Fast Bayesian inference of phylogenies from multiple continuous characters

Time-scaled phylogenetic trees are an ultimate goal of evolutionary
biology and a necessary ingredient in comparative studies. The
accumulation of genomic data has resolved the tree of life to a great
extent, yet timing evolutionary events remains challenging if not
impossible without external information such as fossil ages and
morphological characters. Methods for incorporating morphology in tree
estimation have lagged behind their molecular counterparts, especially in
the case of continuous characters. Despite recent advances, such tools are
still direly needed as we approach the limits of what molecules can teach
us. Here, we implement a suite of state-of-the-art methods for leveraging
continuous morphology in phylogenetics, and by conducting extensive
simulation studies we thoroughly validate and explore our methods'
properties. While retaining model generality and scalability, we make it
possible to estimate absolute and relative divergence times from multiple
continuous characters while accounting for uncertainty. We compile and
analyze one of the most data-type diverse data sets to date, comprised of
contemporaneous and ancient molecular sequences, and discrete and
continuous characters from living and extinct Carnivora taxa. We conclude
by synthesizing lessons about our method's behavior, and suggest
future research venues.