Many questions relevant to conservation decision making are characterized by extreme uncertainty due to lack of empirical data and complexity of the underlying ecological processes, leading to a rapid increase in the use of structured protocols to elicit expert knowledge. Published ecological applications often employ a modified Delphi method, where experts provide judgments anonymously and mathematical aggregation techniques are used to combine judgments. The Sheffield Elicitation Framework (SHELF) differs in its behavioral approach to synthesizing individual judgments into a fully specified probability distribution for an unknown quantity. This study demonstrates the remote use of the SHELF protocol for an extinction risk assessment of three subterranean aquatic species petitioned for listing under the US Endangered Species Act. Experts were provided an empirical threat assessment for each known locality using video conferencing and asked for judgments on the probability of population persistence over four generations using online submission forms and R‐shiny apps available through the SHELF package. Despite large uncertainty for all populations, results reveal key differences between species’ risk of extirpation based on spatial variation in dominant threats, local land use and management practices, and microhabitat use. The resulting probability distributions provide decision makers with a full picture of uncertainty that is consistent with the probabilistic nature of risk assessments, and discussions during the behavioral aggregation stage clearly document dominant threats (e.g., development, timber harvest, animal agriculture, and cave visitation) and their interactions with local cave geology and species’ habitat preferences. Our virtual implementation of the SHELF protocol demonstrates the flexibility of this approach for conservation applications operating on budgets and timelines that can limit in‐person meetings of geographically dispersed experts.
|Title||Using expert knowledge to support Endangered Species Act decision‐making for data‐deficient species|
|Authors||Daniel Bruce Fitzgerald, David R. Smith, David C. Culver, Daniel Feller, Daniel W. Fong, Jeff Hajenga, Matthew L. Niemiller, Daniel C. Nolfi, Wil D. Orndorff, Barbara Douglas, Kelly O. Maloney, John A. Young|
|Publication Subtype||Journal Article|
|Series Title||Conservation Biology|
|Record Source||USGS Publications Warehouse|
|USGS Organization||Leetown Science Center|