The Local Unit Criteria and Indicators Development (LUCID) test was a

collaborative initiative among eight national forests, their leadership teams, and the

Inventory and Monitoring Institute (IMI). IMI has a wealth of additional material

available about the LUCID Test Project, its results, toolkit, and implementation

strategies. To better serve diverse interests, the results of the LUCID test are

presented several ways. This Management Edition, prepared with the assistance of

Forest Service CAT Publishing Arts, summarizes the highlights of the LUCID test and

the recommendations for implementation.

The Technical Edition of the LUCID test, USDA Forest Service Inventory and

Monitoring Report No. 4 (Wright et al. 2002) supplements this Management Edition and

serves as a desk guide. The Technical Edition contains:

 Background and Context,

 Methods,

 Criteria and Indicator Results,

 Analysis and Synthesis, and

 Implementation.

Chapters are followed by summaries of key points and findings. Icons throughout

the document indicate focus areas, such as background information, tools, and additional

resources, which are available in CD-ROM format only. The appendixes provide

additional reference documents, the full core suite of LUCID criteria and indicators,

models and tools, further discussion of technical considerations, and a science review

supplement. The Technical Edition and other supplemental materials, reports,

newsletters, and related links are available at http://www.fs.fed.us/institute/lucid/.

Wright, P. A., G. Alward, J.L. Colby, T.W. Hoekstra, B. Tegler, and M. Turner. 2002. Monitoring

for forest management unit scale sustainability: The local unit criteria and indicators

development (LUCID) test (management edition). Fort Collins, CO: USDA Forest Service

Inventory and Monitoring Report No. 5. 54 p.









 





	


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Principal Author

Pamela A. Wright, Ph.D.1

Co-Author

Jennifer L. Colby2

Contributing Authors

Gregory Alward, Ph.D.3

Thomas W. Hoekstra, Ph.D.3

Brent Tegler, Ph.D.4

Matt Turner, M.S.3

 	




	



	
	 	



			

 	

	



1

 USDA Forest Service Inventory and Monitoring Institute/METI, Inc., Fort Collins, CO.

2

 USDA Forest Service CAT Content Analysis Program, Salt Lake City, UT.

3

 USDA Forest Service Inventory and Monitoring Institute, Fort Collins, CO.

4

 North South Environmental Consultants, Ontario, Canada.

	


The Forest Service has a strong commitment to its motto: “Caring for the Land and Serving

People.” Monitoring for sustainability of our national forests and grasslands, as a result of

ecosystem management, is a key component of this commitment. Our goal in the Forest Service is

to work with our partners, the American public, to strike the right balance between sustainable

social, economic, and ecological systems. In this way we can satisfy the values of the present

without compromising the needs of future generations.

This report documents the second major step the Forest Service has taken to establish

sustainability monitoring on national forests and grasslands. In the first project, completed in 1999,

the Inventory and Monitoring Institute (IMI) tested the application of the Center for International

Forestry Research (CIFOR) forest management unit scale “Criteria and Indicators of

Sustainability.” The Boise National Forest, Forest Service Research, and International Programs

cooperated in this effort. While the CIFOR project showed very promising results for the

application of sustainability assessments on national forests and grasslands, it also highlighted the

need for a more thorough test of the methodology in a variety of social, economic, and ecological

settings.

In 1999, the Chief’s office asked the IMI to undertake the Local Unit Criteria and Indicators

Development (LUCID) project. This report, and supporting documentation, is now available to

assist and guide the development of sustainability assessments on national forests and grasslands,

as part of the planning process. The LUCID monitoring methodology will continue to evolve, as

national forests and grasslands tailor sustainability assessments to local ecological conditions, and to

meet the needs of collaborators in both public and private venues. There is not a single

sustainability assessment for all national forests and grasslands. The LUCID test has identified

common social, economic, and ecological threads that can be woven together to tell the

sustainability stories of our national forests and grasslands. Sustainability is the common ground

between all public values. Sustainability assessments provide the information that we, and the

American people, can use to assure the continued long-term integrity of the social, economic, and

ecological systems we depend on from the lands we manage. I am proud to convey this report to

the Forest Service and to the American people. Sustainability assessments provide a blueprint for

us to use as we work together toward the next 100 years of public land management.

DALE N. BOSWORTH

Chief




The LUCID Project was initiated as a result of the CIFOR-NA test in Boise in 1998. The United

States Department of Agriculture Forest Service Research and International Programs, who sponsored

the original CIFOR-NA test, continued to advocate for, and support, the LUCID Project from the

outset. We benefited in many ways from the strong work of the Center for International Forestry

Research, particularly the assistance of Dr. Ravi Prabhu.

The LUCID Project was a true partnership between the forests and the Washington Office. The

eight national forests, participating in six forest teams took the germ of an idea and a rough protocol and

worked together with the IMI, with their collaborators, and with each other. They were constantly

innovating, providing constructive comments with good humor, and were always willing to go the extra

mile. Most teams were developed through strong partnerships with universities, or staff from other

national forests and regional offices and these individuals provided fresh ideas and alternative

perspectives that were very helpful.

The forest supervisors and deputy forest supervisors for the Allegheny, Malheur, Modoc, Mt. Hood,

Ottawa, Wallowa-Whitman, Tongass, and Umatilla National Forests were advocates for their forest

teams and played an active role throughout the project. We thank in particular the Allegheny National

Forest leadership team who strongly supported the Project during several forest supervisor transitions.

Each of these forests also benefited from strong support of the regional foresters and state foresters

who supported the project from its initiation.

The Boise National Forest, the host for the 1998 CIFOR-NA test, continued to play a valuable role

in the LUCID Project. At their own initiation the Boise, in conjunction with the other forests in the

Southwest Idaho Ecogroup, were able to implement some of the preliminary ideas for incorporating

sustainability monitoring into their forest plan revision. We thank in particular Lynnette Morelan and

David Rittenhouse who championed this initiative and participated in the LUCID Project in a review

capacity throughout and were always willing to make presentations to those interested in

implementation realities.

A number of people from both inside and outside the Forest Service provided valuable suggestions,

support, and review comments throughout the project including: Washington Office Ecosystem

Management Coordination staff; Michael Sieg, Field Sampled Vegetation staff; Andrew Gillespie, Forest

Inventory Analysis; Connie Carpenter, State and Private Forestry; Great Lakes Forestry Association

staff and volunteers; Brad Holt, Boise Cascade Industries; staff and volunteers with the Canadian

Model Forest Network; and the whole staff of the IMI. Ravi Prabhu, Timothy Allen, Joseph Tainter,

Donald Floyd, and Stephen Woodley provided very useful review comments.

Elisabeth Reite, graduate student at Colorado State University, provided assistance in many, many

capacities. Beth helped prepare databases and publication material, provided great suggestions for

revisions, and was always willing to do whatever needed to be done.

We wish to specifically thank Management and Engineering Technologies International, Inc.

(METI), particularly Renard and Al Johnson who managed and coordinated staffing, keeping all of us

happy and well supported. METI staff were consummate professionals and were always interested in

what we were doing and in finding ways they could help.

We would like to extend special thanks to those individuals who helped us pull all the details of the

report together:

 Original Cover Art and Figures: Joyce VanDeWater, USDA Forest Service Rocky Mountain Station

 Database and Computer Models: Richard Hagestedt, Mt. Hood National Forest

 Editor: Karen Mora, USDA Forest Service CAT Publishing Arts

 Layout and Design: Carol LoSapio, USDA Forest Service CAT Publishing Arts

		

 	
 

  1



 
 

  2

Indicators................................................................................................................................... 2

Using Criteria and Indicators Frameworks............................................................................... 2

 

 
 



  
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A Need for Forest Management Unit Scale Criteria and Indicators........................................ 4

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Resilience in Systems................................................................................................................ 5

Systems Properties ................................................................................................................... 6

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Anchoring the LUCID Test in a Systems Framework ................................................................... 9

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LUCID Test Sites ................................................................................................................... 10

Collaborative Learning Approach ........................................................................................... 10

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Systems Framework Benefits................................................................................................. 12

Final Criteria and Indicators Core Suite.................................................................................. 12

An Unexpected Result: The Importance of the Process Itself .............................................. 12

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Step 1: Define The Purpose of Sustainability Monitoring and the Study Area ....................... 13

Step 2: Review Systems Concepts and Adopt a Systems-based Framework ........................ 15

Step 3: Review, Select, and Revise Indicators........................................................................ 16

Step 4: Identify Measures ...................................................................................................... 17

Step 5: Define Reference Values............................................................................................ 18

Step 6: Collect Data ................................................................................................................ 19

Steps 7 and 8: Conduct Evaluations and Develop a Sustainability Assessment ..................... 19



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Integrating the LUCID Process into Local Forest Planning and Management ..................... 22

FMU-Scale Sustainability Monitoring Offers Clear Benefits................................................. 24

Contributions to Multiscale Monitoring ................................................................................... 24

Relationship to National Criteria and Indicators Framework ................................................. 25

Broader Implementation of the LUCID Process ................................................................... 25


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The term sustainability expresses the human

desire for an environment that can provide for our

needs now and for future generations. Our collective journey to find a way to live harmoniously

with each other and within our social, economic,

and ecological environments is a quest for

sustainability.

Sustainability is a compelling societal goal

with widespread public appeal. However, what the

term implicitly conveys and what it explicitly

means are not necessarily the same. Finding a

specific definition of sustainability that is broadly

acceptable is difficult because it is about values

that vary among groups and over time.

The quest ultimately requires decisions about

what to sustain, for whom, for how long, at what

cost, and how. Clearly, this is not a simple task

because issues of generational equity are involved

(i.e., balancing the distribution of benefits and

costs within this generation and across future

generations). Despite ongoing scientific and

political debate regarding specific definitions of

sustainability, the term has proven to be a useful

organizing concept for exploring the relationship

between social, economic, and ecological systems,

their current conditions, and trends (Floyd et al.

2001). Although people may not easily define

sustainability, they more readily recognize its

antithesis.

Over the past several decades, the quest for

sustainability has emerged as a central theme of

economic development, social policy, and natural

resource management at local, regional, national,

and international levels. Scientists, resource

managers, policymakers, and citizens alike

increasingly recognize the interconnectedness,

complexity, and dynamism of social, economic,

and ecological systems. However, the very

complexity of these systems poses significant

challenges to our ability to study and understand

them. Our ever-expanding technological capabilities along with our unique ability to exploit dense

energy sources, such as fossil and nuclear fuels,

and our species’ widespread population dispersal

means that humans are now the dominant keystone species worldwide. Our technological

ability to manipulate the biophysical environment

now extends from the molecular scale of genetic

engineering to local and regional scales of urbanization and land use patterns visible from satellites to the global atmospheric scale of climate.

This ability has caused increasing concern for the

current and future consequences of our actions.

We are beginning to recognize and confront the

undesirable outcomes of past human activities that

may be redressed only with significant effort and

cost, if at all.

Sustainability is a human value, not a fixed,

independent state of social, economic, and ecological affairs. It requires human judgment about the

condition or state of a set of tangibles. Inherent in

sustainability is our positive valuation of tangibles

that we wish to persist in time and space.

Sustainability is not absolute because it is

dependent on social values and involves multiple

dimensions and scales, including those of time and

space. As we become more aware of cross-scale

interactions, decision makers increasingly seek a

triple bottom line from which tradeoffs can be more

clearly defined and simultaneous social, economic,

and ecological benefits can be achieved and

maintained over time. Given the range of human

values and differing objectives for future social,

economic, and ecological conditions, engaging in a

public discourse about sustainability is critical.
		




	
 

			

		



Images of the blue sphere of the earth taken

from the Apollo space missions in the late 1960s

highlighted the finite nature of natural resources.

International concerns about environmental

change led the United Nations to convene various

expert panels to assess current conditions and

trends. One of these panels, the Bruntland Commission, used a report, “Our Common Future”

(World Commission on Environment and Development 1987), which popularized the concept of

sustainable development. Since that time,

sustainability has become a central theme in many

different venues, from natural resource management to community development to business

planning. Within the United States, many agencies, industries, organizations, and citizens have

undertaken the quest for sustainability through

many of their own initiatives.

Nowhere has the struggle for sustainability

and the debate over its meaning and goals been

more focused than in relation to forests. As the

debate has shifted focus from tropical to temperate forests, the larger dialogue about sustainability

has been brought into our backyards. The 1992

United Nations Conference on Environment and

Development held in Rio de Janeiro renewed the

international commitment to protect the integrity

of the global environment while respecting the

interests of all people. The United States and the

other 173 signatory nations negotiated a nonbinding companion agreement for sustainable

forest management, Agenda 21, LUCID Technical

Edition, Appendix 1 (Wright et al. 2002), and

agreed to monitor, evaluate, and report on

progress. The United States has committed to

report periodically on the status of sustainable

forest management across all ownerships, not just

on National Forest System lands.



	
	




In pursuit of sustainability, we first must ask

where we are now, how we are doing, and where

we are going. A primary strategy is to focus on

monitoring and assessment. To do so, we need to

identify the critical components of social, economic, and ecological systems and then attempt to

gather the appropriate information over time to

help answer these questions.

	


Information about specific elements of

interest needs to be measured in a consistent

fashion: defining and using indicators is one

common approach. Indicators are simplified parts

within complex systems that tell us something

about a specific component or process of interest

and are commonly used in everyday life. For

example, an indicator of the state of the economy

is the unemployment rate and an indicator of

personal health is a blood pressure reading. Any

individual measure of an indicator is merely a

signal of the larger phenomenon or event. An

integrated array of indicators is necessary for

understanding a more complete picture. For

instance, an individual’s high blood pressure

reading may signal cardiovascular disease. Blood

pressure is a common indicator of underlying

physiological processes such as plaque deposition

in blood vessels, but it is not a direct measure of

these processes. While results from one indicator

can provide some information, a physician needs a

broader set of tests and a patient history to make a

firm diagnosis, evaluate the relative risk for a

heart attack, and prescribe appropriate treatment.

Individual indicators are neither sustainable

nor unsustainable; they do not reveal causality or

direction. By considering a suite of systems-based

indicators and measuring them over time, we hope

to better understand social, economic, and ecological conditions, albeit in simplified form. This

is one step on our quest for sustainability.



		

	


		


Many government agencies, non-governmental organizations, and academic researchers use

hierarchical frameworks to help design sets of

indicators for sustainability monitoring programs.

These frameworks are typically composed of two

basic groups and are referred to as criteria and

indicators (C&I). A recent Food and Agriculture

Organization (FAO) review of C&I for sustainable

forest management (Castaneda et al. 2001) used

the following definitions:

“Criteria define the range of forest values to be

addressed and the essential elements or

principles of forest management against which

the sustainability of forests may be assessed.
	


 	

	
	 	

		
	

Each criterion relates to a key element of

sustainability and may be described by one or

more indicators.” Examples: landscape

function, capital and wealth.

“Indicators are parameters that measure specific quantitative and qualitative attributes

and help monitor trends in the sustainability

of forest management over time.” Examples:

disturbance process, recreation facility infrastructure.

A C&I approach for assessing sustainability

is an organizational tool. It can provide a common language for understanding sustainable

management and can guide the monitoring

process over time. Given the abstract nature of

sustainability, the C&I hi erarchy provides a

structured approach to defining the parameters

and goals of social, economic, and ecological

sustainability and assessing progress toward

them.

Although the use of a simple two-level

hierarchy is common in most national-level

initiatives such as the Montreal Process, many

people have added levels either formally or

informally. For instance, criteria are often

grouped under three higher organizing categories, called principles. These commonly are

social, economic, and ecological (Figure 1,

Table 1).

Indicators themselves do not define the

specific characteristics to be studied or the

methods to be used in studying them, collectively known as measures. Measures form a

hierarchical level below indicators in the C&I

framework. These characteristics and methods

must be defined before indicators can actually

be put to use. For the sample indicator disturbance processes, the characteristic to monitor

might be fire events, and a methodology would

define the dimensions to measure, the measurement scales for each dimension, and the

frequency of measurements (for example, acres

burned annually by sixth order watershed at

specified fire intensity ranges). Associated

measures form a level below indicators in the

C&I hierarchy. Data elements are the actual

information collected for the measures, and

reference values are comparison values against

which the data may be evaluated.

	




Principle

Criterion Criterion

Indicator Indicator Indicator Indicator

Measure Measure

Data

Element

Data

Element

Principle Principle
		




	
 

			

		




	
	
		

	





Much of the initial focus on developing

indicators resulted from the need to report on

national progress toward sustainable forest

management. However, there has been growing

realization that sustainability issues involve

multiple scales and so achieving the national goals

of sustainability largely rest on actions carried out

at the local or forest management unit (FMU)

scale. This report presents the results of one such

initiative: the Local Unit Criteria and Indicators

Development (LUCID) pilot test, a USDA Forest

Service project to develop a sustainability monitoring program at the FMU scale.

The LUCID test originated from both the need

to report on progress towards sustainability at the

national scale and the need for timely, accurate,

and integrated monitoring at the forest management unit scale. Current national initiatives focus

on the broader policy and institutional frameworks

that foster sustainability, while FMU-scale

monitoring focuses on management activities and

their associated outcomes.



	
	





		

	


The FMU scale is generally the scale at which

management policy is actually implemented with

on-the-ground activity in the USDA Forest

Service. A forest management unit includes—but

is not limited to—national forest system lands; it

can also encompass neighboring communities,

economies, and ecological systems across ownership boundaries. Total land area and ownership

size may vary among forest management units,

but day-to-day decisions about management

activities occur at this scale. Criteria and indicators tailored to the FMU scale can help provide

insight into the sustainability of associated social,

economic, and ecological systems.

The Center for International Forestry

Research (CIFOR), an international forestry

research organization, has pioneered work on

local-unit scale indicators for sustainable forest

management. While their primary focus has been

on tropical forests, CIFOR staff members have

also been interested in applying their assessment

methods to North American temperate forests. As

a first step toward using FMU scale C&I in North

 	
 


		 
	
									

	
		
					

		

			
	

	 
									

						


								
			

					

				
		


		

 
	 
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America, CIFOR staff conducted a test in 1998 in

partnership with the International Programs and

Research branches and the Inventory and

Monitoring Institute (IMI) of the USDA Forest

Service on the Boise National Forest and

surrounding areas. Experts from government,

industry, and nongovernmental organizations from

Canada, Mexico, and the United States

participated. The CIFOR–North America

(CIFOR–NA) test refined and adapted the CIFOR

set of criteria and indicators for tropical forests to

the social, economic, and ecological conditions of

temperate North American forests. The results

from this test were quite promising (Woodley et

al. 1999).

The CIFOR–NA test produced an initial set of

indictors applicable to North America but did not

develop associated measures or reference values

for them. The CIFOR–NA expert team also did

not have time to produce analysis and synthesis

tools for use in evaluating any data collected or

methods for aggregating appropriate data sets. The

CIFOR–NA test did, however, clearly demonstrate

that a set of local-scale C&I could potentially

provide the information needed for sustainable

management of national forests.

The results of this preliminary test were

reported at the 1998 North American Forestry

Commission meeting1. The Chief of the Forest

Service chartered the LUCID project to further

this work and tailor it to the specific needs of

the agency. This pilot project was called the

Local Unit Criteria and Indicators Development

(LUCID) test. The intent of the LUCID test was

for a core team of project coordinators from

IMI to work with personnel (forest teams) at six

national forest test sites to expand the sciencebased development and evaluation of an FMUscale monitoring program for sustainability.

Several participants in the CIFOR–NA test

served on LUCID core team to build on the

knowledge and experience gained from the

Boise site. All LUCID participants hoped to

develop one broadly applicable set of criteria

and indicators that could be used by the Forest

Service for both forested and non-forested

systems.

	

By standard dictionary definitions, systems

are groups of interrelated, interacting, or interdependent elements forming a complex whole. Their

interactions determine the structure and organization of the system and are in turn are influenced

by that structure and organization (Kay and Foster

1999). Because of these interactions, which

include positive and negative feedback and selforganization, systems have emergent properties

that are more than the sum of the physical ingredients of which they are composed. Nutrient cycling, carbon sequestering, and microclimate

maintenance are examples of emergent properties

of ecological systems. The multiplier effects from

direct employment in the forest sector is an

example of an emergent property of economic

systems. King (1993) notes that because a system

is defined by both its components and the interactions between them, a system description “simultaneously involves both structure and

function—what are the components, how are they

connected, and how do they operate together?”








Recent research efforts in understanding

complex social, economic, and ecological systems

have highlighted a property known as resilience

(Holling 2001). In essence, resilience is the

capability or degree to which a self-organized

system can resist perturbation and remain within

the functional boundaries that characterize it

without flipping to a different set of functional

boundaries. Redundancy of elements within

systems, such as biodiversity or regional economic diversity, helps to maintain resiliency. This

is because multiple components can fill similar

functional roles as conditions vary over time. The

resilience of a given system is not a fixed state. It

changes over time in response to changes in

system components and processes.

A highly resilient system alone is neither

desirable nor undesirable; desirability depends on

human valuation of the system in question. For

instance, dictatorships can be extremely resilient,

as can be eutrophic lakes, but these system states

may not provide the desired level of goods and

services for human well-being and sustenance

(Carpenter et al. 2001, Holling 2001) as compared

to other possible system states.

1

Details about this meeting and summarized conclusions

are posted at http://www.fs.fed.us/global/nafc/nafc_reports/

reports/1991/report_1999.htm.
		




	
 

			

		



Resilient systems can withstand greater

amounts of disturbance while remaining within

their characteristic boundaries than can less

resilient systems. Some systems may have little

inherent resilience, but can persist due to lack of

significant perturbation. Highly specialized island

ecosystems commonly fit this category because of

their spatial isolation. Therefore, resilience by

itself cannot serve as a measure of sustainability.

Understanding the concept is important in the

quest for sustainability because systems with

different levels of resilience will respond differently to both natural and human-induced disturbance processes. Given that less desirable system

states can be highly resilient, human activities that

cause productive systems to become less productive may be very difficult or costly restore. One

example is Lake Mendota in Wisconsin, where

the clear lake system has become eutrophic due to

gradual phosphorus accumulation from agriculture

and urban development (Carpenter et al. 2001).




		


The social, economic, and ecological elements

of sustainability are organized into inter-related,

dynamic systems rather than functioning as stand

alone, independent entities, or existing in simple

one-to-one and linear relationships. While this

observation has been central to the worldview of

many human cultures, many individuals and

groups tend to overlook this concept while

addressing real-world problems. Human cognition

is subject to several common conceptual pitfalls

including the following:

 Elements or variables are independent;

 Effects are immediate (no lag time exists);

 Effects are linear and constant;

 Effects are one-way (no feedback or emergent

properties exist); and

 Systems and their components are static (High

Performance Systems 2000).

In a narrow context, any of these assumptions

may seem to hold true. However, failing to

consider system dynamics over time commonly

leads to unwelcome surprises. Decision makers

and managers risk failing to achieve their goals

when they attempt to sustain components independently and at static levels because other processes

and interactions may confound their expectations

(Folke et al. 2002). In practice, people often focus

more intently on maintaining structure than on

function instead of acknowledging the interdependence of the two within functioning systems. The

intensity of the public outcry and debate surrounding the Yellowstone fires of 1988 (Rykiel 1998)

demonstrates this tendency to attach greater value

to static structure than to dynamic process.

Due to the complexity of social, economic,

and ecological systems, it is becoming increasingly evident that sustainability is best achieved

by focusing on the contexts in which systems

operate rather than attempting to sustain specific

system elements. Taken broadly, this means

sustaining the integrity of systems so that as

individual components vary over time, the associated functions and processes characterizing the

system’s functional boundaries and inherent

system resilience are maintained. While the exact

dynamics or variability in systems cannot be

specifically predicted, changes can be anticipated

by explicitly taking these dynamics into account

and by gaining more information about these

patterns over time.

Although modern scientific theories explicitly

recognize the interdependence, non-linearity, and

dynamism of many systems, the scientific method

often approaches systems in a reductionist fashion, focusing on a small number of individual

elements and processes. While this is a crucial

means of gaining information through experimentation and observation, there has traditionally

been far less emphasis placed on synthesis or

system-level analysis.

Scientists tend to work within the confines of

their particular subdisciplines, such as hydrology,

microeconomics, vertebrate population biology,

cultural resource management, or silviculture,

aggravating this tendency to focus on components

rather than systems. Forest managers and staff

also tend to be organized administratively into

activity sectors (such as recreation or silviculture),

or into ecosystem component disciplines (such as

terrestrial vertebrate biology, soils, or hydrology).

These divisions are useful frameworks for carrying out programs of work but do not inherently

encourage systems thinking or collaboration

across disciplines.

A systems-based framework for monitoring

recognizes that systems of interest are a group of

interrelated, interacting, or interdependent constituents forming a complex whole. This framework uses the structures and functions (processes)
	


 	

	
	 	

		
	

of systems as its organizing tools. It focuses on

the contexts that allow for the production of goods

and services, not just the goods and services

themselves. A systems approach focuses on the

outcomes or states of systems. A systems approach is particularly applicable to forests and

rangelands since they are joint production systems

with emergent properties that simultaneously, not

independently, produce soil, water, air, plant, and

animal material. For example, tree growth is

partly dependent on soil fertility, which in turn is

dependent on tree decline, death, and decay for

providing an array of micronutrients and minerals

to feed soil microorganisms, as well as structural

elements to provide physical protection against

drying, compaction, and erosion. Therefore,

failure to consider and manage soil resources may

lead to less than desirable stand regeneration.


		

		
	

Social, economic, and ecological systems

reveal different characteristics both in time and

space. A single tree does not look the same or play

the same interactive role within the parent community at 5, 50, and 500 years; and a forest does

not look or function the same at 500 or 5,000 or

50,000 acres. Simply stated, scale, both spatial

and temporal, matters. Because the systems we are

trying to sustain reveal different characteristics at

every scale, sustainability questions change at

every scale. Allen and Hoekstra (1994) note that

there is “no nature-given scale at which a system

is sustainable or otherwise.” Therefore, we should

monitor for sustainability at a variety of scales.

Although critical in framing the issue of

sustainability, scale is often neglected in

sustainability discussions (McCool and Haynes

1995). Sustainability monitoring initiatives occur

at a range of spatial scales. The national scale of

the Montreal Process focuses on examining the

state of a nation’s forests. At a regional scale, a

number of states are monitoring for

sustainability. Other assessment programs at this

scale are defined by ecological region, such as

the Mid-Atlantic, the Great Lakes, and the Sierra

Nevada.

The question we ask at the local scale will be

specific to the dynamics of the particular place

and its resources and residents. Thus, employment

may be a common factor to many scales, but the

meaning associated with monitoring employment

at the local scale is very different from monitoring

at the national scale.

Monitoring within a systems framework seeks

to collect sets of data over time that can provide

us with information about the state of ecological,

social, and economic systems, not just one-time

images of the state of individual resources or

elements. The goal of monitoring is to detect

change. This means both collecting data and

comparing it to what we understand about the

ranges within which these systems normally

operate (Innes 1998). Since systems are inherently

dynamic rather than static, sustainability monitoring programs are intended to discern both the

baseline patterns and fluctuations for various

components and processes and to estimate humaninduced changes over and above this variability.

Managers may also be better able to design

treatment strategies that mimic natural patterns of

variability.

Monitoring in this context is an iterative

learning process. As more indicator data are

collected and synthesized, the fundamental

understanding of sustainability in a systems

context will increase. The growing scientific and

public consensus surrounding the anthropogenic

causes of climate change is one example in which

data collection and analyses over time have

revealed patterns that allow increasingly accurate

differentiation between baseline fluctuations in

climate and those likely to have be associated with

human activity.

Temporal scale considerations are central to

adaptive management, yet managers often treat

monitoring as a singular, one-time event dependent on project implementation, agency funding,

and reporting requirements. To understand systems, it is vital to consider the temporal scale at

which the elements in question interact, and then

to monitor the elements accordingly. In this

context, sustainability monitoring tracks a suite of

indicators over time to identify the changes in the

states of systems. Monitoring is an ongoing,

multistage process. Periodic sustainability assessments serve to analyze and synthesize data

collected at more frequent intervals. Results of the

sustainability assessments can then inform management decisions.
		




	
 

			

		



 	
	




 	


	


Placed within an adaptive management

context, monitoring can engage agency personnel

in a systematic and rigorous learning effort as they

undertake ongoing applied research to determine

if management actions do indeed result in expected outcomes and if so, at what scales. Consequently, monitoring becomes a vital link in the

larger land management process and the essential

feedback loop of managing for sustainability

(Figure 2).

Forest planners and managers confront an

ongoing need to tie planning and active management activities to on-the-ground results and to

obtain meaningful information from which to

make their decisions. Forest planning requires

asking a series of detailed questions, often as

testable hypotheses:

 What are the desired conditions of the various

land areas that we manage? For what reasons?

Over what time periods?

 Do our projects move us toward desired future

conditions?

 Are our assumptions correct about outcomes,

under what conditions and where? Over what

time period?

 What are any tradeoffs?

 What off-forest activities affect National Forest System lands, and vice versa?

If models or underlying assumptions are not

accurate, then explicit management goals, such as

maintaining non-declining timber volume or

maintaining watershed health, cannot be met

because activities lead to unexpected and potentially undesirable results. Effective adaptive

managers need a clear, scientifically sound

applied research approach. This includes a mechanism for the analysis and interpretation of the data

and a means for the results to feed back into

decision making. Monitoring should focus on

developing the tools necessary to gauge where we

are relative to where we want to be.

The LUCID test aimed to determine whether

adopting a program of sustainability monitoring

could enhance current monitoring programs at the

local scale in the Forest Service. At the FMU

scale, agency monitoring efforts have traditionally

focused on project and forest plan implementation

compliance with minimum legal requirements

along with specific local information needs. These

efforts vary widely from forest to forest and from

year to year. There are widespread concerns, both

internal and external, over current monitoring

strategies. Efforts are often costly, fragmented,

and focused predominantly on project compliance

with standards for individual resources or elements. Implementation monitoring focuses on the

following kinds of questions:

 Are specific species present?

 Were stream buffer widths achieved for this

project?

 What are the road densities in the watershed?

While this work is a vital component of active resource management, it does not necessarily meet

the needs of broader forest planning, especially in

the context of adaptive management.

Monitoring is too often viewed as a costly

incidental activity that takes scarce resources

away from on-the-ground activities and outcomes,

despite the fact that a well designed monitoring

program can greatly assist resource managers.

Barriers to effective monitoring and adaptive

management include costs, staffing, and budgetary

constraints; inadequate scientific methodology

during program design; lack of contact and

collaboration between researchers and managers,

both internal and external; lack of clear connections among monitoring elements or among

"	##$#%
 monitoring elements and management goals and

Assess

Problem

Design

Monitor Implement

Evaluate 

and

Adjust
	


 	

	
	 	

		
	

objectives; overlapping or redundant monitoring

efforts; lack of institutional connections between

data analysis and decision making; lack of reference values; and lack of thresholds or trigger

points for concern or action. Specifically, monitoring efforts should support the following objectives:

 Build a base of understanding about the system by revealing patterns and trends;

 Establish benchmarks of the current state of

the system for comparison to desired future

conditions;

 Detect change in the system and serve as an

early warning of further change;

 Evaluate the effectiveness of programs and

measure progress toward goals;

 Identify changes in baseline conditions for

key indicators that result from management

actions, including restoration activities;

 Support planning and management decisions

through the identification of key issues and

trends;

 Communicate about the state of the environment; and

 Serve as an accountability mechanism for the

public, managers, governments, and international communities.


 	

 
 	

	

A C&I hierarchical approach is predominantly

component-based and can serve as powerful tool

to gather information about specific elements of

systems. Unless individual elements are explicitly

considered within a systems framework, however,

standard C&I monitoring results do not reveal

interactions between elements or the emergent

properties of the systems being studied. One of

the most significant innovations of the LUCID

test was to explicitly place the development if the

criteria and indicators set within a systems framework. The systems approach is a key difference

between the LUCID test and other C&I monitoring frameworks.

LUCID project coordinators hoped that FMUscale sustainability monitoring would provide a

robust approach to achieving many of the

agency’s broad monitoring needs. The core team

felt that a systems framework would help participating forests focus on what is really important

and what is secondary to systems. This could, in

turn, reduce a seemingly infinite number of

potential monitoring elements to a feasible

number capable of producing meaningful information. The core team was also interested in exploring the interdependence of various system

components and addressing scale issues.

The systems approach taken by the core team

was based on the most up-to-date knowledge of

the components and organization of the social,

ecological, and economic systems. The LUCID

test systems framework was also developed for

potential application across all National Forests.

Test participants aimed to produce a broadly

applicable set of indicators to capture the wide

range of local scale social, economic, and ecological systems present within the United States.

The core team also hoped to develop a clear

process that individual forest management units

could use to place monitoring efforts in a systems

framework. Indicators or individual components

alone have little meaning or value in assessing

sustainability until they are considered within the

dynamic systems of which they are a part. However, traditional forest planning and monitoring is

organized primarily within activity sector and

component-based frameworks, so providing a

clear transition to a systems approach is needed.

	







































































	 !

"###
		




	
 

			

		





	
	

Given the national reporting requirements on

forest sustainability and the real needs at the local

forest scale for improved monitoring programs,

the LUCID core team was eager to appraise the

feasibility of sustainability monitoring. They

hoped that their approach could establish a logical

link from sustainability concepts to on-the-ground

monitoring efforts for adaptive management.

Starting from the strong foundation provided by

the CIFOR–NA test, they hoped to discover

whether designing a systems-based C&I monitoring toolkit could provide forest managers and

collaborators with the following: feedback that

could be used to improve Forest land and resource

management plans; enhanced collaboration among

National Forests, other governmental agencies,

private landowners and stakeholders; and a means

to relate forest plan outcomes with regional and

national C&I trends. The following five specific

objectives were set to guide the project:

1. Test, develop, modify, and evaluate C&I to assess the sustainability of social, economic,

and ecological systems at the FMU scale;

2. Develop analysis methods that establish the

relationships among indicators and aggregate

the results for reporting on sustainability;

3. Examine the relationship between nationalscale (e.g., Montreal Process) and FMU scale

indicators;

4. Develop a research agenda based on the above

work to further understanding and application

of FMU scale C&I; and

5. Develop a strategy to implement FMU scale

C&I throughout the Forest Service.


 




In March of 1999, all Forest Service Regional

Offices were asked to nominate pilot areas to

participate in the LUCID test. The pilot forests

were selected based on having the full support of

the forest supervisor and on the extent to which

they met a series of other criteria, such as geographical and ecological diversity, opportunities

for collaboration across ownership boundaries and

with internal and external researchers, and availability of staff resources.

Six interdisciplinary forest teams working on

eight different national forests were ultimately

selected to participate in the LUCID Project.

These included the Ottawa National Forest in the

Upper Peninsula of Michigan; the Allegheny

National Forest in northwestern Pennsylvania; the

Modoc National Forest in northern California; the

Blue Mountain Province Forests of eastern

Oregon (the Wallowa-Whitman, Malheur, and

Umatilla National Forests); the Mt. Hood National

Forest in northwestern Oregon; and the Tongass

National Forest in southeastern Alaska. These test

sites ranged from 500,000 to 17 million acres and

from single national forests to one team of three

national forests working together within one

ecoregion province. In keeping with the fact that

the spatial scales of social, economic, and ecological systems rarely correspond to human administrative boundaries, the study areas were not just

limited to National Forest System lands, but were

left up to each forest team to define.

The LUCID Test forest teams identified three

main benefits they were seeking from participating in sustainability monitoring: (1) improved

resource management; (2) stronger linkage

between inventory, monitoring, and data management; and (3) improved collaboration.

!	"
	
	#

During an approximately two-year period, the

core team worked with each participating forest

team. Both the LUCID core team and the forest

teams required a mix of skills to carry out the test.

Ideally, this meant that each team was composed

of at minimum: a sociologist, an economist and

an ecologist to address the system components for

each principle; an analyst/GIS specialist to

address data management, modeling, and technical aspects; and a team leader, preferably with

some planning background. LUCID test site forest

supervisors also participated actively, providing

policy insight on sustainability monitoring and

making recommendations on practical implementation of FMU-scale sustainability monitoring.

The core team designed a process that provided some degree of flexibility for each of the six

forest teams while ensuring consistency among

the teams. Each forest team started with the same

C&I hierarchy and followed the same steps while

choosing its own collaborative partners, defining
	


 	

	
	 	

		
	

relevant scales, adapting the initial criteria and

indicators, and developing measures, reference

values, and synthesis tools appropriate to the test

site. The forest teams began with the CIFOR–NA

set of indicators (Hoekstra et al. 1999), supplemented with others provided by the core team

based on their gap analysis of the CIFOR–NA set

(see Figure 3). Each forest team then screened the

indicators for applicability to the local unit within

the systems framework. The teams then developed

one or more measures for each indicator, conducted a field test of the set of indicators, and

tried various methods of analyzing, synthesizing,

and reporting their results.

Modifications and changes were documented

as they were made. The core team worked

collaboratively with each forest team, visiting

each site to conduct workshops at each major step

of the process, and consulting with the teams as

needed. The forest team start dates were staggered

over a six-month period. The three teams (Ottawa,

Allegheny, and Blue Mountains) that started early

in the process made suggestions that led to

improvement of some of the tools (e.g., the

database structure, workshop content, and timing)

in order to make them more helpful to other

participants. Between workshops, the forest teams

worked more independently, consulting with the

core team, with other forest staff or collaborators,

and with other forest teams as needed. The

development, analysis, and implementation of a

practical set of local C&I involved forest team

discussions with many affected groups. These

included other staffs within the Forest Service,

other federal agencies administering public lands

adjoining the involved national forests, state

agencies responsible for administering state

interests, university researchers, and local stakeholders at each participating national forest.

	




Following the conclusion of the pilot tests, the

LUCID core team synthesized the results from

each of the forest teams in order to determine

whether they could identify a common systems

framework and core suite of C&I. The core team

found that the six different test site team results

did indeed converge into a common systems

&	'%


Ottawa 

National

Forest

Allegheny

National 

Forest

Blue 

Mountain

Province

Tongass

National

Forest

Modoc

National

Forest

Mt. Hood

National 

Forest

New local

indicators

Locally

relevant

indicators

Locally

inappropriate

indicators

LUCID

set of 

C & I

Revised

LUCID

set of 

C & I

Initial

LUCID

set of 

C & I
		




	
 

			

		



framework, a systems-framed suite of broadly

applicable core indicators, a revised process for

FMU-scale sustainability monitoring, and recommendations for integrating FMU-scale monitoring

into forest planning. The final systems-based C&I

suite consisted of 3 principles (social well-being,

economic well-being, and ecological function), 16

associated criteria and 58 indictors and is described in greater detail below.




		
$


LUCID participants found that the systems

framework provided a new lens through which to

view the topic of sustainability, both conceptually

and practically. They agreed that a systems

framework had several distinct benefits:

 Providing an approach to communicate that

sustainability is more than the sum of the individual components that are monitored;

 Providing a strong theoretical, science-based

link to understanding sustainability;

 Refining the description of elements for

monitoring;

 Developing a more meaningful method for

synthesizing and analyzing results to understand the state of systems;

 Thinking through and refining descriptions of

the systems to be monitored (i.e., it provided a

key tool to learn how components are

interrelated);

 Helping to identify and understand critical

elements for sustainability monitoring; and

 Providing useful information for adaptive

management.

Given that sustainability is a social value,

using a systems framework also fosters opportunities to consider, negotiate, and address differing

values among managers, researchers, and stakeholders. The interdependence of systems elements

and processes at various scales and to differing

degrees may lead to tradeoffs between certain

values. A systems framework also provides a

means for managers and stakeholders to begin

assessing these interrelationships and anticipating

resulting tradeoffs.

A program of sustainability monitoring using

the process pioneered by LUCID test participants

shows great promise in fulfilling current monitoring needs at the FMU scale. As a result of the test,

LUCID participants from forest supervisors to

forest team members concluded that systemsbased monitoring for FMU-scale sustainability is

feasible and can make significant contributions to

improving Forest Service management. The

relationship between the revised LUCID process

and forest planning is discussed in the final

section of this report.


		

	

	
%

By design, the six forest teams represented a

range of different social, economic, and ecological

conditions across eight national forests. Even so,

all forest teams identified a relatively common set

of C&I at the conclusion of the test. These were

evaluated by the core team and consolidated into a

core suite of 58 systems-framed indicators determined to be the minimum set necessary for

sustainability monitoring at the local scale. The

core team recommends this suite for consideration

across the national forest system at the FMU

scale. Some adaptation will likely be necessary to

ensure that the indicators meet the full range of

conditions on each national forest. However,

revision, adaptation, and substitution should be

made within the systems framework in order to

retain the overall context and meaning. Future

forest teams may choose to supplement the core

suite with indicators focusing on specific system

elements or functions of local concern.


&

%'
 #
	


#
	





A major goal of the LUCID test was to test

and refine the LUCID process itself, not just to

produce a final suite of indicators. While this was

an explicit goal, no one anticipated how predominant the process would become, or how important

the forest teams would find it to be as they

customized and tested the revised C&I at their

sites. The process proved to be central to

sustainability monitoring, not just the work

required along the way to get to a final suite of

indicators. The process of implementing a

sustainability-monitoring program at the FMU

scale emerged as a tangible product in its own

right.

Teams also noted that the collaborative

learning approach was fundamental to their

success. The Forest Service does not traditionally
	


 	

	
	 	

		
	

monitor nor conduct work programs within a

systems context. Forest teams were somewhat

surprised to find that their ongoing dialogue about

monitoring for sustainability in a systems framework was extremely beneficial, despite also being

quite challenging.

In the future, local units will be able to start at

a very different point from where the pilot forest

teams began. Adopting a systems-based framework for sustainability monitoring is a learning

tool as well as a work-planning tool. As other

forest management units adopt sustainability

monitoring, working through the process will

provide them an understanding of the supporting

concepts as well as the rationale for each element

in the core suite.

Given the importance test participants placed

on the LUCID process itself, the next section

describes it in some detail from a lessons learned

perspective. This description intended to give a

clearer picture of the experiences of the participants and to help other Forest teams build on them

as they consider implementing a systems-based

sustainability-monitoring program.


	



The revised LUCID process consists of eight

key steps, which can begin after initial preparatory work has been completed, such as gaining

approval and support from Forest leadership, staff,

and the regional office and assembling a forest

team. The revised process starts by clarifying the

purpose for undertaking a sustainability monitoring program and identifying spatial boundaries of

interest, moves on though exploring, further

refining, and applying the systems-framed core

suite of indicators, and finally leads into analyzing

and synthesizing results and incorporating them

into ongoing forest planning efforts and decision

making. These steps are diagrammed in Figure 4.

An important note concerns the feedback loops

and the two-way arrows within the process

diagram: while the steps have a distinct order, the

process is iterative and forest teams found themselves revisiting previous steps based on new

information. Defining the measures for some

indicators led to reconsidering whether the

indicators themselves should be modified or

removed from the suite.


('

 #
%	


%
!

	

#
%
	

To begin testing a program of sustainability

monitoring, forest teams first worked with the

core team to pinpoint the common objectives for

this undertaking. This gave them the opportunity

to consider their current monitoring programs,

results, experiences, and any shortcomings;

evaluate how sustainability monitoring differed

from their current approach; and determine how

the two could complement each other. It led them

to review agency goals at all levels of the organization, and consider how sustainability monitoring

could help achieve them. They could explore how

sustainability monitoring could meet the needs of

adaptive ecosystem management as the organizational focus of the Forest Service shifts from

maximizing individual resource use in the short

term to broader and more holistic long-term goals.

This, in turn, led to discussions of scale issues,

both in time and space, and the various meanings

of sustainability itself.


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Although complementary relationships should

exist between sustainability monitoring initiatives

at different scales, monitoring initiatives at the

FMU scale are not merely an application of

national-scale material. Forest teams defined the

relevant spatial scale for their own local conditions. To do so, they thoroughly discussed the

boundaries of interest, recognizing that the

systems and associated sustainability questions

would help define these boundaries and that

boundaries between social, economic and ecological systems typically do not coincide. Every

LUCID team adopted test boundaries that were

larger than the NFS lands. The Allegheny

National Forest team, for example, used the

unglaciated Allegheny Plateau (Ecoregion 212Ga)

(Bailey 1995) as the rough bounds of the ecological areas of focus. By contrast, the Blue Mountains Province team included three national forests

over a three-state area within the same ecoregion

province. Teams used a flexible set of overlapping

boundaries because social and economic indicators and questions were often associated with

different administrative boundaries such as

counties.
		




	
 

			

		



(	'%)*+

$

Review

Systems

Framework

Review,

Select and

Revise

Indicators

Identify

Measures

Define

Reference

Values

Collect Data

Conduct

Evaluation

Define Purpose

and Study Area

Develop Sustainability

Assessment

Identify information

requirements and

questions for each staff unit

Determine scale

of information

requirements Review available

standardized

measures/protocols

Identify collaborative partners

Identify

interdisciplinary

team

Determine if non-Forest

Service ownerships will be

included in study area

Identify spatial scales and

sub-scales of interest

Regional Assessments Establish context and identify issues using regional assessments Identify common data available

from regional assessments

Compare against reference values

Produce indicator evaluations

Synthesize to describe state of systems

Communicate results to

forest management Share narrative assessment upscale

Identify areas for further

investigation/research

Identify need for

additional data

Identify need for revised

measure/protocol

Identify need to

revisit reference

value

Synthesize and assess

through discussion

Determine

objectives

Discuss meanings of sustainability

Prepare Forest

Plan revision

Coordinate

monitoring

with others

Building

collaborative

partnerships

Store data

Natural Resource

Information System

Obtain data

Construct

conceptual model
	


 	

	
	 	

		
	

From a process perspective, the LUCID teams

found that an explicit discussion of scale issues

was critical as they began testing C&I. Discussing

the bounds of their study area and the relative

influences of boundaries was important for

identifying nontraditional relationships and

linkages. By understanding the regional context

within which they were working, they were better

able to focus on the role of forest resources and

management responsibilities within a

sustainability context that transcended administrative boundaries. These discussions naturally led to

exploring the potential roles and needs for collaboration, both internally and externally. This

exploration helped them to identify potential

partners, such as adjacent private landowners,

other public lands managers, stakeholders, and

scientists.

This process also helped build an understanding of the relationship between FMU-scale

sustainability initiatives and sustainability initiatives at other scales. Forest teams recommend that

a structured discussion be conducted early in the

process of adopting a LUCID monitoring framework to create a common understanding of scale

issues and to focus questions on the FMU scale.

'# #$(#"$)
$ *! %

The process of engaging the National Forest

staff and collaborators in a dialogue about

sustainability and sustainability monitoring was

invaluable and considered a key finding of the

test. Forest-scale sustainability monitoring can

help transform the concept of sustainability into

real outcomes on the ground (Bosworth 2001) by

engaging people in a dialogue about sustainability

and placing it within a broader adaptive management context.

Collaborative efforts can play an important

role in the sustainability monitoring process in a

number of ways:

 Creating a basis for dialogue,

 Helping to identify key components for

monitoring,

 Establishing reference values,

 Finding alternative sources of data, and

 Building a collective vision of desired future

conditions.

Working toward sustainability revolves

around stakeholder and natural resource manager

values. Each has a different set of experiences,

perspectives, and information. Broader societal

values are also embedded in the legal and institutional framework for natural resource management, but may not be explicitly recognized as

such. Moreover, the evaluation of stakeholder and

internal staff values is not often explicit in forest

resource management planning and decision

making. Participants found that the LUCID

process served as a powerful tool for initiating

discussions and clarifying values among stakeholders, managers, and scientists within a collaborative framework. In this way, the LUCID test

process reveals the interplay between human

values, system components and dynamics, and

sustainability.


)'
"











*!

		

Monitoring by individual forests generally is

neither systems-based nor systematic in nature. It

typically focuses on data collection and presentation

for individual components rather than the synthesis

of components to encourage understanding of

complex systems. As a result, the utility of monitoring results to management has been at best piecemeal. Likewise, other C&I monitoring programs to

this point have remained primarily hierarchical in

nature and have also tended to report data for

individual indicators rather than assessing interrelationships within among indicators. The core team set

out to overcome these limitations by solidly grounding the sustainability monitoring C&I development

in a systems framework.

Forest teams were largely unfamiliar with a

systems-based framework, so the core team conducted on-site workshops to discuss systems

concepts and to place the test goals firmly within

this framework. The teams refined the systems

framework and components throughout the process,

starting from the three principles: social well-being,

economic well-being, and ecological function, and

then establishing criteria for each. The social

framework was structured by four criteria:

1. collaborative stewardship;

2. institutional and community capacity;

3. social equity; and

4. social and cultural values/opportunities

examined across a range of kinds of

systems including individuals and communities of place and of interest.
		




	
 

			

		



The economic framework was also defined by

four criteria:

1. capital and wealth;

2. flows of products and services;

3. trade and distributional equity; and

4. efficiency across a range of economic

system scales from individuals to

communities.

The resulting ecological framework was defined

by the criteria of structure and function for

organisms, ecosystems, populations, and landscapes across a range of spatial scales.

Although three distinct frameworks were

developed, one for each of the principles, participants recognized that they must be considered

together as the complete set of structural components and functional interactions that make up

sustainability. In addition, the way that the systems are bounded is somewhat arbitrary, a reality

inherent in conceptual models. For example, the

LUCID test distinguished between frameworks for

social and economic systems. These are somewhat

artificial distinctions because economic systems

are a subsystem of broader social systems. However, for the purposes of FMU monitoring,

participants found great utility in distinguishing

between the two because these systems tend to be

organizationally separated within the Forest

Service, the disciplinary training of individuals in

these fields are distinct, and the world views of

these systems are often different.

The LUCID test participants concluded that a

systems approach provides an effective organizing

framework to develop a sustainability-monitoring

program for National Forests. Specifically, they

found that this approach:

 Leads to a richer and more integrated understanding of social, economic, and ecological

systems;

 Helps to identify, define, and organize critical

indicators and measures for monitoring;

 Serves as a conceptual basis for analysis and

synthesis of monitoring data in order to assess

the emergent properties of systems and the interrelationships between the ecological,

economic, and social spheres; and

 Applies to the FMU scale and provides a

consistent organizational approach to understanding, monitoring and assessing

sustainability. Adopting a consistent framework would have the added benefit of

decreasing the inconsistencies among forests

and improving understanding and transparency for the public.


+'
",
,

"


	


At this stage, forest teams concentrated on

testing and revising their initial sets of indicators.

Monitoring of indicators within a C&I framework

can provide essential information regarding

sustainability, but the information is only useful if

it answers the right questions. Establishing the

appropriate criteria and selecting the right indicators is a critical task in the LUCID process.

Participants worked to produce a suite of indicators that integrated a diverse array of system

components, avoided unconnected and irrelevant

indicators, and contained a sufficient but feasible

number of elements to be monitored. Continual

reference to the principles and criteria within a

systems framework was a key method of successfully identifying the essential structural and

functional components of systems.

While it may seem like there are an infinite

number of possible indicators from which the

participants had to choose during the test, systems

research indicates that a relatively small number

of controlling processes or variables usually

contribute to sustaining the functionality of a

given system within a range of equilibrium states

(Holling 2001). The forest teams continually

revisited sustainability and systems concepts as

they worked to define the set of key elements and

processes relevant to the local scale.


"! +"("" 

The LUCID teams tested, evaluated, and

refined all of the initial criteria and indicators

within the three principles: social well-being,

economic well-being, and ecological function.

However, more work was commonly needed on

social and economic elements for several reasons.

Researchers have applied complex systems theory

to ecological systems extensively over the past

several decades and the bounds of ecological

sustainability are becoming increasingly well

understood (see for example Allen and Hoekstra

1992, Flood and Carson 1993, Kay and Foster

1999, and Sterman 2000). A systems approach to

studying social and economic systems is less

common, however, and so applications to
	


 	

	
	 	

		
	

sustainability are limited. Consequently, less

research and guidance is available for developing

systems-based social and economic frameworks

for assessing sustainability at the FMU scale.

Another challenge in developing social and

economic indicators was that these disciplines

have not traditionally been well represented

within Forest Service staffing, and relatively few

agency sociologists or economists work at individual forest offices as compared to those specializing in ecological fields or activity-related

sectors. Further, teams needed to think about

social and economic aspects of systems across the

ownership boundaries of their study area. Available data is often reported at spatial scales that do

not match administrative boundaries, such as by

county or state. Finally, other sets of C&I have not

focused on communities and outcomes.

Indicators developed for larger scales commonly do not address the local system structures

and functions and local scale questions of

sustainability. Sometimes indicators are so inherently scale-specific (e.g., measures of the contribution of forestry to the gross domestic product)

that they must be significantly revised. And while

very broad and generically worded indicators

(e.g., employment) can be applied at multiple

scales, the indicators typically must be adapted to

meet local conditions for them to have any value.


$ # " 

By working to identify key factors in their

systems of interest at the FMU scale, forest teams

were indeed able to arrive at a core suite of

indicators that they deemed the minimum suite

needed to assess sustainability. Developing and

testing indicators and comparing them among sets

was often a difficult process. The teams used the

same definitions of terms and hierarchical divisions, but some variation resulted from individual

approaches. At the conclusion of the test, forest

team suites included an average of 56 indicators,

ranging from a low of 44 to a high of 77. Indicators were generally equally divided between the

three principles, although the economic indicators

had consistently fewer indicators across all sets.

The core team then compared the indicators

submitted by each forest team and produced the

recommended LUCID C&I suite of 16 criteria and

58 indicators (Wright et al. 2002). (Also see

Appendix at end of this report.)


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  ). 

The core suite of indicators provides a strong

foundation from which additional forest teams can

adopt the LUCID test framework, benefiting from

the extensive work and experience of the original

test participants. This core suite, however, is not

simply a pick list. The suite was carefully designed as an integrated tool for use in a systems

context, so attempting to remove individual

elements from the whole robs them of any particular relevance to sustainability monitoring. Therefore, even as a complete set, the suite itself does

not have any great value without explicitly being

interpreted within a systems framework and

within an interdisciplinary, collaborative environment.


-'



%	


Indicators in and of themselves were designed

to be broadly applicable across a range of potential forest conditions and to have no assigned

directionality. Each indicator needed at least one

measure, or specified methodology for data

collection. The three different kinds of measures

in the final core suite include the following:

 Recommended measures that are relatively

consistent from FMU to FMU. Where possible, these are based on standard Forest

Service protocols or ideas.

 Proxy measures as substitutes for recommended measures. While the recommended

measure is preferred or more common, the

proxy measure provides an alternative means

of obtaining the information.

 Optional measures that forests might use to

supplement the core suite based on local issues of interest and concerns.

As the teams worked, scale-dependency

figured prominently in selecting FMU scale C&I

and in determining how to measure them. Living

systems operate in similarly at multiple scales in

time and space. For example, landscape systems

on a decomposing log can have the same structural and functional characteristics of landscape

systems that operate in a watershed. However, the

tools and protocols used for measuring components of systems are scale-dependent and must be

appropriate to the spatial scale of interest. For

example, the decomposing log system may be

measured with magnifying tools at close range
		




	
 

			

		



whereas remote sensing technologies may be

appropriate for evaluating a watershed landscape

system. Consequently, although an indicator of a

system component may appear to be common

across multiple scales, it may require different

measures and metrics.

The scale of study will impact the ability for

monitoring efforts to detect changes. King (1993)

notes that choosing the grain of detail (increment

points along a given measurement scale) for study

is a key step. Large-scale measures smooth out

fine-scale variability, and this option may remove

noise and make detection of meaningful signals or

trends more obvious. A coarser analysis may also,

however, filter out fine-scale signals of developing problems. These considerations led some

teams to add a fine-scale approach to monitor land

use and change because they felt that some

potential important problems were being overlooked with the current scale of monitoring on

their national forest.

Given the nature of systems and scale, the

choice of measures and associated data elements

cannot be exactly prescribed for every forest

management unit. Selecting measures depends on

the specific questions that are unique to each

FMU. Consequently the suite of possible measures may be used only as a guide and each local

unit will need to identify the specific questions

associated with the indicators for their site and

then determine the appropriate measures for data

collection. Standardized protocols should be used

wherever possible if they address the appropriate

questions and provide the right data at the right

scale.


.'

	
/%


Reference values are comparison values

against which data collected for indicator measures can be gauged. Reference values may be

used to help interpret an indicator. This comparison process may be used to guide discussion about

the meaning of measurement results; to assess

progress toward desired outcomes; and to identify

interactions among other components in the

system. In short, reference values tell us how we

are doing. Consequently, their utility hinges on the

rationale for their selection. The source of reference values can range from current conditions to

legal standards to historic range of variation.

The core team anticipated that the development of reference values would be very difficult

and a task that forest teams would not fully

complete within the test period. However, exploring the utility and feasibility of reference values

was considered an important component of the

test and the forest teams became the real experts

on reference value development. Each team

experimented with different approaches, so the

process of developing reference values varied

from site to site. Some forest teams were able to

access literature or external resource specialists to

help in the development of specific reference

values while other teams had limited time for

external consultations.

Monitoring for sustainability seeks to identify

change. Because system elements are interrelated,

dynamic, and nonlinear, monitoring should filter

out the expected noise of these fluctuations from

potentially out-of-range larger human-induced

fluctuations. For this reason, forest teams often

found that using ranges rather than single target

data points as reference values was more useful.

Comparing measures of an indicator against a

reference value may trigger a range of responses

including management action to correct an

undesired situation: special cause and effect

monitoring; intensified sampling; or changes in

management standards, thresholds, or measurement protocols.

Participants had no preconception of the

complexity and challenge involved in comprehensively using reference values and did not

know what would be learned or what would be

the value gained. Forest teams reported that

developing reference values was the most

difficult part of the process and they provided

many suggestions for modifying it. Key suggestions include the following:

 Take time to clarify the rationale and implications of the reference value;

 Document assumptions used;

 Start early in the process, to clarify and revise

indicators and measures;

 Be specific;

 Establish reference values using a collaborative, interdisciplinary approach rather than

leaving these to individual specialists.

 Discuss interrelationships between reference

values, and use this information to help clarify
	


 	

	
	 	

		
	

systems relationships and tradeoffs between

reference values;

 Recognize that clarifying these tradeoffs will

lead to some conflict;

 Carefully assess the usability of legal standards, their underlying assumptions and

scientific validity, and consider a second reference value if necessary; and

 Seek external expert judgment and input.

The experience of setting reference values proved

to be challenging and often imprecise, but LUCID

participants found it to be a critical part of the

process of monitoring for sustainability.


0'



Forest teams were directed not to develop

indicators based solely on what was already being

monitored at their site or what sources of data

were currently available. In doing so, the core

C&I suite is designed to meet the needs of

sustainability monitoring rather than fit to the

limits of current data availability. While much of

the data are already available and collection is

fairly straightforward, this is an area that will

present many fruitful opportunities for collaboration between researchers and managers and

advances in accepted methodologies for collection

and storage.

Corporate data systems (e.g., NRIS) and

remotely sensed technologies provide a potential

source of data to be used for FMU-scale

sustainability monitoring; the latter holds great

promise in providing efficient and spatially

integrated data. Other useful sources may include

internal and external research partners, other

external databases, and the development of valid

sampling techniques and the tools for applying

them across FMUs with similar systems characteristics or elements. Data collection can also be

integrated into on-the-ground ongoing project and

forest plan monitoring programs of work. Some

preliminary overlaps have been identified, but

additional work and discussions are needed to

ensure the best fit between corporate data sources

and repositories and those required for FMU-scale

sustainability monitoring. Further development is

needed:

 Improved standardized measurement protocols to address questions at the FMU scale.

 Cooperation of various inventorying, monitoring, and data-management initiatives (e.g.,

corporate data collection and storage systems)

to develop an approach for facilitating the

supply, storage, and ready access to data that

are suited to a range of multiscaled questions.



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Ensuring that a monitoring program is useful

involves the functions of synthesis, analysis,

interpretation, and presentation so that monitoring

data are converted to useable knowledge as part of

the broader adaptive management process. The

final step of an FMU-scale sustainability monitoring program involves evaluating monitoring data

and developing a sustainability assessment.

	
	





	

	


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Establishing a sustainability-monitoring

program improves management decisions and

activities by providing feedback on the state of

systems and through engaging collaborators in a

dialogue about sustainability. Too often treated as

an afterthought, the analysis process involves

determining in advance the purpose for the

monitoring program and who needs information

for what purposes at what spatial scale and at

what time intervals.

The sustainability assessment using a suite of

C&I can provide a comprehensive way of looking

at the state of systems, as well as the state of our

knowledge. An assessment provides a way of

analyzing the current state of FMU systems and

developing a better understanding of the place of

national forests within the larger context. It can

also identify the need for change. The evaluation

stage involves comparing current conditions

against a reference value (e.g., trend, standard,

norm, benchmark, or desired future condition) and

is not a consolidated measure of sustainability.

The sustainability assessment involves engaging

stakeholders in a dialogue to help evaluate the

assessment of sustainability, not make an absolute

determination.

!) !

Given the large amounts of data to evaluate,

the LUCID core team looked for tools that could

facilitate indicator analysis, could be structured

according to a systems-based hierarchical model

of criteria and indicators, and could manipulate

spatially variable information with a range of

different types of reference values. LUCID

participants tested and adapted a knowledge-based

modeling tool, NetWeaver2 (Rules of Thumb, Inc.

2000) and developed a spatial extension of this

tool3, GeoNetWeaver, to facilitate the analysis

process. Although their development was challenging, these programs were very useful for

organizing and framing evaluations consistent

with systems-based C&I, for processing large

quantities of data, for normalizing the values on a

common scale, for displaying spatial variation of

results, and for assessing the status of a group of

related phenomena, such as all indicators within a

criterion. Evaluations, either spatial or numeric

generated through NetWeaver analysis were then

further interpreted by team members in narrative

form to inform the sustainability assessment

discussions. Team members concluded though that

technical tools such as models are only aids to

help us organize, synthesize, analyze, and present

large quantities of complex information. Interdisciplinary, collaborative dialogue remains the

primary tool for sustainability assessment.

&!%/). )0$! 

At its most basic level, an analysis procedure

must interpret the results of individual indicator

assessments. This is the most common approach

in sustainability C&I initiatives and often is the

analysis step. As the fundamental unit for monitoring, indicator-based analysis provides the

information necessary for other forms of analysis.

The resulting detailed indicator profiles are useful

in providing specific feedback to assist day-to-day

management and to provide a refernce database.

forest teams included the following information in

their detailed indicator profiles:

 An overview of the indicator and its relationship and importance to sustainability and to

the study area;

 A description of the methods used to verify

the indicator including any measurement or

data challenges;

 Current conditions of the indicator based on

available spatial or non-spatial data;

 A comparison of the current data value over

time (e.g., trend data) or against some other

reference value (e.g., a benchmark); and

 Possible management implications.

At this stage of the LUCID test, most forest

teams prepared summary indicator evaluations

with specific focus on indicators or measures of

concern including those that did and did not meet

reference values. One key weakness of individual

indicator-based analysis and reporting is the

difficulty in highlighting and analyzing interrelationships among indicators. Examining these

interactions is critical to understanding

sustainability. Any single indicator is only a small

signal in the overall assessment of sustainability.

Examined in isolation, these signals may be

incomplete or appear contradictory. Greater

2

 The use of trade names is for the benefit of the reader;

such use does not constitute an official endorsement or

approval by the USDA Forest Service to the exclusion of

others that may be suitable. 3

 Chapters 12 and 13 of the Technical Edition (Wright et

al. 2002) describe this tool in detail.
	


 	

	
	 	

		
	

understanding comes from probing how the

indicators work together as a suite to assess the

status of a system’s structures and functions. The

image that comes from the assessment of the

whole system can help determine if a negative

trend in one indicator affecting the overall system.

A systems-framed suite of C&I is one of the

first techniques to overcome this weakness. The

systems framework and the indicators selected to

represent the key structural and functional

components of those systems is inherently

integrative. A reason for adopting a systems

approach was not only to aid in the selection of

indicators but also to serve as a guide for analysis. The conceptual and analytical models were

designed by the LUCID forest teams as conceptual road maps to guide analysis. Evaluations

that compare data to reference values could be

used for groups of indicators organized at the

criterion level to add context and meaning to

individual indicator results. LUCID forest teams

recommended spatially-based analysis to illustrate relationships among indicators, even if only

conceptually. Finally, LUCID participants noted

that the most powerful analytical tool in understanding relationships was human dialogue and

discussion.

Although forest teams organized their results

differently (by criterion or indicator, by management issue, or by physical area or watershed), the

most popular and meaningful method of presentation was narrative description. Narratives varied

but commonalities included definitions of the

indicator and measures; discussion of the standards set; descriptions of the data or at least

specific pieces of data of concern or interest; and

discussions of missing data, potential causal

influences, and tentative management implications. Adding charts, graphs, or maps displaying

numeric summaries to these narratives can very

effectively improve communication of results.

This approach to analysis and presentation is

relatively easy to follow and is fairly straightforward to assemble. This approach serves as the

cornerstone for broader analysis and communication to many different audiences.

The development of methods and tools for

analyzing indicators and synthesizing results to

help understanding of the state of systems is in its

infancy. Forest teams did not feel they achieved a

complete sustainability assessment product given

the intense amount of time spent on placing the

C&I suite in a systems framework, developing

measures and reference values, and experimenting

with various software tools to aid in analysis. The

LUCID test made strides in identifying how a

systems approach and consistent technical tools

can help guide meaningful sustainability assessments. However, much more work remains to be

done, including improving to existing tools,

examining alternative approaches and techniques,

and developing guidance for widespread application across the National Forest System.

+))) 
 12!

The sustainability assessment process may be

used to engage stakeholders in discussions about

forest management and desired future conditions.

A sustainability dialogue with collaborators from

other government agencies and research institutions facilitates improved participation throughout

the forest plan revision process (see Figure 5). It

can be particularly useful during development of

the analysis of the management situation report,

identification of management issues, and the need

for change.

#() )( 

The final step in sustainability assessment is

communicating results to management including

recommending areas for further research, identifying the need for change, and contributing input for

larger scale monitoring efforts. These topics are

elaborated on in the final sections of this report.

The results of the sustainability assessment may

also lead to revising subsequent monitoring

activities.

			


LUCID test participants concluded that

sustainability monitoring is a valuable approach,

is feasible, and should be adopted more widely at

the FMU scale. Given that the forest teams were

participating in a test process that was new

territory for them and that refining the process

itself was one of the goals, the teams faced

challenges along the way. These challenges were

also opportunities for reappraisal and growth, and

the value in the process itself is a key finding of

the LUCID test.
		




	
 

			

		



The LUCID project was initiated to develop a

method for assessing systems sustainability at the

local scale. The primary intent was to develop a

tool that would provide feedback specifically at

the FMU scale. In their evaluations, however,

LUCID participants noted that the tool and

techniques have application on a daily basis at a

range of scales including at the project scale.

Teams reported many other specific benefits,

including the following:

 Using a systems framework for resource management enhances interdisciplinary work and

provides a forum to discuss and consider how

elements and activities interact among complex social, economic, and ecological

systems;

 Sustainability monitoring can serve as the

core of forest planning and monitoring;

 The C&I framework, a core indicators suite,

and supplemental indicators provide both

consistency and flexibility. Consistency allows for reducing duplication of effort and for

better integration of data; flexibility allows local units to tailor indicators and measures;

 The LUCID process provides a framework for

public collaboration in forest planning and a

forum to discuss and recognize differing staff,

collaborating partner, and stakeholder values;

 The LUCID process forges stronger ties

among managers and external collaborators to

provide a larger pool of data sources and expertise and to build relationships;

 The LUCID process can facilitate coordinated

monitoring efforts among local, regional, and

national administrative levels within the

Forest Service and between agencies;

 Explicitly addressing issues of scale, data collection and aggregation, and reference values

forces forest teams to define and redefine

sustainability within a broader social, economic, and ecological systems context.

	
#

	





	





The LUCID test process and tools for

sustainability monitoring can be used throughout

the forest planning cycle to inform the process of

adaptive management. Sustainability monitoring

at the FMU scale can provide forest managers and

collaborators with assessment information that

can be used to improve land and resource management plans, enhance collaboration between

national forests and other government agencies,

and relate forest plan outcomes with regional and

national C&I trends.

Figure 5 shows the points of the forest planning cycle at which sustainability monitoring can

enhance the process. These are described in

greater detail below.

A sustainability assessment using a suite of

C&I can provide a comprehensive way of looking

at the state of systems, as well as the state of our

knowledge, in preparation for Forest Plan revision. An assessment provides a way of analyzing

the current state of FMU systems, facilitating

understanding of the place of the National Forest

in the larger context, and identifying the need for

change. The sustainability assessment involves

engaging stakeholders in a dialogue to help

evaluate the assessment of sustainability, not

make an absolute determination.

Results from ongoing sustainability monitoring efforts can be used at this stage for issue

identification. Data collection over time can

provide a strong foundation of knowledge around

which to develop desired future conditions and

alternatives.

A collaborative approach to sustainability

monitoring provides an opportunity for more

participatory development of the analysis of the

management situation and identification of the

need for change. Although a C&I-based

sustainability-monitoring program will not

eliminate conflicting perspectives, the approach

can facilitate a deeper understanding of different

perspectives because the topics are discussed

using a common language of C&I. The common

set of C&I can be used to compare scenarios or

alternatives or to discuss the potential outcomes

and interactions among social, economic, and

ecological aspects of alternative scenarios.

Alternatives can be compared against a set of

common reference values or a comparative

analysis can be completed based on differing

perspectives on outcomes (e.g., short-term versus

long-term outcomes or reference values prepared

from different perspectives).

From a forest plan perspective, sustainability

monitoring is focused on FMU conditions rather

than project implementation monitoring. The
	


 	

	
	 	

		
	

forest monitoring plan for the Southwest Idaho

Ecogroup forests, forest planners found that it was

relatively simple to fit the monitoring needs for

each group within a common framework and that

groups usually could agree on measures or data

that would meet a broad range of purposes.

Systems-based sustainability monitoring

supports the analysis and synthesis of information

in a way that is more useful for program management decisions. Comparing indicators to reference

values over time and synthesizing individual

comparisons into an overall system assessment

can help identify whether management actions

and priorities should be revisited. In an active

management context, the process of developing

reference values requires analysis of the question,

“What variation from the reference conditions

would initiate further evaluation and/or change in

management direction?” If progress is being

made, then management actions continue; if

systems approach framing the monitoring protocols can have broader application throughout

forest planning and management by being used as

a framework for understanding complex living

systems. Implementation monitoring and other

monitoring requirements will still be necessary,

but they can be organized within this comprehensive monitoring framework to bridge short-term

actions with long-term outcomes.

A common monitoring framework that

focuses on understanding the broader systems is

also useful for streamlining and coordinating

monitoring efforts. Often, each functional or

ecosystem component group (e.g., soils, aquatics,

or silviculture) will propose monitoring items that

are clearly related if not identical. Developing a

systems-based monitoring program that frames

and coordinates disciplinary measures can help

identify those overlaps and reduce redundancies.

Based on their experience in reorganizing the

1. Identify 

need for 

change

2. Identify 

issues and 

develop 

information 3. Evaluate 

alternatives

6. Monitor 

the plan

5. Implement 

the plan

4. Amend or 

revise the plan

Project is implemented 

to improve sustainable 

conditions. A subset of 

indicators are used to 

monitoring project level 

work.

Systems-based C&I 

become the core 

monitoring variables in 

the Forest Plan.

The systems-based 

C&I are used to 

compare alternative 

management 

options.

A collaborative approach to 

sustainability monitoring provides an 

opportunity for participatory 

development of the analysis of the 

management situation and 

identification of management issues. Sustainability 

assessment of FMU 

systems identifies the 

need for change – e.g., 

indicators that are not 

performing relative to 

their reference values.

Sustainability 

assessment includes 

monitoring and analysis 

of C&I.

This assessment 

contributes to regional 

and national scale 

sustainability 

monitoring efforts.

,	'-*+

$)
		




	
 

			

		



progress is not made, then the plan may need to be

revisited or adjusted. Areas of concern may

include the appropriateness of the indicator or

measure, the quality of the data, and the appropriateness of the reference value. An area of concern

identified through monitoring might require a

more detailed assessment or analysis. Using the

systems framework as a guide, users may be able

to hypothesize the possible effects of a problem in

one area on interrelated issues in order to anticipate future problems.

*
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From a forest planning perspective,

sustainability monitoring switches the focus from

short-term implementation monitoring to monitoring long-term outcomes. A common monitoring

framework that focuses on understanding the

broader systems helps to rationalize and coordinate existing monitoring efforts. LUCID test

participants found that by using a common

framework and indicators to organize monitoring

items, they were able to identify fewer data items

to measure. This ability clearly increases monitoring efficiency.

The system approach that frames the monitoring system can also have broader application

throughout forest planning and management as a

framework for understanding complex living

systems. As the agency’s focus moves toward

ecosystem management goals, sustainability

monitoring can inform our decisions while

contributing to greater public trust and improved

accountability. In this way, forest managers and

stakeholders can better answer how we are doing,

and where we want to be in the future.

Pilot test results clearly demonstrate the great

potential of the LUCID approach for monitoring

at the FMU scale. The suite of core indicators

applied within a system framework can provide

information needed for adaptive management at

the local unit scale, as well as certain types of data

that can be aggregated and utilized at regional and

national scales. Experience shows that

sustainability monitoring can effectively replace

most existing forest plan monitoring. Specific

legal and regulatory monitoring requirements and

required implementation monitoring components

may fit as measures to verify the core indicators

or be supplemental to the core indicators. The

LUCID core suite includes optional measures for

tailoring C&I to local conditions, but forest teams

will need to examine their context and questions

carefully. Revision, adaptation, and substitution

can be made within the systems framework while

maintaining the overall context and meaning as

long as the core suite itself is not treated as simply

a pick list. The core suite itself is designed to be

the basic set needed for systems-based

sustainability monitoring.

While a great deal of work remains in refining

and developing this method and its tools, the

approach is far enough along to begin wider

implementation beyond the initial six test forest

teams. IMI staff can provide technical assistance

during implementation of FMU-scale

sustainability monitoring, including assistance in

using a systems framework and tools such as the

C&I and analytical methods. Additionally, Forest

Service research staff can provide assistance in

identifying the most suitable measures for a

particular FMU area, designing cost effective and

reliable protocols, and researching appropriate

reference values for indicators. Regional office

specialists may also be able to advise and assist in

supporting the use of FMU-scale sustainability

monitoring in forest planning, helping to coordinate initiatives between forests, facilitating access

to specialists, and clarifying relationships with

regional and national monitoring requirements.

	!%

%

	

Sustainability is a multiscaled problem and

consequently there is no right scale to assess or

manage for sustainability. Although sustainability

can be studied at multiple scales, once the components of systems are identified for monitoring,

selecting the correct scale is critical. The context

of the systems that we are trying to sustain change

at every scale because the constraints change.

Using the wrong scale to look at certain system

properties could be like trying to see an elephant

through a microscope. Managing for sustainability

requires consideration all scales, but monitoring

and assessing sustainability must be based on the

recognition that different questions and different

methods are appropriate for different scales.

Just as matching the scale of the question to

the scale of data collection is important, so are

issues of data aggregation. Scale issues are

complicated by whether or not systems are nested

or unnested. For nested systems, the issues of

sampling and data aggregation are straightfor-
	


 	

	
	 	

		
	

ward. Data are typically sampled at least one scale

finer than the question of interest and are then

aggregated upward to the scale appropriate to the

properties of the system component. Sampling and

data aggregation in non-nested systems are more

difficult because the emergent properties of

systems mean that simply aggregating data will

overlook the synergistic effects of systems. For

example, percent soil carbon can be aggregated

using weighted area values because it is a processindependent measure while the volume of soil

carbon/cubic meter cannot be aggregated because

it is a process-dependent measure.

Examination of the relationships across the

various scales of sustainability monitoring requires consideration of the nature of human values

and the role of communities of place and communities of interest in forming these values; the

nature of living systems and the inherent variability in their form and expression; and, the methodological challenges associated with data integrity,

sampling, and aggregation.

#


		


	

		

National and FMU scale C&I programs

represent complementary tools that can be used in

our quest for sustainability. Each tool helps

answer a set of questions and provides feedback

for different kinds of purposes and decisions at

different scales.

A strong national framework provides the

policy context and structures to enable local

management for sustainability. National

sustainability reports, for example, may identify

broad trends and trigger interest and attention on

specific issues. Likewise, improvements in

national progress towards sustainability are

facilitated by local actions. There is clear philosophical overlap and interdependence between the

national and FMU-scale sustainability monitoring

initiatives although the purposes, tools, and

approaches are by intent different and therefore

not easily translated one to the other.

Many of the indicators included in various

C&I initiatives are conceptually similar. In some

cases, not only are the indicators the same, but

also the questions to be addressed are similar

enough between scales that the same measure

could be used for the indicator. Although the same

type of raw data may be useful at multiple scales,

the sampling locations, intensity, and analytical

methods may often vary because the sustainability

questions change between scales. Where shared

data elements can be identified between national

and FMU scales, monitoring efficiencies can be

achieved. As measurement protocols and data

elements are more clearly specified for Montreal

Process monitoring at the national scale, it will be

easier to identify potential opportunities for data

sharing.

In addition, to a desire to identify efficiencies

in shared data, there is some desire in understanding how sustainability assessments at one scale

can contribute to sustainability assessments at

another scale. If we look at the results of the FMU

sustainability assessment as a whole, aggregating

the results of one FMU assessment to another

scale is not appropriate or feasible. The emergent

properties of a system make it unique. In understanding the relationship between initiatives at

different scales, we found that the most value

comes from narrative descriptions that describe

the results in a context fashion. Narratives can be

used to describe this rich picture.

Monitoring programs at the national and FMU

scale have commonalities with respect to the

lessons learned about the process of monitoring.

The growing literature on the Montreal Process

C&I and the suite of indicators provided a valuable context and starting point for the LUCID test.

The 2003 national sustainability report will

highlight many new issues, ideas, data requirements, and data sources that will also be useful for

local forest units. From a process perspective, the

LUCID forest teams learned a series of valuable

lessons about systems approaches, the need for

specificity, the balance between consistency and

flexibility, reference values, and approaches and

tools for analysis and synthesis that may be of

benefit to the national program.

$		


#


	



Several of the original LUCID test sites and a

number of other units are continuing to integrate a

systems-based approach to sustainability monitoring into their work. LUCID products appear to be

valuable in assisting forests currently in their

forest plan revision process, forests re-examining

their monitoring strategies, and for other initiatives from project level application to contributing

to analytical approaches for monitoring at other

scales. The core team continues to work on the
		




	
 

			

		



most effective implementation strategies for the

LUCID process in consultation with local units.

The LUCID Technical Edition (Wright et al.

2002) discusses some of the strategic and tactical

implementation strategies in detail; highlights of

these issues are summarized here.

Adopting the LUCID process of sustainability

monitoring faces several challenges, both in

technical development and in practical implementation. While forest teams did make significant

progress in identifying measures, the social wellbeing measure in particular needs further development; this area is not a traditional agency strength

and staff resources are limited. This challenge is

neither unexpected nor insurmountable. Continuing to define and test reference values is another

challenge. Issues of data availability for indicator

measures are significant. The compatibility among

LUCID data measurement protocols, corporate

data sources such as the Natural Resource Information System (NRIS), and external data sources

present another opportunity for productive advances toward more effective sustainability

monitoring.

Supporting individual national forests in their

efforts to integrate an FMU-scale approach to

sustainability monitoring as a part of their work

plans will require not only the enhancement of

tools, techniques, and relationships among other

Forest Service units but will also require building

skills at each forest. Technical assistance, regional, and national support will be vital to the

success of this process. LUCID participants found

that a forest team consisting of five or more

people, representing the social, economic, and

ecological disciplines, Geographic Information

Systems (GIS) analyst, and a team leader with

planning or public information skills was optimal.

Sociologists, economists, and GIS staff are in

relatively short supply in the Forest Service, but

regional office staff support and use of external

collaborators could be used to help bridge gaps in

expertise. A network of research and regional

office specialists could be built to support forestscale sustainability monitoring initiatives. National guidance and support for creating this

network would mean that individual Forests

would not need to duplicate efforts. Like any new

program, implementing a sustainability-monitoring program would demand additional investments at the outset, but as this approach became

more widespread among Forests and staff move

between units, the skills gained would mean that

in the initial investment levels would gradually

taper off. Adoption and use of this common

systems framework, defined by the principles and

criteria, can provide a consistent organizational

approach to understanding, monitoring, and

assessing sustainability at the local forest scale.





The journey and lessons learned by a group of

dedicated Forest Service employees and stakeholders is documented by this report and the

LUCID Technical Edition (Wright et al. 2002).

Although participants shared a common goal, they

all had different understandings of how to make

progress, how quickly to proceed, and even in

what direction. However, they gradually developed a common language to discuss values and

perspectives. As a result, they made significant

steps in developing a set of tools and a process to

help others monitor progress in the quest for

sustainability.

LUCID test participants affirmed that

sustainability is a social concept and one that is

incredibly valuable in practical application even

though its definition may be elusive. They agreed

that sustainability cannot be achieved by any one

group of people, at one scale, and certainly not by

the Forest Service acting alone. They recognized

that agency personnel need to act on multiple

fronts, on multiple scales, and with internal and

external partners across physical, conceptual, and

administrative boundaries. In the face of uncertainty and a multitude of competing stakeholder

values, sustaining the fundamental systems

contexts that sustain people is the surest way to

move forward.

Establishing and implementing a

sustainability-monitoring program at the forest

management unit scale represents one approach to

sustaining these systems. The LUCID test has

clearly demonstrated that its approach can serve

as a practical tool for managers, stakeholders, and

citizens in their quest for sustainability at the local

scale. The approach provides a means to share

diverse perspectives on individual and collective

values about healthy communities, healthy

economies, and healthy lands in moving toward a

sustainable future.
	


 	

	
	 	

		
	




	

Allen, T. F. H., and T. W. Hoekstra. 1992. Toward

a unified ecology. New York: Columbia

University Press.

Allen, T. F. H., and T. W Hoekstra. 1994. “Toward a definition of sustainability.” In

Sustainable ecological systems: Implementing an ecological approach to land

management. W. W. Covington and L. F.

DeBano, tech. coord. Fort Collins, CO:

USDA Forest Service, Rocky Mountain

Range and Forest Experiment Station

General Technical Report 247. 363 p.

Bailey, R. G. 1995. Description of the ecoregions

of the United States. 2d ed. USDA Forest

Service Misc. Pub. No. 1391, Map scale

1:7,500,000. 108 p.

Bosworth, D. 2001. The Forest Service Role in

Fostering Sustainability. Speech By USDA

Forest Service Chief. Washington, DC:

Society of American Foresters meeting. May

29, 2001. Text available online at http://

www.fs.fed.us/intro/speech/2001/2001may29–

Bosworth.html.

Carpenter, S., B. Walker, M. Anderies, and N.

Abel. 2001. “From metaphor to measurement:

Resilience of what to what?” Ecosystems

4:765–781. Available online at http://

www.resalliance.org/reports/.

Castaneda, F., C., Palmberg–Lerche, and P.

Vuorinen. 2001. Criteria and indicators for

sustainable forest management: A compendium. Rome, Italy: Food and Agriculture

Organization, Forest Resources Development

Service, Forest Resource Division. Forest

Management Working Paper No. 5.

Flood, R. L., and E. R. Carson. 1993. Dealing

with complexity. New York: Plenum Press.

Floyd, D. W., S. L. Vonhof, H. E. Seyfang, J.

Heissenbuttel, R. Cantrell, L. Stocker, B.

Wilkinson, and K. Connaughton. 2001.

“Forest sustainability: A discussion guide for

professional resource managers.” Journal of

Forestry 99(2) 8–31.

Folke, C., S. Carpenter, T. Elmqvist, L.

Gunderson, C. S. Holling, B. Walker, J.

Bengtsson, F. Berkes, J. Colding, K. Danell,

M. Falkenmark, L. Gordon, R. Kasperson, N.

Kautsky, A. Kinzig, S. Levin, K. G. Mäler, F.

Moberg, L. Ohlsson, P. Olsson, E. Ostrom, W.

Reid, J. Rockström, H. Savenije, and U.

Svedin. 2002. Resilience and sustainable

development: Building adaptive capacity in a

world of transformations. Scientific Background Paper for The World Summit on

Sustainable Development on behalf of The

Environmental Advisory Council to the

Swedish Government. Available online at

http://www.resalliance.org/reports/

resilience_and_sustainable_development.pdf].

Grumbine, R. E. 1994. “What is ecosystem management?” Conservation Biology 8(1) 27–38.

High Performance Systems (HPS), Inc. 2001. “A

pressing need: Improving performance.” In

Introduction to systems thinking. ithink©

1992–1997, 2000. Available online at http://

www.hps-inc.com/download/pdfs/

iISTguidechapter1.pdf.

Hobbs, R. J. 1998. “Managing ecological systems

and processes.” In Ecological scale, theory

and applications. Edited by D. L. Peterson

and V. T. Parker. New York: Columbia University Press.

Hoekstra, T. W., T. F. H. Allen, J. Kay, and J. A.

Tainter. 1999. “Appendix H: Criteria and

indicators for ecological and social system

sustainability with system management

objectives.” In North American test of criteria

and indicators of sustainable forestry. Volume

1. Compiled by S. Woodley, G. Alward, L.

Iglesias Gutierrez, T. Hoekstra, B. Holt, L.

Livingston, J. Loo, A. Skibicki, C. Williams,

P. Wright. USDA Forest Service Inventory

and Monitoring Institute Report No. 3.

Available online at http://www.fs.fed.us/

institute/cifor/cifor_1.html.

Holling, C.S. 2001. “Understanding the complexity of economic, ecological, and social

systems.” Ecosystems 4:390–405. Available

online at http://www.resalliance.org/reports.

Innes, John L. 1998. “Measuring environmental

change.” In Ecological Scale, Theory and

Application. Edited by D. L. Peterson and V.

T. Parker. New York: Columbia University

Press. pp. 429-457.

Kay, J. J., and J. Foster. 1999. “About teaching

systems thinking.” In Proceedings of the

HHK conference. Edited by G. Savage and P.

Roe. Ontario: University of Waterloo. June

14–16, 1999. pp. 165–172. Available online

at http://www.fes.uwaterloo.ca/u/jjkay/pubs/

systems/.
		




 	


	





King, A. W. 1993. “Considerations of scale and

hierarchy.” In Ecological integrity and the

management of ecosystems. Edited by S. J.

Woodley and J. Kay and G. Francis. Delray

Beach: FL: St. Lucie Press. pp. viii, 220.

McCool, S. F., and R. Haynes. 1995.

Sustainability. Prepared for the Interior

Columbia Basin Ecosystem Management

Project. Available online at http://

www.icbemp.gov/science/scirpte.html or

http://www.icbemp.gov/science/mccool.pdf.

Rules of Thumb, Inc. 2000. NetWeaver. North

East, PA: Rules of Thumb, Inc. Software

available online at http://

www.bjsoftware.com/.

Rules of Thumb, Inc. 2002. GeoNetWeaver. North

East, PA: Rules of Thumb, Inc. Software

available online at http://

www.bjsoftware.com/.

Rykiel, E. J., Jr. 1998. “Relationships of scale to

policy and decision making.” In Ecological

scale, theory and applications. Edited by D.

L. Peterson and V. T. Parker. New York:

Columbia University Press.

Sterman, J. D. 2000. Business dynamics: Systems

thinking and modeling for a complex world.

McGraw Hill Companies, Inc.

Woodley, S. J., G. Alward, L. I. Gutierrez, T. W.

Hoekstra, B. Holt, L. Livingston, J. Loo, A.

Skibicki, C. Williams, and P. Wright. 1999.

North American test of criteria and indicators

of sustainable forestry. Fort Collins, CO:

USDA Forest Service Inventory and Monitoring Institute Report No. 3. Available online at

http://www.fs.fed.us/institute/cifor/

cifor_1.html.

World Commission on Environment and Development. 1987. Our Common Future. Oxford

University Press.

Wright, P. A., G. Alward, T. W. Hoekstra, B.

Tegler, and M. Turner. 2002. Monitoring for

forest management unit scale sustainability:

The local unit criteria and indicators development (LUCID) test (technical edition). Fort

Collins, CO: USDA Forest Service Inventory

and Monitoring Report No. 4. Available

online at http://www.fs.fed.us/institute/lucid/

final_report/.
	


 	

	
	 	

		
	


 	

	


	


	 

		

Principle P1: Social Well-being

CRITERION INDICATOR MEASURE--RECOMMENDED MEASURE--OPTIONS 

C1.1 Collaborative 

stewardship 

I1.1.1 Contribution of 

local and traditional and 

ecological knowledge 

M1.1.1.1 Projects with traditional 

ecological knowledge/local 

knowledge component (e.g., 

number of projects) 

 MO1.1.1.2 Participation in 

traditional ecological knowledge 

workshops (number workshops, 

person hours participation) 

 

C1.1 Collaborative 

stewardship 

I1.1.2 Collaborative 

decision-making 

M1.1.2.1 Projects done 

collaboratively (e.g., number of 

projects) 

MO1.1.2.1 Assessment of 

Memo of Understand/Memo of 

Agreement and other 

agreements (e.g., number of 

agreements) 

 M1.1.2.2 Participation in public 

review opportunities (e.g., 

number, representativeness, 

satisfaction with) 

 MO1.1.2.3 Environmental 

Assessment/Environmental 

Impact Statements resulting in

appeals/lawsuits (e.g., number 

or proportion resulting in 

appeals) 

 MO1.1.2.4 Requests for 

participation in decision making 

beyond requirements (e.g., 

number by type) 

 

C1.1 Collaborative 

stewardship 

I1.1.3 Stewardship 

activities 

M1.1.3.1 Participation in 

stewardship activities (e.g., 

number of volunteer days, 

number of individuals involved) 

MO1.1.3.1 Cooperative 

stewardship initiatives (e.g., 

number of initiatives by type) 

 MO1.1.3.2 Partnership 

contributions (e.g., dollars, inkind contributions, cost shares)

 

C1.1 Collaborative 

stewardship 

I1.1.4 Local area 

empowerment and 

development 

M1.1.4.1 Skill base development 

and retention (e.g., number of 

training hours, days, sessions) 

 

C1.2 Institutional 

and Community 

Capacity 

I1.2.1 Community 

resiliency 

M1.2.1.1 Community capacity 

index 

MO1.2.1.1 Socioeconomic 

resiliency index 

 

C1.2 Institutional 

and Community 

Capacity 

I1.2.2 Institutional 

adequacy 

M1.2.2.1 Areas with sustainable 

management plans, policies or 

commitments (e.g., acres by 

ownership and type of 

agreement) 

MO1.2.2.1 Assessment of land

tenure policies that restrict 

sustainabile forest management

 M1.2.2.2 Adequacy of 

inventories (e.g., index of 

completeness and currency) 

 
		




	
 

			

		



 

C1.2 

Institutional 

and 

Community 

Capacity 

I1.2.3 Ownership patterns M1.2.3.1 Land ownership/tenure 

(e.g., area by use type) 

MO1.2.3.1 Parcel size by 

ownership type 

 M1.2.3.2 Land value by type of 

use/tenure (e.g., change in per 

capita value of assessed 

property; average property value 

of forest land by 

commercial/private) 

 

C1.2 

Institutional 

and 

Community 

Capacity 

I1.2.4 Government to 

government relationships

M1.2.4.1 Assessment of 

government-to-government 

agreements/plans (e.g., number 

of by Tribe, State, etc.) 

MO1.2.4.1 Government to 

government 

interactions/relationships 

(e.g., number of informal 

interactions/relationships by 

Tribe, State, etc.) 

 

C1.3 Social 

equity 

I1.3.1 Environmental 

justice and civil rights 

M1.3.1.1 Civil rights violations 

(e.g., number or severity by type)

 M1.3.1.2 Small business/minority 

set asides (e.g., number, dollar 

value, extent by program area) 

 M1.3.1.3 Distribution of workforce 

(e.g., gender, ethnicity, age, etc.) 

 M1.3.1.4 Environmental justice 

issues (e.g., number of analysis 

issues identified, remedial actions 

as result of analysis, number of 

complaints, etc.) 

 

C1.3 Social 

equity 

I1.3.2 Disabled access M1.3.2.1 Accessible facilities 

(e.g., proportion/number facilities 

available by activity) 

 

C1.3 Social 

equity 

I1.3.3 Worker health and 

safety 

M1.3.3.1 Worker safety incidents 

(e.g., lost time injury frequency 

rate; number per year) 

MO1.3.3.1 Worker health 

and safety claims (e.g., 

number or value of those 

filed per year) 

 M1.3.3.2 Intensity and extensity 

of workforce trained (e.g., number 

of workers or months of training 

by program/topic area) 

 MO1.3.3.3 Compliance with 

inspections (e.g., number of 

violations or # outstanding

violations by severity) 

 

C1.3 Social 

equity 

I1.3.4 Public health and 

safety 

M1.3.4.1 Public safety incidents 

(e.g., number of incidents by 

type/severity) 

 

C1.3 Social 

equity 

I1.3.5 

Community/environmenta

l health 

M1.3.5.1 Human health violations 

(e.g., number of by type, air 

quality violations, potable water) 

 M1.3.5.2 Fire hazard or risk index 

CRITERION INDICATOR MEASURE--RECOMMENDED MEASURE--OPTIONS 
	


 	

	
	 	

		
	

CRITERION INDICATOR MEASURE--RECOMMENDED MEASURE--OPTIONS 

C1.4 Social and 

cultural values 

I1.4.1 Gathering M1.4.1.1 Participation in 

harvest for personal use (e.g., 

number of persons 

participating in harvest for 

consumption/cultural 

purposes) 

MO1.4.1.1 Availability of 

subsistence use programs (e.g., 

number by type, knowledge of

availability) 

 M1.4.1.2 Participation in 

harvest for Native American 

use (e.g., number of persons 

participating in harvest for 

consumption/cultural 

purposes) 

MO1.4.1.2 Availability of Native 

American subsistence use 

programs (e.g., number by type; 

knowledge of availability) 

 MO1.4.1.3 Conflicts between 

Native American use and other 

activities (e.g., number of by 

type such as FS policy, 

commercial harvest, etc.) 

 

C1.4 Social and 

cultural values 

I1.4.2 Aesthetics and 

Solitude 

M1.4.2.1 Scenic quality index 

(e.g., acres by visual quality 

objective class, acres needing 

rehabilitation by visual quality 

objective class) 

 M1.4.2.2 Availability of places 

for solitude (e.g., acres of 

forest buffered from collector 

or arterial roads, acres of 

forest not under flight paths, 

etc.) 

 

C1.4 Social and 

cultural values 

I1.4.3 Education and 

research 

M1.4.3.1 Interpretive facilities 

available (e.g., number of by 

type) 

 M1.4.3.2 Interpretation, 

education, and research 

participation (e.g., number of 

contacts by type) 

 MO1.4.3.3 Interpretation, 

education, research 

expenditures (e.g., total 

expenditures or proportion of 

total expenditures on 

educational/interpretive/researc

h materials) 

 MO1.4.3.4 Research activities

(e.g., number of efforts to 

promote/enable research) 

 

C1.4 Social and 

cultural values 

I1.4.4 Cultural values 

and historic features

M1.4.4.1 Protection of cultural 

and historic sites (e.g., total 

number, number with 

minimum buffer protection 

widths or other protective 

mechanisms) 

MO1.4.4.1 Cultural and historic 

sites identified (e.g., number of 

sites that have been evaluated)

 M1.4.4.2 Cultural and historic 

sites that have been 

negatively impacted (e.g., 

number of sites impacted) 

MO1.4.4.2 Cultural and historic 

site monitoring for protection 

(e.g., number sites monitored 

annually, archaeological field 

monitoring days) 
		




	
 

			

		



CRITERION INDICATOR MEASURE--RECOMMENDED MEASURE--OPTIONS 

C1.4 Social and 

cultural values 

I1.4.5 Spiritual values 

and special places 

M1.4.5.1 Areas managed for 

special places/values (e.g., 

acres/proportion old growth, 

deer management, wetland 

areas, etc.) 

 

C1.4 Social and 

cultural values 

I1.4.6 Access and 

use rights 

M1.4.6.1 Road access (e.g., 

miles by road class) 

 M1.4.6.2 Access restrictions 

(e.g., acres of forest with 

restricted access by restriction 

type) 

MO1.4.6.2 Access 

restrictions (e.g., special 

acts/closure orders; number

permits denied due to access 

concerns; restrictions by 

inholdings; number of nonaccess days) 

 M1.4.6.3 Special use permits 

(e.g., number of special use 

permits by type) 

 MO1.4.6.4 Areas available for 

recreation (e.g., Scenery 

Management System by 

Recreation Opportunity 

Spectrum) 

 

C1.4 Social and 

cultural values 

I1.4.7 Recreation and 

tourism 

M1.4.7.1 Recreation use (e.g., 

Recreation Visitor Day by 

Recreation Opportunity 

Spectrum) 

MO1.4.7.1 Visitors use (e.g., 

number of visitors) 

 M1.4.7.2 Recreation user 

satisfaction (e.g., visitor survey 

by activity type) 

 MO1.4.7.3 Recreation supply 

(e.g., Persons at One Time 

by Recreation Opportunity 

Spectrum) 

 

C1.4 Social and 

cultural values 

I1.4.8 Customs and 

culture 

M1.4.8.1 Public satisfaction 

with management for sense of 

place (e.g., public survey, 

interviews, etc.) 

 MO1.4.8.2 Facilities available 

to support customary and 

traditional activities (e.g., 

number of facilities) 

 MO1.4.8.3 Services and 

mechanisms available to 

support customary and 

traditional activities (e.g., 

number of special use 

permits for recreation events)

 MO1.4.8.4 Conflicts between 

United States Forest Service 

and customary users (e.g., 

number of conflicts) 

 
	


 	

	
	 	

		
	

Principle P2: Maintain Ecological Integrity

CRITERION INDICATOR MEASURE--RECOMMENDED MEASURE--OPTIONS 

C2.1 Landscape 

function 

I2.1.1 

Disturbance 

processes 

M2.1.1.1 

Intensity/extensity/frequency of 

disturbance processes (e.g., 

degree of organic matter 

removal by fire, proportion of 

landscape disturbed by wind 

annually, return interval for fire) 

 MO2.1.1.2 Risk 

assessment of disturbance

processes (e.g., area at 

high risk of mortality due to

insects or disease, fire risk

condition class) 

 

C2.1 Landscape 

function 

I2.1.2 Hydrologic 

function 

M2.1.2.1 Watershed condition 

index (e.g., hydrologic condition 

assessment) 

 M2.1.2.2 Drought/Flood severity 

measures (e.g., Palmer drought 

severity index) 

MO2.1.2.2 Precipitation 

measures (e.g., inches of 

rainfall/ snowfall, snowpack 

depth) 

 MO2.1.2.3 Snow/Ice 

phenology (e.g., rate of 

glacier recession, date of 

lake ice on/off) 

 

C2.1 Landscape 

function 

I2.1.3 Long-term 

community 

dynamics 

M2.1.3.1 Longevity of current 

community assemblages (e.g., 

changes in dominant species 

based on vegetation history 

derived from pollen cores, 

presence of historic soil profiles, 

fossil evidence of past 

plant/animal communities) 

 

C2.2 Landscape 

structure/composition 

I2.2.1 Landscape 

diversity 

M2.2.1.1 Assessment of 

vegetation community types 

including permanent 

conversions (e.g., acres or 

relative proportion of each type) 

 M2.2.1.2 Horizontal and vertical 

structural diversity of vegetation 

(e.g., number of vegetation 

layers present) 

 MO2.2.1.3 Management 

guidelines present and 

utilized by ecological land 

types 

 

C2.2 Landscape 

structure/composition 

I2.2.2 Landscape 

patterns 

M2.2.2.1 Patch size and patch 

shape metrics (e.g., mean patch 

size and edge to interior ratios 

etc., by forest/non-forest area; 

by succesional stage; by 

community type) 

MO2.2.2.1 Landscape 

pattern effect on habitat 

(e.g., percent of sub-basin

with suitable habitat for 

selected species; 

HABSCAPES model) 
		




	
 

			

		



CRITERION INDICATOR MEASURE--RECOMMENDED MEASURE--OPTIONS 

 M2.2.2.2 Patch distribution 

and connectivity (e.g., 

dispersion index, 

characterization of 

random/regular/clumped 

patches, inter-patch 

distance, nearest neighbor 

measurement) 

 M2.2.2.3 Density and 

distribution of human 

developed features (e.g., 

road density, number of 

road crossings, distance to 

human developed features, 

by use class) 

 

C2.3 Ecosystem 

function 

I2.3.1 Productive 

capacity 

M2.3.1.1 Net primary 

productivity (e.g., remote 

sensing measures of site 

productivity) 

MO2.3.1.1 Productive 

capacity (e.g., estimate 

based on climate, chlorophyll 

per unit area, etc.) 

 M2.3.1.2 Tree growth rate 

(e.g., annual increment, 

basal area growth) 

 M2.3.1.3 Forage condition 

class assessment 

MO2.3.1.3 Forage quantity

and quality (e.g., animal unit 

months, pounds forage 

produced) 

 M2.3.1.4 Animal and fish 

productivity (e.g., number of 

individuals or pounds 

annually, shared measure, 

in part, with M2.6.1.1) 

 

C2.3 Ecosystem 

function 

I2.3.2 Functional 

diversity 

M2.3.2.1 Areas impacted by 

hyper/hypo species 

abundance (e.g., acres 

where deer exceed carrying 

capacity) 

 M2.3.2.2 Diversity within 

selected guild populations 

(e.g., number of species, 

redundancy of species) 

 

C2.3 Ecosystem 

function 

I2.3.3 Invasive 

species 

M2.3.3.1 Presence of exotic 

species (e.g., number of 

exotic plant species) 

 M2.3.3.2 Areas affected by 

invasive species (e.g., acres 

invaded, rate of spread) 

MO2.3.3.2 Risk assessment 

of invasive species (e.g., 

acres at risk) 

 MO2.3.3.3 Adequacy of 

monitoring to detect invasive 

species 

 

C2.3 Ecosystem 

function 

I2.3.4 Nutrient cycling M2.3.4.1 Soil organic matter 

content (e.g., percent 

organic matter in 'A' horizon 

of soil) 

MO2.3.4.1 Litter 

accumulation/decay (e.g., 

pounds per acre, litter extent, 

'O' horizon depth, percent 

cover, percent bare soil) 
	


 	

	
	 	

		
	

CRITERION INDICATOR MEASURE--RECOMMENDED MEASURE--OPTIONS 

 MO2.3.4.2 Soil macro/micro

fauna diversity (e.g., number 

of species) 

 

 I2.3.5 Carbon 

sequestration 

M2.3.5.1 Soil carbon (e.g., 

pounds of carbon in soil per 

acre) 

MO2.3.5.1 Soil carbon 'A' 

horizon (e.g., based on 

depth of 'A" horizon and 

percent carbon present) 

MO2.3.5.1 Soil carbon as 

determined from CWD (e.g., 

pounds of CWD by decay 

class) 

 M2.3.5.2 Biomass carbon 

sink (e.g., timber volume 

equivalents) 

 

 I2.3.6 Stream 

function 

M2.3.6.1 Assessment of 

stream segments in proper 

functioning condition (e.g., 

proportion of stream in 

proper functioning condition)

MO2.3.6.1 Assessment of 

the quality and extent of 

riparian vegetation (e.g., 

stream miles with intact 

riparian vegetation/ thermal

cover) 

MO2.3.6.1 Presence of 

stream structural elements 

(e.g., number and 

distribution of in stream 

structural elements such as

coarse woody debris and 

large boulders) 

 

C2.4 Ecosystem 

structure/composition 

I2.4.1 Air, soil and 

water quality 

M2.4.1.1 Air quality 

attributes (e.g., number of 

days of non-compliance with 

standards such as visual 

quality or ozone level, lichen 

health index based on 

coverage of arboreal 

lichens, direct measures of 

pollutants per volume of air)

 M2.4.1.2 Index of biotic 

integrity for aquatic systems 

(e.g., index biotic integrity by 

stream segment or 

watershed) 

MO2.4.1.2 Water quality 

attributes (e.g., number of 

water bodies that exceed 

water quality standards such 

as temperature, fecal 

coliform, 303(d) list, EPA 

"impaired list", turbidity, etc.; 

number of spills per year, 

number of fish mercury 

advisories; PH measures 

including PH of precipitation)

 M2.4.1.3 Soil quality 

attributes (e.g., number of 

departures from BMP for 

soil quality, level of 

pollutants per unit volume of 

soil, bulk density 

measurement; soil nutrient 

analysis) 

 
		




	
 

			

		



CRITERION INDICATOR MEASURE--RECOMMENDED MEASURE--OPTIONS 

C2.4 Ecosystem 

structure/composition 

I2.4.2 Ecological 

legacies 

M2.4.2.1 Assessment of Coarse 

Woody Debris and Snags (e.g., 

volume of CWD or number of 

snags per acre by decay class, by 

size class, by vegetation 

community type) 

 M2.4.2.2 Assessment of Snags 

(e.g., number of snags per acre 

by decay class, by size class, by 

vegetation community type) 

 MO2.4.2.3 Assessment

of other ecological 

legacies (e.g., number 

of, or acres of features 

such as raptor nests, 

boulder fields, wallows, 

ant hills, beaver dams, 

legacy trees, etc). 

 

C2.4 Ecosystem 

structure/composition 

I2.4.3 Special 

habitats 

M2.4.3.1 Assessment of areas of 

special interest (e.g., reserve 

acres, wilderness acres, winter 

deer yards, etc., by condition 

class and protection status) 

 MO2.4.3.2 Guidelines 

are in place for the 

management of special

interest areas 

 

C2.4 Ecosystem 

structure/composition 

I2.4.4 Species 

richness 

M2.4.4.1 Assessment of species 

richness (e.g., number of species 

present within target species 

group such as birds or by species 

guild such as cavity nesters, by 

vegetation community type, 

management type) 

 M2.4.4.2 Assessment of species 

of concern (e.g., number of 

extirpated or endangered species)

 

C2.5 Population 

function 

I2.5.1 Population 

viability 

M2.5.1.1 Population viability 

analysis of selected species (e.g., 

index based on measures of 

population dynamics, population 

genetics, environmental variation, 

metapopulation structure, and 

habitat suitability, etc.) 

MO2.5.1.1 Habitat 

suitability analysis (e.g.,

acres of suitable habitat

for selected species 

based on habitat 

models) 

 

C2.6 Population 

structure/composition 

I2.6.1 Populations 

of indigenous 

species 

M2.6.1.1 Populations of selected 

species (e.g., population size, 

density, age class, sex ratio) 

MO2.6.1.1 Habitat 

suitability analysis (e.g.,

acres of suitable habitat

for selected species 

based on habitat 

models) 

 M2.6.1.2 Assessment of species' 

metapopulations (e.g., size, 

number and distribution of 

metapopulations) 
	


 	

	
	 	

		
	

CRITERION INDICATOR MEASURE--RECOMMENDED MEASURE--OPTIONS 

 MO2.6.1.3 Number of 

selected species with 

completed conservation 

assessment and recovery 

plans 

 

C2.7 Organism function I2.7.1 Genetic 

mixing 

M2.7.1.1 Assessment of the 

existence and use of native 

and non-native stock rules 

 

C2.7 Organism function I2.7.2 Genetic 

migration 

M2.7.2.1 Assessment of 

migration barriers (e.g., 

number water control 

structures per river mile) 

 M2.7.2.2 Assessment of the 

persistence of species on 

the edge of their range (e.g., 

population size of species 

on edge of range) 

 

C2.7 Organism function I2.7.3 Genetic 

selection 

M2.7.3.1 Assessment of the 

alteration of native species 

gene pools (e.g., percent of 

area where selected species 

do not reach sexual maturity 

prior to harvesting, acres of 

vegetation communities 

where management actions 

or selection pressures lead 

to unnatural selection 

pressures) 

 

C2.8 Organism 

structure/composition 

I2.8.1 Genetic 

diversity 

M2.8.1.1 Assessment of 

areas converted to nonnative gene pool (e.g., acres 

converted, proportion of 

landscape converted) 

 M2.8.1.2 Allele and 

genotype frequencies for 

selected species (e.g., 

difference from expected 

natural heterozygocity) 

MO2.8.1.2 Assessment of 

minimum viable populations

(e.g., population size of 

species with limited gene 

pools) 

 MO2.8.1.3 Presence of 

adequate genetic 

reserves/gene banks (e.g., 

acres) 

 
		




	
 

			

		



Principle P3: Economic Well-being

CRITERION INDICATOR MEASURE--

RECOMMENDED 

MEASURE--OPTIONS 

C3.1 Capital and 

wealth 

I3.1.1 Natural capital - 

forests 

M3.1.1.1 Compare productive 

forest to total forest (e.g., 

proportion by ownership, 

forest type, species, etc.) 

MO3.1.1.1 Total amount of 

forest (e.g., acres) 

 M3.1.1.2 Amount of productive 

forest (e.g., timber volume by 

ownership, forest type, 

species, etc.) 

 M3.1.1.3 Value of productive 

forest (e.g., $ value by 

ownership, forest type, 

species, etc.) 

 

C3.1 Capital and 

wealth 

I3.1.2 Natural capital - 

recreation 

M3.1.2.1 Areas available for 

recreation (e.g., Scenery 

Management System by 

Recreation Opportunity 

Spectrum) 

 M3.1.2.2 Areas of wilderness 

 

C3.1 Capital and 

wealth 

I3.1.3 Natural capital - 

wildlife/fish 

M3.1.3.1 Wildlife populations MO2.6.1.1 Assessment of

suitable habitat for 

selected species (e.g., 

habitat suitability models) 

 MO3.1.3.2 Assessment of

fish bearing streams (e.g., 

stream miles) 

 

C3.1 Capital and 

wealth 

I3.1.4 Natural capital - 

range 

M3.1.4.1 Amount of productive 

range (e.g., acres by forage 

condition class) 

 

C3.1 Capital and 

wealth 

I3.1.5 Other natural 

capital 

M3.1.5.1 Special management 

areas (e.g., acres of research 

natural areas, special interest 

areas, wild scenic rivers, etc.) 

 M3.1.5.2 Water resources 

(e.g., volume available) 

 M3.1.5.3 Special forest 

products (e.g., lbs of edible 

mushrooms) 

MO3.1.5.3 Value of special 

forest products (e.g., $ 

value of annual edible 

mushroom harvest) 

 M3.1.5.4 Areas of oil, gas and 

mineral potential (e.g., 

assessment of proven 

reserves) 

 MO 3.1.5.5 Natural capital 

ownership (e.g., proportion 

public to private forest 

ownership) 

 
	


 	

	
	 	

		
	

CRITERION INDICATOR MEASURE--RECOMMENDED MEASURE--OPTIONS 

C3.1 Capital and 

wealth 

I3.1.6 Built 

infrastructure - roads 

and trails 

M3.1.6.1 Roads and trails 

(e.g., miles of roads/trails by 

use type and capacity) 

 M3.1.6.2 Condition of roads 

and trails (e.g., miles to 

standard) 

MO3.1.6.2 Asset value of 

roads and trails (e.g., $ value 

of roads/trails by use type) 

 MO3.1.6.3 Roads and trails

maintenance cost 

 MO3.1.6.4 Roads and trails

maintenance backlog 

 

C3.1 Capital and 

wealth 

I3.1.7 Built 

infrastructure - 

recreation facilities 

M3.1.7.1 Recreation 

facilities (e.g., number of 

recreation facilities by type) 

 M3.1.7.2 Capacity of 

recreation facilities 

 M3.1.7.3 Condition of 

recreation facilities 

MO3.1.7.3 Recreation 

facilities maintenance cost 

 MO3.1.7.4 Recreation 

facilities maintenance 

backlog 

 MO3.1.7.5 Asset value of 

recreation facilities 

 

C3.1 Capital and 

wealth 

I3.1.8 Built 

infrastructure - other 

facilities 

M3.1.8.1 Other facilities 

(e.g., number of by type) 

 M3.1.8.2 Condition of other 

facilities 

MO3.1.8.2 Other facilities 

maintenance cost 

 MO3.1.8.3 Asset value of 

other facilities 

 MO3.1.8.4 Other facilities 

maintenance backlog 

 

C3.1 Capital and 

wealth 

I3.1.9 Human capital M3.1.9.1 Assessment of 

labor pool (e.g., 

size/distribution by gender, 

ethnicity, age etc.) 

 

C3.2 Flows of 

products and 

services 

I3.2.1 Production of 

marketed goods and 

services 

M3.2.1.1 Timber harvested 

(e.g., million board feet by 

type) 

MO3.2.1.1 Value of timber 

produced 

 M3.2.1.2 Recreation use 

(e.g., recreation visitor day 

by recreation opportunity 

spectrum) 

MO3.2.1.2 Value of 

recreation produced 

 M3.2.1.3 Wildlife harvested MO3.2.1.3 Value of wildlife 

harvested 

 M3.2.1.4 Fish harvested MO3.2.1.4 Value of fish 

harvested 

 M3.2.1.5 Animal unit month 

used 

MO3.2.1.5 Value of animal 

unit month produced 

 M3.2.1.6 Oil, gas and 

minerals produced (e.g., 

volume by type/grade) 

MO3.2.1.6 Value of minerals 

produced 

 M3.2.1.7 Electrical power 

generated (e.g., kw hours) 

MO3.2.1.7 Value of energy 

produced 
		




	
 

			

		



CRITERION INDICATOR MEASURE--RECOMMENDED MEASURE--OPTIONS 

 M3.2.1.8 Water withdrawals 

(e.g., volume) 

MO3.2.1.8 Value of water

withdrawals 

 M3.2.1.9 Contribution to Gross 

Domestic Product/Gross 

Regional Domestic Product 

 

C3.2 Flows of 

products and 

services 

I3.2.2 Production of 

non-marketed goods 

and services 

M3.2.2.1 Special forest 

products harvested (e.g., lbs 

of mushrooms) 

MO3.2.2.1 number of 

special forest product 

permits issued 

 M3.2.2.2 

Wilderness/backcountry 

permits (e.g., number of 

permits issued per year) 

 M3.2.2.3 Special use permits 

(e.g., number of permits 

issued per year) 

 

C3.3 Trade and 

distributional equity

I3.3.1 Trade balance M3.3.1.1 Value of imports to 

exports 

MO3.3.1.1 Timber imports

to exports (e.g., volume of 

timber imports/exports) 

 

C3.3 Trade and 

distributional equity

I3.3.2 Workforce 

diversity 

M3.3.2.1 Distribution of 

workforce (e.g., gender, 

ethnicity, age) 

 M3.3.2.3 Assessment of 

contracts to minority-owned or 

small businesses (e.g., 

proportion/value of contracts) 

 MO3.3.2.4 Average 

commuting time 

 

C3.3 Trade and 

distributional equity

I3.3.3 Income M3.3.3.1 Size distribution of 

income (e.g., percent by 

tenure type) 

 M3.3.3.2 Technical distribution 

of income (e.g., by factor type)

MO3.3.3.2 Technical 

distribution of employment

 M3.3.3.3 National Forest 

System total program 

expenditures (e.g., $ annual 

by program) 

MO 3.3.3.3 National Forest 

System operating budget 

 MO 3.3.3.4 National Forest 

System generated 

revenues (e.g., receipts) 

 

C3.3 Trade and 

distributional equity

I3.3.4 Equity M3.3.4.1 Distribution of rent: 

timber (percent rent by 

recipient) 

 M3.3.4.2 Distribution of rent: 

recreation (percent rent by 

recipient) 

 M3.3.4.3 Distribution of rent: 

range (percent rent by 

recipient) 

 M3.3.4.4 Distribution of rent: 

stewardship/protection 

(percent rent by recipient) 

 MO3.3.4.5 Distribution of 

payments in lieu of taxes 
	


 	

	
	 	

		
	

CRITERION INDICATOR MEASURE--RECOMMENDED MEASURE--OPTIONS 

 MO3.3.4.6 25% Fund 

 MO3.3.4.7 Other FS 

distributions (e.g., range 

betterment fund) 

 MO3.3.4.8 Other non-FS 

distributions (e.g., coal and gas 

royalties) 

 

C3.4 Efficiency I3.4.1 Net Rent M3.4.1.1 Net rent: timber 

(income-expenditures) 

 M3.4.1.2 Net rent: recreation 

(income-expenditures) 

 M3.4.1.3 Net rent: range 

(income-expenditures) 

 M3.4.1.4 Net rent: 

stewardship/protection 

(income-expenditures) 

 
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all prohibited bases apply to all programs.) Persons with disabilities who require

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Room 326-W, Whitten Building, 1400 Independence Avenue, SW, Washington, DC

20250-9410 or call (202) 720-5964 (voice or TDD). USDA is an equal opportunity

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