Abstract
Modeling discrete phenotypic traits for either ancestral character state reconstruction or morphology-based phylogenetic inference suffers from ambiguities of character coding, homology assessment, dependencies, and selection of adequate models. These drawbacks occur because trait evolution is driven by two key processes – hierarchical and hidden – which are not accommodated simultaneously by the available phylogenetic methods. The hierarchical process refers to the dependencies between anatomical body parts, while the hidden process refers to the evolution of gene regulatory networks (GRNs) underlying trait development. Herein, I demonstrate that these processes can be efficiently modeled using structured Markov models equipped with hidden states, which resolves the majority of the problems associated with discrete traits. Integration of structured Markov models with anatomy ontologies can adequately incorporate the hierarchical dependencies, while the use of the hidden states accommodates hidden GRN evolution and mutation rate heterogeneity. This model is insensitive to alternative coding approaches, which is shown by using a previously unknown “two-scientist paradox”, and solving the tail color problem. The practical considerations for implementing this model in phylogenetic inference and comparative methods are demonstrated and discussed.
Footnotes
E-mail: sergxf{at}yandex.ru
The author declares no conflict of interest.