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Strengthen causal models for better conservation outcomes for human well-being

BACKGROUND: Understanding how the conservation of nature can lead to improvement in human conditions is a research area with significant growth and attention. Progress towards effective conservation requires understanding mechanisms for achieving impact within complex social-ecological systems. Caus...

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Autores principales: Cheng, Samantha H., McKinnon, Madeleine C., Masuda, Yuta J., Garside, Ruth, Jones, Kelly W., Miller, Daniel C., Pullin, Andrew S., Sutherland, William J., Augustin, Caitlin, Gill, David A., Wongbusarakum, Supin, Wilkie, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7083336/
https://www.ncbi.nlm.nih.gov/pubmed/32196534
http://dx.doi.org/10.1371/journal.pone.0230495
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author Cheng, Samantha H.
McKinnon, Madeleine C.
Masuda, Yuta J.
Garside, Ruth
Jones, Kelly W.
Miller, Daniel C.
Pullin, Andrew S.
Sutherland, William J.
Augustin, Caitlin
Gill, David A.
Wongbusarakum, Supin
Wilkie, David
author_facet Cheng, Samantha H.
McKinnon, Madeleine C.
Masuda, Yuta J.
Garside, Ruth
Jones, Kelly W.
Miller, Daniel C.
Pullin, Andrew S.
Sutherland, William J.
Augustin, Caitlin
Gill, David A.
Wongbusarakum, Supin
Wilkie, David
author_sort Cheng, Samantha H.
collection PubMed
description BACKGROUND: Understanding how the conservation of nature can lead to improvement in human conditions is a research area with significant growth and attention. Progress towards effective conservation requires understanding mechanisms for achieving impact within complex social-ecological systems. Causal models are useful tools for defining plausible pathways from conservation actions to impacts on nature and people. Evaluating the potential of different strategies for delivering co-benefits for nature and people will require the use and testing of clear causal models that explicitly define the logic and assumptions behind cause and effect relationships. OBJECTIVES AND METHODS: In this study, we outline criteria for credible causal models and systematically evaluated their use in a broad base of literature (~1,000 peer-reviewed and grey literature articles from a published systematic evidence map) on links between nature-based conservation actions and human well-being impacts. RESULTS: Out of 1,027 publications identified, only ~20% of articles used any type of causal models to guide their work, and only 14 total articles fulfilled all criteria for credibility. Articles rarely tested the validity of models with empirical data. IMPLICATIONS: Not using causal models risks poorly defined strategies, misunderstanding of potential mechanisms for affecting change, inefficient use of resources, and focusing on implausible efforts for achieving sustainability.
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spelling pubmed-70833362020-03-30 Strengthen causal models for better conservation outcomes for human well-being Cheng, Samantha H. McKinnon, Madeleine C. Masuda, Yuta J. Garside, Ruth Jones, Kelly W. Miller, Daniel C. Pullin, Andrew S. Sutherland, William J. Augustin, Caitlin Gill, David A. Wongbusarakum, Supin Wilkie, David PLoS One Research Article BACKGROUND: Understanding how the conservation of nature can lead to improvement in human conditions is a research area with significant growth and attention. Progress towards effective conservation requires understanding mechanisms for achieving impact within complex social-ecological systems. Causal models are useful tools for defining plausible pathways from conservation actions to impacts on nature and people. Evaluating the potential of different strategies for delivering co-benefits for nature and people will require the use and testing of clear causal models that explicitly define the logic and assumptions behind cause and effect relationships. OBJECTIVES AND METHODS: In this study, we outline criteria for credible causal models and systematically evaluated their use in a broad base of literature (~1,000 peer-reviewed and grey literature articles from a published systematic evidence map) on links between nature-based conservation actions and human well-being impacts. RESULTS: Out of 1,027 publications identified, only ~20% of articles used any type of causal models to guide their work, and only 14 total articles fulfilled all criteria for credibility. Articles rarely tested the validity of models with empirical data. IMPLICATIONS: Not using causal models risks poorly defined strategies, misunderstanding of potential mechanisms for affecting change, inefficient use of resources, and focusing on implausible efforts for achieving sustainability. Public Library of Science 2020-03-20 /pmc/articles/PMC7083336/ /pubmed/32196534 http://dx.doi.org/10.1371/journal.pone.0230495 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication.
spellingShingle Research Article
Cheng, Samantha H.
McKinnon, Madeleine C.
Masuda, Yuta J.
Garside, Ruth
Jones, Kelly W.
Miller, Daniel C.
Pullin, Andrew S.
Sutherland, William J.
Augustin, Caitlin
Gill, David A.
Wongbusarakum, Supin
Wilkie, David
Strengthen causal models for better conservation outcomes for human well-being
title Strengthen causal models for better conservation outcomes for human well-being
title_full Strengthen causal models for better conservation outcomes for human well-being
title_fullStr Strengthen causal models for better conservation outcomes for human well-being
title_full_unstemmed Strengthen causal models for better conservation outcomes for human well-being
title_short Strengthen causal models for better conservation outcomes for human well-being
title_sort strengthen causal models for better conservation outcomes for human well-being
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7083336/
https://www.ncbi.nlm.nih.gov/pubmed/32196534
http://dx.doi.org/10.1371/journal.pone.0230495
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