Coalescence vs. concatenation: Sophisticated analyses vs. first principles applied to rooting the angiosperms

• (Amborella, Nuphar) resolution by coalescence methods is an artifact of mis-rooting.
• Amborella alone is supported as sister to the remaining extant angiosperms.
• ASTRAL is more robust to incorrectly rooted gene trees than MP-EST or STAR.
• OV and TIGER biased in favor of characters with asymmetrical state distributions.
• Novel methods may be novel sources of systematic errors.

It has recently been concluded that phylogenomic data from 310 nuclear genes support the clade of (Amborellales, Nymphaeales) as sister to the remaining angiosperms and that shortcut coalescent phylogenetic methods outperformed concatenation for these data. We falsify both of those conclusions here by demonstrating that discrepant results between the coalescent and concatenation analyses are primarily caused by the coalescent methods applied (MP-EST and STAR) not being robust to the highly divergent and often mis-rooted gene trees that were used. This result reinforces the expectation that low amounts of phylogenetic signal and methodological artifacts in gene-tree reconstruction can be more problematic for shortcut coalescent methods than is the assumption of a single hierarchy for all genes by concatenation methods when these approaches are applied to ancient divergences in empirical studies. We also demonstrate that a third coalescent method, ASTRAL, is more robust to mis-rooted gene trees than MP-EST or STAR, and that both Observed Variability (OV) and Tree Independent Generation of Evolutionary Rates (TIGER), which are two character subsampling procedures, are biased in favor of characters with highly asymmetrical distributions of character states when applied to this dataset. We conclude that enthusiastic application of novel tools is not a substitute for rigorous application of first principles, and that trending methods (e.g., shortcut coalescent methods applied to ancient divergences, tree-independent character subsampling), may be novel sources of previously under-appreciated, systematic errors.

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