Bayesian Total-Evidence Dating Reveals the Recent Crown Radiation of Penguins.
The total-evidence approach to divergence time dating uses molecular and morphological data from extant and fossil species to infer phylogenetic relationships, species divergence times, and macroevolutionary parameters in a single coherent framework. Current model-based implementations of this approach lack an appropriate model for the tree describing the diversification and fossilization process and can produce estimates that lead to erroneous conclusions. We address this shortcoming by providing a total-evidence method implemented in a Bayesian framework. This approach uses a mechanistic tree prior to describe the underlying diversification process that generated the tree of extant and fossil taxa. Previous attempts to apply the total-evidence approach have used tree priors that do not account for the possibility that fossil samples may be direct ancestors of other samples, that is, ancestors of fossil or extant species or of clades. The fossilized birth–death (FBD) process explicitly models the diversification, fossilization, and sampling processes and naturally allows for sampled ancestors. This model was recently applied to estimate divergence times based on molecular data and fossil occurrence dates. We incorporate the FBD model and a model of morphological trait evolution into a Bayesian total-evidence approach to dating species phylogenies. We apply this method to extant and fossil penguins and show that the modern penguins radiated much more recently than has been previously estimated, with the basal divergence in the crown clade occurring at 
∼12.7
∼12.7
 
 Ma and most splits leading to extant species occurring in the last 2 myr. Our results demonstrate that including stem-fossil diversity can greatly improve the estimates of the divergence times of crown taxa. The method is available in BEAST2 (version 2.4) software www.beast2.org with packages SA (version at least 1.1.4) and morph-models (version at least 1.0.4) installed.
Centre for Computational Evolution, University of Auckland, Auckland, New Zealand.|Department of Computer Science, University of Auckland, Auckland, New Zealand.
Alexandra
A
Gavryushkina
Department of Ecology, Evolution, & Organismal Biology, Iowa State University, Ames, IA, USA.
Tracy A
TA
Heath
Bruce Museum, Greenwich, CT, USA.
Daniel T
DT
Ksepka
Department of Biosystems Science & Engineering, Eidgenössische Technische Hochschule Zürich, Basel, Switzerland.
Tanja
T
Stadler
Centre for Computational Evolution, University of Auckland, Auckland, New Zealand.|Department of Computer Science, University of Auckland, Auckland, New Zealand.
David
D
Welch
Centre for Computational Evolution, University of Auckland, Auckland, New Zealand.|Department of Computer Science, University of Auckland, Auckland, New Zealand.
Alexei J
AJ
Drummond
D000818:Animals; D001499:Bayes Theorem; D005580:Fossils; D049810:Genetic Speciation; D008954:Models, Biological; D010802:Phylogeny; D046429:Spheniscidae / Q000033:anatomy & histology / Q000145:classification* / Q000235:genetics; D013997:Time Factors
Birth–death process; calibration; divergence times; MCMC; phylogenetics