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Power of the concentrated changes test for correlated evolution
1Department of Biology, University of Toronto at Mississauga, Mississauga, Ontario L5L 1C6, Canada. plorch@credit.erin.utoronto.ca
Systematic Biology
|June 25, 2002
Summary
The concentrated changes test (CCT) is generally reliable for detecting correlated evolution but can produce false negatives when traits can be gained but not lost. Tree shape and the proportion of taxa lacking traits also impact accuracy.
Area of Science:
- Evolutionary Biology
- Phylogenetics
- Biostatistics
Background:
- The concentrated changes test (CCT) assesses if binary character changes on a cladogram are random.
- It's crucial for investigating correlated evolution, where one character's state influences another's.
- Previous studies suggest CCT sensitivity to 'white branches' (branches lacking traits) and tree topology.
Purpose of the Study:
- To evaluate the concentrated changes test's sensitivity to type I and type II errors.
- To investigate the impact of white branches and tree topology on CCT accuracy.
- To explore how different evolutionary models (gains only vs. gains and losses) affect CCT performance.
Main Methods:
- Conducted simulation analyses on 100-taxon trees with varying topologies (random, symmetrical, asymmetrical).
- Simulated two evolutionary models: gains only, and gains and losses.
- Evolved character pairs to represent independent evolution or strong trait correlation.
Main Results:
- CCT showed low type I error rates, indicating robustness against false positives with white branches.
- CCT exhibited susceptibility to type II errors (false negatives) when white branches exceeded 20% or in gains-only models.
- Tree shape significantly influenced CCT results, correlating positively with tree imbalance.
Conclusions:
- The CCT is generally reliable for detecting correlations but can be conservative (increased type II error) under specific conditions, particularly when traits are gained but not lost.
- Tree topology and the proportion of taxa lacking traits (sampling universe) critically affect CCT's power to detect true correlations.
- Guidelines are suggested to mitigate limitations and improve the accuracy of CCT in phylogenetic comparative analyses.
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