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.