Data from: Likelihood of tree topologies with fossils and diversification rate estimation
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Since the diversification process cannot be directly observed at the human scale, it has to be studied from the information available, namely the extant taxa and the fossil record. In this sense, phylogenetic trees including both extant taxa and fossils are the most complete representations of the diversification process that one can get. Such phylogenetic trees can be reconstructed from molecular and morphological data, to some extent. Among the temporal information of such phylogenetic trees, fossil ages are by far the most precisely known (divergence times are inferences calibrated mostly with fossils). We propose here a method to compute the likelihood of a phylogenetic tree with fossils in which the only considered time information is the fossil ages, and apply it to the estimation of the diversification rates from such data. Since it is required in our computation, we provide a method for determining the probability of a tree topology under the standard diversification model. Testing 21 our approach on simulated data shows that the maximum likelihood rate estimates from the phylogenetic tree topology and the fossil dates are almost as accurate as those obtained by taking into account all the data, including the divergence times. Moreover, they are substantially more accurate than the estimates obtained only from the exact divergence times (without taking into account the fossil record). We also provide an empirical example composed of 50 Permo-carboniferous eupelycosaur (early synapsid) taxa ranging in age from about 315 Ma (Late Carboniferous) to 270 Ma (shortly after the end of the Early Permian). Our analyses suggest a speciation (cladogenesis, or birth) rate of about 0.1 per lineage and per My, a marginally lower extinction rate, and a considerable hidden paleobiodiversity of early synapsids.
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Related works
- Is cited by
- 10.1093/sysbio/syx045 (DOI)
- Is derived from
- https://github.com/gilles-didier/Diversification (URL)