Data from: Improved estimation of macroevolutionary rates from fossil data using a Bayesian framework

The estimation of origination and extinction rates and their temporal
variation is central to understanding diversity patterns and the
evolutionary history of clades. The fossil record provides the only direct
evidence of extinction and biodiversity changes through time and has long
been used to infer the dynamics of diversity changes in deep time. The
software PyRate implements a Bayesian framework to analyze fossil
occurrence data to estimate the rates of preservation, origination and
extinction while incorporating several sources of uncertainty. Building
upon this framework, we present a suite of methodological advances
including more complex and realistic models of preservation and the first
likelihood-based test to compare the fit across different models. Further,
we develop a new reversible jump Markov chain Monte Carlo algorithm to
estimate origination and extinction rates and their temporal variation,
which provides more reliable results and includes an explicit estimation
of the number and temporal placement of statistically significant rate
changes. Finally, we implement a new C++ library which speeds up the
analyses by orders of magnitude, therefore facilitating the application of
PyRate to large datasets. We demonstrate the new functionalities through
extensive simulations and with the analysis of a large dataset of Cenozoic
marine mammals. We compare our analytical framework against two
alternative methods to infer origination and extinction rates revealing
that PyRate decisively outperforms them across a range of simulated
datasets. Our analyses indicate that explicit statistical model testing,
which is often neglected in fossil-based macroevolutionary analyses, is
crucial to obtain accurate and robust results.