Prioritization of Species Status Assessments for Decision Support

Abstract

Species status assessments are used to inform U.S. Fish and Wildlife Service (USFWS) decision making for Endangered Species Act (ESA) classification decisions, recovery planning, and more. The large number of species that require assessment and uncertainty in the data available impede the process of assigning and completing the assessments, which makes creating a multiyear work plan extremely difficult. An optimized triaging system that maximizes the use of the best available information while managing the complex ESA workload and meeting deadlines is necessary. We used a structured decision-making framework to approach the problem with the goal of creating a prioritization tool that would be effective at scheduling assessments, given the best information available and priorities of the USFWS. We collected data on the species awaiting assessment and developed a value function that incorporates existing deadlines, taxonomic uncertainty, controversy of the species, and population and habitat data availability and quality. We used a constrained linear optimization algorithm to maximize the value function and ensure that workload capacity was not exceeded. A comparison of model scenarios indicates that imposed deadlines impact the model more than capacity constraints. Additionally, differential weighting of the metrics significantly affected the outcome of the model. In the future, elicitation of metric weights should be done routinely before the model is run for use in official planning to ensure alignment with current USFWS priorities. Output from this optimization can be used to inform a five-year work plan, allocate resources, and discuss workforce decisions. History: This paper has been accepted for the Decision Analysis Special Issue on Decision Analysis to Advance Environmental Sustainability. Funding: This work was funded via an inter-agency agreement between the USFWS and the USGS and subsequently by a Research Work Order contract between the USGS and the University of Florida [Grant G21AC00016].