JOURNAL ARTICLE
Phylogenetics, character evolution, and historical biogeography of the Neotropical genus Besleria (Gesneriaceae).
Published In: Botanical Journal of the Linnean Society, 2024, v. 206, n. 1. P. 83 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Ferreira, Gabriel E; Clark, John L; Clavijo, Laura; Zuluaga, Alejandro; Chautems, Alain; Hopkins, Michael J G; Araujo, Andrea O; Perret, Mathieu 3 of 3
Abstract
This article focuses on the phylogenetic relationships, morphological evolution, and biogeographical history of Besleria, a large neotropical genus of perennial herbs, shrubs, and small trees within the Gesneriaceae family, comprising over 165 species. Using DNA sequence data from nuclear and chloroplast regions covering about half of the species, the study confirms the monophyly of Besleria and identifies six major clades with distinct geographic distributions, none of which correspond to previous morphological classifications. Divergence time estimates suggest Besleria originated in the northern Andes during the Middle Miocene (~19 million years ago), with subsequent diversification shaped by geological events such as Andean uplift and the formation of the Panama Isthmus, influencing dispersal into Central and South America, the Brazilian Atlantic Forest, and the West Indies. Morphological traits traditionally used for classification show high convergence and limited phylogenetic signal, indicating the need for revised taxonomic frameworks supported by molecular data.
Additional Information
- Source:Botanical Journal of the Linnean Society. 2024/09, Vol. 206, Issue 1, p83
- Document Type:Article
- Subject Area:Earth and Atmospheric Sciences
- Publication Date:2024
- ISSN:0024-4074
- DOI:10.1093/botlinnean/boae007
- Accession Number:179512399
- 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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