Evolutionary Modeling Using Bayesian Phylogenetics

Abstract

Bayesian phylogenetic methods infer evolutionary trees and divergence times while explicitly accounting for uncertainty in topology, branch lengths, and model parameters through posterior probability distributions, yet their computational cost and methodological complexity have limited adoption relative to faster maximum-likelihood alternatives. We evaluated 214 Bayesian phylogenetic modelling programmes across centres in Spain, Estonia, and Austria between 2016 and 2021, spanning five methodological categories: standard MCMC tree inference, relaxed molecular clock dating, coalescent-based population genetics, phylodynamic epidemic modelling, and variational and amortised Bayesian inference. A Bayesian Phylogenetics Effectiveness Index (BPEI) was constructed from five sub-scores -- topological accuracy, divergence time estimation quality, computational efficiency, model selection robustness, and biological discovery impact -- with weights from regression against sustained adoption into evolutionary biology pipelines. BPEI correlated with adoption at r = +0.84 and discriminated adopted from non-adopted methods with an AUC of 0.882. Standard MCMC inference scored highest (mean BPEI 0.826), while variational methods trailed at 0.602. Only 35.5 percent exceeded the 0.75 threshold. Topological accuracy carried the largest weight (beta = +0.278), followed by computational efficiency (beta = +0.230)