Selection of Character Coding Method Is Not Phylogenetically Neutral: A Test Case Using Hominoids

Abstract

The early stages of phylogenetic inference from morphological data involve a sequence of choices about which analytical methods to employ. At each stage, the selection of one method over another can dramatically impact tree inference. Phylogenetic hypotheses are sensitive to decisions relating to which taxa and characters to select for analysis, whether and how to delimit character states, which taxa to use as outgroups, and how to account for character dependence. Using extant hominoids as a test case, I quantify the degree to which phylogenetic inferences are sensitive to the choice of method used to transform continuously scaled variables into categorical traits. I demonstrate that the character coding strategy significantly impacts hypotheses of character state identity and phylogenetic branching patterns. To avoid biasing evolutionary hypotheses, I recommend that continuously scaled characters be analyzed without prior discretization.

The diverse terminology of reptile eggshell microstructure and its effect on phylogenetic comparative analyses

“…Indeed, the problem of discretizing continuous traits has been the subject of a four‐decade‐long debate in phylogenetic systematics, and none of the many discretization methods available (e.g. divergence weighting, step‐matrix gap‐weighting, implied weighting – Bardin et al, 2014; Gift & Stevens, 1997; Wiens, 2001) have been shown to accurately represent the original distribution of continuous traits (Bardin et al, 2014; Raven & Maidment, 2017; Worthington, 2017). Additionally, the uneven temporal distribution of terrestrial Konservat‐Lagerstätten (Eliason et al, 2017) may differentially impact the record of amniote eggs with a thin or absent CL, and statistical approaches that consider probability of preservation could be used to put confidence bounds on estimated ancestral states (Eliason et al, 2017; Marjanović & Laurin, 2008; Marshall, 2019; Wang et al, 2016).…”