The Strategy and Design of

the Effectiveness Monitoring

Program for the Northwest

Forest Plan

United States

Department of

Agriculture

Forest Service

Pacific Northwest

Research Station

General

Technical Report

PNW-GTR-437

January 1999
Technical

Coordinators

BARRY S. MULDER is a wildlife biologist, U.S. Department of the Interior, Fish

and Wildlife Service, P.O. Box 3623, Portland, OR 97208; BARRY R. NOON and

HARTWELL H. WELSH are research ecologists, U.S. Department of Agriculture,

Forest Service, Pacific Southwest Research Station, 1700 Bayview Dr., Arcata, CA

95521; THOMAS A. SPIES is a research forester and GORDON H. REEVES is a

fisheries biologist, U.S. Department of Agriculture, Forest Service, Pacific Northwest

Research Station, Forestry Sciences Laboratory, 3200 Jefferson Way, Corvallis, OR

97331; MARTIN G. RAPHAEL is a chief research wildlife biologist, U.S. Department

of Agriculture, Forest Service, Pacific Northwest Research Station, 3625 93rd Ave.,

Olympia, WA 98512-9193; CRAIG PALMER is a program director, Harry Reid Center

for Environmental Studies, University of Nevada—Las Vegas, 505 South Maryland

Parkway, Las Vegas, NV 89154-4009; and ANTHONY R. OLSEN is a mathematical

statistician, U.S. Environmental Protection Agency, 200 SW 35th St., Corvallis, OR

97333.
The Strategy and Design of the

Effectiveness Monitoring Program

for the Northwest Forest Plan

Barry S. Mulder, Barry R. Noon, Thomas A. Spies,

Martin G. Raphael, Craig J. Palmer, Anthony R. Olsen,

Gordon H. Reeves, and Hartwell H. Welsh

Technical Coordinators

Published by:

U.S. Department of Agriculture

Forest Service

Pacific Northwest Research Station

Portland, Oregon

General Technical Report PNW-GTR-437

January 1999

In cooperation with:

U.S. Department of the Interior

Bureau of Land Management

Fish and Wildlife Service

Environmental Protection Agency
Abstract Mulder, Barry S.; Noon, Barry R.; Spies, Thomas A.; Raphael, Martin G.;

Palmer, Craig J.; Olsen, Anthony R.; Reeves, Gordon H.; Welsh, Hartwell H.

1999. The strategy and design of the effectiveness monitoring program for the

Northwest Forest Plan. Gen. Tech. Rep. PNW-GTR-437. Portland, OR: U.S.

Department of Agriculture, Forest Service, Pacific Northwest Research Station.

138 p.

This report describes the logic and design of an effectiveness monitoring program

for the Northwest Forest Plan. The program is prospective, providing an early warning

of environmental change before irreversible loss has occurred. Monitoring is focused

at two resource levels: individual species and specific ecosystem types. Selection of

prospective indicators for the status of species or ecosystems is based on the development of conceptual models relating resource change to reliable, early warning

signals of change. Ecosystems, such as late seral stage forest communities, are

monitored on the basis of critical structural and compositional elements that reflect

the state of underlying ecological processes. The assumption is that systems retain

their ecological integrity to the extent that key biotic and physical processes are

sustained. For species of concern, the design integrates animal populations with their

necessary habitat and projects changes in population status by monitoring significant

changes in habitat at several spatial scales. Anticipatory forecasting of changes in

population status assumes habitat to be a reliable surrogate for direct population

measures. A surrogate-based approach requires an active period of model building

that relates population to habitat variation to develop robust wildlife relation models.

Essential components needed for program implementation, such as data collection,

information management, report preparation, and feedback to management, are

discussed. This discussion includes recommendations for staffing, funding, and

establishing a long-term commitment for a large, interagency monitoring program.

Keywords: Northwest Forest Plan, ecological monitoring, effectiveness monitoring,

adaptive management, regional scale, habitat basis, conceptual model, predictive

model, integration, summary report, interpretive report, institutionalize.
Preface Under the direction of an Intergovernmental Advisory Committee that oversees

the implementation and management of the Northwest Forest Plan, interagency

Federal teams have been developing a monitoring program to evaluate the success

of the Forest Plan. This complex and challenging task has required a large commitment of time and agency expertise. This report represents another important step in

implementing a comprehensive monitoring program for the Forest Plan, a program

that eventually will cover the three types of monitoring required by the plan: implementation, effectiveness, and validation monitoring. Overall direction for monitoring

under the Forest Plan began with the description of the key role of monitoring in

adaptive management outlined in FEMAT (July 1993), defined in the interagency

report entitled “Interagency Framework for Monitoring the President’s Forest Ecosystem Plan” (March 1994), and summarized in the record of decision for the Forest

Plan (April 1994).

This report is the second in a series that addresses effectiveness monitoring. The

first report, “Effectiveness Monitoring: An Interagency Program for the Northwest

Forest Plan” (July 1995), describes the general framework for effectiveness monitoring under the Forest Plan. The approach was accepted by the Intergovernmental

Advisory Committee and approved as the appropriate direction for developing an

effectiveness monitoring program. This second report, after taking into consideration

all peer, agency, and other reviews, concludes development by the Effectiveness

Monitoring Team of the overall strategy and guidance for effectiveness monitoring.

The strategy described here has not changed from that reviewed and accepted

by the Intergovernmental Advisory Committee; this document incorporates edits,

clarifications, and further background material, as requested.

This report represents a further step in our understanding of and approach to ecosystem monitoring. It provides the scientific basis for the effectiveness monitoring

program; separate reports or modules provide specific options for monitoring assigned

priority resources: late-successional and old-growth forest, northern spotted owl,

marbled murrelet, and aquatic and riparian ecosystems; and monitoring plans for

survey-and-manage and other late-successional and aquatic species. Reports on

other issues, such as socioeconomic and tribal, will be developed in the future.

Our approach, described here, provides the template for designing these and other

monitoring modules to help address the Forest Plan.

The effectiveness monitoring program will consist of many modules and their supporting guidance and plans. This report and the individual reports or modules for

the individual resources respond to the assignment to identify a range of options for

monitoring these issues from which the Federal agencies can select an appropriate

approach (or approaches). Because of the complexity of the science related to this

issue, the Federal research agencies (USDA Forest Service, Environmental Protection Agency, and U.S. Geological Survey—Biological Resources Division), at the

request of the Intergovernmental Advisory Committee, took responsibility to establish

the underlying scientific framework and develop monitoring options. Federal agency

selection of a set of options will trigger the third and final stage in the development

of an effectiveness monitoring program: assignment by the agencies of the plans,

people, and funding to implement the program. The completed set of documents

will function as integrated guidance to Forest Plan monitoring.

The Effectiveness Monitoring Team
Executive Summary

The Northwest Forest Plan is a large-scale ecosystem management plan for Federal

lands in the Pacific Northwest, encompassing 24 million acres of federally managed

forests over 18 National Forests and 7 Bureau of Land Management Districts in northern California, western Oregon, and western Washington. Three types of monitoring

are mandated by the Forest Plan: implementation, effectiveness, and validation. The

purpose of this report is to provide the strategy and design for effectiveness monitoring of priority resources identified in the Forest Plan.

The primary goals and objectives for the Forest Plan are both ecological and socioeconomic. In the context of the Forest Plan, the primary question that effectiveness

monitoring is designed to answer is, “To what extent are the goals and objectives of

the Forest Plan being achieved?” Following the goals and objectives of the Forest

Plan, the basic scientific premise underlying the proposed program is to implement a

predictive and integrated habitat-based approach to monitoring, intended to produce

useful and timely results more efficiently and cost-effectively than past programs have.

The general approach for developing the effectiveness monitoring program has

been to develop the scientific framework for monitoring; this document describes

that approach. The approach has been used in developing monitoring strategies

for specific priority resources identified by management, including late-successional

and old-growth forests, northern spotted owls, marbled murrelets, and aquatic and

riparian ecosystems. This document also provides the basis for designing future

monitoring modules that may address other important resource issues (for example,

socioeconomic, tribal, survey-and-manage species, or other species associated with

late-successional or aquatic ecosystems).
The goals for effectiveness monitoring are to evaluate the success of the Forest Plan

by assessing the status and trends of selected resources. These goals are consistent

with emerging national and international frameworks for monitoring. The program outlined in this report is designed to build on and improve ongoing monitoring activities

of regional as well as local forest management units to accomplish these goals. Its

scope and complexity, however, mean that it will be significantly different from how

agency activities have traditionally been monitored, a difference that will lead to a

change in thinking about how to manage and operate a monitoring program.

Scientific Approach The task of developing a monitoring system to detect and recognize significant

change is complex because natural systems are inherently dynamic and spatially

heterogeneous. Further, many changes in space and time are not a consequence

of human-induced actions, and many are not amenable to management intervention.

It is not surprising, therefore, that few examples exist of successful monitoring programs at the ecosystem scale. Environmental monitoring programs often are discussed in abstract terms, have little theoretical foundation, try to measure too many

attributes, have vague objectives, and have no institutionalized connections to the

decision process. In times of budget reductions, monitoring programs can be the

first to be eliminated.

To be most meaningful, a monitoring program should provide insights into causeand-effect relations between environmental stressors and anticipated ecosystem

responses. Indicators should be chosen based on a conceptual model clearly linking

stressors and indicators with pathways leading to effects on ecosystem structure and

function. This process enables the monitoring program to investigate the relations

between anticipated stressors and environmental consequences, and provides the

opportunity to develop predictive models to anticipate trends instead of waiting until

trends have been demonstrated.

The emphasis chosen for effectiveness monitoring of the Forest Plan may best be

described as prospective monitoring. This approach incorporates causal relations

between effects and stressors through the judicious selection of indicators. It starts

with characterizing threats (stressors) to the ecological integrity and ecosystem

functioning (effects) of the management unit. A conceptual model then outlines the

pathways from the stressor(s) to the ecological effects. Attributes indicative of the

anticipated changes in specific ecological conditions are then selected for measurement. The ultimate success of this approach depends on the validity of the assumed

cause-effect relations between the stressor(s), their ecological effects, and the

selected indicators of stress.

The essential steps, described in the scientific literature, that we followed in developing the approach to the effectiveness monitoring program for the Forest Plan were:

1. Specify goals and objectives

2. Characterize stressors and disturbances

3. Develop conceptual models—outlines the pathways from stressors to the

ecological effects on one or more resources

4. Select indicators—detects stressors acting on resources

5. Determine detection limits for indicators—to guide sampling design

6. Establish “trigger points” for management intervention
7. Establish clear connections to the management decision process

Given the great diversity of species—plant and animal, vertebrate and invertebrate—

monitoring of all biotic components of managed ecosystems is clearly impossible.

Based solely on pragmatic considerations, only a few surrogate measures can be

used that allow indirect (but reliable) inference to the integrity of the larger set of

biological processes and components. A possible surrogate for the biota is to

measure the pattern and dynamics of habitat structure.

The justification for using habitat structure as surrogate variables for predicting

wildlife populations is based on both pragmatic and theoretical arguments. Habitat

loss and fragmentation were the primary drivers or stressors behind creation of the

Forest Plan. The theoretical argument is based on the belief that animals respond

to habitat adaptively; that is, where an animal selects to live is believed to be an

evolved behavioral response stimulated by structural and compositional features of

the landscape. Predictive habitat suitability models will need to consider the relations

between landscape pattern and life history characteristics of individual species and

population-scale dynamics to provide a realistic portrayal of potential trends. The

assessment strategy, which emphasizes both remotely sensed and ground-plot

habitat data, should allow inferences about habitat quality at different spatial scales

across a range of resource issues.

The foundation of our approach to effectiveness monitoring for the Forest Plan is

to initiate a gradual transition from an intensive, individual species-resource focus

to a more extensive, ecosystems approach. This transition assumes identifying and

measuring surrogate variables that allow reliable inferences about the integrity of

the primary resources. Such a fundamental shift means a movement away from

the current crisis response to individual endangered species-resource issues, to

a prospective evaluation of management decisions in an ecosystem context. The

transition to a habitat-based monitoring program has several advantages:

• Monitoring vegetation change will be more cost-effective than directly monitoring

populations of all the possible species for which agencies are responsible

• Existing forest inventory programs can be the foundation for monitoring programs

• A habitat focus is more in line with the mandates of the Forest Plan to manage

vegetation communities (habitat), not species populations directly

• Estimating the trends in habitat structure and composition represents an anticipatory as opposed to a retrospective approach to ecological monitoring, and

allows evaluation of alternative management strategies

Approach to

Management

To be successful, a monitoring program must be able to collect data, summarize the

data into useful information, and interpret that information to advance understanding

and knowledge to improve management decisions. Key components of a structured

monitoring program include data collection, information management, preparation

of data summaries and interpretive reports, feedback to management, and program

coordination and support.

Many inventory, monitoring, and research projects are currently collecting data

of value to effectiveness monitoring in the region of the Forest Plan. Rather than

duplicate these efforts, we recommend building as much as possible on ongoing

data collection activities. Coordination among these programs will be encouraged

through direct staff links, direct data links, and quality assurance systems.
Two types of reports are integral to the effectiveness monitoring program: data

summaries and interpretive reports. Data summaries are brief, comprehensive

reports of essential data collected for effectiveness monitoring and are to be

produced annually for each resource being monitored. The key products of the

effectiveness monitoring program will be periodic regionwide interpretive reports

produced at 5-year intervals. The purpose of interpretive reports is to evaluate the

ecological significance of status and trends emerging in the monitoring data in

relation to the Forest Plan, and to provide statements of the implications of monitoring results, documented in the summary reports, to management; pertinent

information from other sources or lands also would be considered. The resulting

information is critical to adaptive management; it can be used to change plans,

direction, or policies and contribute to budgetary and other decisions.

As the program develops, the challenges to success will expand because of the

complexity of the data being collected. In addition to assisting in interpreting the

monitoring data, research support will be needed to address emerging information

needs, such as selecting new indicators and associated monitoring designs. Pilot or

test studies also will offer important opportunities to test new methods and concepts,

which will allow the monitoring program to be improved or adapted over time. The

program must provide monitoring results that are legally defensible. Therefore, an

information management and quality assurance system will be needed to assist in

collecting, validating, storing, and retrieving data and in preparing reports.

Strategy for

Implementation

Given the complexity of a monitoring program of this scale, magnitude, and importance, we propose that the initial goal be to develop the first regionwide interpretive

monitoring report at the end of 1999. Not only will this product test the success of

the program, but it also will provide the baseline for assessing future trends and

offer an opportunity to adjust the program for future operation. The challenge to the

effectiveness monitoring program will be to integrate all the critical components into

an efficient and responsive program to meet this goal. This task is daunting, given

the diversity of cooperating agencies, the number of resources being monitored, and

the plethora of different monitoring groups. A primary concern has been to develop a

strategy for integrating the assigned resources. Our approach has been to develop a

scientific and management framework that fosters integration. The monitoring plans

for each resource propose a common monitoring approach, conceptual framework,

indicator-selection strategy and monitoring design, and data assessment and

reporting process. Similarly, strategies to address research needs, pilot studies,

data management, and quality assurance have been identified.

Because this program represents a step forward from how monitoring has been

handled, specific steps will need to be taken to institutionalize all aspects of the

program and to establish base funding to support program activities over the longterm. Assigning permanent monitoring staff and establishing core agency teams

including program managers is critical to foster integration, management, and coordination for the monitoring program. If the approaches to staffing, data sharing, and

quality assurance are followed, integrating all the monitoring efforts is likely and the

information necessary for adaptive management of the Forest Plan will be available.
Contents

1 Chapter 1: Introduction to Effectiveness Monitoring

Barry S. Mulder and Craig J. Palmer

1 Background and Purpose

4 Role of Monitoring in the Northwest Forest Plan

6 Considerations in Developing the Effectiveness Monitoring Program

8 Links to Individual Forest Management Units

9 Relation to Emerging National and International Monitoring Requirements

11 Key Aspects of the Effectiveness Monitoring Program

14 Focus of This Report

14 Acknowledgments

15 References

21 Chapter 2: Conceptual Basis for Designing an Effectiveness

Monitoring Program

Barry R. Noon, Thomas A. Spies, and Martin G. Raphael

21 Scientific Basis for Monitoring

21 What Is Monitoring?

23 Why Monitor?
24 What Can a Monitoring Program Tell Us?

25 The Legacy of Environmental Monitoring Programs

27 Prospective (Predictive) or Retrospective Monitoring?

29 Challenges of Monitoring Ecological Systems

30 Key Steps in Designing the Monitoring Program

31 State the Goals of the Monitoring Program

32 Identify Stressors Relating to Management Goals

33 Develop a Conceptual Model Linking Relevant Ecosystem Components

35 Identify Candidate Indicators Responsive to Environmental Stressors

36 Estimate the Status and Trend of the Indicator

37 Expected Values and Trends

41 Linking Monitoring Results to Decisionmaking

44 Summary of Key Points

44 Acknowledgments

44 References

49 Chapter 3: Scientific Framework for Effectiveness Monitoring

of the Northwest Forest Plan

Barry R. Noon

49 Habitat as a Surrogate for Biotic Populations

49 Logical Foundation—A Habitat-Based Approach

51 Toward a Theory of Habitat

52 Spatial Scales of Habitat Evaluation

53 Developing Predictive Habitat-Relations Models for Animals

54 Tradeoffs Between Habitat and Population Monitoring

55 Focal Species as Ecological Indicators

57 Application to the Northwest Forest Plan

58 Late-Successional and Old-Growth Forests

59 Northern Spotted Owl

61 Marbled Murrelet

62 Aquatic and Riparian Ecosystems

63 Other Forest Plan Resources

63 Essential Data Requirements for Forest Plan Monitoring

63 Minimal Components for Baseline Monitoring of the Forest Plan

64 Attributes of the Habitat Map
65 Summary of Key Points

65 Acknowledgments

65 References

69 Chapter 4: Components of the Effectiveness Monitoring Program

Craig J. Palmer and Barry S. Mulder

69 A Structured Approach to Monitoring

71 Data Collection

71 Review of Ongoing Data-Collection Activities

74 Addressing New Data-Collection Needs

74 Coordination Among Data-Collection Activities

75 Strategy for Continual Improvement

76 Data Summaries

76 Summarizing Data

77 Content of Data Summary Reports

77 Options for Data Summaries

78 Strategy for Continual Improvement

78 Interpretive Reports

79 Preparing Interpretive Reports

80 Content of Interpretive Reports

80 Options for Frequency and Staffing of Interpretive Reports

81 Strategy for Continual Improvement

81 Management Decisions

82 Conceptual Model

82 Roles of Monitoring in Land Management Decisions

83 Strategy for Continual Improvement

84 Program Coordination and Support

84 Integration

84 Research Support

85 Pilot Testing and Development Projects

89 Information Management

89 Quality Assurance

91 Example Application to the Forest Plan: Northern Spotted Owl

92 Data Collection

92 Data Summary
92 Interpretive Report

93 Management Decisions

93 Coordination and Support

93 Summary of Key Points

94 Acknowledgments

94 References

99 Chapter 5: Strategy for Implementing the Monitoring Program

Barry S. Mulder, Craig J. Palmer, Miles Hemstrom, Joseph B. Lint, and

Sarah Madsen

99 Goal for Effectiveness Monitoring

101 Road to Producing Effectiveness Monitoring Reports

103 The First Effectiveness Monitoring Report

104 Long-term Reporting Schedule

105 Infrastructure Requirements: Institutionalizing the Program

109 Costs and Budgets

112 Conclusions

113 Acknowledgments

113 References

115 Appendices

115 Appendix A: Authors, Team Members, and Acknowledgments

120 Appendix B: Acronyms and Key Terms Used in This Report

122 Appendix C: Chronology of Key Meetings and Reviews

124 Appendix D: Supplemental Information on Essential (Type B) and Related

(Type C) Projects That May Contribute to the Effectiveness Monitoring

Program

127 Appendix E: Summary Information Used to Evaluate Schedules, Staffing,

and Costs of the Effectiveness Monitoring Program
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Document continues on next page.
Chapter 1: Introduction to

Effectiveness Monitoring

Barry S. Mulder and Craig J. Palmer

Background and

Purpose

In 1993 President Clinton directed the Forest Ecosystem Management Assessment

Team (FEMAT) to develop long-term alternatives for resolving the conflicts over

managing forest ecosystems on USDA Forest Service and Bureau of Land Management (BLM) lands in the Pacific Northwest. The analysis of the FEMAT alternatives (FEMAT 1993) in an environmental impact statement (USDA and USDI

1994a) led to adoption of the land-allocation strategy contained in the “Record

of Decision for Amendments to Forest Service and Bureau of Land Management

Planning Documents Within the Range of the Northern Spotted Owl” (ROD; USDA

and USDI 1994b), commonly known as the Northwest Forest Plan (or Forest Plan).

The Forest Plan is a large-scale ecosystem management plan for Federal forested

lands in the Pacific Northwest that covers parts of northern California, western

Oregon, and western Washington (fig. 1). It encompasses 24 million acres of

federally managed forest lands over 18 National Forests and 7 BLM Districts.

The scale of the Forest Plan represents unique challenges in ecosystem management, adaptive management, and monitoring.

1
Figure 1—Range of the northern spotted owl in western Washington and

Oregon and northern California.

Federal lands

Physiographic provinces

2
Monitoring is required by the ROD (USDA and USDI 1994b), is mandated under

applicable laws and regulations (for example, National Forest Management Act of

1976 [NFMA]; Federal Land Policy and Management Act of 1976 [FLPMA]; Endangered Species Act of 1973, as amended [ESA]), and also is an action expected by

the public and interest groups (Dwyer 1994). The ROD explicitly states the need to

develop a monitoring strategy for key components of the Forest Plan as part of the

adaptive management process. The Forest Plan describes three types of monitoring:

implementation, effectiveness, and validation. Federal agencies have taken a multiphased approach to building a comprehensive monitoring program to evaluate the

Forest Plan (Mulder et al. 1995, Tolle et al. 1994): implementation monitoring is

already underway (Alverts et al. 1997, Tolle et al. 1995). This and the reports for

specific resources issues, noted below, describe the strategy for effectiveness

monitoring, and a future report will address validation monitoring.

The purpose of this report is to provide guidance for designing and implementing an

effectiveness monitoring program for the Forest Plan. The initial resources assigned

by the Federal agencies for monitoring are late-successional and old-growth forests

(LSOG), the northern spotted owl (Strix occidentalis caurina), the marbled murrelet

(Brachyramphus marmoratus), and aquatic and riparian ecosystems. Table 1 summarizes the general criteria that guided the planning effort of the Effectiveness Monitoring Team (EMT) for these issues (see appendix A for team membership and

summary of the process).

This report describes a program that, if implemented, will generate the information

needed to address whether the regional ecological objectives of the Forest Plan are

being met on Federal lands in the three-State area. Ultimately, we believe that full

implementation of the monitoring program will provide insights to management effectiveness for the whole Forest Plan and associated resources.

Table 1—Criteria guiding development of the effectiveness

monitoring program for the Northwest Forest Plan

Points Criteria

1 Respond to the information needs for assessing goals and

objectives of the Forest Plan

2 Follow an incremental approach to constructing a large-scale

monitoring program

3 Focus on status and trend monitoring

4 Address questions at the regional or ecosystem scale

5 Apply to Forest Plan activities on Federal lands (Forest Service

and BLM)

6 Consider options that are cost-effective

7 Provide a sound science-based framework for monitoring

3
Role of Monitoring

in the Northwest

Forest Plan

The primary goals and objectives for the Forest Plan were both ecological and

socioeconomic (FEMAT 1993). A regional-scale monitoring program is needed to

evaluate these goals. Monitoring is intended to ascertain whether current resource

conditions match an expected outcome or lie within some acceptable confidence

region around that outcome. Because monitoring usually is justified by an explicit

goal or mandate, any planning effort starts with determining the goals and objectives

to be evaluated (NRC 1990, Noss 1990). For the Forest Plan these goals—as

articulated by President Clinton at the beginning of the forest planning process in

1993—are to:

• Maintain and restore biological diversity

• Maintain long-term site productivity of forest ecosystems

• Maintain late-successional and old-growth forest ecosystems

• Maintain sustainable levels of renewable resources

• Maintain rural economies and communities

These goals were embodied in the Forest Plan under three conservation strategies

(FEMAT 1993, USDA and USDI 1994b): terrestrial, aquatic, and social (see Mulder

et al. 1995 for discussion of relations between these strategies; fig. 2). The primary

goal for the terrestrial strategy is “to protect and enhance habitat for late-successional

and old-growth forest related species.” The primary goal for the aquatic conservation

strategy is “to restore and maintain the ecological integrity of watersheds and aquatic

ecosystems.” The goals for the social strategy are “to provide a predictable, sustainable commodity and resource production, maximize the social and economic

benefits, and assist long-term economic development and diversification.” Because

the initial focus was to follow an incremental approach (table 1) in developing an

effectiveness monitoring program for ecological issues, only the terrestrial and aquatic

strategies have been addressed, but the approach to designing a monitoring program

would be the same for any resource issue.

Northwest Forest Plan

Terrestrial

LSOG

ACS

Ripar./aqua.

Northern

spotted owl

Marbled

murrelet

Survey -manage

biodivers ity

Fis hspp.

Figure 2—Forest plan conservation strategies and subcomponents that form the basis for the ecological part

of the effectiveness monitoring program (note that some

LSOG and ACS [aquatic conservation strategy] data

are also pertinent to the socioeconomic strategy); dashed

lines indicate potential monitoring issues (Mulder et al.

1995).

4
These conservation strategies assume successful outcomes. As such, the standards

and guidelines (USDA and USDI 1994b) needed to realize them are the primary

focus or the treatment being evaluated through the monitoring program. The Forest

Plan assumes that if the strategies are successfully implemented, then the goals will

be met. Compliance with standards and guidelines is evaluated through implementation monitoring; validation monitoring will determine the link between cause (standards and guidelines) and effect (trend). Effectiveness monitoring will establish the

status and trends for selected resources under these strategies. Therefore, there

is a strong link between these monitoring programs.

The goals form the basis for the questions that the monitoring program is designed

to answer. These goals are redefined as general monitoring questions that provide

the basis for developing more specific questions that can be addressed with monitoring data for the Forest Plan and for each individual resource (see Mulder et al.

1995 for further discussion). For example, the primary question for effectiveness

monitoring to address the success of the terrestrial and aquatic strategies is, “What

are the status and trends of the ecologically important habitat features and processes,

and populations of key species on Federal lands1 within the Forest Plan area?”

The general approach for developing monitoring questions for each resource follows

from the primary monitoring question above and focuses on the status and trends of

the resource being monitored. For example, for the northern spotted owl, the general

owl question would be, “What is the status and trend of the spotted owl population

and its habitat over the Forest Plan area?” This question is then subsequently subdivided into more specific questions. These more specific questions refine the monitoring goals, and suggest variables (or indicators) for measurement, based on the

components of the resource assumed to be responsive to Forest Plan standards

and guidelines (see monitoring modules for LSOG, northern spotted owl, marbled

murrelet, and aquatic and riparian2 for the specific monitoring questions and candidate indicators). These variables then provide the basis for developing specific

monitoring tasks. Identifying quantifiable questions, measurable indicators, and the

subsequent monitoring design and field protocols, along with an appropriate supporting infrastructure, are key to designing an implementable monitoring program

that meets agency information needs (for example, Davis 1993, MacDonald et al.

1991, NRC 1995, Vora 1997). Providing the guidelines for designing the monitoring

program and its operational components is the purpose of this document.

1 Although not a focus of this exercise, we note that many

species overlap other ownerships; information from these

lands that has a bearing on the resources being monitored is

important to interpreting monitoring results on Federal lands.

2 The effectiveness monitoring program will consist of a number of modules, referred to in this report as LSOG monitoring

module (Hemstrom et al., in press), northern spotted owl module

(Lint et al., in press), the marbled murrelet module (Madsen et

al., in press), and draft annotated outline for aquatic and riparian

monitoring (Furniss et al. 1997). Future modules on other subjects will be added to this program.

5
Considerations in

Developing the

Effectiveness

Monitoring Program

Monitoring is expected to play a major role in implementing and managing the Forest

Plan (Dwyer 1994; FEMAT 1993; USDA and USDI 1994a, 1994b). Credible and

accurate data are needed for future resource planning (U.S. GAO 1994), particularly

given these legal and political expectations. Monitoring is at the core of adaptive

management (fig. 3) and essentially synonymous with effective decisionmaking. In

other words, the more information available, the greater the flexibility to manage.

Monitoring is, however, more complex than we realize, and the Forest Plan adds a

new level of complexity to ecosystem and adaptive management. Our focus therefore

must be on identifying information that is needed and usable to address key issues;

that knowledge must build from information appropriate to the question (Persson and

Janz 1997, Vora 1997). As such, monitoring is more than taking measurements or

making observations; it also includes data management, assessment, and decisionmaking. Thus, decisionmakers should be involved in the planning as well as the

application of a monitoring program (CENR 1997, Noble and Norton 1991).

In the field of natural resources, adaptive management is still somewhat conceptual,

and we are beginning to realize that agencies are having difficulties in applying its

objectives. Although considerable literature exists about both adaptive management

(for example, Bormann et al., in press; Brunner and Clark 1997; Everett et al. 1993;

Gunderson et al. 1995; Holling 1978; Morrison et al., in press; Walters 1986), and

ecosystem management (for example, CENR 1997; Christensen et al. 1996; Duffus

1994; Franklin 1996; Grumbine 1994, 1997; papers in Haeuber and Franklin 1996),

these articles give little explanation or description about monitoring other than to note

that it is part of the management planning process. We believe that this has led to

a lack of appreciation of the complexity and difficulties in carrying out a monitoring

program and in using monitoring information in decisionmaking.

Figure 3—Adaptive management cycle where monitoring plays a

major role by taking measurements and making observations of

key resources (adapted from: Keune and Mandry 1996).

Collect data

Summarize

information

Interpret

results

Make

decisions

Actions

Monitoring

Forest Plan

resources

6
Monitoring is particularly difficult when large geographic areas are being managed.

To be successful, it needs to be a routine and integrated component of land management planning and be managed at a scale directly addressing the policies that

affect land management (CENR 1997, Marcot 1994, Olsen and Schreuder 1997).

The ability to successfully institutionalize monitoring into our day-to-day operations

not only will improve decisions but also will support management actions, make

planning more efficient and responsive, and help focus future expectations (CENR

1997, Olsen and Schreuder 1997, Schreuder and Czaplewski 1992, Vora 1997).

As Persson and Janz (1997) note, however, it is not really worth carrying out

the program, if an infrastructure to support all components is absent. Historically,

monitoring has not been effectively integrated into agency operations, and Federal

agencies do not have a good track record in using or applying monitoring programs

(for example, Bella 1997; Grumbine 1997; Hilbran 1992; Lee 1993; Morrison and

Marcot 1995; NRC 1990; U.S. GAO 1988, 1994). A formal approach to improve

the use of monitoring information in the adaptive management process is needed

(Morrison et al., in press).

Although considerable information exists in the monitoring literature, it is largely

conceptual, particularly at the scale of the Forest Plan (Chapter 2 has numerous

references). In addition, Federal resource agencies have some experience with

monitoring but mostly at a scale not really useful to the design of a large, regionalscale, multidimensional program (CENR 1997). The EMT therefore sought to apply

knowledge gained from a relatively few large-scale programs. Most concepts in

ecological monitoring have been generated by the Environmental Monitoring and

Assessment Program (EMAP) (for example, Stevens 1994; Thornton et al. 1993,

1994), and we relied heavily on the planning done for that program. Additional

insight was gained from EMAP’s Forest Health Monitoring (FHM) program (FHM

1994, Lewis and Conkling 1994) and the U.S. Fish and Wildlife Service’s (FWS)

North American Waterfowl Monitoring Program (Nichols et al. 1995), among others

(for example, International Geosphere-Biosphere Programme 1990, Programme

Center 1989, Ringold 1994).

As a result, we have identified several critical issues that must be considered as

the agencies decide on an optimal approach to monitoring for the Forest Plan.

Trend monitoring is a long-term effort, so we need to consider how to reduce the

response time in obtaining useful information. Trend monitoring is usually retrospective: thus, we need to consider how to anticipate or predict trends so that it

is not too late for management to respond. In addition, the scope and magnitude

of this program, covering parts of 3 states, 18 National Forests, and 7 BLM districts,

will require considering how to collect and manage a large amount of information

among several local and regional administrative units within and among a variety

of agencies. Monitoring at this (regional) scale also raises critical questions about

links from local to international issues, or with nonmonitoring activities (for example,

inventories) that also may use or provide important data. Addressing status and

trend questions about the Forest Plan also will require considering how to link

findings with their causal mechanisms, specifically as they relate to the standards

and guidelines and implementation monitoring.

7
Ultimately, plans to monitor at this scale and over such a long time must address the

total cost of the program, especially in times of declining agency budgets. We need

to monitor, though, to know if our management actions are successful; the more

certainty we want, the higher the cost. This is a question of risk, in knowing that the

methods proposed provide sufficient data to meet our information needs for status

and trend monitoring. Key questions then are whether we can use or increase the

usefulness of existing programs (for example, vegetation surveys), make their data

more accessible, and integrate related data gathering and assessment efforts in a

way that meets local, regional, and larger scale monitoring needs. Because we

know little about monitoring at these large scales, many of these questions lead

to assumptions that need to be tested as we improve and adapt the monitoring

program over time.

Because regional scale monitoring is complex, especially at the scale of the Forest

Plan, the program needs to be dynamic, iterative, and capable of continual modification over time (Olsen and Schreuder 1997, Vora 1997). Monitoring programs, particularly of this size, cannot remain static; they need to adapt to meet emerging or

changing needs. This “adaptive monitoring” process (Ringold et al. 1996; Ringold et

al., in press) is intended to result in improvements to the program and data that will

increase the value of the monitoring results over time, thus leading to more efficiency

and increasingly responsive decisions.

Links to Individual

Forest Management

Units

To assess the success of the Forest Plan, the effectiveness monitoring program

addresses regional-scale monitoring requirements. Individual forest management

units also conduct monitoring, but at a more local scale. Many questions have

arisen regarding the relation between the programs at these two scales: How can

the regional program assist with local-scale monitoring needs? Does the regional

program address all local information interests? What should be the role of individual

forest management units in the regional program? How can an individual management unit benefit from participating in the regional program? and Why is it important

for an individual management unit to support the implementation of the regional

program? These questions can best be answered by understanding the links from

the regional program to individual management units.

This monitoring program needed to build on and improve ongoing monitoring activities such as the Forest Service’s current vegetation survey (CVS; Max et al. 1996).

It thus will rely significantly on data obtained through the assistance of local forest

management units. The expertise of local specialists is also extremely important in

assisting the regional program with interpreting these data. Local specialists are

familiar with their data and may be able to provide information to assist with calibrating models or establishing thresholds of concern.

The regional program, on the other hand, can provide assistance and guidance

for improving local monitoring efforts. The scientific approaches developed for the

regional program—such as the monitoring design, indicator selection, or data interpretation methods—also apply to local-scale monitoring. Many of the effectiveness

monitoring questions apply directly to individual forest management units (for example, Pagel et al. 1997); only the scale of the question has changed. Although the

regional program cannot respond to all local information needs, regional-scale monitoring results can assist with local-scale interpretations by providing a regionwide

8
context for those interpretations (CENR 1997; also see fig. 4). This context should

be particularly useful in regulatory (for example, ESA) and planning assessments in

helping to meet resource goals (for example, for species and commodities).

The importance of regional monitoring to making decisions at individual forest management units, however, is indirect rather than direct. The regional program is

directed to the regional land management policies and decisions that govern local

direction. As such, monitoring information is not needed from every local management unit. Results may be used to establish new regional goals and objectives,

modify standards and guides, or focus management on emerging issues. Changes

to the Forest Plan would then be passed along to the individual forest management

units to assist them in refining their own goals and objectives, standards and guides,

or monitoring requirements through amendments to their forest management plans.

Although resources directed to the regional effort may result in diminished availability

of resources for local monitoring, regional efforts can provide important feedback to

the individual forest management units that replace or complement remaining local

monitoring efforts.

Relation to Emerging

National and

International Monitoring

Requirements

Monitoring is needed to evaluate the success of management practices on public

lands in the Pacific Northwest. Requests for monitoring information also arise at

other geographic scales, including national and international; for example, questions

are being asked about forest trends in the United States, North America, and the

world at large. Considering how effectiveness monitoring in the Northwest fits into

the picture of forest monitoring requirements for the Federal land management

agencies at national and international scales is important.

Regional question: What is the

effect of the Forest Plan on the

owl population over its range?

Forest question:

What is the effect of

a large-scale fireon

owls in the Forest?

Local question: What is

the effect of a thinning on

a known pair of owls?

Figure 4—Comparison of monitoring questions at different scales illustrating

relations and differences among monitoring questions.

9
Two significant issues are emerging as a common requirement for monitoring at

national and international scales: conservation of biodiversity and sustainable management of forests. President Clinton’s Committee on Environment and Natural

Resources recently issued their report (CENR 1997) calling for a strategy and framework for environmental monitoring and research that will enable comprehensive

assessments of the Nation’s natural resources. The strategy emphasizes the need

to increase understanding, improve information links, focus on status and trends,

be predictive, improve methodologies, increase efforts to integrate programs and

activities, and make better use of existing information.

Considerable international effort also has been focused in recent years on criteria

for assessing forest issues. The United States signed the Santiago Agreement

(Anonymous 1995) defining criteria and indicators for conserving and sustainably

managing temperate and boreal forests. Other efforts in Europe (Helsinki Process),

tropical countries (Tarapoto Proposal), and Africa (Dry Zone Africa Initiative) are

helping to develop a worldwide consensus on these topics. These criteria are now

being used to develop the Global Forest Resource Assessment 2000 by the United

Nations, and they served as the program theme for the 1997 World Forestry Congress in Antalya, Turkey (World Forestry Congress 1997). The Santiago Agreement

identifies seven criteria for assessing sustainable forest management:

1. Conservation of biological diversity

2. Maintenance of productive capacity of forest ecosystems

3. Maintenance of forest ecosystem health and vitality

4. Conservation and maintenance of soil and water resources

5. Maintenance of forest contribution to global carbon cycles

6. Maintenance and enhancement of long-term multiple, socioeconomic benefits to

meet the needs of societies

7. Legal, institutional, and economic framework for forest conservation and

sustainable management

The goals for effectiveness monitoring are consistent with this emerging national and

international framework for monitoring. For example, the first criterion above is the

primary focus of current efforts in effectiveness monitoring that also should address

some aspects of criteria 2 and 3 (see footnote 2). Planning is currently underway with

the riparian and aquatic issue (see footnote 2) and the socioeconomic resource areas

in effectiveness monitoring, and these efforts should address some aspects of criteria

4 and 6. Although effectiveness monitoring is not being designed to address all the

criteria and indicators proposed in the Santiago Agreement, it does provide a means

for conducting regional assessments that could be expanded to address additional

emerging monitoring requirements, as recommended by CENR (1997). These efforts

will be extremely useful to agency staff at the national level as they attempt to

develop a national picture or contribute to the international perspective on forest

status and trends.

10
Key Aspects of

the Effectiveness

Monitoring Program

The challenge the EMT faced was to construct an approach to effectiveness monitoring that met the goal of providing useful information (see table 1) to managers at

the local, regional, and national scales and was organized and timely. Development

of this monitoring program has been difficult, and the difficulty little understood. We

have gained considerable knowledge about the complexity of these issues, however,

as investigations have proceeded. The result has been an evolution in thought about

monitoring, what it will take to successfully monitor, and what this will mean to Federal (and other) agencies (see appendices A and E for a summary of the process).

We realized from our experience that the approaches for monitoring that have been

followed in the past were not appropriate for the Forest Plan, but that we needed to

create a more structured and effective monitoring design and operational approach.

The implications led to a realization that for adaptive management to work, a fundamental change would be needed in how agencies monitor, which will inevitably lead

to a change in institutions. A number of barriers will need to be overcome, including

technical, institutional, and philosophical. If we had started with this knowledge, our

approach could have been focused more on helping agencies understand the need

for change, which may have made it easier to interpret and understand the proposals

for Forest Plan monitoring. We recognize that time is needed to gain this understanding before agencies can fully implement a long-term, functional program meeting

their needs. As such, the transition from the current way of doing business to a more

organized and consistent approach will not be easy and cannot be done overnight.

A concerted and coordinated effort by all participating agencies will be needed to

fully fund, implement, and institutionalize the process into our operational culture—a

national as well as a regional question. The approach, described in this report and

summarized below, is intended to provide the framework for accomplishing this goal.

The basic scientific premise underlying the proposed program is to implement a predictive and integrated habitat-based approach to monitoring (fig. 5) that will produce

useful and timely results more efficiently and cost-effectively than past programs

have. Developing an approach to monitoring at any scale requires careful planning;

thus, we began with emphasis on the scientific foundation for monitoring design (see

Chapter 2), based on the use of conceptual models supported by ecological theory

and empiricism. This approach is critical to selecting the focal point or points for

monitoring, thus leading to opportunities for integrating programs and reducing

costs. It allows us to construct a program that builds from a base information set;

for example, by using the LSOG database to evaluate questions about spotted

owl habitat. The approach also helps identify problem areas where pilot studies

or research to test underlying assumptions or develop new methodologies will be

critical to the long-term success of the program; concurrent research is a necessary

part of a monitoring program (CENR 1997, Olsen and Schreuder 1997).

11
Given the great diversity of species—plant and animal, vertebrate and invertebrate—

monitoring all biotic components of managed ecosystems is clearly impossible. It

also would be prohibitively expensive. As such, the foundation of our approach to

effectiveness monitoring for the Forest Plan is to initiate a gradual transition from

an intensive, individual species-resource focus to a more extensive, ecosystems

approach (Chapter 3). This transition assumes identification and measurement of

surrogate variables to allow reliable inferences about the integrity of the primary

resources of the Forest Plan. Conceptual models were used to construct approaches

for collecting and managing monitoring information meeting the information needs

of the Forest Plan about specific resources, such as LSOG, northern spotted owl,

marbled murrelet, and aquatic and riparian (see footnote 2).

The focal point for measuring success of the monitoring program will be on periodically producing interpretive reports that respond to questions about the status

and trends of selected resources, beginning with the initial monitoring modules (see

footnote 2). The EMT realized that the complexity of the design, and the scope and

magnitude of the program needed to produce these reports required an operational

context for data collection and management, and assessment and reporting to meet

land management and regulatory agency information needs for adaptive management

(see Chapter 4). Although the modules for monitoring specific resources emphasize

data collection, the focus is on developing an organized, institutionalized approach

to managing and assessing information to enhance the ability of managers to make

effective and efficient decisions locally (that is, for a Forest or province) and regionally. Data management, assessment, reporting, and the supporting infrastructure are

as important to the success of a monitoring program as the underlying science and

the design for collecting data (Persson and Janz 1997).

Monitoring

program

Forest

vegetation

habitat

trend

monitoring

Species

population

trend

monitoring

Predictive

trend

modeling

Figure 5—Basic components for effectiveness

monitoring of ecological resources.

12
Moving toward implementing an effectiveness monitoring program requires decisions

to establish, implement, and manage a large-scale, multifaceted, and interagency

monitoring program (see Chapter 5). The decisions begin with an understanding of

the goals of the program, the implications of implementing a program of this scale

and magnitude, and the structure and organization needed to manage a comprehensive program. Answering questions about establishing monitoring as a formal

resource program within the participating agencies with long-term base funding are

essential to the success of the program. Necessary steps include developing annual

work plans, multiyear budget allocations, and staffing assignments to operate the

program. Institutionalizing monitoring into agency operations is critical (CENR 1997,

Olsen and Schreuder 1997, Schreuder and Czaplewski 1992, Vora 1997).

The resulting program will provide greater benefits than do existing activities because

it is constructed as a fully integrated process to provide reliable information more

efficiently and cost-effectively; that is, it is designed to yield results. The approach to

monitoring design that we describe also provides the basis for future modules that

may address other important resources (for example, socioeconomic, tribal, surveyand-manage species,3 or other species associated with late-successional or aquatic

ecosystems), and it is intended to be a template for managers in directing future

monitoring, whether for effectiveness monitoring or other objectives. Because of the

comprehensive approach to design and application described here, the program

also will make a contribution to experience in the application of monitoring, and

thus should have long-term benefits to future monitoring and associated resource

management activities in this region and elsewhere.

Many steps will be needed to carry out the monitoring program and achieve longterm success; these additional steps, which include research, are identified in this

report. The approach allows opportunities to ensure that an appropriate and scientifically based program is rigorously tested at each step. Although this statement

may give the impression that significant questions about monitoring at this scale

may warrant delay or smaller scale testing as a first step, following up on these

issues is a long-term, but necessary exercise (Olsen and Schreuder 1997). As

such, the program is structured to respond to emerging needs and improvements

adaptively, so that we can be pragmatic and move ahead to implement a program

of this complexity with minimal concerns.

3 The ROD (USDA and USDI 1994b) identifies a large group

of organisms, referred to as “survey-and-manage species,”

that require special management. Concern has been expressed

about the high cost of monitoring these species as well as other

aspects of biodiversity. We believe that monitoring resources,

such as survey-and-manage species, as a biodiversity component of the LSOG and aquatic and riparian monitoring

programs should be possible by tracking trends in forest and

riparian habitats and evaluating the link to species-habitat

requirements through research or validation monitoring.

13
Focus of This

Report

The intent of this report is to explain the strategy for effectiveness monitoring,

describe the scientific framework underlying the strategy, and establish the guidelines for monitoring design and program implementation and management (fig. 6).

The purpose of Chapter 1 has been to provide background and summary information

on the need for an effectiveness monitoring program for the Forest Plan. Chapter 2

provides a technical description of the underlying science to be considered when

establishing the monitoring program. The intent is to clearly define the scientific

approach for a monitoring program. This approach, as applied to effectiveness

monitoring, is presented in Chapter 3, which focuses on the use of habitat as a

surrogate for measuring biotic populations. This foundation, as described in Chapters

2 and 3, is essential to obtaining usable and credible information for adaptive management under the Forest Plan. Chapter 4 describes the structural components of

an operational program. The intent is to provide a context for implementing and managing a resource program of this magnitude and importance so that the collected

information can be readily and efficiently used. Chapter 5 identifies the strategy or

steps needed to implement, manage, and fund the program.

This report is provided to help people understand and to give context to the specific

monitoring plans for each resource monitored under the Forest Plan. The full set of

documents, including subsequent implementation plans, field manuals, and annual

work plans, are intended to function as integrated guidance to Forest Plan monitoring.

Acknowledgments We especially thank Timothy Lewis, Bruce Marcot, Loyal Mehrhoff, Martin Raphael,

and Paul Ringold for their ideas and comments for this chapter.

Figure 6—Current and potential future modules of the effectiveness

monitoring program for the Northwest Forest Plan (dashed lines

indicate potential modules; S/M (survey-and manage species) and

biodiversity includes terrestrial and aquatic and riparian species.

LSOG

monitoring

plan

Nor. spot. owl

monitoring

plan

Marb. murr.

monitoring

plan

S/M-biodiv.

monitoring

plan

S/M-biodiv.

monitoring

plan

Other future

monitoring

plans

Tribal

monitoring

plan

Socioecon.

monitoring

plan

Fish spp.

monitoring

plan

Rip/Aq

monitoring

plan

Strategy and design

for effectiveness

monitoring

14
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U.S. General Accounting Office [U.S. GAO]. 1988. Environmental Protection

Agency: protecting human health and the environment. GAO/RCED-880-101.

Washington, DC.

U.S. General Accounting Office [U.S. GAO]. 1994. Ecosystem management:

additional actions needed to adequately test a promising approach. GAO/RCED94-111. Washington, DC.

Vora, R.S. 1997. Developing programs to monitor ecosystem health and effectiveness of management practices on Lakes States National Forests, USA. Biological

Conservation. 80: 289-302.

Walters, C. 1986. Adaptive management of renewable resources. New York:

MacMillan Publishing Co. 374 p.

World Forestry Congress. 1997. Proceedings of the 11th World Forestry Congress;

[year unknown] October 13-29; Antalya, Turkey. Rome: United Nations, Food and

Agriculture Organization: Vol. 1.

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Chapter 2: Conceptual Basis for

Designing an Effectiveness

Monitoring Program

Barry R. Noon, Thomas A. Spies, and Martin G. Raphael

Scientific Basis for

Monitoring

What Is Monitoring?

Monitoring is the “measurement of environmental characteristics over an extended

period of time to determine status or trends in some aspect of environmental quality”

(Suter 1993:505). The challenge in this definition, and the topic of this chapter, is to

clearly understand why monitoring is an important activity, to decide which characteristics of the environment to measure, to determine what information these characteristics indicate about environmental quality, and to use that information to make

better management decisions about the Forest Plan.

Monitoring is purpose oriented (Goldsmith 1991). In general, monitoring data are

intended to detect long-term environmental change, provide insights to the ecological consequences of these changes, and to help decisionmakers determine

if the observed changes dictate a correction to management practices. Monitoring

is conducted at regular intervals to assess the current status and the time trend

in various environmental attributes. By its very nature, monitoring is a dynamic

exercise; that is, it is a continuing activity and its temporal span may be indefinite.

The time frames for monitoring programs are frequently unspecified, because human

behavior and continuing human population growth lead to ongoing environmental

change with unexpected ecological events as unavoidable consequences.

21
In the following discussion, environmental attributes are broadly defined to include

any biotic or abiotic feature of the environment that can be measured or estimated.

The convention is to refer to the measured attributes as “indicators,” under the assumption that their values are somehow indicative of the quality, health, or integrity of

the larger system to which they belong (refer to definitions in EMAP 1993, Hunsaker

and Carpenter 1990).

The most common reason to monitor a specific indicator is to detect differences in

its value among locations at a given moment (status), or changes in value across

time at a given location (trend). Changes in the value of an indicator are useful and

relevant to the extent that they provide an early warning of adverse changes to an

ecosystem before unacceptable loss has occurred. Trend, viewed as the estimated

time trajectory of a state variable (a variable that describes some fundamental attribute of the system), is particularly relevant because even if the value of an indicator

is currently acceptable, a declining (or increasing) trend may indicate a trajectory

towards system degradation, or an undesired state.

The task of detecting and recognizing meaningful change is complex because natural systems are inherently dynamic and spatially heterogeneous. Further, many

changes in space and time are not a consequence of human-induced effects, and

many are not amenable to management intervention. For example, at least three

kinds of change are intrinsic to natural systems: stochastic variation, successional

trends after natural disturbance, and cyclic variation. Assuming that sustained ecosystems maintain these dynamic variations with predictable bounds of variation

(Chapin et al. 1996), management intervention may be appropriate even when

change is not human induced. For example, developing an underlying structural

model that predicts the expected magnitude of change in state variables arising

from natural variation may be possible. Values of indicators could then be viewed

in the context of deviations from expectations based on the structural models. Repeated observations of indicator variables whose values appeared “out of range”

could trigger a management response.

Extrinsically driven changes to biological indicators that arise as a consequence

of some human action are of most interest to environmental monitoring programs.

Concern arises when extrinsic factors, acting singly or in combination with intrinsic

factors, drive ecosystems outside the bounds of sustainable variation. Thus, one

key goal of a monitoring program is to discriminate between extrinsic and intrinsic

drivers of change; that is, a mechanism is needed to filter out the effects of expected

intrinsic variation or cycles (noise) from the effects of additive, human-induced patterns of change (signal).

Intrinsic and extrinsic drivers of change can, for the most part, be collectively referred

to as “disturbances events.” Disturbances alter processes or act as physiological

disruptors that elicit a response from the biota; they generate a change in the value

of the state variables that characterize an organism or an ecosystem. The term

“stressor” is used to refer to disturbance events that result in significant ecological

effects. These effects can be either positive or negative; however, our focus is

usually on those resulting in undesired outcomes. In this context, stressors are

22
considered as the proximate causes of adverse effects on an organism or system.

This terminology is consistent with the literature on monitoring (for example, Suter

1993). The focus is on stressors arising from human activities because they are

amenable to management intervention and changes in policy. Further, we focus on

stressors that cannot be incorporated within the natural disturbance dynamics of a

system, exceed the resilience of the system, and drive an ecosystem to new state.

Stressor effects are evaluated in the context of induced changes in one or more

indicators. The magnitude of indicator change that could generate a management

response, however, is difficult to determine a priori. This uncertainty arises primarily

from an incomplete understanding of the dynamics of ecosystems and the bounds

of variation to which they are resilient. Interpretation of the significance of changes

in the value of an indicator is also complicated by nonlinear, cause-effect relations

between the indicator and its stressor(s). The assumption of linearity implies that

marginal increases in the magnitude of the stressor generate fixed, marginal changes

in the value of the indicator. Such assumptions fail to recognize the fundamental

nonlinearity of most ecological systems (see Jones and Lawton 1994 for examples).

The real danger for monitoring programs, however, is that assumptions of linearity

fail to acknowledge the possible existence of thresholds. Thresholds are regions

of change in the value of a stressor that generate precipitous declines in the value

of the indicator or, more seriously, the larger ecosystem. A familiar analogy is an

acid-base titration in analytical chemistry. Increasing acidity (the stressor) is indicated by changes in color (the indicator) of the liquid, but the change in color is not

uniform with marginal increases in acidity; rather, the change is precipitous when the

buffering capacity (threshold) of the liquid has been surpassed.

A lesser known, but extremely relevant example in public land management, considers the effects of habitat loss and fragmentation on the extinction process. Loss

of some area from relatively continuous habitat may have no effect for some time.

But, at some point, landscape connectivity is lost, and populations become isolated

and vulnerable to stochastic processes (Opdam et al. 1993). Computer simulations

of these scenarios suggest that critical threshold amounts and distribution of habitat

exist, below which species populations rapidly decline (Lamberson et al. 1992, Lande

1987).

Why Monitor? The ultimate rationale for monitoring arises from the fact that long-term human welfare and environmental integrity are inseparable. Monitoring is usually justified in the

context of a more immediate goal or mandate; however, on multiple-use public lands,

management actions are subject to many environmental standards. The public demands information about whether these standards are being realized and resources

sustained; for example, monitoring is mandated on National Forest lands to ascertain

the degree of compliance with the population viability requirement of National Forest

Management Act (NFMA) and with minimum water quality standards of the Clean

Water Act of 1972, as amended. Even for lands reserved from resource extraction

and multiple use, such as the National Parks, compliance with the broad mandate

to sustain “wild” resources for the enjoyment of future human generations must be

assessed. In this document, we are developing a monitoring program for the Forest

Plan to determine whether the goals and objectives of that plan are being met.

23
Determining compliance with a monitoring goal requires a predetermined standard

or norm for comparison. The degree of deviation of the indicator from its desired

value serves as a signal of noncompliance or a measure of environmental degradation. Standard or benchmark values for indicators are particularly important when

monitoring is part of a large restoration project. In highly degraded ecosystems (for

example, coastal watersheds in the Pacific Northwest [Bisson et al. 1997, Reeves

et al. 1995]), some time may elapse before indicator values begin to approach the

standard, but evidence that the indicator is changing in the direction of the benchmark value is evidence that the restoration effort is working.

One way to establish the benchmark value of an environmental indicator is to refer

to documented historical values or to conduct preliminary, baseline monitoring of a

nonaffected (“pristine”) system. Given the scarcity of truly pristine systems, however,

benchmarks may have to be based on some concept of a “desired condition” (see

discussion of this concept in Bisson et al. 1997). Therefore, in the absence of reference systems, some other method must be used to generate expected values or

time trajectories of indicator variables.

In addition to assessing compliance, environmental monitoring programs have great

value as early warning systems. By providing measures, in the early stages of decline, of those attributes indicative of ecological change, monitoring can result in

prompt intervention before unacceptable environmental losses occur. Note, however,

that compliance monitoring and early warning monitoring can lead to selection of very

different indicators. A simple example will demonstrate this difference. On a parcel

of public land, the ESA may require compliance monitoring for a top-level, vertebrate

predator, such as the northern spotted owl. The life history of this species (long lived,

high survival rate, low fecundity, high site fidelity) may introduce lags in its response

to environmental change, however, and thus make it a particularly poor choice as an

early warning indicator of all but large-scale changes in old-growth ecosystems.

Thus monitoring, whether for compliance or early warning, is undertaken to ascertain

whether the current state of the system matches the expected norm or lies within

some acceptable confidence region about the norm. If monitoring results indicate

that conditions lie outside the acceptance region, then some specific attribute of

land management practice or resource policy should be changed. Alternatively, the

information from monitoring can be used to investigate the response of the system

to specific management actions. This information will allow the question, “Is the system responding as predicted?” to be addressed.

What Can a Monitoring

Program Tell Us?

Before a monitoring program can be developed for the Forest Plan or any given

management unit (for example, National Wildlife Refuge, state park, National Forest),

understanding and agreement on what environmental monitoring is must be reached.

What are realistic goals for a monitoring program? What biological insights can and

cannot be inferred from a monitoring program. How are the costs of a monitoring

program justified?

24
To determine if a monitoring program is, by itself, adequate to assess attainment of

management objectives, what can and cannot be legitimately inferred from the results

of monitoring must be understood. A logical first step, often referred to as “implementation monitoring,” is to determine if the management guidelines or environmental

regulations have been implemented. Given implementation, a monitoring program

can help to evaluate the effectiveness of current management practices, develop a

predictive understanding (in the form of one or more testable hypotheses) of why an

environmental indicator is changing, and decide when more active management or

intervention is required.

If the purpose of the monitoring program is to provide an early warning of ecosystem

decline (or signs of improvement), then its success depends on having selected an

appropriate indicator or indicators, and knowledge of how much change in the value

of the indicator signals a significant biological change. By itself, however, a monitoring

program cannot unambiguously determine the cause of a change; help decide on

how much change is acceptable—that is, whether the observed change is still within

the range of acceptable variation; decide on threshold values of the indicator that

trigger specific management actions; or avoid false alarms—that is, concluding the

state of the system has changed significantly when no meaningful change has

occurred.

Because changes in the status of an indicator are of limited value without evidence

of causation, cause-effect relations are best established by concurrent assessment

of suspected ecosystem stressors. The second and third limitations are largely research problems; minimizing these deficiencies clearly demonstrates the complementary nature of research and monitoring programs. The last limitation is, to varying

degrees, unavoidable. For a fixed sampling effort, limiting false positives (type I

errors) occurs at the cost of increasing the likelihood of type II errors (that is, failing

to detect a significant biological effect). The tradeoff between these risks is determined by which error is considered most important to avoid.

The Legacy of

Environmental

Monitoring Programs

Monitoring to estimate the viability of an individual species or a group of related

species is more straightforward than assessing the integrity of an entire ecosystem.

Therefore, it is not surprising that the few examples of successful, long-term monitoring programs that exist have a narrow focus on specific taxa. Arguably, the best

example of such a program is the North American waterfowl monitoring program

administered by the U.S. Fish and Wildlife Service (Nichols et al. 1995).

Despite the obvious value of holistic environmental monitoring, few examples exist

of successful monitoring programs at the ecosystem scale. Unfortunately, little evidence supports the idea that such programs have contributed to informed management decisions, or proved valuable in averting biological crises (NRC 1990, U.S.

GAO 1988). In fact, the most ambitious (and expensive) monitoring program to date,

EMAP, has little tangible evidence of success and has been heavily criticized both

scientifically and technically (NRC 1990, 1994a, 1994b, 1995). Given the obvious

importance of knowledge of the status and trends of the Nation’s natural resources,

and the integrity of the ecosystems that provide these resources, why have monitoring programs contributed so little to environmental decisions or policy formulation

at the ecosystem scale?

25
One fundamental reason for consistent failure is that monitoring costs are perceived

by managers and the public to be prohibitively high, so there is reluctance to commit

to implementation. In addition, environmental monitoring programs often are discussed in abstract terms, have little theoretical foundation, try to measure too many

attributes, have vague objectives, and have no institutionalized connections to the

decision process. The result has been a shallow comprehension of the need for,

and components of, an effective monitoring program. Further, almost all previous

programs have been given low priority, seldom have been fully implemented, and

have been insufficiently funded. In times of budget reductions, monitoring programs

often are the first to be eliminated.

The limited investment in environmental monitoring by most public land management

agencies demonstrates its low priority and lack of appreciation. One example is in the

U.S. Department of Agriculture. To assess whether resource management practices

are maintaining biological diversity on National Forest lands, environmental monitoring

is required under NFMA; however, a review of existing Forest Service monitoring

programs indicates that they often exist in name only and are funded at a fraction

of programs for resource extraction. And what monitoring has been done is often

ad hoc, has little foundation in ecological theory, or fails to follow the fundamental

statistical principles of sampling and estimation. Those few National Forests that

have implemented scientifically defensible monitoring programs have not developed

a formal mechanism to link the results of monitoring to management decisions

(Morrison and Marcot 1995). The primary reasons for the failure of monitoring

programs are:

• Minimal foundation in ecological theory or knowledge

• Little logic to support selection of indicators

• No necessary understanding of causation

• Trigger points not identified

• No connection to decisionmaking

To gain institutional support, the concept of environmental monitoring must become

less abstract, its purposes more relevant, and its contributions more apparent. At a

minimum, a defensible monitoring program should do the following:

1. Clearly state management goals and objectives, emphasizing how periodic

information about the status of the resources is needed for informed management decisions.

2. Provide a clear statement of why the monitoring program has value, what information it will provide, and how the interpretation of that information will lead to a

more responsible management response.

3. Establish the relation between those factors that may compromise the management

goals and their ecological expression. This action is best accomplished by developing

a conceptual model of how the system works and how it will be affected by external

stresses.

26
4. Provide a clear exposition of the logic and rationale underlying the selection of the

environmental attributes (indicators) to be measured. Recognizing that every species

or physical or biological process of interest cannot (and need not) be measured, on

what basis should attributes to be monitored be selected from among all possible

candidates? Inherent in this step is the need to select indicators that can be

measured simply and cost-effectively.

5. Outline the sampling design and methods of measurement to estimate the value

of the indicator variable. This element includes, but should not be limited to, the

sampling and measurement protocols.

6. Ensure statistical precision of the measurement protocols. For example, the

sampling design must address the necessary precision of indicator estimation to

detect a given magnitude of change, and the likelihood of detecting this change

should it occur (for a good example, see Zielinski and Stauffer 1996).

7. Include those procedures that connect the monitoring results to the decision

process. For example, determine what magnitude of change in a given indicator

should trigger a management response, and what the response or responses

should be?

Most existing monitoring programs frequently omit the first, second, third, sixth, and

seventh elements or address them only superficially. Most attention has been given

to the fifth element, and even here, the focus has been narrow, often restricted to an

exhaustive discussion of the sampling and measurement protocols. It is not unusual

to discover that great thought and deliberation have gone into how, when, and where

to measure a given indicator, but little discussion of why that particular attribute is

being measured or what magnitude of change needs to be detected (that is, issues

of monitoring design and management decisionmaking).

Prospective (Predictive)

or Retrospective

Monitoring?

To be most meaningful, a monitoring program should provide insights into causeand-effect relations between environmental stressors and anticipated ecosystem

responses; that is, prior scientific knowledge and an understanding of the factors

likely to stress ecosystem functions should be incorporated into the selection of

variables to measure and the sampling design (NRC 1995). Indicators should be

chosen based on a conceptual model clearly linking stressors and indicators with

pathways that lead to effects on ecosystem structure and function (NRC 1995).

This process enables the monitoring program to investigate the relations between

anticipated stressors and environmental consequences and provides the opportunity

to develop predictive models.

Prospective and retrospective studies focus on determining if a cause-and-effect relation exists as postulated. In epidemiology, a prospective study begins by selecting

cases with and without a suspected antecedent cause and following cases to determine if the anticipated effect is associated with the antecedent cause. Conversely, a

retrospective study begins by selecting cases with and without an effect and tracing

back the cases to determine whether the effect is associated with the suspected

antecedent cause. Both approaches have their foundation in identifying a supposed

causal relation between an antecedent cause and its expected effect. The two perspectives differ only about whether the study begins with a set of cases with or

without a suspected antecedent (stressor) or with a set of cases with or without

an anticipated effect.

27
The NRC report (1995) states that “retrospective or effects-oriented monitoring is

monitoring that seeks to find effects by detecting changes in status or condition

of some organism, population, or community,” and “predictive or stress-oriented

monitoring is monitoring that seeks to detect the known or suspected cause of an

undesirable effect (a stressor) before the effect has had a chance to occur or to

become serious.” Effects-oriented monitoring does not require knowing a causeeffect relation, but if stressors and effects are both included in the monitoring, then

the program permits analyses directed at establishing cause-effect relations. Stressoriented monitoring assumes that a cause-effect relation is known. See Thornton et

al. (1994) and Suter (1993) for additional discussions. A specific effort must be made

to gather cause-effect data; this effort was not part of the current assignment for

effectiveness monitoring of the Forest Plan (FEMAT 1993, USDA and USDI 1994)

but rather left for future research.

The emphases chosen for effectiveness monitoring of the Forest Plan may best

be described as anticipatory monitoring and predictive effects monitoring. Each

incorporates supposed causal relations between effects and stressors through the

judicious selection of indicators. Anticipatory monitoring starts with a characterization

of threats (stressors) to the ecological integrity and ecosystem functioning (effects)

of the management unit. A conceptual model then outlines the pathways from the

stressor(s) to the supposed ecological effects. Attributes that indicate the anticipated

changes in specific ecological conditions are then selected for measurement. The

ultimate success of this approach depends on the validity of the assumed causeeffect relations among the stressor(s), their ecological effects, and the selected

indicators of stress. Anticipatory monitoring does not require monitoring ecological

condition or assessment endpoints of interest. It attempts to detect effects as they

are occurring by measuring anticipatory indicators, rather than describing effects after

they have occurred. An advantage of this approach at the local and regional scales

is that the emphasis on anticipated cause-effect relations allows an earlier and more

focused management response to environmental change. Given that all potential

stressors cannot be identified, complete reliance on this approach is not without

some risk. A possibility exists of failing to detect the ecological effects of significant

but unanticipated stressors.

Predictive effects monitoring incorporates the basis of anticipatory monitoring and

extends it to predicting ecological effects. Not only does this extension require the

assumption of an assumed cause-effect relation but it also requires developing a

predictive model for the relation. As an example, an anticipatory monitoring program

could be established to measure the vegetation characteristics necessary to support

northern spotted owls. Based on these characteristics, a model is developed to predict

the probable distribution or population status of spotted owls. The model may assume

the vegetation characteristics remain as measured, hence predicting presence under

steady-state conditions, or it may predict future vegetation characteristics under natural growth or harvest assumptions to allow a prediction of population trend. In this

case, predictive monitoring focuses on estimating the future effects of changes in

habitat. Initial phases of a predictive monitoring program would include additional

monitoring of the effect of interest (that is, population response) to construct the

predictive models and establish their reliability. Subsequently, the direct monitoring

of owl populations would be conducted periodically as required for model validation

and model refinement.

28
Challenges of

Monitoring Ecological

Systems

Ecosystems are poorly understood, complex systems subject to stochastic variation

and unpredictable behaviors. In addition, the process of ecosystem adaptation and

accommodation to stress is not well explored scientifically (Rapport and Reiger

1995). Given this reality, it is not surprising that the task of monitoring ecosystems,

and drawing reliable inferences to system integrity before irreversible degradation,

has proved such a daunting task. Incomplete understanding of ecosystem process

and function, and limited ability to predict system response to stress will remain for

the foreseeable future. As a consequence, research and monitoring are inextricably

entwined and mutually dependent; a successful monitoring program will require a

parallel research program.

Despite the complexity of ecosystems and the limited knowledge of their functions,

to begin monitoring, we must first simplify our view of the system. The usual method

has been to take a species-centric approach, focusing on a few high-profile species;

that is, those of economic, social, or legal interest. Because of the current wide (and

justified) interest in all components of biological diversity, however, the species-centric

approach is no longer sufficient. This wide interest creates a conundrum; we acknowledge the need to simplify our view of ecosystems to begin the process of monitoring,

and at the same time we recognize that monitoring needs to be broadened beyond

its usual focus to consider additional ecosystem components.

To address this dilemma, we need information about a small number of surrogate

variables whose status and trend provide insights to the integrity of the larger system.

This is the logical basis for the indicator variable concept. But, no body of ecological

theory or empiricism that will unambiguously tell us what to measure currently exists.

To develop a step-down process to move towards a solution requires that we begin

to build on experience and existing ecological knowledge and theory.

One step toward a comprehensive but simplified approach to ecosystem monitoring

is to focus on the structural and composition elements of the landscape that express

underlying process and function (fig. 7). Applying this logic to managing public lands,

such as through the Forest Plan, suggests an emphasis on living and nonliving elements that collectively define the habitat of a species. Thus, an assessment of the

status and trend of habitat types and key habitat elements may be a useful surrogate

set of variables to substitute for the direct monitoring of numerous biotic populations.

Indicators may vary, however, depending on the class or classes of organisms being

addressed.

Processes Integrates Predicts Biodiversity Structure and

composition

Figure 7—The conceptual model that is the basis for identifying indicators from structural

and compositional landscape elements. We assumed that these elements reflect underlying

ecological processes and allow predictions of the biodiversity response.

29
Key Steps in

Designing the

Monitoring Program

A key premise of our effort is that improving the framework for monitoring, specifically grounding the process in ecological theory and empiricism, will result in a

defensible and useful monitoring program for the Forest Plan. The value of monitoring will become more apparent because the process will become less abstract

and better focused; the relevance of the indicators to the integrity of the larger

system will be more obvious; and a better theoretical framework should prove to be

more cost-effective. We therefore sought to apply the concepts presented here by

following a logical process to creating a monitoring program. The steps we followed

are summarized in table 2.

These represent, in a step-down fashion, the key components in the design of the

effectiveness monitoring program for the Forest Plan. Figure 8 is a model to guide

the reader through each step discussed in the following sections.

The principles and concepts discussed in this chapter are general, and they can

be applied to environmental monitoring programs regardless of spatial scale: local,

regional, or national. The process and recommendations expressed here, however,

are targeted to regional-scale monitoring programs, as required for the Forest Plan.

Monitoring programs to evaluate the effects of specific local projects are more amenable to the experimental designs of environmental impact studies (see Schmitt and

Osenberg 1996).

Our intent here is to provide a brief overview of the scientific basis underlying development of the monitoring proposals by the team; a more thorough understanding can

be gained from the literature we applied to this effort. Because monitoring is an ongoing, active process, however, implementing these components will never be completely finished. These components must constantly be revisited and revised as

scientific knowledge is acquired and as the threats to the integrity of ecosystem

functions change.

Table 2—A sequential list of key issues to address in the design

of a prospective monitoring program

Steps Design topics

1 Specify goals and objectives

2 Characterize stressors and disturbances

3 Develop conceptual models—outlines the pathways from stressors

to the ecological effects on one or more resources

4 Select indicators—detects stressors acting on resources

5 Determine detection limits for indicators—to guide sampling design

6 Establish “trigger points” for management intervention

7 Establish clear connections to the management decision process

Specify

goals

Identify

stressors

Develop

conceptual

model

Select

indicators

Establish

sampling

design

Define

response

criteria

Ensure links to

decisionmaking

Figure 8—Steps in the design of a monitoring program.

30
No universal set of goals characterizes a “quality” environment, assures the maintenance of biological diversity, and applies to all ecosystems experiencing a diversity

of stresses. No single benchmark condition applies to all ecosystems. The concept

of ecological integrity (Karr 1991), however, serves as a broad unifying concept and

provides a universal set of goals for ecosystem management. Ecological integrity

has been defined as the capacity to support and maintain a balanced, integrated,

adaptive community of organisms having a species composition, diversity, and

functional organization comparable to that of natural habitats of the region (Karr

1987, 1991, 1996). The key aspect of this definition is that it ties ecological integrity

to evolution—the ability of the biota to persist by way of adaptive responses to environmental variation. In this broad context, the goal of monitoring of the Forest Plan is

to provide the information needed to answer the question, “Are current management

practices maintaining the ecological integrity of the ecosystem, including human uses

of these resources for needed goods and services?”

A relevant example of a human-induced disturbance leading to a loss of biological

integrity is silviculture as practiced in the forests of the Pacific Northwest from 1950

to 1990. The conversion of old-growth coniferous forests to intensively managed

forests has resulted in significant changes in forest structure, decreased biological

diversity, and a loss of resilience to natural disturbance events such as fire and

windthrow (Spies 1991; Spies and Franklin 1991, 1995). The ecological integrity

of these forests has been compromised.

Invoking the concept of ecological integrity puts the problem in the context of an

ecological system composed of integrated biological components (individual organisms, populations, species, and communities) connected by exchanges of matter

and energy. This model represents the traditional notion of an ecological hierarchy,

and it will be a comfortable starting point for most ecologists, though it may not be

a good starting point for decisionmakers responsive to societal, not necessarily biological, values. A connection, therefore, must be made between measured biological and physical attributes and what society values. This link requires a conceptual

framework identifying the relations between societal values (the ultimate assessment

endpoints for an environmental monitoring program) and biotic integrity.

The Forest Plan (FEMAT 1993, USDA and USDI 1994) provides general direction

for management in the form of standards and guidelines, and makes qualitative predictions of anticipated changes in the forest ecosystem, given their implementation.

To develop a monitoring program for the Forest Plan, the general management objectives and predictions of the Forest Plan must first be refined into a set of specific

monitoring questions. The monitoring program is designed to answer these questions,

restated as parameters to be estimated or formal hypotheses to be tested with monitoring data.

Specify

goals

Develop

conceptual

model

Identify

stressors

Select

indicators

Establish

sampling

design

Define

response

criteria

Ensure link to

decisionmaking

State the Goals of the

Monitoring Program

31
A key ecological resource identified in the Forest Plan, and mandated for monitoring

in subsequent legal decisions (USDA and USDI 1994), is late-successional and oldgrowth forests (Hemstrom et al., in press [see footnote 2, Chapter 1]). Monitoring

goals, stated in the form of questions that can be addressed with monitoring data

from late-successional forests, include:

• What are the amounts and distribution of forest age classes (including LSOG) at

the landscape scale?

• What are the patch size distribution, patch interior area distribution, and interpatch

distance distribution for LSOG at the landscape scale?

• Based on stand sample data, what changes have been produced by stressors in

the amount and distribution of forest age classes, beginning with data collected for

the 1993 FEMAT analysis?

• What are the effects of silvicultural treatment and salvage logging on LSOG

structure and composition at the stand scale?

• Are the standards and guidelines leading to an increase in the amount and

distribution of late-successional forest?

These questions refine the monitoring goals and suggest attributes (indicators) to

measure. Measured attributes are those components of late-successional forests

assumed to be indicative of the successful implementation of the standards and

guidelines of the Forest Plan.

This step usually will take the form of identifying the anticipated extrinsic environmental stressors that may compromise the integrity of the ecosystem and its component species and resources. From previous studies of disturbed ecosystems (for

example, Delcourt et al. 1983), we know if the effects of an extrinsic stressor exceed

the resilience or adaptational limits of the ecosystem, change occurs, the ecosystem

moves to a new state, and the management goal may be compromised. Stressors,

as envisioned here, can be both human-induced and “natural.” Examples include

(see Barber 1994):

• Loss of late-successional habitat by fire

• Alterations of hydrologic cycles because of dams or water diversions

• Reduction, loss, or fragmentation of critical habitat

• Increased sediment loads to streams after storm events

Identify Stressors Relating

to Management Goals

Specify

goals

Develop

conceptual

model

Identify

stressors

Select

indicators

Establish

sampling

design

Define

response

criteria

Ensure link to

decisionmaking

32
• Overharvest of game species

• Changes in the transport of minerals and nutrients resulting from road construction

• Increased pollution from point sources or diffuse input of toxins

To retain the possibility of establishing cause-effect relations from the monitoring

program, the status of the stressor also must be periodically estimated; that is, to

infer causation from an observed change in the value of an indicator requires concurrent estimates of the status of the indicator and the magnitude of the supposed

stressor.

To aid the process of indicator selection, identifying the ecological resource(s) likely

to be affected by a given stressor, is important. A resource is broadly defined as an

ecological entity subject to stressor effects. In practice, a resource is usually a key

component of the larger ecosystem or management unit. Examples include freshwater lakes and montane meadows in National Forests. A resource can be either

discrete or extensive (EMAP 1993). Examples from the Forest Plan of extensive

resources include late-successional forests and aquatic-riparian ecosystems; discrete

resources include the northern spotted owl and marbled murrelet. Establishing the

functional relations between stressors (natural or human-induced) and resources is

an essential first step in developing the conceptual model.

To select indicators that reflect underlying ecological structure and function requires

well-developed conceptual models of the resources of concern (Barber 1994; NRC

1990, 1995). The conceptual model outlines the interconnections among ecosystem

resources (key system components), the strength and direction of those links, and

the attributes that characterize the state of the resources. The model should demonstrate how the system works, with particular emphasis on anticipated system

responses to stressor input. The model also should indicate the pathways by which

the system accommodates natural disturbances and how the system may acquire

resilience to disturbance. These processes could be portrayed by illustrating the

acceptable bounds of variation of system components, and normal patterns of

variation in input and output among the model elements.

As a general goal, management will strive to maintain ecological processes. These

functions, however, are often difficult or impossible to measure directly. Conceptual

models should identify structural and compositional elements of the resources affected by, and affecting, the underlying processes. A heuristic device to guide the

model development would link process and function to measurable aspects of structure and composition. These elements, in turn, can be used to make predictions of

expected biological response (see fig. 7).

Develop a Conceptual

Model Linking Relevant

Ecosystem Components

Specify

goals

Develop

conceptual

model

Identify

stressors

Select

indicators

Establish

sampling

design

Define

response

criteria

Ensure link to

decisionmaking

33
Measurements and inferences from biological systems are affected by the scale

of observation. Therefore, to determine the appropriate scale for measuring an indicator, the temporal and spatial scales at which processes operate and resources

respond must be estimated (at least to a first approximation) and clearly identified

in the conceptual model. As a result, the most useful conceptual models will have a

hierarchial structure; that is, a given structural-compositional resource in the model

will reflect processes operative at smaller temporal and spatial scales, and indicate

the constraints operating at larger scales (Allen and Hoekstra 1992, Allen and Starr

1982).

To make the process of scale an explicit component of the conceptual models developed for the Forest Plan, we developed a worksheet to characterize stressors and

their anticipated effects on the ecosystem and its components (fig. 9). The purpose of

this exercise is to assist with the development of the conceptual models leading to

the selection of indicators for measurement. Scale was considered by allocating the

effects of specific stressors to various levels in the ecological hierarchy: landscape,

community-ecosystem, population-species, or genetic (see Noss 1990). Formalizing

of the conceptual model required identifying the scale associated with each model

component (fig. 9). As a result, insights to both the resolution and the range of the

measured indicators become apparent in the conceptual model.

To illustrate the use of the worksheet in developing a model, consider the addition

of roads as a stressor. The biotic consequences at the landscape scale for road

building could be a disruption of landscape connectivity for plants and animals

(function-process) leading to the isolation of habitats or species (structure-composition). A consequence at the community-ecosystem level could be changes in the

dynamics of predator-prey systems resulting in changes to the species abundance

distribution. At the population-species scale, a decrease in connectivity among individuals within a population may result in inbreeding depression. At the genetic scale,

gene flow is altered via the barriers to dispersal and migration, thereby resulting in a

change to the distribution of genotypes.

Developing the conceptual model should highlight that the links between stressors

and biotic responses may be indirect. The building of roads, for example, may lead

to an increase in erosion resulting in excess fine particle sedimentation in streams

Ecological hierarchy;

components

Biotic

consequence

Measurable

attributes

Scale of

measurement

Sampling

methods

Landscape:

Function-process

Structure-composition

Community-ecosystem:

Function-process

Structure-composition

Population-species:

Function-process

Structure-composition

Genetic:

Function-process

Structure-composition

Figure 9—The stressor-specific worksheet used to identify the biotic consequences of

stressor action at several scales of the ecological hierarchy. The attributes that reflect

the biotic consequence (that is, indicators) and their measurement also are listed.

34
and associated biotic responses. The preliminary effects of stressors on the physicalchemical components of the ecosystem also should be considered hierarchically

during development of the conceptual models (Ulrich 1994).

The indicators arising from the conceptual model are the attributes that characterize

structural and compositional resources of the system. Their values indicate the current state of those resources. The indicators subsequently selected for measurement

are those best reflecting known or suspected cause-effect relations among system

components as identified in the model. Resources occupying central positions in the

model should receive increased weight when the indicators are selected. As a result,

in terms of contemporary ecological principles and theory, the model justifies the

indicator or indicators selected for monitoring, and demonstrates how knowledge of

the status and trend of the indicator reflects underlying process and function and will

meet the goal of the monitoring program. Usually, modeling a restricted, but relevant

component of the system will be sufficient. Thus, a complete model of an ecosystem

is seldom necessary before proceeding with a reliable monitoring program.

On the basis of the conceptual model and characterization of its central components,

indicators are proposed for monitoring and subsequent field testing. At this point, the

primary criteria for selecting indicators are that they reflect underlying ecological processes and changes in stressor levels, represent the larger resource of which they

are a structural or compositional component, and are measurable. We begin with

candidate indicators because our knowledge of the stressors affecting the system is

limited. Thus, we identify a set of indicators that, based on our current knowledge,

best meets our needs, but with the understanding that these may change as the

program is implemented and new knowledge is gained.

Before field or simulation testing, the list of candidate indicators can be narrowed by

focusing on those with the following properties:

• Their dynamics parallel those of the larger environmental component or system of

ultimate interest.

• They each show a short-term but persistent response to change in the status of

the environment.

• They can be accurately and precisely estimated (that is, a high signal-to-noise

ratio).

• The likelihood of detecting a change in their magnitude is high, given a change in

the status of the system being monitored.

• Each demonstrates low natural variability, or additive variation, and changes in

their values can readily be distinguished from background variation.

• The costs of measurement are not prohibitive.

Identify Candidate

Indicators Responsive

to Environmental

Stressors

Specify

goals

Develop

conceptual

model

Identify

stressors

Select

indicators

Establish

sampling

design

Define

response

criteria

Ensure link to

decisionmaking

35
Additional evaluative criteria for screening candidate indicators are in NRC (1990)

and Barber (1994).

Even if a monitoring program is fully funded and implemented for many years, it will

fail if the wrong indicators were selected. Thus, the ultimate success or failure of the

program may be determined by this one step. The likelihood of choosing appropriate

indicators is greatly improved if the conceptual model thoroughly characterizes the

dynamics of the system, and accurately reflects stressor inputs. (A review of the

effort by EPA to produce a strategy for developing indicators for EMAP [for example,

Barber 1994] and subsequent criticism [NRC 1995] clearly shows the difficulty of this

task.)

We find the following a useful analogy for the process of indicator selection. Imagine

a funnel-shaped filter into which are poured all possible attributes of an ecological

system that can possibly be measured. The fabric of filter is composed of scientific,

political, and social threads. Our goal is to design the scientific fibers of the filter so

that only those attributes that allow the most comprehensive and reliable inferences

to the status of the ecosystem, constrained by cost functions, remain in the filter.

Those attributes retained by the filter become the indicators.

In general, determining the status of an indicator is a problem in estimating the value

of an unknown parameter (that is, state variable) within some specified bounds of

precision. Estimates of trend address the pattern of change over time in the status

of the indicator. These problems are to be addressed by statisticians using the tools

of survey and sample design (for example, Cochran 1977). As indicators change, the

sampling design may change, so continual effort is needed to ensure that the design

meets our monitoring needs. This topic is broad, and proper design requires substantial statistical expertise. Fortunately, a large body of statistical literature exists on

parameter estimation, hypothesis testing, and trend estimation that is relevant to this

problem (for example, Larsen et al. 1994, Overton and Stehman 1995, Sauer and

Droege 1990, Stevens 1994).

Debate exists over the correct statistical framework for monitoring: parameter estimation or hypothesis testing (for example, Stewart-Oaten 1996). For the moment, we

frame the monitoring question in terms of a statistical null hypothesis of no difference

between the estimated value of the indicator and its hypothesized baseline value. The

choice of significance level (α) for tests of the null hypothesis of no difference in the

status of an indicator must be balanced against the likelihood of failing to detect a

significant biological difference. Determining the α risk-level is a burden borne by

decisionmakers. The β risk-level, in contrast, is a burden borne by those charged

with maintaining ecological integrity.

Estimate the Status and

Trend of the Indicator

Specify

goals

Develop

conceptual

model

Identify

stressors

Select

indicators

Establish

sampling

design

Define

response

criteria

Ensure link to

decisionmaking

36
The managers’ responsibility is to implement an environmental monitoring program

with sufficient statistical power (that is, an acceptable value of 1-β) to detect meaningful changes in the values of the indicators. For the monitoring design and analyses

to be meaningful, statistical power must be considered a priori when determining

sample sizes, and post hoc to interpret the result of statistical tests that failed to

reject the null hypothesis (Skalski 1995, Zielinski and Stauffer 1996). In practice, to

address questions of statistical power requires that the minimal magnitude of change

in the indicator variable that is of biological significance be stated (this critical value

must be estimated by some defensible process). Given this information, practical

sampling issues, such as number of samples and resampling interval, can be

addressed.

One of the most difficult challenges is to determine the value of an indicator, or

the magnitude of change in its value over some interval, that indicates a significant

biological effect. In statistical terms, this amount is referred to as the effect size (∆)

or magnitude of change in the value of the indicator that the monitoring program

should be able to detect. Initial estimates of an appropriate effect size can be based

on the spatial or temporal variation in the indicator (σ2 ) under baseline or reference

conditions (Skalski 1995). In sum, specification of acceptable levels for type I and II

errors (α and β), natural variability of the indicator (σ), and the sensitivity of the test

(∆), determine the sampling effort for a given effect size. A comprehensive discussion

of statistical power, and its relevance to decisionmaking in the context of responsible

management of natural resources, is found in Peterman (1990).

An essential component of a monitoring program is the generation of expected

values or expected time trends of the indicator variables; that is, the system is

observed at time i and its state projected at time i + ∆ given some management

action. Only by comparing observed with expected values or trends can a determination be made about the effectiveness of management practices. The close

approach to, or the passing of, an expected value is the threshold point that triggers a change in management practices. Estimating expected values (that is, benchmark conditions), however, is difficult and imprecise for five reasons: (1) the limited

availability of pristine, undisturbed ecosystems to provide insights to benchmark

conditions; (2) an incomplete understanding of the relation between the value of

an indicator and the desired ecosystem state(s); (3) inadequate knowledge of the

expected variability, over time and space, of the indicator of ecosystem state (or

species status); (4) the nonlinear relations between indicator values and ecosystem

processes (including the existence of sharp threshold regions); and (5) the fact that

indicator benchmarks may be best represented by probability distributions rather than

single target values.

Expected Values and

Trends

Specify

goals

Develop

conceptual

model

Identify

stressors

Select

indicators

Establish

sampling

design

Define

response

criteria

Ensure link to

decisionmaking

37
Expected values and thresholds implicitly assume that an ecosystem will evolve to

(or was historically at) a steady state of ecosystem integrity. This concept, often

referred to as “the balance of nature,” has been replaced by one that recognizes the

dynamic nature of ecosystems (Pickett et al. 1992). Therefore, when evaluated for

periods ranging from decades to hundreds of years, the assumption of a steady state

is clearly false. The dynamic nature of ecosystems argues for specifying a probability

distribution of values rather than an expected value at a single moment. A second

aspect of the nonequilibrium paradigm concerns the predicted time trajectory for an

indicator (by trajectory, we mean how the value of the state variables change through

time). Given the long delay between management actions and the response of the

ecosystem to those actions, a monitoring program needs to be designed to predict

the future, expected trajectory of the indicator. This prediction will require developing

a mechanistic model that simulates the system response to management and whose

state variables reflect both current and future ecological conditions.

Determining threshold values first requires the selection of a spatial scale to observe

the ecosystem. If the spatial scale is a point in space, for example, when stream temperature is measured at a single location, an indicator threshold may be specified as

a single value. An example would be a maximum water temperature beyond which

conditions become lethal for cutthroat trout (temperature >22

o

C). If the spatial scale

includes a complete watershed, or the range of the species, however, then expecting

the water temperature of all stream reaches within this area to be≤ 22

o

C may be

unreasonable. Specifying an expected distribution of temperature values over the

area would be more appropriate. Thus, two different categories of indicators may be

described: those that lend themselves to threshold values (for example, water temperature for some fish and amphibians), and those best categorized by a target distribution (for example, number of snags and logs per acre). In practice, few indicators

will be characterized by a single target value.

In addition, because the physical and biological processes and structural-compositional elements that characterize ecosystems differ in space and time, most

indicators are best considered random variables; that is, when integrated across

space, at a given moment, a specific process or landscape element is characterized

by a dynamic distribution. To illustrate, assume that we have selected “forest stand

age” as our measured indicator. We know that under a natural disturbance regime

a dynamic distribution of stand ages would differ according to the spatial scale of

aggregation of forest stands. If the goal of management is to mimic natural disturbance processes, then the scientific challenge is to estimate the benchmark distribution of stand ages that management should aspire to achieve. This distribution,

however, depends on spatial scale. The age distribution would change as it is estimated for different-sized areas. As a consequence, a threshold value or an objective

distribution cannot be specified without having some idea of the “correct” spatial and

temporal scale for measuring the indicator, and the “correct” spatial scale for aggregating the measurements.

Once the scale of observation has been determined, indicator values can be aggregated into a frequency distribution. For a given moment, the observed distribution of

indicator values would be compared to the expected distribution to detect both the

magnitude and pattern of deviation from desired conditions. The concept of a spatial

distribution of indicator values as the appropriate evaluative statistic is critical to the

monitoring of ecological systems.

38
Given the inherent dynamic nature of ecosystems, the value of a given ecosystem

component (for example, process or landscape element) will follow a probability

distribution. Based on this understanding, a monitoring program must address two

distinct questions: Is the observed value of the process (or its indicator) at a specific

area on the landscape, or at some moment in a time series, within acceptable bounds

of the expected probability distribution? and When the observed value of the indicator, at a given time and space, is considered in the context of neighboring locations

on the landscape, or in the context of a longer time series, does the expected distribution of indicator values result? For a given resource on the landscape (for

example, a segment of stream, a forest stand, a riparian corridor), establishing

a target value for a given indicator may be appropriate. When deviation from the

desired ecosystem state at the landscape scale is evaluated, however, inferences

drawn from the indicator’s value at a site are of limited use without considering that

signal in the broader context of values from neighboring landscape sites.

The concept of the distribution of indicator values as a collective index of ecosystem

state at the landscape scale is illustrated in figure 10. This figure shows a historical

distribution used as a benchmark, the current distribution of indicator values, and the

future targeted distribution. In recognition of the impossibility of returning to preindustrial, pristine conditions, the target distribution is not identical to the historical distribution. Despite the need to establish benchmark distributions, the process of establishing such benchmarks is subject to some degree of arbitrariness. For example, the

Past

Future

Current

Condition index

t 0

t 1

t 2

inappropriate

trajectories

appropriate

trajectories

value at

t 2

Figure 10—Frequency distribution of indicator values

showing environmental condition at various points on

the landscape. The past benchmark distribution, current

distribution, and future, desired distribution are shown

as they change through time. Current-to-figure changes

are a consequence of management intervention.

39
appropriate temporal reference point and uncertainty on how benchmark conditions

are to be estimated from historical data are being debated. No clear guidance has

been given on how far back in time to go to find an appropriate point of reference.

Finally, how the concept of benchmarks can be reconciled with the dynamic nature

of landscapes is unclear, especially when viewed over long time scales. For the time

being, any evaluation of the ecological consequences of human activity will inescapably depend on value judgments.

In the interim, benchmark distributions, and the critical values that separate degraded

from nominal conditions (fig. 11), will be based on best available information. Evaluating local conditions relative to these threshold points will be the basis of management decisions even though the location of threshold points is subject to change as

ecological understanding increases. In the absence of decision thresholds or explicit

objectives that management seeks to achieve, monitoring will be disconnected from

management and policy formulation. Because of the complexity of this issue, the

EMT did not believe this could be adequately addressed in this planning effort. This

is an area that needs further work to improve sampling designs and make the program more responsive as we implement the program (see Chapter 4, “Research

Support”).

Most natural systems and resources recover slowly and will be slow to respond to

changes in management practices. In the interim, while ecological resources are

moving in the direction of a more desired ecosystem state, it is useful to identify

appropriate trajectories of change in indicator values that, if continued, would lead

to the target distribution (fig. 10). Thus, periodic estimates of the direction and magnitude of indicator change provide an ongoing evaluation of the appropriateness of

the management strategy.

Degraded

Conditions

Marginal

conditions

Nominal

conditions

Condition index

Threshold at upper limit

of degraded conditions

Threshold at lower limit

of nominalconditions

Figure 11—The distribution of indicator scores showing ecological

condition at various points on the landscape. Index values relate

to ecological conditions considered degraded, marginal, and

nominal.

40
A useful way to evaluate the integrity of a given ecological resource, concurrently

across many locations, is to compute the cumulative distribution of indicator scores

(fig. 12; Barber 1994). This distribution allows computing the proportion of sites below

(or above) a given indicator value (that is, the lower or upper acceptable value of the

indicator). In addition, the observed and expected distributions can be compared by

using statistical tests (that is, Kolmogorov-Smirnov goodness-of-fit test; Zar 1984) to

evaluate the deviation of the current distribution from the target distribution. Unacceptable test statistics for accepting goodness-of-fit, for example, can be used as pseudothreshold points to trigger a change in management policy.

Monitoring programs do not end with the collection of data, its analysis and synthesis, or even with summary reports. The results of monitoring programs are of

value to the extent that they provide information for management decisions, and

provide early warnings of ecosystem degradation. The link between monitoring and

decisionmaking begins with the formulation of and agreement on the monitoring

questions. The “correct” questions allow monitoring to be directed at areas where

management requires information to adjust activities to mitigate unplanned and

undesirable outcomes. Because the behaviors of complex systems are frequently

unpredictable (Smith 1997), the link between decisionmaking and monitoring is

essential.

1.0

0.75

0.50

0.25

10 20 30 40

Condition index

Degraded

sites

Upper confidence

interval

Figure 12—Cumulative distribution of indicator

scores showing the collective ecological condition

of many locations on the landscape. The figure

illustrates that about 20 percent of the sample

locations show degraded conditions.

Linking Monitoring

Results to

Decisionmaking

Specify

goals

Develop

conceptual

model

Identify

stressors

Select

indicators

Establish

sampling

design

Define

response

criteria

Ensure link to

decisionmaking

41
Decisionmakers begin by asking questions such as, “Is the Forest Plan achieving its

objectives for late-successional and old-growth forests?” A simple yes or no answer

is not necessarily useful. A process must be instituted to connect the decisionmakers’

questions to the analysis and summary of the monitoring data. One formalization

uses principles of statistical decision theory (Lindley 1985).

Statistical decision theory involves determining the potential alternative ecological

outcomes, assessing the probability each of these outcomes is valid, describing the

management decisions under consideration, and associating a “utility” with each

combination of decision and outcome (table 3). A few examples applying decision

theory to the management of natural resources exist: Maguire and Boiney (1994)

used decision analysis in conjunction with dispute-resolution techniques to resolve

a public policy dispute in Zaire over the best policy for managing an endangered

species; and Conroy and Noon (1996) applied decision theory to the question of

reserve selection and species conservation.

A simple, but nonetheless relevant, example illustrates the value of monitoring

data and how it is integrated with decision analysis to improve decisionmaking

under the Forest Plan. Assume the plan is responsible for conserving a species

listed as threatened under the ESA. In their simplest form, the possible management decisions are to take no action at all (the status quo decision) or to institute

conservation measures. Based on available data, particularly the monitoring data

relevant to status and trend, we estimate some probability that the species is stable

or increasing (p(θ1)), or in decline (p(θ2)). (Note: Because of the impossibility of

knowing the “true” status of a population, all we can do is to estimate the likelihood of

the different status categories based on the best available data. The combination of

alternative decisions by possible states of the population are presented in a two-way

decision table [fig. 13].)

Table 3—A sequential list of the steps to follow to make an optimal

decision in the context of uncertainty and incomplete information

Steps Decisions

1 Determine the bounds of the management decision space

2 Provide a range of possible management responses to the

monitoring data

3 Estimate the probabilities associated with each possible interpretation

of the monitoring data

4 Estimate the utilities associated with each possible combination of

decision and monitoring data interpretation (that is, the costs of

wrong decisions and misinterpretation of the monitoring signal)

5 Determine the decision that maximizes utility

42
The task now is to assign values to each combination of decisions and population

states (fig. 13). These values are the utilities, u(di, θj), associated with the various

outcomes. Utilities are scaled to the unit interval, with u = 1 “best” and u = 0 worst;

u(di) is the expected utility for decision i, over the probability space of the possible

outcomes (Conroy and Noon 1996). Although utility is arguably subjective in many

instances, certain outcomes (for example, the species goes extinct) are unequivocally

the worst possible [u(d1, θ2) = 0], and others (for example, the species persists with

no economic costs) are the best [u(d1, θ1) = 1]. The other outcomes have intermediate utilities. In this example, taking conservation action when none was needed (that

is, the population was not declining) was assigned a lower utility because of the

economic costs (for example, opportunity costs) that accompany most conservation

actions.

Once the elements of the table are complete (di, θj, and ui, j; fig. 13), the management decision is chosen that maximizes the expected (average) utility:

That is, the decision (di) with the largest u(di) is chosen.

As new data become available (for example, through monitoring the behavior of the

ecosystem) the probabilities associated with the possible states of the system (the

p(θi) values) are recomputed. The decision process is then revisited to determine

if a different decision now maximizes overall utility. This iterative process is the

substance of adaptive management.

ud d p i ij j

i

u

( ) ( )( ) = ,

=

∑ θ θ

1

Population status - likelihood (2)

21: increasing/stable 22: declining

1.0 0.0

0.5 0.75

p(22 p(2 ) 1)

Probabilities

No action (d1)

Conservation (d2

)

Figure 13—Hypothetical utility table illustrating the likelihood

of different population states, the possible management

decisions, and the utilities associated with the combinations

of states and decisions.

43
In summary, application of decision analysis under uncertainty involves specifying

management objectives and criteria for measuring success in achieving them;

identifying alternatives to achieving the objectives; describing the uncertain events

in the ecological and sociopolitical environment that influence the outcome of actions

taken; assessing the outcome of each combination of management alternative and

uncertain events in terms of the decision criteria; estimating the likelihood, or probability, of each uncertain event; calculating the expected values of the decision

criteria for each alternative; resolving any tradeoffs among conflicting criteria; and

reexamining the “optimal” decision by analyzing its sensitivity to changes in input

parameters (Maguire et al. 1988). This is an area of current research and one not

carried out by the EMT. We expect this to be addressed as we gather monitoring

information for each resource issue and begin to study how to make the results

useful to management (see Chapter 4, “Research Support”).

Summary of Key

Points

The purpose of this chapter has been to explain our scientific framework for effectiveness monitoring. The definition and purpose of monitoring were presented along

with an attempt to address the challenges and experiences encountered by others

when developing monitoring programs for complex ecological systems. The concepts

of prospective and retrospective monitoring were introduced and the reason for our

selection of the former approach was given. Seven steps for developing a prospective monitoring program were explained, including specifying goals or monitoring

questions, identifying stressors, developing conceptual models relating stressors to

ecological responses, selecting indicators, establishing sample designs, defining

response criteria, and linking monitoring results to decisionmaking. These seven

steps have been used as guidance for developing the modules for each of the

resources to be monitored under the Forest Plan. They are intended to be used

as the template for developing future modules.

Acknowledgments This chapter includes contributions from Anthony Olsen and Hart Welsh.

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48
Chapter 3: Scientific Framework

for Effectiveness Monitoring of the

Northwest Forest Plan

Barry R. Noon

Habitat as a

Surrogate for Biotic

Populations

Logical Foundation—A

Habitat-Based Approach

The foundation of our approach to effectiveness monitoring for the Northwest Forest

Plan is to initiate a gradual transition from an intensive, individual species-resource

focus to a more extensive, ecosystems approach. This transition assumes identifying

and measuring surrogate variables that allow reliable inferences about the integrity

of the primary resources. Such a fundamental shift means a movement away from

the current crisis response to individual endangered species-resource issues, to a

prospective evaluation of management decisions in an ecosystem context. For

agency requirements, this change in focus means an eventual shift away from

directly monitoring species to a habitat-based (primarily vegetation) monitoring

program. Implicit in this approach is the validity of the assumption that inferences

to species viability, biodiversity, and ecological integrity can be reliably drawn from

assessments of the status and trends in attributes of habitat structure and composition. The assumption underlying this is that we have a solid base of information

from which to address questions about a range of related resources.

49
A simple conceptual model describes our approach: process ↔ structure-composition

→ biodiversity (see fig. 7, Chapter 2). For forested ecosystems in the Pacific Northwest, we propose that measurable, biotic and abiotic structure and composition reflect

underlying driving forces. In turn, knowledge of habitat structure and composition

(amount and distribution) allows reliable predictions of biological diversity and the

integrity of the ecosystem.

We recognize that knowledge of the status and trends of a small set of habitat

attributes will not provide comprehensive insight into species viability, biodiversity,

and ecological integrity; that is, monitoring the change in habitat structure from

management actions is simply a surrogate for directly measuring biodiversity and

ecological integrity. As such, it is accompanied by uncertainty. To reduce this uncertainty, regular, local-scale validations of assumed relations between habitat (primarily vegetation structure and composition) and species viability and fundamental

ecological processes will be required. Despite this important caveat, accurate and

timely monitoring of changes in the status and trends of habitat should provide a

reliable early warning system of changes in biological diversity and ecological

processes.

The transition to a habitat-based monitoring program has several advantages:

• Monitoring vegetation change will be more cost-effective than directly monitoring

populations of all the possible species for which agencies are responsible. Costs

savings may be greatest if much of the monitoring can be based on remotely

sensed data.

• Existing forest inventory programs can be the foundation for habitat-based

monitoring programs.

• A habitat focus is more in line with the agencies’ mission to manage vegetation

communities (habitat), not species populations directly.

• Estimating the trends in habitat structure and composition represents an anticipatory

approach to ecological monitoring, and allows alternative management strategies

to be evaluated through predictive models.

Some important limitations to a strictly habitat-based approach to monitoring also

must be addressed in the design of a monitoring program:

• Some unknown proportion of the variation in species’ population dynamics is not

driven by changes in habitat amount and distribution.

• Changes in habitat will not predict population responses to other stressors, for

example, environmental toxins and climate change.

• On the basis of the first two limitations, a strictly habitat-based monitoring program

may have limited ability to predict changes in the viability of some species and

prove unreliable for some ecosystem processes.

• Most existing vegetation inventories (for example, Forest Inventory and Analysis

[FIA] data) do not measure the complete complement of attributes relevant to the

status and trends of species populations.

• Model validation efforts are required to ensure the continued validity of the habitatbased predictive models.

50
Given the possible limitations of defaulting to just habitat measures, several precautions are needed. The most important is that models predicting population status

on the basis of habitat predictors must be subject to frequent validation. If validation

tests are unsatisfactory, the structure of these models may need to be expanded to

include additional predictors. Unfortunately, this addition would increase the scope

of the monitoring program to include measuring additional indicators. Finally, the

prudent course is to not devote the entire program to monitoring surrogate variables.

Some effort needs to be devoted to direct measurement of the resources of interest.

Toward a Theory of

Habitat

Given the great diversity of species—plant and animal, vertebrate and invertebrate—

monitoring all biotic components of managed ecosystems clearly is impossible. Based

solely on pragmatic considerations, only a few surrogate measures can be used that

allow indirect (but reliable) inference to the integrity of the larger set of biological

processes and components. A possible surrogate for the biota is to measure the

pattern and dynamics of habitat structure, defined as the physical arrangement

of objects in space (Bell et al. 1991). These objects, tangible and with discrete

boundaries, can be either biotic or abiotic in origin. They have defining attributes

including type, size (length by width by height), volume, mass, form (geometry),

and if biotic, characteristic renewal rates. Most relevant to dynamic ecosystems

are objects that create and modify environments, differ in their effects, and can

be affected by human-induced and natural disturbance processes. These objects

include dominant geologic features (for example, mountains, rivers) or biotic,

structure-producing organisms (for example, coral reefs, trees, kelp forests) that

create environments favorable to structure-using organisms. Over time scales

relevant to management, the geologic features are assumed to be static. Thus,

our focus is primarily on the biotic components, particularly plants, as the dominant

objects in the space of landscapes that modify the environment and are affected by

human activities.

A theoretical framework for organizing the effects of living objects on ecosystems is

beginning to develop (for example, Brown 1995, Jones and Lawton 1994, Jones et

al. 1994); that is, ecologists have begun to recognize that organisms cause physical

and chemical changes in their environments, altering the flows of matter and energy,

and thereby create environments favorable to other organisms. Jones and his colleagues focused primarily on animals that perform mechanical engineering resulting

in such things as gopher-mound disturbances, bison wallows, and beaver dams.

Our focus in the Forest Plan is more on vegetation that, through autogenic processes, controls flows of matter and energy in ecosystems and creates environments favorable to other species.

Given this framework, we return to our heuristic model (see fig. 7, Chapter 2),

where underlying processes are reflected in and affected by structural and compositional elements of the biota. Focusing on the biotic components, some small

subset of the vegetation or aspects of plant community structure and pattern would

be directly monitored. Most plant species would not be directly monitored, however.

Rather, those aspects of the vegetation that are measured would be chosen, at least

in part, on their ability to provide surrogate information back to the integrity of the

larger plant community and provide predictive insights into animal responses. These

structural and compositional components would be biotic elements that are persistent, change or ameliorate the local environment, and create microenvironments

favorable to many other species (plant and animal).

51
Justification—The justification for using habitat structure as a surrogate variable for

predicting wildlife populations is based on both pragmatic and theoretical arguments.

Habitat loss and fragmentation were the primary drivers or stressors behind creation

of the Forest Plan. The theoretical argument is based on the belief that animals respond to habitat adaptively (Noon 1986); that is, where an animal selects to live is

believed to be an evolved behavioral response stimulated by structural and compositional features of the landscape. The fitness gain accrued by making the “correct”

decision is an increase in lifetime reproductive success. According to the conceptual

model of Southwood (1977, 1988), by the processes of discrimination and eventual

selection, habitat acts as the templet that guides the evolution of ecological strategies.

Many behavioral studies support the understanding that animals evaluate a habitat’s

“quality” on the basis of proximal cues received from the environment. Habitat selection therefore would evolve if different decisions were associated with different probabilities of survival and reproduction.

Casting the theory of animal-habitat relations in an evolutionary framework serves

two key purposes. First, it establishes a theoretical justification for attempting to

model the relations between animals and habitat structure. Second, it suggests that

predictive models relating patterns of a species’ distribution to components of habitat

structure are likely to be useful. Given such models, management-induced change

in specific structural or distributional components of the landscape can be simulated

and the likely biotic response estimated (see fig. 7, Chapter 2). Such tools would

allow management alternatives to be evaluated in an anticipatory context.

Spatial Scales of Habitat

Evaluation

Traditionally, most studies of habitat suitability have been conducted at the within-site

scale; that is, ecologists have focused on the relations between individual patterns of

occurrence on the landscape and features of the environment within the immediate

neighborhood. We are now aware, however, that the distribution and abundance of

most plants and animals also are strongly influenced by the spatial arrangement of

suitable habitats across the landscape (for example, Flather et al. 1992, Lamberson

et al. 1992, Short and Turner 1994). The spatial arrangement of habitats, however, is

dynamic and subject to a variety of stressors; one of the primary causes of declines

in biodiversity and in individual species has been a consequence of human-induced

habitat fragmentation (reviewed in Noss and Csuti 1994). Thus, conservation efforts

for any species must include analyses at multiple scales.

Species respond to environmental patterns at different scales—no single correct

scale for habitat evaluation exists. In addition, the “correct” spatial arrangement of

habitat elements is a function of the degree of population organization; what may

be a fragmented habitat at the scale of the individual home range may not be fragmented at the scale of the population of the same species (Wiens 1996). Thus, habitat quality must be evaluated at a minimum of two spatial scales: the scale of the

individual and the population scale. A third scale of assessment becomes important

when long-term persistence of populations is considered in the context of environmental variation. Given this consideration, what is needed are many distinct populations, widely distributed across the landscape, with fluctuations spatially asynchronous (Den Boer 1981).

52
Developing predictive habitat suitability models requires consideration of the relations

between landscape pattern and life history characteristics of individual species and

population-scale dynamics (Rickers et al. 1995). These needs will require monitoring

changes in the status and trend of habitat quality at the scale of the individual, the

local population, and the metapopulation (see example for spotted owls in Noon and

McKelvey 1996). Thus, managers and policymakers also will need to consider the

effects of their decisions (that is, land use) at all three scales; decisions affecting

habitat quality and species persistence can no longer be assessed just locally. The

assessment strategy for northern spotted owls and marbled murrelets, which uses

both remotely sensed and ground-plot habitat data, should allow inferences to habitat

quality at all three spatial scales.

Developing Predictive

Habitat-Relations

Models for Animals

Two general types of models are possible: heuristic and mechanistic (Pielou 1981).

Those that simply fit a statistical model to the observed data (that is, correlational

approaches) often have no obvious biological interpretation; their coefficients do

not necessarily reflect fundamental biological processes or elements. In contrast,

mechanistic models attempt to simulate key biological process. As a result, mechanistic models go beyond forecasting to explain the behavior of the system.

In practice, developing models for selected components of the biota will be an

iterative process moving from relatively crude to more refined models. A logical

first step would be to explore the spatial relations between remotely sensed vegetation (habitat) data and existing data on patterns of species distributions. These

relations could be integrated with higher resolution wildlife habitat relations models

that already exist (for example, Morrison et al. 1994, Verner et al. 1986). This synthesis will allow the development of initial models to forecast changes in animal

species distributions based on possible future changes in vegetation structure and

composition. These models are largely heuristic.

Predictive models that reflect biological mechanisms, however, will need to explicitly

incorporate demographic processes affected by habitat change at the individual, local

population, and metapopulation scales. Such models directly evaluate changes in

habitat quality as they affect key demographic processes: birth, death, and dispersal

rates. This type of model has been developed for the northern spotted owl and used

to investigate the response of the subspecies to landscape change on the Olympic

Peninsula, Washington (Holthausen et al. 1995).

Population processes are affected by habitat factors operating at several spatial

scales; for example, the survival and fecundity of a pair of spotted owls is a function

of habitat quality at the scale of the home range. The likelihood of their offspring

finding a suitable site and a mate are a function of the distribution of suitable territories at the population scale. And, the persistence likelihood of a regional population is a function of the number and distribution of local populations across the landscape. Therefore, to invoke habitat as an appropriate surrogate for anticipatory

monitoring will require a multiscaled approach to modeling the relations between

population dynamics and habitat. Only when population dynamics is considered

in this holistic context will the monitoring program be sufficient.

53
Model validation—Predictive models lend themselves readily to validation. When

habitat changes, as a result of management or natural disturbance, for example,

field survey data can be used to test whether the species responds as predicted

by the model. (Note the absence of a 1:1 mapping of habitat to population status

requires that other drivers of change be treated as covariates and be controlled

for statistically.) If little correspondence is found between the observed and predicted response, then the model is revised to incorporate new understandings,

and validation begins again. Model validation and revision are ongoing and iterative

processes.

Species populations are affected by many factors other than their immediate habitat;

for example, disturbance history, availability of source populations, and current or

past weather greatly affect observed population abundance and distribution. The

correspondence between observed and predicted states therefore is seldom going

to be 1:1. The challenge is to estimate the contribution of habitat structure; that is,

What component of the variation in a species population is due to habitat variation?

This question requires that the influence of habitat structure on organisms (and

processes) be separated from all other environmental influences; that the majority

of the structural components of habitats affecting organisms be identified; and that

habitat structure be examined at all spatial scales relevant to the organism of interest. All these factors must be considered as habitat-based models are constructed,

validated, and revised.

Tradeoffs Between

Habitat and Population

Monitoring

By choosing to measure a surrogate, instead of a direct measure of the variable of

interest, inference to status and trend of the resource becomes indirect. The step

back from direct inference inescapably introduces additional uncertainty to estimates

of “true” status and trend of the population. The uncertainty associated with surrogate

measures can be so large that no substitute exists for directly measuring the population. The results of attempts to predict population status from habitat predictors

suggest that surrogate measures should be viewed with skepticism (see for example,

papers in Verner et al. 1986).

Reasons can be cited for why inferences from habitat state to population status are

uncertain: the realized habitat distribution of a species is affected by more than just

habitat quality; and actual habitat use is affected by the species’ density, the density

and competitive interactions with other species, the abundance and distribution of

biotic resources not directly related to vegetation, history of the population, and

abiotic drivers such as climatic variation (Block and Brennan 1993). As a consequence, a species’ pattern of habitat use is dynamic in both space and time. This

realization has an important consequence for the use of habitat as a surrogate

indicator of population status. To some degree, habitat attributes, particularly those

components assessed by vegetation measures, will not be sufficient predictors of

population status. Some component of population variation (that is, variation in distribution or abundance) will be unrelated to the current value of habitat attributes.

54
Focal Species as

Ecological Indicators

An often-asked question concerns the degree to which the population status of a few

selected species reflects the state of the larger ecosystems to which they belong.

This is a reasonable question. Even if the species-based metrics are not selected as

the most appropriate measures during indicator development, often legal mandates

(for listed species under the ESA) or social mandates (for economically valuable

species) exist for directly monitoring the population status of some species. The

answer to the question, however, is not clear; for example, the interpretation and

usefulness of “indicator species” have been widely debated. Most of the criticism has

been directed at the reliability of drawing inferences to the status of unmonitored

species from the status of a monitored species (Landres 1992, Landres et al. 1988).

For the most part, criticism has not been directed at the use of species as sentinels

of system change, as early warning of the action of one or more stressors, or as

indicators of ecosystem state.

Current research has revived interest in the value of measuring aspects of species

populations as useful indicators of ecological integrity; for example, multispecies

conservation issues in Africa have been addressed by concentrating on “focal

species” (Davis 1996). Often these species are simply those with large area

requirements in habitats experiencing loss and fragmentation. Recently, simple

models have been used to compare and rank species with similar life histories in

terms of their sensitivities to habitat loss and fragmentation (Noon et al. 1997). This

work represents a first step in moving from single to multispecies conservation

planning. Of most interest to ecological monitoring, however, may be the recent

quantitative formulation of the keystone species concept into an expression that

formally links species dynamics to ecological processes (Power et al. 1996). An

extended discussion of this paper is in order because it develops a framework

for the selection of focal species as indicators of ecosystem state.

The most abundant species are well known for playing major roles in controlling

the rates of ecosystem processes simply because of their numeric dominance. In

contrast, keystone species differ from dominant species in that their effects are much

greater than would be predicted from their abundance (Power et al. 1996). To make

the concept more operational, Power et al. measure the impact of a species by its

community importance (CI)—the change in a community or ecosystem trait per unit

change in the abundance of a given species, which is expressed mathematically as:

where the right side of the equation represents the per capita rate of change in the

ecosystem trait given a change, dp, in the proportional abundance (or biomass) of

a given species. For example, the ecological trait of interest might be total carbon

storage capacity in a forest ecosystem. We are interested in the per capita rate

of change in carbon storage capacity with a reduction in the abundance of largediameter pine trees. Deleting this species from the forest and recomputing carbon

storage capacity provides an estimate of the CI of pine trees. In this example, our

focus has been on species; however, the arguments about CI values could be

computed for landscape elements as well as for species (for example, the CI

of all trees > 30 inches in diameter at breast height).

CI =

d trait

dp

1

trait

a f

a f •

L

N

M O

Q

P

55
Estimating the CI of a species or landscape element is difficult, but management

“experiments” are often inadvertently performed that eliminate, or greatly reduce, a

species (or element) from a community. Then, a measure of the species’ importance

(CI), given as the change in an ecosystem trait before and after its deletion, can be

computed as:

where tn is a quantitative measure of an ecosystem trait (that is, primary productivity)

before deletion, tp is a measure of the trait after species i has been deleted, and pi is

the species proportional abundance (or biomass) before deletion (Power et al. 1996).

Despite the practical difficulties in estimating CI values for a community of species,

this formalization offers a worthwhile perspective when contemplating which species

to monitor.

The current understanding of the relative importance of species in communities and

ecosystems does not allow a listing of life history traits that unambiguously characterize keystone species. Most known examples include species at high trophic levels

(that is, top of the food chain) with high rates of prey consumption relative to prey

production (Power et al. 1996). Given the criterion of disproportionate per capita

effects relative to abundance or biomass, however, keystone species may occur at

any trophic level. Perhaps the quickest way to gain insight into which species act as

keystones is to better exploit management actions as ecological experiments (Walters

1986). Management actions on public forest lands cause changes in the abundance

and occurrence of species in the management unit. By viewing these as “deletion

experiments” relative to some ecosystem trait, we can more rapidly gain an understanding of which species most contribute to ecological integrity.

In the Pacific Northwest, one of the best examples of keystone species may be

the anadromous fishes (Willson and Halupka 1995). Before their current population

reductions because of habitat destruction, dams, and over-fishing (Stouder et al.

1997), salmon and steelhead made significant contributions to the base of the

terrestrial food chain. In addition, they represented a significant vector of nutrient

and biomass transmission from the marine to the terrestrial environment (Willson

and Halupka 1995). Their contribution to the terrestrial food web and their role in

nutrient transport into riverine and riparian ecosystems greatly exceeds their numerical abundance, qualifying them as a keystone species group.

In addition to the idea of keystone species, the “umbrella species” concept also may

be useful when selecting indicator species for the Forest Plan. The umbrella species

concept rests on the simple principle of animals with large area requirements, as

Murphy and Wilcox (1986) stated, by protecting the minimum areas needed for a

viable population of a single, large-bodied species, sufficient space should also be

maintained for the viability of smaller and more numerically abundant species in the

area. Implicit in this concept is that both the area and habitat requirements of the

umbrella species encompass those needed for viable populations of other sympatric

species and that the umbrella species’ population is itself viable. A recent application

involves the black rhinoceros in South Africa, and its role as an umbrella for the conservation of other large-bodied herbivorous species with which it is sympatric (Berger

1997).

CI

t t

t

1

p

n p

n i

=

−

•

L

N

M O

Q

P d i

56
For umbrella species, a preliminary list of ecological attributes could be compiled.

Other than large area requirements, these species should generally be year-round

residents (that is, nonmigratory), have low vagility, and select habitat elements that

also limit the population sizes of sympatric species. Compelling arguments could

be made that, in the Pacific Northwest, the northern spotted owl serves as a strong

candidate umbrella species for much of the late-seral forest community (for example,

Noon 1997).

Application to the

Northwest Forest

Plan

Key ecological resources are identified in the ROD (USDA and USDI 1994), among

them the northern spotted owl and marbled murrelet populations, late-successional

and old-growth forest, aquatic and riparian habitats and their associated species, and

other associated late-successional organisms, including survey-and-manage species.

To move toward a habitat-based approach to monitoring, each resource category is

assumed to be represented by several specific, but surrogate, habitat elements expressed as quantifiable attributes of habitat structure and composition. This concept

is illustrated in a model showing the ordination of each resource category along a

pair of habitat gradients (fig. 14). Based on the current understanding of these resource categories, we believe that considerable overlap exists among the sets of

habitat attributes that characterize the vegetation components of their ecological

niches (fig. 14). Habitat attributes in the area of overlap should receive top priority

for quantification. These concepts guided our application of the design principles (see

table 2, Chapter 2) for each of the modules for the resource issues, as summarized

in the following sections (see also “Research Support” in Chapter 4).

Habitat gradient 1

S-M

MaMu

NSO

Rip/Aq

LSOG

Figure 14—Hypothetical relation between important habitat

elements in the Forest Plan. LSOG = late-successional old

growth; S-M = survey-and-manage; MaMu = marbled

murrelet; NSO = northern spotted owl; and Rip/Aq =

riparian and aquatic.

57
Late-Successional and

Old-Growth Forests

The core ecological resource of the Forest Plan, and the one about which we are

most concerned, is late-successional and old-growth forest. To differing extents,

providing for the vegetation elements that characterize such habitats will meet

many of the requirements of other resources (fig. 15). Given this understanding,

the first task is to develop a conceptual model for these habitats that retains our

concept: biotic and abiotic processes are reflected in vegetation structure and composition, which in turn can be used to predict biological diversity and ecological

integrity (see fig. 7, Chapter 2). Following the procedure outlined in Chapter 2,

we have made an initial attempt to develop the late-successional and old-growth

conceptual model by elucidating key processes, specifying the spatial scales at

which vegetation structure and composition are evaluated, and listing the main

components of biodiversity (fig. 15; Hemstrom et al., in press [see footnote 2,

Chapter 1]).

We elaborate further on this conceptual model by resolving the components of the

model into measurable attributes (fig. 16). For example, we list the key disturbance

processes (stressors) likely to affect Pacific Northwest forests. These include wind

throw, insect outbreaks, fire, logging, and road construction (fig. 16). Importantly,

disturbance processes arising from human behavior are distinguished from those

disturbances that would occur in the absence of people.

The most important task at this step, however, is to exhaustively list measurable

attributes that describe vegetation structure and composition at various spatial scales.

Because our focus is on measuring the key elements of vegetation structure and

composition through time, these attributes constitute the list of candidate indicator

variables for measurement by the monitoring program. The final selection of indicator

variables at this step is done in the context of our underlying paradigm: process ↔

structure-composition → biodiversity. That is, we must select for measurement

Landscape/

watershed

Stand/

microsite

LSOG

aggregate

indicators

Terrestrial

habitat

Aquatic

habitat

Species

diversity

Foodweb/

trophic

structure

Natural

disturbance

Vegetation

development

Other ecosystem

processess

Regional

processes

Historical

processes

Human caused

Processes Structure/composition Biodiversity

Figure 15—Conceptual model for late-successional and old-growth

forest. LSOG = late-successional old growth.

58
those attributes that best reflect underlying ecological processes, and at the same

time serve as the best predictors of biological diversity and ecosystem integrity. This

process must be scientifically credible: based on the most current data, analyses,

and understandings of Pacific Northwest ecosystems, and on the most applicable

ecological theory.

Northern Spotted Owl Given the Forest Plan’s concentration on late-successional and old-growth forests,

the task for monitoring individual species is to identify those components of the

resource that overlap with critical components of the species’ habitats. For the

northern spotted owl (Lint et al., in press [see footnote 2, Chapter 1]), we are initially

most interested in those elements of vegetation structure and function that simultaneously characterize the forests and spotted owl habitat (see fig. 14). Some spotted

owl habitat requirements may lie outside the vegetation space that defines these

forests (fig. 14). To the extent this is true, and to feel confident that the measured

habitat attributes serve as a reliable surrogate for owl population status, we may

need to expand the monitoring program. In this case, we would then elect to measure

vegetation attributes beyond those required solely to monitor changes in the latesuccessional forests.

Landscape/

watershed:

- patch size

- edge density

- patch shape

- juxtaposition

- patch diversity

- fractal dimension

Stand/microsite:

- tree size density

and distribution

- snags; fallen trees

- herb/shrub layer

- tree crown/

platforms

- forest floor

- tree composition

LSOGaggregate

indicators:

- abundance

- diversity of types

- landscape

connectivity

- seral stage

diversity

Terrestrial habitat

populations:

- NSO

- MaMu

Aquatic habitat

populations:

- anadromous fish

- amphibians

Species diversity

- LSOGspecialists

- exotics

- canopy species

- below ground

Foodweb/

trophic structure

Natural

disturbance:

pest, pathogen,

wind, fire

Vegetation

development: growth,

regeneration, seed source,

site availability,

succession

Other ecosystem

process ess: hydrology,

nutrient cycling, carbon storage,

primary productivity, site

productivity, trophic

dynamics

Regional process es:

climate, geology,

topography

Historical process es:

fire history, land uses

Human caused:

logging, roads,

silviculture, dams

Processes Structure/composition Biodiversity

Figure 16—Expanded conceptual model for late-successional and old-growth forests showing measurable attributes that represent candidate indicator variables (Hemstrom et al., in press [see footnote 2,

Chapter 1]). LSOG = late-successional and old growth; NSO = northern spotted owl; and MaMu =

marbled murrelet.

59
To make the transition from the current demographic-based monitoring program

for spotted owls to a habitat-based program requires an active period of habitat

modeling (fig. 17). We must identify those aspects of vegetation structure and

composition that have the greatest power to predict the distribution of owls on

the landscape, as well as to explain the observed variation in demographic rates

(owl birth and death rates) at a local scale. To accomplish this task will require

an active research program characterizing vegetation at a variety of spatial scales

based on data from the existing demographic study areas. The intersection of

spatially referenced data from both the owl demographic studies and vegetation

samples provides the fundamental data for the model-building phase. The degree

to which these models explain the observed variation in owl distribution and population performance shows the certainty with which habitat variation predicts population variation. Explained variation is thus a direct measure of the confidence we

have in habitat as an appropriate monitoring surrogate for population attributes.

We are aware that not all observed, population-scale variation will be explained by

habitat predictors. Many factors, in addition to habitat quality, affect population size

and demographics. The first task will be to estimate the expected proportion of

population variance explained by habitat. To the extent the variance explained by

habitat falls below expectation, the models will need to be refined to increase their

predictive ability. Validating model predictions by independent field surveys also is

essential; that is, the models will be used to predict an owl population response that

must then be verified by direct field measurement from one or more owl populations

(fig. 17). Finally, a period of concurrent monitoring of owl populations and habitat may

provide additional insights into how they covary and accelerate the process of model

development.

Population-based monitoring

NSO demographic studies

(rates of population growth; birth and death rates)

Demographic/habitat studies

(linking demographic variation to habitat variation)

Development of habitat-based models

(predict demographic response to habitat variation at numerous

spatial scales)

Habitat-based monitoring

Model validation

(field tests in LSRs)

Figure 17—Key steps in the predictive modeling of species-habitat

relations (Lint et al., in press [see footnote 2, Chapter 1]). NSO =

northern spotted owl; and LSR = late-successional reserve.

60
Marbled Murrelet We take a similar approach with the marbled murrelet as for the northern spotted

owl. We first identify those components of late-successional and old-growth forests

that overlap with critical components of its habitat. A key difference, however, is

that the murrelet spends most of its life at sea and uses forest habitat primarily for

nesting. Our interest relative to the Forest Plan therefore is in the elements of vegetation structure and function that simultaneously characterize late-successional

forests and murrelet nesting habitat (fig. 14; also see Madsen et al., in press [see

footnote 2, Chapter 1]). Certain important murrelet habitat requirements, however,

may not be included in the broad vegetation definition of the forests (fig. 14); therefore, we need to measure vegetation attributes beyond those required solely to

monitor changes in the late-successional resource. At the initial stage of the monitoring program, analyses will be conducted to refine identification of definitions of

these habitat attributes.

An active period of habitat modeling will be required to identify those aspects of

vegetation structure and composition that have the greatest power to explain the

distribution of murrelets on the landscape, as well as to explain the observed

variation in population and demographic rates from the marine environment. To

accomplish this task will require an active research program characterizing vegetation at a variety of spatial scales in known murrelet nesting habitat. As for the

spotted owl, initial models may be heuristic (that is, correlational), but the ultimate

goal is to develop mechanistic models.

In contrast to the spotted owl, the most appropriate place for evaluating population

status and trends of murrelets, and to estimate demographic parameters, is in the

marine environment. Murrelet status and trends will be evaluated through at-sea

surveys to estimate population size and compute age ratios (young of the year to

adults) as estimates of breeding success. If the terrestrial habitat management plan

is appropriate, and at-sea conditions suitable, then an effective Forest Plan should

result in a stable or increasing number of murrelets in at-sea counts, as well as

stable or increasing age ratios.

Development of predictive models will focus on nesting habitat. The intersection

of spatially referenced data from terrestrial surveys and corresponding vegetation

samples provides the fundamental data for the model-building phase. As with the

spotted owl, the degree to which these models explain the observed variation in

murrelet distribution and population performance estimates the certainty with which

habitat variation predicts population variation. Explained variation is thus a direct

measure of the confidence we have in nesting habitat as an appropriate monitoring

surrogate for population attributes. We know that not all observed, population-scale

variation will be explained by forest habitat predictors, particularly for the murrelet,

because of its reliance on the marine environment and because a large percentage

of its nesting habitat is on non-Federal lands. To the extent the explained variation

falls below that expected on the basis of habitat predictors, the models will be refined

to increase their predictive ability. Validating model predictions by independent field

surveys also is essential in both terrestrial and marine environments.

61
Aquatic and Riparian

Ecosystems

The objective of the aquatic and riparian monitoring program is to assess the degree

to which the goals of the aquatic conservation strategy (ACS; FEMAT 1993:V30-V31)

are attained. In general, the ACS seeks to preserve or return ecological integrity to

these systems by maintaining and restoring physical and biological processes, water

quality, and the structural and compositional diversity of biologic (plant and animal)

and geologic (shorelines, banks, and bottom configurations) elements that characterize “healthy” watersheds. Focusing on these key elements help form a basis for

monitoring other aquatic and riparian resources—similar to the stepdown approach

from LSOG to the spotted owl and marbled murrelet—such as fish and survey-andmanage species or other aquatic and riparian-associated species.

Following the approach outlined previously in this chapter, we begin the planning

effort by identifying the human-caused stressors that may compromise attainment

of the ACS goals (Furniss et al. 1997 [see footnote 2, Chapter 1]). The effects of

these stressors on specific processes (biotic and abiotic) will be assessed at the

site, reach, and watershed scales. The interactions among natural processes, humancaused disturbances, the structural and compositional elements of the watershed,

and the response of the biota are characterized in a multiscale conceptual model.

The model relies on an integrated indicator called watershed condition that combines

the range of crucial processes necessary to sustain populations in specific watersheds. The multiscale model provides candidate indicator variables for both structuralcompositional elements and direct measures of biological indicators. Finally, the

approach is to use the conceptual model to aid developing empirical models linking

land management to aquatic-riparian habitat conditions, and then to aquatic and

riparian species.

When completed, the monitoring plan for aquatic and riparian resources will be

formed around key components consistent with the overall design for effectiveness

monitoring:

• An evaluation of current watershed condition based on available data and expert

opinion, aggregated to basin, province, and regional scales

• Design and implementation of a statistically based survey to provide estimates

of temporal trend for selected indicators of watershed condition

• Development of empirical models that characterize the relations between upslope

disturbances and watershed condition

Evaluation of watershed condition would occur on a 5- to 10-year cycle. This would

include evaluation of the effectiveness of forest practices (that is, standards and

guidelines) and of the ongoing watershed analysis process relative to its intended

role in scaling the ACS objectives to specific watersheds, and in providing the ecological context of watersheds at larger scales. These components will provide a

foundation for developing modules of other aquatic and riparian resources and

species.

62
Other Forest Plan

Resources

We believe the step-down process that we developed for prospective, habitat-based

monitoring (see table 2, Chapter 2) should apply to most ecological resources of

concern in the forest planning area; for example, the concepts described above for

monitoring spotted owl and marbled murrelet populations should apply to the surveyand-manage species group. Given the many species included in that list (USDA and

USDI 1994), however, a focal-species approach may be essential. For system-based

resources, such as old-growth forests and watersheds, we encourage the development of conceptual models first to facilitate indicator selection.

The approach we have outlined in this and the preceding chapters (see table 2 and

subsequent discussion) provides only general guidance to the prospective monitoring

process. To appreciate the full details required of a monitoring program, the reader is

referred to the reports for each monitoring module (Hemstrom et al., in press; Lint et

al., in press; Madsen et al., in press; also see Furniss et al. 1997 [see footnote 2,

Chapter 1]).

Essential Data

Requirements for

Forest Plan

Monitoring

In a habitat-based approach to environmental monitoring, the initial data requirements

are estimates of the amount and distribution of vegetation communities (habitats),

their defining attributes, and landscape features in the entire planning area. These

baseline data—combining remotely sensed (aerial photographs, LANDSAT imagery)

and plot data (current vegetation survey data; CVS)—will provide a preliminary characterization of habitat structure and composition in a spatially explicit format (that is,

a map) across a range of related resource issues. Remeasurement at regular intervals will estimate the magnitude and pattern of change in major landscape features.

Given these patterns of change, including information on changes in habitat amount

and geometry, the expected biotic response can be predicted (see fig. 7, Chapter 2).

Minimal Components

for Baseline Monitoring

of the Forest Plan

The minimal data requirements to begin effectiveness monitoring under the Forest

Plan are spatially referenced data drawn from surveys of the entire planning area;

that is, a map of the structural and compositional features of the landscape, including information drawn from both public and private lands. The map will be based on

data collected from plot surveys and remote imagery (Hemstrom et al., in press [see

footnote 2, Chapter 1]). Standardized methods of change detection will need to be

devised and applied at two spatial scales: local and landscape. Before the mapping

exercise can begin, consensus on a standardized vegetation classification system

and the minimum spatial resolution of the data is required (Vegetation Strike Team

1996).

Guidelines for the base map include:

• Use a spatially explicit framework that allows for easy and rapid updating. Create

an “electronic map” via geographic information system (GIS) technology. Attributes

to include as data layers should be carefully considered before data collection

begins, and emphasis should be strong on data reliability.

• Monitor trends in several response variables: for example, the area of each vegetation type; the patch-size distribution of vegetation types; statistics of spatial pattern (for example, degree of contagion of late-successional stage communities; contrast between vegetation types; degree of connectivity within various vegetation

communities). Trend should be estimated at a variety of spatial scales (local to

regional).

63
• Manipulate the electronic maps, in the context of GIS, to simulate various stressor

effects (for example, timber harvest or road construction). These simulations can

be used to project changes in the response variables through time under various

stressor regimes, and to explore the extent to which they could be mitigated by

management actions.

• Select landscape elements that integrate disturbances across time and space (for

example, watersheds or provinces) as a basic unit for spatial monitoring. Change

statistics would be estimated at this scale and accumulated for provincial and

regional summary statistics.

The primary function of the map, and its regular updating, would be to provide a

straightforward tool for change detection at the landscape scale. Although a 10-year

cycle may be realistic, we propose the above list as a minimum set of analyses to be

conducted on no longer than a 5-year cycle, assuming annual updates to the map.

We also suggest that the remotely sensed data be cross-referenced with CVS plot

data (Max et al. 1996) to allow accuracy assessment for validation of the map classification and to provide estimates of classification error. Such a cross-reference also

should allow inferences to habitat change at the stand scale.

To be useful for modeling and to reliably forecast the effects of changes in land use

practices on ecological processes, the map, at the landscape scale, must be wall-towall to include attribute information on non-Federal as well as Federal lands.

Attributes of the Habitat

Map

The attributes of the habitat map will determine the initial set of variables available

for predictive modeling for owls and murrelets at the landscape scale. The key variables for inclusion in the map will be determined by the intersection between the

habitat attributes that can be estimated from remotely sensed data and the attributes

relevant to owl and murrelet ecology. This map will be produced as part of the monitoring program for late-successional and old-growth forest resources.

The effectiveness monitoring program uses two perspectives of habitat, one from

remotely sensed attributes and the other from ground plot data collected at the stand

scale. Habitat is estimated at two distinct scales: landscape and forest stand. These

scales are appropriate to owls and murrelets and correspond to population processes

operative at the scales of the individual animal and the population. By some process

(to be determined by future research), stand-scale habitat quality metrics can be

accumulated upward to draw inferences to habitat quality at the population scale.

Consequently, inference to habitat status and trend for owls and murrelets can be

made at two independent scales of assessment.

Maps will be developed at two spatial scales, with preference given to

landscape-scale mapping. The minimal attributes to include are listed below:

Landscape scale—maps and analyses:

• Forest class (potentially forested; seedling and sapling forest; small, single-storied

stands; medium to large single-storied stands; large multistoried stands)

• Forest land class type (coniferous, mixed, deciduous)

• Vegetation unit (combination of forest class and land class type, resulting in 16

possible mapping units)

• Acreage by forest land class type

64
• Spatial distribution of land class types

• Distribution of patch interior areas by land class type

• Distribution of interpatch distances

Stand scale—data tables and analyses:

• Acreage by forest class and land class

• Tree-diameter distribution by species

• Canopy structure and height-class distribution by species

• Snag height and diameter distribution by species

• Down woody debris

The development of a reliable landscape-scale map of forest types, accompanied by

annual updating for change analyses, is an essential landscape habitat component of

an effectiveness monitoring program for the Forest Plan. Another essential site-scale

habitat component is the existing CVS survey information; additional measurements

are required for CVS to obtain site-scale habitat measures for the spotted owl and

marbled murrelet. Because these data have utility beyond the monitoring program,

a consolidated ecological classification, inventory, and mapping program crossing

agency and program lines would be most effective and useful.

Summary of Key

Points

The application of the scientific approach presented in Chapter 2 to effectiveness

monitoring for the Forest Plan is presented in this chapter. The scientific justification

for a gradual transition to a habitat-based approach is discussed along with the basis

for the selection of focal species. The dependence of this approach on the development of reliable habitat-relation models is presented, and the application of these

concepts to late-successional and old-growth forests, northern spotted owls, marbled

murrelets, and aquatic-riparian resources is discussed. The data requirements for

these modules are reviewed, emphasizing the importance of both remote sensing

and ground-plot surveys of vegetation to all aspects of the monitoring program.

Acknowledgments This chapter includes contributions from Thomas Spies and Martin Raphael.

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68
Chapter 4: Components of the

Effectiveness Monitoring Program

Craig J. Palmer and Barry S. Mulder

A Structured

Approach to

Monitoring

The monitoring program for the Forest Plan will produce considerable information

throughout the life of the program, including a wide variety of data tables, maps,

and reports. Numerous individuals and groups will participate in this effort at many

localities across Washington, Oregon, and northern California. The mechanics of how

this information is collected and managed will dictate how successful the monitoring program will be in the adaptive management process.

Efforts to plan monitoring programs almost always focus on the technical aspects

of design, with little consideration given to the specifics of who will actually do the

work and, even more important, how that information will be maintained and used.

The basic problem in implementing a monitoring program is that the full process of

monitoring—including data collection, management, analysis, and reporting—has

not been well organized and usually not institutionalized in existing agency cultures

(Bella 1997, CENR 1997, Grumbine 1997, Morrison and Marcot 1995, NRC 1990,

Noble and Norton 1991, Schreuder and Czaplewski 1992, U.S. GAO 1988). Structured, periodic reporting is critical (Vora 1997), but people often take for granted that

information will appear when it is needed, without much thought about how it actually

gets there. In addition, few models at this scale are available to illustrate what will be

needed to adequately support an ecosystem-based monitoring program (Persson and

Janz 1997).

69
To respond to this concern, we have attempted in this chapter to answer the question, What is a monitoring program? Although not a major part of the assigned task,

our experience made us realize that the scope, complexity, and magnitude of the

proposed monitoring designs for the combined resources required an operational

context. A monitoring program is more than data collection, and more than a source

of information in the adaptive management cycle. In developing and implementing

a monitoring program, equal consideration must be given to collecting, managing,

and reporting information (Persson and Janz 1997). The program resulting from

these considerations for the Forest Plan will be different, however, from how we

have traditionally handled monitoring activities and will require giving thought to

the operational components of a monitoring program. Close collaboration during

all phases of planning is required between decisionmakers and those designing a

monitoring program (CENR 1997; Morrison et al., in press; Noble and Norton 1991;

Vora 1997).

To understand the myriad decisions that managers must make to implement the

monitoring program for the Forest Plan, we offer a conceptual model (fig. 18). The

model focuses on the five operational components of a structured and organized

monitoring system and illustrates the relations among these components. Each box

represents people, assignments, and products, and the model helps illustrate how

decisions need to be made to manage the different elements successfully. It can

help to identify needed actions and documentation. By referring to this model, staff

can be assigned specific tasks, for specific purposes, and essential collaborators

can be identified. Although not emphasized in this diagram, feedback occurs among

each of the components to encourage continual improvement and ensure ongoing

utility in the program.

The model also can clarify how staff responsible for the different components

contribute to the adaptive management process. Each box in figure 18 identifies

a process or activity. Data collection results in developing databases that must be

maintained. Databases, especially large ones, are most valuable when they have

been summarized into useful information. This information must then be interpreted

in the context of other information (such as recent scientific research) to improve

the understanding and knowledge of the systems we are evaluating (U.S. GAO

1994). This knowledge can then become an important component in contributing

to wise management decisions (Keune and Mandry 1996, Roots 1992, Thow-Yick

1994). A representation of these concepts is presented in figure 19 (see also fig. 2,

Chapter 1). This chapter will describe each of the critical components that are part

of a monitoring system.

Figure 18—Conceptual model of the components for implementing an operational monitoring program.

Collect

data

Summarize Interpret Make

decisions

Coordinate

and support

70
Data collection represents the largest component of a monitoring program, usually

employs the most staff, and is the most costly part. To ensure that collected data

meet our needs, the monitoring design is critical (see Chapter 2, “Key Steps in Designing the Monitoring Program”). Agencies often are data rich because of the myriad

programs involved in collecting information (CENR 1997, Olsen and Schreuder 1997),

so part of the exercise is to determine if existing activities can contribute to the monitoring effort. An objective of this section is to identify the role of existing projects in

the region of the Forest Plan that may provide critical data needed for effectiveness

monitoring. A second objective is to provide a strategy to address important monitoring-data needs not currently being met by ongoing projects. Because these projects will need to be integrated into effectiveness monitoring, an approach also is

presented for coordinating these efforts and improving data collection over time.

Review of Ongoing

Data-Collection

Activities

Many existing agency inventory, research, and monitoring activities are collecting

data of value to effectiveness monitoring in the region of the Forest Plan. For example, current data collection in the region of the Forest Plan can be arranged into four

categories as illustrated in figure 20. This figure shows the relations between these

four categories and how they may contribute to the effectiveness monitoring program.

Although some data collection activities may be initiated solely for the effectiveness

program (type A), there will be many other data collection activities that may contribute only partially (type B or C) or may not contribute to this program (for example,

type D).

Data

Figure 19—Conceptualized feedback between

data and decision for

adaptive management.

Information

Knowledge

Wisdom

Collect

data

Summarize Interpret Make

decisions

Coordinate

and support

Data Collection

71
Rather than duplicate projects that may contribute needed information, the monitoring

modules (Hemstrom et al., in press; Lint et al., in press; and Madsen et al., in press

[see footnote 2, Chapter 1]) build as much as possible on ongoing activities, which—

in a real sense—makes this a “value added” program to enhance the usefulness

of results from other programs. It also reduces or avoids new monitoring costs.

Programs should be evaluated to determine whether they could provide needed

information.

To meet the needs of the monitoring program, however, a project must meet certain

standards. General criteria to assist in placing data collecting activities into one of

the four categories given in figure 20 are presented in table 4. The purpose is to

evaluate the utility and importance of a project to effectiveness monitoring. These

criteria should be considered individually and then combined to develop a rating for

a specific activity. The criteria also can be used to identify changes to improve the

utility of a project for the monitoring program.

Because of the potential to adapt existing activities1 to meet the needs of the

monitoring program, no type A activities have been proposed at this time. Examples

of existing Federal activities considered essential (type B) to effectiveness monitoring

include (also see appendix D, table 12):

• Forest Service and BLM’s current vegetation surveys (CVS)

• Forest Service and BLM remote sensing programs

1 Most existing activities proposed as essential sources of

data for effectiveness monitoring are not permanent resource

programs with base funding, but instead have been funded

and staffed as short-term projects (see Chapter 5 for funding

needs).

B

C

D

A

EM

Internal

External

essential

External

useful

unrelated

External

Figure 20—How data-collection projects are related to

effectiveness monitoring. EM = data collection needs

of effectiveness monitoring; type A = monitoring activity

initiated solely for the effectiveness program and internal to it; type B = external activity, initiated for other

purposes, that makes a significant contribution to the

data requirements of effectiveness monitoring; type

C = external program that provides useful but not

critical data; and type D = external activity with no

apparent connection to effectiveness monitoring.

72
• Northern spotted owl demographic studies (Federal research agencies)

• Watershed analyses as required under the ROD (USDA and USDI 1994)

Several projects in this category, such as CVS (Max et al. 1996), have recently

been established with a major purpose of meeting the needs of programs such

as effectiveness monitoring. As the Forest Plan monitoring program is established,

these projects may move to the type A category.

Table 4—General criteria for evaluating utility of existing or new projects for effectiveness monitoring (EM)

Internal to EM External essential External useful External unrelated

Criteria (type A) (type B) (type C) (type D)

Objectives Focus on EM Addresses one or more Related to EM objectives Not related to EM

objectives EM objectives objectives

Monitoring

questions Answers specific Contributes to or answers Useful to EM questions Not related to EM

EM question(s) one or more EM questions questions

Geographic

coverage or

scale Rangewide on Rangewide on Federal Lands— Single province or partial Local or limited

Federal lands— Forest Plan area—or when coverage coverage

Forest Plan combined with other components,

becomes a rangewide program

Indicators and

measurements Focus on EM Includes EM indicators within Other useful data (e.g., Not related or unknown

indicators regular project indicators stressor data) value

Sampling design Statistically valid Statistically valid for EM objectives Statistically valid for other Not statistically valid

for EM objectives in addition to original project objectives objectives design or no design

Project duration New Ongoing Likely to end Has ended

Historical record New Long term Short term Minimal

Quality assurance Under EM quality- Rangewide comparability Inconsistent quality No quality assurance

assurance program assurance

Database

management Designed for EM Well designed, efficient, and Inconsistent No database managemaintained ment

Database access Accessible Accessible electronically with Accessible but not in Database not accessible

electronically project permission electronic format

through the EM

program

Project reports

available To be available Available on annual or other Occasional None available

annually planned period

Ability to adapt

to changing EM Intended to be

needs improved over

time Can be improved Low flexibility No flexibility

Uses (for planning,

budgets, reporting) Intended for these Contributes to these uses Occasional use Not of use

uses

Costs; availability

of base funding New Currently funded Uncertain None or unknown

Costs; additional

base funding

needed to address

EM objectives New Small to none Medium Large

73
Type C activities collect data that are of value but not essential to the success of

the program. One method of evaluating whether a program is type B or C is to

decide how the utility of the monitoring program would be affected by terminating

these activities. If the monitoring program could not meet some of its objectives

because a type C project was discontinued, then it probably belongs in type B. If

the program could continue to meet its goals even though the analyses might be

diminished through loss of type C activities, then the activities fall in the C category.

Examples of existing projects that provide information of value (type C) to effectiveness monitoring include FHM, FIA, and murrelet marine surveys (also see appendix

D, table 13). Additional projects that collect useful information likely will be identified

as monitoring plans are developed and implemented. If these activities are consistent (see table 4) in protocol and design required for monitoring (for example, see

the murrelet module [Madsen et al., in press] for discussion on marine surveys), the

data would be essential (type B) to effectiveness monitoring. Of particular interest

will be how the large number of projects currently involved in gathering information

on aquatic and riparian resources will meet the goals of the effectiveness monitoring

program (Furniss et al. 1997 [see footnote 2, Chapter 1]).

Addressing New

Data-Collection Needs

We anticipate that some data requirements for effectiveness monitoring may not be

met by ongoing data-collection activities. Two approaches should be considered to

address these additional needs. First is to enhance or adapt existing efforts by adding

to them the new measurement variables required for effectiveness monitoring. The

additional expenses from adding a new variable to an existing survey are much less

than starting a completely new program. The CVS program, for example, is testing

the possibilities of adding measures for murrelet nesting habitat to ongoing surveys

(Madsen et al., in press [see footnote 2, Chapter 1]). Although minimizing the cost

of collecting these data, this strategy also will improve the use of data from existing

survey efforts in the monitoring program. But before new measurement variables

are added to existing projects, they need to be tested. In addition, existing projects

should have appropriate spatial and temporal coverages to meet the needs of effectiveness monitoring (see table 4).

The second approach is to develop entirely new data-collection projects for the montoring program (type A). Each project would follow the design criteria described in

Chapter 2 (“Key Steps in Designing the Monitoring Program”), have a detailed project

plan, obtain funding, and be fully integrated into the monitoring program.

Coordination Among

Data-Collection Activities

Numerous projects will contribute data to the monitoring program. Coordinating the

integration of these activities into the effectiveness monitoring program will have multiple benefits, such as improving the ability to use data for multiple resources, thus

reducing costs (see “Integration,” below, this chapter). Any data-collection activities

that fall into the type A category would be directly managed as an integrated part of

the program. Type D data-collection activities would not need to be integrated unless

data of particular interest to the program are collected, which then moves them into

another category. What requires thought, however, is how to integrate information

from the B and C categories into the program.

74
Type B monitoring projects collect data critical to the success of the effectiveness

monitoring program. Many of these projects (for example, CVS and spotted owl

demographic studies) are funded by various Federal agencies and would need to

be continued to be useful to effectiveness monitoring. A decision by an agency to

alter or terminate a type B program could have profound implications to the success

of the monitoring program. In other words, information provided by these projects is

so critical that close coordination and integration with the monitoring program are

essential.

Although type C activities may not be essential to the effectiveness program, we

recommend that some coordination with the monitoring program be considered for

these activities. Monitoring staff should keep informed about results coming from

these activities by periodically reviewing project reports and attending program

reviews. Emerging needs of monitoring should be communicated to managers as

well. Significant changes in these projects should be identified, in case data from

the projects become essential for effectiveness monitoring.

Coordinating and integrating the data collected through all these independent

efforts is critical to the success of the monitoring program. The following steps

would contribute to achieving this integration:

• Direct staff links. Monitoring staff would be responsible for coordinating mutual

activities. Direct staff links will foster awareness of any changes contemplated for

these projects that could affect the monitoring program.

• Direct data links. A direct link to data from type B activities by effectiveness monitoring staff is essential. Monitoring staff and managers will require rapid and consistent access to these data and associated information about the data (metadata)

to complete the required monitoring reports.

• Quality-assurance (QA) systems. All data used in monitoring must be of documented quality to be legally defensible (see “Quality Assurance,” below, this

chapter). Assessments of data originating from many sources are most useful

when data are comparable, compatible, and verifiable (Burke 1996, Schroder

et al. 1996). Because most current monitoring projects do not have established

QA systems, the monitoring program will need such oversight for its activities.

Oversight would include implementing QA project plans, QA management reviews,

and QA reports.

Strategy for Continual

Improvement

A major goal of the monitoring program is to continually improve the quality and

utility of data. Because many of the essential data-collection activities are external

(type B and some type C), the program will need to provide feedback to the managers of those activities about the utility and quality of the data to keep them informed

or reminded of the requirements of the effectiveness monitoring program. Internal

(type A) data collection can be improved through annual debriefings of field crews,

review of the quality of data, reports from data analysts about the utility of the

collected data, and feedback from policymakers (see “Management Decisions,”

below, this chapter).

75
The reporting of information has been a major problem in environmental monitoring

(CENR 1997). To facilitate this process, two essential types of reports are described:

data summaries and interpretive reports. Each has an important role in making data

collected for effectiveness monitoring available to decisionmakers; interpretive reports

are covered in the next section.

Data summaries are brief, comprehensive reports of essential data collected for the

monitoring program by internal (type A) or external (type B) data-collection projects.

The primary intent of a summary is to present data in an organized and useful manner. Some evaluations of the significance of the results also may be presented, if

readily apparent. Data summaries should be prepared for each module each year,

or as appropriate to the resource being monitored (for example, some activities may

not collect data every year); therefore they can be preplanned.

The routine preparation of data summaries will provide several important benefits

to the program. If prepared on a predictable and recurring basis, the summaries can

foster program support by establishing a client base for the reports (Ember 1995).

Preparing them also serves to motivate data collectors to process their data in a

timely manner so that assessment and reporting needs can be met. These reports

provide a tangible product for which staff and agencies can be held accountable each

year. Most important, data summaries are essential building blocks for preparing

interpretive reports and provide intermediate progress reports for assessing Forest

Plan objectives. In this section, we discuss the steps required to summarize data

and prepare these reports (fig. 21). Two options will be considered for the spatial

coverage of individual reports, and a strategy for continually improving these reports

is included.

Summarizing Data The first step in summarizing and evaluating ecological data is to conduct a quality

check of the data to be used in preparing the report. The process—called data

validation—results in the acceptance, rejection, correction, or qualification of data.

Although this step often is overlooked, it is essential to producing credible and valid

results (see “Quality Assurance,” below, this chapter).

Collect

data

Summarize Interpret Make

decisions

Coordinate

and support

Data Summaries

Data

validation

Collected

data

Data

analysis

Data

presentation

Report

preparation

Figure 21—Data summarization process.

→→→ →

76
Once data have been accepted or corrected through validation, the summary process

begins. An important first step may be to consider combining many measurements

into simple indices useful for interpretation (see Chapter 2, “Expected Values and

Trends”). Examples of some useful indices might be adult female survival rate, biotic

integrity, species diversity, down woody debris per acre, or connectivity between

old-growth patches on the landscape (for example, see Hemstrom et al., in press;

Lint et al., in press; Madsen et al., in press; also see Furniss et al. 1997 [see footnote 2, Chapter 1]). Regional standards should be developed for how these indices

are calculated (Thomas 1996); the indices can then be presented in the summary

reports as statistical tables, bar charts, cumulative distribution functions, figures, or

maps. Trends also can be calculated by comparing results with those reported in

previous data summaries.

Staff members responsible for collecting or working with the monitoring data would

be expected to play the major role in preparing data summaries. These individuals

would be responsible for obtaining data from external monitoring projects, validating

these data, synthesizing them into useful indices, evaluating them, and preparing the

summary reports.

Content of Data

Summary Reports

The data summaries would include monitoring results for each of the resource issues

(that is, late-successional and old-growth forests, spotted owl, marbled murrelet, and

aquatic and riparian). Monitoring questions for which data have been collected should

be addressed in the data summary, and results should be reported as quickly as possible to avoid a delay in the evaluation if major issues arise. Data can be presented

in many forms including statistical tables, charts, figures, and maps. We recommend

developing standardized formats for each resource report so that new data can be

readily substituted each year into subsequent reports.

The first data summaries prepared by the monitoring program will be important milestones because they will be the model for future summaries. These first reports must

undergo extensive review to evaluate their utility and the accuracy of the assessments. This review also should include a detailed examination of the data-assessment algorithms, because they will be used for future reports. This examination

should check whether the appropriate calculations have been made and if they

have been correctly programmed for computer or other use.

Options for Data

Summaries

A unique challenge for the monitoring program will be to prepare summary reports

with data from many different monitoring efforts. Each effort may cover different areas

and have different remeasurement cycles, and complete data sets may not be available each year from every monitoring activity. Note that a monitoring program for a

specific resource may consist of more than one field project; for example, in the eight

demographic studies for the spotted owl, data are collected every year, managed,

and reported separately by project. On the other hand, in any one year, data are

collected for only part of the range of the Forest Plan for the Forest Service CVS

project. Regardless, the information from these different projects needs to be combined for use in the regional summary reports.

Several options exist for developing data summaries. One is to report all monitoring

data collected during any given year from across the Forest Plan region for all resource issues. This approach would tend to encourage timely evaluation of all data.

An implication of this approach is that monitoring would need to be active in all parts

of the region every year, which would allow frequent regionwide analyses or interpretations of monitoring data.

77
A second approach is to select a subregion (such as two or three provinces) or a

specific resource, and complete a summary report for those on a rotating basis.

The report could include all data from the most recent remeasurement cycle that

may have taken a monitoring group several years to collect. Over a period of 4 or

5 years, all areas could be covered. This approach would mean that the synthesis

or interpretation of results for the Forest Plan could be done only after analyses for

all areas had been completed. The region could be divided by political or ecological

boundaries. The choice of political boundaries would foster cooperation among

management units, and the choice of meaningful ecological units would foster the

ecological interpretation and presentation of data. Focusing on a specific resource

issue may help address emerging questions that arise unexpectedly (for example,

under the ESA).

Strategy for Continual

Improvement

One goal for the staff preparing data summaries should be to continually improve

them over time to make them more useful and responsive. Obtaining feedback

about the quality and utility of the data summaries through peer reviews, management reviews, or client surveys is important. These surveys can be included with

each report, and feedback from the surveys and reviews can be used to improve

subsequent reports.

Publishing monitoring data as well as reports also would be useful, for example;

by providing access through the Internet. This strategy offers external reviewers

an opportunity to look at the data and verify or identify trends. Some may not want

their data published, but the program could provide a link to data users by listing data

sources in summary reports. The goal is to improve methods for evaluating

data by encouraging as many people as possible to use monitoring results.

The primary product of the effectiveness monitoring program will be a periodic regionwide interpretive report. The purpose of the interpretive process is to evaluate the

ecological significance of status and trends emerging in the monitoring data, as

identified in data summaries (fig. 22). The role of interpretive reports is to present a

synthesis of monitoring results and statements of their implications to management

for each resource being monitored, and how those results may relate to continuing

implementation of the Forest Plan. This information is critical to the adaptive management process mandated in the Forest Plan; it will be used to change plans, direction,

or policies and contribute to budgetary and other decisions.

Interpretive reports differ from data summaries in several key features. Interpretive

reports are more analytical and comprehensive than data summaries. Considerable

effort and planning are required to develop successful interpretive reports, and they

will require significant participation by knowledgeable agency scientists. This section

describes a process for preparing interpretive reports, including options for staffing,

the reporting frequency, and a strategy for future improvement.

Collect

data

Summarize Interpret Make

decisions

Coordinate

and support

Interpretive Reports

78
Preparing Interpretive

Reports

The key task of interpretive reporting is to address the effectiveness monitoring

questions by using all available data (fig. 23). The focus will be on analyzing and

interpreting trend data. Analyses should emphasize past trends, current status, and

projected future trends. These trends should be explained in terms of their ecological

context and their implications to management (for example, Southern Appalachian

Man and the Biosphere Cooperative 1996).

Sources of data include all monitoring activities producing types A and B data and

useful type C data. Summary reports provide an important starting point for additional

data analyses, as do relevant research findings, especially new ones. In addition,

other information that may have a bearing on the monitoring questions should be

considered and addressed, as appropriate, such as information from other lands (for

example, National Park Service or non-Federal lands) or from other related programs

(for example, FHM, FIA, or local databases). All data used in preparing interpretive

reports need to be validated, although much of this validation will have been completed in preparing data summaries.

Interpretive

report

LSOG

summary

reports

NSO

summary

reports

Other

summary

reports

Project

reports

Maps ,

GISdata Models,

other data

Other

information

Figure 22—General flow of information from projects to

annual data summaries to periodic interpretive reports using

LSOG as an example. LSOG = late-successional old growth,

and NSO = northern spotted owl.

Figure 23—Information sources and topics

addressed in effectiveness monitoring.

Summary

Reports

Relation to

goals and

objectives

Implications

for

management

Emerging

issues and

research

needs

General

condition

status and

trends

Other

validated

data

Recent

research

Models

Interpretive

reports

• Resource

issues

• Whole

program

→

→

↓

↓

↓

↓

→

→

79
Content of Interpretive

Reports

The ultimate goal from an ecosystem perspective is to address status and trend

questions about the Forest Plan, as gained through knowledge of the status and

trends for the resources being monitored. As a starting point, all effectiveness

monitoring questions outlined in the modules for each resource (Hemstrom et al.,

in press; Lint et al., in press; Madsen et al., in press; also see Furniss et al. 1997

[see footnote 2, Chapter 1]) would be evaluated in the interpretive reports. Questions

may be prioritized or additional questions may be added depending on current information needs. The report may be organized to address specific resources, such as

spotted owls, or it may be organized to foster a synthesis of results across resources

that would facilitate conclusions about the status of the Forest Plan relative to these

resources. As with summary reports, a standardized format should be developed.

These reports would interpret monitoring signals in the context of all relevant

data and contemporary ecological theory, and a range of possible management

responses to the monitoring results should be suggested for each resource. Discrimination among possible management responses to the monitoring data should

be made in a formal decision-theoretic framework (Marcot 1994; also see Chapter 2,

“Linking Monitoring Results to Decisionmaking”). That is, the possible decisions

should be listed, the likelihoods associated with each possible interpretation of the

monitoring data estimated, and the costs and benefits associated with each possible combination of decision and likelihood should be thoroughly discussed. By

this process, the monitoring data fully contribute to the decision process. Each step

should be fully documented in the interpretive report.

Options for Frequency

and Staffing of

Interpretive Reports

Given the long time frame of monitoring, interpretive reports would not be needed

every year, but should be prepared at periodic intervals (for example, see monitoring

modules [Hemstrom et al., in press; Lint et al., in press; Madsen et al., in press]).

Reporting would be regional, although a more focused report addressing a specific

question or resource may sometimes be needed. Options can range from intervals

of 1, 2, 5, or 10 years, although a 5-year interval may be the most practical. Oneor two-year intervals would be too frequent, given the intensity of effort required to

prepare the reports and because of the time required for significant trends in data

to emerge. Because agency planning occurs on about a 10-year cycle, a 5-year

reporting interval offers land management agencies the benefit of two interpretive

reports on which to base changes to management plans in the normal planning cycle,

or to make adjustments between cycles if the results so warrant (see Chapter 5 for

milestones). More frequent reports could be provided if necessary or requested.

A significant amount of preparatory work is associated with developing these reports,

and several options exist for preparing them. One would be to assign specific agency

staff who would be dedicated to preparing the report. The problem with this approach

is that a 5-year cycle may be too infrequent to justify hiring staff solely for this task.

Staff responsible for collecting monitoring data may participate, but a range of other

disciplines including both staff and managers needs to be involved. Specific expertise

would include data managers, statisticians, subject-area specialists, ecological modelers, and remote-sensing and GIS specialists. The regional monitoring staff would be

most logical to manage this task with specialists assigned as needed. The number

would differ depending on the nature and size of the expected report (see “Infrastructure Requirements: Institutionalizing the Program” in Chapter 5).

80
A second option would be to select a consulting panel of experts to assist in-house

monitoring staff. The panel would be composed of agency scientists with expertise

to address the monitoring questions. Program staff responsible for preparing data

summaries would provide technical support to the panel of experts. The panel would

have responsibility to evaluate the implications of answers to the monitoring questions

and identify important followup actions. For the panel of experts to be most effective,

they would need to be assigned 2 years before an interpretive report is to be completed. This timing would allow them to organize data, evaluate the summary reports,

identify other relevant external data, and prepare the reports. This approach also

would allow the participating agencies to budget for this periodic activity and allow

research agencies to adjust assignments of key research staff needed to participate.

A third option would be to contract the preparation of the interpretive reports to a

separate panel of scientists or consultants. An example could be a contract with the

National Research Council to prepare an interpretive report on monitoring questions

for a specific resource issue; other supporting factors noted under the second option

above would be applicable.

Strategy for Continual

Improvement

As with the data summary reports, obtaining feedback through peer reviews and

client surveys on the quality of the interpretive reports is vitally important. An additional method of feedback could be from encouraging involved scientists to publish

key results of data analyses in peer-reviewed journals and using the results to

improve the process.

Readers may wonder why a step for making decisions has been included as a

separate component of the effectiveness monitoring program. As Noble and Norton

(1991) concluded “Many monitoring programs fail not because of poor design or

measurement but due to a lack of appreciation or understanding by managers of the

purposes of the exercise.” The monitoring program is set up to generate a continual

output of information and reports. A major problem for the adaptive management

process will be knowing how and when to use this information. This section discusses

how to integrate this information into the planning and decision processes, and it

identifies ways of making monitoring more useful to those making decisions; the

latter area, particularly the emphasis on use of decision theory and risk analysis,

needs further investigation (see “Research Support,” below, this chapter).

Management

Decisions

Collect

data

Summarize Interpret Make

decisions

Coordinate

and support

81
Conceptual Model A general schematic of the relation of monitoring to agency policies is presented in

figure 24. Changes in resource condition result from a combination of natural stressors, land management practices, and regulatory practices. The role of monitoring is

to provide feedback about status and trends in resource condition that will be useful

in evaluating the effects of land management or regulatory practices. If undesired

effects are encountered, agency administrators can consider changing policies, or

delaying implementation of policies until the full consequences are better understood.

Implementing a change in policy affects land management and regulatory practices,

which in turn would affect resource conditions over time. Subsequent monitoring can

then provide feedback to management on the effectiveness of these new practices.

Roles of Monitoring in

Land Management

Decisions

Monitoring data and the resulting reports are important to administrators because

they are sources of feedback about results of past and current practices and help

identify need for change to future practices (Morrison et al., in press; Vora 1997).

Although adaptive management is proposed as an ongoing, iterative process that

implies flexibility, it is not easy to do in an agency environment (Bella 1997; Hilbran

1992; Lee 1993; Morrison and Marcot 1995; NRC 1990; U.S. GAO 1988, 1994). A

formalized process of evaluating and responding to new information is needed that

makes routine the interactions of monitoring staff and decisionmakers (Marcot 1994;

Morrison et al., in press); to our knowledge this process does not exist.

The program will be monitoring, for example, status and trends in spotted owl habitat

across the region of the Forest Plan, in terms of areal extent, connectivity, fragmentation, and other indicators (see northern spotted owl monitoring module [Lint et al.,

in press]). The implications of these trends would be described in the interpretive

report and provided to the appropriate administrators who would then consider the

conclusions, including their reliability, and make a decision relative to current policies

Figure 24—Conceptual model of how monitoring is related

to management.

Regulatory

practices

Natural and

human-caused

stressors

Regulatory

policies

Land

management

policies

Land

management

practices

Monitoring

and

research

Resource

condition

Regulatory

agencies

Land

management

agencies

←

↓

↓

↓

←

↓

→

→

→

82
(fig. 25). Policymakers also would consider several other factors, such as politics,

economics, and availability of resources. Decisions could include improving Forest

Plan implementation, continuing on the same path, reformulating implementation,

changing the standards and guides, altering monitoring strategies, requesting assistance from research agencies, or other actions. Any change in the Forest Plan, of

course, would require National Environmental Policy Act (NEPA) consideration and

would be handled on an interagency basis.

Strategy for Continual

Improvement

Given our experience with past monitoring efforts, a significant need exists to improve

the monitoring process so that relevant feedback is provided to managers that will be

useful in making timely decisions. This effort is collaborative, and land management,

research, and regulatory agencies involved with the Forest Plan can benefit from participating in and supporting monitoring. Monitoring staff and managers should take

several steps to ensure that monitoring continually improves.

The first step is to make sure that the questions most relevant to management are

being addressed in monitoring reports; feedback should be solicited from managers

before and after these reports are prepared (Vora 1997). The feedback should come

from land management, research, and regulatory agencies. A second step is to assure that the data in monitoring reports are presented in ways useful to management

(Marcot 1994; Morrison et al., in press). Periodic meetings should be held among key

monitoring agency staff, researchers, and managers to discuss the implications of

data summary and interpretive reports (see Chapter 2, “Linking Monitoring Results to

Decisionmaking”). Important findings should be highlighted rather than simply

presented to managers as many multicolumned tables and complicated charts. A

third step is to assure the data are sufficiently accurate to meet the needs of management and reduce uncertainty (see “Quality Assurance,” below, this chapter). If

uncertainty in interpreting monitoring results is high, managers may have difficulty

making reliable decisions (U.S. GAO 1994, Williams et al. 1996). Considerable dialogue will be needed between management and program participants to identify

requirements for the quality of results.

Summary

reports

Management

decisions

Interpretive

reports

Possible outcomes

• Changes in

plans, policy,

practices

• Research

needs

• Changes in

monitoring

program

Other input

• Social and

economic

• Funding

limitations

• Priority issues

• Policy

• Other

monitoring

• Research

↓

↓

→ →

Figure 25—Factors affecting decisionmaking.

83
The monitoring system component, called program coordination and support, spans

all aspects of monitoring from data collection through reporting to decisionmaking.

This section describes critical support functions underlying the program, focusing

on integration, research, pilot and testing projects, information management, and

quality assurance.

Integration A goal of the monitoring program is to integrate all the critical components, including

data collection, information management, and assessment and reporting, by using

common databases to answer monitoring questions across a range of resource

issues; linking monitoring efforts with research, implementation monitoring, or

monitoring programs of state or other agencies; or integrating assessments and

methods to evaluate trends. The outcome will maximize use of collected data,

increase cost effectiveness of the program, and improve the ability to evaluate

data from an ecosystem rather than an issue-by-issue perspective. Integration is

becoming a major theme in recent literature (CENR 1997, Marcot 1994, Olsen and

Schreuder 1997, U.S. GAO 1994). This task is challenging, given the diversity of

cooperating agencies, the number of resources being monitored, and the variety

of different monitoring groups.

A primary concern has been developing a strategy to integrate the monitoring components of the different resources (for example, late-successional and old-growth

forests, spotted owls, marbled murrelets, and aquatic and riparian resources), so

that the program to facilitate evaluation of the Forest Plan is unified. This will not be

easy. Our approach has been to develop a scientific and management framework

that fosters integration. The monitoring plans for each resource follow a common

monitoring approach, conceptual framework, indicator-selection strategy and monitoring design, and data-assessment and reporting process. Similarly, strategies to

address research needs, pilot studies, data management, and quality assurance are

part of each plan. Developing these strategies has already fostered integration in the

program by identifying common areas, such as using regional vegetation maps to

assess LSOG and spotted owl and murrelet habitat. Further efforts are needed to

improve methods of integrating appropriate activities.

Research Support The scientific development of the effectiveness monitoring program has presented

many challenges, particularly in attempting to apply the concepts and theory in the

literature to this program. As the program develops, these challenges will expand

because of the complexity of the issues being monitored. Given the conceptual nature

and complexity of the program, drawing a clear distinction between research and

monitoring is difficult; rather it is becoming clear that they are meant to be companion

programs (CENR 1997, Marcot 1994, Olsen and Schreuder 1997). At the present

time, research agencies are highly involved in ongoing monitoring projects, such as

Program Coordination

and Support

Collect

data

Summarize Interpret Make

decisions

Coordinate

and support

84
the owl demographic studies, and they will have a major role in fostering development of the conceptual approach and framework for additional monitoring through the

interagency strategic research plan (Benson and Owston 1997) for the Forest Plan.

Research will be needed to address emerging needs, such as selecting new

indicators, establishing associated sampling designs, and estimating thresholds for

assessing monitoring trends. Research expertise also is needed to assist managers

in interpreting monitoring data (Marcot 1994), particularly with integrated data sets

pertinent to regional monitoring questions.

Priority research issues relevant to the effectiveness monitoring program—

Table 5 represents an abbreviated listing of key scientific issues, discussed by the

EMT, critical to the effectiveness monitoring program, particularly to support the

interpretation and use of monitoring results (see Chapter 2, “Expected Values and

Trends” and “Linking Monitoring Results to Decisionmaking”). To a large degree,

these topics are only partially understood and represent priority areas for continued

research within the field of applied ecology. We list them here to demonstrate the

close connection between monitoring and research and to encourage Federal

agencies to support research relevant to effectiveness and validation monitoring.

Research issues for individual monitoring modules—Critical research needs

also have been identified for each of the resource issues; for example, the need

to develop improved landscape-and stand-scale indicators for monitoring LSOG,

develop improved predictive models relating spotted owl demographics to measures

of owl habitat, improve estimates of murrelet populations and nesting habitat, and

improve remote-sensing evaluation of riparian conditions (for example, see monitoring modules [Hemstrom et al., in press; Lint et al., in press; Madsen et al., in

press; also see Furniss et al. 1997]). Although some of these research questions

may already be under investigation, all these examples suggest a continuing need

to include research as a component of the monitoring program.

Two approaches are suggested to address research needs. The first is to develop

a process for communicating the research needs to the research agencies, so that

the needs can be integrated into the long-term strategic research plan; needs should

be prioritized so higher priority items can be given preference. Although research support for monitoring will need to compete with other priorities for available research

budgets, needs should be identified in monitoring reports and provided to the heads

of research agencies and agency scientists. Scientists participating in evaluating

monitoring data also may identify key research activities relating to their areas of

expertise. A second approach is to use pilot or test studies to investigate critical

research needs for monitoring (see next section).

Pilot Testing and

Development Projects

Pilot or test projects have several roles in monitoring (Brunner and Clark 1997;

Ringold et al., in press). One is as a proving ground for new ideas. Because some

aspects of the monitoring program could have significant budgetary implications,

pilot studies offer opportunities to test whether the appropriate approaches are

being taken, with less stress on budgets. Besides testing, pilot studies also can

help to refine and improve selected monitoring methods, indicators, sampling

designs, or data evaluation techniques.

85
Table 5—Priority research issues to support effectiveness monitoring

Research topic Explanation of need

Make predictions

across temporal

and spatial scales Observations often are limited to a single spatial and temporal scale (that is, the scale at which

measurements are taken), but our interests usually extend beyond the scale of observation. How

do we reliably make predictions from phenomena observed at one scale to other scales in the

ecological hierarchy?

Select an optimum

set of indicators Of all the possible attributes of the environment that can be measured, does a step-down algorithm

exist to enable the selection of a small subset of attributes whose values allow reliable inference to

the “integrity” of the larger ecosystem?

Estimate “normal”

rates of change of

ecosystem processes Ecological systems are inherently dynamic; that is, we expect their attributes to change through

space and time. What rate of change (for example, in the sense of a time derivative) is an

appropriate benchmark for a given ecological process?

Define the expected

“range of variation”

in natural processes We are aware that the concept of ecosystems occupying a steady-state equilibrium position is flawed.

What magnitude of observed variation should be labeled “unexpected” and therefore treated as an

early warning of system degradation?

Identify threshold

regions of change

that trigger management responses This problem is closely tied to the previous one. Assume many possible states of an ecosystem are

integrated over some time interval or spatial domain, and these states can be characterized by a

probability distribution. What frequency of states in the right (left) tail of an observed distribution

should signal a change in management policy? How can threshold regions be established within

the range of the probastates that we accept

Define a “desired future

condition” for dynamic

systems If natural systems are inherently dynamic, how do we define the appropriate distribution of system

states that we accept as “normal”? What is an abnormal state? How are benchmark conditions

determined. How far back in time is an appropriate interval to establish benchmarks?

Detect cause in the

presence of time

lags and synergistic

effects Monitoring programs are most easily designed to detect the action of a single stressor leading to a

change in the state of a single indicator variable. How do we identify cause when the state of the

indicator reflects past disturbances, or the joint effects of multiple stressors, each of such limited

magnitude as to appear innocuous?

Link physical and

biological process

in the form of predictive

models The focus of most environmental monitoring programs has been exclusively on biological resources

and processes, but the key drivers of change in many ecosystems (for example, aquatic and riparian

communities) may be physical processes. How do we begin to link biological processes to poorly

understood or difficult to measure physical drivers of change?

Draw inferences to

the population of

interest from nonprobabilitybased samples The realities of sampling large areas under limited budgets, and with the complexity of private property

restrictions, often result in unorthodox survey designs. Given that our population of interest is usually

the entire ecosystem (which crosses ownership boundaries), how do we make valid statistical inference

when all possible components of an ecosystem are not within the sampling frame?

Address the uncertainty

in the monitoring “signal” The integrity of an ecosystem will be inferred from the nature of the signal based on the values of the

indicator variables. Given that the meaning of the signal is subject to sampling, measurement, and

interpretational error, how is this uncertainty addressed in the decision process?

86
Another role of pilot projects is to provide examples of potential products from monitoring, such as databases and reports. The importance of developing examples is

to help cultivate the support and understanding required to make long-term monitoring successful. A monitoring plan can describe in abstract terms the products it

could provide, but a pilot study can present prototype and concrete examples of

these products for consideration by monitoring clients. These types of interim or

test products are critical to successfully conducting a monitoring program.

A flow chart depicting the use of a pilot study in effectiveness monitoring is presented

in figure 26. Scientific, technical, and management issues in need of testing or evaluation should be identified. The issues are then incorporated into a pilot study plan

that identifies an approach to addressing them. After review and approval of the

study plan, the pilot study is funded and implemented. The results from the pilot

study are then used to improve monitoring procedures, to provide example products,

and possibly to achieve other objectives.

Pilot studies, however, should not be considered a surrogate or necessary first

step for implementing a monitoring program. Although important for addressing

some issues, many of these issues may take considerable time to investigate. Pilot

studies, as with research, should be viewed as companion components of a complete monitoring system and carried out concurrently such that continual improvements can be made to the program.

Oregon Coast Province effectiveness monitoring pilot—As a result of the effort

by the EMT to develop a monitoring strategy for the Forest Plan, a province-scale

monitoring pilot study was begun in October 1996 in the Oregon Coast Province to

evaluate monitoring strategies and identify problems that could be addressed while

the planning process was underway (Plumley et al. 1996). The study is comprehensive in that it attempts to test monitoring strategies for all four resource issues. It is

the proving ground for the majority of concepts described in the monitoring plans and

merits continued support from management, as well as agency funding. The project

is of critical importance to the effectiveness monitoring program; it is planned to be

completed concurrently with the first year of implementation of the program. The

results of this effort are intended to be used to help direct implementation; the project

thus needs to be carefully coordinated with program implementation.

! ! Pilot

study

Scientific

and technical

issues

Pilot

study

plan

Management

issues

Review

Funding

Results

Improve

EM

plan

Examples

Other

products

Figure 26—Use of pilot studies to improve the effectiveness monitoring

program. EM = effectiveness monitoring.

87
A summary of the objectives, key contacts, geographic scope, budget, and timeline for this study is presented in table 6. Proposed monitoring questions from each

resource module will be reviewed (for example, see monitoring modules [Hemstrom

et al., in press; Lint et al., in press; Madsen et al., in press; also see Furniss et al.

1997]). The availability and suitability of current inventory and monitoring data to

answer these questions will be evaluated. If possible, a provincewide assessment will

be conducted by using available data. Of particular interest is the objective to revise

monitoring plans of the Siuslaw National Forest and the Salem and Eugene BLM

Districts. These endeavors should help coordinate monitoring efforts for the Forest

Plan with the ongoing local National Forest and District monitoring and planning

efforts and help address questions about the utility of the regional program at the

local scale.

This pilot study also will foster the development of technologies required to prepare

summary and interpretive reports; for example, data assessment algorithms will be

developed and tested. Definitions and models for specific ecological conditions, such

as old-growth forests or marbled murrelet nesting habitat, will be refined. Procedures

for providing confidence limits for statistical estimates will be determined, and remotely sensed data will be compared to ground-plot data. This pilot offers a unique opportunity to foster the technical development of the effectiveness monitoring program in

concert with other activities proposed for the monitoring program.

Additional pilot studies—Several additional pilot or development projects are being

conducted by agencies with an interest or stake in monitoring in the Pacific Northwest. For example, the Environmental Protection Agency (EPA) is funding a project

to assess the use of remote imagery for characterizing riparian systems, and the

FWS is cofunding a project with Forest Service Research to evaluate different remote

Table 6—Oregon Coast Province effectiveness monitoring pilot

Topic area Descriptive information

Scientific and technical objectives Evaluate monitoring questions

Evaluate utility of existing and readily available data

Develop examples

Evaluate analytical methods for data analysis

Test integration of data across emphasis areas

Management objectives Develop strategy for integrating results into one framework

Determine links to adaptive management

Revise local monitoring plans

Identify costs

Key contacts Harriet Plumley, Siuslaw NF, Corvallis, OR

Tom Spies, PNW Research Station, Corvallis, OR

Scope and geographic area Federal lands in the Oregon Coast Province

Budget FY1997: $250,000

FY1998: $250,000

FY1999: $250,000

Timeline October 1996 to September 1998

88
sensing methods. Cooperative links with these projects should be developed because

they will contribute to the success of the monitoring program. As the program is implemented, additional scientific and management issues are likely to develop, such

as those identified in the preceding section. Pilot studies offer a cost-effective way

to develop strategies to address future issues while providing opportunities to continually improve and adapt the monitoring program (Brunner and Clark 1997). Pilot

proposals should be identified through the annual reporting process. Some monitoring staff will need to participate in approved pilot studies to foster coordination

and integration with the program. Additional funding for pilot studies may be needed.

Information Management The role of information management is to assist in collecting, organizing, validating,

storing, and retrieving data and in preparing reports. An effective information management system is essential to any monitoring program (CENR 1997) but especially to

one of this scope and complexity. Information management may be the most important support function needed for the monitoring program, but currently no information

management system is in place within the agencies that can meet the needs of this

program. Failure to address this issue can have profound implications to the value

and success of the program.

An intergovernmental committee, The Interorganizational Resource Information Coordinating Council, has been coordinating development of interagency data standards

for inventory, monitoring, and other information (Vegetation Strike Team 1996). These

standards will provide an important tool for sharing monitoring information on forest

resources within and across agencies in the region of the Forest Plan. It also can

serve as a starting point for developing an information management system for the

program.

Given the size of the program, an interagency information management plan needs

to be developed. This is particularly important because much of the program is based

on using integrated data sets that provide a basis for evaluating monitoring questions

across a range of related issues (for example, vegetation databases). Because the

program is a cooperative effort, this plan would detail organizational responsibilities

for managing and operating the program, such as identifying data managers. The

plan would identify how data will be handled and made available, from collecting

through preparing reports, with a focus on maintaining data integrity and fidelity while

encouraging data accessibility. The plan also would identify who will be responsible

for managing the information, where and how information will be stored, and how

metadata will be developed. The importance of preparing the plan early in a monitoring program cannot be overstated (Appleton 1996).

Quality Assurance An important characteristic of the monitoring program is that it has been legally mandated by the ROD (USDA and USDI 1994) and emphasized by Judge Dwyer in his

decision declaring the Forest Plan legally acceptable (Dwyer 1994). The program

must therefore provide monitoring results that are legally defensible, which is one

purpose of a structured quality-assurance system.

89
Implementing quality assurance for monitoring programs is relatively new for land

management agencies. Regulatory agencies, such as the EPA and the U.S. Department of Energy, have required quality assurance as integral components of datacollection programs for many years. These agencies have learned through experience

that courts will not accept monitoring results unless the quality of the data can be

established. Currently, no specific mandate exists within the Forest Service or BLM

that requires quality assurance for monitoring. Only components of the program that

may be funded by the EPA would have that requirement. The probability that all

Federal agencies will be requiring some type of quality assurance in the future for

environmental monitoring activities is high (CENR 1997).

Quality assurance can provide many benefits. Quality is often evaluated according

to the ability of a product to meet the needs and expectations of an end user. One

benefit of structured quality assurance is that it provides a process for identifying

and meeting client needs. Other benefits are to ensure that data-collection programs

provide and document high-quality data, and to ensure that analyses of these data

are repeatable and defensible.

A regional quality-assurance system for effectiveness monitoring should comply with

the “American National Standard: Specifications and guidelines for quality systems

for environmental data collection and environmental technology programs" (ANSI and

ASQC 1995). This standard, developed jointly by several government agencies and

industry, identifies a minimum set of quality management specifications required to

conduct programs with environmental data collection and evaluation. It is directly

applicable to effectiveness monitoring and describes the need for a quality management system to manage environmental programs and quality system specifications

to guide collecting and evaluating of environmental data.

Quality-management system—A quality management plan should be prepared

that reflects the quality-assurance philosophy of continuous quality improvement.

The quality-assurance system will support all monitoring and research activities (for

example, a pilot study) associated with monitoring. The system emphasizes sufficient

assistance in planning data-collection activities and documenting all aspects of each

study to assure that a complete record exists; that results are repeatable; and that

the scientific interpretations are traceable, reviewable, defensible, and logical.

Because the monitoring program is cooperative, the quality management plan will

detail organizational responsibilities for managing the program and for technical staff

to ensure that quality is integrated into it. An important component of a high-quality

management system is to ensure that it is formally assessed and the results regularly

documented. Audits will objectively evaluate quality-related practices, procedures,

instructions, activities, and items, including review of documents and records. The

audits would be performed in accordance with written procedures, with checklists,

and by appropriately trained personnel. Evaluations of data quality would be included

in summary and interpretive reports or as separate quality-assessment reports.

90
Quality-system specifications—A quality-assurance plan should be prepared that

lists the specific activities contributing to project quality. These activities would include

defining study objectives; experimental design; procurement; measurement procedures; calibration procedures and frequency; training and certification requirements;

preventive procedures; quality controls; corrective action; data collection, reduction,

and verification; and data validation and reporting. To ensure that the data meet the

needs of the study, realistic quality objectives should be established during the planning phase and experimental design of each project. Procedures for conducting accuracy (measurement-error) assessments for all monitoring data should be provided.

All data analysis methods should be thoroughly documented and tested.

The additional costs from a quality-assurance system need not be excessive. Many

large-scale data-collection activities contain most quality-assurance requirements

specified by ANSI and ASQC (1995) (for example, the CVS has a very tight qualitycontrol program for plot data). The expertise to develop a quality-management plan

that addresses the needs of effectiveness monitoring is available, primarily through

EPA. Training will be required in quality assurance concepts to assist with developing

individual quality-assurance project plans and assessing data (estimating measurement error). Commitment by all cooperating agencies will be needed to carry out an

appropriate quality-assurance for monitoring.

Example

Application to the

Forest Plan:

Northern Spotted

Owl

This chapter has introduced several concepts related to the key structural components that will be needed to manage and successfully operate the effectiveness monitoring program. To help clarify this process, an example is presented of several

activities collecting data essential to the program (fig. 27). Each of the steps, from

data collection, summarization, and interpretation through use in management

decisions, will be discussed.

The activities chosen for this example are the demographic studies that are part of

the proposed monitoring program for the northern spotted owl (Lint et al., in press

[see footnote 2, Chapter 1]; also see appendix D, table 12). These eight studies

have been collecting data for up to 12 years and are classified as external projects

providing essential data (type B). To visualize how the effectiveness monitoring

program is expected to operate, the year chosen for this example is 1998.

Northern spotted owl

monitoring program

←

←

←

←

←

←

←

Figure 27—Spotted owl projects submitting information to the

effectiveness monitoring program. OP = Olympic Peninsula;

NW CA = northern California; and NSO = northern spotted owl.

Other NSO

projects:

• Habitat

studies

• Predictive

modeling

• GIS/remote

snesing

NW CA

demographic

study

Other

demographic

studies

Roseburg

demographic

study

OP

demographic

study

91
Data Collection The demographic studies collect data important to answering the monitoring questions related to spotted owl populations. In 1998, these projects will continue to collect

owl demographic data according to standardized procedures (Lint et al., in press

[see footnote 2, Chapter 1]) that would also be described in a quality-assurance

project plan. The procedures have been in use for several years and are described

in Forsman et al. (1996). These and other owl projects also collect habitat data useful in developing predictive models relating owl population variation to habitat change.

The procedures for habitat assessment will be evaluated as part of the current pilot

project in the Oregon Coast Province (see “Pilot Testing and Development Projects,”

above, this chapter) and results adopted for use in assessing owl habitat.

Note that other data important to answering the spotted owl effectiveness monitoring

questions also may be collected or analyzed, or both, from other projects in 1998.

For example, satellite data may be collected that would provide data to assess abundance and distribution of late-successional forests (see LSOG monitoring module

[Hemstrom et al., in press]) and aid interpretation of owl habitat. Regional ground

surveys, such as CVS, also may collect data useful to owl habitat characterization.

Research on developing and refining predictive models will be conducted, and preliminary results made available.

Data Summary The spotted owl data summary will be an important component of the annual summary report for the effectiveness monitoring program. An evaluation team representing all the owl demographic studies would be responsible for preparing an annual

summary at the end of the field season. This team would report information from

each individual study area into one summary report for the spotted owl. Data collected in 1998 would be validated to ensure internal consistency and accuracy. The data

summary then would be prepared by using statistical approaches, data-aggregation

algorithms, tables, and maps in formats adopted for the program (for example, the

annual report format used by Federal research agencies). This report will identify key

issues from the individual study reports and, if appropriate, highlight any requiring

management consideration, such as budget needs. Note that habitat data would be

summarized following similar procedures and included in the summary report, if

available in 1998.

Interpretive Report The 1998 and 1999 summary reports will be included as important source documents

during the preparation of the first interpretive report in 1999. The spotted owl report

would be written by a panel of agency scientists, including those conducting research

on owl demography and other specialists. The results from previous years of the

demographic studies will contribute to the interpretive report, which will include information from other projects, such as spotted owl research and habitat data derived

from vegetation maps through remotely sensed data interpretation and ground vegetation surveys. The results of past and current meta-analyses of owl demographic

data (for example, see Burnham et al. 1996) would be included.

The interpretive report will include an analysis of population trends; if available, the

report may include projected trends from predictive models of habitat change. These

analyses will combine information about the relation of owl demographics and habitat,

and the report will provide an evaluation of all owl information and draw conclusions

about current status and trends and their implications to the Forest Plan. The primary

benefit of the first interpretive report will be to establish the baseline for future trend

analyses.

92
Management Decisions The implications of the results of the owl monitoring studies to management will be

identified at several steps in the reporting process. Monitoring results will be summarized in the 1998 summary report and subsequent summary reports. The first

regional interpretive report in 1999 will identify the implications of the monitoring

results to the extent that any are apparent this early in the monitoring program and

will put them into a regional perspective. Data-interpretation tools, such as ecological

risk assessments and statistical decision theory, will assist in highlighting the management implications of these monitoring results following the guidance in Chapter 2

(“Linking Monitoring Results to Decisionmaking”). Other information will be considered when pertinent to understanding the current situation relative to the Forest

Plan (for example, owl surveys or studies on other lands).

Managers will have several options available to them for the decisions they undertake based on monitoring results from the spotted owl monitoring program. Changes

may be undertaken in plans, policies, or practices; critical research needs may be

identified; or changes may be made to the monitoring program.

Coordination and

Support

A coordinated effort is required to accomplish these steps with staff and managers

assigned tasks at every level of the owl program from each individual owl study up

through the regional offices within and among agencies. The demographic studies

and other spotted owl monitoring activities would be coordinated through a regional

team consisting of project leaders from each of the demographic studies, project

leaders responsible for other owl tasks, regional monitoring support staff, and the

assigned team leader for the owl monitoring effort. The team leader will coordinate

the efforts of the spotted owl team with the other modules of the effectiveness monitoring program to ensure program consistency; coordinate the preparation of an annual summary report; plan the interpretive report; provide feedback to management

regarding budgetary and personnel requirements; and identify emerging research

needs. The monitoring support staff will be responsible for preparing the summary

and interpretive reports, planning and supporting annual monitoring activities, and

managing databases, among other duties.

An important responsibility for the owl monitoring team leader and the project leader

for the owl demographic study area in the Oregon Coast Ranges will be participation

in the Oregon Coast Province effectiveness monitoring pilot in 1998. The technical

approaches used to answer the owl monitoring questions in this pilot will provide the

template for interpretation of monitoring data from the remainder of the region in summary and interpretive reports. The pilot also will provide examples of monitoring

results to foster support for ongoing data collection efforts.

Summary of Key

Points

This chapter presents the principal components of an effectiveness monitoring program including data collection, data summaries, interpretive reports, management

decisions, and program coordination and support. Data collection will build on existing

monitoring programs whenever possible. One goal of effectiveness monitoring will be

to prepare annual data summaries for each resource being monitored. These summaries will serve as an important component for the preparation of 5-year interpretive

reports. Effectiveness monitoring will be of value only if feedback is provided to managers to assist with land management decisions. Support will be required for all

these components through research, pilot studies, information management systems,

and quality assurance procedures. An example of how these components would

function together is presented for the northern spotted owl module.

93
Acknowledgments We appreciate the contributions to the thoughts in this chapter from David Cassell,

Timothy Lewis, Joseph Lint, Paul Ringold, Martin Raphael, and Thomas Spies.

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Agriculture Organization: 17-29. Vol. 1.

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pilot study plan. Corvallis, OR: U.S. Department of Agriculture, Forest Service,

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Thomas, L. 1996. Monitoring long-term population change: Why are there so many

analysis methods? Ecology. 77(1): 49-58.

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Thow-Yick, L. 1994. The basic entity model: a fundamental theoretical model of

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U.S. Department of Agriculture, Forest Service; U.S. Department of the Interior,

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74 p.

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Document continues on next page.
Chapter 5: Strategy for

Implementing the Monitoring

Program

Barry S. Mulder, Craig J. Palmer, Miles Hemstrom,

Joseph B. Lint, and Sarah Madsen

Goal for

Effectiveness

Monitoring

The ultimate goal of the monitoring program is to produce regionwide interpretive

monitoring reports, at regular intervals, that address regional assessment questions

(table 7) about our success in meeting the goals of the Forest Plan1 (USDA and

USDI 1994). An organized and stepwise approach to program design, data collection

and management, and information assessment and reporting is needed to provide

timely and efficient access to critical information to meet this goal. The challenges

are great, but without this type of approach the monitoring program is unlikely to

succeed.

1 Implicit is this statement is the expectation that, to fully

address questions about the Forest Plan, implementation

and validation monitoring will apply the same strict scientific

standards to monitoring design, and the three programs will

be closely linked and integrated.

99
The reader should realize from the previous chapters that this program represents

a relatively new approach to ecosystem monitoring, an approach different from how

most monitoring has been conducted. We realize that shifting to a new way of doing

business will be difficult, and the transition will take time. Many barriers need to be

overcome to set this program in place, including scientific, technical, and institutional

(Ringold et al., in press). Organizational and cultural differences in how information is

collected, maintained, and shared will need to be addressed, permanent monitoring

programs established, links with research developed, annual monitoring responsibilities formally assigned, and stable base funding provided. Addressing these complexities will require moving away from the traditional ad hoc approach to one that

institutionalizes monitoring into day-to-day operations (CENR 1997; Morrison and

Marcot 1995; Morrison et al., in press; Noble and Norton 1991; Olsen and Schreuder

1997; Vora 1997).

The issues of immediate interest are related to schedules (What is the work? and

When does it need to be done?), people (Who will do it?), and money (What will it

cost?) (fig. 28). Addressing these questions will require close collaboration among all

participating research, land management, and regulatory agencies. The information in

this chapter is provided to help agencies begin to address these questions. A general

strategy is described to implement and manage the monitoring program, a strategy

applicable regardless of the options selected for monitoring the different resource

issues.

Table 7—Goals and objectives for effectiveness monitoring

Topic areas Criteria and questions

Goals of the

monitoring

program Evaluate the success of the Forest Plan in achieving the objectives on Federal

lands of:

a. Protecting and enhancing habitat for late-successional and old-growth forest

and related species

b. Restoring and maintaining the ecological integrity of watersheds and aquatic

ecosystems

c. Maintaining sustainable amounts of renewable resources and rural economies

and communities

Objectives of

the monitoring

program 1. Assess the status and trends of:

a. The amount and distribution of late-successional and old-growth forests, related

species, and their habitats

b. The ecological integrity of watersheds and aquatic ecosystems

c. The production of sustainable amounts of renewable resources in maintaining

rural economies and communities

2. Compare the observed status and trends with those expected under the Forest

Plan to assess whether Forest Plan goals and objectives are being met

3. Identify the implications of the results to adaptive management for the Forest Plan

4. Identify any emerging issues requiring management or research attention

Regional

assessment

questions 1. What are the status and trends for the resources identified above?

2. Are the goals and objectives of the Forest Plan for these resources being achieved?

3. What are the implications to the Forest Plan of the status and trend information

from the monitoring program?

4. Are any issues emerging that need management or research attention?

100
The effectiveness monitoring system implemented by the Federal agencies in the

Pacific Northwest will result in a complex and large-scale program consisting of

multiple parts, including the current modules (Hemstrom et al., in press; Lint et al.,

in press; and Madsen et al., in press; also see Furniss et al. 1997 [see footnote 2,

Chapter 1]), new modules, and links between modules and other monitoring, research, and related activities (fig. 29). In addition, each module is multifaceted and

includes subcomponents of data collection, information management, assessment,

and reporting that will result in numerous databases, products, and reports.

Milestones Infrastructure Cost

Figure 28—Conceptual model for the primary

issues to be addressed in implementing the

monitoring program.

Road to Producing Milestones Infrastructure Cost

Effectiveness

Monitoring Reports

NSO

species

studies

NSO

predictive

model

NSO

habitat

survey

NSO

habitat

map

LSOG

predictive

model LSOG

map

LSOG

vegetation

survey

MaMu

species

studies

Mamu

habitat

map

MaMu

habitat

survey

MaMu

predictive

model

Other

monitoring

modules

Regional

vegetation

cover map

Regional

vegetation

surveys

Figure 29—Generalized relation between monitoring components and

base forest and vegetation data; each component represents different

projects that produce information to be used in evaluating status and

trends. LSOG = late-successional old growth; NSO = northern spotted

owl; and MaMu = marbled murrelet.

101
Because many years will be needed to begin to discern trends in resources under

the Forest Plan, this will be a program that spans many decades. And, because this

is a relatively new approach to resource monitoring, the program will need to be

phased in over several years and will require periodic changes and improvements.

As a result, a planned sequence will be needed to time decisions and implementation each year. This sequencing will be most critical in the first few years as monitoring activities for individual resources are implemented and decisions are made

about pilot projects, research, and other necessary supporting efforts to develop or

refine methodologies, technologies, and new study designs. Deciding on a schedule

for producing key monitoring reports will set the schedule for intermediate tasks and

products. Immediate needs associated with initial implementation of the monitoring

program are listed in table 8 (see appendix E, table 18); see individual modules for

specific tasks for initiating the monitoring plans.

The steps followed to address these issues will help identify opportunities and needs

for establishing an infrastructure, allocating resources among participating agencies,

and setting a schedule for completing necessary products for the long-term success

of the program. Considerations include the following:

1. Staffing and funding allocations for collecting, managing, and assessing monitoring

data

2. Work plans with annual work objectives, timelines, and performance standards tied

to monitoring and ecological resource goals—including training needs

3. Budget requests for annual congressional appropriations hearings

Table 8—Tasks needing to be addressed in the initial phases of implementing

the effectiveness monitoring program for the Forest Plana

Subject area Issue to address

Program management Managers and program support staffing

Plans for:

data-information management system

quality-assurance system

Guidance for:

methods for integrating program components

methods for linking effectiveness to implementation monitoring

approaches to using new information (adaptive management process)

Monitoring modules Project leaders and field-project staffing

Allocation of funds

Annual work plans

Manuals, field protocols, forms

Database management system

Research support Project leaders-research support staffing

Investigations into:

candidate indicator usage

indicator thresholds and values

habitat-population variables

methods for linking results to decisionmaking

other research studies to support monitoring

Oregon coast pilot project Support for project completion

Methods to apply findings to effectiveness monitoring

a See Furniss et al. 1997; Hemstrom et al., in press; Lint et al., in press; Madsen et al., in press

for details (see footnote 2, Chapter 1).

102
4. Research plans identifying the research needed to support monitoring, pilot and

other projects important to improving the program

5. Supporting documentation for monitoring staff, including field manuals, reporting

forms, protocols and methods, and formats for data analysis and reporting

6. Specific guidance for monitoring staff, including approaches for quality assurance

and control, and a system for data management

7. Management and coordination of all the above activities among agencies

The First Effectiveness

Monitoring Report

The collective interpretation of information from the monitoring modules will allow a

regional assessment of the Forest Plan (see table 7 and Mulder et al. 1995). Two

questions will be used to evaluate whether that assessment, and hence the monitoring program, is successful: How useful is the information produced by the first

regionwide interpretive monitoring report? and Is this information used as part of

the decision process? That is, to what extent do the monitoring reports allow the

agencies to assess progress towards achieving the goals and objectives of the

Forest Plan? And, if progress is unsatisfactory, do the monitoring reports provide

insights into how standards and guidelines may need to be changed?

To initiate the monitoring program, we propose that the first regionwide interpretive

report be completed at the end of 1999; subsequent interpretive reports would follow

on a 5-year schedule. In addition to being a test of the functioning of the program,

the first report will establish the baseline for future comparisons of trend information

for all resources being monitored. Several immediate steps are needed to produce

this report; otherwise our ability to produce the first report by 2000 will be delayed.

Between the time of implementing the program and publishing this first regionwide

report, data need to be collected, organized, and analyzed; results of the pilot project

applied (see Plumley et al. 1996 for details); supporting research initiated (see Chapter 4); and summary and interpretive reports prepared for each module (fig. 30 and

table 8).

1998 1999 2000

Complete

OR pilot

Initiate

monitoring

program

Provide annual

summaryreports:

- LSOG - NSO

- MaMu - Rip/Aq

- (other modules)

Issue first

regional

interpretive

report

Figure 30—Milestones leading to the first regionwide interpretive report

for effectiveness monitoring. LSOG = late-successional old growth; NSO =

northern spotted owl; MaMu = marbled murrelet; Rip/Aq = riparian and

aquatic; and OR pilot = Oregon coast pilot project.

103
There will be more work associated with producing this report than seems apparent.

Initial results from the Oregon Coast Range pilot (see Chapter 4) indicate about a

2-year schedule for a variety of staff to evaluate the information and produce the

report. Steps for producing this report include:

1. Assemble a planning group for the interpretive report

2. Prepare a draft outline to identify information and staff needs

3. Conduct a peer and user review of the draft approach

4. Assign the staff expertise needed to conduct the review

5. Collect the pertinent information, summary reports, and other input

6. Conduct special studies (for example, modeling) needed for the review

7. Prepare a draft interpretive report

8. Conduct peer and user review of the draft report

9. Finalize the draft report

10. Document lessons learned from the exercise

These steps assume that the infrastructure, staff, and work plans for individual monitoring activities at the field level (see table 8) are in place. It will take a concerted and

planned effort both within and among agencies, and will require specific assignments

to a variety of staff and managers representing a range of disciplines with sufficient

expertise (see “Infrastructure Requirements: Institutionalizing the Program,” below,

this chapter), to handle the needed tasks.

Long-Term Reporting

Schedule

The initial results or output from the monitoring program will represent only the first

data points in a long-term program; multiple data points gathered over several additional years will be needed to discern trends. As such, the reporting schedule for the

effectiveness program is portrayed over two decades to illustrate the long-term nature

of the program and to highlight the relations among the various monitoring components (fig. 31). Figure 31 also is intended to give managers a sense of the importance

of the products and how they fit into the research, regulatory, and land management

planning processes (for example, recovery planning under the ESA; land management planning under NEPA, NFMA, and FLPMA).

1998 2003 2008 2013 2018

First interpretive

report - FY-00 2d IR

FY-05

3d IR

FY-10

4th IR

FY-15

5th IR

FY-20

Annual

summary reports

Figure 31—Schedule for interpretive reporting (IR) through the first two decades

of the effectiveness monitoring program. FY = fiscal year.

104
Timing for intermediate products and reports is critical; interdependencies in data

needs among different components of the program require careful scheduling and

close coordination. Delays in initiating monitoring activities may change the schedule

but will not change the need for the expected products. Delays also may disrupt the

quality or consistency in ongoing or long-term data sets (for example, spotted owl

demographic studies), and careful consideration should be given to avoiding breaks

in data sets that may be critical to evaluating future monitoring trends (Wolfe et al.

1987).

The effectiveness monitoring program focuses on the Forest Plan at the regional

scale, which will require managing and coordinating numerous monitoring modules

(see fig. 29) as well as other resource programs important to monitoring among

several Federal agencies, over a three-state area, and for a long time. This means

collecting and then moving a considerable amount of information to the appropriate

locations on a timely basis. This will be a large increase in work from what we have

been used to. The primary question to be addressed then is, Who is responsible for

conducting and managing the program and its individual components? This question

involves all Federal agencies cooperating under the Forest Plan; the primary land

management agencies (Forest Service and BLM), and the cooperating agencies

(Bureau of Indian Affairs, EPA, FWS, National Marine Fisheries Service, National

Park Service, and the U.S. Geological Survey—Biological Resources Division).

Managing a program of this nature will be new to agency cultures, and managing

interagency programs has not been very successful (CENR 1997, U.S. GAO 1994).

Most monitoring has been handled in the field with little regional management, but

managing operations regionally, as with any other resource program, is critical to the

long-term success of this program so that data are available to address the policy

questions that govern land management planning decisions. As such, ties between

local and regional administrative units are essential to help increase efficiencies,

avoid duplication or redundancy, and reduce costs. As the U.S. GAO (1994) notes

from its evaluation of current efforts in ecosystem management, the result requires

an era of unparalleled coordination and cooperation within and among agencies for

all aspects of this program to ensure:

• Program design that meets agency information needs

• Data collection that includes standards, methods, and quality control within and

across agencies

• Data management that is accessible to all interested parties within and across

agencies

• Assessment and evaluation of information across agency boundaries

• Summary and interpretive reporting that is consistent across agencies

• Adaptive management responses to monitoring results coordinated between

agencies

• Improvements to the monitoring program to meet emerging needs across agencies

Infrastructure

Requirements:

Institutionalizing

the Program

Milestones Infrastructure Cost

105
To satisfy these requirements, the monitoring program should be institutionalized into

the day-to-day activities at both the regional and project scales. And an infrastructure

that addresses all aspects of the program, including data collection, information management, assessment, and reporting (Persson and Janz 1997), is needed. Questions

of this nature may be more appropriately addressed at the national than the regional

level.

Annual assignments and scheduled products are basic to the program at project

(for example, data collection and management) and regional scales (for example,

assessment and reporting), and dedicated and accountable staff and managers will

be needed from all participating agencies to carry these out. Figure 32 illustrates the

management, coordination, and staffing needs for one of the monitoring resources as

an example; a similar structure is applicable to support each module. Core staff will

be the focal point for collecting (project scale) and using information (regional scale)

and need to be kept involved throughout the entire process to ensure consistency

and responsiveness. Specific staff at the project (for example, see individual monitoring modules [Hemstrom et al., in press; Lint et al., in press; and Madsen et al.,

in press]) and regional scales will need to be assigned monitoring responsibilities

associated with program design, data collecting, information management, and

assessment and reporting. Similarly, other staff will be needed at both scales to

manage and coordinate day-to-day activities within and among agencies. As Vora

Figure 32—Example (using LSOG) of the supporting infrastructure

for effectiveness monitoring. LSOG = late-successional old growth;

NSO = northern spotted owl; MaMu = marbled murrelet; Rip/Aq =

riparian and aquatic; IM = information management; spec. = specialist; and QA/QC = quality assurance and quality control.

Project management:

LSOG coordinator

Project leaders/staff

GIS/rem. sensing/IM spec.

Modeling spec.

Subject area spec.

LSOG

monitoring

module

NSO

monitoring

module

Mamu

monitoring

module Rip/Aq

monitoirng

module

Other

monitoring

modules

Regional management:

Program managers

GIS/rem. Sensing/IM spec.

Statistician

QA/QC spec.

Subject area spec.

Interagency

monitoring program

←→

←→

←→

←→

←→

→

←

→

←

106
(1997) notes from the study of National Forest monitoring in the Great Lakes region,

“the expertise needed to evaluate monitoring data are not always available.” Areas

of needed staff expertise include knowledge of special analytical tools (GIS, statistical

software) and expertise in applied statistics and plant and animal ecology. In addition,

expertise from the Federal research agencies will be needed to develop methodologies and support analyses and interpretations.

Permanent and full-time administration also will be needed to manage the large

number of involved staff to meet schedules, provide consistent direction and guidance, produce needed products, and address planning and budgetary needs at the

regional and project scales. Administrators must be able to provide clear expectations about tasks, products, and due dates, and develop performance standards and

job descriptions that assign responsibilities and accountability to monitoring and associated resource goals.

Answering questions about adding new staff, reallocating staff, and prioritizing staff

assignments will be necessary to ensure the availability of needed expertise for

program activities. A combination of approaches for each module and for the region

as a whole may be appropriate to handle these operations, including contracts and

in-house staffing (fig. 33); the agencies will have to weigh the available options given

funding and other limitations. See table 9 (and appendix E, table 19) for a summary

of anticipated staffing needs; actual full-time staff needs were not evaluated, but we

anticipate the need for a range of full-time, part-time, and some temporary staff.

At this time no infrastructure exists in Federal agencies to take full responsibility

for handling a program of this size; the closest model is the Forest Service’s CVS

program (Max et al. 1996). In the recent past, the Forest Service’s Research, Development, and Application program provided informal coordination among a variety of

entities involved with monitoring the northern spotted owl. Although the scope and

magnitude of these two examples is large, they do not directly compare with the size

of the effectiveness monitoring program for the Forest Plan. These considerations

lead to the conclusion that a permanent management team may be necessary that

includes managers for each of the monitoring modules as well as for the overall

program; an evaluation of the management and staffing needs is essential to the

long-term success of this program.

Figure 33—Options for operating the program.

- Program mgmt

- Program funds

Centralized

Contract In-house

Decentralized

↑

→

↓

←

107
Given the complexity of this program, it may be appropriate for the agencies to consider establishing an initial or interim management team to be assigned full-time to

oversee implementation of this program through completion of the first interpretive

report at the end of 1999 (fig. 34). An interim team should consist of a manager for

each of the monitoring modules, plus an overall team leader. These individuals would

represent both their agencies (for links to budgets and staff) and the individual monitoring modules (for links to work plans and products).

Table 9—Summary of staff expertise needed to support the effectiveness

monitoring programa

Subject area Needed disciplines and specialties

Program level Program managers

Information management specialists

Quality assurance and control specialists

GIS-remote sensing specialists

Statisticians

Subject area specialists (data analysts, modelers, ecologists, etc.)

Monitoring modules Module managers-coordinators

Field project leaders-field technicians and staff

Database managers

Data analysts, statisticians

Computer, mathematical modelers

GIS-remote sensing specialists

Subject area specialists (see monitoring modulesa)

a See Furniss et al. 1997; Hemstrom et al., in press; Lint et al., in press; Madsen et al., in press,

for details (see footnote 2, Chapter 1).

NSO

program

manager

LSOG

program

manager

MaMu

program

manager

Rip/Aq

program

manager

Soc/Econ

program

manager

Other

future

modules

EM

program manager

Monitoring

activities,

staff, etc.

Agency

managers,

funds, work

plans

S/M-Biodiv.

program

manager

Figure 34—Suggested interim team structure to coordinate the initial implementation

of the effectiveness monitoring (EM) program through completion of the first interpretive

report at the end of 1999, showing links and feedback among the agencies and on-theground monitoring activities. NSO = northern spotted owl; LSOG = late successional old

growth; MaMu = marbled murrelet; Rip/Aq = riparian and aquatic; and S/M = surveyand-manage species.

108
Costs and Budgets

The costs of the monitoring program will be directly related to the degree of risk or

uncertainty in the amount and type of data collected and how those data will be made

available for future decisions. That is, the more confident that people want to be that

implementing the standards and guidelines (USDA and USDI 1994) is having the

desired ecological outcome, the greater the investment in the monitoring program.

The ability to successfully conduct the program, however, depends on our understanding of the system or resources being monitored (fig. 35). For example, for the

marbled murrelet (Madsen et al., in press [see footnote 2, Chapter 1]; also see appendix E, tables 15 to 17), the early phases of the proposed program emphasize

the need for research to improve the ability to design and implement a monitoring

program for that species. On the other hand, the more that is known about a resource (for example, LSOG), the greater the likelihood of reducing the scope and

cost of the program. For ecological systems, in most cases our knowledge or

ecological understanding is at the low end of the continuum (U.S. GAO 1994).

Given the scope and magnitude of the monitoring program, a stable and long-term

base commitment to funding and staffing will be required for each individual module

and the integrated program responsibilities. Because most current funding is not

part of existing agency base appropriations, it needs to become a routine part of

the annual budget appropriations with links from the modules to the offices within

Federal agencies controlling the funds. This commitment is essential regardless of

the options selected for monitoring. This is an interagency program, however, and as

the CENR (1997) report notes, funding interagency programs has not always been

workable, with continuing questions about availability and allocations of funds within

and among agencies—questions that may need to be addressed at the national level.

Milestones Infrastructure Cost

Ecological understanding High

Low

High High

t1 — t2

Research Monitor

Low

Low

Figure 35—Value of a monitoring program where certainty in decisionmaking

is related to ecological understanding.

109
The projected costs of the monitoring program are based on meeting the suggested

timeline for implementing the recommended options (table 10; appendix E, tables 20

and 21; and see individual monitoring modules [Hemstrom et al., in press; Lint et al.,

in press; and Madsen et al., in press]). Our assessment focused on the costs associated primarily with data collection. Because information was not available, we did

not evaluate costs for administration, management, research, or other support. These

are, however, important parts of the program and funding will be needed. The immediate implications from our assessment are the need to establish base funds and to

obtain additional funds to collect data and support monitoring staff in the first year of

the program.

Costs will differ by year and agency. Because this program is new, current estimates

should be viewed as starting points only. As a result, our primary focus should be on

what is needed to support the first three years of the program up to and through the

production of the first interpretive report. Although most of the funds will be needed

for collecting data, equal consideration needs to be given to funding data management, assessment, and reporting (fig. 36), and the organization and infrastructure

that will underlie all the components of the monitoring program, including:

• Assessing and reporting monitoring information

Table 10—Summary of estimated costs for

some components of the effectiveness

monitoring programa b

Fiscal year

Topic areas 1998 1999 2000

Thousand dollars

Program management:

Agency managers and staff tbd tbd tbd

Monitoring modules:

LSOGc 70 170 —

Northern spotted owl 2240 2585 2520

Marbled murreletd 994 879 940

Aquatic and riparian tbd tbd tbd

Other modules tbd tbd tbd

Oregon coast pilot project 240 tbd tbd

Totals (estimated) 3784 3634 3460

tbd = to be determined

a See Furniss et al. 1997; Hemstrom et al., in press; Lint et al.,

in press; and Madsen et al., in press, for details (see footnote 2,

Chapter 1).

b These costs are illustrative only; actual costs will differ as

the programs are implemented.

c Costs of monitoring LSOG (about $700,000) are covered by

other agency vegetation inventory programs; LSOG costs noted

here are for data analysis and reporting only (see appendix E,

table 19).

d Costs of monitoring murrelets will be revised and are expected

to be higher.

110
• Technical support and oversight and management of the program regionally and

at the project scale

• Future program design and modification

• Collecting and managing monitoring information

• Pilot projects, and research studies

Questions about future allocations of funds will need to be considered among

the agencies as decisions are made about implementing the specific monitoring

modules. Changes or delays in timing of funding for key components may change

the total annual costs for a particular year, but would not change the total costs of

the full program over time. The program will be phased in over several years, and

new modules (for example, socioeconomic or survey-and-manage species) or special

projects (for example, research or pilot tests) may be added to the program. As a

result, the annual allocation of resources may change in the future, in both staffing

and funding. Although annual resource allocations may differ, support for the base

program will need to be relatively stable, particularly to provide a constant resource

base for data management, quality assurance, and assessment and reporting.

Because a large part of the monitoring program will rely on existing external (type B)

activities, identifying costs solely for effectiveness monitoring is difficult. This adds

another complexity to the funding issue. For example, the annual costs associated

with activities such as vegetation inventories (for example, CVS), species research

(for example, spotted owl studies), and other agency monitoring programs (for example, FHM) would be distributed among various agency programs and not attributed

solely to effectiveness monitoring. The proposed monitoring program should not

increase the costs of these other efforts, but should help refine them and make

better use of collected data. How funds and staff will be allocated relative to those

programs and the monitoring program, however, will require careful review and

evaluation among the agencies. Changes in decisions about essential activities

expected to contribute to meeting monitoring needs, such as vegetation inventories

or species research, will affect both timing and cost of the monitoring program. If

funding rates change, either the effectiveness monitoring program will be delayed

or costs will increase proportionally because the program will then have to develop

or fund alternative sources of information.

Current situation Desired future condition

Data

collection

Data processing

Reporting

Res earch support

Data

processing

Data

collection Res earch

support

Figure 36—Generalized cost distribution comparison. The dotted

lines are representational only.

111
Conclusions The goal of the effectiveness monitoring program is to provide periodic interpretive

reports addressing success in achieving the goals and objectives of the Northwest

Forest Plan. The selection of this monitoring goal has provided the focus for developing the effectiveness monitoring program. From our investigations, we have come

to realize that the scope, complexity, and magnitude of the resulting program mandate

that an innovative approach be taken to monitoring important resources. We only

wish that this knowledge could have been gained earlier in the process—it might

have made the task easier. We hope that our efforts, described here, have advanced

the understanding of ecosystem monitoring, so that necessary changes can be successfully managed.

The new approach will require a shift in thinking from how the agencies have traditionally viewed and managed monitoring within each agency’s culture, and it provides

a unique challenge. Key concepts emerging from our efforts include:

• Taking a more structured and scientific approach to monitoring design, data

collection, assessment, and reporting

• Integrating monitoring and related programs to make better use of existing data

and data collected for other purposes

• Adopting an adaptive approach to monitoring, in collaboration with Federal

research agencies, that improves the program over time

• Institutionalizing the monitoring program on a par with other traditional resource

programs

• Establishing dedicated programs for data collection, data management, information

assessment, and reporting

• Maintaining permanent monitoring staff and stable base funding that cross agency

boundaries

The ultimate test of the success of the proposed program will be whether, in upcoming years, staff and managers routinely ask, “What does our monitoring program

show?” The approach to monitoring the Forest Plan that we describe, including the

steps needed to support implementation and to improve the program as it proceeds,

is intended to be responsive to the information needs of the participating agencies,

and to improve management decisions. To do so, however, will require careful consideration of the activities, milestones, and projected costs for implementing the monitoring program and how best to manage the program over time. Although seemingly

complicated, future management should be routine and straightforward, if steps are

taken to institutionalize it into our operational culture. Establishing a core-resource

program with full-time staff and base funding will overcome the barriers to successful

application of the monitoring program and will make the long-term program more

cost-effective and responsive.

The agencies have an opportunity to be pragmatic and begin to implement these

concepts and proposals, but it will take time and commitment to make the transition

to a new way of doing business. The first test will be in producing the first regional

interpretive report in 1999, and whether it meets the needs for adaptive management.

Immediate decisions about staffing and funding should focus on meeting that shortterm goal. Decisions about the long-term implementation of the program, and its related costs, should be reevaluated after the first report is completed, so that appropriate and necessary improvements can be made for the future.

112
Acknowledgments The following individuals contributed to the ideas expressed in this chapter: Jim

Alegria, Timothy Lewis, Paul Ringold, Martin Raphael, and Tim Tolle.

References Committee on Environment and Natural Resources [CENR]. 1997. Integrating

the Nation’s environmental monitoring and research networks and programs: a

proposed framework. Washington, DC: National Science and Technology Council;

report; Environmental Monitoring Team. 102 p.

Furniss, M.; Hohler, D.; Larsen, P. [and others]. 1997. Northwest Forest Plan

aquatic and riparian effectiveness monitoring. [Draft outline]. Portland, OR: U.S.

Department of Agriculture, Forest Service, Six Rivers National Forest. On file

with: Six Rivers National Forest, 1330 Bay Shore Way, Eureka, CA 95501.

Hemstrom, M.; Spies, T.; Palmer, C. [and others]. [In press]. Late-successional

and old-growth forest effectiveness monitoring plan for the Northwest Forest Plan.

Gen. Tech. Rep. PNW-GTR-438. Portland, OR: U.S. Department of Agriculture,

Forest Service, Pacific Northwest Research Station. 37 p.

Lint, J.; Noon, B.; Anthony, R. [and others]. [In press]. Northern spotted owl

effectiveness monitoring plan for the Northwest Forest Plan. Gen. Tech. Rep.

PNW-GTR-440. Portland, OR: U.S. Department of Agriculture, Forest Service,

Pacific Northwest Research Station.

Madsen, S.; Evans, D.; Hamer, T. [and others]. [In press]. Marbled murrelet

effectiveness monitoring plan for the Northwest Forest Plan. Gen. Tech. Rep.

PNW-GTR-439. Portland, OR: U.S. Department of Agriculture, Forest Service,

Pacific Northwest Research Station.

Max, T.A.; Schreuder, H.T.; Hazard, J.W. [and others]. 1996. The Pacific

Northwest Region vegetation and inventory monitoring system. Res. Pap.

PNW-RP-493. Portland, OR: U.S. Department of Agriculture, Forest Service,

Pacific Northwest Research Station. 22 p.

Morrison, M.L.; Marcot, B.G. 1995. An evaluation of resource inventory and

monitoring program used in National Forest Planning. Environmental Management. 19: 147-156.

Morrison, M.L.; Marcot, B.G.; Mannan, R.W. [In press]. Wildlife habitat

relationships: concepts and applications. 2d ed. Madison, WI: University

of Wisconsin Press.

Mulder, B.S.; Alegria, J.; Czaplewski, R. [and others]. 1995. Effectiveness

monitoring: an interagency program for the Northwest Forest Plan. Portland, OR:

U.S. Department of Agriculture, Forest Service; U.S. Department of the Interior,

Bureau of Land Management [and others]; report; Monitoring Design Group,

Intergovernmental Advisory Committee.

Noble, I.; Norton, G. 1991. Economic aspects of monitoring for National Park management. In: Margules, C.R.; Austin, M.P., eds. Nature conservation: cost-effective

biological surveys and data analysis. Canberra, Australia: Commonwealth Scientific

and Industrial Research Organization (CISRO): 69-73.

Olsen, A.R.; Schreuder, H.T. 1997. Perspectives on large-scale natural resource

surveys when cause-effect is a potential issue. Environmental and Ecological

Statistics. 4: 167-180.

113
Persson, R.; Janz, K. 1997. Assessment and monitoring of forest and tree resources.

In: Forest and tree resources: Proceedings of the 11th World Forestry Congress:

[year unknown] October 13-29; Antalya, Turkey. Rome: United Nations, Food and

Agriculture Organization: 17-29. Vol. 1.

Plumley, H.; Spies, T.A.; Palmer, C.J. 1996. Province scale effectiveness monitoring pilot study plan. Corvallis, OR: U.S. Department of Agriculture, Forest

Service, Siuslaw National Forest.

Ringold, P.; Mulder, B.; Alegria, J. [and others]. [In press]. Establishing a

regional monitoring strategy: the Pacific Northwest Forest Plan. Environmental

Management.

U.S. Department of Agriculture, Forest Service; U.S. Department of the Interior,

Bureau of Land Management [USDA and USDI]. 1994. Record of decision for

amendments to Forest Service and Bureau of Land Management planning documents within the range of the northern spotted owl. [Place of publication unknown]. 74 p.

U.S. General Accounting Office [U.S. GAO]. 1994. Ecosystem management:

additional actions needed to adequately test a promising approach. GAO/RCED94-111. Washington, DC.

Vora, R.S. 1997. Developing programs to monitor ecosystem health and effectiveness of management practices on Lakes States National Forests, USA. Biological

Conservation. 80: 289-302.

Wolfe, D.A.; Champ, M.A.; Flemer, D.A.; Mearns, A.J. 1987. Long-term biological

data sets: their role in research, monitoring, and management of estuarine and

coastal marine systems. Estuaries. 10(3): 181-193.

114
Appendices

Appendix A:

Authors, Team

Members, and

Acknowledgments

History and Process

The effort to develop a monitoring program to support the Northwest Forest Plan

began after completion of the FEMAT (1993) report when it was realized that a land

management plan of this scope and complexity would require a new and more comprehensive approach to ecosystem monitoring. The initial definitions and concepts

for the different types of ecosystem monitoring—implementation, effectiveness, and

validation—were described by Tolle et al. (1994) and incorporated into the ROD

(USDA and USDI 1994) as Forest Plan requirements. Under the direction of the

Regional Interagency Executive Committee (RIEC), the first step was to develop

a plan for implementation monitoring (Tolle et al. 1995); the resulting program has

been conducted since 1996 (Alverts et al. 1997). Concurrently, a more detailed

introduction into effectiveness monitoring was developed (Mulder et al. 1995) that

describe general concepts and identified the considerable difficulties associated with

large-scale monitoring for resource issues, such as late-successional old-growth

forests, aquatic and riparian systems, northern spotted owl, marbled murrelet, and

survey-and-manage species. This 1995 report served as the catalyst for intensifying

interagency efforts to develop a strong science-based effectiveness monitoring

program.1

1 Considerable appreciation is given to Tim Lewis (group

leader), Craig Palmer, Barry Noon, Martin Raphael, Thomas

Spies, David Cassell, and Martin Stapanian for their ideas and

struggles in 1996 to begin development of the effectiveness

monitoring program.

115
In response, the Federal research executives2 from the USDA Forest Service, EPA,

and USGS—BRD took responsibility for establishing and directing a formal team

of Federal scientists—called the Effectivess Monitoring Team (EMT)—to complete

development of the effectiveness monitoring program. These executives, relying on

recommendations from the RIEC and Intergovernmental Advisory Committee (IAC),

established the sideboards, tasks, and schedules that governed the EMT efforts.

Most importantly they established a formal process to manage the complexity of this

planning effort. A core science team was established (the EMT) that was responsible

for developing the strategy and design of a large-scale effectiveness monitoring program and for providing day-to-day guidance for work groups assigned to develop

monitoring plans for specific resources. The core team included management representatives from the BLM and Forest Service who provided liaison with and feedback

from the land management agencies; the team also included the work group leaders3

to ensure consistency and enhance understanding of key concepts with the work

groups. The Regional Ecosystem Office (REO) and Research and Monitoring Committee (RMC) provided day-to-day support for this large effort.

The core team followed a fairly rigorous research-based approach for carrying out

its assignment, emphasizing peer review and feedback at every step in designing

the overall strategy and developing the individual monitoring modules (Hemstrom

et al., in press; Lint et al., in press; Madsen et al., in press; also see Furniss et al.

1997 [see footnote 2, Chapter 1]), beginning with review and consensus on the

core attributes of a good monitoring plan—goals and objectives, conceptual models,

monitoring questions, and candidate indicators (see appendix E). Formal peer reviews

also were conducted for the final draft products (see appendix C). Finally, agency

feedback was solicited throughout the effort through briefings with the RIEC and

IAC, and at numerous discussion sessions with key agency personnel (see appendix C) to ensure acceptance and understanding of the final proposed approaches

to effectiveness monitoring. The final products were reviewed and accepted for

implementation by the RIEC and IAC.

Effectiveness

Monitoring Team

Technical coordinators—

Barry S. Mulder, Wildlife Biologist, U.S. Fish and Wildlife Service, Portland, Oregon.

He served as the overall coordinator for the Effectiveness Monitoring Team

(EMT) and effectiveness monitoring work groups. He has participated in or led

development of the monitoring plans for the Forest Plan since FEMAT (1993).

Since 1987, he has served as coordinator for the spotted owl activities and

related forest ecosystems projects for the FWS.

2 Credit for the EMT’s success in completing development

of the effectiveness monitoring program is due to the leadership

of Thomas Mills (PNW), Thomas Murphy (EPA), and Michael

Collopy (USGS—BRD).

3 Work group leaders were selected from the land management

agencies to establish a link with the agencies responsible for implementing these monitoring plans; work group members were

scientists and specialists who provided the expertise to develop

credible monitoring plans.

116
Barry R. Noon, Research Ecologist, USDA Forest Service, Pacific Southwest

Research Station, Arcata, California. He served as the lead scientist for the

effectiveness monitoring effort and was a member of the northern spotted owl

monitoring work group. His recent research includes demography and habitat

relations of spotted owls, effects of forest fragmentation on biological diversity,

and ecological modeling. He is currently with the Department of Fishery and

Wildlife Biology, Colorado State University, Fort Collins, Colorado.

Anthony R. Olsen, Mathematical Statistician, Environmental Protection Agency,

National Health and Environmental Effects Research Laboratory, Corvallis,

Oregon. In addition to serving on the EMT, he was an advisor to the marbled

murrelet work group. Since 1990, he has led research and coordination of statistical monitoring designs for the EPA Environmental Monitoring and Assessment

Program and has led the development of monitoring designs applied to streams,

rivers, lakes, Great Lakes, estuaries, coastal waters, and forests.

Martin G. Raphael, Chief Research Wildlife Biologist, USDA Forest Service, Pacific

Northwest Research Station, Olympia, Washington. In addition to serving on the

EMT, he was a member of the northern spotted owl work group and was an advisor to the marbled murrelet work group. He is a team leader for research with

emphasis on habitat relations and population dynamics of threatened and endangered species, landscape ecology, and ecology of old-growth forest communities.

Gordon H. Reeves, Research Fisheries Biologist, USDA Forest Service, Pacific

Northwest Research Station, Corvallis, Oregon. In addition to serving on the

EMT, he was a member of the riparian and aquatic work group. His recent

research focuses on freshwater ecology of anadromous salmonids and the

influence of land management on aquatic systems.

Thomas A. Spies, Research Forester, USDA Forest Service, Pacific Northwest

Research Station, Corvallis, Oregon. In addition to serving on the EMT, he was

a member of the late-successional and old-growth work group. His research

includes old-growth ecosystems, landscape ecology, and remote-sensing applications to monitoring. He is coleading the Oregon Coast Pilot monitoring

project to develop techniques to support monitoring.

Hartwell H. Welsh, Jr., Research Ecologist, USDA Forest Service, Pacific Southwest Research Station, Arcata, California. In addition to serving on the EMT, he

served as an advisor to the riparian and aquatic work group. His current research

focuses on the aquatic-upland interface as it relates to forest management and

reptile and amphibian biology and the use of these species as indicators of ecosystem stress.

Technical consultant—

Craig J. Palmer, Research Scientist-Soil Scientist, Harry Reid Center for Environmental Studies, University of Nevada, Las Vegas, Nevada. In addition to

serving as technical advisor to the EMT, he was a member of the late-successional and old-growth work group. He conducts research related to the development of monitoring programs for terrestrial ecosystems nationally and internationally. He formerly coordinated development of forest health monitoring

efforts for the EPA Environmental Monitoring and Assessment Program.

117
Management consultants—

Michael Crouse, Natural Resource Policy Advisor, BLM Oregon and Washington

State Office, Portland, Oregon. He served as the BLM management representative to the EMT and other policy level forums related to Forest Plan monitoring

and implementation. He is currently serving as a policy level liaison between

Federal agencies and the State of Oregon for the development and implementation of the Oregon Plan, a cooperative statewide restoration strategy to address

salmon and water quality issues.

Arnie Holden, Deputy Director, Strategic Planning, USDA Forest Service, Pacific

Northwest Region, Portland, Oregon. He served as the Forest Service management representative to the EMT and other policy level forums related to Forest

Plan monitoring and implementation. He manages planning activities relating to

Forest Service monitoring and Forest Plan development and revision.

Monitoring work group leaders—

Michael J. Furniss, Watershed Group Leader, USDA Forest Service, Six Rivers

National Forest, Eureka, California. He led the riparian and aquatic work group.

His work includes managing and coordinating activities for the Forest Service on

issues relating to geomorphology, hydrology, and soils. He helped develop the

guidance for conducting watershed analysis for the Forest Plan.

Miles Hemstrom, Regional Ecologist, USDA Forest Service, Pacific Northwest Region,

Portland, Oregon. He served as leader of the late-successional and old-growth

work group. He coordinates activities of National Forest ecology programs in the

Pacific Northwest Region. His major emphases include sampling and classification

of vegetation, developing models and maps of potential natural vegetation, describing vegetation successional relations, and predicting vegetation response to management.

Joseph B. Lint, Wildlife Biologist, BLM Oregon and Washington State Office, Roseburg District Office, Roseburg, Oregon. He led the northern spotted owl work

group. He functions as a coordinator in the BLM state office. His responsibilities

include threatened and endangered species management with emphasis on

northern spotted owls, marbled murrelets, and implementation of the Forest Plan.

Sarah Madsen, Wildlife Biologist, USDA Forest Service, Siuslaw National Forest,

Corvallis, Oregon. She led the marbled murrelet work group. She served as the

Forest’s threatened, endangered, and sensitive species coordinator, and as the

marbled murrelet coordinator for the Pacific Northwest Region of the Forest

Service with emphasis on Forest-related issues and regional coordination of

marbled murrelet management issues. She currently is with the Regional Office

in Portland, Oregon.

118
Acknowledgments Primary direction came from the research executives of the Federal research

agencies, Tom Mills (Forest Service, PNW), Mike Collopy (USGS—BRD), and

Tom Murphy (EPA). Their strong support and continuing dedication to this project

was greatly appreciated. We also thank members of the RMC (Dan McKenzie and

Dave Busch), Jerry McIlwain (Forest Service, PNW), and Bob Alverts (BLM) for their

valuable day-to-day support and guidance. Many people provided reviews, feedback,

support, and ideas throughout this effort, with special thanks to Tim Lewis (Humboldt

State University), Paul Ringold (EPA), David Cassell (BLM), Jim Alegria (BLM), and

Tim Tolle (Forest Service). In addition, many agency staff and managers provided

valuable feedback through briefings and workshops (see appendix C), including members of the IAC, RIEC, REO, and RMC, as well as participating Federal agencies

at the local and regional level. Finally, we especially thank Martha Brookes (Forest

Service, PNW) for her excellent comments, and Karen Esterholdt for her fine editing

and publication assistance. Their help, as well as the support and input from all those

noted above, is greatly appreciated.

The peer reviews were invaluable. Tim Lewis, Bruce Marcot (Forest Service, PNW),

Doug Powell (Forest Service), Paul Ringold, and Kurt Ritters (Tennessee Valley

Authority) reviewed the management concepts (Chapters 1, 4, and 5). Jon Bart,

John Emlen, Dan Fagre, Paul Geissler, Jim Nichols, Rusty Rodriguez, Peter Stine

(USGS—BRD); Hart Welsh (Forest Service, PSW); Gary Davis and Dave Graber

(NPS); and Ted Case (University of California, San Diego), L. Scott Mills (University

of Montana), David Peterson (University of Washington), James Quinn (University of

California, Davis), and Gerald Wright (University of Idaho) participated in an expert

workshop, held in Olympic National Park, to review the science and theory (Chapters

2 and 3).

References Alverts, R.; Horton, A.; Stone, B. 1997. Results of the FY 1996 (pilot year) implementation monitoring program. Portland, OR: U.S. Department of Agriculture,

Forest Service; U.S Department of the Interior, Bureau of Land Management [and

others]; report; Regional Implementation Monitoring Team, Intergovernmental

Advisory Committee.

Forest Ecosystem Management Assessment Team [FEMAT]. 1993. Forest

ecosystem management: an ecological, economic, and social assessment.

Portland, OR: U.S. Department of Agriculture; U.S. Department of the Interior

[and others]. [Irregular pagination].

Furniss, M.; Hohler, D.; Larsen, P. [and others]. 1997. Northwest Forest Plan

aquatic and riparian effectiveness monitoring. [Draft outline]. Portland, OR: U.S.

Department of Agriculture, Forest Service, Six Rivers National Forest. On file

with: Six Rivers National Forest, 1330 Bay Shore Way, Eureka, CA 95501.

Hemstrom, M.; Spies, T.; Palmer, C. [and others]. [In press]. Late-successional

and old-growth forest effectiveness monitoring plan for the Northwest Forest Plan.

Gen. Tech. Rep. PNW-GTR-438. Portland, OR: U.S. Department of Agriculture,

Forest Service, Pacific Northwest Research Station. 37 p.

Lint, J.; Noon, B.; Anthony, R. [and others]. [In press]. Northern spotted owl

effectiveness monitoring plan for the Northwest Forest Plan. Gen. Tech. Rep.

PNW-GTR-440. Portland, OR: U.S. Department of Agriculture, Forest Service,

Pacific Northwest Research Station.

119
Madsen, S.; Evans, D.; Hamer, T. [and others]. [In press]. Marbled murrelet

effectiveness monitoring plan for the Northwest Forest Plan. Gen. Tech. Rep.

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest

Research Station.

Mulder, B.S.; Alegria, J.; Czaplewski, R. [and others]. 1995. Effectiveness monitoring: an interagency program for the Northwest Forest Plan. Portland, OR: U.S.

Department of Agriculture, Forest Service; U.S. Department of the Interior, Bureau

of Land Management [and others]; report; Monitoring Design Group, Intergovernmental Advisory Committee.

Tolle, T.; Crouse, M.; Mulder, B. [and others]. 1994. Interagency framework for

monitoring the President’s Forest Ecosystem Plan. Portland, OR: U.S. Department of Agriculture, Forest Service and U.S. Department of the Interior, Bureau

of Land Management [and others]; report; Interagency Monitoring Workgroup,

Regional Interagency Executive Committee.

Tolle, T.; Hyzer, M.; Mulder, B. [and others]. 1995. Implementation monitoring

for management of habitat for late-successional and old growth forest related

species within the range of the northern spotted owl. Portland, OR: U.S. Department of Agriculture, Forest Service; U.S. Department of the Interior, Bureau of

Land Management [and others]; report; Implementation Monitoring Team, Intergovernmental Advisory Committee.

U.S. Department of Agriculture, Forest Service; U.S. Department of the Interior,

Bureau of Land Management [USDA and USDI]. 1994. Record of decision

for amendments to Forest Service and Bureau of Land Management planning

documents within the range of the northern spotted owl. [Place of publication

unknown]. 74 p.

ACS—Aquatic conservation strategy

BIA—Bureau of Indian Affairs

BLM—Bureau of Land Management

CVS—Current vegetation survey

EM—Effectiveness monitoring

EPA—Environmental Protection Agency

EMAP—Environmental Monitoring and Assessment Program

EMT—Effectiveness Monitoring Team

ESA—Endangered Species Act

FEMAT—Forest Ecosystem Management Assessment Team

FIA—Forest Inventory and Analysis

FHM—Forest health monitoring

FLPMA—Federal Land Policy Management Act

FWS—U.S. Fish and Wildlife Service

GIS—Geographic Information System

Appendix B:

Acronyms and Key

Terms Used in This

Report

Acronyms

120
IAC—Intergovernmental Advisory Committee

IRICC—Interorganizational Resource Information Coordinating Committee

LSOG—Late-successional old growth

MaMu—Marbled murrelet

NEPA—National Environmental Policy Act

NFMA—National Forest Management Act

NPS—National Park Service

NSO—Northern spotted owl

QA/QC—Quality assurance and quality control

REO—Regional Ecosystem Office

RIEC—Regional Interagency Executive Committee

Rip/Aq—Riparian and aquatic

RMC—Research and Monitoring Committee

ROD—Record of decision

S/M—Survey-and-manage species

U.S. GAO—U.S. Government Accounting Office

USGS-BRD—U.S. Geological Services-Biological Resources Division

Key Terms Although many words or phrases could or should be explained, a few that appear

many times in this report are listed below because they are critical to the proposed

program. They are provided as explanations, not definitions.

Adaptive management—the process of action-based planning, monitoring, research,

evaluating, and adjusting to accelerate learning and improve the ability to manage

resources effectively

Adaptive monitoring—a continuing approach to refine the monitoring program as

a result of experience in implementing it, assessing its results, and interacting

with users of the information

Conceptual model—a method to outline the interconnections among ecosystem

processes, structure, composition, and function, the strength and direction of

those links, and the attributes that characterize the state of the ecosystem

Effectiveness monitoring—monitoring to document the status and trends of

resource conditions; in the context of the Forest Plan, to evaluate whether the

goals and objectives of the plan are being achieved

Implementation monitoring—monitoring to document compliance with direction;

in the context of the Forest Plan, to evaluate whether the agencies are implementing the standards and guidelines in the ROD

Indicator—any living or nonliving feature of the environment that can be measured

or estimated and that provides insights to the state of the ecosystem

121
Information management—an organized system to assist in collecting, storing, and

retrieving data needed for reporting and making decisions and that is computer

based and linked to GIS

Integrated monitoring system—an approach to monitoring that brings together all

the critical data collection, information management, and reporting components

into a unified interactive program

Interpretive report—a report that evaluates and interprets the ecological significance

of trends demonstrated by the monitoring data and evaluates these trends based

on summary reports and other associated information

Monitoring—collecting and assessing information on measurable ecosystem attributes to evaluate whether the goals of the Forest Plan are being met and to

determine whether the integrity of the ecological system and its processes are

being maintained

Pilot project—a short-term study to test, evaluate, or develop methods or processes

for the monitoring program

Regional scale—the spatial scale or extent addressed by the monitoring program;

this scale implies answering questions about status and trends across the entire

area covered by the Forest Plan

Stressor—intrinsic and extrinsic drivers of change, either positive or negative; in the

context of this report it refers to natural and human-induced disturbance events

resulting in significant ecological effects

Summary report—brief, comprehensive summaries, tables, and maps of essential

data periodically collected to address the monitoring questions for specific resources

Validation monitoring—monitoring to document cause-and-effect relations; in

the context of the Forest Plan, to evaluate the link between implementing the

standards and guidelines and the observed effects

Appendix C:

Chronology of Key

Meetings and

Reviews

Numerous briefings, discussions, and meetings occurred among the effectiveness

monitoring team (EMT), Intergovernmental Advisory Group (IAC), Regional Interagency Executive Committee (RIEC), Federal research executives, and agency

staff specialists and managers on guidance, issues, and proposals for effectiveness monitoring of the Forest Plan. Table 11 lists only formally scheduled sessions

applicable to the current effort. Day-to-day interactions, discussions, preparation

sessions for these meetings, ongoing EMT and monitoring work group meetings,

and other work sessions are not included; they were too numerous to list here.

122
Table 11—List of formal briefings and meetings on effectiveness monitoring

Meeting date Type of presentation Participants Comments and issues Followup and documentation

May 30, 1996 Briefing on planning effort IAC meeting Discussion IAC issue summary

Aug. 2, 1996 (none) Research executives Assignments and direction Letters to participants

Aug. 28, 1996 Planning meeting for EMT EMT, REO, and Assignments and direction Informational

Research executives

Sept. 19, 1996 Briefing on status IAC meeting Discussion IAC issue summary

Nov. 26, 1996 Briefing on status and FS, BLM, FWS, NMFS, Discussion Informational

approaches EPA mid-level managers

Dec. 5, 1996 Briefing on status IAC meeting Discussion IAC issue summary

Jan. 24, 1997 Briefing on status and FS, BLM, FWS, NMFS, Discussion Informational

approaches EPA mid-level managers

Feb. 2, 1997 Briefing on status and Research executives Discussion Summarized comments

approaches

Mar. 20, 1997 Briefing on status and BLM State, District, Res. Verbal and written Written response

approaches Area staff and managers questions

April 3, 1997 Briefing on status and IAC meeting Verbal questions IAC issue summary

approaches

April 29, 1997 (none) Selected peer reviewers Peer review comments Peer review summary

(mgmt. concepts)

April 30, 1997 Briefing on status and IRICC meeting Verbal questions and Followup briefing; copies of

approaches and discussion comments draft EM reports

on information mgmt

May 8, 1997 Briefing on status and Forest Service and FWS Discussion Followup briefing

approaches mid-level field managers

May 10-12, 1997 Science peer review Selected scientists and Discussion on science Peer review summary

workshop monitoring specialists theory and concepts

May 19, 1997 Briefing on status and Forest Service regional Verbal questions Followup briefing

approaches managers

May 22, 1997 Update on status and IRICC meeting Verbal questions Summarized comments

approaches and discussion

on information mgmt.

June 3, 1997 Workshop on EM proposals Interagency staff and Verbal and written Summarized comments

managers (regional and questions and

field) comments

June 11, 1997 Workshop on EM proposals Research executives and Verbal questions and Summarized comments;

and science discussion interagency staff and comments written response

managers

June 13, 1997 Discussion on EM proposals RIEC members and staff Verbal questions and Summarized comments

and decision process comments

June 19, 1997 Briefing on status, proposals, IAC meeting Verbal questions and IAC issue summary

and decision process for comments

draft reports

June 23, 1997 Briefing on proposals Forest Service and BLM Verbal questions and Summarized comments

(staffing and costs) planning staff comments

July 7, 1997 Meeting on staffing and Agency policy and man- Discussion and verbal Further review; followup

costs agement team questions materials

July 31, 1997 Meeting on staffing and Agency policy and man- Discussion Further review

costs agement team

Aug. 7, 1997 Briefing on status IAC meeting Verbal questions and IAC issue summary

comments

Sept. 4, 1997 Meeting on staffing and Agency policy and man- Discussion Further review

costs agement team

Oct. 2, 1997 Conference call on final IAC members Discussion; final approval IAC issue summary

draft reports

Nov. 6, 1997 Briefing on implementation RIEC and IAC meetings Discussion; agreement on IAC issue summary;

implementation summary comments

123
Tables 12 and 13 provide lists of existing data collection activities that may contribute

useful information to the effectiveness monitoring program. This is not a complete list

but is intended only as examples of these kinds of activities (refer to Chapter 4).

Appendix D:

Supplemental

Information on

Essential and

Related Projects

That May Contribute

to the Effectiveness

Monitoring Program

Table 12—Examples of existing projects that provide essential data to effectiveness monitoring

(type B projects)

Title Coordinating agency Geographic scope Duration Description

Vegetation inventories and survey effortsa

Vegetation map USDA Forest Service Region of NW Forest Planned Remotely sensed map characterized to

Plan (OR, WA, CA) Vegetation Strike Teamb data standards

Current vegetation

survey USDA Forest Service National Forests (WA, 4 years 3.4- and 1.7-mile grids of 1-hac plots, live

OR) and dead trees, plant indicators, down

woody material

Current vegetation

survey USDA Forest Service National Forests (CA) 3.4-mile grid of 1-ha plots, live and dead

trees, plant indicators, down woody

material

Current vegetation

survey BLM BLM Districts (OR) Begin in 1997 3.4-mile grid of 1-ha plots, live and dead

trees, plant indicators, down woody

material

Ecology program;

potential vegetation

classification and

mapping USDA Forest Service National Forests (WA, Classification based on current vegetation,

OR, CA) environment, disturbance competition,

and time

Forest inventory BLM BLM Districts (CA) Forest mensuration variables

Northern spotted owl demographic studiesd

Olympic Peninsula

owl demographic

study Pacific Northwest Olympic Peninsula 10 years Owl survey data

Research Station (WA) (814 544 ha)

and USGS-BRD

Cle Elum owl

demographic study Pacific Northwest

Research Station Cle Elum (WA) 8 years Owl survey data

(176 272 ha)

H. J. Andrews owl

demographic study Pacific Northwest H.J. Andrews, Willamette 10 years Owl survey data

Research Station NF (OR) (234 400 ha)

North Coast Range

owl demographic

study Pacific Northwest North Coast Range, 8-10 years Owl survey data

Research Station (OR) (391 392 ha)

124
Table 12—Examples of existing projects that provide essential data to effectiveness monitoring

(type B projects) (continued)

Title Coordinating agency Geographic scope Duration Description

Northern spotted owl demographic studiesd (continued)

Roseburg owl

demographic study Pacific Northwest

Research Station,

BLM, and USGS-BRD Roseburg (OR) (105 584 ha) 12 years Owl survey data

Klamath owl

demographic study BLM Klamath Province (CA) 12 years Owl survey data

(137 728 ha)

South Cascades owl

demographic study USDA Forest Service South Cascades (OR) 9 years Owl survey data

(240 528 ha)

NW California owl

demographic study USDA Forest Service National Forests (NW CA) 12 years Owl survey data

(178 416 ha)

Aquatic and riparian analysese

Watershed analysis USDA Forest Service Watersheds throughout 3 years Characterize watershed and ecological

and BLM Forest Plan region processes

(OR, WA, CA)

a See Hemstrom et al., in press (see footnote 2, Chapter 1). b See Vegetation Strike Team 1996. c Although grid plots are spaced 1.7 or 3.4 miles apart, data are collected for 1-hectare (ha) units. d See Lint et al., in press (see footnote 2, Chapter 1). e See Furniss et al. 1997 (see footnote 2, Chapter 1).

Table 13—Examples of existing projects that provide information of value to effectiveness monitoring

(type C)

Title Coordinating agency Geographic scope Duration Description

Vegetation inventories and survey effortsa

Plant association

grouping BLM Salem-Eugene Districts (OR) 2 years Plant association mapping

Ecological unit

inventory USDA Forest Service National Forests (WA, OR) — Stratification of ecological units based on

soil, climate, potential natural vegetation,

landform, and lithology

Forest inventory

and analysis Pacific Northwest Non-federal forest lands — 3.4-mile grid of live and dead trees,

Research Station (WA, OR, CA) vegetation profiles, down woody material,

and tree cover and species

Forest health

monitoring Pacific Northwest National Forests 5 yr in CA; 27-km triangular grid of 1-ha plots, growth,

Research Station (CA, WA, OR) began WA, regeneration, crown condition, tree species,

OR in 1997 damage, and mortality

Insect and disease

survey USDA Forest Service National Forests Annual aerial surveys of insect and

(WA, OR, CA) disease damage

(continues on next page)

125
Table 13—Examples of existing projects that provide information of value to effectiveness monitoring

(type C) (continued)

Title Coordinating agency Geographic scope Duration Description

Marbled murrelet surveysb

Marbled murrelet

marine surveys Pacific Northwest San Juan Islands, 3 years Marbled murrelet population and reproductive

Research Station, Hood Canal (WA) data

National Council for

Air and Stream

Improvement

Marbled murrelet

marine surveys WA Department of

Fish and Wildlife Puget Sound, San Juan 5 years Marbled murrelet population and reproductive

Islands, coast (WA) data

Marbled murrelet

marine surveys Sustainable Ecosystems Puget Sound, Hood 2 years Marbled murrelet population and reproductive

Inst.; NW Indian Canal (WA) data

Fisheries Commission

Marbled murrelet

marine surveys OR Department Fish OR coast 4 years Marbled murrelet population and reproductive

and Wildlife data

Marbled murrelet

marine surveys Pacific Southwest Northern CA coast 8 years Marbled murrelet population and reproductive

Research Station, data

CA Department of

Fish and Game,

Fish and Wildlife

Service

Marbled murrelet

marine surveys CA Department of Central CA coast 4 years Marbled murrelet population and reproductive

Fish and Game data

Marbled murrelet

aerial surveys MARZET Marine and North and central CA, 4 years Marbled murrelet population data

Estuarine Research Co. OR and WA coast

Aquatic and riparian inventoriesc

Aquatic Inventories USDA Forest Service National Forests — Channel morphology, habitat attributes, and

(WA, OR, CA) riparian vegetation

a See Hemstrom et al., in press (see footnote 2, Chapter 1). b See Madsen et al., in press (see footnote 2, Chapter 1). c See Furniss et al. 1997 (see footnote 2, Chapter 1).

126
The Effectiveness Monitoring Team (EMT) used a rigorous, science-based approach

to design the program for effectiveness monitoring and identify monitoring options for

the assigned resources. The resulting information was used to propose a schedule

for implementing program components, identify potential staffing needs, and estimate

costs. The following summarizes the steps used by the EMT and monitoring work

groups to evaluate and develop the information presented in the following tables.

Part 1: Steps To Identify

Options

The effort to identify options for monitoring selected resources followed the general

multistep process for designing a monitoring plan (see Chapter 2). The primary focus

of the effort was to identify the information needed to evaluate the Forest Plan within

the criteria provided by the IAC so that methods for obtaining that information could

be evaluated. This effort can be described as a series of distinct steps:

1. Evaluation of scientific literature on monitoring and the needs of the Forest Plan

led to identification of a basic framework for effectiveness monitoring: monitoring

for status and trends in forest vegetation and species habitat, and in species populations. Predictive modeling was proposed as a means to improve the use of monitoring data, anticipate trends, and reduce long-term costs; species-habitat relations

can be addressed only if both of the other components are included (see table 14

for narrative description of basis monitoring components).

EMT conclusions—

• Application of the two-component approach for status and trend monitoring

addresses the primary ecological assumptions underlying the Forest Plan

• Inclusion of predictive modeling is a desired component of monitoring

2. This framework was applied to each of the selected resource issues. Following

the multistep process, each EMT work group identified the information needed to

address status and trends for the selected resource (see table 15; also see monitoring modules [Hemstrom et al., in press; Lint et al., in press; and Madsen et al.,

in press; also see Furniss et al. 1997]).

EMT conclusions—

• The general framework is applicable to monitoring status and trends under the

Forest Plan for the assigned resources.

• Information at the landscape and stand scales and understanding species-habitat

relations are basic information needs.

3. Sampling methods were identified and evaluated for gathering status and trend

(see Chapter 3; also see monitoring modules [Hemstrom et al., in press; Lint et al.,

in press; Madsen et al., in press; also see Furniss et al. 1997]). The primary evaluation criterion was whether the method provided the information needed to address

the Forest Plan (see table 16). In addition, at a minimum the methods had to be

scientifically valid (consistent, comparable, repeatable, quantifiable, etc.), contribute

to trend prediction, link to other uses, be synergistic, and provide adequate spatial

coverage. As a result of this evaluation, alternative methods not meeting these

criteria were dropped from further consideration; in general there are not many

competing approaches to trend monitoring.

Appendix E:

Summary Information Used To

Evaluate Schedules,

Staffing, and Costs

of the Effectiveness

Monitoring Program

127
EMT conclusions—

• The process to evaluate sampling methods identified specific methods to meet

Forest Plan information needs for status and trends.

• In most cases existing methods (remote sensing, vegetation surveys, species

habitat and population studies) met these criteria, although techniques may need to

be improved or modified.

• New methods were identified where needed.

4. After eliminating unproductive methods, the work groups identified a range of

options for implementing those methods meeting agency information needs (see

Chapter 3; also see monitoring modules [Hemstrom et al., in press; Lint et al., in

press; and Madsen et al., in press; also see Furniss et al. 1997]). These options

are summarized in table 17.

EMT conclusions—

• The recommended options will provide information to meet agency needs to

evaluate the Forest Plan and key resources.

• The options provide a range of funding levels for gathering status and trend

information for the assigned resources.

• The quality and utility of the information collected under the different options is

primarily related to the intensity of the monitoring effort and the amount of funds

available.

Part 2: Information To

Support Management of

the Program

A framework for managing a monitoring program was described (see Chapter 4).

The structure is identified in the monitoring literature as basic to successful monitoring programs and was used as the template for program operations. It includes

an infrastructure for data collection and management and for information assessment and reporting (see table 18). This structure is independent of the options; it

was provided to give context for evaluating the options from a program management perspective, such that schedules and estimates of funding and staffing could

be made.

EMT conclusions—

• The framework identifies the major components of an operational program from

data collection and management to assessment and reporting.

• The framework needs to be applied to each separate module as well as the whole

program to maintain continuity in information quality and availability.

• The success of a large-scale, multifaceted program depends on institutionalizing

the components and providing stable funding and staffing for these components.

128
Part 3: Information To

Support Implementing

the Program

Material was evaluated around a schedule for implementing the program through

its first 3 years for each of the resources being monitored (see monitoring modules

[Hemstrom et al., in press; Lint et al., in press; Madsen et al., in press; also see

Furniss et al. 1997]), and for management and support at the regional scale. The

intent was to provide a strategy for implementing the program and producing the

first regionwide interpretive report (see Chapter 5). This schedule does not depend

on the selection of options, although funding levels and staffing requirements will

differ by option selected (see tables 19 and 20); although not fully evaluated by the

EMT, information also is provided for needs at the regional level.

EMT conclusions—

• Tasks, assignments, and schedules will exist at both regional and project levels.

• Long-term, multiyear managerial and staff support is necessary at both regional

and project levels.

• Documentation and guidance are needed to implement a program, including field

manuals, annual work plans, staff assignments, and schedules.

• The program should be evaluated and redirected, if necessary, after completion

of the first report.

Part 4: Information to

Evaluate Costs

An evaluation was conducted on the estimated costs for each of the completed

monitoring modules based on costs of activities in previous years (see monitoring

modules [Hemstrom et al., in press; Lint et al., in press; Madsen et al., in press; also

see Furniss et al. 1997]). This included an evaluation of the full- and part-time (and

temporary) staff needed to manage and carry out each component of the program.

Because there were few examples of what it would take to manage and operate this

program at the regional level, only general information is provided (see Chapter 5).

The information is based on the recommended options for assigned resources (see

tables 20 and 21).

EMT conclusions—

• Funds will be needed at both regional and project scales.

• Long-term, multiyear, stable funding is necessary.

129
Table 14—Narrative description of proposed activities for the effectiveness monitoring program

Monitoring

topic Activity Description

Regional Program management Management and oversight of interagency efforts for all monitoring modules at the

regional office level

Technical support Provide plans and technical support, including research support, for monitoring program

at regional and field levels

Assessment and reporting Analysis of regional monitoring results to identify regional Forest Plan trends

Late-successional

old growth (LSOG) Vegetation trend Use of agency remote sensing and vegetation surveys (grid plot) to develop regionwide

databases

Trend model development Development and use of modeling to link remotely sensed and grid plot data to anticipate

trends

Assessment and reporting Analysis of remotely sensed and grid plot data to identify regional vegetation trends

Management and coordination Management and oversight of project and regional efforts

Northern spotted

owl Population trend:

Population studies Use of demographic studies to determine population trends

Habitat association (stand) Refinement of owl habitat relations and habitat definitions for use in predictive modeling

Habitat trend (landscape) Analysis of remotely sensed and grid plot data to identify regional vegetation trends for

owl habitat

Trend model development Development and use of modeling to link owls and habitat and remotely sensed data

to anticipate trends

Assessment and reporting Analysis of demographic and habitat data to identify regional trends

Management and coordination Management and oversight of project and regional efforts

Marbled murrelet Population trend Test and improve marine survey methods for tracking population trends

Habitat trend:

Landscape studies Develop consistent trend model classification methods for murrelet habitat

Stand level studies Define key murrelet nesting habitat variables and link ground-based variables to trend

model imagery

Trend model development Development and use of modeling that links remotely sensed and grid plot data to

anticipate trends

Assessment and reporting Analysis of population and habitat data to identify regional trends

Management and coordination Management and oversight of project and regional efforts

Coast pilot project Evaluate methods and reporting Test approaches to answering key effectiveness monitoring questions by using existing

data and techniques; provide example monitoring report in FY1998 (Siuslaw NF, Salem

and Eugene BLM, PNW)

130
Table 15—Comparison of proposed approaches and methods for monitoring assigned resources under

the Forest Plan

General approach Methods to Options for level Operating

Monitoring topic to monitoring apply approach of expenditure assumptions Comments

Late-successional

old growth (LSOG) Vegetation trend Remote sensing 2 levels Continued funding Decision on vegetation

(landscape) and of remote sensing map is critical to all

grid plot (standscale) and grid plot programs programs

Predictive trend Develop vegetation yes or no Availability of grid plot

model models data

Assessment and Develop analytic and Necessary to draw

reporting reporting methods results from data

Northern spotted

owl Population trend Demographic and 3 levels (phase 1) Habitat (stand) and Outcome of phase 1

habitat (stand) population studies may affect direction

studies needed for habitat- and cost of phase 2

species association

Habitat trend Remote sensing and 3 levels Continued funding of

grid plot studies remote sensing and

grid plot programs

Predictive trend Develop species- yes or no Availability of popumodel habitat models lation and habitat

(stand-scale) data

Assessment and Develop analytic and Necessary to draw

reporting reporting methods results from data

Marbled murrelet Population trend Marine surveys 4 levels (phase 1) Initial development Outcome of phase 1

of new survey may affect direction

techniques and cost of phase 2

Habitat trend Remote sensing, 4 levels (phase 1) Initial refinement of Outcome of phase 1

grid plot, and habitat nesting habitat may affect direction

(stand) studies (continued funding and cost of phase 2

of remote sensing

and grid plot programs); habitat

(stand) and population studies needed

for habitat-species

association

Predictive trend Develop species- yes or no Availability of popu- No immediate decision

model habitat models lation and habitat necessary

(stand-scale) data

Assessment and Develop analytic Necessary to draw

reporting and reporting results from data

methods

Aquatic and riparian Watershed trend Under development yes Effectiveness of agency Continued availability of

watershed analyses; watershed analyses

initial development of

sampling techniques

Predictive trend Under development yes Initial development of

model techniques

Assessment and Develop analytic Necessary to draw

reporting and reporting methods results from data

Other modules tbd tbd tbd

tbd = to be determined.

131
Table 16—Description and rationale for recommended options for effectiveness monitoring of assigned

resources

Recommended method or option

Monitoring

topic Approach Option Description Selection rationale

Late-successional

old growth (LSOG) Vegetation trend 2 Analyze vegetation Higher quality and more useful information using

maps and grid plots landscape and stand-scale views

Improved accuracy for detecting “real” change

Synergistic use of data and funds

Predictive trend — Refine trend estimates Extrapolation, prediction, cost reductions

model

Northern spotted

owl Population trend 1 Habitat assessment Wider coverage given range variation

(for model development) Maintain capability and improve quality of metaand population surveys analyses

in 8 demographic study Develop species-habitat association data for

areas model

Habitat trend 2 Rangewide habitat assess- Same rationale as LSOG vegetation above

ment using LSOG vegetation maps

Predictive trend

model 1 Pursue model development Extrapolation, prediction, cost reductions

Marbled murrelet Population trend 2 Test and improve marine Establish consistency in survey techniques to

survey methods with obtain higher quality and more useful data

Federal and non-Federal Improve methods for monitoring

participation (phase 1) Improve collaboration and acceptance

Habitat trend 4 Establish baseline by using Obtain better characterization of habitat given

4 study areas and develop range variation

process for monitoring Improve baseline for monitoring (same rationale

trends using LSOG vege- as LSOG above)

tation maps and grid plots

(phase 1)

Predictive trend

model — Pursue model development Extrapolation, prediction, cost reductions

Aquatic and riparian Watershed trend tbd Under development Under development

Predictive trend

model tbd Pursue model development Extrapolation, prediction, cost reductions

Other modules tbd tbd tbd tbd

tbd = to be determined.

132
Table 17—Summary of existing and new monitoring activities proposed for developing methods and

conducting activities for assigned resources

Developing methods Monitoring activities

Monitoring

topic Approach Existing New Existing New

Late-successional

old growth (LSOG) Vegetation trend Remote sensing Assessment and reporting

and grid plot efforts process; trend projection

Predictive trend

model Develop and

validate model

Assessment and Develop procereporting dures

Northern spotted owl Population trend Demographic and

habitat association

studies

Habitat trend Rangewide habitat assessment

Predictive trend

model Develop model Validate model

Assessment and Develop procereporting dures

Marbled murrelet Population trend Develop new Marine surveys

survey tech- (to be modified)

niques

Habitat trend Refine habitat Habitat surveys

definitions and (to be modified)

analytic techniques

Predictive trend model Develop and

validate model

Assessment and reporting Develop procedures

Aquatic and riparian Watershed trend Develop sampling

methods FS and BLM watershed analyses;

existing monitoring

activities to be

evaluated

Predictive trend model Develop and validate model

Assessment and reporting Develop procedures

Other modules tbd tbd tbd tbd tbd

tbd = to be determined.

133
Table 18—Summary of expected monitoring reports, proposed reporting schedule, and responsibilities

Monitoring Monitoring Reporting Database Responsible

topic report schedule Information content location monitoring staff Important disciplinesa

Regional Regionwide

data summary Annual Combined summaries: Project Regional and QA/QC; statistician;

Maps and tables summaries module moni- information management;

Tables and reports toring managers GIS and remote sensing

Regionwide

interpretive

report 5-year Forest Plan analysis Regional Regional and Statistician-modeler

summaries module monitoring managers

Late-successional

old growth

(LSOG) Data summary: Evaluate project Project sum- Project and QA/QC; statistician;

Landscape 5-year information: maries regional man- information management;

Stand-level Annual Maps and tables agers GIS and remote sensing

Tables and reports

Interpretative report 5-year Regional trend analysis Regional sum- Regional and Statistician-modeler

maries managers

Northern

spotted

owl Data summary: Evaluate project Project sum- Project and QA/QC; information

information: maries regional man- management; GIS

Demographic sites Annual Tables and reports agers and remote sensing

Demographic site

habitat 5-year Tables and reports

Rangewide habitat 5-year Maps and tables

Interpretive report:

Population metaanalysis 4-year Demographic analysis Regional Project and Statistician-modeler

managers

Trends 5-year Regional trend analysis Regional summaries Regional and

managers

Marbled

murrelet Data summary: Evaluate project Project sum- Project and QA/QC; statistician;

information: maries regional man- information management;

Marine Annual Tables and reports agers GIS and remote sensing

Terrestrial Annual Tables and reports

Landscape 5-year Maps and tables

Stand-level Annual Tables and reports

Interpretive report 5-year Regional trend analysis Regional sum- Regional and Statistician-modeler

maries managers

Aquatic

and

riparian Data summary: Annual Evaluate project Project sum- Project and QA/QC; statistician;

information: maries regional man- information management;

Watershed analysis Maps, tables, reports agers GIS and remote sensing

Sampling Maps and tables

Interpretive report 5-year Regional trend analysis Regional sum- Regional and Statistician; modeler

maries managers

Other

modules tbd tbd tbd

Coast pilot

project Example inter- FY 1998 Data analysis for Siuslaw NF, PNW; FS; BLM GIS and remote sensing;

pretive report monitoring questions Salem and staff and module information management;

Eugene BLM managers statistician; modeler

tbd = to be determined.

a In addition to the disciplines identified, additional support may be needed from subject area specialists.

134
Table 19—Staffing and technical expertise needs for conducting the effectiveness monitoring program

through the first 3 years (fiscal year 2000)

Monitoring Part-time or

topic Subject area Position type Full-time staff temporary staff

Regional Program management Regional monitoring managers x

Regional monitoring module managers x

Technical support Information management specialists

GIS and remote sensing specialists x

QA/QC specialists x

Statisticians x

Subject area specialists x

Assessment and reporting Data analysts and modeling specialists x

Late-successional

old growth

(LSOG) Vegetation trend Project managers and field staff xa x

a

Predictive trend model Model specialists x

Assessment and reporting Data analysts x

Northern

spotted owl Population trend Project managers, coordinators, and field staff x x

Habitat trend Project managers and coordinators x

GIS and remote sensing specialists x x

Subject area specialists x x

Predictive trend model Coordinator and model specialists x x

Assessment and reporting Data analysts x

Marbled

murrelet Population trend Project managers, coordinators, and field staff x x

Habitat trend Project managers, coordinators, and field staff x x

GIS and remote sensing specialists x x

Predictive trend model Coordinator and model specialists x x

Assessment and reporting Data analysts x

Aquatic and

riparian tbd tbd tbd tbd

Other modules tbd tbd tbd tbd

Coast pilot

project Evaluate methods and Coordinator and subject area specialists xb

report

tbd = to be determined.

a Many of these staff would be part of an agency’s existing GIS-remote sensing programs who would contribute information to the monitoring

program.

b Staff are included in the funding identified for the pilot project (including coordinators and technical support).

135
Table 20—Implementation schedule and summary of estimated annual funding for monitoring

(recommended options) and related programsa

Funding fiscal year

Monitoring Existing

topic funds, 1997 1998 1999 2000 2001 2002 2003 2004 2005

−−−−−−−−−−−−−−−− Thousand dollars −−−−−−−−−−−−−−−−

Regional — tbd tbd tbd tbd tbd tbd tbd tbd

Late-successional

old growth (LSOG) See below 70 170 — — — 70 70 —

Northern spotted owl – – – – – – – – – – Phase 1b ––––––––– ––––– Phase 2 –––––

1,884 2,240 2,585 2,520 3,290 3,155 1,265 1,228 1,328

Marbled murrelet – – Phase 1b –– –––––––––––– Phase 2 ––––––––––––

600 994 879 940 880 435 435 435 435

Aquatic and riparian tbd tbd tbd tbd tbd tbd tbd tbd tbd

Other modules: tbd tbd tbd tbd tbd tbd tbd tbd tbd

Socioeconomics

Tribal

Biodiversity

Coast pilot project 240 240

Estimated totals 2,724 3,544 3,634 3,460 4,170 3,590 1,770 1,733 1,763

Estimated funding for producing vegetation information (for comparison)c

Image analysis

(remote sensing) Data not 680 1,000 May May May May May May

available differ differ differ differ differ differ

Grid plot data

(e.g., CVS) 2,500- 2,500- 2,500- 2,500- 2,500- 2,500- 2,500- 2,500- 2,500-

3,500 3,500 3,500 3,500 3,500 3,500 3,500 3,500 3,500

tbd = to be determined.

a Costs will change as the programs are implemented. b The spotted owl and murrelet monitoring programs are divided into phases based on research needed to improve

or modify the programs; costs will change as the research is completed (see Lint et al., in press; Madsen et al., in press).

c These funding estimates are for comparison purposes only; these costs are covered by other agency programs

and would not be expected to change regardless of the size or scope of the EM program.

136
Table 21—Summary of estimated annual funding for each specific monitoring component (for completed

modules only)a

Fiscal year funding

Monitoring Existing

topic Activity funds 1997 1998 1999 2000 2001 2002 2003 2004 2005

−−−−−−−−−−−−−− Thousand dollars −−−−−−−−−−−−−−−−

Regional Program management — tbd tbd tbd tbd tbd tbd tbd tbd

Technical support — tbd tbd tbd tbd tbd tbd tbd tbd

Assessment and

reporting — tbd tbd tbd tbd tbd tbd tbd tbd

Late-successional

old growth (LSOG) Vegetation trend See table 20 Same Same Same Same Same Same Same Same

Predictive trend

model 0 30 30 — — — 70 70 —

Assessment and

reporting 0 40 140 — — — — — —

Management and

coordination 0 tbd tbd tbd tbd tbd tbd tbd tbd

Northern spotted owl Population trend 1,690 1,870 2,050 2,050 2,150 2,150 1,100 1,100 1,200

Habitat trend 0 50 200 120 40 45 45 48 48

Predictive trend

model 194 320 335 350 1100 960 120 80 80

Assessment and

reporting — tbd tbd — — — tbd tbd —

Management and

coordination — tbd tbd tbd tbd tbd tbd tbd tbd

Marbled murrelet Population trend 400 175 60 400 340 340 340 340 340

Habitat trend 200 819 819 540 540 95 95 95 95

Predictive trend

model 0 0 0 tbd tbd tbd tbd tbd tbd

Assessment and

reporting — tbd tbd — — — tbd tbd —

Management and

coordination — tbd tbd tbd tbd tbd tbd tbd tbd

Coast pilot project Evaluate methods

and report 240 240

Totals, estimated 2,724 3,544 3,634 3,460 4,170 3,590 1,770 1,733 1,763

tbd = to be determined.

a Costs will change as the programs are implemented.

137
References Furniss, M.; Hohler, D.; Larsen, P. [and others]. 1997. Northwest Forest Plan

aquatic and riparian effectiveness monitoring. [Draft outline]. Portland, OR: U.S.

Department of Agriculture, Forest Service, Six Rivers National Forest. On file

with: Six Rivers National Forest, 1330 Bay Shore Way, Eureka, CA 95501.

Hemstrom, M.; Spies, T.; Palmer, C. [and others]. [In press]. Late-successional

and old-growth forest effectiveness monitoring plan for the Northwest Forest Plan.

Gen. Tech. Rep. PNW-GTR-438. Portland, OR: U.S. Department of Agriculture,

Forest Service, Pacific Northwest Research Station. 37 p.

Lint, J.; Noon, B.; Anthony, R. [and others]. [In press]. Northern spotted owl

effectiveness monitoring plan for the Northwest Forest Plan. Gen. Tech. Rep.

PNW-GTR-440. Portland, OR: U.S. Department of Agriculture, Forest Service,

Pacific Northwest Research Station.

Madsen, S.; Evans, D.; Hamer, T. [and others]. [In press]. Marbled murrelet

effectiveness monitoring plan for the Northwest Forest Plan. Gen. Tech. Rep.

PNW-GTR-439. Portland, OR: U.S. Department of Agriculture, Forest Service,

Pacific Northwest Research Station.

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Mulder, Barry S.; Noon, Barry R.; Spies, Thomas A.; Raphael, Martin G.; Palmer,

Craig J.; Olsen, Anthony R.; Reeves, Gordon H.; Welsh, Hartwell H. 1999.

The strategy and design of the effectiveness monitoring program for the Northwest

Forest Plan. Gen. Tech. Rep. PNW-GTR-437. Portland, OR: U.S. Department of

Agriculture, Forest Service, Pacific Northwest Research Station. 138 p.

This report describes the strategy and design of an effectiveness monitoring program

for the Northwest Forest Plan. The described premise is to implement a prospective

and integrated habitat-based approach to monitoring that provides a gradual transition

from an intensive, individual species-resource focus to a more extensive, ecosystems

approach by using surrogates to measure the pattern and dynamics of habitat structure in place of monitoring biota. The report describes the scientific framework for

monitoring, starting with conceptual models, that is the basis for designing plans

for monitoring specific resources.

Keywords: Northwest Forest Plan, ecological monitoring, effectiveness monitoring,

adaptive management, regional scale, habitat basis, conceptual model, predictive

model, integration, summary report, interpretive report, institutionalize.

The Forest Service of the U.S. Department of

Agriculture is dedicated to the principle of multiple

use management of the Nation’s forest resources

for sustained yields of wood, water, forage, wildlife,

and recreation. Through forestry research, cooperation with the States and private forest owners, and

management of the National Forests and National

Grasslands, it strives—as directed by Congress—to

provide increasingly greater service to a growing Nation.

The United States Department of Agriculture (USDA)

prohibits discrimination in its programs on the basis

of race, color, national origin, sex, religion, age, disability,

political beliefs, and marital or familial status. (Not all

prohibited bases apply to all programs.) Persons with

disabilities who require alternative means of communication of program information (Braille, large print, audiotape,

etc.) should contact the USDA’s TARGET Center at

(202) 720-2600 (voice and TDD).

To file a complaint, write the Secretary of Agriculture,

U.S. Department of Agriculture, Washington, DC 20250,

or call (800) 245-6340 (voice), or (202) 720-1127 (TDD).

USDA is an equal employment opportunity employer.

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