JOURNAL ARTICLE
Genomic and geographic diversification of a "great-speciator" (Rhipidura rufifrons).
Published In: Ornithology (Oxford University Press), 2023, v. 140, n. 1. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Klicka, Lukas B.; Campillo, Luke C.; Manthey, Joseph D; Andersen, Michael J.; Dumbacher, John P.; Filardi, Christopher E.; Joseph, Leo; Uy, J. Albert C.; Weidemann, Douglas E.; Moyle, Robert G. 3 of 3
Abstract
This article investigates the genomic and geographic diversification of the Rufous Fantail (Rhipidura rufifrons), a bird complex considered a "great speciator" due to its rapid differentiation across the southwest Pacific despite high dispersal ability. Using thousands of single nucleotide polymorphisms (SNPs) from 89 individuals representing 19 taxa, the study identified seven genetically distinct lineages within R. rufifrons, with evidence of gene flow between geographically isolated populations and discordance between plumage variation and genetic relationships. The findings suggest a recent radiation (1.35–2.31 million years ago) characterized by rapid phenotypic differentiation and multiple independent lineages, supporting the classification of R. rufifrons as a great speciator. Biogeographically, the Louisiade Archipelago appears to have played a key role as an early stepping stone in diversification, while remote island populations such as those in the Northern Mariana and Santa Cruz Islands show unexpected sister relationships, highlighting complex colonization and gene flow patterns across the Pacific.
Additional Information
- Source:Ornithology (Oxford University Press). 2023/01, Vol. 140, Issue 1, p1
- Document Type:Article
- Subject Area:Geography and Cartography
- Publication Date:2023
- ISSN:2732-4613
- DOI:10.1093/ornithology/ukac049
- Accession Number:161947815
- Copyright Statement:Copyright of Ornithology (Oxford University Press) 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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