JOURNAL ARTICLE
A revision and histological investigation of Saltoposuchus connectens (Archosauria: Crocodylomorpha) from the Norian (Late Triassic) of south-western Germany.
Published In: Zoological Journal of the Linnean Society, 2023, v. 199, n. 2. P. 354 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Spiekman, Stephan N F 3 of 3
Abstract
This article focuses on a comprehensive revision of the Late Triassic crocodylomorph species *Saltoposuchus connectens*, clarifying its taxonomy, morphology, and phylogenetic relationships. Through detailed osteological comparisons of all referred specimens, including previously contentious skulls SMNS 12591a and SMNS 12352, the study confirms their referral to *Saltoposuchus connectens* and distinguishes this taxon from the closely related but distinct *Terrestrisuchus gracilis*. Histological analysis of a femur (SMNS 12596) reveals highly vascularized fibrolamellar bone tissue indicative of sustained fast growth and a high metabolic rate, supporting an active, cursorial lifestyle. Phylogenetic analysis defines the clade Saltoposuchidae, comprising *Saltoposuchus connectens*, *Terrestrisuchus gracilis*, and *Litargosuchus leptorhynchus*, as a group of gracile, long-legged non-crocodyliform crocodylomorphs with distinct cranial and postcranial synapomorphies. These findings contribute to understanding early crocodylomorph diversity, growth strategies, and ecological roles during the Late Triassic.
Additional Information
- Source:Zoological Journal of the Linnean Society. 2023/10, Vol. 199, Issue 2, p354
- Document Type:Article
- Subject Area:Zoology
- Publication Date:2023
- ISSN:0024-4082
- DOI:10.1093/zoolinnean/zlad035
- Accession Number:172759115
- 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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