Performance of Phylogenetic Methods in Simulation

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Abstract
Computer simulations are useful because they can characterize the expected performance of phylogenetic methods under idealized conditions. However, simulation studies are also subject to several sources of bias that make the results of different simulation studies difficult to interpret and often contradictory. In this study, I examined the performance of 26 commonly used methods of phylogenetic inference for three statistical criteria: consistency, efficiency, and robustness. Methods examined included parsimony (general, weighted, and transversion), maximum likelihood (assuming Jukes-Cantor and Kimura models of DNA substitution), and UPGMA, minimum evolution, and weighted and unweighted least squares (with uncorrected, Jukes-Cantor, Kimura, modified Kimura, and gamma distances). The performance of methods was examined under three models of DNA substitution for four taxa. The branch lengths of the four-taxon trees were varied extensively in this simulation. The results indicate that most methods perform well (i.e., estimate the correct tree 2:95% of the time) over a large portion of the four-taxon parameter space. In general, maximum likelihood performed best, followed by the additive distance methods and the parsimony methods. Lake's method of invariants and UPGMA are, respectively, inefficient and extremely sensitive to branch-length inequalities. In general, differential weighting of character-state transformations increases the performance of methods when the weighting can be applied appropriately. Although methods differ in their consistency, efficiency, and robustness, additional criteria-mainly falsifiability-are extremely important considerations when choosing a method of phylogenetic inference.

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