Data from: Total-evidence dating under the fossilized birth-death process

Bayesian total-evidence dating involves the simultaneous analysis of
morphological data from the fossil record and morphological and sequence
data from recent organisms, and it accommodates the uncertainty in the
placement of fossils while dating the phylogenetic tree. Due to the
flexibility of the Bayesian approach, total-evidence dating can also
incorporate additional sources of information. Here, we take advantage of
this and expand the analysis to include information about fossilization
and sampling processes. Our work is based on the recently described
fossilized birth-death (FBD) process, which has been used to model
speciation, extinction and fossilization rates that can vary over time in
a piecewise manner. So far, sampling of extant and fossil taxa has been
assumed to be either complete or uniformly at random, an assumption which
is only valid for a minority of datasets. We therefore extend the FBD
process to accommodate diversified sampling of extant taxa, which is
standard practice in studies of higher-level taxa. We verify the
implementation using simulations and apply it to the early radiation of
Hymenoptera (wasps, ants and bees). Previous total-evidence dating
analyses of this dataset were based on a simple uniform tree prior and
dated the initial radiation of extant Hymenoptera to the late
Carboniferous (309 Ma). The analyses using the FBD prior under diversified
sampling, however, date the radiation to the Triassic and Permian (252
Ma), slightly older than the age of the oldest hymenopteran fossils. By
exploring a variety of FBD model assumptions, we show that it is mainly
the accommodation of diversified sampling that causes the push towards
more recent divergence times. Accounting for diversified sampling thus has
the potential to close the long-discussed gap between rocks and clocks. We
conclude that the explicit modeling of fossilization and sampling
processes can improve divergence time estimates, but only if all important
model aspects, including sampling biases, are adequately addressed.