JOURNAL ARTICLE
The earliest-diverging avemetatarsalian: a new osteoderm-bearing taxon from the Triassic (?Earliest Late Triassic) of Madagascar and the composition of avemetatarsalian assemblages prior to the radiation of dinosaurs.
Published In: Zoological Journal of the Linnean Society, 2023, v. 199, n. 2. P. 327 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Nesbitt, Sterling J; Patellos, Emily; Kammerer, Christian F; Ranivoharimanana, Lovasoa; Wyss, Andre´ R; Flynn, John J 3 of 3
Abstract
This article focuses on the discovery and description of *Mambachiton fiandohana* gen. et sp. nov., an early-diverging avemetatarsalian (bird-line archosaur) from the ?Earliest Late Triassic (~235 million years ago) 'basal Isalo II'/Makay Formation of Madagascar. Represented by well-preserved postcranial material including a distinctive cervical vertebra series with numerous osteoderms, *Mambachiton* is phylogenetically placed at the base of Avemetatarsalia, outside the clade containing aphanosaurs and ornithodirans. The presence of osteoderms in this taxon supports the hypothesis that early avemetatarsalians retained such armor, which was subsequently lost in more crownward lineages. The co-occurrence of *Mambachiton* with a lagerpetid (*Kongonaphon kely*) and a silesaurid dinosauriform in the same formation documents a diverse non-dinosaurian avemetatarsalian fauna in Gondwana near the Middle–Late Triassic transition, providing new insights into early archosaur evolution and morphology.
Additional Information
- Source:Zoological Journal of the Linnean Society. 2023/10, Vol. 199, Issue 2, p327
- Document Type:Article
- Subject Area:Zoology
- Publication Date:2023
- ISSN:0024-4082
- DOI:10.1093/zoolinnean/zlad038
- Accession Number:172759118
- Copyright Statement:Copyright of Zoological Journal of the Linnean Society is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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