Rob Lanfear, Matthew Hahn 

As phylogenomic datasets have grown in size, researchers have developed new ways to measure biological variation and to assess statistical support. Larger datasets have many more sites and many more loci, and therefore less sampling variance. While this means that we can more accurately measure the mean signal in these datasets. the lower sampling variance is often reflected in widely used measures of branch support— such as the bootstrap and posterior probability—being uniformly high, limiting their utility. Larger datasets have also revealed a large amount of biological variation in the topologies found across individual loci, such that the single species tree inferred by most phylogenetic methods represents a limited summary of the data. In contrast to measures of statistical support, the degree of underlying topological variation among sites or loci should be approximately constant regardless of the size of the dataset. “Concordance factors” and similar statistics have therefore become increasingly important tools in phylogenetics. In this review, we explain why concordance factors should be thought of as descriptors of topological variation, rather than as measures of statistical support, and argue that they provide important information not contained in measures of support. We review a growing suite of statistics derived from various ways of measuring concordance, comparing them in a common framework that reveals their interrelationships. We also discuss how measures of topological variation might change in the future as we move beyond estimating a single “tree of life” towards estimating the myriad evolutionary histories underlying genomic variation.