Do missing data influence the accuracy of divergence-time estimation with BEAST?

Time-calibrated phylogenies have become essential to evolutionary biology. A recurrent and unresolved question for dating analyses is whether genes with missing data cells should be included or excluded. This issue is particularly unclear for the most widely used dating method, the uncorrelated lognormal approach implemented in BEAST. Here, we test the robustness of this method to missing data. We compare divergence-time estimates from a nearly complete dataset (20 nuclear genes for 32 species of squamate reptiles) to those from subsampled matrices, including those with 5 or 2 complete loci only and those with 5 or 8 incomplete loci added. In general, missing data had little impact on estimated dates (mean error of similar to 5 Myr per node or less, given an overall age of similar to 220 Myr in squamates), even when 80% of sampled genes had 75% missing data. Mean errors were somewhat higher when all genes were 75% incomplete (similar to 17 Myr). However, errors increased dramatically when only 2 of 9 fossil calibration points were included (similar to 40 Myr), regardless of missing data. Overall, missing data (and even numbers of genes sampled) may have only minor impacts on the accuracy of divergence dating with BEAST, relative to the dramatic effects of fossil calibrations. (C) 2015 Elsevier Inc. All rights reserved.