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 the 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 chains equipped with hidden states, which resolves the majority of the problems associated with discrete traits. Integration of structured Markov chains with anatomy ontologies adequately incorporates the hierarchical dependencies, while 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 solving the Maddison’s tail color problem. Additionally, this model provides new insight into character concept and homology assessment. The practical considerations for implementing this model in phylogenetic inference and comparative methods are discussed.
Footnotes
E-mail: sergxf{at}yandex.ru
The author declares no conflict of interest.