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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.
Afiliação
  • McIntire EJB; Pacific Forestry Centre, Canadian Forest Service, Natural Resources Canada, Victoria, British Columbia, Canada.
  • Chubaty AM; Faculty of Forestry, Forest Resources Management, The University of British Columbia, Vancouver, British Columbia, Canada.
  • Cumming SG; Département des sciences du bois et de la forêt, Pavillon Abitibi-Price, 2405, rue de la Terrasse, Université Laval, Québec City, Québec, Canada.
  • Andison D; Pacific Forestry Centre, Canadian Forest Service, Natural Resources Canada, Victoria, British Columbia, Canada.
  • Barros C; Département des sciences du bois et de la forêt, Pavillon Abitibi-Price, 2405, rue de la Terrasse, Université Laval, Québec City, Québec, Canada.
  • Boisvenue C; FOR-CAST Research & Analytics, Calgary, Alberta, Canada.
  • Haché S; Département des sciences du bois et de la forêt, Pavillon Abitibi-Price, 2405, rue de la Terrasse, Université Laval, Québec City, Québec, Canada.
  • Luo Y; Faculty of Forestry, Forest Resources Management, The University of British Columbia, Vancouver, British Columbia, Canada.
  • Micheletti T; Bandaloop Landscape-Ecosystem Services Ltd., Nelson, British Columbia, Canada.
  • Stewart FEC; Faculty of Forestry, Forest Resources Management, The University of British Columbia, Vancouver, British Columbia, Canada.
Ecol Lett ; 25(6): 1345-1351, 2022 Jun.
Article em En | MEDLINE | ID: mdl-35315961
ABSTRACT
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Ecologia / Modelos Teóricos Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Ecol Lett Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Canadá

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Ecologia / Modelos Teóricos Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Ecol Lett Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Canadá