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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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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. |
format | Online Article Text |
id | pubmed-9310704 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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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