Data from: Integration of anatomy ontologies and evo-devo using structured Markov models suggests a new framework for modeling discrete phenotypic traits

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 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
evolution of gene regulatory networks and substitution rate heterogeneity.
I assess the new models using simulations and theoretical synthesis. The
new approach solves the long-standing "tail color problem," in
which the trait is scored for species with tails of different colors or no
tails. It also presents a previously unknown issue called the
"two-scientist paradox," in which the nature of coding the trait
and the hidden processes driving the trait's evolution are
confounded; failing to account for the hidden process may result in a
bias, which can be avoided by using hidden state models. All this provides
a clear guideline for coding traits into characters. This paper gives
practical examples of using the new framework for phylogenetic inference
and comparative analysis.