Optimal Adaptive Management for the Translocation of a Threatened Species

• Published in: Ecological applications Vol. 19; no. 2; pp. 515 - 526
• Main Authors: Rout, Tracy M., Hauser, Cindy E., Possingham, Hugh P.
• Format: Journal Article
• Language: English
• Published: United States Ecological Society of America 01-03-2009
• Subjects: active adaptive management; Animals; Applied ecology; Australia; Bayes Theorem; Bayesian statistics; bridled nailtail wallaby; Carrying capacity; Conservation biology; Conservation of Natural Resources - methods; decision theory; Environment; Extinction, Biological; Learning rate; Macropodidae - physiology; Markov Chains; Mortality; Onychogalea fraenata; optimization; Population ecology; Population Growth; Population growth rate; Population size; Queensland; reintroduction; stochastic dynamic programming; translocation; Wallabies; Wildlife management; Queensland; Australia, Queensland
• ISSN: 1051-0761; 1939-5582
• DOI: 10.1890/07-1989.1

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 optmal 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.