Adaptive Management

Adaptive management (AM), also known as adaptive resource management (ARM), is a structured, iterative process of robust decision making in the face of uncertainty, with an aim to reducing uncertainty over time via system monitoring. In this way, decision making simultaneously meets one or more resource management objectives and, either passively or actively, accrues information needed to improve future management. Adaptive management is a tool which should be used not only to change a system, but also to learn about the system (Holling 1978). Because adaptive management is based on a learning process, it improves long - run management outcomes. The challenge in using the adaptive management approach lies in finding the correct balance between gaining knowledge to improve management in the future and achieving the best short - term outcome based on current knowledge (Allan & Stankey 2009).

There are a number of scientific and social processes which are vital components of adaptive management, including:

1. Management is linked to appropriate temporal and spatial scales
2. Management retains a focus on statistical power and controls
3. Use of computer models to build synthesis and an embodied ecological consensus
4. Use of embodied ecological consensus to evaluate strategic alternatives
5. Communication of alternatives to political arena for negotiation of a selection

The achievement of these objectives requires an open management process which seeks to include past, present and future stakeholders. Adaptive management needs to at least maintain political openness, but usually aims to create it. Adaptive management must therefore be a scientific and social process. It must focus on the development of new institutions and institutional strategies in balance with scientific hypothesis and experimental frameworks (resilliance.org).

Adaptive management can proceed as either passive adaptive management or active adaptive management, depending on how learning takes place. Passive adaptive management values learning only insofar as it improves decision outcomes (i.e. passively), as measured by the specified utility function. In contrast, active adaptive management explicitly incorporates learning as part of the objective function, and hence, decisions which improve learning are valued over those which do not(Holling 1978; Walters 1986). In both cases, as new knowledge is gained, the models are updated and optimal management strategies are derived accordingly. Thus, while learning occurs in both cases, it is treated differently. Often, deriving actively adaptive policies is technically very difficult, which prevents it being more commonly applied.

Key features of both passive and active adaptive management are:

• Iterative decision-making (evaluating results and adjusting actions on the basis of what has been learned)
• Feedback between monitoring and decisions (learning)
• Explicit characterization of system uncertainty through multi-model inference
• Bayesian inference
• Embracing risk and uncertainty as a way of building understanding

Adaptive management is particularly applicable for systems in which learning via experimentation is impractical. However, any one of five process failures can seriously compromise effective adaptive management decision making (Elzinga et al. 1998; Alana & Michael, 2009):

• The monitoring is never completed.
• The monitoring data are not analyzed and cannot ensure it's definitely accurate.
• The analyzed results are not conclusive.
• The analyzed results (Boormann et al. 1999).