JOURNAL ARTICLE

The evolutionary history of the Central Asian steppe-desert taxon Nitraria (Nitrariaceae) as revealed by integration of fossil pollen morphology and molecular data.

  • Published In: Botanical Journal of the Linnean Society, 2023, v. 202, n. 2. P. 195 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Woutersen, Amber; Jardine, Phillip E; Silvestro, Daniele; Bogotá-Angel, Raul Giovanni; Zhang, Hong-Xiang; Meijer, Niels; Bouchal, Johannes; Barbolini, Natasha; Dupont-Nivet, Guillaume; Koutsodendris, Andreas; Antonelli, Alexandre; Hoorn, Carina 3 of 3

Abstract

This article focuses on the evolutionary history of the plant genus Nitraria (family Nitrariaceae) through the integration of fossil pollen morphology and molecular phylogenetics. The study presents the oldest known Nitraria fossil pollen (>53 million years old) from Central Asia, supporting a Central Asian origin rather than a purely coastal one. It reveals a significant decline in Nitraria diversity at the Eocene-Oligocene Transition (EOT), coinciding with global cooling, aridification, and the retreat of the proto-Paratethys Sea, followed by a Miocene diversification that gave rise to modern species. The research highlights discrepancies between fossil and extant-only molecular data, demonstrating the importance of combining fossil and molecular evidence in total evidence phylogenies to accurately reconstruct plant evolutionary histories.

Additional Information

  • Source:Botanical Journal of the Linnean Society. 2023/06, Vol. 202, Issue 2, p195
  • Document Type:Article
  • Subject Area:Earth and Atmospheric Sciences
  • Publication Date:2023
  • ISSN:0024-4074
  • DOI:10.1093/botlinnean/boac050
  • Accession Number:163872395
  • Copyright Statement:Copyright of Botanical 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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