JOURNAL ARTICLE
Polychaetes distributed across oceans—examples of widely recorded species from abyssal depths of the Atlantic and Pacific Oceans.
Published In: Zoological Journal of the Linnean Society, 2023, v. 199, n. 4. P. 906 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Meißner, Karin; Schwentner, Martin; Götting, Miriam; Knebelsberger, Thomas; Fiege, Dieter 3 of 3
Abstract
This article investigates the distributional ranges of selected deep-sea annelid species using an integrative approach combining mitochondrial COI genetic markers, 18S rRNA sequences, and detailed morphological analyses. Specimens collected from multiple deep-sea expeditions across the Pacific and Atlantic Oceans between 1998 and 2015 were studied, focusing on species with documented widespread deep-sea distributions and sufficient sample sizes. The study confirms pan-oceanic distributions for three species—*Sigambra magnuncus* (Pilargidae), *Progoniada regularis*, and *Bathyglycinde profunda* (both Goniadidae)—and identifies several new species, including *Octomagelona borowskii* (Magelonidae) and two *Spiophanes* species (Spionidae). Genetic data reveal high haplotype diversity and suggest ongoing dispersal and gene flow between oceans, likely facilitated by larval transport and sediment-mediated dispersal via abyssal storms and near-bottom currents. The findings highlight the complexity of deep-sea species connectivity and emphasize the need for further sampling and integrative studies to better understand deep-sea biodiversity and biogeography.
Additional Information
- Source:Zoological Journal of the Linnean Society. 2023/12, Vol. 199, Issue 4, p906
- Document Type:Article
- Subject Area:Zoology
- Publication Date:2023
- ISSN:0024-4082
- DOI:10.1093/zoolinnean/zlad069
- Accession Number:173959503
- 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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