Deep learning for mass extinction detection on fossilized phylogenies: power, limitations and lessons for simulation-based birth–death inference

Abstract Detecting mass extinction events from phylogenies is a fundamental yet challenging task. While traditional likelihood-based methods are available, deep learning offers a powerful, simulation-based alternative. Here, we evaluate a deep learning approach using a novel hybrid model that combines graph neural networks with long short-term memory networks. This model analyses phylogenies—containing both extant species and fossils—simulated under a complex skyline fossilized birth–death model