Using decision models to assist Vital Signs Monitoring in National Parks: A prototype using sea otters (Enhydra lutris kenyoni) in coastal Alaska

Background/Question/Methods

Federal and state management agencies are faced with the difficult task of long-term protection and management of resources under their jurisdiction. Achieving this task requires multiple approaches, including the conservation of essential habitats and the identification and elimination of potential threats to biota and habitats. To accomplish these goals, many agencies have implemented inventory and monitoring programs to monitor key biological, chemical, and physical components of ecosystems. While detecting and quantifying change is important to conservation efforts, it is of limited use if not collected in such a manner as to resolve key uncertainties. The agency primarily responsible for implementing monitoring of northern sea otter populations in southwest Alaska is the National Park Service, while the U.S. Fish and Wildlife Service is responsible for managing sea otters pursuant to both the Marine Mammal Protection Act and the Endangered Species Act.  We present structured decision making (SDM) as a means to link interagency monitoring to management decision-making and to identify key uncertainties to help prioritize monitoring and research needs.  Formal decision making frameworks such as SDM provide a means by which to evaluate and optimize natural resource decision making in an integrated framework that explicitly links research and monitoring to management decision making.  Using this approach, we developed an integrated modeling and decision support program appropriate for the management of northern sea otters in Southwest Alaska Network Park Units. 

Results/Conclusions

Objectives, values and decision alternatives were identified and structured into a means objective hierarchy based on feedback from both management agencies.  We then constructed a Bayesian Belief Network (BBN), a probabilistic form of an influence diagram, to depict causal relations among demographic, environmental, and anthropogenic factors that could potentially influence sea otter population status.  Sensitivity analysis was used to assess the relative influence of each model component on the population outcome (sea otter persistence).  Sensitivity analysis revealed that model components representing predation, disease and contaminant exposure were key model uncertainties (i.e. the population outcome was highly variable depending on the state of these model components).  Juvenile and pre-weaning survival model components were more sensitive to contaminant exposure than the adult survival model component.  Future monitoring and research efforts explicitly designed at reducing these key uncertainties would be valuable.  We believe that formal decision analysis provides a useful framework for linking interagency management and for prioritizing monitoring and data collection needs.