Stock, A., E.J. Gregr and K.M.A. Chan (2023). “Data leakage jeopardizes ecological applications of machine learning.” Nature Ecology & Evolution. Doi: 10.1038/s41559-023-02162-1
Machine learning is a popular tool in ecology but many scientific applications suffer from data leakage, causing misleading results. We highlight common pitfalls in ecological machine-learning methods and argue that discipline-specific model info sheets must be developed to aid in model evaluations.