Function-Valued Traits in Evolution

TL;DR

This paper introduces a statistical method combining dimension reduction and phylogenetic Gaussian process regression to infer ancestral function-valued traits and estimate evolutionary processes from biological data.

Contribution

It presents a novel, practical approach for analyzing continuous function-valued traits in evolution, accounting for phylogenetic relationships and enabling ancestral trait reconstruction.

Findings

Method performs well on simulated data

Robust to limited trait data at tips

Applicable to diverse function-valued traits

Abstract

Many biological characteristics of evolutionary interest are not scalar variables but continuous functions. Given a dataset of function-valued traits generated by evolution, we develop a practical statistical approach to infer ancestral function-valued traits, and estimate the generative evolutionary process. We do this by combining dimension reduction and phylogenetic Gaussian process regression, a nonparametric procedure which explicitly accounts for known phylogenetic relationships. We test the methods' performance on simulated function-valued data generated from a stochastic evolutionary model. The methods are applied assuming that only the phylogeny and the function-valued traits of taxa at its tips are known. Our method is robust and applicable to a wide range of function-valued data, and also offers a phylogenetically aware method for estimating the autocorrelation of function-valued traits.