Function-Valued Traits in Evolution
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.
Cite
@article{arxiv.1212.3730,
title = {Function-Valued Traits in Evolution},
author = {Pantelis Z. Hadjipantelis and Nick S. Jones and John Moriarty and David A. Springate and Christopher G. Knight},
journal= {arXiv preprint arXiv:1212.3730},
year = {2015}
}