Components of adaptive management

Abstract

Adaptive Management of ecosystems for production and preservation is a cyclical process with four components: learning, describing, predicting and doing. Learning involves monitoring and evaluation, describing uses models to summarise and represent systems, prediction and gaming are used to test policies and proposed actions, and the doing is done through management experiments. Successful adaptive management needs clear objectives, data and knowledge, the right participants, science skill, willing partners, and money and time. Additionally, in doing adaptive management it is necessary to drive and steer the process, keep momentum, embrace uncertainty, and beware of the danger of half measures. Adaptive management supports decisions and resource allocation, and provides a framework for action directed to changing ecosystem state while learning through and from such change. It focuses conversation and reduces arguments and finger pointing; and also reduces excuses for inaction, provides system understanding, identifies data and knowledge gaps, and sets up a time and space framework for explanation of key processes. Adaptive management won't make decisions, won't do the work or the thinking, and has scientific, social, political and economic aspects that may cause failure, but which, when understood and embraced, provide the framework for successful ecosystem improvement.