Sabine Schreiber, Andrew Bearlin, Simon Nicol and Charles Todd are scientists with the Arthur Rylah Institute for Environmental Research (Department of Sustainability and Environment Victoria) and the Co-operative Research Centre for Freshwater Ecology (both institutions contactable through PO Box 137, Heidelberg, Victoria 3084, Australia. Tel. +61-3 9450 8640, Email: Sabine.Schreiber@dse.vic.gov.au). The work is associated with the Cooperative Research Center's project C210, Adaptive Management in Restoration Ecology, which included this review and an application of Adaptive Management to the management of an endangered fish species.
Adaptive management: a synthesis of current understanding and effective application
Article first published online: 10 NOV 2004
Ecological Management & Restoration
Volume 5, Issue 3, pages 177–182, December 2004
How to Cite
Schreiber, E. S. G., Bearlin, A. R., Nicol, S. J. and Todd, C. R. (2004), Adaptive management: a synthesis of current understanding and effective application. Ecological Management & Restoration, 5: 177–182. doi: 10.1111/j.1442-8903.2004.00206.x
- Issue published online: 10 NOV 2004
- Article first published online: 10 NOV 2004
- adaptive environmental assessment and management;
Summary Adaptive management (AM) remains a commonly cited, yet frequently misunderstood, management approach. The aim of AM is to improve environmental management through ‘learning by doing’ and understand the impact of incomplete knowledge, but AM more commonly consists of ad hoc changes in managing environmental resources in the absence of adequate planning and monitoring. Here, we trace and review the development of AM, the central roles of consultation, collaboration and of monitoring, and of quantitative models and simulations. We identify a series of formalized, structured steps included in one AM cycle and review how current AM programs build upon such cycles. We conclude that the best AM outcomes require rigorous and formalized approaches to planning, collaboration, modelling and evaluation. Finally, simulating potential outcomes of an AM cycle in the presence of existing uncertainty can help to identify management strategies that are most likely to succeed in relation to clearly articulated goals.