An implementation of the Brownian motion model for Bayesian phylogenetic inference using continuous traits with missing states

Abstract Continuous morphological traits play a crucial role in phylogenetic inference, yet they are often discretized due to limited software support and challenges of handling missing data efficiently. We present a new implementation of the Brownian motion model for continuous trait evolution in the Bayesian phylogenetics software MrBayes. Our approach efficiently accommodates any proportion of missing data and supports evolutionary rate variation across characters and data partitions. It is compatible with both non-clock and relaxed clock models. We validate the implementation through simulations and apply it to empirical datasets of pterosaurs and ancient humans, demonstrating that continuous traits can improve phylogenetic resolution. This development expands the methodological tool kit for morphological and total-evidence phylogenetics and is applicable across diverse taxonomic groups.