Resolving competing evolutionary histories in joint ancestral state reconstruction - James D. Boyko; Kyle J. Gontjes; Evan S. Snitkin; Stephen A. Smith (Proceedings of the National Academy of Sciences, 2026)
Ancestral state reconstruction (ASR) is a foundational tool in comparative biology, offering insights into the evolutionary history of lineages. With each new evolutionary model, our ability to estimate ancestral states with increased biological realism has improved. However, the field has primarily relied on marginal reconstructions, which focus on individual nodes. This framework is analytically tractable and appropriate for node-specific hypotheses, but it is not designed to identify the most probable sequence of evolutionary events across a tree. We argue that for researchers interested in evolutionary trajectories, joint reconstructions provide a more effective way to characterize the full history of transitions. Traditionally, joint reconstruction algorithms focused only on the single most likely sequence, but here we use conditional probabilities derived from stochastic mapping to sample the distribution of plausible ancestral histories efficiently. Furthermore, we provide tools to quantify and summarize this joint uncertainty. Through simulations and an empirical case study, we demonstrate that joint reconstructions more effectively recover simulated trait histories than node-wise marginal estimates and that the uncertainty surrounding these histories can be biologically meaningful. We apply our methods to epidemic multidrug-resistant Klebsiella pneumoniae and find that the evolution of antibiotic resistance is not a single narrative but a series of competing histories. Each of these histories exhibits distinct phenotype–genotype transitions that node-wise approaches would struggle to identify, yet have critical implications for predicting and understanding resistance evolution.