Abstract
It is generally the case that a significant degree of uncertainty exists concerning the behavior of ecological systems. Adaptive management has been developed to address such structural uncertainty, while recognizing that decisions must be made without full knowledge of how a system behaves. This paradigm attempts to use new information that develops during the course of management to learn how the system works. To date, however, adaptive management has used a very limited information set to characterize the learning that is possible. This paper uses an extension of the Partial Observable Markov Decision Process (POMDP) framework to expand the information set used to update belief in competing models. This feature can potentially increase the speed of learning through adaptive management, and lead to better management in the future. We apply this framework to a case study wherein interest lies in managing recreational restrictions around golden eagle (Aquila chrysaetos) nesting sites. The ultimate management objective is to maintain an abundant eagle population in Denali National Park while minimizing the regulatory burden on park visitors. In order to capture this objective, we developed a utility function that trades off expected breeding success with hiker access. Our work is relevant to the management of human activities in protected areas, but more generally demonstrates some of the benefits of POMDP in the context of adaptive management.
Highlights
There is currently a paradigm shift in many conservation organizations
To overcome its limitations we have applied a Partial Observable Markov Decision Process (POMDP) approach (‘‘extended POMDP’’; Fackler and Pacifici [7]) that allows for both structural uncertainty and partial observability to be handled in a common framework; here we focus on structural uncertainty
In the remainder of the paper we briefly describe the extended POMDP approach and highlight several features of this approach by modifying an existing case study wherein interest lies in managing recreational restrictions around golden eagle nesting sites [8]
Summary
There is currently a paradigm shift in many conservation organizations These organizations are asked to come up with clear conservation objectives, and to increase the efficiency in their use of conservation funds. Scientific experiments represent the most rigorous and efficient way to reduce structural (model) uncertainty, i.e., uncertainty about the behavior of a managed system. This observation sometimes leads to a recommendation to conduct adaptive management as a sequential, 2step ‘‘learn manage’’ process. It is rarely optimal to postpone management in order to wait until the analyses of experiments are complete These perspectives argue against this sequential ‘‘learn manage’’ approach to adaptive management
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