Handling Logical Character Dependency in Phylogenetic Inference: Extensive Performance Testing of Assumptions and Solutions Using Simulated and Empirical Data Article Swipe
Tiago R. Simões
,
Oksana Vernygora
,
Bruno A. S. de Medeiros
,
April Wright
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/sysbio/syad006
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/sysbio/syad006
Logical character dependency is a major conceptual and methodological problem in phylogenetic inference of morphological data sets, as it violates the assumption of character independence that is common to all phylogenetic methods. It is more frequently observed in higher-level phylogenies or in data sets characterizing major evolutionary transitions, as these represent parts of the tree of life where (primary) anatomical characters either originate or disappear entirely. As a result, secondary traits related to these primary characters become “inapplicable” across all sampled taxa in which that character is absent. Various solutions have been explored over the last three decades to handle character dependency, such as alternative character coding schemes and, more recently, new algorithmic implementations. However, the accuracy of the proposed solutions, or the impact of character dependency across distinct optimality criteria, has never been directly tested using standard performance measures. Here, we utilize simple and complex simulated morphological data sets analyzed under different maximum parsimony optimization procedures and Bayesian inference to test the accuracy of various coding and algorithmic solutions to character dependency. This is complemented by empirical analyses using a recoded data set on palaeognathid birds. We find that in small, simulated data sets, absent coding performs better than other popular coding strategies available (contingent and multistate), whereas in more complex simulations (larger data sets controlled for different tree structure and character distribution models) contingent coding is favored more frequently. Under contingent coding, a recently proposed weighting algorithm produces the most accurate results for maximum parsimony. However, Bayesian inference outperforms all parsimony-based solutions to handle character dependency due to fundamental differences in their optimization procedures—a simple alternative that has been long overlooked. Yet, we show that the more primary characters bearing secondary (dependent) traits there are in a data set, the harder it is to estimate the true phylogenetic tree, regardless of the optimality criterion, owing to a considerable expansion of the tree parameter space. [Bayesian inference, character dependency, character coding, distance metrics, morphological phylogenetics, maximum parsimony, performance, phylogenetic accuracy.]
Related Topics
Concepts
Inference
Character evolution
Weighting
Character (mathematics)
Phylogenetic tree
Coding (social sciences)
Bayesian probability
Dependency (UML)
Maximum parsimony
Computer science
Character encoding
Algorithm
Artificial intelligence
Mathematics
Statistics
Biology
Clade
Geometry
Medicine
Radiology
Biochemistry
Gene
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/sysbio/syad006
- OA Status
- green
- Cited By
- 20
- References
- 80
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4320033035
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4320033035Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/sysbio/syad006Digital Object Identifier
- Title
-
Handling Logical Character Dependency in Phylogenetic Inference: Extensive Performance Testing of Assumptions and Solutions Using Simulated and Empirical DataWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-02-11Full publication date if available
- Authors
-
Tiago R. Simões, Oksana Vernygora, Bruno A. S. de Medeiros, April WrightList of authors in order
- Landing page
-
https://doi.org/10.1093/sysbio/syad006Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://pmc.ncbi.nlm.nih.gov/articles/PMC10276625/pdf/syad006.pdfDirect OA link when available
- Concepts
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Inference, Character evolution, Weighting, Character (mathematics), Phylogenetic tree, Coding (social sciences), Bayesian probability, Dependency (UML), Maximum parsimony, Computer science, Character encoding, Algorithm, Artificial intelligence, Mathematics, Statistics, Biology, Clade, Geometry, Medicine, Radiology, Biochemistry, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
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20Total citation count in OpenAlex
- Citations by year (recent)
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2025: 10, 2024: 4, 2023: 5, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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80Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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