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.