Modelling trait dependent speciation with Approximate Bayesian Computation
This work addresses the lack of robust software for phylogenetic modeling in evolutionary biology, providing a flexible tool for researchers, but it is incremental as it builds on existing Approximate Bayesian Computation methods.
The authors tackled the problem of estimating parameters in trait-dependent speciation models by developing an R package called pcmabc, which implements three novel phylogenetic algorithms using Approximate Bayesian Computation, and demonstrated its effectiveness through a simulation-reestimation study on a branching Ornstein-Uhlenbeck process.
Phylogeny is the field of modelling the temporal discrete dynamics of speciation. Complex models can nowadays be studied using the Approximate Bayesian Computation approach which avoids likelihood calculations. The field's progression is hampered by the lack of robust software to estimate the numerous parameters of the speciation process. In this work we present an R package, pcmabc, based on Approximate Bayesian Computations, that implements three novel phylogenetic algorithms for trait-dependent speciation modelling. Our phylogenetic comparative methodology takes into account both the simulated traits and phylogeny, attempting to estimate the parameters of the processes generating the phenotype and the trait. The user is not restricted to a predefined set of models and can specify a variety of evolutionary and branching models. We illustrate the software with a simulation-reestimation study focused around the branching Ornstein-Uhlenbeck process, where the branching rate depends non-linearly on the value of the driving Ornstein-Uhlenbeck process. Included in this work is a tutorial on how to use the software.