Fast and accurate estimation of species-specific diversification rates using data augmentation

Diversification rates vary across species as a response to various factors, including environmental conditions and species-specific features. Phylogenetic models that allow accounting for and quantifying this heterogeneity in diversification rates have proven particularly useful for understanding clades diversification. Recently, we introduced the cladogenetic diversification rate shift model (ClaDS), which allows inferring subtle rate variations across lineages. Here we present a new inference technique for this model that considerably reduces computation time through the use of data augmentation and provide an implementation of this method in Julia. In addition to drastically reducing computation time, this new inference approach provides a posterior distribution of the augmented data, that is the tree with extinct and unsampled lineages as well as associated diversification rates. In particular, this allows extracting the distribution through time of both the mean rate and the number of lineages. We assess the statistical performances of our approach using simulations and illustrate its application on the entire bird radiation.
Methods
These additionnal data contains supplementary figures supporting the paper, as well as a tutorial for the use of the Julia package.

The .jld2 file is the result of the run of ClaDS on the complete bird phylogeny computed with molecular data from Jetz (2012) with the Hackett backbone, containing 6670 species. We use TreeAnnotator from the software Beast with the Common Ancestor option for node height (Bouckaert 2019) to obtain a Maximum Clade Credibility (MCC) tree computed from a sample of 1000  trees from the posterior distribution. We fix the sampling fractions for each of the subtrees of the tree from Jetz (2012) as the ratio between the number of species in the molecular phylogeny over that in the phylogeny including all bird species. We attach the results of this analysis as a supplementary material to this paper.

Bouckaert, R., T. G. Vaughan, J. Barido-Sottani, S. Duchêne, M. Fourment, A. Gavryushkina,

J. Heled, G. Jones, D. Kühnert, N. De Maio, et al. 2019. Beast 2.5: An advanced software

platform for bayesian evolutionary analysis. PLoS computational biology 15:e1006650.

Jetz, W., G. Thomas, J. Joy, K. Hartmann, and A. Mooers. 2012. The global diversity of birds in

space and time. Nature 491:444.