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Along the Santa Clara River in California, populations of the federally and state-listed Least Bell's Vireo (Vireo bellii pusillus) are recovering from near extirpation. Habitat protection and restoration, as well as controlling rates of brood parasitism, are thought to be the primary drivers of this recovery. Continuing successful management of this population faces multiple challenges due to the highly dynamic and unpredictable nature of the system, lack of clearly defined and measurable recovery criteria, parametric and stochastic uncertainty, and data limitations. Many of these management challenges are not unique to Least Bell's Vireo and require careful balancing of limited resources into the future. We developed a decision support tool as a user interface for exploring the underlying uncertainty in a population viability analysis under an array of different management scenarios. The tool was designed to assist with the planning and coordination between conservation partners in the region in three distinct aspects of the decision-making process: defining the problem and setting clear goals and objectives, exploring the consequences of potential alternative actions, and identifying criteria for ongoing evaluation and monitoring. The general framework for the design of this decision support tool is broadly applicable to many management and decision-making scenarios that share these common challenges.

Recovery planning in a dynamic system: integrating uncertainty into a decision support tool for an endangered songbird