Keywords Stony coral tissue loss disease; Etiology; Coral reefs; Disease ecology; Wildlife disease; Hypothesis testing Highlights * We developed methods for rapidly assessing hypotheses about disease etiology. * Methods included formal expert elicitation and Bayesian hierarchical modeling. * We illustrate this approach for a recently emerged disease in coral reefs. * These methods are useful for fast decision-making for conservation issues. * These methods can help adapt management decisions over time as knowledge accumulates. Abstract Emerging diseases can have devastating consequences for wildlife and require a rapid response. A critical first step towards developing appropriate management is identifying the etiology of the disease, which can be difficult to determine, particularly early in emergence. Gathering and synthesizing existing information about potential disease causes, by leveraging expert knowledge or relevant existing studies, provides a principled approach to quickly inform decision-making and management efforts. Additionally, updating the current state of knowledge as more information becomes available over time can reduce scientific uncertainty and lead to substantial improvement in the decision-making process and the application of management actions that incorporate and adapt to newly acquired scientific understanding. Here we present a rapid prototyping method for quantifying belief weights for competing hypotheses about the etiology of disease using a combination of formal expert elicitation and Bayesian hierarchical modeling. We illustrate the application of this approach for investigating the etiology of stony coral tissue loss disease (SCTLD) and discuss the opportunities and challenges of this approach for addressing emergent diseases. Lastly, we detail how our work may apply to other pressing management or conservation problems that require quick responses. We found the rapid prototyping methods to be an efficient and rapid means to narrow down the number of potential hypotheses, synthesize current understanding, and help prioritize future studies and experiments. This approach is rapid by providing a snapshot assessment of the current state of knowledge. It can also be updated periodically (e.g., annually) to assess changes in belief weights over time as scientific understanding increases. Synthesis and applications: The rapid prototyping approaches demonstrated here can be used to combine knowledge from multiple experts and/or studies to help with fast decision-making needed for urgent conservation issues including emerging diseases and other management problems that require rapid responses. These approaches can also be used to adjust belief weights over time as studies and expert knowledge accumulate and can be a helpful tool for adapting management decisions. Author Affiliation: (a) Contract Quantitative Ecologist, US Geological Survey, Wetland and Aquatic Research Center, Gainesville, FL, USA (b) U.S. Geological Survey, Montana Cooperative Wildlife Research Unit, University of Montana, Missoula, MT, USA (c) U.S. Geological Survey, Eastern Ecological Science Center, Laurel, MD, USA (d) U.S. Geological Survey, National Wildlife Health Center, Honolulu Field Station, Honolulu, HI, USA (e) U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, USA (f) Florida Sea Grant, Dania Beach, FL, USA (g) U.S. Geological Survey National Wildlife Health Center, Western Fisheries Research Center, Seattle, WA, USA (h) Smithsonian Marine Station, Fort Pierce, FL, USA (i) Nova Southeastern University, Halmos College of Arts and Sciences, Dania Beach, FL, USA (j) Fish and Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission, St. Petersburg, FL, USA (k) Hollings Marine Laboratory, Center for Coastal Environmental Health and Biomolecular Research, National Oceanic and Atmospheric Administration's National Ocean Service, Charleston, SC, USA (l) Department of Soil, Water, and Ecosystem Sciences, University of Florida, Gainesville, FL, USA (m) Cooperative Institute for Marine and Atmospheric Studies, University of Miami, Miami, FL, USA (n) Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, Miami, FL, USA (o) Moore Ecological Analysis and Management, LLC, Gainesville, FL, USA (p) Center for Marine and Environmental Studies, University of the Virgin Islands, St. Thomas, USVI, USA (q) Florida Keys National Marine Sanctuary, NOAA, Key Largo, FL, USA * Corresponding author. Article History: Received 1 November 2022; Revised 10 February 2023; Accepted 2 March 2023 (footnote)1 co-first-authors Byline: Ellen P. Robertson [robertsonep@gmail.com] (a,*), Daniel P. Walsh [dwalsh@usgs.gov] (b,1), Julien Martin [julienmartin@usgs.gov] (c,1), Thierry M. Work (d), Christina A. Kellogg (e), James S. Evans (e), Victoria Barker (f), Aine Hawthorn (g), Greta Aeby (h), Valerie J. Paul (h), Brian K. Walker (i), Yasunari Kiryu (j), Cheryl M. Woodley (k), Julie L. Meyer (l), Stephanie M. Rosales (m,n), Michael Studivan (m,n), Jennifer F. Moore (o), Marilyn E. Brandt (p), Andrew Bruckner (q)