Testing Phylogenetic Stability with Variable Taxon Sampling.

Over the past three decades, computational capabilities have grown at such a rapid rate that they have given rise to many computationally heavy science fields such as phylogenomics. As increasingly more genomes are sequenced in the three domains of life, larger and more species-complete phylogenetic tree reconstructions are leading to a better understanding of the tree of life and the evolutionary histories in deep times. However, these large datasets pose unique challenges from a modeling and computational perspective: accurately describing the evolutionary process of thousands of species is still beyond the capability of current models, while the computational burden limits our ability to test multiple hypotheses. Thus, it is common practice to reduce the size of a dataset by selecting species to represent a clade (taxon sampling). Unfortunately, this process is subjective, and comparisons of large tree of life studies show that choice and number of species used in a dataset can alter the topology obtained. Thus, taxon sampling is, in itself, a process that needs to be fully investigated to determine its effect on phylogenetic stability. Here, we present the theory and practical application of an automated pipeline that can be easily implemented to explore the effect of taxon sampling on phylogenetic reconstructions. The application of this approach was recently discussed in a study of Terrabacteria and shows its power in investigating the accuracy of deep nodes of a phylogeny.

Quantifying the Error of Secondary vs. Distant Primary Calibrations in a Simulated Environment.

Using calibrations to obtain absolute divergence times is standard practice in molecular clock studies. While the use of primary (e.g., fossil) calibrations is preferred, this approach can be limiting because of their rarity in fast-growing datasets. Thus, alternatives need to be explored, such as the use of secondary (molecularly-derived) calibrations that can anchor a timetree in a larger number of nodes. However, the use of secondary calibrations has been discouraged in the past because of concerns in the error rates of the node estimates they produce with an apparent high precision. Here, we quantify the amount of errors in estimates produced by the use of secondary calibrations relative to true times and primary calibrations placed on distant nodes. We find that, overall, the inaccuracies in estimates based on secondary calibrations are predictable and mirror errors associated with primary calibrations and their confidence intervals. Additionally, we find comparable error rates in estimated times from secondary calibrations and distant primary calibrations, although the precision of estimates derived from distant primary calibrations is roughly twice as good as that of estimates derived from secondary calibrations. This suggests that increasing dataset size to include primary calibrations may produce divergence times that are about as accurate as those from secondary calibrations, albeit with a higher precision. Overall, our results suggest that secondary calibrations may be useful to explore the parameter space of plausible evolutionary scenarios when compared to time estimates obtained with distant primary calibrations.