AUTHOR=Iacona Gwenllian D. , Avery-Gomm Stephanie , Maloney Richard F. , Brazill-Boast James , Crouse Deborah T. , Drew C. Ashton , Epanchin-Niell Rebecca S. , Hall Sarah B. , Maguire Lynn A. , Male Tim , Newman Jeff , Possingham Hugh P. , Rumpff Libby , Runge Michael C. , Weiss Katherine C. B. , Wilson Robyn S. , Zablan Marilet A. , Gerber Leah R. TITLE=Hurdles to developing quantitative decision support for Endangered Species Act resource allocation JOURNAL=Frontiers in Conservation Science VOLUME=3 YEAR=2022 URL=https://www.frontiersin.org/journals/conservation-science/articles/10.3389/fcosc.2022.1002804 DOI=10.3389/fcosc.2022.1002804 ISSN=2673-611X ABSTRACT=

The U.S. Fish and Wildlife Service oversees the recovery of many species protected by the U.S. Endangered Species Act (ESA). Recent research suggests that a structured approach to allocating conservation resources could increase recovery outcomes for ESA listed species. Quantitative approaches to decision support can efficiently allocate limited financial resources and maximize desired outcomes. Yet, developing quantitative decision support under real-world constraints is challenging. Approaches that pair research teams and end-users are generally the most effective. However, co-development requires overcoming “hurdles” that can arise because of differences in the mental models of the co-development team. These include perceptions that: (1) scarce funds should be spent on action, not decision support; (2) quantitative approaches are only useful for simple decisions; (3) quantitative tools are inflexible and prescriptive black boxes; (4) available data are not good enough to support decisions; and (5) prioritization means admitting defeat. Here, we describe how we addressed these misperceptions during the development of a prototype resource allocation decision support tool for understanding trade-offs in U.S. endangered species recovery. We describe how acknowledging these hurdles and identifying solutions enabled us to progress with development. We believe that our experience can assist other applications of developing quantitative decision support for resource allocation.