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A Practical Guide to Phylogenetics for Nonexperts
Published on: February 5, 2014
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Measuring Support for Phylogenies: The "Proportional Support Index"
1Department of Zoology, University of Queensland, Brisbane, QLD 4072, Australia.
Cladistics : the International Journal of the Willi Hennig Society
|December 14, 2021
Summary
The total support index is affected by uninformative data. A new proportional support index offers a more accurate measure of phylogenetic signal strength by focusing on informative characters.
Area of Science:
- Phylogenetics
- Systematic Biology
- Computational Biology
Background:
- Cladogram support indices are crucial for evaluating phylogenetic tree reliability.
- The total support index (Bremer, 1994) is widely used but can be influenced by uninformative characters and character incongruence.
- Accurate assessment of phylogenetic signal strength is vital for robust evolutionary inferences.
Purpose of the Study:
- To address the limitations of the total support index.
- To propose a modified index that is not distorted by uninformative characters.
- To provide a more accurate method for comparing phylogenetic signal strength across different datasets.
Main Methods:
- A modified version of the total support index was developed, termed the proportional support index.
- The proportional support index measures cladogram support relative to the maximum potential support.
- The calculation is based on the number of informative characters in the dataset.
Main Results:
- The proposed proportional support index is not distorted by uninformative characters.
- This new index provides a more accurate reflection of the actual support for a cladogram.
- It allows for a more reliable comparison of phylogenetic signal strength between datasets.
Conclusions:
- The proportional support index offers a superior alternative to the total support index for evaluating cladogram robustness.
- This method enhances the accuracy of phylogenetic signal assessment in systematic biology.
- It facilitates more reliable evolutionary analyses by mitigating biases from uninformative data.
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