JOURNAL ARTICLE
Fossil Equidae in the Linxia Basin with Biostratigraphic and Paleozoogeographic Significance.
Published In: Acta Geologica Sinica (English Edition), 2024, v. 98, n. 1. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Sun, Boyang 3 of 3
Abstract
The Linxia Basin is characterized by an abundance of Cenozoic sediments, that contain exceptionally rich fossil resources. Equids are abundant in the Linxia Basin, the fossil record of equids in this region including 16 species that represent 10 genera. In comparison to other classic late Cenozoic areas in China, the Linxia Basin stands out, because the fauna and chronological data accompanying Linxia equids render them remarkably useful for biostratigraphy. The anchitheriines in the region, such as Anchitherium and Sinohippus, represent early equids that appeared in the late stages of the middle and late Miocene, respectively. Among the equines, most species of Chinese hipparions have been identified in the Linxia Basin and some species of the genera Hipparion and Hippotherium have FAD records for China. Furthermore, Equus eisenmannae is one of the earliest known species of Equus in the Old World and is well‐represented at the Longdan locality. Some species with precise geohistorical distributions can serve as standards for high‐resolution chronological units within this framework. Located at the eastern margin of the Tibetan Plateau and subject to considerable uplift, the Linxia Basin has served as a biogeographic transition area for equids throughout the late Cenozoic. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Acta Geologica Sinica (English Edition). 2024/02, Vol. 98, Issue 1, p1
- Document Type:Article
- Subject Area:Earth and Atmospheric Sciences
- Publication Date:2024
- ISSN:1000-9515
- DOI:10.1111/1755-6724.15097
- Accession Number:175327164
- Copyright Statement:Copyright of Acta Geologica Sinica (English Edition) is the property of Wiley-Blackwell 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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