JOURNAL ARTICLE
Integrative systematics of the taxonomically complex gobiid genus Glossogobius Gill, 1859 (Teleostei: Gobiidae) from the south-western Indian Ocean, with a description of a new species.
Published In: Zoological Journal of the Linnean Society, 2025, v. 203, n. 4. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Zarei, Fatah; Sithole, Yonela; Schliewen, Ulrich; Bills, Roger; Chakona, Albert 3 of 3
Abstract
This article focuses on the taxonomic diversity, phylogenetics, and biogeography of the gobiid fish genus Glossogobius in the south-western Indian Ocean. Through integrative molecular and morphological analyses, the study identifies nine distinct lineages in the region, including five newly recognized lineages and the formal description of a new species, Glossogobius hanisii sp. nov., distributed across southern Africa and Madagascar. The research highlights the south-western Indian Ocean as a significant hotspot of Glossogobius endemism, with 65% of Indian Ocean species/lineages endemic to this area, and underscores Madagascar's central role in the genus's diversification, particularly in subterranean habitats. The study also revises the taxonomy of key species complexes, clarifies phylogeographic patterns influenced by marine currents and biogeographic provinces, and provides a taxonomic key to distinguish Glossogobius species in Africa and Madagascar, thereby informing conservation strategies for these ecologically important fishes.
Additional Information
- Source:Zoological Journal of the Linnean Society. 2025/04, Vol. 203, Issue 4, p1
- Document Type:Article
- Subject Area:Zoology
- Publication Date:2025
- ISSN:0024-4082
- DOI:10.1093/zoolinnean/zlaf023
- Accession Number:185321548
- 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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