JOURNAL ARTICLE
New information on the anatomically derived millerettid Milleretta rubidgei from the latest Permian based on µCT data.
Published In: Zoological Journal of the Linnean Society, 2025, v. 203, n. 3. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Jenkins, Xavier A; Benson, Roger B J; Elliott, Maya; Jeppson, Gabriel; Dollman, Kathleen; Fernandez, Vincent; Browning, Claire; Ford, David P; Choiniere, Jonah; Peecook, Brandon R 3 of 3
Abstract
This article focuses on a detailed re-examination of the cranial anatomy of the fossil reptile *Milleretta rubidgei*, a member of the Millerettidae family from the middle to late Permian of South Africa, using propagation phase-contrast synchrotron radiation X-ray micro-computed tomography (PPC-SRµCT). The study reveals that many anatomical features previously interpreted as primitive or plesiomorphic are instead unique derived traits of *Milleretta*, challenging its traditional placement as an early diverging parareptile and suggesting closer affinities with Neodiapsida, the clade that includes the reptile crown group. The research also clarifies ontogenetic changes, such as the closure of the lower temporal fenestra during growth and the development of cranial osteoderms, which have historically complicated phylogenetic interpretations. These findings underscore the importance of including anatomically conservative and ontogenetically varied specimens in phylogenetic analyses to better understand early reptile evolution.
Additional Information
- Source:Zoological Journal of the Linnean Society. 2025/03, Vol. 203, Issue 3, p1
- Document Type:Article
- Subject Area:Geology
- Publication Date:2025
- ISSN:0024-4082
- DOI:10.1093/zoolinnean/zlaf004
- Accession Number:184296391
- 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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