Modelling Trait-dependent Speciation with Approximate Bayesian Computation

Abstract

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 nonlinearly on the value of the driving Ornstein-Uhlenbeck process. Included in this work is a tutorial on how to use the software.

Building an adaptive trait simulator package to infer parametric diffusion model along phylogenetic tree

“…It is known that statistical inference accounts for the uncertainty. Similar to the simple OU model [2] , ABC estimation for the models in Jhwueng [12] results in wide histograms of the estimator (see Fig. 3 ).…”

“…Note that a popular package pcmabc [2] available on CRAN offers the same functionality as ouxy except for a wider class of models, including a trait dependent speciation one. While pcmabc allows arbitrary class of Markov process and requires users to specify the drift coefficient and diffusion coefficient in the diffusion model, ouxy focus on expanding the Ornstein-Uhlenbeck based processes with non-Gaussian process (CIR) on the rate of evolution and functional optimal regression where the optimal of the dependent variable depends on another stochastic covariates.…”

Phylogenetic Curved Optimal Regression for Adaptive Trait Evolution

“…Since both the MLE for mean and variance can be written as a function of , can be estimated by optimizing the likelihood function over its domain . Those statistics resulted in a great interest in evolutionary-biology research [ 19 , 41 , 42 ]. Euclidean distance measure corresponds to those statistics S , where and are computed from observed and simulated-trait data, respectively.…”

PhylSim: A New Phylogenetic Tool for Modeling Trait Evolution and Speciation

“…It also has the function 'covdist', which only uses the covariance matrices to calculate the distance. For distance between trees, PhylSim uses functions provided by the 'pcmabc' package, namely 'bdcoeffs', 'node heights', and 'logweighted node heights' [3].…”

“…The mvSLOUCH framework was further improved by Bartoszek and Lio (2019) [3] with the 'pcmabc' package. This package uses Approximate Bayesian Computation to fit parameters of the stochastic process, thereby making the computation more efficient.…”