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Details of Adaptive management

A systematic process for continually improving management policies and practices by learning from the outcomes of previously employed policies and practices. In active adaptive management, management is treated as a deliberate experiment for the purpose of learning.

Adaptive management, also known as adaptive resource management (ARM), is a structured, iterative process of optimal decision-making in the face of uncertainty, with an aim to reducing uncertainty over time via system monitoring. In this way, decision-making simultaneously maximizes one or more resource objectives and, either passively or actively, accrues information needed to improve future management. AM is often characterized as "learning by doing."

History

'Adaptive environmental assessment and management' was the original name given to this approach which was developed by the ecologists C.S. Holling and Carl J. Walters at the University of British Columbia, Canada in the 1970s. The approach was further developed at IIASA in Vienna, Austria, while C.S. Holling was director of the Institute. Adaptive management has probably been most frequently applied in Australia and North America, initially applied in fishery management, but received more broad application in the 1990s and 2000s. One of the most successful applications of adaptive management have been in the area of waterfowl harvest management in North America, most notably for the Mallard.^{[citation needed]}

Explanation

Adaptive management can be considered either passive or active. The former begins by using predictive modeling based on present knowledge to inform management decisions. As new knowledge is gained, the models are updated and management decisions adapted accordingly.

Active adaptive management, on the other hand, involves changing management strategies altogether in order to test completely new hypotheses. So while the goal of passive adaptive management is to improve existing management approaches, the goal of active adaptive management is to learn by experimentation in order to determine the best management strategy.

Key features of both passive and active adaptive management are:

• Iterative decision-making (evaluating results and adjusting actions based on 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

ARM is particularly applicable for systems in which learning via experimentation is impractical.

Because ARM is used to make decisions regarding the management of valuable natural resources, it directly affects (and is affected by) public policy.

Key books

• Holling, C. S. (ed.) (1978). Adaptive Environmental Assessment and Management. Chichester: Wiley. ISBN 0-471-99632-7. 
• Lee, Kai N. (1993). Compass and Gyroscope: Integrating Science and Politics for the Environment. Washington, D.C.: Island Press. ISBN 1-55963-197-X. 
• Walters, Carl (1986). Adaptive Management of Renewable Resources. New York: Macmillan. ISBN 0-02-947970-3. 

Other references

• Nichols, J.D.; and Johnson, F.A.; and Williams, B.K. (1995). "Managing North American waterfowl in the face of uncertainty.". Annu. Rev. Ecol. Syst. 26: 177-199. 

• Johnson, F.A.; and Williams, B.K. (1999). ""Protocol and practice in the adaptive management of waterfowl harvests."". Conservation Ecology 3 (8). 

• Marmorek, D.R.; D.C.E. Robinson, C. Murray and L. Grieg (2006). Enabling Adaptive Forest Management. National Commission on Science for Sustainable Forestry, 94 pp. 

• Murray, Carol; and David Marmorek (2003). "Adaptive management and ecological restoration", in in Peter Friederici (ed.),: Ecological Restoration of Southwestern Ponderosa Pine Forests. Washington, D.C.: Island Press, 417-428. ISBN 1-55963-652-1. 

• Peterman, Randall M.; and Calvin N. Peters (1998). "Decision analysis: taking uncertainties into account in forest resource management", in in Vera Sit and Brenda Taylor (eds).,: Statistical Methods for Adaptive Management Studies. Victoria, B.C.: B.C. Ministry of Forests, 105-127. ISBN 0-7726-3512-9. 

• Stankey, George H; Roger N. Clark and Bernard T. Bormann. 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. 

See also

• Conservation ecology
• Decision Theory
• Ecology
• Fisheries
• Forestry
• Optimization (mathematics)
• Operations research
• Uncertainty