BEST-FIT MAXIMUM-LIKELIHOOD MODELS FOR PHYLOGENETIC INFERENCE - EMPIRICAL TESTS WITH KNOWN PHYLOGENIES

Despite the proliferation of increasingly sophisticated models of DNA sequence evolution, choosing among models remains a major problem in p hylogenetic reconstruction. The choice of appropriate models is though t to be especially important when there is large variation among branc h lengths. We evaluated the ability of nested models to reconstruct ex perimentally generated, known phylogenies of bacteriophage T7 as we va ried the terminal branch lengths. Then, for each phylogeny we determin ed the best-fit model by progressively adding parameters to simpler mo dels. 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 condi tions of extreme terminal branch-length variation, there were not only dramatic differences among models, but best-fit models were always am ong the best at overcoming long-branch attraction. The performance of minimum-evolution-distance methods was generally lower than that of di screte maximum-likelihood methods, even if maximum-likelihood methods were used to generate distance matrices. Correcting for among-site rat e variation was especially important for overcoming long-branch attrac tion. The generality of our conclusions is supported by earlier simula tion studies and by a preliminary analysis of mitochondrial and nuclea r sequences from a well-supported four-taxon amniote phylogeny.