Kolstrom, R., T. L. Wilson, and L.M. Gigliotti. 2020. Using a Structured Decision Analysis to Evaluate Bald Eagle Vital Signs Monitoring in Southwest Alaska National Parks. Ecology and Evolution, Open Access: Manuscript ID: ECE-2019-10-01387.R1 https://doi.org/10.1002/ece3.6499
Monitoring programs can benefit from an adaptive monitoring approach, where key decisions about why, where, what, and how to monitor are revisited periodically in order to ensure programmatic relevancy. The National Park Service (NPS) monitors status and trends of vital signs to evaluate compliance with the NPS mission. Although abundant, The Southwest Alaska Network (SWAN) monitors bald eagles because of their inherent importance to park visitors and role as an important ecological indicator. Our goal is to identify an optimal monitoring program that may be standardized among participating parks. We gathered an expert panel of scientists and managers, and implemented a Delphi Process to gather information about the bald eagle monitoring program. Panelists generated a list of means objectives for the monitoring program: minimizing cost, minimizing effort, maximizing the ability to detect change in bald eagle populations, and maximizing the amount of accurate information collected about bald eagles. We used a swing-weighting technique to assign importance to each objective. Collecting accurate information about bald eagles was considered the most important means objective. Combining panelist-generated information with objective importance, we analyzed the scenarios and defined the optimal decision using linear value modeling. Through our analysis, we found that a “Comprehensive” monitoring scenario, comprised of all feasible monitoring metrics, is the optimal monitoring scenario. Even with greatly increased cost, the Comprehensive monitoring scenario remains the best solution. We suggest further exploration of the cost and effort required for the Comprehensive scenario, to determine whether it is in the parks’ best interest to begin monitoring additional metrics.