The Invariant Nature of a Morphological Character and Character State: Insights from Gene Regulatory Networks.

Abstract

What constitutes a discrete morphological character versus character state has been long discussed in the systematics literature but the consensus on this issue is still missing. Different methods of classifying organismal features into characters and character states (CCSs) can dramatically affect the results of phylogenetic analyses. Here, I show that, in the framework of Markov models, the modular structure of the gene regulatory network (GRN) underlying trait development, and the hierarchical nature of GRN evolution, essentially remove the distinction between morphological CCS, thus endowing the CCS with an invariant property with respect to each other. This property allows the states of one character to be represented as several individual characters and vice versa. In practice, this means that a phenotype can be encoded using a set of characters or just one complex character with numerous states. The representation of a phenotype using one complex character can be implemented in Markov models of trait evolution by properly structuring transition rate matrix.