LAUSR

The scale of datasets used to infer phylogenies has grown dramatically in the last decades, providing researchers with an enormous amount of information with which to draw inferences about evolutionary history. However, standard approaches to assessing confidence in those inferences (e.g., nonparametric bootstrap proportions and Bayesian posterior probabilities) are still deeply influenced by statistical procedures and frameworks that were developed when information was much more limited. These approaches largely quantify uncertainty caused by limited amounts of data, which is often vanishingly small with modern, genome-scale sequence datasets. As a consequence, today's phylogenomic studies routinely report near complete confidence in their inferences, even when different studies reach strongly conflicting conclusions and the sites and loci in a single dataset contain much more heterogeneity than our methods assume or can accommodate. Therefore, we argue that bootstrap proportions and marginal posterior probabilities of bipartitions have outlived their utility as the primary means of measuring phylogenetic support for modern, phylogenomic datasets with large numbers of sites relative to the number of taxa. Continuing to rely on these measures will hinder progress towards understanding remaining sources of uncertainty in the most challenging portions of the Tree of Life. Instead, we encourage researchers to examine the ideas and methods presented in this special issue of Systematic Biology and to explore the area further in their own work. The papers in this special issue outline strategies for assessing confidence and uncertainty in phylogenomic datasets that move beyond stochastic error due to limited data and offer promise for more productive dialogue about the challenges that we face in reaching our shared goal of understanding the history of life on Earth.