Data from: Improved estimation of macroevolutionary rates from fossil data using a Bayesian framework
Silvestro, Daniele; Salamin, Nicolas; Antonelli, Alexandre; Meyer, Xavier (2019), Data from: Improved estimation of macroevolutionary rates from fossil data using a Bayesian framework, Dryad, Dataset, https://doi.org/10.5061/dryad.j3t420p
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.