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PERFICT: A Re‐imagined foundation for predictive ecology

Ecology Letters2022Vol. 25(6), pp. 1345–1351

Citations Over TimeTop 10% of 2022 papers

Eliot J. B. McIntire, Alex M Chubaty, Steven G. Cumming, Dave Andison, Ceres Barros, Céline Boisvenue, Samuel Haché, Yong Luo, Tatiane Micheletti, Frances E. C. Stewart

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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