Data from: Evaluating UCE data adequacy and integrating uncertainty in a comprehensive phylogeny of ants

While some relationships in phylogenomic studies have remained stable
since the Sanger sequencing era, many challenging nodes remain, even with
genome-scale data. Incongruence or lack of resolution in the phylogenomic
era is frequently attributed to inadequate data modeling and analytical
issues that lead to systematic biases. However, few studies investigate
the potential for random error or establish expectations for the level of
resolution achievable with a given empirical dataset, and integrate
uncertainties across methods when faced with conflicting results. Ants are
the most species-rich lineage of social insects and one of the most
ecologically important terrestrial animals. Consequently, ants have
garnered significant research attention, including their systematics.
Despite this, there has been no comprehensive genus-level phylogeny of the
ants inferred using genomic data that thoroughly evaluates both signal
strength and incongruence. In this study, we provide insight into and
quantify uncertainty across the ant tree of life by utilizing the most
taxonomically comprehensive Ultraconserved Elements dataset of ants to
date, including 277 (81%) of recognized ant genera from all 16 extant
subfamilies, and representing over 98% of described species. We use
simulations to establish expectations for resolution, identify branches
with less-than-expected concordance, and dissect the effects of data and
model selection on recalcitrant nodes. Simulations show that hundreds of
loci are needed to resolve recalcitrant nodes on our genus-level ant
phylogeny. This demonstrates the continued role of random error in
phylogenomic studies. Our analyses provide a comprehensive picture of
support and incongruence across the ant phylogeny, while offering a more
nuanced depiction of uncertainty and significantly expanding generic
sampling. We use a consensus approach to integrate uncertainty across
different analyses and find that assumptions about root age exert
substantial influence on divergence dating. Our results suggest that
advancing the understanding of ant phylogeny will require not only more
data but also more refined phylogenetic models. We also provide a workflow
for identifying under-supported nodes in concatenation analyses, outline a
pragmatic way to reconcile conflicting results in phylogenomics, and
introduce a user-friendly locus selection tool for divergence dating.