Taxon sampling revisited
Steven Poe () and
David L. Swofford
Additional contact information
Steven Poe: MRC 162, National Museum of Natural History, Smithsonian Institution
David L. Swofford: Laboratory of Molecular Systematics, Smithsonian Institution
Nature, 1999, vol. 398, issue 6725, 299-300
Abstract:
Abstract Phylogenies that include long, unbranched lineages can be difficult to reconstruct. This is because long-branch taxa (such as rapidly evolving species) may share character states by chance more often than more closely related taxa share derived character states through common ancestry1. Despite Kim's warning that added taxa can decrease accuracy2, some authors have argued that the negative impact of this error, called ‘long-branch attraction’, is minimized when slowly evolving lineages are included to subdivide long branches3,4,5. From this they have concluded that increasing the number of species sampled per lineage results in better accuracy than increasing the number of characters per species6. We find, using computer simulations, that adding characters can be the more favourable strategy, even for long-branched trees, and that adding slowly evolving taxa to subdivide long branches can reduce accuracy.
Date: 1999
References: Add references at CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
https://www.nature.com/articles/18592 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:398:y:1999:i:6725:d:10.1038_18592
Ordering information: This journal article can be ordered from
https://www.nature.com/
DOI: 10.1038/18592
Access Statistics for this article
Nature is currently edited by Magdalena Skipper
More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().