PERFICT: A Re-imagined foundation for predictive ecology. McIntire, E. J. B., Chubaty, A. M., Cumming, S. G., Andison, D., Barros, C., Boisvenue, C., Haché, S., Luo, Y., Micheletti, T., & Stewart, F. E. C. Ecology Letters, 00:1– 7, 2022. 1 citations (Semantic Scholar/DOI) [2022-04-25] 0 citations (Crossref) [2022-04-25] _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ele.13994
PERFICT: A Re-imagined foundation for predictive ecology [link]Paper  doi  abstract   bibtex   
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.
@article{mcintire_perfict_2022,
	title = {{PERFICT}: {A} {Re}-imagined foundation for predictive ecology},
	volume = {00},
	issn = {1461-0248},
	shorttitle = {{PERFICT}},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ele.13994},
	doi = {10.1111/ele.13994},
	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.},
	language = {en},
	urldate = {2022-03-22},
	journal = {Ecology Letters},
	author = {McIntire, Eliot J. B. and Chubaty, Alex M. and Cumming, Steven G. and Andison, Dave and Barros, Ceres and Boisvenue, Céline and Haché, Samuel and Luo, Yong and Micheletti, Tatiane and Stewart, Frances E. C.},
	year = {2022},
	note = {1 citations (Semantic Scholar/DOI) [2022-04-25]
0 citations (Crossref) [2022-04-25]
\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ele.13994},
	keywords = {FAIR data, computational workflows, cross-disciplinary, ecological forecasting, open models, predictive ecology, predictive validation, science-policy integration},
	pages = {1-- 7},
}

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