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Assumptions in ecosystem service assessments: Increasing transparency for conservation

Conservation efforts are increasingly supported by ecosystem service assessments. These assessments depend on complex multi-disciplinary methods, and rely on a number of assumptions which reduce complexity. If assumptions are ambiguous or inadequate, misconceptions and misinterpretations may arise w...

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Autores principales: Schröter, Matthias, Crouzat, Emilie, Hölting, Lisanne, Massenberg, Julian, Rode, Julian, Hanisch, Mario, Kabisch, Nadja, Palliwoda, Julia, Priess, Jörg A., Seppelt, Ralf, Beckmann, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7782657/
https://www.ncbi.nlm.nih.gov/pubmed/32915448
http://dx.doi.org/10.1007/s13280-020-01379-9
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author Schröter, Matthias
Crouzat, Emilie
Hölting, Lisanne
Massenberg, Julian
Rode, Julian
Hanisch, Mario
Kabisch, Nadja
Palliwoda, Julia
Priess, Jörg A.
Seppelt, Ralf
Beckmann, Michael
author_facet Schröter, Matthias
Crouzat, Emilie
Hölting, Lisanne
Massenberg, Julian
Rode, Julian
Hanisch, Mario
Kabisch, Nadja
Palliwoda, Julia
Priess, Jörg A.
Seppelt, Ralf
Beckmann, Michael
author_sort Schröter, Matthias
collection PubMed
description Conservation efforts are increasingly supported by ecosystem service assessments. These assessments depend on complex multi-disciplinary methods, and rely on a number of assumptions which reduce complexity. If assumptions are ambiguous or inadequate, misconceptions and misinterpretations may arise when interpreting results of assessments. An interdisciplinary understanding of assumptions in ecosystem service science is needed to provide consistent conservation recommendations. Here, we synthesise and elaborate on 12 prevalent types of assumptions in ecosystem service assessments. These comprise conceptual and ethical foundations of the ecosystem service concept, assumptions on data collection, indication, mapping, and modelling, on socio-economic valuation and value aggregation, as well as about using assessment results for decision-making. We recommend future assessments to increase transparency about assumptions, and to test and validate them and their potential consequences on assessment reliability. This will support the taking up of assessment results in conservation science, policy and practice. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s13280-020-01379-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-77826572021-01-14 Assumptions in ecosystem service assessments: Increasing transparency for conservation Schröter, Matthias Crouzat, Emilie Hölting, Lisanne Massenberg, Julian Rode, Julian Hanisch, Mario Kabisch, Nadja Palliwoda, Julia Priess, Jörg A. Seppelt, Ralf Beckmann, Michael Ambio Perspective Conservation efforts are increasingly supported by ecosystem service assessments. These assessments depend on complex multi-disciplinary methods, and rely on a number of assumptions which reduce complexity. If assumptions are ambiguous or inadequate, misconceptions and misinterpretations may arise when interpreting results of assessments. An interdisciplinary understanding of assumptions in ecosystem service science is needed to provide consistent conservation recommendations. Here, we synthesise and elaborate on 12 prevalent types of assumptions in ecosystem service assessments. These comprise conceptual and ethical foundations of the ecosystem service concept, assumptions on data collection, indication, mapping, and modelling, on socio-economic valuation and value aggregation, as well as about using assessment results for decision-making. We recommend future assessments to increase transparency about assumptions, and to test and validate them and their potential consequences on assessment reliability. This will support the taking up of assessment results in conservation science, policy and practice. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s13280-020-01379-9) contains supplementary material, which is available to authorized users. Springer Netherlands 2020-09-11 2021-02 /pmc/articles/PMC7782657/ /pubmed/32915448 http://dx.doi.org/10.1007/s13280-020-01379-9 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Perspective
Schröter, Matthias
Crouzat, Emilie
Hölting, Lisanne
Massenberg, Julian
Rode, Julian
Hanisch, Mario
Kabisch, Nadja
Palliwoda, Julia
Priess, Jörg A.
Seppelt, Ralf
Beckmann, Michael
Assumptions in ecosystem service assessments: Increasing transparency for conservation
title Assumptions in ecosystem service assessments: Increasing transparency for conservation
title_full Assumptions in ecosystem service assessments: Increasing transparency for conservation
title_fullStr Assumptions in ecosystem service assessments: Increasing transparency for conservation
title_full_unstemmed Assumptions in ecosystem service assessments: Increasing transparency for conservation
title_short Assumptions in ecosystem service assessments: Increasing transparency for conservation
title_sort assumptions in ecosystem service assessments: increasing transparency for conservation
topic Perspective
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7782657/
https://www.ncbi.nlm.nih.gov/pubmed/32915448
http://dx.doi.org/10.1007/s13280-020-01379-9
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