Finely preserved fossil assemblages (lagerstätten) provide crucial insights into evolutionary innovations in deep time. We report an exceptionally preserved Early Triassic fossil assemblage, the Guiyang Biota, from the Daye Formation near Guiyang, South China. High-precision uranium-lead dating shows that the age of the Guiyang Biota is 250.83 +0.07/–0.06 million years ago. This is only 1.08 ± 0.08 million years after the severe Permian-Triassic mass extinction, and this assemblage therefore represents the oldest known Mesozoic lagerstätte found so far. The Guiyang Biota comprises at least 12 classes and 19 orders, including diverse fish fauna and malacostracans, revealing a trophically complex marine ecosystem. Therefore, this assemblage demonstrates the rapid rise of modern-type marine ecosystems after the Permian-Triassic mass extinction.

The rapid and accurate taxonomic identification of fossils is of great significance in paleontology, biostratigraphy, and other fields. However, taxonomic identification is often labor-intensive and tedious, and the requisition of extensive prior knowledge about a taxonomic group also requires long-term training. Moreover, identification results are often inconsistent across researchers and communities. Accordingly, in this study, we used deep learning to support taxonomic identification. We used web crawlers to collect the Fossil Image Dataset (FID) via the Internet, obtaining 415,339 images belonging to 50 fossil clades. Then we trained three powerful convolutional neural networks on a high-performance workstation. The Inception-ResNet-v2 architecture achieved an average accuracy of 0.90 in the test dataset when transfer learning was applied. The clades of microfossils and vertebrate fossils exhibited the highest identification accuracies of 0.95 and 0.90, respectively. In contrast, clades of sponges, bryozoans, and trace fossils with various morphologies or with few samples in the dataset exhibited a performance below 0.80. Visual explanation methods further highlighted the discrepancies among different fossil clades and suggested similarities between the identifications made by machine classifiers and taxonomists. Collecting large paleontological datasets from various sources, such as the literature, digitization of dark data, citizen-science data, and public data from the Internet may further enhance deep learning methods and their adoption. Such developments will also possibly lead to image-based systematic taxonomy to be replaced by machine-aided classification in the future. Pioneering studies can include microfossils and some invertebrate fossils. To contribute to this development, we deployed our model on a server for public access at www.ai-fossil.com.

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