title: Maximum likelihood supertrees.
abstract: We analyze a maximum likelihood approach for combining phylogenetic trees into a larger "supertree." This is based on a simple exponential model of phylogenetic error, which ensures that ML supertrees have a simple combinatorial description (as a median tree, minimizing a weighted sum of distances to the input trees). We show that this approach to ML supertree reconstruction is statistically consistent (it converges on the true species supertree as more input trees are combined), in contrast to the widely used MRP method, which we show can be statistically inconsistent under the exponential error model. We also show that this statistical consistency extends to an ML approach for constructing species supertrees from gene trees. In this setting, incomplete lineage sorting (due to coalescence rates of homologous genes being lower than speciation rates) has been shown to lead to gene trees that are frequently different from species trees, and this can confound efforts to reconstruct the species phylogeny correctly.
authors.affiliation: Allan Wilson Centre for Molecular Ecology and Evolution, Department of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand. m.steel@math.canterbury.ac.nz
authors.forename: Mike
authors.initials: M
authors.lastname: Steel
authors.forename: Allen
authors.initials: A
authors.lastname: Rodrigo
mesh_terms: D003198:Computer Simulation; D016013:Likelihood Functions; D008957:Models, Genetic; D010802:Phylogeny