We now look at a more sophisticated way to calibrate the tree that overcomes these issues.
The following discussion assumes you have a basic grasp of Bayesian statistics, are familiar with BPP (or other similar Bayesian coalescent inference software), and with the R environment for statistical computing.
I think the tutorial should take less than one hour. Please read the documentation in the package (bpp DOC.pdf) before using the program.
Please let me know if you find the tutorial confusing or if you have comments or suggestions. If you are new and want to get started with the program, you can go through the command-line tutorial at the top of this page (choose the right pdf file depending on whether you use windows, mac, or linux), and then go through the tutorial in the following paper: Yang, Z. A tutorial of BPP for species tree estimation and species delimitation. pdf If you have questions, after having struggled through the tutorial, please post them at the google bpp discussion site. This also includes the two exponential priors on internal and external branch lengths described by Yang & Rannala (2005) and Yang (2007). If you use the modified program, please cite Mr Bayes as well as the papers that describe the modifications.
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The advantage of the method is that incomplete lineage sorting (the discrepancy between gene trees and the species tree), and ancestral polymorphism are accounted for during phylogenetic inference.
Bayesian implementations of the method are computationally expensive, and are best suited for inference among closely related species (or populations).
The RADseq data used to build the phylogeny above represents a random sample of the genome, and thus we may assume that most RAD-fragments are evolving neutrally.
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This is perhaps the simplest procedure to calibrate a coalescent tree to geological time.
The procedure outlined above has at least two serious limitations however.