Efficient recycled algorithms for quantitative trait models on phylogenies

TL;DR

This paper introduces a fast, flexible algorithm for calculating phenotypic trait likelihoods on phylogenies, combining pruning and numerical quadrature, applicable to various models and uncertainties.

Contribution

It presents a novel, efficient method that integrates Felsenstein's pruning with numerical quadrature, extending likelihood computation beyond Gaussian models.

Findings

Developed algorithms for likelihood calculation and ancestral state reconstruction.

Applied methods to trait data of 839 Labales species with successful results.

Demonstrated adaptability to different trait models and uncertainties.

Abstract

We present an efficient and flexible method for computing likelihoods of phenotypic traits on a phylogeny. The method does not resort to Monte-Carlo computation but instead blends Felsenstein's discrete character pruning algorithm with methods for numerical quadrature. It is not limited to Gaussian models and adapts readily to model uncertainty in the observed trait values. We demonstrate the framework by developing efficient algorithms for likelihood calculation and ancestral state reconstruction under Wright's threshold model, applying our methods to a dataset of trait data for extrafloral nectaries (EFNs) across a phylogeny of 839 Labales species.