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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Naturearrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Nature
Article . 1999 . Peer-reviewed
License: Springer TDM
Data sources: Crossref
Nature
Article . 1999
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Taxon sampling revisited

Authors: S, Poe; D L, Swofford;

Taxon sampling revisited

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.

Keywords

Animals, Reproducibility of Results, Computer Simulation, Models, Biological, Phylogeny

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
182
Top 10%
Top 1%
Top 1%
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