A Standardized Definition of Rapid Evidence Assessment for Environmental Applications
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2026
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Details
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Journal Title:Conservation Letters
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Personal Author:Webb, J. Angus
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Schofield, Kate A.
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Cook, Carly N.
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Fisher, Jonathan R. B.
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Cheng, Samantha H.
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Christie, Alec
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Cooke, Steven J.
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Dubois, Natalie S.
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Frampton, Geoff
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Macura, Biljana
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Nichols, Susan J.
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Richards, Rob
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Aicher, Rebecca J.
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Mason, Sara
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Anderson, Erik
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Betley, Erin
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Borsuk, Mark
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Busch, Jonah
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Carlson, Sara
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Dubois, Jean‐Jacques B.
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Eales, Jacqualyn
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Game, Edward T.
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Irvine, Robyn L.
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Muir, Matthew
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Olander, Lydia
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Pollard, Amina
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Porzecanski, Ana
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Radke, Elizabeth
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Randall, Nicola
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Riley, Trevor
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Ritchie, Stephanie
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Salafsky, Nick
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Sigouin, Amanda
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Stevens, Kara
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Ridley, Caroline E.
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NOAA Program & Office:
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Description:Evidence assessment—identifying, evaluating, and synthesizing data and findings from previous studies—is important to inform environmental decision-making but can be slow and resource intensive. Users seeking efficiency have developed multiple definitions and methods for rapid evidence assessment (REA), raising concerns about consistency and rigor. To improve consistency and confidence in REA, we convened an international group of evidence users and researchers to define REA for environmental applications. Through a consensus-driven and iterative approach, we define REA as: a structured review process that aims to maximize rigor and objectivity given assessment needs and resource constraints; is transparent about trade-offs, risks, and biases; and can integrate multiple types of evidence. Our standardized definition of REA will improve transparency and facilitate decisions about the appropriate levels of rigor required for those who commission, conduct, and use REAs for environmental decision-making.
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Source:Conservation Letters, 19(1)
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DOI:
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ISSN:1755-263X ; 1755-263X
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Format:
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Publisher:
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License:
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Rights Information:CC BY
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Compliance:Submitted
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Main Document Checksum:urn:sha-512:d660b1db5e093ecdc9a9c62dfd0f943ae137f405a22583855a512fdbe474492cce90a657ef2be7c7d50004d877cf0b8c4aaa264db799c0a28563dd1bfd30a7b9
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