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The tree prior distribution describes the diversification process where fossil and extant species are treated as samples from this process.

The placement of fossils and absolute branch times are determined in one joint inference rather than in separate analyses.

Advances in molecular biology and computer science have enabled increasingly sophisticated methods for inferring phylogenetic trees.

In the total-evidence approach to dating (Lee et al. 2012a), one specifies a probabilistic model that encompasses the fossil data, molecular data and morphological data and then jointly estimates parameters of that model, including a dated phylogeny, in a single analysis using all available data.

It builds on previously described methods for combining molecular and morphological data to infer phylogenies (Nylander et al.

However, this approach must be executed with caution and attention to the quality of the fossil record for the clade of interest, as posterior estimates of divergence times are very sensitive to prior calibration densities of selected nodes (Warnock et al. 2015) meaning that erroneous calibrations lead to erroneous results (Heads 2012).

The second major concern about node calibration is that the fossilization process is modeled only indirectly and in isolation from other forms of data.

Typically the oldest fossil in the clade is chosen as the minimum clade age but there is no agreed upon method of specifying the prior density beyond that.

One way to specify a prior calibration density is through using the fossil sampling rate that can be estimated from fossil occurrence data (Foote and Raup 1996).

Bayesian Markov chain Monte Carlo (MCMC) methods are now the major tool in phylogenetic inference (Yang and Rannala 1997; Mau et al. Stochastic branching models describing the diversification process that generated the tree are typically used as prior distributions for the tree topology and branching times (Yule 1924; Kendall 1948; Nee et al.

1999; Huelsenbeck and Ronquist 2001) and are implemented in several widely used software packages (Lartillot et al. 1994; Rannala and Yang 1996; Yang and Rannala 1997; Gernhard 2008; Stadler 2009).

2004) using a probabilistic model of trait evolution (the Mk model of Lewis 2001).


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