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In phylogenetic analysis, one possible approach to minimize missing data in DNA supermatrices consists in sampling sequences from different species to obtain a complete sequence for all genes included in the study. We refer to those complete sequences as composite taxa because DNA sequences that are combined belong to different species. An alternative approach is to analyze incomplete supermatrices by coding unavailable DNA sequences as missing. The accuracy of phylogenetic trees estimated using matrices that include composite taxa has recently been questioned, and the best approach for analyzing incomplete supermatrices is highly debated. Through computer simulations, we compared the phylogenetic accuracy of the 2 competing approaches. We explored the effect of composite taxa when inferring higher level relationships, that is, relationships between monophyletic groups. DNA sequences were simulated on a 42-taxon model tree and incomplete supermatrices containing different percentages of missing data were generated. These incomplete supermatrices were analyzed either by coding the missing data with "?" or by reducing the amount of missing data through the combination of 2 or more taxa to generate composite taxa. Of 180 comparisons (18 simulation cases with 2 different inference methods and 5 levels of incompleteness), we observed significantly higher phylogenetic accuracies for composite matrices in 46 comparisons, whereas missing data matrices outperformed composites in 8 comparisons. In all other cases, the phylogenetic accuracy obtained with composite matrices was not significantly different from that of missing data matrices. This study demonstrates that composite taxa represent an interesting approach to minimize the amount of missing data in supermatrices and we suggest that it is the optimal approach to use in phylogenomic studies to reduce computing time.

The Use and Validity of Composite Taxa in Phylogenetic Analysis