Active adaptive conservation of threatened species in the face of uncertainty

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


Author McDonald-Madden, E
Probert, WJM
Hauser, CE
Runge, MC
Possingham, HP
Jones, ME
Moore, JL
Rout, TM
Vesk, PA
Wintle, BA
Title Active adaptive conservation of threatened species in the face of uncertainty
Journal name Ecological Applications   Check publisher's open access policy
ISSN 1051-0761
Publication date 2010-07
Year available 2010
Sub-type Article (original research)
DOI 10.1890/09-0647.1
Volume 20
Issue 5
Start page 1476
End page 1489
Total pages 14
Place of publication United States of America
Publisher Ecological Society of America
Collection year 2011
Language eng
Subject C1
960599 Ecosystem Assessment and Management not elsewhere classified
050205 Environmental Management
Formatted 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.
Keyword Active adaptive management
Bayesian updating
Decision theory
Learning
Markov decision process
Sarcophilus harrisii
Stochastic dynamic programming
Tasmania, Australia
Tasmanian devil facial tumor disease
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Sun, 11 Jul 2010, 00:06:43 EST