# Modelling trait dependent speciation with Approximate Bayesian Computation

## TL;DR

This paper introduces pcmabc, an R package utilizing Approximate Bayesian Computation for flexible trait-dependent speciation modeling, enabling estimation of complex evolutionary parameters without likelihood calculations.

## Contribution

The work presents three novel phylogenetic algorithms for trait-dependent speciation within an accessible R package, expanding modeling capabilities beyond predefined models.

## Key findings

- Successful simulation-reestimation of trait-dependent branching rates
- Flexible modeling of non-linear trait effects on speciation
- Tutorial provided for practical software application

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