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The ClaDS rate-heterogeneous birth-death prior for full phylogenetic inference in BEAST2

Cite this dataset

Barido-Sottani, Joëlle; Morlon, Hélène (2023). The ClaDS rate-heterogeneous birth-death prior for full phylogenetic inference in BEAST2 [Dataset]. Dryad.


Bayesian phylogenetic inference requires a tree prior, which models the underlying diversification process which gives rise to the phylogeny. Existing birth-death diversification models include a wide range of features, for instance lineage-specific variations in speciation and extinction rates. While across-lineage variation in speciation and extinction rates is widespread in empirical datasets, few heterogeneous rate models have been implemented as tree priors for Bayesian phylogenetic inference. As a consequence, rate heterogeneity is typically ignored when reconstructing phylogenies, and rate heterogeneity is usually investigated on fixed trees. In this paper, we present a new BEAST2 package implementing the cladogenetic diversification rate shift (ClaDS) model as a tree prior. ClaDS is a birth-death diversification model designed to capture small progressive variations in birth and death rates along a phylogeny. Unlike previous implementations of ClaDS, which were designed to be used with fixed, user-chosen phylogenies, our package is implemented in the BEAST2 framework and thus allows full phylogenetic inference, where the phylogeny and model are co-estimated from a molecular alignment. Our package provides all necessary components of the inference, including a new tree object and operators to propose moves to the MCMC. It also includes a graphical interface through BEAUti. We validate our implementation of the package by comparing the produced distributions to simulated data, and show an empirical example of the full inference, using a cetaceans dataset.


The data contained in this dataset was created and analyzed using the code in the Software section.

Usage notes

Software required:

BEAST2 ( version 2.6.6

R (


European Commission, Award: 101022928