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.