Premium
PERFICT: A Re‐imagined foundation for predictive ecology
Author(s) -
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.
Publication year - 2022
Publication title -
ecology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.852
H-Index - 265
eISSN - 1461-0248
pISSN - 1461-023X
DOI - 10.1111/ele.13994
Subject(s) - workflow , interoperability , computer science , ecology , foundation (evidence) , data science , management science , quality (philosophy) , engineering , geography , biology , philosophy , archaeology , epistemology , database , operating system
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.