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PERFICT: a Re-imagined Foundation for Predictive Ecology
  • +7
  • Eliot McIntire,
  • Alex Chubaty,
  • Steve Cumming,
  • David Andison,
  • Ceres Barros,
  • Céline Boisvenue,
  • Samuel Hache,
  • Yong Luo,
  • Tatiane Micheletti,
  • Frances Stewart
Eliot McIntire
Natural Resources Canada

Corresponding Author:[email protected]

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Alex Chubaty
FOR-CAST Research & Analytics
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Steve Cumming
Université Laval
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David Andison
Bandaloop Landscape-Ecosystem Services Ltd.
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Ceres Barros
The University of British Columbia
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Céline Boisvenue
Natural Resources Canada
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Samuel Hache
Environment and Climate Change Canada
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Yong Luo
Canadian Forest Service
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Tatiane Micheletti
University of British Columbia Faculty of Forestry
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Frances Stewart
Natural Resources Canada
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Abstract

Making predictions from ecological models – and comparing these predictions to data – offers a coherent approach to objectively evaluate model quality, regardless of model complexity or modeling 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 inadequate integrated approaches. We present an updated foundation for Predictive Ecology that is based on 7 principles applied to ecological models: 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: 1) accelerating science; 2) bridging to data science; and 3) improving science-policy integration.
22 Mar 2022Published in Ecology Letters. 10.1111/ele.13994