A formula for maximum possible steps in multistate characters: isolating matrix parameter effects on measures of
Jennifer F Hoyal Cuthill1,2, Simon J Braddy2, Philip C J Donoghue2
1Department of Earth Sciences, University of Cambridge, Cambridge CB2 3EQ, UK.
Cladistics : the International Journal of the Willi Hennig Society
|December 8, 2021
Summary
The number of character states in phylogenetic data influences homoplasy measurement. More states reduce measurable homoplasy, impacting evolutionary analyses.
Area of Science:
- Evolutionary Biology
- Phylogenetics
- Computational Biology
Background:
- Homoplasy, or recurrent evolution, is crucial for understanding evolutionary processes.
- Accurate measurement of homoplasy is essential for reliable phylogenetic inference.
- Non-biological factors can obscure the true biological signal in homoplasy patterns.
Purpose of the Study:
- To investigate how the number of character states affects homoplasy measurement in phylogenetic analyses.
- To isolate the impact of character states from biological signals in homoplasy.
Main Methods:
- Developed a formula to calculate the maximum number of parsimony steps for a character based on its states and taxa.
- Analyzed the relationship between the number of character states and the proportion of homoplasy inferred.
- Evaluated the effect of character states on the consistency index for measuring homoplasy.
Main Results:
- The number of states per character sets a limit on the maximum number of evolutionary steps inferable via parsimony.
- Increasing the number of character states decreases the maximum proportion of steps attributable to homoplasy.
- Higher numbers of character states reduce the measurable homoplasy when using the consistency index.
Conclusions:
- The number of character states is a critical non-biological factor that must be accounted for when measuring homoplasy.
- Phylogeneticists should consider the number of character states to avoid over or underestimating homoplasy.
- This finding aids in distinguishing genuine evolutionary signals from methodological artifacts in phylogenetic data.
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