ArticleSystematic biology2019
Integration of Anatomy Ontologies and Evo-Devo Using Structured Markov Models Suggests a New Framework for Modeling Discrete Phenotypic Traits.
Article in Systematic biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Building phenotypic character matrices for phylogenetic inference: exploration of 35 years of practice.Biological reviews of the Cambridge Philosophical Society · 2026Pooled it
- Phylogenetic congruence, conflict and consilience between molecular and morphological data.BMC ecology and evolution · 2023Pooled it
- Unidentifiability and false-positive inference in state-dependent diversification models.Nature communications · 2026Article
- An Evolving View of Character Macroevolution.Systematic biology · 2026Review
- The Dermal Skeleton of Stem-Actinopterygian Moythomasia durgaringa and Its Implications for the Nature of the Ancestral Osteichthyan.Journal of morphology · 2026Article
- Estimating Ancestral States of Complex Characters: A Case Study on the Evolution of Feathers.Systematic biology · 2026Article
- Practical Guide and Review of Fossil Tip-Dating in Phylogenetics.Systematic biology · 2026Review
- Independent origins of spicules reconcile paleontological and molecular evidence of sponge evolutionary history.Science advances · 2026Article
- How many characters are needed to reconstruct a phylogeny?Biology letters · 2025Article
- Integrating Deep Learning Derived Morphological Traits and Molecular Data for Total-Evidence Phylogenetics: Lessons from Digitized Collections.Systematic biology · 2025Article
- Multiple evolutionary pressures shape identical consonant avoidance in the world's languages.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Evaluating the Accuracy of Methods for Detecting Correlated Rates of Molecular and Morphological Evolution.Systematic biology · 2023Article
- Extended embryo retention and viviparity in the first amniotes.Nature ecology & evolution · 2023Article
- New Phylogenetic Markov Models for Inapplicable Morphological Characters.Systematic biology · 2023Article
- Handling Logical Character Dependency in Phylogenetic Inference: Extensive Performance Testing of Assumptions and Solutions Using Simulated and Empirical Data.Systematic biology · 2023Article
- A New Molecular Phylogeny of Salps (Tunicata: Thalicea: Salpida) and the Evolutionary History of Their Colonial Architecture.Integrative organismal biology (Oxford, England) · 2023Article
- Assessing Bayesian Phylogenetic Information Content of Morphological Data Using Knowledge From Anatomy Ontologies.Systematic biology · 2022Article
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- Exceptional evolutionary lability of flower-like inflorescences (pseudanthia) in Apiaceae subfamily Apioideae.American journal of botany · 2022Article
- New opabiniid diversifies the weirdest wonders of the euarthropod stem group.Proceedings. Biological sciences · 2022Article
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1 author.
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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 (SMM) equipped with hidden states, which resolves the majority of the problems associated with discrete traits. Integration of SMM with anatomy ontologies can adequately incorporate the hierarchical dependencies, while the use of the hidden states accommodates hidden evolution of GRNs 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 article gives practical examples of using the new framework for phylogenetic inference and comparative analysis.
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