Optimal adaptive management for the translocation of a threatened species

Rout, Tracy M., Hauser, Cindy E. and Possingham, Hugh P. (2009) Optimal adaptive management for the translocation of a threatened species. Ecological Applications, 19 2: 515-526. doi:10.1890/07-1989.1

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
UQ181588_OA.pdf Full text (open access) application/pdf 820.28KB 0

Author Rout, Tracy M.
Hauser, Cindy E.
Possingham, Hugh P.
Title Optimal adaptive management for the translocation of a threatened species
Journal name Ecological Applications   Check publisher's open access policy
ISSN 1051-0761
Publication date 2009-03
Year available 2009
Sub-type Article (original research)
DOI 10.1890/07-1989.1
Open Access Status File (Publisher version)
Volume 19
Issue 2
Start page 515
End page 526
Total pages 12
Editor Schimel, D.
Place of publication United States
Publisher Ecological Society of America
Collection year 2010
Language eng
Formatted abstract
Active adaptive management (AAM) is an approach to wildlife management that acknowledges our imperfect understanding of natural systems and allows for some resolution of our uncertainty. Such learning may be characterized by risky strategies in the short term. Experimentation is only considered acceptable if it is expected to be repaid by increased returns in the long term, generated by an improved understanding of the system. By setting AAM problems within a decision theory framework, we can find this optimal balance between achieving our objectives in the short term and learning for the long term. We apply this approach to managing the translocation of the bridled nailtail wallaby (Onychogalea fraenata), an endangered species from Queensland, Australia. Our task is to allocate captive-bred animals, between two sites or populations to maximize abundance at the end of the translocation project. One population, at the original site of occupancy, has a known growth rate. A population potentially could be established at a second site of suitable habitat, but we can only learn the growth rate of this new population by monitoring translocated animals. We use a mathematical programming technique called stochastic dynamic programming, which determines optimal management decisions for every possible management trajectory. We find optimal strategies under active and passive adaptive management, which enables us to examine the balance between learning and managing directly. Learning is more often optimal when we have less prior information about the uncertain population growth rate at the new site, when the growth rate at the original site is low, and when there is substantial time remaining in the translocation project. Few studies outside the area of optimal harvesting have framed AAM within a decision theory context. This is the first application to threatened species translocation.
Keyword active adaptive management
Bayesian statistics
Bridled natiltail wallaby
decision theory
Onychogalea fraenata
Queensland, Australia
stochastic dybamic programing
wildlife management
Q-Index Code C1
Q-Index Status Confirmed Code

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 47 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 58 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Thu, 03 Sep 2009, 08:36:29 EST by Mr Andrew Martlew on behalf of School of Biological Sciences