Adaptive Management 

of Natural Resources: 

Theory, Concepts, and 

Management Institutions

George H. Stankey, Roger N. Clark, Bernard T. Bormann

United States 

Department of 

Agriculture

Forest Service

Pacific Northwest 

Research Station

General Technical 

Report

PNW-GTR-654

August 2005

DEPA RTMENTOFAGRICULT URE

Evaluate Plan

Monitor Act
Authors

George H. Stankey is a research social scientist and Bernard T. Bormann is a 

principal plant physiologist, Forestry Sciences Laboratory, 3200 SW Jefferson Way, 

Corvallis, OR 97331; Roger N. Clark is a research forester, Pacific Wildland Fire 

Sciences Laboratory, 400 N 34th Street, Suite 201, Seattle, WA 98103. 

Cover Photos

Background photo, forest stream: Photo by Ron Nichols, USDA Natural Resources Conservation Service.

Background circle, river viewed from hill: Dave Powell, USDA Forest Service, www.forestryimages.org.

Upper left, two people standing pointing from hillside: Photo by Gary Wilson, USDA Natural Resources 

Conservation Service.

Upper right, four people looking at a map: Photo by Jeff Vanuga, USDA Natural Resources Conservation 

Service.

Lower left, two people measuring tree: Photo courtesy of USDA Natural Resources Conservation Service.

Lower right, person with drip torch: Photo by Roger Ottmar, PNW Research Station.

The Forest Service of the U.S. Department of Agriculture is dedicated to the principle of 

multiple use management of the Nation’s forest resources for sustained yields of wood, 

water, forage, wildlife, and recreation. Through forestry research, cooperation with the states 

and private forest owners, and management of the national forests and national grasslands, 

it strives—as directed by Congress—to provide increasingly greater service to a growing 

Nation.

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Independence Avenue, S.W., Washington, D.C. 20250-9410 or call (800) 795-3272 (voice) or 

(202) 720-6382 (TDD). USDA is an equal opportunity provider and employer.
Abstract

Stankey, George H.; Clark, Roger N.; Bormann, Bernard T. 2005. Adaptive 

management of natural resources: theory, concepts, and management institutions. Gen. Tech. Rep. PNW-GTR-654. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station. 73 p.

This report reviews the extensive and growing literature on the concept and application of adaptive management. Adaptive management is a central element of 

the Northwest Forest Plan and there is a need for an informed understanding of the 

key theories, concepts, and frameworks upon which it is founded. Literature from 

a diverse range of fields including social learning, risk and uncertainty, and institutional analysis was reviewed, particularly as it related to application in an adaptive 

management context. The review identifies opportunities as well as barriers that 

adaptive management faces. It concludes by describing steps that must be taken to 

implement adaptive management.

Keywords: Adaptive management, social learning, public policy, research 

design, risk and uncertainty, natural resource management.
Contents

 1 Introduction

4 The Concept of Adaptive Management

8 Key Premises of Adaptive Management

11 Alternative Models of Adaptive Management

14 Learning: A Driver and Product of Adaptive Management

 15 What Is Learning?

 17 Is Learning the Result of Technical Processes, Social Processes, or Both?

 20 Organizational Learning or Learning Organizations?

27 Risk and Uncertainty

31 Institutional Structures and Processes for Adaptive Management

33 Increasing Knowledge Acquisition

36 Enhancing Information Flow

40 Creating Shared Understandings

41 Institutional Attributes Facilitating Adaptive Management

55 Summary and Conclusions

61 Literature Cited
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

1

Introduction

A common feature of contemporary natural resource management issues is the 

underlying uncertainty regarding both cause (What causal factors account for the 

problem?) and effect (What will happen if a particular management strategy is 

employed?). These uncertainties are, in part, a product of the growing emphasis 

on long-term, multiscale, and integrative aspects of resource management. These 

involve multiple disciplinary perspectives, multiple jurisdictions and associated 

management objectives, and a growing concern with cause and effect over large 

spatial scales and long timeframes. 

In the face of such issues, traditional approaches to scientific inquiry increasingly have been found inadequate, particularly with regard to the ability to predict 

consequences and effects. As many have argued (e.g., Herrick and Sarewitz 2000, 

Kuhn 1970), the central strategy of mainstream science has been to break phenomena into distinct components (disciplines), remove those components from their 

larger context, and identify mechanisms or processes to frame specific research 

questions. Although this paradigm has served science and society well (and will 

continue to do so), its capacity to contribute effectively to addressing many contemporary environmental problems is problematic.

These limits generally are acknowledged. Calls for ecosystem-based, integrative resource management explicitly or implicitly are grounded in the need for 

innovative institutional structures and processes (Cortner et al. 1996). Such approaches acknowledge the critical role of ongoing monitoring and evaluation as the 

basis from which learning would inform subsequent action. The iterative relation 

between learning and action is a hallmark of social learning planning models 

(Friedmann 1987).

The concept of adaptive management has gained attention as a means of linking 

learning with policy and implementation. Although the idea of learning from experience and modifying subsequent behavior in light of that experience has long been 

reported in the literature, the specific idea of adaptive management as a strategy for 

natural resource management can be traced to the seminal work of Holling (1978), 

Walters (1986), and Lee (1993). These scholars have framed and articulated the idea 

of an approach that treats on-the-ground actions and policies as hypotheses from 

which learning derives, which, in turn, provides the basis for changes in subsequent 

actions and policies.

This contemporary concept of adaptive management has been applied across a 

range of resource sectors (agriculture, water resource management, fisheries, etc.) 

as well as a variety of sociopolitical contexts (Australia, Canada, Europe, Southeast 

Asia, South Africa, United States). The potential of adaptive management makes it 
GENERAL TECHNICAL REPORT PNW-GTR-654

2

an attractive strategy in situations where high levels of uncertainty prevail. It was 

this quality that led to adaptive management becoming a central component of the 

Forest Ecosystem Management Assessment Team (FEMAT) report (1993) and the 

subsequent Northwest Forest Plan (hereafter, the Plan) (USDA USDI 1994). 

Implementation of the Plan began in 1994. The Plan’s goal was to initiate 

an ecosystem-based management approach across 24 million acres (9.7 million 

hectares) of federal land in a three-state region in which sharp conflicts over 

objectives and values existed. These conflicts were exacerbated by high levels of 

uncertainty. Most existing science had been undertaken at the site or stand level, 

and its applicability at the watershed and regional level was not well understood. 

Moreover, the precarious status of endangered species and the diminishing extent 

of old-growth forests in the region combined to create a situation in which there 

was great concern—among citizens, managers, policymakers, and scientists—that 

it was important to be cautious in not aggravating the problem (fig. 1). As a consequence, the Plan placed a heavy emphasis on reserves; about 80 percent of the 

planning region is in an administrative or statutory reserve. The reserve allocations 

were augmented by a set of restrictive standards and guidelines (S&Gs) that set 

performance standards for on-the-ground activities.

The Plan also acknowledged that improving understanding within and among 

the complex biophysical, social-economic-political systems in the region would 

require an increased emphasis on new knowledge. As a result, it called for adoption of an adaptive management strategy to gain new understanding. It proposed a 

four-phase adaptive management cycle (fig. 2). In the first phase, plans are framed, 

based on existing knowledge, organizational goals, current technology, and existing 

inventories. In phase two, on-the-ground actions are initiated. Phase three involves 

monitoring results of those actions and, in phase four, results are evaluated. The 

cycle could then reinitiate, driven by emerging knowledge and experience. Results 

could validate existing practices and policies or reveal the need for alterations in the 

allocations, S&Gs, or both. 

To facilitate the adaptive strategy, about 6 percent of the area was allocated to 

10 adaptive management areas (AMAs) distributed across the three-state region to 

represent the diversity of biophysical and socioeconomic conditions (fig. 3). The 

AMAs provided areas where there would be latitude to experiment with management practices, where the S&Gs could be tested and validated, and where innovative relations between land managers and citizens would be encouraged. 

The Plan has been in place for more than a decade. A key question regarding 

implementation concerns the extent to which adaptive management has achieved its 

A key question 

regarding the Plan’s 

implementation 

concerns the extent 

to which adaptive 

management has 

achieved its intended 

objectives.
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

3

Figure 1—In the Northwest Forest Plan, the diminishing extent of old-growth forests 

in the region has raised concerns whether these forests can be sustained and restored. 

National Park Service 

intended objectives; has it provided a framework within which key uncertainties contained in the Plan have been critically examined, tested, and, as appropriate, modified? 

A companion report1 of this literature review describes this evaluation.

The use of an adaptive management strategy for forest management has been 

given additional importance by the revised planning rule that guides implementation 

1Stankey, G.H.; Bormann, B.T.; Ryan, C.; Shindler, B.; Sturtevant, V.; Clark, R.N.; Philpot, 

C., eds. Learning to manage a complex ecosystem: adaptive management and the Northwest 

Forest Plan. Draft manuscript on file with G.H. Stankey.
GENERAL TECHNICAL REPORT PNW-GTR-654

4

of the National Forest Management Act (NFMA). The new rule replaces the former 

chapter dealing with “regional planning,” replacing it with “The Adaptive Planning Process” (see Forest Service Handbook 1909_12 chapter 20) and outlining the 

procedures responsible planning officials are to follow in implementing the new 

approach.

As suggested above, the adaptive management concept has been pursued in 

diverse fields, from agriculture, fisheries, and forestry in the natural resource arena 

to business and education. It incorporates diverse academic perspectives including 

learning theory, public policy, and experimental science. In some cases, relevant 

concepts and experiences derive from literature or policy experiments where 

the explicit notion of adaptive management is either absent or only of tangential 

interest. In this review, we have attempted to blend the results of substantive and 

technical analyses and discussions of the key conceptual components of an adaptive 

approach, with results from various implementation efforts.

The Concept of Adaptive Management

Haber (1964) traced the origins of adaptive management to the ideas of scientific 

management that took root in the early 1900s. The idea is linked to disciplines 

outside natural resource management; for example, adaptive management, or 

closely-related notions, are found in business (total quality management, continuous improvement, and learning organizations [Senge 1990]), experimental science 

Goals Knowledge Technology Inventory

Revised goals

New knowledge

Inventory

New technology

Adaptive

management

MONITOR

EVA

L

UATE

A

CT

PLAN

Figure 2—The adaptive management cycle (USDA USDI 1994: E–14).
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

5

0 25 50 100 Miles

San Francisco

Portland

Seattle

Northern Coast

Range

AMA

Applegate

AMA

Olympic

AMA

Finney

AMA

Snoqualmie Pass

AMA

Cispus

AMA

Central Cascades

AMA

Little River

AMA

Goosenest

AMA

Hayfork

AMA

I-80

I-5

I-5

I-5

I-84

I-82

I-90

I-90

I-84

Adaptive

management areas

Northwest Forest

Plan region

Major lakes

and rivers

Major roads

Metropolitan

areas

States

Figure 3—The 10 adaptive management areas in the Northwest Forest Plan provide a diverse range of biophysical, 

political, and socioeconomic conditions.
GENERAL TECHNICAL REPORT PNW-GTR-654

6

(hypothesis testing [Kuhn 1970]), systems theory (feedback control [Ashworth 

1982]), industrial ecology (Allenby and Richards 1994), and social learning (Korten 

and Klauss 1984).

The concept has drawn particular attention in natural resource management 

(Bormann et al. 1999). In 1978, with publication of Holling’s Adaptive Environmental Assessment and Management, its potential as a framework for dealing with complex environmental management problems began to be recognized. The subsequent 

publication of Adaptive Management of Renewable Resources (Walters 1986), 

Compass and Gyroscope: Integrating Science and Politics for the Environment 

(Lee 1993), and Barriers and Bridges to the Renewal of Ecosystems and Institutions (Gunderson et al. 1995a) added increasing sophistication and elaboration to 

the concept and its potential. Key elements of adaptive management were explored 

in these texts; the importance of design and experimentation, the crucial role of 

learning from policy experiments, the iterative link between knowledge and action, 

the integration and legitimacy of knowledge from various sources, and the need 

for responsive institutions. A growing professional literature, reflecting a diverse 

body of interest and experience in application of adaptive management, has now 

developed. For example, in a literature search of the Cambridge Scientific Abstracts 

and SciSearch for 1997–98, Johnson (1999) found 65 papers that used adaptive 

management in their title, abstract, or keywords, covering issues from wildlife 

management, wetland and coastal restoration, and public involvement.

Holling (1995: 8) hypothesized that expanding interest in adaptive management 

has been driven by three interlocking elements:

The very success in managing a target variable for sustained production of food or fiber apparently leads inevitably to an ultimate pathology of less resilient and more vulnerable ecosystems, more rigid 

and unresponsive management agencies, and more dependent 

societies. This seems to define the conditions for gridlock and irretrievable resource collapse [emphasis added].

In confronting these conditions, societies have sought strategies to forestall 

collapse. McLain and Lee (1996) reported that ethnographic evidence indicates 

humans long have relied on ad hoc hypothesis testing as a means of learning from 

surprise and increasing the stock of knowledge on which future decisions to use 

environmental resources are made. For example, Falanruw (1984) described how 

the Yap of Micronesia for generations sustained a high population despite resource 

scarcity by practicing adaptive techniques. Such techniques resulted in the production of termite-resistant wood and the creation and maintenance of coastal mangrove depressions and seagrass meadows to support fishing. The Yap altered their 
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

7

environment by using adaptive management processes; they undertook actions, 

observed and recorded results through story and songs, and codified practices 

through rituals and taboos. In short, at one level, the Yap experience embraces the 

modern concept of adaptive management: “policies are experiments: learn from 

them” (Lee 1993: 9).

Despite examples of the potential of an adaptive approach, contemporary 

examples of successful implementation are meager. In many ways, this seems paradoxical. On the one hand, adaptive management offers a compelling framework; 

i.e., learn from what you do and change practices accordingly. Yet, the literature 

and experience reveal a consistent conclusion; while adaptive management might 

be full of promise, generally it has fallen short on delivery. This dilemma is widely 

recognized (Halbert 1993, McLain and Lee 1996, Roe 1996, Stankey and Shindler 

1997, Walters 1997), leading Lee (1999: 1) to conclude “adaptive management has 

been more influential, so far, as an idea than as a practical means of gaining insight 

into the behavior of ecosystems utilized and inhabited by humans.” 

In part, the root of the difficulties might lie in the general level of familiarity 

with the notion of adaptation. As the Yap experience demonstrates, humans have 

long demonstrated the capacity to adapt to new information and contexts. Environmental stimuli provide feedback that inform us and modify subsequent behavior. 

Over time, individuals, groups, societies, and cultures learn to respond to changes; 

i.e, they adapt (or conversely, they don’t and eventually inherit the consequences). 

There are a host of adaptive mechanisms, some more conscious and explicit than 

others. In sum, however, most people have personal experiences with “learning by 

doing” and as a behavior, it therefore seems obvious, even unexceptional.

Adaptive management, as discussed in the contemporary literature, stands in 

contrast to these conventional conceptions. Although it shares the general premise 

of learning by doing, it adds an explicit, deliberate, and formal dimension to framing questions and problems, undertaking experimentation and testing, critically 

processing the results, and reassessing the policy context that originally triggered 

investigation in light of the newly acquired knowledge. Thus, adaptive management 

in this context involves more than traditional incrementalism; learning derives from 

purposeful experimentation that, in turn, derives from deliberate, formal processes 

of inquiry, not unlike scientific study. In this sense, assertions that resource agencies have long been adaptive are less than persuasive.

Carl Walters, a contemporary proponent of experimental adaptive management, 

offered a pessimistic appraisal of recent progress. He noted “I have participated 

in 25 planning exercises for adaptive management of riparian and coastal ecosystems over the past 20 years; only seven...have resulted in relatively large-scale 

Adaptive management 

learning derives 

from deliberate formal 

processes of inquiry.
GENERAL TECHNICAL REPORT PNW-GTR-654

8

management experiments and only two of these experiments would be considered 

well planned in terms of statistical design” (Walters 1997: 2–3). His critique is 

grounded, in part, on the question of what constitutes an experiment. As used here, 

we see it “...loosely as an action whose outcome we cannot predict completely 

in advance or specific beforehand” (Bernstein and Zalinski 1986: 1024). To Lee 

(1999), experimentation has three components: (1) a clear hypothesis, (2) a way of 

controlling factors extraneous to the hypothesis, and (3) an opportunity to replicate 

the experiment to test reliability. However, the general disappointment about the 

effectiveness of implementing adaptive management derives from more than a 

definitional conundrum. There is a growing appreciation of the various cultural, institutional, social-psychological, and political-legal challenges confronting adaptive 

management (Miller 1999). But despite these challenges, there is a growing body of 

experience and scholarly commentary reporting alternatives for addressing them.

Key Premises of Adaptive Management 

A foundational premise of adaptive management is that knowledge of ecological 

systems is not only incomplete but elusive (Walters and Holling 1990). Moreover, 

there is a growing conviction that expanding knowledge through traditional 

scientific inquiry will always be limited by resources and time. When these limiting factors are linked to the contextual conditions of resource scarcity, potential 

irreversibility, and growing demands, the need for new ways in which understanding and learning not only occur but directly inform decisionmaking and policy 

processes becomes apparent (Bormann et al. 1994b). Adaptive management offers 

both a scientifically sound course that does not make action dependent on extensive 

studies and a strategy of implementation designed to enhance systematic evaluation 

of actions (Lee and Lawrence 1986). 

As noted earlier, adaptive management has attracted attention for its emphasis 

on management experiences as a source of learning. This has produced a variety of 

phrases that emphasize the idea that adaptive management is learning to manage 

by managing to learn (Bormann et al. 1994a). This idea is not new; in a variation of 

the phrase, Michael (1973) entitled his book On Learning to Plan—and Planning 

to Learn. Whatever the particular phrase, the central idea is the presence of an 

iterative process that links knowledge to action (Friedmann 1987) and, conversely, 

action to knowledge (Lee 1993).

A critic of adaptive management might contend it is little more than a variant of Lindblom’s (1959) “disjointed incrementalism” or, as commonly described, 

“muddling through” model. Natural resource management long has demonstrated 

an ability to build on previous actions and outcomes; policies are always subject to 
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

9

revision in the light of past performance (Kusel et al. 1996). Some learning occurs 

irrespective of the particular management approach taken; Gunderson (1999c: 35) 

commented, “trial-and-error is a default model for learning...people are going to 

learn and adapt by the simple process of experience.” However, what distinguishes 

adaptive management from Lindblom’s incrementalism is its purposefulness

(Dovers 2003); agreed-upon goals and objectives serve as a basis against which 

progress can be measured and lessons gained. Adaptive management mimics the 

scientific method by highlighting uncertainties, specifying and evaluating hypotheses, and structuring actions to test those hypotheses through field application 

(Gunderson 1999c). In Walters’ (1997) terms, adaptive management replaces management learning by ad hoc, trial and error (an incremental, evolutionary process) 

with learning by careful tests (a process of directed selection).

Use of the scientific method to improve understanding of the effects of natural 

resource management actions is not without limits and liabilities. Although adaptive 

management “rests on a judgment that a scientific way of asking questions produces 

reliable answers at lowest cost and most rapidly, this may not be the case very 

often” (Lee 1999: 4) and might even be the opposite; i.e., slow and costly. Although 

Walters (1997: 10) agreed that environmental management changes needed to 

resolve key uncertainties might prove unacceptably costly, he argued “most debates 

about cost and risk have not been...well founded, and appear instead to be mainly 

excuses for delay in decision making.” It must also be recognized that the capacity 

of adaptive management to resolve value-based conflicts (e.g., forest management 

to meet economic as opposed to environmental objectives) might prove no more 

effective than traditional planning approaches.

There are many definitions of adaptive management (Bormann et al. 1999, 

Halbert 1993). As Failing et al. (2004) have observed, this widespread use of the 

term has propagated various interpretations of its meaning and, as a result, there 

are only vague notions about what it is, what is required for it to be successful, or 

how it might be applied. Not surprisingly, given recent attention by the scientific 

community, many definitions frame the discussion around a structured process that 

facilitates learning by doing; i.e., “adaptive management does not postpone action 

until ‘enough’ is known, but acknowledges that time and resources are too short to 

defer some action” (Lee 1999: 5). Holling (1978) and Walters (1986) specified two 

major components to the adaptive management process:

1. An effort to integrate existing interdisciplinary experience and scientific 

information into dynamic models to frame predictions about the impacts 

of alternative policies; this step performs three key functions:
GENERAL TECHNICAL REPORT PNW-GTR-654

10

• Problem clarification and enhanced communication among 

scientists, managers, and other stakeholders.

• Policy screening to eliminate options unlikely of doing much good because of inadequate scale or type of impacts.

• Identification of key knowledge gaps that make predictions suspect.

2. Design of a specific management experiment.

A third component to be added to this list links the results of a management 

experiment with the policymaking process; i.e., in light of the actions taken in an 

experimental setting, how do those results translate into changes in ongoing land 

management practices. In many ways, this third component is where the idea of 

“adaptive” comes into play, based on feedback from the results of experimentation.

These components contain important implications. Step 1 emphasizes the 

importance of problem framing, i.e., getting the question(s) right (Bardwell 1991, 

Miller 1999). This is a crucial phase; as Walters (1986: 9) noted, in system analysis 

terms, “bounding the problem” is where “most resource policy analyses go astray.” 

For example, Smith et al. (1998) described how conflicts over appropriate management strategies for salmon in the Pacific Northwest are confounded by differing 

assessments regarding the underlying causes of the salmon’s decline. Managers 

emphasize habitat loss, commercial fishers point to predators, and others identify 

water pollution. Failure to focus on problem definition can lead to inappropriate 

attention to symptoms and solutions (Van Cleve et al. 2003). Framing effective 

strategies in the face of such differences is also challenging because it is ultimately 

a social undertaking, involving a variety of perspectives and experiences; it must 

transcend its limitations as a technical-scientific endeavor. For example, Butler et 

al. (2001) argued that it is important that resource users (e.g., fishers) understand 

the benefits and costs associated with an adaptive approach. Without such information, adaptive adjustments can become nothing more than “tinkering in pursuit of 

fruitless equilibrium” (p. 797). Finally, the problem-framing phase needs to encourage a deliberate and informed “working through” process (Yankelovich 1991) in 

which options and their costs and efficacy are identified, debated, and evaluated. 

It can best achieve this through a process of informing all concerned of the inevitable risks and uncertainties involved. This helps focus future inquiry on the most 

important questions (or to gaps in knowledge that carry the greatest liability for the 

resource and stakeholders).

Two further comments on this process can be made. First, although step 1 refers 

to model development, it is the modeling process that is particularly important as 

it is the means through which the three principal functions of step 1 are achieved. 

Failure to focus on 

problem definition can 

lead to inappropriate 

attention to symptoms 

and solutions.
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

11

Whether a specific model emerges from this or not is not necessary; the modeling 

process helps facilitate learning, which in turn, informs future decisions. McLain 

and Lee (1996) noted that evidence from case studies in British Columbia and the 

Columbia River basin supports the idea that models can be useful for enhancing 

information flow by stimulating discussion among stakeholders about values, goals, 

objectives, and management options. 

Second, this learning process is information-intensive and requires active, 

ongoing participation from “those most likely to be affected by the policies being 

implemented” (Lee 1999: 7). This emphasizes the social and political aspect of 

adaptive management. Lee (1993: 161) noted “Managing large ecosystems should 

rely not merely on science, but on civic science; it should be irreducibly public in 

the way responsibilities are exercised, intrinsically technical, and open to learning 

from errors and profiting from successes.” Civic science, he argues, is a political 

activity; “Ecosystem-scale science requires political support to be done...Learning 

in such a setting cannot take place without active political support; there are too 

many ways for things to go wrong without it” (Lee 1993: 165). This view was reiterated in FEMAT: “People will not support what they do not understand and cannot 

understand that in which they are not involved” (FEMAT 1993: VII–113). It is this 

political element of adaptive management that provides Lee’s “gyroscope” (i.e., “the 

pragmatic application of politics”) to the companion notion of the “compass” of 

science (i.e., “the idealistic application of science to policy”) (Lee 1993: 10–11).

Alternative Models of Adaptive Management

Walters and Holling (1990) suggested three ways in which adaptive processes could 

be structured. First, there is an evolutionary or trial-and-error model2 (Holling 

1978; Kusel et al. [1996] used the term incremental adaptive management and 

Hilborn [1992] referred to it as a reactive approach). Under such approaches, the results of external decisions and choices are used to frame subsequent decisions that, 

we hope, lead to improved results. In many ways, this form of adaptive management 

is reminiscent of muddling through, in which some learning inevitably results from 

whatever management experience is undertaken. There is no purposeful direction 

to it and one simply reaps whatever benefits derive from earlier experiences.

Second, there is the concept of passive adaptive management; Bormann et al. 

(1999) used the term sequential learning. In it, historical data are used to frame a 

single best approach along a linear path assumed to be correct (i.e., there is a belief 

2 “Models,” as used in this report, include a variety of depictions intended to 

simplify complexity.
GENERAL TECHNICAL REPORT PNW-GTR-654

12

that the underlying assumptions and antecedent conditions that were applicable 

earlier still prevail). This model applies a formal, rigorous, albeit post facto analysis 

to secondary data and experiences as a means of framing new choices, understanding, or decisions.

Passive adaptive management can be informative. Walters and Holling (1990) 

reported on work in the Florida Everglades focused on the effects of various interventions in the region’s water regime. The work was driven by the single hypothesis 

that wildlife in the area require a natural pattern of water availability. This led to 

changes in both the timing and distribution of waterflows, with the intention that 

the plan would be the first step in a longer, iterative testing process that could lead 

to shifts in hydrological regimes (fig. 4). This could produce, over time, important 

benefits for the ecosystem. Nonetheless, Walters and Holling (1990) argued that 

alternative hypotheses should have been framed; e.g., what were the effects of 

natural changes in nesting habitat outside the area? Such alternatives could have led 

to different analyses and, potentially, to new management strategies.

Two fundamental problems limit passive adaptive approaches. First, such 

approaches can confound management and environmental effects because it is 

often unclear whether observed changes are due to the way the land was treated or 

to changes in environmental factors (e.g., global warming). Second, such analyses 

Figure 4—The timing and distribution of waterflows in Florida’s Everglades is the focus of an adaptive management study designed to protect the region’s ecosystem.

National Park Service 
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

13

can fail to detect opportunities for improving system performance when the “right” 

model and the “wrong” model predict the same results and the system is managed 

as though the wrong model were correct. 

Active adaptive management is a third model. It differs from other versions in 

its purposeful integration of experimentation into policy and management design 

and implementation (Kusel et al. 1996). In other words, policies and management 

activities are treated as experiments and opportunities for learning (Lee 1993). Active adaptive management is designed to provide data and feedback on the relative 

efficacy of alternative models and policies, rather than focusing on the search for 

the single best predictor. Bormann et al. (1999) referred to active approaches as 

examples of parallel learning because they involve the design of suites of policies 

that can be directly and simultaneously compared and evaluated.

Adaptive management is inevitably a sociopolitical action as well as a technical-scientific undertaking. Kusel et al. (1996) addressed the social dimension in 

terms of the relationships among scientists, resource managers, and the public. 

They argued that adaptive processes, as opposed to traditional resource management approaches, are “fundamentally about changing the relationships between 

these three groups” (Kusel et al. 1996: 612–613). Participation-limited adaptive 

management focuses on the interface of scientists and managers. Here, citizens 

stand apart from the dialogue and interaction between scientists and managers 

and are connected only via traditional public information venues, such as public 

meetings. This model is consistent with the historical reliance on the expert-driven, 

command/control approach that characterized social reform planning during much 

of this century. In contrast, integrated adaptive management can dramatically 

change the relationships among participants, with the public engaging as peers and 

partners with their manager and scientist colleagues to build active working relationships among themselves (Buck et al. 2001). Such relationships are central to the 

ideas of social learning.

In summary, the literature reports a variety of ways to undertake adaptive management, although there are no standard templates to guide decisions about what is 

best. The focus on formal learning, however, coupled with creation of forums that 

facilitate improved problem identification and framing; mutual, ongoing learning; 

and informed debate about alternatives, options, and consequences are central elements that an adaptive approach seeks to foster.

But the question of how to structure and design an adaptive management 

process is only one challenge confronting resource managers. Next, we turn to a 

variety of issues, challenges, and problems identified in the literature; each of these 

must also be addressed effectively if adaptive approaches are to be effective.

Adaptive management 

is a sociopolitical 

action as well as a 

technical-scientific 

undertaking.
GENERAL TECHNICAL REPORT PNW-GTR-654

14

Learning: A Driver and 

Product of Adaptive Management

The concept of learning is central to adaptive management and is grounded in recognition that learning derives from action and, in turn, informs subsequent action. 

Lee (1999) argued that the goal of implementing management experiments in an 

adaptive context is to learn something; he also argued that surprise is an inevitable 

consequence of experimentation and that it is often a source of insight and learning. Yet, such observations beg the question as to what learning is. What is implied 

when we say we have learned? Does any change in the phenomena being studied 

represent learning or only certain changes? Is learning measured at the individual 

level, at some small collective (e.g., a planning team), or at a larger, organizational 

level? A related question concerns the idea of organizational learning. Is it simply 

the sum of individual learning within the organization, or does collective learning 

take on an emergent quality (i.e., properties that can be attributed to a system as 

a whole, but not to any individual components [Clayton and Radcliffe 1996]) that 

exceeds the sum of that held by individuals within the organization? What distinguishes change based on learning from other change (Parson and Clark 1995)? 

Further, how do we best organize to learn? Michael (1995: 484) contended “there 

are two kinds of learning: one for a stable world and one for a world of uncertainty 

and change.” In a world of rapid change and high uncertainty, acquiring more 

facts—data—might not be as important as improving the capacity to learn how 

to learn, or what Ackoff (1996) has described as deutero-learning. In other words, 

what might have once facilitated learning might no longer do so.

Four commonalities emerge from the learning literature. First, learning is 

initiated when some dilemma or tension appears regarding a problem. For example, 

previously held assumptions might prove unfounded or dysfunctional and there is 

a need to learn how to proceed (Mezirow 1995). Or, new problems emerge for 

which little is known. In either case, the discrepancy between what is known and 

what is needed creates tensions that can only be resolved through learning. Of 

course, learning itself can be anxiety-producing (Michael 1995), so the need for 

and benefits of learning must outweigh the anxiety produced during the learning 

process.

Second, much learning derives from experience and, in particular, from experiences in which mistakes were made. Mistakes or what operations research would 

call “negative feedback” have the potential to be powerful sources of insight. 

Dryzek (1987: 47) described it as a “highly desirable quality.” Such feedback and 

the learning it can produce, is a central premise of adaptive management (Lee 

1993). However, as we shall discuss in more detail later, risk-aversion at both the 
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

15

individual and institutional levels can combine to hamper such learning. A management culture that ignores or even punishes failures and mistakes can seriously 

retard the learning process.

Third, learning almost always involves change. This begins by acknowledging a dilemma, discussed above, that initiates learning behavior. The subsequent 

learning must then be transferred into the organizational system in such a way that 

future behavior (policies, programs) reflects the new information. Also, because 

an organization is imbedded in a wider biophysical and socioeconomic environment, where change is ongoing, it must also be open to continuous learning that 

permits it to operate effectively as that wider environment changes. Again, this is 

the fundamental premise of the adaptive management process. However, individual 

and institutional behavior is often biased toward maintenance of the status quo, and 

such continuous change can be difficult and anxiety-producing (Parson and Clark 

1995). As Dovers and Mobbs (1997) concluded, adaptive, learning institutions do 

not always survive.

Fourth, learning involves what is referred to as reframing. Reframing is the 

process of reinterpreting the world in light of alternative perspectives and values. In 

simple terms, it involves seeing problems in a different way. Because reframing can 

lead to critiques of current policies, processes, or structures, it can be psychologically uncomfortable and resisted by others. Nonetheless, the reframing process is an 

essential component of a learning organization and can be facilitated by purposefully incorporating diverse perspectives on planning teams (Yorks and Marsick 

2000).

Learning manifests itself in distinctive forms, including data, information, 

knowledge, understanding, and wisdom (Ackoff 1996). Data are simply “1s and 0s” 

stored in a spread sheet. They reflect and describe actual observations. Information

includes data, but provides details regarding who, what, when, and where. Knowledge concerns questions relative to “how to” and offers insight as to how a system 

might be managed. Understanding clarifies questions related to cause and effect; 

here, we begin to understand why systems act and respond as they do. Finally, 

wisdom, as Ackoff (1996: 16) suggested “is the ability to perceive and evaluate 

the long-run consequences of behavior.” Adaptive management, in a contemporary 

sense, is particularly concerned with advancing learning at the knowledge, understanding, and wisdom levels.

What Is Learning?

Opinion is divided on the question of what it means to learn. The debate turns on 

whether the appropriate indicators of learning involve a change in cognition (a 

Learning manifests 

itself in distinctive 

forms, including data, 

information, knowledge, 

understanding, and 

wisdom.
GENERAL TECHNICAL REPORT PNW-GTR-654

16

change in knowledge), a change in behavior (observable changes in organizational 

practices and policies), or both (Tsang 1997). Given an emphasis in the adaptive 

management literature on the role of action informed by knowledge, it seems that 

appropriate indicators of learning necessarily involve both cognition and behavior. 

Knowledge that lacks a link to action would seem to constitute little more than facts 

on the shelf; conversely, action that lacks a base in improved knowledge is little 

more than hopeful activity. Thus, learning would seem to require both a cognitive 

dimension as well as an observable behavioral manifestation grounded in improved 

knowledge. It is also clear that significant barriers grounded in organizational 

processes, belief systems, or other factors act to stymie the acquisition of improved 

knowledge or its implementation into action. Inkpen and Crossan (1995) drew 

attention to how organizational norms and sanctions can operate to stymie learning 

or thwart behavioral change, effectively maintaining the status quo.

Learning encompasses knowledge acquisition; to say we have learned implies 

that we know more than previously (which might include that we now know how 

little we knew). Michael (1995) argued that learning implies more than increasing 

the stock of facts: it suggests we know what needs to be done, how to do it, whether 

it worked, and how to apply learning to emerging consequences. In other words, 

learning is not an end in itself, but a means to informing subsequent action. He 

also argued that learning involves what “must be unlearned” (p. 461). We all have 

certain trained incapacities, and learning must acknowledge and accommodate 

these. However, to do so can evoke feelings of psychological discomfort, denial, anger, and fear (Miller 1999). Michael (1995: 468) added “...most people under most 

circumstances are not all that eager to learn...most...are content with believing and 

doing things as they have always been done” and individuals (including scientists) 

are rewarded for maintaining and sustaining certain beliefs and behaviors because 

these are “the way things are and should be.” 

The literature identifies a number of factors that facilitate or constrain the 

learning process. Various categories can be defined: structural/organizational (e.g., 

laws, policies, organizational structure), sociocultural (e.g., values and beliefs), 

emotional (e.g., concerns with risk and failure), and cognitive (e.g., whether additional information leads to learning or simply overload).

The literature also discusses the concept of learning styles. People learn in different ways. For example, learning differs in terms of perception (the way in which 

information is taken in) as well as in the way we order that information (the way 

we use the information we perceive). There are differential capacities in dealing 

with information in a concrete versus abstract or conceptual manner. And, there are 
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

17

a variety of ways in which people best organize the information around them: as 

facts, as principles, in terms of relevance, or in terms of underlying reasons.

Learning occurs through various means. A classroom teacher, for example, 

facilitates the learning process for his or her students. In terms of new knowledge 

(i.e., learning) about the world, Lee (1999) and Marcot (1998) suggested that 

experimentation is not the only way to learn, or even the most obvious way. Table 1 

depicts different learning modes.

The processes through which learning occurs change as people age. This 

has led to a significant literature of adult learning theories. As with many of the 

literatures we examine in this review, this is a large, diverse area. However, for 

our purposes, this literature suggests that a key feature of the learning process for 

adults is that learning occurs not so much through incremental accumulation of 

understanding (e.g., more facts), but via “leaps” of understanding when existing 

information is examined in a new light. In particular, this process is triggered by a 

critical reexamination or reframing of an individual’s past experiences and underlying beliefs and assumptions about the world. This critical assessment, in turn, leads 

to a reassessment of previous understanding and, more importantly, to a realization 

that new options and alternatives exist and that previous presumed constraints 

and bounds on one’s thinking no longer prevail. Reflection is a key element of this 

process because it offers people an opportunity to determine whether previous assumptions still are relevant and applicable to the decisions that face them (Mezirow 

1995). These views of learning are especially important in an adaptive context, 

given that one’s assumptions are open to critical review by other parties in the 

problem-framing stage and previous experiences, subject to new perspectives and 

insight, can provide opportunities for identifying plausible hypotheses (policies) for 

critical examination in the field.

Perhaps the most controversial issue with regard to the notion of learning and 

the processes and structures that facilitate it links to two related questions: is learning a technical or social process (or both) and, as noted earlier, is organizational 

learning simply the sum of individual learning within that structure or is it an 

emergent product that is more than the sum of the learning of individuals within the 

organization?

Is Learning the Result of Technical Processes, 

Social Processes, or Both?

Advocates of learning as a technical process argue that it primarily involves 

processing information. For example, Argyris and Schön (1978: 2) took the position that learning “involves the detection and correction of error.” In this view, 
GENERAL TECHNICAL REPORT PNW-GTR-654

18

management organizations, such as the Bureau of Land Management, constitute 

social technologies designed to perform a specific set of tasks; i.e., they represent a 

working model of a theory for solving a particular and specific set of problems. To 

the extent that this system works well, it reflects the notion of single-loop learning

(Argyris and Schön 1978). Single-loop learning occurs when individuals perceive a 

mismatch between their intentions (i.e., what they wanted to have happen) and actual events (i.e., what actually takes place) and then take steps to correct that action. 

Such a process is driven by existing assumptions about how a system works and 

that the organization has the capacity to detect error or problems and solve them. 

However, new problems often emerge or are reconfigured in ways that are 

neither recognized nor soluble by the theory embodied in the current organizational 

structure. For example, the FEMAT (1993) social assessment chapter addressed the 

changing nature of the demands, uses, and values associated with forests in the 

Source: Lee 1999: 3.

Each mode of 

learning

makes 

observations...

and 

combines 

them...

to inform 

activities...

that 

accumulate 

into usable 

knowledge.

Example

Laboratory 

experimentation

Controlled 

observation to 

infer cause

Replicated to 

assure reliable 

knowledge

Enabling 

prediction, 

design, control

Theory (it 

works, but 

range of 

applicability 

may be narrow)

Molecular 

biology and 

biotechnology

Adaptive 

management 

(quasiexperiments in 

the field

Systematic 

monitoring to 

detect surprise

Integrated 

assessment to 

build system 

knowledge

Informing 

model-building 

to structure 

debate

Strong 

inference (but 

learning may 

not produce 

timely prediction 

or control)

Green 

Revolution 

agriculture

Trial and error Problemoriented 

observation

Extended to 

analogous 

instances

To solve 

or mitigate 

particular 

problems

Empirical 

knowledge (it 

works but may 

be inconsistent 

and surprising)

Learning by 

doing in mass 

production

Unmonitored 

experience

Casual 

observation

Applied 

anecdotally

To identify 

plausible 

solutions to 

intractable 

problems

Models of 

reality (test 

is political, 

not practical, 

feasibility)

Most statutory 

policies

Table 1—Modes of learning
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

19

Pacific Northwest and the increasing inability of current organizations and policies 

to deal with those changes. To overcome these types of problems requires rethinking 

the fundamental purposes, rules of operation, and assumptions on which an organization is founded so that it has the capacity to more accurately diagnose the problems of theory driving the search for answers to practical problems. This involves 

a capacity for critical self-examination; it requires what Argyris and Schön defined 

as double-loop learning. Such learning addresses basic questions of why problems 

occurred in the first place, whether the management solution is correct, and if not, 

how to make corrections (British Columbia Ministry of Forests 2000). Through 

hypothesis testing and theories about how the world works, and the comparison of 

the results of these tests against experience, the potential for informed, grounded 

revision is enhanced. But, as Argyris and Schön (1978) warned, organizations often 

inhibit this type of learning because it requires critical assessment of current organizational assumptions, beliefs, and norms.

The concept of double-loop learning has important implications for adaptive 

management. First, it reemphasizes the importance of sound problem-framing 

processes (Bardwell 1991). The way in which questions and problems are framed 

directly affects the way in which solutions are defined and pursued. Second, as noted 

above, redefining the questions and problems confronting an organization can be a 

painful process; it often reveals liabilities and shortcomings in organizational culture 

and structure that, if left untended, leave that organization at risk. For example, in 

the case of the conflicts between environmentalists and timber interests in the Pacific 

Northwest during the 1990s, reliance on technical assessments and studies—key 

elements of contemporary resource management culture—has done little to resolve 

the crippling debate; “the failure of technical studies to assist in the resolution of 

environmental controversies is part of a larger pattern of failures of discourse in 

problems that put major societal values at stake. Discussions of goals, of visions of 

the future, are enormously inhibited” (Socolow 1976: 2). Under these conditions, any 

management approach, including adaptive management, that fails to embrace the 

social and value-based dimensions of a problem as well as technical dimensions, will 

be limited in its ability to foster resolution.

An alternative conception of learning focuses on learning as the product of social processes. Here, learning results from participation and interactions with others 

in social life (Easterby-Smith and Araujo 1999). The distinguishing feature of this 

conception is that learning is a process of social construction; i.e., people “construct” 

reality in ways meaningful to them. From this perspective, scientific data do not hold 

objective, unequivocal meaning, but are given meaning and interpretation by people. 

Thus, in natural resource management, problems characterized by complexity and 
GENERAL TECHNICAL REPORT PNW-GTR-654

20

uncertainty also will be characterized by varying interpretations and, by inference, 

different solutions.

Within natural resource organizations, knowledge is continually constructed and 

reconstructed as different people interact with one another and as new information 

becomes available. Thus, a social constructivist perspective also focuses attention on 

the ways in which institutional structures and processes can facilitate, enhance, or 

constrain the construction and dissemination of learning. Thus, the notion of “learning to learn,” an idea promoted by Ackoff (1996) in the theoretical literature, as well 

as in the Northwest Forest Plan, becomes an important feature. 

Clearly, the emphasis in adaptive management on learning, although important, 

also introduces an extraordinarily complex arena. At the core of this is the reality that 

learning needs to derive from both technical and social processes. For instance, we 

might hypothesize that the lack of learning is attributable to the lack of data and the 

associated knowledge. In other cases, the lack of learning derives not from the lack of 

information, but the manner in which it is presented (abstract vs. concrete), the social 

processes and structures (or lack thereof) to facilitate communication and discussion 

among organizational members, or because of its presentation as a set of principles as 

opposed to its potential relevance to a particular problem. In any case, the information 

is effectively inaccessible and learning fails to occur.

Organizational Learning or Learning Organizations?

A second, correlate question regarding learning concerns the relationship between 

individual learning and a more collective form of learning that ascribes to the organization. 

Two predominant arguments are found in the literature: (1) organizations do 

not learn; what is called “organizational learning” is simply the sum of individual 

learning, and (2) organizations as a system can learn, with that learning reflecting an 

emergent quality that exceeds the sum of individual learning.

Proponents of the first argument argue that “organizational learning” only occurs 

when individual learning becomes institutionalized into organizational norms and 

memory (Watkins 1996). Organizational learning, in this schema, becomes successful when structures exist to encourage individual learning and there are processes for 

transferring and codifying that learning into the organization.

The alternative view contends that organizational learning surpasses the sum of 

individual members. For example, Yorks and Marsick (2000: 253) argued that “groups 

can learn as discrete entities in a way that transcends individual learning within the 

group.” This perspective views organizations as systems that have the capacity to 

produce learning characterized by an emergent quality; i.e., the collective learning 
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

21

is more than the sum of individual learning. As suggested earlier, the notion of 

emergent properties derives from systems thinking; from this perspective, individual learning becomes a necessary, but not sufficient, condition for organizational 

learning. It further contends that “new” learning emerges through the interaction of 

organizational members who collectively create new knowledge not attributable to 

any one individual. It thus also becomes closely linked to the idea of learning as the 

product of social processes.

Although knowledge is clearly linked to the learning process, it is also an issue 

in and of itself and there is a significant literature surrounding it. Knowledge is 

defined in a variety of ways; e.g., Webster’s dictionary defines it as “the sum of 

what is known...the body of facts accumulated...in the course of time.” But a common view of the concept of knowledge is that it reveals the way in which we know 

the world.

The concept of adaptive management implies the production of knowledge 

(through policy and management actions); it also implies that such knowledge is 

transmitted or distributed among various interests (scientists, managers, and 

citizens) and that it is used. In our assessment of adaptive management, the issue 

of knowledge is critical. In terms of knowledge production, questions arise as to 

where knowledge is created and by whom. In the positivist model that underlies 

modern scientific inquiry, research scientists are seen as the principal knowledge 

producers. The formal knowledge that emerges from scientific inquiry is a powerful 

form of knowing; done properly, it is characterized by being replicable and reliable. Scientific inquiry attempts to analyze the world through formal concepts and 

theories, involving the systematic dissection of problems into smaller components 

(reductionism) and isolating and controlling external factors (Holzner and Marx 

1979, Kloppenburg 1991). There is also a presumption that scientific inquiry is 

independent of social context; i.e., it is value-free and not subject to social influence 

(Gurvitch 1971). The value of such inquiry and knowledge is deeply imbedded in 

modern resource management philosophy and institutions; it is a fundamental element of the social-reform movement in planning (Friedmann 1987) and the foundation of modern forest management. 

There is growing recognition of the importance of alternative forms of knowledge or knowing. Known variously as “personal,” “local,” “experiential,” or “indigenous” knowledge, this form of knowing emerges from experience gained through 

living, working, and playing in the world. Buttolph and Doak (2000) argued that 

such knowledge, rather than being less valid or legitimate, highlights other ways of 

seeing and knowing (fig. 5). Yet, such knowledge often is trivialized, marginalized, 

or rejected in modern planning processes. Kloppenburg (1991: 529) suggested that 
GENERAL TECHNICAL REPORT PNW-GTR-654

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scientific knowledge has come to hold “undisputed intellectual hegemony” with 

local knowledge relegated to the “epistemic peripheries.” Thus, the core precept of 

social reform planning—that science serves society—is predicated on the caveats 

that (1) only a certain form of knowledge (formal science), controlled by a certain 

group of people (scientists), is admitted to the decisionmaking arena and (2) science 

possesses accurate insight as to society’s needs.

Yet, there is also growing recognition of the limits of formal, scientific knowledge in resolving the complex issues confronting society. Often, such knowledge is 

inadequate for the kinds of analyses required and for the development of functional 

predictions and useful management strategies (Friedmann 1987). Herrick and 

Sarewitz (2000) argued further that high levels of scientific complexity mean that 

predictive scientific assessments inherently are limited in their ability to guide 

policy development. They contend that a more appropriate and useful role for such 

assessments would be in conducting ex post evaluations, a role consistent with 

adaptive approaches that seek insight through critical analyses of policy implementation results. 

Figure 5—There are many ways of “knowing” the world around us. Knowledge grounded in technical understanding and the personal or experiential knowledge gained from living and working in a place are both needed.

Gary Wilson, USDA Natural Resources Conservation Service.

Citizens and managers 

are seen not only as 

the source of values 

and objectives... 

but also the source 

of improved 

understanding and 

knowledge.
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

23

Recognizing the limits of formal knowledge is critical to fashioning programs 

of knowledge creation, distribution, and utilization in an adaptive management 

model. In this model, citizens and managers are seen not only as the source of 

values and objectives or as reviewers and reactors to proposals, but also the source 

of improved understanding and knowledge about the complex systems with which 

we are concerned. If barriers to the recognition, acceptance, and legitimization of 

alternative forms of knowledge exist—cognitive, structural, or procedural—the 

adaptive process will be adversely affected.

Finally, the literature highlights the importance of two forms of knowledge; 

explicit knowledge (so-called articulated or substantive knowledge, composed 

of facts, data, etc. and recorded in books, reports, etc.) and tacit knowledge (the 

intuition, perspectives, beliefs, and values created as a result of experience). As 

Saint-Onge (1996) noted, tacit knowledge forms a “mental grid” within which 

explicit knowledge is filtered and interpreted. “[T]acit knowledge is made up of 

the collective mindsets of everyone in the organization. Out of its experience, the 

organization assumes a unique set of beliefs and assumptions through which it 

collectively filters and interprets how it sees the world and reacts to it” (Saint-Onge 

1996: 10). Thus, tacit knowledge becomes a critical factor in shaping the paradigm 

underlying how some group (e.g., resource managers) establishes professional 

standards, behavioral norms, and conceptual approaches to problem-solving (Kuhn 

1970, Wondolleck 1988). In short, it can be a powerful, formative, and enduring 

type of knowledge.

Assessing knowledge, from whatever source, and using it to build understanding, framing such understanding into questions and hypotheses, formulating options and alternatives, and testing, monitoring, and validating the outcomes of these 

alternatives requires explicit design (Haney and Power 1996). The issue of adequate 

design permeates the adaptive management literature; in essence, it addresses a 

straightforward question: How and when do we know we have learned something? 

Does the action taken lead to the results observed, or were results due to other, 

perhaps unknown, factors or chance (Bednar and Shainsky 1996)? Real learning 

is dependent on a capacity to discern the answer to such questions. This challenge 

explains why the protocols, methods, and philosophy of science have attracted attention in the adaptive management literature, for it represents a method of inquiry 

grounded on establishing cause-and-effect relationships. As Lee (1999: 4) noted, 

“in principle, the scientific approach leads to reliable determination of causes; in 

practice, that means being able to learn over time how management does and does 

not affect outcomes...an experimental approach may be costly and onerous in 
GENERAL TECHNICAL REPORT PNW-GTR-654

24

the near term, but it is probably the only way to root out superstitious learning—

erroneous connections between cause and effect.”

Adequate research design to facilitate sound learning in adaptive management 

experiments often is lacking (Walters 1997). In part, this derives from a persistent 

lack of formal and systematic documentation. Lee (1993) pointed to the critical 

need for an intellectual paper trail that provides an explicit record of the chain of 

reasoning underlying any action. Lacking such documentation, it is difficult if not 

impossible to later review assumptions, data, methods, analytical treatments and so 

on to help understand why differences between outcomes and predictions occurred.

In northeast Victoria, Australia, Allan and Curtis (2003) reported on a project 

designed to use an adaptive approach to developing alternative options for the 

management of salinity. The implementation of on-the-ground works, such as tree 

planting, became the highest priority, but program administrators failed to recognize that such plantings could be viewed as experimental treatments. Coupled with 

a lack of formal monitoring, the sum effect has been that it has proven difficult to 

assess the efficacy of different salinity management options and an opportunity to 

learn more systematically from implementation has been lost.

Walters (1997) identified design of management experiments as the second 

key step in the adaptive management process. He concluded, with some notable 

exceptions, that literature reporting well-designed field applications of adaptive 

management is sparse. In particular, few efforts included either adequate controls 

or designs for replication. He also was critical of efforts that have not progressed 

beyond continued investments in baseline information gathering and in complex 

simulation modeling. He concluded “what probably drives these investments is 

the presumption that sound predictions (and, hence, good baseline policies) can 

somehow be found by looking more precisely, in more mechanistic detail, at more 

variables and factors” (Walters 1997: 3).

Walters’ comments suggest limits to the benefits derived from more data or 

better models. In discussing adaptive management planning models for riparian and 

coastal ecosystem situations, he described some of the complex technical issues that 

need to be accommodated in experimental design. One example involves problems 

that derive from cross-scale linkages between physical/chemical and ecological 

processes. Hydrodynamic and chemical processes that operate on short time scales 

and fine spatial scales must interact with ecological processes in the marine and 

estuarine setting that operate over long periods and broad spatial scales. To resolve 

the burdensome computational process, the various subcomponent models are 

sometimes decoupled, but the process of disconnecting inextricably connected 

systems leads to problematic outcomes. He concluded “we must rely on empirical 
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

25

experience, not modeling or physical principles, to tell us how much averaging and 

selecting we can safely do” (Walters 1997: 5).

Lee (1993) identified three circumstances that reinforce the need to consider 

large-scale experimentation. First, large-scale ecosystems manifest emergent 

properties that do not occur or cannot be detected at smaller scales; salmon abundance in the Columbia River system is different from that in any stream within the 

larger system. Second, some effects are too small to observe at the laboratory scale; 

e.g., the introduction of a new chemical as a constituent of an agricultural fertilizer 

might not result in the immediate death of fish when lab tested, but when released 

in a larger, more complex system, could lead to adverse effects. In the absence of 

explicitly designed controls, these effects might go undetected until it is too late. 

Third, ecosystem-level interventions might already be underway in the form of 

existing policy decisions, or decisionmakers might be unwilling (or unable) to 

postpone action until more is known. Such events provide opportunities for largescale experimentation, as long as it is recognized that the outcomes of the experiments are poorly understood and the potential for significant adverse impacts (e.g., 

extirpation) exists. 

Lee (1999) argued that explicit, well-designed experimentation also helps address what he describes as two social misdirections of learning. First, the concept of 

the “regression to the mean” needs to be kept in mind. Many environmental issues 

with which we struggle today initially attracted attention because of their extreme 

condition (e.g., declining fisheries), but in a dynamic world, extreme events often 

are followed by less-extreme ones; “there is a regression to the mean, not because 

something has been remedied but simply because the mix of fluctuating causal 

factors has changed...[producing]...fertile ground for erroneous conclusions” (e.g., 

because we presume some intervention either caused or resolved the problem, when 

in fact, it was driven by external conditions or cycles) (Lee 1999: 4).

Second, he elaborated on the idea of superstitious learning, the illusion that 

something has been learned when “evaluations of success are insensitive to the actions taken” (Levitt and March 1988: 326). Explanations for why something worked 

or failed often are incorrect; we simply might not understand why things worked as 

they did, and the relation to any particular intervention or event is only coincidental. 

Lee concluded that when “resource managers are held to standards that have no 

grounding in ecological science, the more likely it is that accountability itself will 

induce superstitious learning” (1999: 5).

Lee (1993: 74) concluded “for some policy questions, statistical concepts 

promote understanding of the nature of the policy judgments required.” His argument derives from the idea that although technical and statistical analyses are 
GENERAL TECHNICAL REPORT PNW-GTR-654

26

necessary, their presence is not sufficient to fully inform policymakers of the effects 

of their actions. He elaborated on this in a discussion of the distinction between the 

statistical concepts of type I and type II errors. A type I error occurs when what 

one believes to be true actually is false. This is a fundamental precept on which 

Western law is founded. As a society, we accept that it is better to occasionally let a 

guilty party go free than it is to punish an innocent person. Science is also a field in 

which avoidance of type I errors is part of the culture; we tend to be conservative in 

accepting something as true. In the case of environmental management, we impose 

high standards of proof because we are reluctant to accept something as true (e.g., 

the minimum acceptable level of water quality for salmon survival), because if we 

later find this to be false, we might have already imposed irreversible impacts on 

the species. 

Type II errors occur when something is rejected that later turns out to be true. 

For example, a scientific panel convened in New Brunswick, Canada, sought to 

determine whether the use of pesticides to control a spruce budworm epidemic was 

implicated in the deaths of children from a disease called Reye’s syndrome. Central 

to their deliberations was the question of what constituted scientific proof of harm. 

The provincial government took the view that only incontrovertible scientific proof 

of harm would lead them to change their spraying policy (Miller 1993). A survey in 

the province identified over 3,000 cases of illness with symptoms similar to Reye’s 

syndrome (at the time, Reye’s syndrome was not a reportable illness in the province 

and most physicians were unfamiliar with it). A subsequent screening, focused 

on identifying the specific disease, reduced this to about a dozen, excluding from 

consideration the possibility that pesticides might have been a factor in the etiology 

of some, or all, of the excluded cases. A scientific panel reviewing the data concluded no incontrovertible scientific proof existed to establish a causal link between 

spraying and the disease. Their conclusion reveals the difficulty in determining the 

etiology of a rare disease; it provided little in terms of understanding the effects of 

spraying on more common viral diseases plaguing the community. By focusing on 

a narrow hypothesis (Reye’s syndrome), the “panel appears to have a committed a 

type 2 error by accepting false negative findings...”; the analytical methods chosen 

to conduct the study provided an “opportunity to look for clearly defined needles in 

a poorly documented haystack” (Miller 1993: 567). Reliance on a narrow, analytically confined problem definition served to obscure the real problem, providing 

instead a dubious scientific basis for sustaining the status quo policy position.

What are the implications for adaptive management? It reveals the kind of 

tension that exists in many natural resource management debates today, including 

those between forest management and endangered species management. On the 
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

27

one hand, the role of regulatory agencies, such as the Fish and Wildlife Service, 

is to avoid type I error; i.e., they want to avoid approving an action, taken to be 

sound (true) based on the best science, that later proves to be unsound (false). For 

example, a proposal to test an alternative silvicultural technique in riparian zones 

might be supported by considerable evidence and theory showing it would have 

beneficial effects on stream conditions. However, a strong predisposition to avoid 

type I errors would deny such a proposal on the grounds that implementation of the 

experimental treatment might endanger salmon. On the other hand, denying the 

experiment might engender a type II error, given that the experiment might prove 

more beneficial to salmon than the current prescription. Moreover, denial limits 

opportunities for learning in the face of uncertainty (Wildavsky 1988). Nonetheless, 

there remain concerns about the social and environmental costs of allowing type II 

errors to occur, and the argument is made that a shifting burden of proof calls for an 

unequivocal demonstration that no adverse consequences will eventuate from some 

policy (M’Gonigle et al. 1994). The resulting tension between these perspectives 

creates a “Catch-22” dilemma: permission to experiment is denied until such time 

as clear, rigorous, and unequivocal scientific evidence is available, but permission to 

undertake the work that might produce such evidence also is denied. This dilemma 

leads to a discussion of risk and uncertainty.

Risk and Uncertainty

The concepts of risk and uncertainty are inextricably linked to adaptive management. In the most basic terms, if there were no risk or uncertainty, there would 

be no need for adaptive management. It is only when we are faced with uncertainty 

as to what is the most appropriate course of action that the concept of adaptive management becomes a strategy that offers a means of acting. Although the terms of 

risk and uncertainty often are used interchangeably, they are not synonyms. Risk is 

typically defined as the possibility that an undesirable state of reality might occur as 

a result of natural events or human activities (Renn 1992). Risk definitions typically 

involve a known probability distribution; e.g., we know there are only 5 chances out 

of 100 that a particular catastrophic event will occur in the next 100 years. 

Risk is increasingly recognized as a social construct, holding different meanings for different people. Risk analysis and assessment involve efforts to estimate 

both the probabilities of occurrence and the severity or seriousness of such occurrences, along with the distribution of those effects. Risk assessment, then, becomes 

more than a technical endeavor, involving social judgments of importance of varying events along with equity issues related to the distribution of costs and benefits 

(Mazaika et al. 1995). The challenge is all the more formidable because many of the 

Risk is...a social 

construct. ...Uncertainty involves situations 

in which the probability 

distribution is not 

known.
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consequences with which we are concerned are not only unanticipated, they cannot 

be anticipated (Schwarz and Thompson 1990). 

Uncertainty is a more complex issue. Typically, uncertainty involves situations 

in which the probability distribution is not known. One major concern is when risk 

and uncertainty are treated as synonyms; e.g., treating a situation as one involving 

risk when, in reality, it is a situation of uncertainty. Walters (1986) suggested three 

types of uncertainty: (1) that which arises from exogenous (i.e., external) disturbances; (2) uncertainty about the values of various functional responses (e.g., how 

production rates of a species vary according to size of the stock); and (3) uncertainty about system structure, or more basically, what are the variables one should 

consider. 

In some situations, uncertainty is assumed away; e.g., former Secretary of 

the Interior Bruce Babbit’s promise of “no surprises” in the implementation of 

new policies for management of endangered species (Reichhardt 1997). Another 

response is to replace the uncertainty of the resource issue (e.g., Is the species 

threatened?) with the certainty of a process, be it a new policy or new institution. 

Gunderson (1999b) described the 9-year adaptive management experiment in the 

Florida Everglades where the uncertainty of chronic resource issues (e.g., water 

levels and distribution) has been replaced by the certainty of a planning process 

and formalization of interactions between management agencies and stakeholders. 

These processes are not without benefit—they have helped spawn ideas for future 

action—but whether they also produce learning or reduce risk remains unknown. 

To protect certain species in the Pacific Northwest, guidelines were instituted 

calling for surveys before ground-disturbing effects take place, extensive regional 

surveys within specified timeframes, and the development of management plans for 

these species (Nelson 1999). However, the survey and manage requirement also has 

stifled experimental management and research policies that could provide understanding needed to ensure species survival. 

Bioregional assessments, such as FEMAT, have been driven by growing unease 

regarding the risks and uncertainties (regarding both biophysical and socioeconomic systems) facing society. FEMAT (1993) concluded that the levels of risk and 

uncertainty facing policymakers are greater than acknowledged (they are also why 

an adaptive approach was seen as essential). Accounting for risk is an essential part 

of such assessments because of the stochastic nature of processes that characterize ecological and socioeconomic systems. The risks associated with predicting 

outcomes can be offset to some degree by explicit portrayal and discussion of the 

underlying cause-and-effect relationships and working assumptions about those 

relationships (Thomas 1999: 19).
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

29

Uncertainties are inevitable, which is why surprise (Gunderson 1999c, Lee 

1993) must be formally incorporated into the adaptive management process. Lee 

(1995) identified two critical elements confronting society’s efforts to achieve 

sustainability: biological uncertainty and institutional complexity (which we turn 

to shortly). He argues that in moving the “unsustainable vitality of industrialism 

to a sustainable order, learning from experience is the only practical approach” 

(p. 228). He noted the difficulties facing those who seek guidance for what to do; 

namely, data are sparse, theory is limited, and surprise is unexceptional. Wilson 

(2002) argued that removing uncertainty from public discussion can retard learning by engendering the belief that adequate knowledge exists (e.g., Gunderson’s 

[1999b] “spurious certitude”). If the pretense of surety dominates policy discussions 

(Dovers and Mobbs 1997), science can be discredited when events lead to contrary 

outcomes, thus diminishing the ability to manage sustainably. Uncertainties play 

a key role in the adaptive management process; highlighting them helps frame 

hypotheses and initiate actions to test them (Gunderson 1999c). If results confirm 

the hypotheses, then actions and policies can be adjusted accordingly. If we fail to 

confirm the hypotheses, nonetheless we have acquired useful information that can 

inform revised hypotheses, which can be subsequently tested.

However, this process, however logical and straightforward, depends on two 

key conditions; there must be both permission and a willingness to experiment. 

This means explicitly confronting uncertainty and risk. Unfortunately, uncertainty 

is not always acknowledged. “Judged from a traditional point of view, uncertainty 

and the lack of predictive capabilities equal ignorance” (Pahl-Wostl 1995, cited in 

Wilson 2002: 332). If acknowledging and operating under uncertainty are deemed 

unacceptable—within the organizational culture, through external sanctions such 

as statutes, or because of public scrutiny and intervention—then adaptive management is not possible. In other words, if action in the face of uncertainty must be 

accompanied by an assurance that nothing will go wrong, then we have a recipe 

for inaction. As Wildavsky (1988) argued, requiring that no action be undertaken 

without a prior guarantee of no risk is a restrictive decision criterion. Volkman and 

McConnaha (1993) contended that invocation of the Endangered Species Act in the 

Columbia River basin effectively has halted any attempt at active adaptive management experimentation, in large part because of the uncertainties of experiments on 

fish. A consequence of such a stance is “no new trials, no new errors—but also no 

new experience and hence no new learning” (Wildavsky 1988: 31). Unfortunately, 

as Huber (1983) has remarked “Statutes almost never explicitly address the lost opportunity costs of screening out a product” (cited in Wildavsky 1988: 35). In other 
GENERAL TECHNICAL REPORT PNW-GTR-654

30

words, the costs of lost learning are seldom accounted for when experimentation is 

restricted or prohibited.

Resistance to experimentation can also derive from those who perceive adverse 

impacts on their interests. For example, Johnson and Williams (1999) described 

how the short-term risks to harvest levels (fish, wildlife) associated with experimentation can mobilize opposition to adaptive approaches. Implementation of a 

regulatory experiment can mean that traditional harvest objectives are replaced 

with learning objectives, with a result that hunters or fishers bear the costs of the 

experimentation in the form of reduced take levels. 

Lang (1990) offered an alternative typology of uncertainty:

1. Uncertainty concerning the specific problem and its context. This leads to 

conflicts over what data are needed, what new research should be undertaken, how forecasts might be improved, and how strategies such as risk 

assessment might better inform discussions.

2. Uncertainty about how to address the problem, with respect to both ends 

and means. This means that clear policy guidance is required, but it also 

implies a thorough assessment about what the problem is before the search 

for solutions begins (Bardwell 1991, FEMAT 1993).

3. Uncertainty concerning what others might do about the problem. This 

means that dealing with uncertainty must also embrace processes of collaboration and coordination.

These different forms of uncertainty are interrelated. For example, to act 

without clearly understanding what the problem is likely will result in a failure to 

reduce uncertainty. To act in an absence of understanding what others are doing 

risks inefficiency, duplication, and the possibility of working at cross purposes. 

Such concerns underlie the social, political, and collaborative nature of the challenges facing adaptive management (Buck et al. 2001, Lee 1993).

Dealing with risk and uncertainty are major challenges to adaptive management. Despite the difficulty of operating under such conditions, principles to guide 

organizational behavior do exist. Ludwig et al. (1993: 36) suggested such principles 

are “common sense”; e.g., consider a variety of hypotheses and strategies; favor 

actions that are robust to uncertainty, informative, and reversible; monitor; etc. 

However, effective and informed operation in the face of uncertainty is confounded 

when institutions responsible for adaptive management implementation are, at their 

core, risk averse; the term is not used in a pejorative sense, but simply means that 

organizational behavior emphasizes the prevention of harm (Wildavsky 1988). 
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

31

Estill (1999: 20) (emphasis added) argued that “one of the primary roles of 

Forest Service managers in American society is to guard against risk...protecting 

against risk is one of the few principles managers can use to identify appropriate 

points of balance and compromise in gut-wrenching situations.” Her comments 

are not without merit, but they imply an organizational capacity for control that is 

neither possible nor realistic. “The primary expectation of adaptive management 

is the unexpected...systems are unpredictable” (Gunderson et al. 1995b: 490). It 

hints at the kind of spurious certitude to which Gunderson (1999b) referred and 

ignores how embracing risk (and uncertainty) is requisite to learning and discovery 

(Michael 1995).

Institutional Structures and Processes 

for Adaptive Management

Holling hypothesized that success in managing a target variable for some commodity output leads inevitably to “an ultimate pathology of less resilient and more 

vulnerable ecosystems, more rigid and unresponsive management agencies, and 

more dependent societies” (1995: 8). Our attention now turns to the issue of institutions—including those “rigid and unresponsive management agencies”—but also 

the array of laws, policies, and other rules by which we live. Why have institutions, 

designed to better serve our needs and wants, become barriers to the very goals to 

which we aspire?

Institutions generally are taken to include the array of mechanisms society 

employs to achieve desired ends (Cortner et al. 1996). Scholars (e.g., Ostrom 1986) 

have described institutions as sets of rules, as standards of behavior, or as political 

structure, yet there is little agreement of what the term means or how to undertake 

studies of them. Some argue that institutions also include norms and values and 

their interaction with the rules and behaviors (McCay 2002). Institutions are both 

formal and informal and profoundly affect how society defines problems of significance and organizes itself to formulate responses to those problems.

Wilson (2002) offered insight into this question and although his focus was on 

marine management, his conclusions seem applicable in other resource contexts. 

He contended that the scientific uncertainty associated with managing complex 

systems has created a more difficult conservation problem than necessary because 

current governing institutions assume more control over natural processes than 

in fact is possible. He concluded that managing complex, uncertain systems that 

manifest highly adaptive qualities requires that the governing institutions also be 

adaptive and learning-driven. 
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32

In a critique of efforts to implement adaptive management policies in riparian 

and coastal ecosystems, Walters (1997: 3) identified four reasons for the low success 

rates observed. “All,” he noted, “in some sense, are institutional reasons” (emphasis added): (1) modeling for adaptive management planning has been supplanted by 

ongoing modeling exercises, (2) effective adaptive management experiments are 

seen as excessively expensive or ecologically risky, (3) there is often strong opposition to experimental policies by people protecting self-interests in the bureaucracies, and (4) there are value conflicts within the community of ecological and 

environmental interests.

Gunderson concurred, noting how a “rigidity or lack of flexibility in management institutions and extant political power relationships has precluded adaptive 

experiments” (1999c: 35), even in situations, such as the Everglades, where the 

ecological system had sufficient resiliency to accommodate such experimentation. 

Lee (1993) devoted attention to the need for improved institutional structures and 

processes to facilitate the practice and exercise of civic science. In his assessment, 

the challenges of overcoming “inappropriate social organization” (p. 153) loom as a 

major barrier to the successful implementation of adaptive management. Organizations and policies often are entrenched (e.g., Western water law) in the pursuit of 

some particular goal, yet institutions find learning leads to a change in goals, which 

in turn trigger changes in order, structure, power, and other institutional currencies. 

Such changes produce ambiguity and stress, and a common response is to resist the 

changes that produce those effects. Lee (1999: 7) observed that “adaptive management is an unorthodox approach for people who think of management in terms of 

command.”

In a review of six case studies from North America and Europe, Gunderson 

et al. (1995b: 495) reported that one of the major insights revealed during their 

analyses was the “extreme nature of the recalcitrance or inertia of institutions, and 

the almost pathological inability to renew or restructure.” They concluded that the 

extent and depth of the resulting institutional rigidity has led to a failure to effectively engage and resolve underlying resource conflicts. Based on a study of adaptive management efforts in New Brunswick, British Columbia, and the Columbia 

River Basin, McLain and Lee (1996) concluded that efforts fell short of the promise 

of adaptive management because of an over-reliance on rational-comprehensive 

planning models, a tendency to discount nonscientific (i.e., personal or experiential) 

knowledge, and a failure to create processes and structures to facilitate shared 

understandings among stakeholders.

Scholars generally are in accord as to the central role of institutions in implementing adaptive approaches. Indeed, Gunderson (1999a: 54) argued that if there is 
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

33

any hope for the future of natural resource management, it must be founded on “developing and creating new ways to think about and manage issues of the environment...it is time to rethink the paradigms or foundations of resource management 

institutions.” Yet, McLain and Lee (1996: 446) observed “the adaptive management 

literature pays little attention to the question of what types of institutional structures and processes are required for the approach to work on a large-scale basis.” 

Lee (1995: 230) also acknowledged the institutional challenge; “...it is not clear how 

the adaptive approach can work in the presence of institutional complexity.”

Yet the reality is that we do have institutions in place—management agencies, 

laws, policies, standards and guides, norms and belief systems—and we need to 

consider how the adaptive management concept, with all its compelling appeal and 

logic, can be made to work. In particular, we face the challenge of framing innovative and effective alternatives to structures and processes that have long been in 

place and that have a long history of successful implementation. This results in a 

“if it ain’t broke, why fix it?” mentality. Wilson (2002: 332) described the dilemma 

facing management institutions in framing innovative models for the future:

We can create institutions nicely tailored to a particular scientific 

theory and preconception of the nature of the uncertainty (we believe) 

we face, or we can design institutions on an alternative basis, one that 

assumes as little as possible about the nature of causal relationships 

and emphasizes the role of collective learning and institutional evolution. The appropriateness of one or the other approach would appear 

to depend on the state of our scientific knowledge or, alternatively, our 

ability to test and validate.

McLain and Lee (1996) argued that the rationale for adaptive learning in 

management systems rests on three key elements: (1) rapid knowledge acquisition; 

(2) effective information flow; and (3) processes for creating shared understandings. 

These constitute a useful framework within which to examine some of the literature 

relative to the institutional challenges of implementing adaptive management.

Increasing Knowledge Acquisition

The concept of scientific adaptive management rests on the notion that the 

formal methods of scientific inquiry, based on hypothesis testing, represent the 

most effective and efficient means of acquiring new knowledge. However, evidence 

from case studies from across North America and around the world question this 

assumption. A variety of factors contribute to this problematic assessment. As 

noted earlier (e.g., Walters 1997), heavy reliance on models has contributed to a 

bias in knowledge acquisition of quantifiable data. This leads to distortion in the 

Reliance on models 

results in a tendency 

to frame problems as 

technical when often 

they involve valuebased issues.
GENERAL TECHNICAL REPORT PNW-GTR-654

34

problem-framing stage, resulting in a tendency to frame problems as technical in 

nature when often they involve value-based issues (e.g., what goods and services are 

desired from the forests of the Pacific Northwest?). Despite the prevailing conception 

of objective science, many issues confronting resource managers and scientists today 

are trans-science: “Though they are, epistemologically speaking, questions of fact 

and can be stated in the language of science, they are unanswerable by science; they 

transcend science” (Weinberg 1962; cited in Lowe 1990: 138). Allen and Gould (1986) 

arrived at a similar conclusion, describing a set of problems they define as wicked that 

arise from disputes over questions of importance and preference, rather than technical 

merit. Genetic or bioengineering and large-scale environmental modifications are 

examples of such undertakings.

Thus, increasing the rate of knowledge acquisition is confounded by differences 

in problem perception and the corollary issue of the types of knowledge required in 

addressing such problems. Challenges also derive from deeply imbedded convictions 

that scientific knowledge is more valid than other forms of knowing (e.g., personal or 

experiential knowledge) and that decisions based on scientific knowledge will lead to 

better decisions (McLain and Lee 1996).

Finally, the literature points to the cost of data acquisition for adaptive management as a major hurdle; the necessary monitoring and evaluation efforts to support 

adaptive approaches are expensive in both money and time. The risks associated 

with adaptive experimentation are judged excessively costly. McLain and Lee (1996) 

noted that the costs of monitoring and evaluation were especially controversial in 

the New Brunswick spruce budworm experiments because only one stakeholder was 

responsible for both the action and its evaluation. In the Pacific Northwest, the Northwest Power Planning Council attempted to avoid this by involving a wide range of 

stakeholders in the monitoring and evaluation process (McLain and Lee 1996). This 

proved costly, raising questions as to whether it would prove possible to continue to do 

this into the future. Although Walters (1997) acknowledged that the costs of adaptive 

experimentation can be great, he contended that costs in the form of risks to resources 

are even greater. He argued that the debate about costs and risks lacks adequate evaluation and scrutiny, suggesting that cost concerns tend to be used more as an excuse 

for avoiding contentious decisions.

There is a complex asymmetry in the distribution of the risks and costs of adaptive management. For instance, Walters (1997) noted that the costs of experiments 

that might benefit fish typically are borne largely by economic interests (agriculture, 

industry). It has been estimated that losses to commercial and recreational fisheries in 

British Columbia owing to experimental reduction of hatchery salmon releases could 

range from $10 to $100 million per year (Perry 1995). Although acknowledging that 
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

35

costs can be substantial for economic interests, Walters (1997) argued that these 

interests will inevitably face costs associated with change, given the nature of shifting public interests and concerns. He observed (Walters 1997: 11):

If...there is even a 10% chance that legislative or legal decisions 

will result in massive and permanent policy change, the expected 

cost (0.1 x cost of massive change) of trying to maintain current 

policy would be radically higher than the cost of an experiment to 

demonstrate that radical change is unnecessary.

The tension between short-term costs and long-term benefits produces a 

complex situation. Any benefits of treatments undertaken today to manipulate 

biophysical systems likely will not appear until later; their costs, however, are borne 

by today’s individuals, organizations, and society. There are both financial and risk 

costs involved. As Walters (1997: 11) noted, the “legacy of response information 

(i.e., learning) from these treatments will mainly be useful to the next generation 

of managers and users.” The time differential between incursion of costs and 

receipt of benefits contributes to tensions between managers and scientists, on the 

one hand, and political and public officials on the other. For example, Lee (1993) 

described a goal of the Columbia River Basin Fish and Wildlife Program as doubling salmon populations over an unspecified time. This goal implies that salmon 

restoration must be seen as a long-term undertaking, measured in generations 

of salmon. These long-term undertakings are being dealt with in a political and 

budgeting world of 1- to 3-year cycles and the similarly short tenure of members of 

the Northwest Power Planning Council (McLain and Lee 1996). 

The timeframes involved and the asymmetry between costs and benefits also 

have implications for how experimentation risks are perceived, particularly by 

resource managers. Volkman and McConnaha (1993: 6) argued that because the 

benefits of learning about flow-survival relationships on the Columbia River are less 

clear than the costs posed by dramatic flow manipulations, the concept of adaptive 

management faces an unusually difficult test in practice; i.e., “how (can) biological 

risks and political considerations be accommodated while taking an aggressive 

approach to learning?” Gray (2000), reviewing progress on the North Coast AMA, 

concluded that managers perceived the “inordinate amount of supporting data, 

energy, and political support” needed to modify any of the standards and guidelines 

“not worth their while” (p. 18). At one level of analysis, such unwillingness makes 

sense; the potential costs of an experiment can be substantial, immediate, and 

personal, whereas any benefits are long-term, uncertain, and diffuse. However, this 

complex issue warrants more attention and we shall return to it in discussing the 

attributes of an adaptive institution.
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The issue of rapid knowledge acquisition also raises questions about who 

participates in the knowledge creation process and how. Wondolleck (1988) argued 

that resource management organizations must provide opportunities for joint 

fact-finding. “To facilitate both meaningful and satisfying participation by national 

forest users in agency decision-making requires that each group and individual be 

operating with equal information” (p. 198). It is critical that people not only understand the implications of different outputs, but that they are “a part of the process 

that goes about obtaining and analyzing this information” (Wondolleck 1988: 198).

Enhancing Information Flow

Once information is acquired, it must be communicated to stakeholders—those 

charged with decisionmaking and implementation responsibilities and those whose 

interests might be affected by an impending decision. In traditional agency planning processes, the information communication process is often restricted to the 

former group (i.e., decisionmakers and implementers). In democratic systems open 

to public scrutiny, a host of stakeholders influence the decisionmaking process; in 

effect, they possess veto power. McLain and Lee (1996), for example, pointed to 

how adaptive management modelers in New Brunswick assumed that federal and 

provincial foresters and politicians were the key political actors in the debate over 

spruce budworm spraying, thereby marginalizing members of the environmental 

movement. Later, environmentalists moved to mobilize public opposition to the 

spraying program, effectively stymieing implementation.

The efficient flow of information to relevant parties, both internal and external, 

is impacted by information complexity. Environmental problems, and potential solutions to them, require qualified, technical expertise. This problem is confounded 

by the inability of many research scientists to communicate results and potential 

implications clearly. Resource managers, faced with heavy workloads, different 

priorities, and limited staff and time, often are not eager to wade through research 

papers and reports, particularly given that doing so might require them to change 

their behavior (Michael 1973).

Efforts to span boundaries and create more efficient and effective flows of 

information have attracted attention. Addressing the challenge of an organization 

striving to adapt to change, Michael (1973) noted two underlying aspects that 

require attention. First, organizations often work to eliminate the need for boundary 

spanning in the first place (and its turbulent consequences) by attempting to control 

their environment; e.g., a resource management agency tries to convince a skeptical 

public that its programs are appropriate and sound. Second, and somewhat contrary 
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

37

to the first, the “societal conditions that create the need for [boundary spanning] 

mean that the potential for controlling the environment will be low” (Michael 1973: 

238).

The idea of boundary spanners (i.e., people to link across functions such as 

research and management) has attracted the attention of natural resource agencies. 

Ideally, these would be people with sound backgrounds in both management and 

science. Although there are arguments as to where these individuals might reside 

organizationally (i.e., within the research or management organization?), in general, 

the idea is that they would help communicate and interpret research results to managers, provide feedback to researchers on the results of the application of results, 

and play a coordinating role among the respective players.

The concept of spanning has drawn only limited attention in the adaptive management literature. One notable exception is reported from Australia, involving application of an adaptive management strategy for the water cycle in the urban fringe 

of three areas. In each experiment, the presence (or absence) of an institutional

champion for the project was identified as a key factor. For example, in the Tuggerah Lakes project, north of Sydney, the presence of such a champion was deemed 

critical in obtaining acceptance of the adaptive management approach by both 

the local council and community participants (Gilmour et al. 1999). The authors 

noted that the absence of such a champion, or in one case, the loss of that person 

to another job, resulted in little enthusiasm and a reduced likelihood of successful 

implementation. They concluded there is a strong need for a person within the lead 

management agency to act as the change agent—the institutional champion. Such 

persons should be sufficiently influential in the decisionmaking process to ensure a 

continued focus on the experiment-review-feedback cycle; they also need excellent 

communication skills to work at multiple levels within and outside the organization. The relative scarcity of such individuals makes efforts to implement adaptive 

management strategies vulnerable to organizational change.

An absence of champions also can detract from the ability to capitalize upon 

learning and knowledge from outside the immediate area of concern. Ewing et al.

(2000: 455) argued that “ensuring the incorporation of, and access to, R & D 

[research and development] outcomes from nonlocal projects is...problematic since 

there is not necessarily a ‘champion’ who is aware of other research and ensures 

that it is incorporated.” They cited the absence of a local champion in a rural 

planning exercise in southwest Western Australia as having an adverse impact on 

efforts to ensure effective, ongoing communication between, and within, various 

subgroups working on the project.

The presence of an 

institutional champion 

for the project was 

identified as a key 

factor.
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In reviewing efforts to implement adaptive management in the operation of 

the Glen Canyon dam and its effects on the Grand Canyon ecosystem, the National 

Research Council evaluation team observed “an advocate is needed for the adaptive 

management experiments themselves, particularly regarding their scientific coherence and the long-term integrity of the Grand Canyon ecosystem. There is currently 

no voice among the stakeholders that represents the interests of these scientific 

experiments” (National Research Council 1999: 61).

The critical role of key individuals in fostering and facilitating the flow of information, and serving as champions of adaptive management also was recognized 

by Gunderson et al. (1995b). They cited three roles for such individuals: visionary 

activist, respected integrator, or rebel bureaucrat. The latter two roles are especially 

critical, given their position within the bureaucracy. Such persons have a particular 

capacity to speak “truth to power” (Wildavsky 1979), an important role when the 

results of adaptive experiments run counter to prevailing policy and the status 

quo. In an early evaluation of adaptive management in Canada, the lack of a “wise 

person” to shepherd projects was identified as a major factor contributing to project 

failures (ESSA 1982). Duinker and Trevisan (2003) concluded these individuals 

were especially critical in gaining understanding and support among front-line staff 

that might otherwise have been reluctant to participate cooperatively in the project. 

These advocates served as teachers who helped create and sustain the organizational support necessary for effective implementation.

Feedback is a key process in enhancing information flow, particularly in an 

adaptive management context. The flow of information from an action back into the 

decisionmaking process provides a basis for evaluating that action and for guiding 

future actions. In particular, negative feedback, reporting on the negative consequences of some action, is especially informative; i.e., “learn from your mistakes.” 

Such feedback is critical in situations involving complexity and uncertainty. As 

Dryzek (1987: 47) noted,

negative feedback is the presence of deviation-counteracting input 

within a system...In an environment of complexity and uncertainty, one 

cannot completely understand that environment...As a substitute for 

perfect understanding of the insides of the “box” (i.e., the environment), 

any intelligent choice mechanisms will be so structured as to respond to 

signals emanating from the “box.”

As an ideal, feedback is the process through which decisionmakers acquire the 

information necessary to deal with uncertainty. Walters (1986: 233) observed that 

“it is a truism to state that the best management decision to make at any point in 

time is some function of all the information available at that time.” He also argued 
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

39

that effective feedback policies are designed not only to report on the results of 

previous actions, but also provide some anticipation of future responses to those actions. In short, feedback informs decisionmakers not only about what has occurred, 

but what is likely to happen in the future.

When effective feedback processes are absent, adaptive management is handicapped. Yet, organizational receptiveness to feedback, particularly negative feedback, often is hampered by structural and cultural barriers that resist acknowledging information contrary to existing practices, policies, and beliefs (Miller 1999). 

In some cases, this stems from efforts to avoid overloading people with excessive 

amounts of information, especially given that the implications of that information might not be clear. Often, however, resistance stems from more deep-seated 

sources. Michael (1973: 271) argued “People have structured organizations (and 

organizations have structured people) to avoid unfamiliar feedback...Organizations 

arrange to receive a minimum of turbulence-generating feedback, and to use as 

little as possible of that to generate further turbulence.” He added “Feedback which 

is disrupting because it is unfamiliar is also avoided by structuring the feedback 

retrieval process so that it selects from the environment only those signals that are 

compatible with the structure and norms of the organization.” In sum, feedback is 

not assimilated, processed, and evaluated evenly; the process is highly selective, 

filtering out, discounting, or ignoring that which stands contrary to contemporary 

policies, beliefs, or dogma. As Schiff (1962) has recounted with regard to the role 

of fires in longleaf pine forests of the Southern United States, research inconsistent 

with prevailing policy was suppressed, and the published work only surfaced 6 

years after completion. Feedback was interrupted because of its conflict with the 

dominant ideology. 

With regard to the concept of feedback, there is a lack of clarity as to the effects 

of differing characteristics of that feedback. For example, in applications of continuous improvement models in a business context, often there is relative clarity and 

agreement as to what information is critical in a feedback loop and what implications it contains. Moreover, such feedback becomes apparent relatively quickly in 

the system; changes in demand for products, impacts of price changes or changes 

in product quality, etc. However, in ecological systems, feedback is often both 

delayed, perhaps substantially so (years or decades) and the meaning and implications of change are often neither clear nor agreed upon. Indeed, the very complexity 

of ecological systems that has made the concept of adaptive management appealing 

also contributes to the limits of the utility of feedback in assessing performance, 

causation, or management implications. Although it is correct that adaptive processes have been productively used in business contexts, fundamental structural 
GENERAL TECHNICAL REPORT PNW-GTR-654

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differences in the underlying complexity of ecological systems mean that feedback 

processes must be understood to be inherently uncertain. As Roe (1998: 96) has 

remarked “The more you search complexity, the more perverse feedback cycles you 

will find.”

Given the critical role of feedback in the adaptive management model, what can 

be done to overcome or mitigate these hampering effects? Michael (1973) suggested 

two important strategies, one internal and the other external. First, he called for 

creation of organizational structures and norms that sustain and reward learning. 

This means cultivating a capacity and acceptance of self-criticism that encourages 

critical thinking and openness to new information. It is also a critical step in restoring trust among competing interests (Michael 1995). Second, it is important in the 

external environment that there be a capacity to force openness and responsiveness 

to feedback. Here, the onus is on the ability of voluntary or other external organizations to both provide feedback and to insist upon organizational attention to it.

Creating Shared Understandings

McLain and Lee (1996: 445) concluded “the scientific adaptive management approach has failed to provide adequate forums for the creation of shared understandings among stakeholders.” The emphasis here is not on trying to create a single 

perspective, or even a consensus of values and meanings, but to create civic places 

where respect, legitimacy, and credibility of diverse interests and perspectives can 

be fostered (Shannon 1987). However, such forums are notably absent in the natural 

resource arena and across society in general.

Yankelovich (1991) called for creation of forums designed to facilitate the 

process of working through, a phrase drawn from psychology that describes the 

process of coming to grips with change. In part, this involves processing information, but even more so, it involves confronting the pressures and conflicts that 

engulf individuals and organizations and which they must, somehow, accommodate. He went on to point out “society is not well equipped with the institutions or 

knowledge it needs to expedite working through. Our culture does not understand 

it very well and by and large does not do a good job with it” (p. 65). The concept 

is especially germane here, for in the process of creating shared understandings 

and achieving the condition where individuals and organizations have a capacity to 

understand, respect, and legitimize diverse interests, values, and perspectives, new 

processes and institutions might be needed. The lack of such processes and institutions, however, can constrain the ability to implement adaptive management.

Walters (1997) contended that self-interest and unresolved value conflicts often 

combine to stymie adaptive management. For example, Allan and Curtis (2003) 
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

41

described efforts to use electromagnetic mapping as a means of assessing the extent 

to which salinity had affected agricultural lands in northeast Victoria, Australia. 

Although introduction of the technique raised community interest in the salinity issue, the lack of a framework describing what the mapping was intended to 

achieve, how the information could be used, and how it related to other information 

collected in the area contributed to confusion and a subsequent disillusionment with 

the program. 

But this leaves the vexing question: If current institutional structures and 

processes lack capacity to behave adaptively, what type of organization would be 

capable of doing so? We examine this issue next.

Institutional Attributes Facilitating 

Adaptive Management

Two central issues confront efforts to more effectively implement adaptive management. First, what would an adaptive management organization look like (and 

implicitly, how would it differ from existing structures)? Second, what transformation processes must be undertaken?

We can examine these questions from two perspectives. First, there is a body of 

empirical work examining the various factors associated with “successful” applications of adaptive management as well as those where adaptive management has 

fallen short of expectations. Second, there is a more theoretically-based literature 

that discusses the necessary attributes of adaptive organizations. Combined, the 

literature provides important insight about the steps needed to implement adaptive 

approaches in natural resource management.

Ladson and Argent (2002) conducted a comparative assessment of three 

projects in the United States in which adaptive approaches were used—the Columbia, Colorado, and Mississippi Rivers—and compared those results with efforts to 

implement adaptive approaches in Australia’s Murray-Darling Basin. Their purpose 

was to gain a better sense of the conditions that affect efforts to implement adaptive 

management. They concluded there were different degrees of success in the three 

case studies, but that the Colorado River program—involving efforts to evaluate the 

impacts of alternative low-flow releases through Glen Canyon Dam on ecological 

conditions in Grand Canyon National Park—was the most successful (fig. 6). They 

identified seven variables that contributed to this success: (1) the relatively simple 

jurisdictional situation (the area involved only the state of Arizona and Grand 

Canyon National Park); (2) few points of possible legal or political intervention in 

the river’s management; (3) credible science was present, with all reports subject to 

peer review and an independent scientific panel overseeing research efforts; 
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(4) system modeling on the Colorado was “complex enough to obtain credibility 

but simple enough that it could be completed and used in a reasonable time frame” 

(p. 96); (5) managers could point to early achievements in experimental management, such as a 1996 beach-building flood that gained both scientific and political 

support; (6) a sense of community among the various stakeholders that enabled a 

consensus regarding goals and objectives of the project; and (7) the project was, in a 

sense, the “only game in town,” requiring that all interested stakeholders participate 

in the process.

Mapstone (2003), describing experiences in implementing strategies for reef 

line fisheries on Australia’s Great Barrier Reef, expanded on the desirable contextual situations for successful implementation of adaptive management. These 

included (1) the presence of options for alternative management strategies (an 

idea closely aligned to Bormann et al.’s [1999] concept of “multiple pathways”), 

(2) specific objectives for assessing performance, (3) effective monitoring and 

feedback systems, (4) cross-sectoral support from stakeholders, (5) clear and open 

mechanisms for information transfer, (6) assurance of continuity of organizational 

and governmental commitment, and (7) protection against political expedience. 

Meppem and Bellamy (2003), reviewing efforts to implement a 4-year research and 

development project on integrated resource use planning in the central highlands 

Figure 6—An adaptive management project on the Colorado River focuses on the impacts of alternative 

low-flow releases through Glen Canyon Dam on ecological conditions in Grand Canyon National Park. 

National Park Service
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

43

of Queensland, Australia, added three additional “cornerstones of a healthy adaptive management system”: (1) support for individuals and sectors to develop their 

own planning capacity, (2) facilitating understanding of both socioeconomic and 

biophysical processes within the systems, and (3) strong institutional arrangements 

that facilitate negotiations among interests.

Consistent with theories of adaptive management, negative feedback on the 

performance of alternative approaches can be important sources of insight. In 

Ladson and Argent’s (2002) critique of adaptive management on the three American river systems, they were particularly critical of the situation on the Columbia 

River. Here, they found four contextual conditions that constrained efforts, including (1) “modeling wars” between specialists, with the result that excessive attention 

focused on details of modeling, rather than its use as a basis for problem framing; 

(2) institutional complexity involving both multistate and multinational jurisdictions; (3) the stultifying effects of the Endangered Species Act, which constrained 

experimentation; and (4) the complex web of values at stake, leading to a resistance 

to changes in the status quo. The vast scale of the Columbia River experiment also 

increased the likelihood that the institutional, socioeconomic, and biophysical complexity found across the region would lead to unintended consequences, exacerbating concerns among stakeholders about impacts on their interests and values (Butler 

et al. 2001).

Meppem and Bellamy (2003) also acknowledged problems from the central 

highlands project in Queensland, including (1) lots of plans but little on-the-ground 

evidence of implementation, (2) lack of understanding of relationships within 

and between systems, (3) an organizational inability to respond to change (i.e., an 

inability to move beyond the status quo), and (4) inadequate problem definition, 

driven in part because not all stakeholders were involved.

Earlier, we noted that traditional planning models and management organizations increasingly are subject to criticism as lacking a capacity (in terms of 

underlying philosophy as well as structure and process) to facilitate what is commonly defined as ecosystem management. Table 2 contrasts the qualities of two 

perspectives. It suggests that the underlying operational assumptions, organizational structure, and methods of operation of the alternatives stand in stark contrast. 

In particular, the traditional model is predicated on a world characterized by clarity 

in problem definition; on the ability to resolve problems through scientific, rational, 

quantitative, and objective means; and on a strong sense of order. In short, it is a 

model firmly grounded in the social reform planning tradition (Friedmann 1987).

This does not discount the value of traditional planning models in natural 

resource management. Nonetheless, organizing effective management programs 
GENERAL TECHNICAL REPORT PNW-GTR-654

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Table 2—Traditional resource management versus ecosystem management 

Traditional management Ecosystem management

Nature To be dominated and mastered Complex, changing, interrelated

Ethics Compartmentalized; Holistic, interrelationships important

interrelationships marginal

Science and models Deterministic, linear, static, Stochastic, nonlinear, dynamic,

steady-state equilibrium variable-rate dynamics, with temporary

equilibria upset periodically by chaotic

moments that set the stage for the next 

temporary equilibrium

Robust, well-defined theory; Embryonic, beginnings of theory, 

discrete data and highly predictable theory and practice intertwined, 

outcomes interrelated data, and unreliable 

outcomes

Maps, linear optimization, monetized Geographic information systems, 

cost-benefit analysis, quantitative relational databases, nonlinear 

simulation (time and space dependent),

quantitative and qualitative evaluation 

for social, economic, and political 

aspects 

Management and Centralized, rigid; little focus on Decentralized, interrelated teams,

organization incentives or innovation adaptive, flexible; focus on incentives,

innovation, shared learning

Hierarchical, top-down bureaucracy Adaptive, bottom-up, cooperative, open

Planning Comprehensive, rational Interrelated, chaotic, looking for order 

in chaos, imaginative

Decisionmaking Rigid, command-and-control, Deliberated, inclusive

authoritarian, expert-driven

Science provides “the answers” Science provides information; alone, 

it cannot provide answers

Adapted to context of problems, 

interrelated to other problems, 

considers externalities

Participation Influence, money Discursive, deliberative

Leadership Authoritarian, leaders designated Situational; leaders arise from the 

community when needed

Source: Cortner and Moote 1999: 38.
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

45

requires a critical review of existing structures and processes and their capacity to deal with the world around us. This world is dominated by complexity and 

uncertainty, and these qualities compromise the ability of many contemporary 

organizations to frame effective approaches, given imbedded norms of risk-aversion and resistance to change. Lee (1993: 85) has summarized the key institutional 

challenges that account for this reluctance.

Rigidities that complicate 

Adaptive management conditions experimentation

There is a mandate to take action Experimentation and learning are at

in the face of uncertainty. most secondary objectives in large 

ecosystems. Experimentation that 

conflicts with primary objectives will 

often be pushed aside or not proposed.

Decisionmakers are aware Experimentation is an admission there 

they are experimenting. may be no positive return. More 

generally, specifying hypotheses to 

be tested raises the risk of perceived 

failure.

Decisionmakers care about improving Costs of monitoring, controls, and 

outcomes over biological time scales. replication are substantial and they will

appear especially high at the outset 

when compared with the costs of 

unmonitored trial and error. Individual

decisionmakers rarely stay in office 

over times of biological significance.

Preservation of pristine environ- Remedial action crosses jurisdictional

ments is no longer an option, and human boundaries and requires coordinated 

intervention cannot produce desired implementation over long periods.

outcomes predictably. 

Resources are sufficient to Data collection is vulnerable to 

ecosystem behavior. external disruptions, such as budget

cutbacks, policy changes, and 

controversy. After changes in leadership, decisionmakers may not be 

familiar with the purposes and values 

of an experimental approach.

Theory, models, and field methods are Interim results may create panic 

available to estimate and infer or a realization that the experimental 

ecosystem-scale behavior. design was faulty. More generally, 

experimental findings will suggest 

policy changes; controversial changes 

could disrupt the experimental program.
GENERAL TECHNICAL REPORT PNW-GTR-654

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Rigidities that complicate 

Adaptive management conditions experimentation

Hypotheses can be formulated. Accumulating knowledge may shift perceptions of what is worth examining via 

large-scale experimentation. For this 

reason, both policy actors and experimenters must adjust the tradeoffs among 

experimental and other policy objectives

during the implementation process.

Organizational culture encourages Advocates of adaptive management are 

 learning from experience. likely to be staff, who have professional 

incentives to appreciate a complex process and a career situation in which 

long-term learning can be beneficial. 

Where there is a tension between staff 

and policy leadership, experimentation

can become the focus of an internal 

struggle for control.

There is sufficient stability to measure Stability is usually dependent on factors

long-term outcomes; institutional outside the control of experimenters

patience is essential. and managers.

A useful framework for conceptualizing the decisionmaking environment 

occupied by natural resource agencies is described by Thompson and Tuden (1987) 

and also used by Lee (1993, 1999). Thompson and Tuden argued that when organizations make choices, they face two issues. First, choices are based on understanding causation. Do we understand why things work and can we predict what will 

happen when action is taken? Although Shannon and Antypas (1997) characterized 

uncertainty along a continuum (from relative surety to the unknown), Thompson 

and Tuden provided a simplified scheme, asking whether there is agreement or 

disagreement (fig. 7). 

Second, choices involve the nature of agreement regarding preferences about 

outcomes. This is a goal-oriented dimension; Shannon and Antypas (1997) described this as ambiguity. A major challenge confronting society today is that there 

is no clear, unequivocal sense of social purpose or single, unified public interest 

(Schubert 1960). Rather, the world is composed of numerous, often contradictory 

demands, creating an ambiguous context within which decisionmakers operate.

Despite the limits of treating uncertainty (causation) and ambiguity (preference) as dichotomous variables, Thompson and Tuden’s schematic helps frame an 

understanding of the institutional choices confronting organizations implementing 

adaptive approaches. Consider the following. In cell A of figure 7, agreement exists 
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

47

on both causation and preferred outcomes. Here, decisionmakers are faced with 

issues primarily of a technical or computational nature; as a result, decisionmaking 

is routinized. Under such conditions, the most appropriate institutional structure 

is the bureaucracy, embodying specialists operating in a formal, hierarchical, and 

routinized environment.

In cell B, although there is accord on preference, disagreement exists about causation. For example, although there is considerable agreement about the importance 

of maintaining old-growth conditions in forests, there is considerable disagreement 

about how this can best be accomplished (e.g., different thinning strategies, role of 

fire). Here, organizations must rely on expert judgment to help guide the decisionmaking process. Because of differences in problem perception and interpretations 

of the scientific evidence, the collective wisdom of the decision unit needs to be 

brought to bear on the problem. The decisionmaking strategy is judgment by 

majority and the preferred institutional structure to facilitate such choices is the 

collegium. Castleberry et al. (1996) provided a useful example by describing how 

the use of expert opinion among a variety of stream ecologists helped establish 

instream flow requirements.

In cell C, the situation is opposite to that in cell B; here, agreement exists on 

causation, but not on preference. When conflict exists over goals, inevitably there 

will be winners and losers; some will find their goals satisfied, others will not. This 

presents decisionmakers with a world where the choice process is driven by bargaining and negotiation, where compromise (the art of politics) must be practiced. 

Preferences About Outcomes

Agree

B

eliefs

A

b

o

ut C

a

u

s

atio

n

Disagree

DP = Decision process

I = Institutional structure

Agree Disagree

A

DP: Bargaining

C

B

DP: Learning and

consensus building

I: ?

D

DP: Computation

I: Bureaucracy

I: Collegium

DP: Judgment

I: Representative

Figure 7—Thompson-Tuden model.

Different approaches 

are called for 

depending on whether 

there is disagreement 

about preferences for 

outcomes or about 

causation.
GENERAL TECHNICAL REPORT PNW-GTR-654

48

It also means that decisionmakers need to be aware of what interests and values are 

involved, and who those losers and winners are. Thus, it is important that decisionmaking processes involve as wide a representation of the multiple interests involved 

as possible (the United States Congress would be an example).

This brings us to the conditions that define cell D. Here, both causation and 

preference lack agreement; i.e., there are high levels of uncertainty and ambiguity. 

Thompson and Tuden described this as “a...dangerous situation for any group...; 

certainly by definition and probably in fact, the group...is nearing disintegration” 

(1987: 202).

Yet, this is a common situation today. For many resource managers, technical 

complexity, coupled with variation in natural processes, makes understanding causation problematic. Experts often disagree about what data mean, further confounding the exercise of informed decisions (Schwarz and Thompson 1990, Yankelovich 

1991). In a pluralistic society, with high levels of political ambiguity, the search for 

preferred outcomes (e.g., some agreed-on purpose of public forests and forestry) 

faces formidable barriers. Fragmentation, intense political rivalries, and strategic 

posturing stymie efforts to find accord. Yet, despite this uncertainty and ambiguity, 

bureaucratic institutions, founded on the assumptions of certainty and clarity of 

purpose (the conditions of cell A), still dominate efforts to make choices. The result 

is a growing mismatch between current institutional structures and processes and 

the character of the decision environment within which those institutions operate.

However, “if not a bureaucracy, then what?” Thompson and Tuden described 

the context implied by cell D as one in which there is a state of anomie, of normlessness, where former goals and values have lost their meaning. But the search for 

appropriate institutional structures and processes capable of operating in such an 

environment reveals little of promise. Thompson and Tuden could only recommend 

a “structure for inspiration,” an institution in which a charismatic leader offers a 

new set of ideals or preferences that “rally unity out of diversity” (1987: 202). 

Lee (1993: 108) offered “two conceptually distinct strategies for intervening in 

a conflict (i.e., the conditions of cell D): either attempting to move toward agreement on causation, leaving preferences to be reconciled later; or attempting to move 

first on preferences.” The second strategy involves consensus-building; contending 

parties strive to turn some initial consensus of goals into a plan to which all can 

agree. In short, he suggests through planning that it is possible to convert cell D 

conditions to those of cell B, where there is goal agreement. Alternatively, contesting parties strive to

alter the character of the dispute by obtaining agreement on causation...This intervention method may be called settling, since the aim 

of the negotiation is not to achieve final resolution of conflict, but 
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

49

rather to hammer out joint actions within a relationship in which all 

parties are aware of and retain opposed interests...The reason parties...try to work together is that they have to (Lee 1993: 108–109).

In both cases, rather than developing a specific strategy for operating in the cell 

D domain, efforts are made to reframe the basic problem so that it fits elsewhere 

where decision processes and institutions are available.

Roe (1998) objected to Thompson and Tuden’s conclusion, arguing there is 

something “which sticks in the throat” (p. 3) about hoping for revitalized institutions 

dependent on the appearance of inspirational, charismatic leaders. Rather than rejecting analysis, he argues that analysis remains the only viable option. What requires 

reassessment is the manner in which analysis is undertaken; “The analytic methods 

required for these sometimes desperate situations are...not those taught in most of 

our method courses and seminars...yet we proceed ahead today as if the old methods 

will get us across this complex public policy terrain” (Roe 1998: 4). The distinguishing characteristic of a more useful analytical approach is one that triangulates; i.e., 

that uses multiple methods, procedures, or theories to gain insight as to appropriate 

responses to complex policy questions. It also implies that multiple forms of governance might be required; traditional hierarchical/bureaucratic (command-andcontrol) systems, market-based systems, local self-governance. This would facilitate 

creation of a diverse set of decision rules affecting incentives, information flow, and 

compliance that would enhance the capacity to operate effectively in this turbulent 

domain (Dietz et al. 2003). On the other hand, the diversity of rules, structures, and 

processes might result in confusion and chaos that exacerbates turbulent conditions.

The challenge of finding institutional structures and processes capable of operating effectively in a world of uncertainty and ambiguity is formidable. Johnson and 

Williams (1999: 10) argued “...unresolved value judgments, and the lack of effective 

institutional structures for organizing debate...present the great threat to adaptive...

management as a viable means for coping with...uncertainty.” Yet, the literature 

contains important insight as to the desirable attributes such a structure might possess. Many of these qualities are consistent with the vision of adaptive management 

outlined in FEMAT.

Could adaptive management represent the kind of innovative strategy for more 

effectively operating in the ambiguity and uncertainty of a cell D world? It is probably not possible to answer the question definitively at this time, but the potential 

is there nonetheless. From a social and political perspective, adaptive management 

offers an opportunity for a more collaborative, multiparty and multi-interest approach, beginning at the problem-framing stage and extending through monitoring 

and evaluation. From a technical perspective, it offers the experimental foundation 
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needed to operate more effectively relative to uncertainty and provides a means by 

which action can take place in the absence of a full understanding of consequences 

and implications. 

Operational-level problems derive from inadequate or inappropriate organization of economic and public affairs and manifest themselves as ineffective management programs, insufficient or flawed information, inadequate legal processes, 

poorly-defined standards, etc. As Caldwell (1990: 72) noted, “the objective at this 

level is to rectify behavior without attempting to alter prevailing economic or 

institutional arrangements.” In response to such problems, managerial organizations 

employ a variety of mechanisms designed to enhance efficiency: increasing incisive 

internal action, exercising increased control over what goes in and out of the organization, or strengthening management supervision and oversight (Michael 1973).

Because the nature of problems confronting organizations (of whatever 

structure) are systemic rather than operational, the required changes similarly 

must be systemic. Systemic environmental problems result from the underlying 

assumptions, goals, and values of modern technological and economic systems and 

priorities. Effective solutions must be similarly framed; adding another decimal 

point to the data or writing a new standard and guideline is inadequate to deal with 

the underlying causes of such problems. Rather, Caldwell (1990: 73) wrote, “the 

remedy (to systemic problem resolution) is sought in progressive adaptation and 

innovation in institutional arrangements.”

Costanza et al. (2000: 153) identified several principles of governance around 

which a reconstituted organization capable of responding to complex environmental 

challenges might be built. These include:

1. Responsibility—Access to environmental resources carries attendant 

responsibilities.

2. Scale-matching—Institutions match the scale of the environmental 

problem.

3. Precaution—In the face of uncertainty about irreversible environmental 

impacts, humans should err on the side of caution.

4. Adaptive management—Decisionmakers acknowledge uncertainty 

and continuously gather and integrate information, with the goal of adaptive improvement.

5. Full-cost allocation—All internal and external costs and benefits of resource use are identified and appropriately allocated.

6. Participation—All affected stakeholders are engaged in the formulation 

and implementation of decisions concerning environmental resources.
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

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These principles are consistent with attributes for effective environmental 

administration suggested by Paehlke and Torgerson (1990):

1. Non-compartmentalized—Organizations should resist the “bureaucratic 

tendency toward compartmentalization” (p. 292) and develop the capacity 

to embrace diverse disciplines as well as affected authorities.

2. Open—Institutional decisionmaking is open and transparent. Given 

citizen access through legal processes as well as access to information 

through electronic means, the ability of organizations to bound debate

and discussion within bureaucratic walls is unlikely.

3. Decentralized—Environmental management focuses on local, idiosyncratic issues. It must be sensitive to, and aware of, local knowledge and 

initiative, but also acknowledge external factors and large-scale processes. The resulting “paradox of scale” (Lee and Stankey 1992: 35) places 

responsibility for ecological regulation in small-scale institutions, while 

locating coordinating responsibilities in collaborative structures at the ecological scale of the regulated system.

4. Anti-technocratic—Although scientific understanding is necessary for 

environmental administration, it is insufficient for handling environmental 

problems. Organizational processes must be conducted in a manner that 

educates both citizens and experts.

5. Flexible—The emergent quality of many environmental problems 

necessitates development of an adaptive capacity and an ability to operate 

under uncertainty and ambiguity.

Dietz et al. (2003) also examined the conditions necessary to foster what they 

described as “adaptive governance,” a term selected to help convey the difficulty 

of control, the need to proceed in the face of uncertainty, and the importance of 

dealing effectively with diverse values, perspectives, and knowledge. They suggested that the requirements for operating in such a context include the provision of 

knowledge, a capacity to deal with conflict, developing strategies to induce compliance with rules, providing infrastructure, and fostering a capacity to operate in the 

face of change.

Collectively, the principles and concepts contained in these analyses (and 

others; see Dietz et al. 2003) lead to two basic conclusions. First, although various 

concepts have been posited in the literature, there is a growing consensus on the 

attributes institutions need to operate effectively in today’s complex and uncertain 

environment. Second, the nature of effective institutions defies pressures for standardized models; Dovers and Mobbs (1997) suggested the precise nature of institutions capable of implementing adaptive management will always be a function of a 
GENERAL TECHNICAL REPORT PNW-GTR-654

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particular context. Indeed, there is evidence that institutional diversity might prove 

as important as biological diversity for long-term survival (Ostrom et al. 1999). It is 

also apparent that institutions must be able to change (i.e., adapt) as the larger social 

and ecological context within which they exist change; Andries et al. (2004) have 

argued that this adaptive capacity within institutions is essential to ensuring their 

effectiveness.

However relevant the kinds of changes discussed above might appear, they also 

constitute changes in traditional natural resource management culture and beliefs 

(table 2) and in the relationship between management organizations and the public. 

Therefore, efforts to change likely will face resistance. Danter et al. (2000: 539) 

noted, “making such organizational changes creates significant changes in agency 

values and culture.” Such values and the culture of natural resource management 

are deeply ingrained; collectively, they form a belief system highly resistant to 

change. But as we confront the issue of how best to facilitate adaptive management, 

we need to recognize that adaptive mechanisms, structures, and processes—qualities that often stand in sharp contrast to traditional management—are required 

(Grumbine 1997). It also means an increased capacity for learning must be present.

Michael (1995) described nine attributes of a learning organization: 

1. The organization understands that language impacts understanding.

Language can be a barrier (it discourages understanding and learning) or 

bridge (it facilitates understanding and learning). People concerned with 

adaptive management need to pay attention to how we express ourselves 

and learn the language of others.

2. Organizations operate on fundamental premises and assumptions 

about the world. Both the Bureau of Land Management and Forest 

Service define themselves as problem-solving organizations. Such a belief 

can be used to act adaptively; these ideas serve as the basis for valuing 

learning.

3. Such organizations explicitly acknowledge that the world is uncertain 

and that they need to accept error. Typically, errors are treated as evidence of incompetence, miscalculation, and failure (Michael 1973). People 

who make errors, it follows, should be punished lest they become indifferent. Such a belief discourages the search for new knowledge—for thinking 

and acting adaptively—because such efforts often result in errors. Harvey 

(1988: 59) wrote “when we make it difficult for organization members to 

acknowledge their mistakes and have them forgiven, we have designed organizations that reduce risk taking, encourage lying, foment distrust, and, 
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

53

as a consequence, decrease productivity.” Learning organizations operate 

in contrast to this.

4. Learning organizations actively work to reduce the individual’s fear 

of failure. If error is inevitable, and if people associate such an outcome 

with sanctions and punishment, it is not surprising that risk-taking, innovation, and creativity suffer. Learning organizations explicitly acknowledge these fears and recognize concerns about power, status, and security. 

This makes it possible to discuss them openly, to deal with them positively, and to build intra-organizational trust. Lacking such acknowledgement, there will always be distrust and fear of others (e.g., they’ll use new 

knowledge to their advantage and my disadvantage), thereby discouraging 

learning.

5. Learning needs facilitators, not chairpersons. Group and organizational learning requires guidance and a capacity to recognize and productively respond to subtle motives and behaviors that play out in any group. 

Direction from leaders often suppresses, rather than facilitates, learning.

6. Learning organizations recognize the importance of training people 

in group process skills. “Learning to learn depends on...skills that enhance task group behavior...(and)...is a necessary prelude to other types of 

learning in which persons must work together” (Michael 1995: 480-481). 

Providing constructive feedback, reflective thinking (Schön 1983), and 

joining are teachable abilities and are necessary in learning organizations.

7. Learning organizations provide short-term reinforcements and rewards. Generally, people react positively to rewards and recognition. This 

is important both in and outside the organization. In the Applegate AMA, 

for example, we find examples of the beneficial role of positive feedback 

from the community for agency staff (Rolle 2002). Rewards and reinforcements are essential to sustain a learning environment.

8. We learn best when we educate others. Teaching requires that one possess a comprehensive grasp of the topic. It also requires recognition that 

teaching means more than imparting knowledge to others; it involves the 

reciprocal processes of listening, processing, and evaluating. Learning organizations view members as educators (fig. 8).

9. Crises can be opportunities for learning. Typically, organizations view 

their mission as one of preventing crises from occurring (Wildavsky 

1988). However, crises are an inevitable part of the ecological processes 

around us, making the idea of prevention and control of such forces an 
GENERAL TECHNICAL REPORT PNW-GTR-654

54

illusion (Holling 1995). Such occurrences are opportunities for learning, 

and learning organizations capitalize on them by asking “How might we 

respond differently if we had the opportunity? Why did this event occur? 

or “What functions does it play?”

Danter et al. (2000) provided an example of the challenges facing the U.S. Fish 

and Wildlife Service (FWS) as it grappled with implementation of ecosystem management. They described four key areas facing the agency: (1) shifts in professional 

emphasis, (2) interdisciplinary collaboration, (3) the role of decisionmaking, and (4) 

organizational values and culture.

Professional emphasis was shifting from stable, linear, and largely internal processes to constantly changing, nonlinear, and external pressures, conditions deemed 

largely outside the experience of most agency personnel. Demands for interdisciplinary collaboration were also stressful, as the bureaucratic structure, organized 

along compartmentalized, disciplinary boundaries, and exercising considerable 

control (at least in theory), was challenged to not only share (coordinate) information with external interests, but actually work collaboratively. Danter et al. (2000: 

539) argued this shift from specialized and compartmentalized expertise to an 

Figure 8—Learning involves more than acquiring more facts. It occurs best when there is a reciprocal 

process in which individuals listen, process, and evaluate information from one another. In this sense, 

effective learning organizations treat everyone as educators.

Gary Wilson, USDA Natural Resources Conservation Service
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

55

interdisciplinary focus requires a “fundamental transformation of agency culture, 

power relationships, and professional norms.” Decisionmaking, undertaken with 

the expectation that it would facilitate an increase in predictability and a reduction 

in surprise, was forced to recognize the inevitable provisional nature of knowledge 

and the need for ongoing, adaptive management. Finally, traditional agency values 

and culture, featuring top-down control and communication, a concern with efficiency, and the bureaucratic organization itself were challenged.

Danter et al. (2000: 540) concluded “ecosystem management itself requires 

transformational leadership after implementation because of the adaptive, nonlinear nature of ecosystem management.” Although leadership is often defined in a 

bureaucratic and hierarchical manner, it is more appropriately seen as processes 

that establish direction, align people, and motivate and inspire—with the goal of 

producing change (Kotter 1995, Stankey et al. 2003a). Leadership, as opposed to 

management skills, among upper- and mid-level FWS officials, was the critical 

attribute in making the transition from a traditional management paradigm to 

ecosystem management as well as in maintaining an ongoing capacity to sustain 

that change. Danter et al. (2000: 544) noted:

Ecosystem management demands continuous agency change, in 

that stable, linear, and predictable organizational processes will be 

replaced by adhocracy. For this reason, after implementation of 

ecosystem management, agency governance must be more leadership 

oriented than was previously required under earlier resource management models [emphases added].

The literature confirms the view of many observers that the major challenge 

facing adaptive management is fundamentally institutional in character. Such 

institutions are built on major premises and beliefs deeply imbedded in educational 

systems, laws, policies, and norms of professional behavior (Miller 1999). Although 

easy to say, institutional change is hard to do; Wilkinson (1992) described how the 

“lords of yesterday”—mining laws, timber and water resource development policies, and other natural resource laws and policies—framed a century ago, persist 

today, despite widespread recognition of the need for change.

Summary and Conclusions

In summary, what can be drawn from this extensive literature on adaptive 

management? In some cases, findings seem marked by consistency, whereas for 

others, the results are mixed or inconclusive. Yet, there are discernable patterns that 

foster efforts to assess the performance of adaptive management in the plan and the 

steps needed to increase its effectiveness. 
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56

1. Although the concept of adaptive management is widely acclaimed in 

the literature as a model for resource management under conditions 

of risk and uncertainty, it remains primarily an ideal rather than a 

demonstrated reality. In a review of bioregional assessments, Johnson 

and Herring (1999: 361) concluded “adaptive management is more of an 

abstraction than an acceptable enterprise, and institutions still do not allow managers to risk failure.” Similarly, Lee (1999: 2) concluded “adaptive management has been much more influential as an idea than as a way 

of doing conservation.” Although ideas are important and can serve as the 

basis for change and innovation, major challenges remain in translating 

adaptive management from rhetoric to reality. An important first step is 

to acknowledge that much remains to be done and that past experiences in 

incremental adjustments in light of new information typically do not meet 

the rigorous standards implied by contemporary notions of adaptive management.

2. There are many definitions of adaptive management. Often, the 

term includes any process in which incremental adjustments occur. 

Typically, however, these do not involve the core characteristics of 

an adaptive approach as envisioned in the Plan or as discussed in the 

contemporary literature. Although organizations long have relied on 

past experiences as a source of information to change subsequent policies and actions, such efforts generally lack the explicit hypothesis testing, 

monitoring, and evaluation that characterize contemporary definitions of 

adaptive management. In essence, adaptive management, as a process to 

accelerate and enhance learning based on the results of policy implementation, mimics the scientific method. Successful implementation of experimentally driven adaptive management requires incorporation of these 

distinctive characteristics, as opposed to simply a continuation of learning 

by incrementalism and trial and error. As Van Cleve et al. (2003: 21) noted 

“adaptive management is a very powerful, yet poorly understood natural 

resource management tool...but (it) must be understood by those who use, 

support, fund, and challenge it.” Adaptive management, as described in 

the contemporary literature, is not simply the latest term embracing ad hoc 

or laissez faire management (MacKay et al. 2003). However, the literature 

reports few examples of formal structures and processes for implementing adaptive management. In the worst case, adaptive management has 

become a code phrase for “we’ll make it up as we go.” One unfortunate 

outcome of these disparate conceptions is that they confound efforts to 
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

57

undertake a comprehensive appraisal and evaluation of progress in implementing adaptive management. 

3. Experimentation is the core of adaptive management, involving hypotheses, controls, and replication. Although adaptive management 

invites experiments involving tests of alternative resource management 

policies and institutional arrangements, it is rare to find examples of such 

experiments, particularly involving controls and replication. Such characteristics are difficult to impose in the complex, multijurisdictional settings 

commonly found at the landscape level. However, there has been a reluctance or even resistance to experimenting with alternative institutional 

structures and processes, such as integrating local knowledge into decisionmaking processes.

4. Adaptive management requires explicit designs that specify problemframing and problem-solving processes, documentation and monitoring protocols, roles, relationships, and responsibilities, and assessment 

and evaluation processes. This suggests that various ways to implement 

adaptive approaches exist, differing by context, organizational capacity, 

resources, etc. Unfortunately, clear documentation describing details of the 

experimentation process often fails to be undertaken, thereby diminishing the potential for feedback and learning. Guidelines and protocols to 

aid managers and policymakers in fashioning useful adaptive management 

models generally are lacking (an exception to this is Salafsky et al. 2001).

5. Adaptive management is irreducibly sociopolitical in nature. Effective 

implementation must involve the active involvement and support of the 

full set of partners and stakeholders. An inclusive approach is required not 

only to build understanding, support, credibility, and trust among constituent groups (Van Cleve et al. 2003, MacKay et al. 2003), but also to ensure 

adequate problem-framing and access to the knowledge, experience, and 

skills held by these groups. Because natural resource management problems are social in origin and potential solutions are framed in a social context, effective management programs must embrace both biophysical and 

social elements. Agee (1999: 292) argued adaptive management can only 

work “if simultaneously adopted in the sociopolitical world” and although 

“the political world does not have to embrace uncertainty itself...it must 

fund activities that reduce or define uncertainty...” This has proven challenging because of the reluctance of parties to work collaboratively and 

because organizational and professional biases continue to define problems 

in technical, scientific terms (Miller 1999).
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58

6. An adaptive management approach is grounded in a recognition 

and acceptance of risk and uncertainty. When working in a complex, 

chaotic, and contingent world characterized by imperfect knowledge and 

unpredictability, improved management and policymaking is dependent 

upon a learning process undertaken in a deliberate, thoughtful, and reflective manner (Buck et al. 2001). A key element of this is explicit acknowledgement and acceptance of the limits of understanding and the risks 

that accompany decisions undertaken in the face of such uncertainty. Yet, 

management organizations have been reluctant to do this; concerns with 

political and legal criticism and sanctions often lead to a denial of uncertainty and an unfounded confidence in the tentative, provisional nature of 

most policies (e.g., the S&Gs).

7. Learning is a key output of the adaptive management process. 

Learning can include improved technical knowledge regarding biophysical 

and socioeconomic systems and their interactions as well as greater insight 

as to how new understanding can be communicated, enhanced, and incorporated into organizational policies, programs, and procedures. Learning 

is driven by treating management policies as hypotheses and the resulting 

knowledge as input to subsequent actions. However, as Failing et al. (2004) 

have argued, the probability of acquiring useful information (i.e., learning) 

must be weighed against the likely impact of that information on decisions, 

and the costs incurred in acquiring that information. Adaptive management is both a good investment and an appropriate strategy when the probability of gaining useful information is high and the consequences of that 

information for pending decisions is also high. If the probability is high, 

but the consequences of information for decisions are low, there is a risk 

of investing scarce resources into a management strategy with a low probability of significant impact. In short, adaptive management is not always 

necessarily an appropriate strategy for proceeding in the face of uncertainty.

8. Adaptive management focuses attention on the meaning and significance of learning. Despite the importance of learning, it remains fundamentally inferential in nature; i.e., it must be inferred based on observations of behavior or communications that suggest learning. Parson 

and Clark (1995) suggested four questions to facilitate determination of 

whether learning has occurred: (1) Who or what learns; i.e., where does 

learning reside in an organization? (2) What kinds of things are learned; 

i.e., in terms of Ackoff’s (1996) five dimensions of learning, does learning 
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

59

manifest itself as more data, improved understanding, wisdom, etc.? (3) 

What counts as learning; i.e., does learning occur at the cognitive or behavioral level or both? What criteria, established through what processes, help 

identify whether the outcomes of an adaptive management approach constitute an adequate basis for changing or maintaining a policy or management 

strategy? (4) Why bother asking; i.e., are the results merely interesting or 

do they have consequences for organizational behavior? 

9. Effective adaptive management is open and responsive to varying 

forms and sources of knowledge. This requires processes and structures 

that enable alternative forms of knowledge to be obtained and incorporated 

into the decisionmaking process. Performance here is spotty, with public 

involvement venues and processes geared primarily to informing citizens of 

organizational intent or of obtaining some sense of public support or 

opposition to potential plans or policies.

10. In the presence of risk and uncertainty, the adaptive management process provides a capacity to act in an informed, judicious manner. This 

involves an acknowledgment that mistakes and failures are normal when 

working in uncertain situations, rather than unwanted feedback deriving 

from incompetence or inability (Schelhas et al. 2001). It highlights the importance of documentation, which provides a basis for examining differences between predicted and actual outcomes. All too often, negative outcomes 

are viewed as liabilities or even denied, rather than being seen as a source 

of learning and insight that could inform and improve subsequent decisionmaking.

11. A variety of institutional barriers confront effective implementation 

of adaptive management. These include legal and political constraints 

(e.g., Endangered Species Act), socio-psychological barriers (e.g., riskaversion; Miller 1999), and technical-scientific constraints (e.g., lack of 

adequate knowledge bases or appropriate monitoring protocols) (Stankey 

et al. 2003a). McLain and Lee (1996: 446) noted that “the adaptive management literature pays little attention to the question of what types of institutional structures and processes are required for the approach to work on a 

large-scale basis.” There clearly is no single template or model most suited 

to an adaptive approach. However, the literature identifies several qualities 

that would characterize an adaptive institution; an atmosphere that is open, 

participatory and inclusive, integrative, collaborative, risk tolerant, and flexible. The search for design principles upon which adaptive institutions are 

founded will continue to draw attention (Andries et al. 2004). Ultimately, 
GENERAL TECHNICAL REPORT PNW-GTR-654

60

however, decisions about appropriate institutional structures and processes 

are linked to the specific context within which an adaptive approach is being considered. 

12. Effective implementation of adaptive management requires organizational leadership and political support, coupled with skilled advocates 

and champions at the field level. A sustained commitment to adaptive 

management requires leadership and ongoing capacity-building efforts 

by organizations. Such commitment must be present at all organizational 

levels. Creation of the AMA coordinators and lead scientists was an important action in efforts to implement an adaptive approach in the Plan, 

and the loss of organizational commitment and support for these positions 

seriously constrains the future of adaptive management in the Plan.

13. A commitment to adaptive management requires transition strategies 

that enable the transformation from a command-control system to 

one built upon learning, collaboration, and integrative management. 

Ongoing assessments of needed changes in organizational structures and 

processes are essential. However, strong legal, organizational, and psychosocial forces work to sustain the status quo and resist efforts to change 

(Miller 1999). The ability of agencies to implement the systemic changes 

required in reframing existing conceptions of resource management—the 

role of citizens, managers, and scientists, the reality of dealing with a 

world characterized by chaos, complexity, and uncertainty, rather than 

order and predictability, etc.—remains problematic. Yet, as Holling (2004) 

has argued, transformational learning is necessary to enable truly novel 

strategies and processes to take root. Although such changes are inherently 

uncertain and unpredictable, they are essential to creating and sustaining 

the innovative environment within which an adaptive approach can successfully operate.

Implementing successful structures and processes to support adaptive management and the transitions through which organizational members must pass are 

formidable but essential. What is involved here is a need for transformation, a 

process with which the private and corporate sectors are well-acquainted (Blumenthal and Haspeslagh 1994, Holling 2004, Kotter 1995). These transformations 

often involve tensions among competing interests both internal and external to the 

organization. Bridges (1991) defined change as an objective and observable state 

that differs from the way things previously were. But the potential effectiveness 

of a change depends on the way individuals in the organization work through the 

transition from one state of conditions to another.
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

61

Transition is a psychological process; it begins with an ending. Traditional ways 

of operating within the organization have changed and members must come to grips 

with that fact. This is never easy, but it is an essential first step. Bridges (1991: 4) 

noted “nothing so undermines organizational change as the failure to think through 

who will have to let go of what when change occurs.” Bridges described the second 

step in transition as negotiating through the neutral zone, a time and place of instability, ambiguity, and uncertainty. This can be threatening. What used to work no 

longer does; the rules that used to apply no longer fit. During this period, “anxiety 

rises and motivation falls,” polarization among people increases, and the organization can become vulnerable to outside attack (Bridges 1991: 35–36). But it is also a 

creative period. Because old ways no longer work, there is a need to find new ways 

that do, and these provide organizational members with opportunities for creativity, 

innovation, and reinvention. Finally, the third step involves arrival at a new state of 

affairs. And the cycle begins again.

Although barriers continue to face implementation of adaptive management, 

the concept remains an important, even essential, component of efforts to deal 

more effectively with today’s complex, uncertain world. In the absence of an 

adaptive grounded approach, rule-based planning—administrative or legal—will 

continue to dominate management, with a further diminution of the ability of 

managers to modify actions and policies in light of new knowledge and experience. 

To avoid this will call for renewed innovation and leadership from all interested 

parties: managers, policymakers, scientists, and citizens.

Literature Cited

Ackoff, R.L. 1996. On learning and the systems that facilitate it. Reflections. 1(1): 

14–24.

Agee, J.K. 1999. Science review. In: Johnson, K.N.; Swanson, F.; Herring, 

M.; Greene, S., eds. Bioregional assessments: science at the crossroads of 

management and policy. Washington, DC: Island Press: 288–292.

Allan, C.; Curtis, A. 2003. Regional scale adaptive management: lessons from 

the North East Salinity Strategy (NESS). Australasian Journal of Environmental 

Management. 10(June): 76–84. 

Allen, G.M.; Gould, E.M., Jr. 1986. Complexity, wickedness, and public forests. 

Journal of Forestry. 84(4): 20–23.

Allenby, B.R.; Richards, D.J. 1994. The greening of industrial ecosystems. 

Washington, DC: National Academy Press. 259 p.
GENERAL TECHNICAL REPORT PNW-GTR-654

62

Andries, J.M.; Janssen, M.A.; Ostrom, E. 2004. A framework to analyze the 

robustness of social-ecological systems from an institutional perspective. 

Ecology and Society. 9(1): 18. http://www.ecologyandsociety.org/vol9/iss1/138. 

(June 17, 2004).

Argyris, C.; Schön, D. 1978. Organization learning: a theory of action perspective. 

Reading, MA: Addison-Wesley. 344 p.

Ashworth, M.J. 1982. Feedback design of systems with significant uncertainty. 

Chichester, UK: Research Studies Press. 246 p.

Bardwell, L.V. 1991. Problem-framing: a perspective on environmental problemsolving. Environmental Management. 15(5): 603–612.

Bednar, L.F.; Shainsky, L.J. 1996. The concept of overcontrolled systems: 

implications for forest management. Journal of Forestry. 94(8): 29–33.

Bernstein, B.B.; Zalinski, J. 1986. A philosophy for effective monitoring. 

In: Oceans 86 monitoring strategies symposium. Washington, DC: Marine 

Technology Society: 1024–1029. Vol. 3.

Blumenthal, B.; Haspeslagh, P. 1994. Toward definition of corporate 

transformation. Sloan Management Review. 35(3): 101–106.

Bormann, B.T.; Brookes, M.H.; Ford, E.D.; Kiester, A.R.; Oliver, C.D.; 

Weigand, J.F. 1994a. Volume 5: a framework for sustainable-ecosystem 

management. Gen. Tech. Rep. PNW-GTR-331. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station. 61 p.

Bormann, B.T.; Cunningham, P.G.; Brookes, M.H.; Manning, V.W.; Collopy, 

M.W. 1994b. Adaptive ecosystem management in the Pacific Northwest. Gen. 

Tech. Rep. PNW-GTR-341. Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station. 22 p.

Bormann, B.T.; Martin, J.R.; Wagner, F.H.; Wood, G.W.; Alegria, J.; 

Cunningham, P.G.; Brookes, M.H.; Friesema, P.; Berg, J.; Henshaw, J.R. 

1999. Adaptive management. In: Johnson, N.C.; Malk, A.J.; Sexton, W.T.; Szaro, 

R., eds. Ecological stewardship: a common reference for ecosystem management. 

Oxford, United Kingdom: Elsevier Science Ltd.: 505-534. Vol. 3. 

Bridges, W. 1991. Managing transitions: making the most of change. Reading, 

MA: Addison-Wesley Publishing Co. 130 p.

British Columbia Ministry of Forests. 2000. Single and double loop learning. 

Adaptive Management Newsletter. Summer: 1–2.
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

63

Buck, L.E.; Geisler, C.C.; Schelhas, J.; Wollenberg, E., eds. 2001.

Biological diversity: balancing interests through adaptive collaborative 

management. New York: CRC Press. 465 p.

Butler, M.J.; Steele, L.L.; Robertson, R.A. 2001. Adaptive resource management 

in the New England groundfish fishery: implications for public participation and 

impact assessment. Society and Natural Resources. 14(9): 791–801.

Buttolph, L.P.; Doak, S.C. 2000. The integration of knowledge in place-based 

ecosystem management. Report to the People and Natural Resources Program. 

Portland, OR: Ecotrust. 51 p.

Caldwell, L.K. 1990. Between two worlds: science, the environmental movement, 

and policy choice. Cambridge, United Kingdom: Cambridge University Press. 

224 p.

Castleberry, D.T.; Cech, J.J., Jr.; Erman, D.C.; Hankin, D.; Healey, M.; 

Kondolf, G.M.; Mangel, M.; Mohr, M.; Moyle, P.B.; Nielsen, J.; Speed, T.P.; 

Williams, J.G. 1996. Uncertainty and instream flow standards. Fisheries. 21(8): 

20–21.

Clayton, A.M.H.; Radcliffe, N.J. 1996. Sustainability: a systems approach. 

Boulder, CO: Westview Press. 258 p.

Cortner, H.J.; Moote, M.A. 1999. The politics of ecosystem management. 

Washington, DC: Island Press. 179 p.

Cortner, H.J.; Shannon, M.A.; Wallace, M.G.; Burke, S.; Moote, M.A. 1996.

Institutional barriers and incentives for ecosystem management: a problem 

analysis. Gen. Tech. Rep. PNW-GTR-354. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station. 35 p.

Costanza, R.; Daly, H.; Folke, C.; Hawken, P.; Holling, C.S.; McMichael, 

A.J.; Pimentel, D.; Rapport, D. 2000. Managing our environmental portfolio. 

BioScience. 50(2): 149–155.

Danter, K.J.; Briest, D.L.; Mullins, G.W.; Norland, E. 2000. Organizational 

change as a component of ecosystem management. Society and Natural 

Resources. 13(6): 537–547.

Dietz, T.; Dolšak, N.; Ostrom, E.; Stern, P.C. 2003. The drama of the commons. 

In: Ostrom, E.; Dietz, T.; Dolšak, N.; Stern, P.C.; Stonich, S.; Weber, E.U., eds. 

The drama of the commons. Washington, DC: National Academy Press: 3–35.
GENERAL TECHNICAL REPORT PNW-GTR-654

64

Dovers, S. 2003. Processes and institutions for environmental management: why 

and how to analyse. In: Dovers, S.; River, S.W., eds. Managing Australia’s 

environment. Sydney, Australia: The Federation Press: 3–12.

Dovers, S.R.; Mobbs, C.D. 1997. An alluring prospect? Ecology, and the 

requirements of adaptive management. In: Klomp, N.; Lunt, I., eds. Frontiers 

in ecology: building the links. Oxford, United Kingdom: Elsevier Science Ltd.: 

39–52. 

Dryzek, J.S. 1987. Rational ecology. New York: Basil Blackwell, Inc. 270 p.

Duinker, P.N.; Trevisan, L.M. 2003. Adaptive management: progress and 

prospects for Canadian forests. In: Burton, P.J.; Messier, C.; Smith, D.W.; 

Adamowicz, W.L., eds. Towards sustainable management of the boreal forest. 

Ottawa, Ontario: NRC Research Press: 857–892.

Easterby-Smith, M.; Araujo, M.L. 1999. Organizational learning: current debates 

and opportunities. In: Easterby-Smith, M.; Burgoyne, J.; Araujo, M.L., eds. 

Organizational learning and the learning organization: developments in theory 

and practice. London: Sage Publications: 2–21.

Environmental and Social Systems Analysts, Ltd. [ESSA]. 1982. Review and 

evaluation of adaptive environmental assessment and management. Ottawa, ON: 

Environment Canada. 116 p. 

Estill, E. 1999. Blazing trails in the Forest Service: ecosystem management and 

social science. In: Cordell, H.K.; Bergstrom, J.C., eds. Integrating social sciences 

with ecosystem management. Champaign, IL: Sagamore Press: 13–23.

Ewing, S.A.; Grayson, R.B.; Argent, R.M. 2000. Science, citizens, and 

catchments: decision support for catchment planning in Australia. Society and 

Natural Resources. 13(5): 443–459.

Failing, E.; Horn, G.; Higgins, P. 2004. Using expert judgment and stakeholder 

values to evaluate adaptive management options. Ecology and Society. 9(1): 13. 

http://www.ecologyandsociety.org/vol9/iss1/13. (June 17, 2004).

Falanruw, M.V.C. 1984. People pressure and management of limited resources 

on Yap. In: McNeely, J.A.; Miller, K.R., eds. National parks, conservation, and 

development: the role of protected areas in sustaining society. Washington, DC: 

The Smithsonian Institution Press: 348–354.

Forest Ecosystem Management Assessment Team [FEMAT]. 1993. An 

ecological, economic, and social assessment. Portland, OR: U.S. Department of 

Agriculture; U.S. Department of the Interior [et al.]. [Irregular pagination].
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

65

Friedmann, J. 1987. Planning in the public domain: from knowledge to action. 

Princeton, NJ: Princeton University Press. 501 p.

Gilmour, A.; Walkerden, G.; Scandol, J. 1999. Adaptive management of the 

water cycle on the urban fringe: three Australian case studies. Conservation 

Ecology. 3(1): 11. http://www.consecol.org/vol3/iss1/art11. (May 17, 2001).

Gray, A.N. 2000. Adaptive ecosystem management in the Pacific Northwest: a case 

study from coastal Oregon. Conservation Ecology. 4(2): 6. http:///www.consecol.

org/vol4/iss2/art6. (April 22, 2001).

Grumbine, R.E. 1997. Reflections on “What is ecosystem management?” 

Conservation Biology. 11(1): 41–47.

Gunderson, L. 1999a. Red queens to mad hatters—a wonderland of landscapes 

and institutions. In: Views from the ridge: considerations for planning at the 

landscape level. Portland, OR: U.S. Department of Agriculture, Forest Service, 

Pacific Northwest Research Station; Western Forestry and Conservation 

Association: 49–57. 

Gunderson, L. 1999b. Resilience, flexibility and adaptive management—antidotes 

for spurious certitude? Conservation Ecology. 3(1): 7. http://www.consecol.

org/vol3/iss1/art7. (January 12, 2000).

Gunderson, L. 1999c. Stepping back: assessing for understanding in complex 

regional systems. In: Johnson, K.N.; Swanson, F.; Herring, M.; Greene, S., 

eds. Bioregional assessments: science at the crossroads of management and 

policy. Washington, DC: Island Press: 27–40.

Gunderson, L.H.; Holling, C.S.; Light, S.S., eds. 1995a. Barriers and 

bridges to the renewal of ecosystems and institutions. New York: Columbia 

University Press. 593 p.

Gunderson, L.H.; Holling, C.S.; Light, S.S. 1995b. Barriers broken and 

bridges built: a synthesis. In: Gunderson, L.H.; Holling, C.S.; Light, S.S., 

eds. Barriers and bridges to the renewal of ecosystems and institutions. 

New York: Columbia University Press: 489–532.

Gurvitch, G. 1971. The social frameworks of knowledge. Oxford, United 

Kingdom: Basil Blackwell. 292 p.

Haber, S. 1964. Efficiency and uplift: scientific management in the progressive 

era, 1890–1920. Chicago, IL: University of Chicago Press. 181 p.

Halbert, C.L. 1993. How adaptive is adaptive management? Implementing 

adaptive management in Washington state and British Columbia. Reviews 

in Fisheries Science. 1: 261–283.
GENERAL TECHNICAL REPORT PNW-GTR-654

66

Haney, A.; Power, R.L. 1996. Adaptive management for sound ecosystem 

management. Environmental Management. 20(6): 879–886.

Harvey, J.B. 1988. The Abilene paradox and other meditations on management. Lexington, MA: Lexington Books. 150 p.

Herrick, C.; Sarewitz, D. 2000. Ex post evaluation: a more effective role for 

scientific assessments in environmental policy. Science, Technology, and 

Human Values. 25(3): 309–331.

Hilborn, R. 1992. Institutional learning and spawning channels for sockeye 

salmon (Oncorhynchus nerka). Canadian Journal of Fisheries and Aquatic 

Sciences. 49: 1126–1136.

Holling, C.S. 1978. Adaptive environmental assessment and management. 

London: John Wiley. 377 p.

Holling, C.S. 1995. What barriers? What bridges? In: Gunderson, L.H.; 

Holling, C.S.; Light, S.S., eds. Barriers and bridges to the renewal of ecosystems and institutions. New York: Columbia University Press: 3–34.

Holling, C.S. 2004. From complex regions to complex worlds. Ecology and 

Society. 9(1): 11. http://www.ecologyandsociety.org/vol9/iss1/art11. (June 

17, 2004).

Holzner, B.; Marx, J.H. 1979. Knowledge application: the knowledge system 

in society. Boston: Allyn and Bacon, Inc. 388 p.

Huber, P. 1983. The old-new division in risk regulation. The Virginia Law 

Review. 69(6): 1025–1107.

Inkpen, A.C.; Crossan, M.M. 1995. Believing is seeing: joint ventures and 

organizational learning. Journal of Management Studies. 32(5): 595–618.

Johnson, B.L. 1999. Introduction to the special issue: Adaptive management—

scientifically sound, socially challenged? Conservation Ecology. 3(1): 8. 

http://www.consecol.org/vol3/iss1/art10. (January 4, 2000)

Johnson, K.N.; Herring, M. 1999. Understanding bioregional assessments. 

In: Johnson, K.N.; Swanson, F.; Herring, M.; Greene, S., eds. Bioregional 

assessments: science at the crossroads of management and policy. 

Washington, DC: Island Press: 341–376.

Johnson, F.; Williams, K. 1999. Protocol and practice in the adaptive management of waterfowl harvests. Conservation Ecology. 3(1): 8. http://www.

consecol.org/vol3/iss1/art8. (January 7, 2000).

Kloppenburg, J., Jr. 1991. Social theory and the de/reconstruction of agricultural science: local knowledge for an alternative agriculture. Rural 

Sociology. 56: 519–548.
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

67

Korten, D.C.; Klauss, R., eds. 1984. People-centered development: contributions toward theory and planning frameworks. Hartford, CT: Kumarian 

Press. 333 p.

Kotter, J.P. 1995. Leading change: why transformational efforts fail. Harvard 

Business Review. March-April: 59–67.

Kuhn, T. 1970. The structure of scientific revolutions. Chicago: University of 

Chicago Press. 210 p.

Kusel, J.; Doak, S.C.; Carpenter, S.; Sturtevant, V.E. 1996. The role of the 

public in adaptive ecosystem management. In: Sierra Nevada ecosystem project: 

final report to Congress. Vol. II, assessments and scientific basis for management 

options. Davis, CA: University of California, Centers for Water and Wildland 

Resources: 611–624.

Ladson, A.R.; Argent, R.M. 2002. Adaptive management of environmental flows: 

lessons for the Murray-Darling Basin from three large North American rivers. 

Australian Journal of Water Resources. 5(1): 89–101.

Lang, R. 1990. Achieving integration in resource planning. In: Lang, R. (ed.), 

Integrated approaches to resource planning and management. Calgary, AB: 

University of Calgary, Banff Centre for Continuing Education: 27–50.

Lee, K.N. 1993. Compass and gyroscope: integrating science and politics for the 

environment. Washington, DC: Island Press. 243 p.

Lee, K.N. 1995. Deliberately seeking sustainability in the Columbia River basin. 

In: Gunderson, L.H.; Holling, C.S.; Light, S.S., eds. Barriers and bridges to the 

renewal of ecosystems and institutions. New York: Columbia University Press: 

214–238.

Lee, K.N. 1999. Appraising adaptive management. Conservation Ecology. 3(2):3. 

http://www.consecol.org/vol3/iss2/art3. (January 4, 2000).

Lee, K.N.; Lawrence, J. 1986. Adaptive management: learning from the Columbia 

River basin fish and wildlife program. Environmental Law. 16: 431–460.

Lee, R.G.; Stankey, G.H. 1992. Evaluating institutional arrangements for 

regulating large watersheds and river basins. In: Adams, P.W.; Atkinson, W.A., 

comps. Watershed resources: balancing environmental, social, political and 

economic factors in large basins. Corvallis, OR: Forest Engineering Department, 

Oregon State University: 30–37.

Levitt, B.; March, J.G. 1988. Organizational learning. Annual Review of 

Sociology. 14: 319–340.
GENERAL TECHNICAL REPORT PNW-GTR-654

68

Lindblom, C. 1959. The science of muddling through. Public Administration 

Review. 19(2): 79–99.

Lowe, I. 1990. Scientific objectivity and values. In: Webb, L.J.; Kikkawa, J., eds. 

Australian tropical forests: science—values—meaning. Melbourne, Victoria, 

Australia: Commonwealth Scientific and Industrial Research Organisation: 

133–141.

Ludwig, D.; Hilborn, R.; Walters, C. 1993. Uncertainty, resource exploitation, 

and conservation: lessons from history. Science. 260: 17, 36.

MacKay, H.M.; Rogers, K.H.; Roux, D.J. 2003. Implementing the South African 

water policy: holding the vision while exploring an uncharted mountain. Water 

SA. 29(4): 353–358.

Mapstone, B. 2003. Institutional and objective certainty: obstacles to the 

implementation of active adaptive management. In: Allan, C.; Curtis, A., comps. 

Notes from an adaptive management workshop. Report 171. Albury, New South 

Wales, Australia: Johnstone Centre: 17–18.

Marcot, B.G. 1998. Selecting appropriate statistical procedures and asking the 

right questions: a synthesis. In: Sit, V.; Taylor, B., eds. Statistical methods for 

adaptive management studies. Victoria, BC: Ministry of Forests, Research 

Branch: 129–143.

Mazaika, R.; Lackey, R.T.; Friant, S.L., eds. 1995. Ecological risk assessment: 

use, abuse, and alternatives. Human and Ecological Risk Assessment. 1(4): 

337–458.

McCay, B.J. 2002. Emergence of institutions for the commons: contexts, situations, 

and events. In: Ostrom, E.; Dietz, T.; Dolšak, N.; Stern, P.C.; Stonich, S.; Weber, 

E.U., eds. The drama of the commons. Washington, DC: National Academy 

Press: 361–402.

McLain, R.J.; Lee, R.G. 1996. Adaptive management: promises and pitfalls. 

Environmental Management. 20(4): 437–448.

Meppem, T.; Bellamy, J. 2003. Building capacity for adaptive management: 

experiences from two community based regional initiatives. In: Allan, C.; 

Curtis, A., comps. Notes from an adaptive management workshop. Rep. 171. 

Albury, New South Wales, Australia: Johnstone Centre: 15–16.

Mezirow, J. 1995. Transformation theory of adult learning. In: Welton, M.R., ed. In 

defense of the lifeworld: critical perspectives on adult learning. New York: State 

University of New York: 39–70.
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

69

M’Gonigle, R.M.; Jamieson, T.L.; McAllister, M.K.; Peterman, R.M. 

1994. Taking uncertainty seriously: from permissive regulation to preventative design environmental decision making. Osgoode Hall Law Journal. 

32(1): 99–169.

Michael, D.N. 1973. On learning to plan—and planning to learn. San Francisco: 

Jossey-Bass Publishers. 341 p.

Michael, D.N. 1995. Barriers and bridges to learning in a turbulent human ecology. 

In: Gunderson, L.H.; Holling, C.S.; Light, S.S., eds. Barriers and bridges to the 

renewal of ecosystems and institutions. New York: Columbia University Press: 

461–488. 

Miller, A. 1993. The role of analytical science in natural resource decision making. 

Environmental Management. 17(5): 563–574.

Miller, A. 1999. Environmental problem solving: psychosocial barriers to adaptive 

change. New York: Springer-Verlag. 239 p.

National Research Council. 1999. Downstream: adaptive management of Glen 

Canyon Dam and the Colorado River ecosystem. Washington, DC: National 

Academy Press. 230 p.

Nelson, J.E. 1999. Management review (of the FEMAT case study). In: Johnson, 

K.N.; Swanson, F.; Herring, M.; Greene, S., eds. Bioregional assessments: 

science at the crossroads of management and policy. Washington, DC: Island 

Press: 121–126.

Ostrom, E. 1986. An agenda for the study of institutions. Public Choice. 48: 3–25.

Ostrom, E.; Burger, J.; Field, C.B.; Norgaard, R.B.; Policansky, D. 1999. 

Revisiting the commons: local lessons, global challenges. Science. 284: 278–282.

Paehlke, R.; Torgerson, D. 1990. Environmental politics and the administrative 

state. In: Paehlke, R.; Torgerson, D., eds. Managing leviathan: environmental 

politics and the administrative state. Peterborough, ON: Broadview Press: 

285–301.

Pahl-Wostl, C. 1995. The dynamic nature of ecosystems: chaos and order 

entwined. Chichester, England: John Wiley & Sons. [Pages unknown].

Parson, E.A.; Clark, W.C. 1995. Sustainable development as social learning: 

theoretical perspectives and practical challenges for the design of a research 

program. In: Gunderson, L.H.; Holling, C.S.; Light, S.S., eds. Barriers and 

bridges to the renewal of ecosystems and institutions. New York: Columbia 

University Press: 428–460.
GENERAL TECHNICAL REPORT PNW-GTR-654

70

Perry, E.A. 1995. Salmon stock restoration and enhancement: strategies and 

experiences in British Columbia. American Fisheries Society Symposium. 15: 

152–160.

Reichhardt, T. 1997. Endangered species bill faces battle against property lobby. 

Nature. 307: 321–326.

Renn, O. 1992. Concepts of risk: a classification. In: Krimsky, S.; Golding, D., eds. 

Social theories of risk. Westport, CT: Praeger Publishing: 53–82.

Roe, E. 1996. Why ecosystem management can’t work without social science: an 

example from the California northern spotted owl controversy. Environmental 

Management. 5: 667–674.

Roe, E. 1998. Taking complexity seriously: policy analysis, triangulation and 

sustainable development. Boston: Kluwer Academic Publishers. 138 p.

Rolle, S. 2002. Measures of progress for collaboration: case study of the Applegate 

Partnership. Gen. Tech. Rep. PNW-GTR-565. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station. 13 p.

Saint-Onge, H. 1996. Tacit knowledge: the key to the strategic alignment of 

intellectual capital. Strategy and Leadership. 24(2): 10, 12–14.

Salafsky, N.; Margoluis, R.; Redford, K. 2001. Adaptive management: a tool 

for conservation practitioners. Washington, DC: Biodiversity Support Program, 

World Wildlife Fund, Inc. 136 p. http://fosonline.org/resources/Publications/

AdapManHTML/adman_1.html. (February 6, 2004).

Schelhas, J.; Buck, L.E.; Geisler, C.C. 2001. Introduction: the challenge of 

adaptive collaborative management. In: Buck, L.E.; Geisler, C.C.; Schelhas, J.; 

Wollenberg, E., eds. Biological diversity: balancing interests through adaptive 

collaborative management. New York: CRC Press: xix-xxxv.

Schiff, A.L. 1962. Fire and water: scientific heresy in the Forest Service. 

Cambridge, MA: Harvard University Press: [Pages unknown].

Schön, D.A. 1983. The reflective practitioner: how professionals think in action. 

New York: Basic Books. 374 p.

Schubert, G. 1960. The public interest: a critique of the theory of a political 

concept. Glencoe IL: The Free Press of Glencoe. 244 p.

Schwarz, M.; Thompson, M. 1990. Divided we stand: redefining politics, 

technology and social choice. Philadelphia, PA: University of Pennsylvania 

Press. 176 p.
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

71

Senge, P.M. 1990. The fifth discipline: the art and practice of the learning 

organization. New York: Currency Doubleday. 423 p.

Shannon, M.A. 1987. Forest planning: learning with people. In: Miller, M.L.; Gale, 

R.P.; Brown, P.J., eds. Social science in natural resource management systems. 

Boulder, CO: Westview Press: 233–247. 

Shannon, M.A.; Antypas, A.R. 1997. Open institutions: uncertainty and 

ambiguity in 21st-century forestry. In: Kohm, K.A.; Franklin, J.F., eds. Creating a 

forestry for the 21st century: the science of ecosystem management. Washington, 

DC: Island Press: 437–445.

Smith, C.L.; Gilden, J.; Steel, B.S.; Mrakovcich, K. 1998. Sailing the 

shoals of adaptive management: the case of salmon in the Pacific Northwest. 

Environmental Management. 22(5): 671–681.

Socolow, R.H. 1976. Failures of discourse: obstacles to the integration of 

environmental values into natural resource policy. In: Tribe, L.H.; Schelling, 

C.S.; Voss, J., eds. When values conflict: essays on environmental analysis, 

discourse, and decision. Cambridge, MA: Ballinger Company: 1–33.

Stankey, G.H.; Bormann, B.T.; Ryan, C.; Shindler, B.; Sturtevant, V.; Clark, 

R.N.; Philpot, C. 2003a. Adaptive management and the Northwest Forest Plan: 

rhetoric and reality. Journal of Forestry. 101(1): 40–46.

Stankey, G.H.; McCool, S.F.; Clark, R.N. 2003b. Building innovative institutions 

for ecosystem management: integrating analysis and inspiration. In: Shindler, 

B.A.; Beckley, T.M.; Finley, M.C., eds. Two paths toward sustainable forests: 

public values in Canada and the United States. Corvallis, OR: Oregon State 

University Press: 271–295.

Stankey, G.H.; Shindler, B. 1997. Adaptive management areas: achieving the 

promise, avoiding the peril. Gen. Tech. Rep. PNW-GTR-394. Portland OR: U.S. 

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

21 p. 

Thomas, J.W. 1999. Learning from the past and moving to the future. In: Johnson, 

K.N.; Swanson, F.; Herring, M.; Greene, S., eds. Bioregional assessments: 

science at the crossroads of management and policy. Washington, DC: Island 

Press: 11–25.

Thompson, J.D.; Tuden, A. 1987. Strategies, structures, and processes of 

organizational decision. In: Thompson, J.D.; Hammond, P.B.; Hawkes, R.W.; 

Junker, B.H.; Tuden, A., eds. Comparative studies in administration. New York: 

Garland Publishing Company: 197–216.
GENERAL TECHNICAL REPORT PNW-GTR-654

72

Tsang, E.W.K. 1997. Organizational learning and the learning organization: a 

dichotomy between descriptive and prescriptive research. Human Relations. 

50(1): 73–89.

U.S. Department of Agriculture, Forest Service; U.S. Department of the 

Interior, Bureau of Land Management [USDA USDI]. 1994. Record of 

decision for amendments to Forest Service and Bureau of Land Management 

planning documents within the range of the northern spotted owl. [Place of 

publication unknown.] 74 p. [Plus attachment A: standards and guidelines].

Van Cleve, F.B.; Simenstad, C.; Goetz, F.; Mumford, T. 2003. Application 

of “best available science” in ecosystem restoration: lessons learned from 

large-scale restoration efforts in the U.S. Puget Sound Nearshore Ecosystem 

Restoration Project. 38 p. http://www.pugetsoundnearshore.org. [Date accessed 

unknown].

Volkman, J.M.; McConnaha, W.E. 1993. Through a glass, darkly: Columbia 

River salmon, the Endangered Species Act, and adaptive management. 

Environmental Law. 23(4): 1249–1272.

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

Macmillan. 374 p.

Walters, C.J. 1997. Challenges in adaptive management of riparian and coastal 

ecosystems. Conservation Ecology. 1(2):1. http://www.consecol.org/vol1/iss2/

art1. (October 5, 1999).

Walters, C.J.; Holling, C.S. 1990. Large-scale management experiments and 

learning by doing. Ecology. 71(6): 2060–2068.

Watkins, K.E. 1996. Individuals who learn create organizations that learn. In: 

Workplace learning: debating five critical questions of theory and practice. 

Directions for Adult and Continuing Education. San Francisco: Jossey-Bass. 

72:(Winter): 89–96.

Weinberg, A. 1962. Science and trans-science. Minerva. 9: 220–232.

Wildavsky, A. 1979. Speaking truth to power: the art and craft of policy analysis. 

Boston: Little, Brown, and Company. 431 p.

Wildavsky, A. 1988. Searching for safety. Bowling Green, OH: Social Philosophy

and Policy Center and Transaction Publishers. 253 p.

Wilkinson, C.F. 1992. Crossing the next meridian: land, water, and the future of 

the West. Washington, DC: Island Press. 376 p.
Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions

73

Wilson, J. 2002. Scientific uncertainty, complex systems, and the design of 

common-pool institutions. In: Ostrom, E.; Dietz, T.; Dolšak, N.; Stern, P.C.; 

Stonich, S., Weber, E.U., eds. The drama of the commons. Washington, DC: 

National Academy Press: 327–359.

Wondolleck, J.M. 1988. Public lands conflict and resolution: managing national 

forest disputes. New York: Plenum Press. 263 p.

Yankelovich, D. 1991. Coming to public judgment: making democracy work in a 

complex world. Syracuse, NY: Syracuse University Press. 290 p.

Yorks, L.; Marsick, V.J. 2000. Organizational learning and transformation. In: 

Mezirow, J., ed. Learning as transformation: critical perspectives on a theory in 

progress. San Francisco: Jossey Bass Publishers: 253–281.

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