth Forest Wildlife Habitat Program

to forest management in the Pacific Northwest Region of the

Forest Service, and to identify needed research on old-growth

Douglas-fir forests.

The analysis of vertebrate community data presented in this

book was designed primarily to identify vertebrate species

associated with old growth and to describe habitat features

that may account for observed patterns of association. While

it does not lend itself to precise predictions about the effects

of management on those species, it could and should play an

important and immediate role in implementing Forest Plans.

Information presented here allows, for the first time, the

development of a reliable and scientifically defensible list of

vertebrate species associated with old-growth Douglas-fir

forests. All of these species should be carefully considered

when allocations for late-seral habitat on the National Forests

are actually implemented. Information on the habitat associations of these species will help managers make better

decisions about the types of forests to include in old-growth

management areas, and how best to provide critical habitat

features, such as large snags and down wood, in managed

stands. Information on the effects of forest fragmentation on

wildlife will enable managers to make better decisions about

the sequence and spatial arrangement of management

activities scheduled in Forest Plans.

During the time this information is being used to help guide

the implementation of Forest Plans, it should also be evaluated to see if amendments to Plans are appropriate. Amendments will be appropriate if these results, and other new

research and monitoring information, indicate that the Plans

will not meet objectives for wildlife habitat, including

maintaining viable populations within diverse communities.

Recognizing that absolute knowledge is not attainable, managers must make reasonable judgments about the viability of

species based on existing information. Amendments may

also be made for other reasons, such as the identification of

new issues or unexpected effects from implementing Forest

Plans. Experience from other Regions of the Forest Service

suggests that such amendments may be fairly common and

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serve to keep the Plans up to date between major revisions.

Finally, this information (along with other new findings from

monitoring and continued research) will be used to revise

Forest Plans either after 10 years or sooner.

Goals, Objectives, and Legal Requirements for

Old Growth

For old-growth research information to be applied to management, the goals, objectives, and legal requirements for old

growth must be understood. Goals for old-growth management on National Forests include providing recreation,

esthetic experiences, and opportunities for scientific study;

maintaining water quality; producing high-quality wood

products; maintaining biological diversity and long-term

productivity; and providing wildlife habitat. The specific

goals for wildlife habitat management are established in the

National Forest Management Act of 1976 and pursuant

regulations (36 CFR 219.19 and 219.27). They are:

. Manage “to maintain viable populations of existing

native and desired non-native vertebrate species in the

planning area;”

. “...establish objectives for the maintenance and

improvement of habitat for management indicator

species” selected during the planning process; and

. “...provide for diversity of plant and animal

communities...in order to meet overall multiple-use

objectives.”

Information presented here can eventually help managers

address all three of these mandates. Results of the

community studies provide the initial scientific basis for

determining whether viability is a concern for any of the

species demonstrated to be closely associated with old

growth. The results will also allow managers to do a better

job of selecting management indicator species for future

amendments and revisions of Forest Plans. Although using

indicator species in forest planning has been controversial,

we believe that all species found to be closely associated

with old growth should be considered in the management

process. Results from these studies will provide information

on how to establish and meet objectives for such species.

Finally, these results will help to answer questions about

biological diversity. A fundamental aspect of managing for

diversity of plant and animal communities is to provide for

persistence of the full array of seral stages in all forest types.

To ensure that we have identified all significant types and

stages, forest (and especially, old-growth) classifications

should be based on both plant and wildlife communities.

Information presented here will be fundamental to

developing these classifications.

Information Needed From Old-Growth

Research

To develop very specific objectives for management, devise

alternative actions that could meet those objectives, and

evaluate the effectiveness of such alternatives, very specific

information about old-growth habitats and associated species

is required. Some specific questions that must be addressed

include:

. What species occur in old-growth forests, and how consistent and close is their association with old growth?

. What stand attributes are associated with each species?

Can species be classified according to sets of stand

attributes? What is the effect on species of different

amounts and patterns of these attributes in unmanaged

and managed stands?

. What amounts and patterns of old-growth forests should

be provided across the landscape to maintain viable and

well-distributed populations of old-growth-associated

species? What sizes and shapes of old-growth stands are

best for providing for old-growth species? What kinds

and patterns of edges adversely affect old-growth forest

habitat conditions, and how can such effects be mitigated? How is dispersal affected by different forest

conditions and the juxtaposition of those conditions?

. What forest management activities (for example, roads,

recreation, hunting, timber production) can take place in

or near old-growth stands and still be compatible with

maintaining adequate habitat for old-growth-associated

species? What is the effect of various rotation lengths on

old-growth attributes in managed stands? How suitable

are stands where elements of old growth (for example,

large snags and logs) are maintained, but the surrounding

stand is modified by timber harvest? Can old-growth

conditions be created in areas currently lacking oldgrowth forest habitats?

Research information available in this book goes a long way

toward answering the first set of questions and provides

some information on the second. Ongoing research, especially

at the landscape (multi-stand) scale, will provide additional

insights into the second and third set of questions. Autecological studies will provide better information on the relationship of old-growth species to their environment, and

proposed manipulative experiments will directly test the

effects of habitat type, edge, size, and configuration on

population and community response.

Answers to the fourth set of questions may be inferred from

correlations between habitat attributes and abundances of

old-growth species. We believe, however, that providing

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old-growth conditions or attributes in managed stands and

landscapes as a means of increasing the viability of oldgrowth wildlife populations should be treated as a working

hypothesis that needs to be tested empirically. Experimental

forests may be ideal settings for testing how well we can

create old-growth systems through management, and whether

or not old-growth species will persist if provided with oldgrowth components in habitats modified by timber harvesting.

Developing Information From Research Into

Needed Tools and Evaluation Procedures

Once basic information on the ecology and biology of oldgrowth forests is available from research, it must be assimilated by managers to guide specific management prescriptions. Ultimately, the information must be incorporated into

existing administrative procedures and policies for assessing

and planning old-growth forest management. This process

entails developing the research information into classification

and inventory systems, particularly on the abundance and

distribution of old-growth forest types and associated wildlife

species; summaries of habitat conditions or attributes associated with old-growth wildlife species; summaries of demographics of old growth-associated species; and models

depicting the response of wildlife populations to habitat

conditions at both stand and landscape scales.

Forest Service wildlife biologists at both Forests and Districts

will use the information and assessment procedures in daily

operational tasks. These tasks include assessing the effects of

proposed forest management activities, and identifying activities consistent with conserving old-growth resources. In addition, specialists in related disciplines-specifically, fisheries

biologists, silviculturists, botanists, ecologists, and landscape

architects-must also play central roles. They would help

bring old-growth evaluation tools and information to the

forefront of multiple-resource planning. Educating specialists

and managers alike should be part of the development and

application of old-growth research information.

Classification Systems

Applying old-growth information to habitat management

requires a system of classifying habitats and wildlife communities. Five major objectives and uses define the need for

habitat and community classification systems: to reliably

predict the successional development of habitats and related

changes in wildlife communities; to identify forest conditions

with which specific sets of old-growth-dependent wildlife

species may be associated; to predict responses of habitat

conditions and wildlife populations to management activities;

to serve as precursors to inventories and monitoring; and to

provide a basis for planning and implementing both research

and management activities. In particular, predicting successional development is important for projecting future habitat

conditions and response by vegetation and wildlife communities to proposed management activities.

Plant community classifications-Classifications of oldgrowth forest types should be based on the ecological characteristics of climax or subclimax forest stands and their

successional states. The objective is to array ecological forest

types along a successional gradient. Successional stages can

be inferred from studies on vegetation structure across an age

gradient (chronosequence) of young, mature, and old-growth

forests. A classification of old-growth habitats may build on

the Franklin and Spies (this volume) approach of an index of

“old-growthness,” much as Raphael and Barrett (1984) developed multivariate correlations of vegetation conditions with

stand age in old-growth forests in northwestern California.

An old-growth index would describe the degree to which a

forest stand, given its dominant plant species and its age,

provides various attributes associated with increasing stand

age and successional development. These attributes in turn

can be related to use by wildlife species. An old-growth index

of this type encourages the view of late-successional forests

as developmental gradients rather than as discrete types.

Forest habitat conditions associated with each wildlife

species and community can be further analyzed to produce a

classification of old-growth forest habitats. Several analytical

techniques may prove useful. For example, a hierarchical

clustering algorithm (see Gauch and Whittaker 1981, Hill

and others 1975) applied to the vegetation data from each

stand may help identify sets of stands with unique conditions.

This approach, however, may merely serve to mirror the

criteria that were used originally to select the study stands.

Other techniques with which a vegetation classification could

be developed include discriminant function analysis and

various ordination techniques, such as principal component

analysis (see Hill and Gauch 1980, Kantrud and Kologiski

1982, Whittaker 1987).

These classification techniques rely on analysis of vegetation

data alone. An old-growth habitat classification could also be

developed by correlating the abundance of wildlife species

with various old-growth forest attributes. One useful approach is canonical correlation analysis (Gauch 1973,

Goldstein and Grigal 1972, Smith 1981), in which variation

in the vegetation data is explained through correlations with

variation in wildlife abundance data. Conversely, canonical

correlation could be used to explain variation in wildlife

data, given the vegetation data (see McIntire 1978). Results

of canonical correlation analysis are sometimes difficult to

465
interpret, however, and the procedure requires many samples

(study stands) relative to the number of variables (vegetation

and wildlife attributes of each stand) (Pimentel 1979, Smith

1981). Also, like other ordination techniques, canonical correlation analysis does not produce a classification per se, although component scores can be ordinated along the canonical vectors and a classification of sites (or species) can be

derived (see Noy-Meir 1973).

Care must be taken to avoid errors when interpreting results

of multivariate analyses (Garsd 1984, Rexstad and others

1988, Wilson 1981). Errors may result from spurious correlations, inadequate sample sizes, and violations of assumptions of normality, linearity, and independence of the data.

Ideally, old-growth habitat classifications would be based

on variables found in forest inventory information used by

Ranger Districts and National Forests. Forest inventories

currently available include Total Resource Inventory (TRI)

data bases and ongoing updates to Vegetative Resource

Surveys (VRS). Results of Mature and Overmature (MOM)

inventories are reported elsewhere in this volume by Marcot

and others. A first step will be to determine if TRI, VRS, and

MOM data bases contain the necessary habitat variables for

use in an old-growth habitat classification system, and if the

data have acceptable precision and resolution (scale). At

present, the kinds, accuracy, and precision of data bases on

old-growth forest habitats vary among National Forests and

Districts. Old-growth classification strategies should be

flexible enough to be useful in the short term with data bases

of varying resolution and accuracy. They should also help

identify additional inventory needs in the longer term-that

is, define new variables or refine existing variables to greater

precision or resolution.

Wildlife community classifications-The objective in developing wildlife community classifications is to identify

wildlife guilds and species assemblages that are associated

with various forest types and successional stages. A wildlife

community classification would identify sets of species

associated with specific old-growth conditions and stages,

which could be done by producing classifications of species

occurring in various successional stages or along gradients of

various habitat attributes. Analysis techniques include clustering algorithms, as discussed above. One approach might be

to apply gradient analyses on the abundance of each wildlife

species, guild, or assemblage for various habitat attributes.

Analytical techniques may include ordinations and various

multivariate methods that produce mathematical models of

species distribution or abundance as functions of habitat

conditions.

We anticipate that both classification and gradient approaches

will prove useful. Classifications would reveal which groups

of wildlife species are likely to be associated with specific

sets of habitat conditions. Various species groups may overlap. Gradient analyses may provide information on how the

relative abundances of species or species groups change

along various environmental gradients, such as stand development, elevation, latitude, or moisture regime in old-growth

forests (see Smith and MacMahon 1981).

Specifically, what is needed is a test of how well wildlife

species can be classified based on various gradients associated with old-growth forests. Clustering or ordination

techniques may be used to test further associations of

wildlife species, guilds, or assemblages with unmanaged

forest age-classes, moisture-classes, and especially oldgrowthness gradients and indices. For the latter, a much

better understanding of the contribution to wildlife species

presence and abundance from old-growth attributes in

younger stands is needed, particularly in intensively managed, even-aged stands. One approach might be to use scatter

diagrams, simple correlations, stepwise multiple linear

regression, logistic regression, and discriminant function

analysis to predict the presence or abundance of wildlife

species as a function of old-growth stand attributes in young

unmanaged forests. Prediction variables may include density

of large snags, large logs, large-diameter live trees, and high

foliage volumes. Then, the models would be applied to

young managed forests, to help identify the kinds and

amounts of late-successional forest conditions that might

provide for old-growth wildlife species.

Inventories

Inventories are required to apply predictive models of oldgrowth habitats and species. Although uses of inventory data

will vary, basic guidelines for inventories should be rigorously standardized among National Forests.

Habitat-The main objective of producing inventories of

old-growth forest habitats is to provide reliable information

on location, distribution, and amount of old-growth and

younger forests that contain old-growth attributes, such as

large live trees, large snags, and large logs. Appropriate

sampling and inventory techniques should be applied to

various forest types at stand, landscape, and National Forest

scales, and under several standards of reliability.

Habitat inventories should ultimately provide vegetation

class-specific and stand-specific data on vegetation structures

and flora. Inventories on all National Forests must be conducted by supplementing remotely sensed data with field

surveys. Identifying which old-growth habitat attributes to

sample will come from analysis of both community and

species-specific old-growth research data.

Ongoing VRS and MOM inventories on National Forests in

Washington and Oregon (Marcot and others, this volume) are

currently scheduled for completion by 1994. The MOM

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inventory will include data on structures of late-successional

forest stands for each plant association. Once entered into a

geographic information system, these data can provide information on the distribution and extent of stand conditions

specified by the user. Such specifications could include, for

example, the quantitative “ecological” definitions of old

growth advanced by the Old-Growth Definition Task Group

(1986). They may also include definitions of stands corresponding to quantitative descriptions of habitat for wildlife

species or species groups associated with various forest conditions. Forest inventory data must be accessible to Ranger

Districts to help in planning and executing projects in an

integrated approach to resource management (Chalk and

others 1984).

Wildlife species-Inventories of selected wildlife species

associated with late-successional forests are also needed.

Such species include those identified in this volume as

“closely associated” with old-growth conditions (table 1;

see also Ruggiero and others, this volume).

Other species classified as “associated” with old growth,

which are expected to decline in a managed forest landscape,

may also need to be inventoried. These include cavity-nesting

birds (Lundquist and Mariani, this volume; Manuwal, this

I volume), birds associated with old-growth forests during

winter (Huff and others, this volume), some amphibians

(Aubry and Hall, this volume; Bury and others, this volume),

and vertebrate species sensitive to edges and other landscape

patterns created by forest management activities. Also of

interest are species recognized by administrative or legal

directives, and keystone species whose functions in latesuccessional forest ecosystems affect the presence and abundance of other plant and animal species (table l), such as

mycophagous small mammals and prey species for spotted

owls.

Developing inventories of the distribution, abundance, and

L trends of populations for each of these species--especially

those that are wide-ranging, rare, or secretive-is a formidable and expensive task (see Franzreb 1977, Raphael and

I

Rosenberg 1983, Ratermann and Brode 1983). Complete

censuses of all of these species (table 1) will never be possible because of the huge investments of time and money

required for even one species in a small area. Sample

surveys, however, can determine distributions, relative

abundances, and population trends. Sampling could be

undertaken among physiographic provinces, National

Forests, or any given land allocation. Validated models of

habitat relationships could be used to identify appropriate

habitats to inventory within such strata.

/

Table I-Categories of species that may require extensive

inventories or additional study

Species closely associated with old-growth conditions in

one or more physiographic provinces

Northern spotted owl

Vaux’s swift

Marbled murrelet

Hairy woodpecker

Red-breasted sapsucker

Brown creeper

Western flycatcher

Big brown bat

Fringed myotis

Long-legged myotis

Silver-haired bat

Shrew-mole

Red tree vole

Olympic salamander

Northwestern salamander

Del Norte salamander

Roughskin newt

Tailed frog

Species associated with old-growth forests that may suffer

declines in managed forest landscapes

Cavity-nesting birds

Birds and mammals closely associated with latesuccessional forests during winter

Chestnut-backed chickadee

Red-breasted nuthatch

Red crossbill

Gray jay

Douglas’ squirrel

Species associated with interior late-successional forest

conditions

Keystone species

Selected prey species of the spotted owl

Northern flying squirrel

Mycophagous mammals

Insectivorous birds, especially those preying on

forest insect pests

Species with special administrative or legal status

Old-growth indicator species

Northern spotted owl

Pileated woodpecker

Marten

Rare, threatened, endangered, or sensitive species

Northern spotted owl

Northern goshawk

Great gray owl

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In addition to sampling habitats for the presence and distribution of late-successional forest wildlife species, appropriate

techniques may need to be developed for estimating the absolute abundance and reproductive attainment of certain

species. Substantial progress has been made on region-wide

inventories of northern spotted owls, although at high cost

(O’Halloran and others, this volume). Similarly, survey

protocols are being developed for determining the distribution and relative abundance of marbled murrelets at inland

sites (Nelson, this volume; Paton and Ralph, this volume).

No reliable methods for assessing the reproductive success

or absolute abundance of marbled murrelets are currently

available, however. Such intensive efforts to gather data on

the absolute abundance and reproductive success of other

wildlife species associated with late-successional forests may

be initiated only for those species that are listed (or proposed

to be listed) by the U.S. Fish and Wildlife Service as

threatened or endangered.

Information on Wildlife Habitat Relationships

Understanding habitat relationships of old-growth wildlife

species is crucial to predicting their responses to present and

future habitat conditions in managed landscapes. Habitat

relationships should be evaluated throughout the geographic

range in which each species occurs, and for each habitat that

may be used for various life-history functions, such as

feeding, resting, reproduction, migration, or dispersal.

Habitat relationships information can be used to predict the

abundance of wildlife species in various structural or successional stages of late-successional or other forest types. Information on the habitat relationships of old-growth-associated

species should include specific habitat elements associated

with the species’ presence, variation in abundance and reproductive success, and other life-history traits. Applying such

information for each species would help identify the range of

occurrence of various habitat elements in the landscapedensity, size, and quality of habitats-and effects on species’

presence and abundance. Of particular concern are the effects

on wildlife population sizes and trends of fragmenting oldgrowth forests. Fragmentation greatly changes spacing, size,

shape, context, and amount of edge of old growth in the

landscape.

Managers need to understand to what degree different vegetation structures and species assemblages (at both stand and

landscape scales) provide for viability of each old-growth

wildlife species. Integral to this understanding is predicting

stand and landscape conditions that result from management

activities by all land owners throughout each species’ range.

To help develop such predictions, researchers and managers

need to know what stand and landscape attributes to track.

The old-growth research results presented in this volume

represent the first step toward developing information for

such uses. To use such information, we also need to test how

well variation in species presence, abundance, survival,

reproductive success, and long-term population viability can

be predicted by using existing habitat data or that which can

be collected through typical forest inventory procedures.

One main objective is to produce area-analysis models for

assessing cumulative effects on late-successional forest wildlife species from management activities at both the stand and

landscape scale. Such analyses may initially be useful only

for predicting the presence of various species. Ongoing

studies of landscape-scale patterns (Lehmkuhl and others,

this volume) will provide essential information for this

purpose.

In such models, attributes would be defined along gradients

rather than by discrete stand age-classes. An example would

be a discriminant function equation that estimates the probability that a species is present based on habitat conditions.

The equation would discriminate between stand conditions

with which the species’ presence is highly likely and those

with which it is highly unlikely. Of course, species’ presence

is not simply a function of habitat: historical factors, random

catastrophic events, and barriers to colonization are also

important determinants.

Predictive species models developed from the old-growth

research data should be validated, especially against an

independent data set. Where additional data sets are lacking,

predictive models can be developed with cross-validation

techniques, such as bootstrap or jackknife analyses (Meyer

and others 1986, Solow 1989). In such tests, the robustness

of the models are assessed by developing prediction equations

or correlations from multiple, randomly chosen subsets of the

data base and testing how values of the model parameters

vary. Cross-validation also entails testing the predictions of

a model derived from a portion of a data base against a

complementary portion not used to develop the model.

Demography Information

Finding through field inventories that a wildlife species is

present in a particular area, or predicting its presence by a

habitat capability model, does not ensure that existing habitat

conditions or landscape patterns will provide for either

reproductive success or long-term population viability (Van

Home 1983). Empirical information on demographic

parameters is critical for making such determinations. Such

expensive and time-consuming studies, however, will likely

be conducted on only a few species of key scientific,

administrative, or social concern, such as spotted owls and

their prey.

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Priorities among species may need to be set for these studies.

Forest species with a high priority for demographic studies

may include those that are associated with late-successional

forests; have restricted or disjunct ranges and small population sizes; or that are known or suspected to have suffered

recent population declines, especially those resulting from

management activities. Forest-scale inventories may need to

be conducted to determine which species associated with

late-successional forests would fall into these priority

categories.

Advisor or Prescription Models

To aid resource managers, advisor or prescription models can

be built to help evaluate old-growth habitat conditions at

stand and landscape scales and to recommend appropriate

courses of action to meet specified management objectives

(Marcot and others 1988). Such models may include expert

systems, in which knowledge bases of if-then rules represent

expert assessments of habitat conditions (Marcot 1986). An

example is the expert system developed for guiding habitat

management for black-tailed deer in coastal British Columbia

(McNay and others 1987).

Advisory models are used to assess habitat conditions for

various species and to set priorities and guide management

prescriptions. At a landscape scale, they can also be used to

guide cumulative effects analysis. That such models be evaluated through peer review and validated with independent

empirical data is critical, however (Marcot 1987). Also, such

models should not be used to make decisions, but merely to

provide information. For example, an expert systems model

could be designed to advise on priorities for retention or

silvicultural treatment of late-successional forest stands in a

given landscape. Especially if tied to a geographic information system to visualize stand conditions and habitat patch

patterns, such a decision-aiding tool would provide a consistent and reliable means of interpreting old-growth forest

conditions for wildlife.

Development of these models is several years away. Key

information still needs to be gathered or developed to

produce prediction equations at stand and landscape scales

and expert advice on habitat and species’ responses to

various management presciptions. Inventories of vegetative

conditions at the stand scale need to be conducted or refined.

Regardless of whether such an advisory system is produced

in the form of a model per se, or as an evaluation process for

resource managers to follow, a thorough understanding of

species-habitat relationships is needed to guide such

prescriptions.

Further Research Needs

We consider further research needs to fall into five basic

categories: autecological studies, studies on developmental

patterns in old-growth forests, landscape studies, research on

inventory and monitoring techniques, and studies on the

effects of management activities and natural disturbances

such as fire.

The team that planned the research reported in this book has

identified autecological studies as the next phase of research

after the community studies (Research Work Unit Description on file at the Forestry Sciences Laboratory in Olympia,

Washington). The results presented in this volume will go a

long way towards identifying species for which autecological

study is needed. In addition, researchers and managers can

work together to identify priorities for other species that

were not included in the sampling design for the community

studies reported here. The species presented in table 1 should

all be considered candidates for more detailed study. Autecological studies must be conducted over a wide range of stand

conditions so that the effects of various stand conditions can

be adequately evaluated. Such studies may sometimes require

manipulation of existing stands to simulate conditions likely

to exist in the managed stands of the future.

Further information on developmental patterns in old-growth

forests is essential to providing full understanding of options

for managing old growth. Many areas currently contain

forests that have some of the characteristics of old growth

but fail to meet all criteria for ecological old growth (see

Franklin and Spies, this volume; Morrison 1988; Old-Growth

Definition Task Group 1986). We need to fully understand

developmental patterns in older forests so that we better

predict their future characteristics.

Landscape ecology studies must focus on two essential

questions. The first is how fragmentation affects old-growth

forest conditions. How the integrity of old-growth habitats

can be maintained at the landscape scale must be understood.

Research is needed to determine how well adjacent stands of

different ages and structures act to buffer the effects of fragmentation on old-growth stands. Some of this research is

already being conducted by Forest Service scientists in the

Pacific Northwest. The second essential question at the

landscape scale concerns the effects of various amounts and

patterns of old growth and other forest types on populations

of wildlife species that are associated with old growth. Such

studies must focus (to the extent possible) on long-term

rather than short-term effects on species. Although these

studies are likely to prove very difficult and expensive, they

469
offer the only real means of evaluating management hypotheses about landscape conditions that will sustain species’

viability over time. These studies must include demographic

measures of the species being investigated. They must also

be designed to look at a broad array of landscape conditions.

The careful setting of priorities of species to be studied at the

landscape scale, and of needed standards of reliability, will

be critical because of the expense and time required for such

studies.

The third category of studies needed are those directed at

developing more effective inventory and monitoring techniques for both old-growth habitats and wildlife species

associated with those habitats. Such inventories are time

consuming and expensive, but management plans cannot be

properly implemented without site-specific knowledge of

habitats and species. In addition to more efficient techniques,

evaluation systems that would set priorities among stands for

species-specific inventories would be useful. Inventories

could then be conducted in those habitats that are most likely

to support species of interest.

Finally, studies are needed on the effects of management

and of natural disturbance on old-growth conditions. Studies

of management techniques should ask what techniques are

useful to retain elements of old-growth forests in newly

regenerated stands; accelerate the development of these

characteristics in young, unmanaged stands; and accelerate

the development of these characteristics in stands that have

already come under management. Studies of natural disturbances should address questions about the frequency, size, and

intensity of natural disturbances and the ways in which they

may be influenced by various stand and landscape

characteristics.

Conclusions

The information presented in this volume provides clear

evidence about species’ associations with old-growth forests.

That information should be further analyzed to provide a

better understanding of the stand characteristics associated

with each of these species. Community-scale analyses of this

data set has provided information on the diversity of

old-growth forest communities and associated wildlife

communities. This information will allow managers to make

more-informed decisions about which stands to include in

old-growth management areas. Such information can be

applied directly in implementing Forest Plans. A

combination of further analysis of this information and

collection of additional data will elucidate how different

amounts and patterns of old-growth stands will affect the

species that are most closely associated with them. Such

analyses will also play a role in determining if amendments

to Forest Plans are needed.

It is vitally important that the research and management

branches of the Forest Service work closely together to

further develop this information and identify priorities for

needed information. Although we acknowledge that

additional information is always desirable, we urge resource

managers to make the best possible use of the results

presented here. A cooperative effort between researchers and

managers is currently underway to make this information

more readily accessible to potential users. The products of

this effort should provide essential tools to managers faced

with tough decisions about maintaining old-growth forest

conditions in a multiple-use context. q

470
Research on Wildlife in Old-Growth Forests: An

Attempt at Perspective

Jack Ward Thomas

Author

JACK WARD THOMAS is chief research wildlife biologist,

U.S. Department of Agriculture, Forest Service, Pacific

Northwest Research Station, 1401 Gekeler Lane, La Grande,

Oregon 97850.

This volume presents the results of research conducted under

the auspices of the Forest Service’s Old-Growth Forest Wildlife Habitat Program. At least another 100 papers have been

prepared from research conducted by or funded by the Program; more are to come. Some of these papers are published,

some in press, and some undergoing review. A vast amount

of data was gathered by the Old-Growth Program. Such a

comprehensive, detailed data set on animal communities

related to the successional or structural stages in Douglas-fir

forests of western Oregon and Washington is unlikely ever to

be collected again.

New evidence and insights about the relationships (or lack

thereof) of a myriad of wildlife species to Douglas-fir forests

in western Oregon and Washington has been presented in

this volume. And, more specifically, the degree of association between many wildlife species and old-growth forests,

as compared to earlier stages of forest development, has been

extensively examined.

In my introductory paper (Thomas, this volume), I suggested

that the research reported here would be used to formulate an

answer to an overriding question-do old-growth Douglas-fir

forests represent unique wildlife habitat compared to other

forested environments? In my opinion, the answer to this

question is yes. The papers included here have shown that

many wildlife species in Douglas-fir forests occur in greater

abundances in old-growth than in young or mature forest

stages, and that several wildlife species are closely associated

with old growth.

The studies reported here, however, dealt with a comparison of wildlife and habitat relationships across a spectrum of

unmanaged forest conditions in young, mature, and oldgrowth stands (and sometimes, in clearcuts). The stands

examined in these studies were naturally regenerated, usually

after fire, and had not been silviculturally treated during their

development. Such stands are not likely to contain communities representative of those in managed stands that have

been subject to site preparation, planting with spacing to

selected stock of selected species, thinning to predetermined

spacing, control of competing vegetation, and so on.

Intuition tells me (and I submit that for scientists to offer

intuition and opinion is acceptable, provided they are

identified as such) that fully managed stands are apt to be

more simplified in structure than unmanaged stands. And,

in turn, managed stands would be expected to support

simplified plant and animal communities compared to

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unmanaged stands of similar age. The comparisons of animal

communities across the spectrum of unmanaged successional

stages reported here must not be uncritically assumed to

apply to managed stands or forested landscapes of the future.

Information presented on habitat features in these successional stages typically showed a carryover of some of the

ecological attributes of the preceding old-growth stand in

younger stands. Such attributes include large, down woody

material, large snags, and some large living trees, all of

which provide important habitat for wildlife (Thomas 1979).

Do these carryover attributes influence the habitat quality of

the young and mature stands studied? That seems likely.

Initiating wildlife studies in intensively managed stands will

be critical, regardless of the limitations on available study

sites. Obviously, locating fully managed stands much older

than 60 to 80 years will be impossible. In most areas, only

fully managed stands of 50 years of age or less will be available for study. Stands that are studied should be as large as

possible, however, to accommodate the broadest array of

wildlife species that might occur there. Comparisons of

wildlife densities in younger managed stands with those in

unmanaged stands of the same age will be required to

adequately evaluate the quality of wildlife habitat provided

in managed forests. Such studies are necessary to determine

if, as several authors have suggested, simplication of forest

structure and tree species composition in managed stands is

indeed reflected in simplication of the attendant animal

community.

Any research effort usually ends with the researchers’

cautioning against inappropriate use of the data presented or

the conclusions drawn, and identifying additional needed

research. There is no final truth in ecological research--only

better and better approximations of what passes as truth; and

there are no final questions-only better and more specific

questions whose answers put us closer to truth. Managers

will, of course, use the research to guide management in

spite of the researchers’ cautions; they have no other choice.

Today’s forest planning procedures rely heavily on the use of

linear mathematical models that consider the effect of several

variables and their interactions to predict outcomes of various

biological processes-say timber production or population

sizes of a wildlife species. The data that goes into these

models are seldom precise, and the interactions between the

variables considered are even less perfectly understood. Such

models were not intended by their developers to provide

precise information. Rather, they were intended to provide

indications of the direction of change, rough estimates of the

magnitude of expected change, and the time frames surrounding such change.

Unfortunately, because the predictions of these models are

numbers that have been used to guide management allocations, some have taken the results to be more precise than is

justified. Political conflict over the allocation of resources

results in political demands for more and more precision,

whether or not more precision is possible. The models are

then, sometimes, “tweaked” over and over to satisfy demands

for greater accuracy and “politically correct” decisions. Such

continued tweaking often only provides an illusion of

precision that produces increasingly unwarranted faith in

what finally appears on the computer printouts. Such events

make monitoring the results of management applications (as

required by law and regulations, and promised in the Forest

Plans) so very critical. Only adequate monitoring can enable

the necessary mid-course corrections in management activities to be made if the models have produced projections that

are out of line with reality-whether too high or too low. All

concerned have a vested interest in ensuring that adequate

monitoring is carried out. And that means adequate design

and resources to get the job done.

The biology of certain wildlife populations and wildlife

habitat relationships are not conducive to precise estimates,

no matter how much they are studied. The precision of such

estimates can only become marginally better regardless of

how politically desirable that may be. The answer will

always involve a mean and be qualified with confidence

intervals. Year-to-year perturbations in number will occur

that are unrelated to habitat. Extremes or exceptions will

always tempt those who long for a different answer than that

indicated by the mean. Collecting enough data to determine

long-term trends associated with habitat quality and quantity

will be difficult and expensive. But good monitoring will

require that.

The political process is not yet attuned to dealing with

science as part of that process. When land-use decisions

are to be made that have large-scale economic, social, and

political impacts, individuals who stand to lose from those

decisions typically demand unreasonable degrees of certainty

in the information on which those decisions are based. All

concerned need to recognize that the provision of a degree

of certainty adequate to satisfy those whose welfare is

threatened by the result is not likely to be attained. This

problem is exacerbated when the decision criteria involve

biological systems that are dynamic and highly variable.

When is the information base adequate to support emotionally

laden, economically explosive decisions so that all parties

will be satisfied? The answer, likely, is never,

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In addition, scientists do not seem well suited by training or

inclination to deal with the political process. Some biologists

dwell on qualification of extant data and lean toward emphasizing uncertainty and the need for more information.

Although they are quite correct, scientists also need to be

cognizant that the world marches on, regardless of the state

of knowledge. So the scientific community, while imparting

the caution that science demands, needs to emphasize the

considerable knowledge that is available, rely on extant

theory, and contribute in whatever way they can to forest

planning and management.

Decision-makers in land management agencies are usually

not biological scientists. That may be fortunate. Some say

that scientists think too much, feel too little, and have inadequate appreciation for economics and politics. Biologists

should recognize that biological considerations are only part

of any political land-use decision-making process-including

any resulting legal proceedings. Such recognition implies

some degree of sympathy, if not empathy, for those who

must ultimately make land-use decisions. Those decisionmakers must consider the law, policy, public desire, political

pressures, economics, politics, and budgetary direction from

Congress, as well as any biological considerations. To

pretend otherwise is both naive and foolish.

In the United States, the management of old-growth forests

in the Pacific Northwest, and the political events that surround this issue, could emerge as the most significant natural

resource issue of this century. The ultimate resolution of this

controversy will set the tone for natural resource management in the first half of the 21st century. What lies ahead?

Intensified conflict? Total preservation? Management for

old-growth habitat features? Emergence of a “new” forestry?

Compromise? Changes in law? Abandonment of multiple-use

concepts in favor of dedication of land to various uses?

Other actions? Who knows?

Certainly there is much more to understand about the

ecological aspects of this issue. Given the huge potential

economic and social impacts of land-use decisions on

managing old growth, support seems likely to be available

for additional ecological studies. But it is critical, now, for

the scientific community to step back and carefully examine

our ecological knowledge of old-growth forests. At what

questions should new research on old growth be directed?

How much more of the limited funds available for wildlife

and forest ecology research should be shifted to the oldgrowth arena? Perhaps the questions and approaches that

would emerge from debate within the scientific community

would differ markedly from the “hot issues” that may be

targeted by the political and congressional appropriations

processes. These issues may well be settled, one way or

the other, before adequate research can be conducted and

brought to bear on them. The scientific community and those

in the research arena must be careful not to be used inappropriately in the fight over old-growth management. The

scientific community should not take part in premature

actions predicated on inadequate understanding, nor in the

use of research as a means of delaying management decisions

where adequate information exists to justify action.

Undoubtedly, hot-issue research on potentially threatened

and endangered species will be given increased funding. But,

at the same time, scientists and managers should strive to

ensure that additional research on the community ecology of

managed and unmanaged forests goes on simultaneouslyperhaps as part of the hot-issue research. For therein lies the

information of more lasting scientific and political value as

the old-growth issue evolves to encompass the broader issues

to be faced by forest managers over the next decades.

Foremost among these will be the need to determine the size

and spacing of old-growth stands within managed forest

landscapes that will provide for the “maintenance of viable

populations of native species...well-distributed” throughout

public lands.

The results presented in this volume have moved us a bit

closer to truth. The challenge, now, is to synthesize this

information so that it may be incorporated into decisionmaking processes, political debates, and legal proceedings on

the management of old-growth forests.

As a last word, I am reminded of the admonitions of several

who philosophize about ecology, mixed together here in paraphrase. There is no final ecological truth. All knowledge is a

current approximation, and each addition to that knowledge

is but a small, incremental step toward understanding. For

not only are ecosystems more complex than we think-they

are more complex than we can think. 0

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