Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If You Use a Screen Reader

This content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.

Best-Fit Maximum-Likelihood Models for Phylogenetic Inference: Empirical Tests with Known Phylogenies

C. W. Cunningham, H. Zhu and D. M. Hillis
Evolution
Vol. 52, No. 4 (Aug., 1998), pp. 978-987
DOI: 10.2307/2411230
Stable URL: http://www.jstor.org/stable/2411230
Page Count: 10
  • Read Online (Free)
  • Download ($4.00)
  • Subscribe ($19.50)
  • Cite this Item
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Best-Fit Maximum-Likelihood Models for Phylogenetic Inference: Empirical Tests with Known Phylogenies
Preview not available

Abstract

Despite the proliferation of increasingly sophisticated models of DNA sequence evolution, choosing among models remains a major problem in phylogenetic reconstruction. The choice of appropriate models is thought to be especially important when there is large variation among branch lengths. We evaluated the ability of nested models to reconstruct experimentally generated, known phylogenies of bacteriophage T7 as we varied the terminal branch lengths. Then, for each phylogeny we determined the best-fit model by progressively adding parameters to simpler models. We found that in several cases the choice of best-fit model was affected by the parameter addition sequence. In terms of phylogenetic performance, there was little difference between models when the ratio of short:long terminal branches was 1:3 or less. However, under conditions of extreme terminal branch-length variation, there were not only dramatic differences among models, but best-fit models were always among the best at overcoming long-branch attraction. The performance of minimum-evolution-distance methods was generally lower than that of discrete maximum-likelihood methods, even if maximum-likelihood methods were used to generate distance matrices. Correcting for among-site rate variation was especially important for overcoming long-branch attraction. The generality of our conclusions is supported by earlier simulation studies and by a preliminary analysis of mitochondrial and nuclear sequences from a well-supported four-taxon amniote phylogeny.

Page Thumbnails

  • Thumbnail: Page 
978
    978
  • Thumbnail: Page 
979
    979
  • Thumbnail: Page 
980
    980
  • Thumbnail: Page 
981
    981
  • Thumbnail: Page 
982
    982
  • Thumbnail: Page 
983
    983
  • Thumbnail: Page 
984
    984
  • Thumbnail: Page 
985
    985
  • Thumbnail: Page 
986
    986
  • Thumbnail: Page 
987
    987