 
Summary: Accounting for Uncertainty in the Tree Topology Has Little Effect on the
DecisionTheoretic Approach to Model Selection in Phylogeny Estimation
Zaid Abdo,* ā Vladimir N. Minin,§ Paul Joyce,* ā and Jack Sullivan* {
*Initiative in Bioinformatics and Evolutionary Studies (IBEST), Program of Bioinformatics and Computational Biology, and
āDepartment of Mathematics, University of Idaho, Moscow; §Department of Biomathematics, David Geffen School of Medicine,
University of California, Los Angeles; and {Department of Biological Science, University of Idaho, Moscow
Currently available methods for model selection used in phylogenetic analysis are based on an initial fixedtree topology.
Once a model is picked based on this topology, a rigorous search of the tree space is run under that model to find the
maximumlikelihood estimate of the tree (topology and branch lengths) and the maximumlikelihood estimates of the
model parameters. In this paper, we propose two extensions to the decisiontheoretic (DT) approach that relax the fixed
topology restriction. We also relax the fixedtopology restriction for the Bayesian information criterion (BIC) and the
Akaike information criterion (AIC) methods. We compare the performance of the different methods (the relaxed,
restricted, and the likelihoodratio test [LRT]) using simulated data. This comparison is done by evaluating the relative
complexity of the models resulting from each method and by comparing the performance of the chosen models in
estimating the true tree. We also compare the methods relative to one another by measuring the closeness of the estimated
trees corresponding to the different chosen models under these methods. We show that varying the topology does not
have a major impact on model choice. We also show that the outcome of the two proposed extensions is identical and is
comparable to that of the BIC, ExtendedBIC, and DT. Hence, using the simpler methods in choosing a model for
analyzing the data is more computationally feasible, with results comparable to the more computationally intensive
methods. Another outcome of this study is that earlier conclusions about the DT approach are reinforced. That is, LRT,
