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PERFICT: A Re‐imagined foundation for predictive ecology

Making predictions from ecological models—and comparing them to data—offers a coherent approach to evaluate model quality, regardless of model complexity or modelling paradigm. To date, our ability to use predictions for developing, validating, updating, integrating and applying models across scient...

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Autores principales: McIntire, Eliot J. B., Chubaty, Alex M., Cumming, Steven G., Andison, Dave, Barros, Ceres, Boisvenue, Céline, Haché, Samuel, Luo, Yong, Micheletti, Tatiane, Stewart, Frances E. C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9310704/
https://www.ncbi.nlm.nih.gov/pubmed/35315961
http://dx.doi.org/10.1111/ele.13994
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author McIntire, Eliot J. B.
Chubaty, Alex M.
Cumming, Steven G.
Andison, Dave
Barros, Ceres
Boisvenue, Céline
Haché, Samuel
Luo, Yong
Micheletti, Tatiane
Stewart, Frances E. C.
author_facet McIntire, Eliot J. B.
Chubaty, Alex M.
Cumming, Steven G.
Andison, Dave
Barros, Ceres
Boisvenue, Céline
Haché, Samuel
Luo, Yong
Micheletti, Tatiane
Stewart, Frances E. C.
author_sort McIntire, Eliot J. B.
collection PubMed
description Making predictions from ecological models—and comparing them to data—offers a coherent approach to evaluate model quality, regardless of model complexity or modelling paradigm. To date, our ability to use predictions for developing, validating, updating, integrating and applying models across scientific disciplines while influencing management decisions, policies, and the public has been hampered by disparate perspectives on prediction and inadequately integrated approaches. We present an updated foundation for Predictive Ecology based on seven principles applied to ecological modelling: make frequent Predictions, Evaluate models, make models Reusable, Freely accessible and Interoperable, built within Continuous workflows that are routinely Tested (PERFICT). We outline some benefits of working with these principles: accelerating science; linking with data science; and improving science‐policy integration.
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spelling pubmed-93107042022-07-29 PERFICT: A Re‐imagined foundation for predictive ecology McIntire, Eliot J. B. Chubaty, Alex M. Cumming, Steven G. Andison, Dave Barros, Ceres Boisvenue, Céline Haché, Samuel Luo, Yong Micheletti, Tatiane Stewart, Frances E. C. Ecol Lett Viewpoint Making predictions from ecological models—and comparing them to data—offers a coherent approach to evaluate model quality, regardless of model complexity or modelling paradigm. To date, our ability to use predictions for developing, validating, updating, integrating and applying models across scientific disciplines while influencing management decisions, policies, and the public has been hampered by disparate perspectives on prediction and inadequately integrated approaches. We present an updated foundation for Predictive Ecology based on seven principles applied to ecological modelling: make frequent Predictions, Evaluate models, make models Reusable, Freely accessible and Interoperable, built within Continuous workflows that are routinely Tested (PERFICT). We outline some benefits of working with these principles: accelerating science; linking with data science; and improving science‐policy integration. John Wiley and Sons Inc. 2022-03-22 2022-06 /pmc/articles/PMC9310704/ /pubmed/35315961 http://dx.doi.org/10.1111/ele.13994 Text en © 2022 Her Majesty the Queen in Right of Canada. Ecology Letters published by John Wiley & Sons Ltd. Reproduced with the permission of the Minister of Natural Resources Canada. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Viewpoint
McIntire, Eliot J. B.
Chubaty, Alex M.
Cumming, Steven G.
Andison, Dave
Barros, Ceres
Boisvenue, Céline
Haché, Samuel
Luo, Yong
Micheletti, Tatiane
Stewart, Frances E. C.
PERFICT: A Re‐imagined foundation for predictive ecology
title PERFICT: A Re‐imagined foundation for predictive ecology
title_full PERFICT: A Re‐imagined foundation for predictive ecology
title_fullStr PERFICT: A Re‐imagined foundation for predictive ecology
title_full_unstemmed PERFICT: A Re‐imagined foundation for predictive ecology
title_short PERFICT: A Re‐imagined foundation for predictive ecology
title_sort perfict: a re‐imagined foundation for predictive ecology
topic Viewpoint
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9310704/
https://www.ncbi.nlm.nih.gov/pubmed/35315961
http://dx.doi.org/10.1111/ele.13994
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