Rapid prototyping for quantifying belief weights of competing hypotheses about emergent diseases

Details

• Journal Title:
  Journal of Environmental Management
• Personal Author:
  Robertson, E.P. ; Walsh, D.P. ; Martin, J. ; Work, T.M. ; Kellogg, C.A. ; Evans, J.S. ; Barker, V. ; Hawthorn, A. ; Aeby, G. ; Paul, V.J. ; Walker, B.K. ; Kiryu, Y. ; Woodley, C.M. ; Meyer, J.L. ; Rosales, S.M. ; Studivan, M.S. ; Moore, J.F. ; Brandt, M.E. ; Bruckner, A.
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research) ; AOML (Atlantic Oceanographic and Meteorological Laboratory) ; NOS (National Ocean Service) ; NCCOS (National Centers for Coastal Ocean Science) ; CIMAS (Cooperative Institute for Marine and Atmospheric Studies)
• Description:
  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.
• Keywords:
  Coral Reefs And Islands Scleractinia Wildlife Diseases Coral Reefs And Islands Wildlife Diseases Disease Ecology Etiology Hypothesis Testing Rapid Prototyping Stony Corals Stony Coral Tissue Loss Disease

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• Source:
  Journal of Environmental Management 337 (2023) 117668
• DOI:
  https://doi.org/10.1016/j.jenvman.2023.117668
• Format:
  PDF
• CoRIS Project ID:
  CRCP Project ID ; 31252
• Document Type:
  Journal Article
• Rights Information:
  Accepted Manuscript
• Rights Statement:
  The NOAA IR provides access to this content under the authority of the government's retained license to distribute publications and data resulting from federal funding. While users may legally access this content, the copyright owners retain rights that govern the reproduction, redistribution, and re-use of this work. The user is solely responsible for complying with applicable copyright law.
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:0ce198126051320d3120f563cea146d51c95b87c8ff3c7da767925e968aa871c898b648f1ef304da28627e9e8ac5040c6f5a0508c92d605aa016934a39534434
• Download URL:
  https://repository.library.noaa.gov/view/noaa/49695/noaa_49695_DS1.pdf
• File Type:
  [PDF - 1.22 MB ]