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Performance of tree-building methods using a morphological dataset and a well-supported Hexapoda phylogeny
Felipe Francisco Barbosa1, José Ricardo M Mermudes1, Claudia A M Russo2
1Zoology, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil.
Peerj
|January 12, 2024
Summary
Maximum likelihood and Bayesian inference methods, using the k-state Markov (Mk) model, demonstrated superior performance in reconstructing phylogenetic trees from discrete morphological data. These methods offer higher precision and resolution for evolutionary studies.
Area of Science:
- Evolutionary Biology
- Systematics
- Bioinformatics
Background:
- Phylogenetic tree-building methods are crucial for understanding evolutionary relationships.
- Previous performance evaluations primarily used simulated data, leaving a gap for discrete morphological datasets.
- There is no established consensus on the best phylogenetic methods for discrete morphological data.
Purpose of the Study:
- To evaluate and compare the performance of different phylogenetic tree-building methods using an empirical dataset.
- To determine which methods best recover accurate and precise phylogenies from discrete morphological characters.
- To assess the reliability of tree topology tests for phylogenetic accuracy.
Main Methods:
- Applied normalized indices, including the normalized Robinson-Foulds metric (nRF) for accuracy and 1-Colless' consensus fork index (1-CFI) for precision.
- Utilized an empirical discrete morphological dataset from extant Hexapoda.
- Compared reconstructed trees against a well-supported phylogenomic tree as a reference.
- Calculated statistical power and type I error rates, and constructed receiver operating characteristic plots.
Main Results:
- Maximum likelihood and Bayesian inference, employing the k-state Markov (Mk) model (with or without gamma distribution), exhibited superior performance.
- These methods demonstrated higher precision (resolution) compared to others.
- Most tested tree topology tests reliably estimated the applied performance measures.
- Morphological datasets were found to possess robust phylogenetic signal.
Conclusions:
- Likelihood-based methods (maximum likelihood and Bayesian inference) with the Mk model are recommended for phylogenetic analyses of discrete morphological data.
- Tree topology tests are reliable tools for assessing phylogenetic performance in such studies.
- The study confirms the presence of robust phylogenetic signal within morphological datasets.
Keywords:
AccuracyInsectsLikelihood-based methodsMK modelMorphological discrete dataPrecisionResolutionTree topology testsMore Related Videos
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