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Tytuł:
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On the uncertainty and confidence in decision support tools (DSTs) with insights from the Baltic Sea ecosystem.
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Autorzy:
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van Beest FM; Department of Bioscience, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark. .
Nygård H; Finnish Environment Institute SYKE, Marine Research Centre, Latokartanonkaari 11, 00790, Helsinki, Finland.
Fleming V; Finnish Environment Institute SYKE, Marine Research Centre, Latokartanonkaari 11, 00790, Helsinki, Finland.
Carstensen J; Department of Bioscience, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark.
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Źródło:
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Ambio [Ambio] 2021 Feb; Vol. 50 (2), pp. 393-399. Date of Electronic Publication: 2020 Sep 03.
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Typ publikacji:
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Journal Article; Review
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Język:
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English
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Imprint Name(s):
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Publication: Stockholm : Royal Swedish Academy of Sciences
Original Publication: Oslo, Universitetsforlaget.
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MeSH Terms:
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Ecosystem*
Baltic States ; Humans ; Uncertainty
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References:
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Sci Adv. 2018 May 09;4(5):eaar8195. (PMID: 29750199)
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Grant Information:
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185 BONUS
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Contributed Indexing:
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Keywords: Baltic Sea; Confidence; Decision support tools; Uncertainty
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Entry Date(s):
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Date Created: 20200905 Date Completed: 20210106 Latest Revision: 20240216
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Update Code:
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20240216
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PubMed Central ID:
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PMC7782639
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DOI:
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10.1007/s13280-020-01385-x
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PMID:
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32885402
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Ecosystems around the world are increasingly exposed to multiple, often interacting human activities, leading to pressures and possibly environmental state changes. Decision support tools (DSTs) can assist environmental managers and policy makers to evaluate the current status of ecosystems (i.e. assessment tools) and the consequences of alternative policies or management scenarios (i.e. planning tools) to make the best possible decision based on prevailing knowledge and uncertainties. However, to be confident in DST outcomes it is imperative that known sources of uncertainty such as sampling and measurement error, model structure, and parameter use are quantified, documented, and addressed throughout the DST set-up, calibration, and validation processes. Here we provide a brief overview of the main sources of uncertainty and methods currently available to quantify uncertainty in DST input and output. We then review 42 existing DSTs that were designed to manage anthropogenic pressures in the Baltic Sea to summarise how and what sources of uncertainties were addressed within planning and assessment tools. Based on our findings, we recommend future DST development to adhere to good modelling practise principles, and to better document and communicate uncertainty among stakeholders.