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The wealth of data available for molecular phylogenetic analyses is expanding at an exponential pace. As data sets have become larger, it has become increasingly critical to understand the advantages and disadvantages of using various phylogenetic inference methods. Four inference methods based on three optimization criteria are commonly used to reconstruct evolutionary history from molecular data: neighbor joining (NJ), minimum evolution (ME), maximum parsimony (MP), and maximum likelihood (ML). The overall efficiency and performance of these methods in reconstructing the true tree is known to vary with substitution rate, transitiontransversion ratio, and sequence divergence (Miyamoto and Cracraft 1991; Nei and Kumar 2000). Computer simulation has proven to be an excellent means of assessing the performance of tree-building methods (reviewed in Nei and Kumar 2000, chapter 9). It can be used to examine the overall performance of a method or specific aspects of its performance (e.g., Hillis 1996; Strimmer and von Haeseler 1996; Kim 1998; Nei, Kumar, and Takahashi 1998). Kumar and Gadagkar (2000) extended the use of simulation to examine the relative performance of NJ in reconstructing deep and shallow nodes. They found that NJ reconstructs deep branches as efficiently as shallow branches, where branch depth is defined as the minimum number of terminal taxa found between the two subtrees joined by the branch. This measure is appropriate because it estimates the complexity of inferring a branch by defining the minimum number of taxa that must be joined; deeper branches require more taxa and thus have higher complexities than shallow branches (Kumar and Gadagkar 2000). In this paper, we expand that study to include the ME, MP, and ML methods to examine how these methods perform under Jukes-Cantor (JC) model (Jukes and Cantor 1969) and a more complex Hasegawa, Kishino, and Yano (1985) (HKY) model of nucleotide substitution. We also look at how differences in choices within an inference method, e.g., the use of step matrix versus unweighted parsimony or Jukes-Cantor versus TamuraNei distance, affects the outcome. These simulations will allow us to examine the relative efficiencies among branch depths and among phylogenetic inference methods.

Traditional Phylogenetic Reconstruction Methods Reconstruct Shallow and Deep Evolutionary Relationships Equally Well