Active adaptive management for conservation

McCarthy, M. A. and Possingham, H. P. (2007) Active adaptive management for conservation. Conservation Biology, 21 4: 956-963. doi:10.1111/j.1523-1739.2007.00677.x


Author McCarthy, M. A.
Possingham, H. P.
Title Active adaptive management for conservation
Journal name Conservation Biology   Check publisher's open access policy
ISSN 0888-8892
Publication date 2007
Sub-type Article (original research)
DOI 10.1111/j.1523-1739.2007.00677.x
Volume 21
Issue 4
Start page 956
End page 963
Total pages 8
Editor Meffe, G. K.
Place of publication United States
Publisher Blackwell Publishing
Collection year 2008
Language eng
Subject 270708 Conservation and Biodiversity
C1
779902 Land and water management
Abstract Active adaptive management balances the requirements of management with the need to learn about the system being managed, which leads to better decisions. It is difficult to judge the benefit of management actions that accelerate information gain, relative to the benefit of making the best management decision given what is known at the time. We present a first step in developing methods to optimize management decisions that incorporate both uncertainty and learning via adaptive management We assumed a manager can allocate effort to discrete units (e.g., areas for revegetation or animals for reintroduction), the outcome can be measured as success or failure (e.g., the revegetation in an area is successful or the animal survives and breeds), and the manager has two possible management options from which to choose. We further assumed that there is an annual budget that may be allocated to one or both of the two options and that the manager must decide on the allocation. We used Bayesian updating of the probability of success of the two options and stochastic dynamic programming to determine the optimal strategy over a specified number of years, The costs, level of certainty about the success of the two options, and the timeframe of management all influenced the optimal allocation of the annual budget. In addition, the choice of management objective had a large influence on the optimal decision. In a case study of Merri Creek, Melbourne, Australia, we applied the approach to determining revegetation strategies. Our approach can be used to determine how best to manage ecological systems in the face of uncertainty.
Keyword Biodiversity Conservation
Ecology
Environmental Sciences
active adaptive management
Bayesian statistics
decision theory
revegetation
urban ecology
Optimal Translocation Strategies
Optimal Fire Management
Populations
Uncertainty
Model
Q-Index Code C1
Q-Index Status Confirmed Code

 
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Created: Mon, 18 Feb 2008, 14:32:43 EST