Ministry of Forests and Range
 
Highlights
"Adaptive Management is a systematic process for continually improving management policies and practices"
 

Defining Adaptive Management

Adaptive management has been defined in various ways since its development in the early 1970s. This presents a challenge to practitioners who must reach a common understanding with partners, stakeholders, managers, scientists and decision makers. In order to bring some consistency to what the BC Forest Service means by “adaptive management”, the following standard working definition was adopted.

Adaptive management is a systematic process for continually improving management policies and practices by learning from the outcomes of operational programs. Its most effective form–"active" adaptive management–employs management programs that are designed to experimentally compare selected policies or practices, by evaluating alternative hypotheses about the system being managed.

Another MFR definition is:
A systematic, rigorous approach for deliberately learning from management actions with the intent to improve subsequent management policy or practice (MFR, 2008)

Although definitions of adaptive management vary by source, several key characteristics of the concept are universal and fundamental:

• Learning; reducing key uncertainties
There is explicit acknowledgement of uncertainties and knowledge gaps about the response of the system to management actions. Reducing these uncertainties (i.e. learning) becomes one objective of management.

• Using what is learned to change policy and practice
Process in place to make certain that what is learned informs decisions (i.e. closing the loop). It is essential to have a good idea at project design stage of what policies and practices may change and what institutional mechanisms are in place to support that change.

• Focus is on improving management
AM integrates the worlds of science and management, ensuring applied science is well directed to key uncertainties and scientific advances are transferred to managers (i.e. this is where the learning is applied)

• Often called experimental management
AM is about thoughtfully applying management activities as experiments to see which are most effective in achieving desired goals

• It is formal, structured, systematic
AM is a deliberate process, not ad-hoc or simply reactionary. However, flexibility in the approach is important to allow the creativity that is crucial to dealing with uncertainty and change.

The adaptive management approach assumes natural resource management policies and management actions are not static but adjusted based on the combination of new scientific and socio-economic information in order to improve management by learning from the ecosystems being affected. Often people think adaptive management simply means “trial and error”, in which management policies and practices evolve in response to past performance and changing priorities, but in fact this misses an essential element of the concept, which is deliberate experimentation.

We often portray the adaptive management process as a six-step cycle, and emphasize that successful adaptive management usually requires managers to complete all six steps. Within each of the steps, several elements are identified. Although the full suite of elements may not be implemented for every AM project, it is important to understand them and the implications of omitting any.

Adaptive Management 6 Step Process Cycle
Step 1: Assess and Define the Problem Step 2: Design Management Plan Step 3: Implementation Step 4: Monitoring Step 5: Evaluation of Results Step 6: Adjustment/ Revision of Hypotheses & Management

For more details on the elements within each of these steps, click on the step in the diagram above or see An Introductory Guide to Adaptive Management

Adaptive Co-Management

When the iterative learning of adaptive management is combined with the linkages of collaborative management, adaptive co-management (ACM) is the result. The emerging concept of ACM is a collaborative approach to adaptive management that engages governments, proponents and planning participants explicitly in defining issues, developing management plans and monitoring outcomes. (Ruitenbeek, J. and C. Cartier. 2001)

Active & Passive Adaptive Management

Adaptive management takes a couple of forms: passive or active (Walters, 1986)

Passive Adaptive Management is an approach whereby,

  • faced with uncertainty, managers implement the alternative they think is ‘best’ (with respect to meeting management objectives), and then monitor to see if they were right, making adjustments if desired objectives are not in fact met (Figure 2a).

Figure 2a Passive AM

Management Goals/ Objectives Management Alternative (usually best management practice) Monitor & Evaluate
(management outcome)

Active Adaptive Management is an experimental approach whereby,

  • when faced with uncertainty, managers implement more than one alternative as concurrent experiments to see which will best meet management objectives. It is characterised by "actively probing" the system in order to distinguish between competing hypotheses (where the different hypotheses suggest different "optimal" actions). The key is that there are alternatives that can be more confidently compared (Figure 2b).

Figure 2b Active AM

Management Goals/ Objectives Management Alternative 1 Monitor & Evaluate
(management outcome)
Management Alternative 2
Management Alternative 3

Other Definitions of Adaptive Management

Definitions Sources/ Citations
Adaptive management (AM) is a formal process for continually improving management policies and practices by learning from their outcomes. Taylor et al., 1997. Adaptive Forest Management in B.C.
AM is a structured process of learning by doing that involves more than simply better ecological monitoring and response to unexpected management impacts. It should begin with a concerted effort to integrate existing interdisciplinary experience and scientific information into dynamic models that attempt to make predictions about the impacts of alternative policies. Walters, 1997. Challenges in Adaptive Management of Riparian and Coastal Ecosystems.
AM is an approach to managing complex natural systems that builds on learning - based on common sense, experience, experimenting, and monitoring - by adjusting practices based on what was learned. Bormann et al., 1999. Adaptive Management In: Ecological Stewardship: A common reference for ecosystem management.
AM is a systematic process for addressing the uncertainties of resource management policies by implementing the policies experimentally and documenting the results. MacDonald et al., 1999. AM Forum: Linking Management and Science to Achieve Ecological Sustainability.
AM is a structured method for "learning by doing" that includes establishing clear goals, defining practices to achieve those goals, implementing the practices, monitoring the outcome of the practices, assessing how those practices are succeeding relative to the goals, and adjusting management in response to the assessments. It is designed to address questions such as: Where do we want to go? How do we get there? How do we know if we're there? If we're not there, how do we change to improve? Kremsater, Perry and Dunsworth. 2002.
AM treats actions and policies as experiments that yield learning (it mimics the scientific method: specifies hypotheses, highlights uncertainties, structures actions to expose hypotheses to field tests, processes and evaluates results, and adjusts subsequent actions in light of those results), and embraces risk and uncertainty as opportunities for building understanding that might ultimately reduce their occurrence. Stankey et al., 2003. Adaptive Management and the Northwest Forest Plan: Rhetoric and Reality.
AM is "learning by doing" with the addition of an explicit, deliberate and formal dimension to framing questions and problems, undertaking experimentation and testing, critically processing results, and reassessing the policy context that originally triggered investigation in light of the newly acquired knowledge. The concept of learning is central to AM. It is a process to accelerate and enhance learning based on the results of policy implementation that mimics the scientific method: experimentation is the core of adaptive management, involving hypotheses, controls and replication. It is also irreducibly socio-political in nature. Stankey, Clark and Bormann, 2005. Adaptive Management of Natural Resources: Theory, Concepts, and Management Institutions.
AM is a systematic process for continually improving management policies and practices by learning from the outcomes of operational programs. It’s most effective form – "active" AM – employs management programs that are designed to experimentally compare selected policies or practices, by evaluating alternative hypotheses about the system being managed. BC Ministry of Forests and Range Adaptive Management web page.
AM is an innovative technique that uses scientific information to help formulate management strategies in order to 'learn' from programs so that subsequent improvements can be made in formulating both successful policy and improved management programs. Halbert, C.L. 1993. How adaptive is adaptive management? Implementing adaptive management in Washington State and British Columbia.
AM is a formal process for continually improving management practices by learning from the outcomes of operational and experimental approaches. Four elements of this definition are key to its utility. First, it is adaptive, and intended to be self-improving. Second, it is a well-designed, formal approach that connects the power of science to the practicality of management. Third, it is an on-going process for continually improving management, so the design must connect directly to the actions it is intended to improve. Fourth, although experimental approaches can be incorporated into adaptive management effectively, operational approaches and scales are emphasized to permit direct connection to the efforts of managers. Bunnell et al., 2007. Forestry and biodiversity - learning how to sustain biodiversity in managed forests.

Full references for the citations can be found on the References / Reading List page.