Liaodactylus is a genus of filter-feeding ctenochasmatid pterosaur from the Jurassic of China. The genus contains one species, L. primus, described by Zhou et al. in 2017. As an adaptation to filter-feeding, Liaodactylus had approximately 150 long, comb-like teeth packed closely together. It is both the earliest known ctenochasmatid and the first filter-feeding pterosaur from the Jurassic Yanliao Biota. Later and more specialized ctenochasmatids differ from Liaodactylus in having longer snouts, smaller openings (or fenestrae) in the skull, and more teeth. Within the Ctenochasmatidae, Liaodacty
Liaodactylus is a genus of filter-feeding ctenochasmatid pterosaur from the Jurassic of China. The genus contains one species, L. primus, described by Zhou et al. in 2017. As an adaptation to filter-feeding, Liaodactylus had approximately 150 long, comb-like teeth packed closely together. It is both the earliest known ctenochasmatid and the first filter-feeding pterosaur from the Jurassic Yanliao Biota. Later and more specialized ctenochasmatids differ from Liaodactylus in having longer snouts, smaller openings (or fenestrae) in the skull, and more teeth. Within the Ctenochasmatidae, Liaodactylus was most closely related to the European Ctenochasma.
==Discovery and naming== thumb|left|Map of the type locality of Liaodactylus, with its position in the stratigraphic column There is one specimen of Liaodactylus known, namely the holotype PMOL-AP00031, which is stored at the Palaeontological Museum of Liaoning. It consists of a complete skull and lower jaws, along with the first two cervical vertebrae. It originates from outcrops located about west of the village of Daxishan, in Jianchang County, Liaoning, China. These outcrops are considered to belong to the Late Jurassic Tiaojishan Formation, which has been dated to range from 161.8 ± 0.4 to 159.5 ± 0.6 million years ago (Oxfordian) based on argon-argon dating. However, a 2023 study instead suggested that these deposits pertain to the Haifanggou Formation instead.
Discovered by embedding cosine similarity (sentence-transformers MiniLM, 384-dim).