Elicited qualitative value of information scores for eastern black rail uncertainties on the Atlantic Coast from a 2020 adaptive management workshop

The eastern black rail (Laterallus jamaicensis jamaicensis; hereafter rail) is a small, cryptic marshbird that was recently listed as threatened under the U.S. Endangered Species Act. We organized a rapid prototyping workshop to initiate development of an adaptive management for rails on the Atlantic Coast. The in-person workshop spanned 2.5 days and was held in Titusville, Florida in January 2020. Workshop participants, comprised of species experts and land managers of rail habitats, chose to focus the framework on testing habitat management techniques to maximize rail occupancy, in which uncertainties could be reduced through a combination of field management experiments and coordinated monitoring. We used the qualitative value of information to prioritize uncertainties (stated as alternative hypotheses developed by participants in habitat-based breakout groups) that could serve as the basis for experiments within the adaptive management framework. Qualitative value of information (QVoI) is a newly-developed decision analysis tool that scores uncertainties in three areas: (1) Magnitude of uncertainty which reflects the strength of theoretical foundation and empirical support of the hypothesized relationship; (2) Relevance to management decisions which indicates how likely the preferred management alternative is to change if the uncertainty were resolved; and (3) Reducibility which is the degree to which the uncertainty could be resolved through research and monitoring. Magnitude is scored on a scale of 0–4, whereas Relevance and Reducibility can vary from 0–3. These data are the anonymized workshop participant (n=26) scores for nine hypotheses focused on testing habitat management techniques, to determine which hypotheses should serve as the basis for management experiments in an adaptive management framework. The data are contained in a .csv file that can be opened using a spreadsheet program such as Microsoft Excel, or read into a statistical analysis program such as Program R.