JOURNAL ARTICLE
Disentangling the stylonychine ciliates (Ciliophora: Hypotricha): the establishment of two new genera using an integrative taxonomic approach.
Published In: Zoological Journal of the Linnean Society, 2024, v. 202, n. 4. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Omar, Atef; Jung, Jae-Ho 3 of 3
Abstract
This article focuses on the taxonomic revision and phylogenetic analysis of stylonychine ciliates collected from South Korea, resulting in the establishment of two new genera, Apostylonychia and Antetetmemena, alongside the genus Tetmemena. Using integrative approaches combining morphology, morphogenesis, and multigene molecular phylogeny, the study differentiates these genera primarily by the arrangement of transverse cirri (one group in Tetmemena versus two groups in the new genera), body shape, frontal area characteristics, and the origin of specific frontal–ventral–transverse (FVT) cirral anlagen during ontogenesis. Phylogenetic analyses based on 18S rRNA and concatenated rRNA gene sequences support the distinctiveness of Apostylonychia and Antetetmemena, while rejecting the monophyly of Tetmemena as previously circumscribed. The research highlights the importance of combining morphological and molecular data to resolve classification challenges within stylonychine ciliates and calls for further molecular sampling and neotypification to clarify species boundaries, especially within the Tetmemena pustulata complex.
Additional Information
- Source:Zoological Journal of the Linnean Society. 2024/12, Vol. 202, Issue 4, p1
- Document Type:Article
- Subject Area:Anatomy and Physiology
- Publication Date:2024
- ISSN:0024-4082
- DOI:10.1093/zoolinnean/zlae144
- Accession Number:182905098
- 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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