JOURNAL ARTICLE
Quantitative paleoenvironmental reconstructions based on large mammal communities in Björn Kurtén's work and since then — revising the case of later Late Miocene Old World "Hipparion faunas".
Published In: Annales Zoologici Fennici, 2024, v. 61, n. 1. P. 179 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Saarinen, Juha; Liu, Liping 3 of 3
Abstract
Functional traits of herbivorous mammals provide tools for reconstructing past environments. In 1952, Björn Kurtén used distribution of ecomorphological features in fossil herbivorous mammal communities from Late Miocene "Hipparion faunas" of Eurasia to characterize their paleoenvironments as "steppe", "forest" and "mixed" types. We tested Kurtén's results with a revised set of ecometric methods. We used dental ecometric estimates of mean annual temperature and precipitation, net primary productivity, and normalized difference vegetation index to compare Miocene localities with modern biomes, and dental mesowear to estimate woody and grass cover in the paleoenvironments. Our results agree with Kurtén's, indicating steppe-edge environments in northern China, wooded paleoenvironments in Pikermi, Greece, and central Europe, and open woodland-grassland environment in Maragheh, Iran. Our analyses indicate the presence of wooded grassland savanna in Lothagam and tropical forest in Lukeino in East Africa, further demonstrating paleoenvironmental variation and ecological diversity within later Late Miocene "Hipparion faunas". [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Annales Zoologici Fennici. 2024/01, Vol. 61, Issue 1, p179
- Document Type:Article
- Subject Area:Biology
- Publication Date:2024
- ISSN:0003-455X
- DOI:10.5735/086.061.0115
- Accession Number:181469840
- Copyright Statement:Copyright of Annales Zoologici Fennici is the property of Finnish Zoological & Botanical Publishing Board 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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