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Folders: ROOT > ScienceBase Catalog > Eastern Ecological Science Center > Quantitative Methods & Decision Science > Fire Effects in Gulf of Mexico Marshes: Adaptive Management for Black Rails, Yellow Rails, and Mottled Ducks ( Show all descendants )

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__Eastern Ecological Science Center
___Quantitative Methods & Decision Science
____Fire Effects in Gulf of Mexico Marshes: Adaptive Management for Black Rails, Yellow Rails, and Mottled Ducks
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This data set was collected as part of a structured decision-making workshop designed to identify sources of uncertainty and articulate alternative hypotheses about prescribed fire in high marshes of the Gulf of Mexico. Workshop participants independently scored alternative hypotheses based on a standard rubric using an online system. Following the workshop, we used the scores to compute QVoI for each participant. We used QVoI to prioritize the sources of uncertainty based on their magnitude of uncertainty, relevance for decision making, and reducibility.
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This data set was collected as part of structured decision-making workshops designed to build a Bayesian Decision Network of prescribed fire in high marshes of the Gulf of Mexico. As part of a formal elicitation process for expert judgment, workshop participants independently created conditional probability tables based on a conceptual model of prescribed fire in high marsh ecosystems. Following the workshop, we aggregated the conditional probability tables across experts and used them to create a Bayesian Decision network in probability network software.
    map background search result map search result map Qualitative value of information for the effects of prescribed fire in Gulf of Mexico marshes: Expert judgment scores from a 2020 adaptive management workshop Expert judgment data for a Bayesian decision network of prescribed fire in Gulf of Mexico marshes Qualitative value of information for the effects of prescribed fire in Gulf of Mexico marshes: Expert judgment scores from a 2020 adaptive management workshop Expert judgment data for a Bayesian decision network of prescribed fire in Gulf of Mexico marshes