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Active adaptive conservation of threatened species in the face of uncertainty

Citation

McDonald-Madden, E and Probert, WJM and Hauser, CE and Runge, MC and Possingham, HP and Jones, ME and Moore, JL and Rout, TM and Vesk, PA and Wintle, BA, Active adaptive conservation of threatened species in the face of uncertainty , Ecological Applications, 20, (5) pp. 1476-1489. ISSN 1051-0761 (2010) [Refereed Article]


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Copyright Statement

Copyright © 2010 by the Ecological Society of America

DOI: doi:10.1890/09-0647.1

Abstract

Adaptive management has a long history in the natural resource management literature, but despite this, few practitioners have developed adaptive strategies to conserve threatened species. Active adaptive management provides a framework for valuing learning by measuring the degree to which it improves long-run management outcomes. The challenge of an active adaptive approach is to find the correct balance between gaining knowledge to improve management in the future and achieving the best short-term outcome based on current knowledge. We develop and analyze a framework for active adaptive management of a threatened species. Our case study concerns a novel facial tumor disease affecting the Australian threatened species Sarcophilus harrisii: the Tasmanian devil. We use stochastic dynamic programming with Bayesian updating to identify the management strategy that maximizes the Tasmanian devil population growth rate, taking into account improvements to management through learning to better understand disease latency and the relative effectiveness of three competing management options. Exactly which management action we choose each year is driven by the credibility of competing hypotheses about disease latency and by the population growth rate predicted by each hypothesis under the competing management actions. We discover that the optimal combination of management actions depends on the number of sites available and the time remaining to implement management. Our approach to active adaptive management provides a framework to identify the optimal amount of effort to invest in learning to achieve long-run conservation objectives.

Item Details

Item Type:Refereed Article
Keywords:active adaptive management; Bayesian updating; decision theory; learning; Markov decision process; Sarcophilus harrisii; stochastic dynamic programming; Tasmania, Australia; Tasmanian devil facial tumor disease
Research Division:Biological Sciences
Research Group:Evolutionary Biology
Research Field:Speciation and Extinction
Objective Division:Environment
Objective Group:Ecosystem Assessment and Management
Objective Field:Ecosystem Assessment and Management at Regional or Larger Scales
Author:Jones, ME (Associate Professor Menna Jones)
ID Code:64723
Year Published:2010
Web of Science® Times Cited:50
Deposited By:Zoology
Deposited On:2010-08-18
Last Modified:2011-04-08
Downloads:810 View Download Statistics

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