Reducing the biases in false correlations between discrete characters

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1093/sysbio/syac066. This is version 2 of this Preprint.

This Preprint has no visible version.

Download Preprint
Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.

Downloads

Download Preprint

Supplementary Files
Authors

James Boyko , Jeremy Beaulieu

Abstract

The correlation between two characters is often interpreted as evidence that there exists a significant and biologically important relationship between them. However, Maddison and FitzJohn (2015) recently pointed out that in certain situations find evidence of correlated evolution between two categorical characters is often spurious, particularly, when the dependent relationship stems from a single replicate deep in time. Here we will show that there may, in fact, be a statistical solution to the problem posed by Maddison and FitzJohn (2015) naturally embedded within the expanded model space afforded by the hidden Markov model (HMM) framework. We demonstrate that the problem of single unreplicated evolutionary events manifests itself as rate heterogeneity within our models and that this is the source of the false correlation. Therefore, we argue that this problem is better understood as model misspecification rather than a failure of comparative methods to account for phylogenetic pseudoreplication. We utilize HMMs to develop a multi-rate independent model which, when implemented, drastically reduces support for correlation. The problem itself extends beyond categorical character evolution, but we believe that the practical solution presented here may lend itself to future extensions in other areas of comparative biology.

DOI

https://doi.org/10.32942/osf.io/e2kj8

Subjects

Ecology and Evolutionary Biology, Evolution, Life Sciences, Other Ecology and Evolutionary Biology

Keywords

Dates

Published: 2022-04-02 01:30

Last Updated: 2022-06-23 01:46

Older Versions
License

CC-By Attribution-ShareAlike 4.0 International