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.

中文翻译：

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PERFICT：预测生态学的重新构想基础

从生态模型进行预测——并将它们与数据进行比较——提供了一种评估模型质量的连贯方法，无论模型复杂性或建模范式如何。迄今为止，我们使用预测来开发、验证、更新、整合和应用跨科学学科的模型，同时影响管理决策、政策和公众的能力一直受到对预测的不同观点和整合方法不充分的阻碍。我们基于应用于生态建模的七项原则为预测生态学提供了一个更新的基础：进行频繁的预测、评估模型、使模型可重用、可自由访问和可互操作，构建在经过常规测试的连续工作流程 (PERFICT) 中。我们概述了使用这些原则的一些好处：加速科学；与数据科学联系；并改进科学与政策的整合。