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Evaluating the clade size effect in alternative measures of branch support

Authors

  • María Amelia Chemisquy,

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    • División Mastozoología, Museo Argentino de Ciencias Naturales “Bernardino Rivadavia”, Buenos Aires, Argentina - CONICET
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  • Francisco J. Prevosti

    1. División Mastozoología, Museo Argentino de Ciencias Naturales “Bernardino Rivadavia”, Buenos Aires, Argentina - CONICET
    2. Departamento de Ciencias Básicas, Universidad Nacional de Luján, Buenos Aires, Argentina
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Corresponding author: María Amelia Chemisquy (amelych80@gmail.com)

Abstract

The clade size effect refers to a bias that causes middle-sized clades to be less supported than small or large-sized clades. This bias is present in resampling measures of support calculated under maximum likelihood and maximum parsimony and in Bayesian posterior probabilities. Previous analyses indicated that the clade size effect is worst in maximum parsimony, followed by maximum likelihood, while Bayesian inference is the least affected. Homoplasy was interpreted as the main cause of the effect. In this study, we explored the presence of the clade size effect in alternative measures of branch support under maximum parsimony: Bremer support and symmetric resampling, expressed as absolute frequencies and frequency differences. Analyses were performed using 50 molecular and morphological matrices. Symmetric resampling showed the same tendency that bootstrap and jackknife did for maximum parsimony and maximum likelihood. Few matrices showed a significant bias using Bremer support, presenting a better performance than resampling measures of support and comparable to Bayesian posterior probabilities. Our results indicate that the problem is not maximum parsimony, but resampling measures of support. We corroborated the role of homoplasy as a possible cause of the clade size effect, increasing the number of random trees during the resampling, which together with the higher chances that medium-sized clades have of being contradicted generates the bias during the perturbation of the original matrix, making it stronger in resampling measures of support.

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