Adaptive Management

© Springer Science+Business Media Dordrecht (outside the USA) 2015
Craig R. Allen and Ahjond S. Garmestani (eds.)Adaptive Management of Social-Ecological Systems10.1007/978-94-017-9682-8_1

1. Adaptive Management

Craig R. Allen  and Ahjond S. Garmestani 

U.S. Geological Survey, Nebraska Cooperative Fish and Wildlife Research Unit, University of Nebraska, 68583 Lincoln, USA

Office of Research and Development, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, 45268 Cincinnati, OH, USA



Craig R. Allen (Corresponding author)


Ahjond S. Garmestani

Adaptive managementEcologyUncertaintyResilienceNatural resource management


Management is complicated by social pressures, which are often poorly understood and affect the application of science to management problems. Often natural resource managers are under pressure from stakeholders and mandates from central offices to promote a narrow focus on single-species management (e.g., game species). Thus, managers are often forced to select between particular kinds of resource use, weighing ecosystem services against one another (Millennium Ecosystem Assessment 2005, Rodríguez et al. 2005). If tradeoffs between services are ignored, future problems may be created that can result in expensive remedial actions to restore previously available ecosystem services.

An additional problem in developing effective management for complex systems is that social and ecological components may not be aligned at the appropriate scales to achieve consistent regional and local management (Conroy et al. 2003, Cumming et al. 2006). It is not uncommon for agency administrators to demand local management actions that are impossible or inappropriate. For example, state-wide hunting regulations may be inappropriate where local wildlife populations are overabundant or on the verge of extinction. Conversely, many ecosystem processes are difficult to manage at the local scale, and appropriate regional authorities and mandates may not exist.

Paradigms for multi-species and ecosystem management have existed for decades, but their implementation within management agencies lags in acceptance, despite compelling arguments for their usefulness (Barrows et al. 2005). The failure of many federal and state management agencies to embrace ecosystem management may be attributable to restrictive institutional mandates and agendas, inflexibility in their ability to adopt new approaches and avoidance of risk taking, and lack of funding (particularly for long-term monitoring and intensive schemes that ecosystem management often demands). Additionally there are real and perceived shortcomings in the associated science, especially in basic understanding of social-ecological systems and in translating theory-derived guidelines into practical, unambiguous recommendations for managers.

Changes in natural resource management through time have been driven by changes in scientific understanding, as well as by a wide range of changes in society and politics. The usual goal of management is to ensure that one or more properties of a system of interest are maintained through time. This is often interpreted as a need for managers to either seek to maintain system stability , or to maintain particular system components and relationships while allowing or encouraging the system to change. In considering the dynamics of management and system change, an understanding of resilience is particularly relevant.

Attempts to optimize economic returns, physical connectivity, or other single system properties are typically doomed in the long-term to failure because related, critical variables are often negatively affected by such management (Holling and Meffe 1996) . Available evidence suggests that managing for single variables usually fails because such approaches do not account for potential feedbacks , thresholds , or surprises arising from interactions with other components of the system (Holling and Meffe 1996). Optimization or efficiency approaches applied to single variables or to maximize output over short time frames often fail because of the complex interactions between social and ecological components of the system (Mascia et al. 2003). Unfortunately, strategies for managing multiple variables are seldom applied, and if they are, appropriate factors for maintaining resilience are rarely identified, monitored, and enforced.

Adaptive management , applied in an appropriate way to an appropriate problem, can speed the process of learning about complex natural resource problems. Adaptive management is an approach to natural resource management that emphasizes learning through management where knowledge is incomplete, and when, despite inherent uncertainty , managers and policymakers must act (Walters 1986) . Although the concept of adaptive management has resonated with resource management scientists and practitioners following its formal description (Holling 1978) , it has been and continues to remain frequently misapplied and misunderstood. Misunderstanding is largely based upon the belief that adaptive management is a trial and error attempt to improve management outcomes, that is, the adaptive component is interpreted as a willingness to try something new when current approaches fail, rather than a structured approach focused on learning. Unlike a traditional trial and error approach, adaptive management has explicit structure, including a careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. Regardless of the particular definition of adaptive management used, and there are many, adaptive management emphasizes learning and subsequent adaptation of management based upon that learning. The process is iterative , and serves to reduce uncertainty , build knowledge and improve management over time in a goal-oriented and structured process. Adaptive management is a poor fit for solving problems of intricate complexity, high external influences, long time spans, high structural uncertainty and with low confidence in assessments (Gregory et al. 2006) (e.g., climate change) . However, even in such situations, adaptive management may be the preferred alternative, and can be utilized to resolve or reduce structural uncertainty.

Adaptive management is now common to a variety of resource management issues, and represents an evolving approach to natural resource management in particular, and structured decision making in general. Founded in the decision approaches of other fields (Williams 2010) including business (Senge 1990), experimental science (Popper 1968), systems theory (Ashworth 1982) and industrial ecology (Allenby and Richards 1994), the first reference to adaptive management philosophies in natural resource management may be traced back to the work of Beverton and Holt (1957) in fisheries management, though the term adaptive management was yet to be used (reviewed in Williams 2010). The term adaptive management would not become common vernacular until C.S. Holling , widely recognized as the “father” of adaptive management, edited “Adaptive Environmental Assessment and Management” in 1978 (Holling 1978) . The work was spawned by the experiences of Holling and colleagues at the University of British Columbia following from the development of resilience theory (Holling 1973). The concept of resilience , predicated upon the existence of more than one alternative stable state for ecosystems, had several ramifications. For one, it meant that managers should be very careful not to exceed a threshold that might change the state of the system being managed; and the location of those thresholds is unknown. Second, for ecological systems in a favorable state, management should focus on maintaining that state, and its resilience. Adaptive management then, was a method to probe the dynamics and resilience of systems while continuing with ‘management’ via management experiments developed to enhance learning and reduce uncertainty.

Eventually Carl Walters (1986) built upon Holling’s original book (1978) and further developed the ideas, especially in the realm of mathematical modeling. Whereas Holling’s original emphasis was in bridging the gap between science and practice, Walters emphasized treating management activities as designed experiments to reduce uncertainty . Both scientists sought an approach that allowed resource management and exploitation to continue while explicitly embracing uncertainties and seeking to reduce them through that management. Walters (1986) described the process of adaptive management as beginning “with the central tenet that management involves a continual learning process that cannot conveniently be separated into functions like research, ongoing regulatory activities, and probably never converges to a state of blissful equilibrium involving full knowledge and optimum productivity.” He characterized adaptive management as the process of defining and bounding the management problem, identifying and representing what we know through models of dynamics that identify assumptions and predictions so experience can further learning, identifying possible sources of uncertainty and alternate hypotheses, and designing policies to allow continued resource management while enhancing learning.

The confusion over the term “adaptive management” may stem from the flexibility inherent in the approach which has resulted in multiple interpretations of “adaptive management” that fall upon a continuum of complexity and a priori design, starting from the simple (e.g., “learning by doing”) and progressing to the more explicit (e.g., “a rigorous process that should include sound planning and experimental design with a systematic evaluation process that links monitoring to management”) (Wilhere 2002, Aldridge et al. 2004). Obviously there is a clear distinction in intent, investment and success between approaches that propose to learn from prior management decisions and those that outline a concise feedback mechanism dependent upon sound scientific principles on which future management decisions will be made. Central to the success of the structured decision making process is the requirement to clearly articulate fundamental objectives, explicitly acknowledge uncertainty, and respond transparently to stakeholder interests in the decision process. The conceptual simplicity inherent in structured decision making makes the process useful for all decisions from minor decisions to complex problems involving multiple stakeholders .

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