4.6 Article

A standardized effect size for evaluating and comparing the strength of phylogenetic signal

期刊

METHODS IN ECOLOGY AND EVOLUTION
卷 13, 期 2, 页码 367-382

出版社

WILEY
DOI: 10.1111/2041-210X.13749

关键词

comparative analysis; macroevolution; RRPP

类别

资金

  1. National Science Foundation [DBI-1902511, DBI-1902694]

向作者/读者索取更多资源

This study introduces a non-parametric statistical method for assessing the strength of phylogenetic signal across different traits, allowing for effective comparison of phylogenetic signal in multiple traits. Simulation experiments revealed that this method has greater statistical power for detecting phylogenetic signal in small trees.
Macroevolutionary studies frequently characterize the phylogenetic signal in phenotypes; however, analytical tools for comparing the strength of that signal across traits remain largely underdeveloped. We developed a non-parametric, permutation test for the log-likelihood of an evolutionary model, plus a standardized statistic, Z, from this test, which can be considered a phylogenetic signal effect size. This statistic can be used in two-sample tests to compare the strength of phylogenetic signal for multiple traits. We performed simulation experiments that revealed that Z had a linear association with Pagel's lambda, which could be predicted by tree size, plus could be quickly interpreted as a hypothesis for phylogenetic signal based on a standard normal distribution. We additionally found that the permutation test had greater statistical power for detecting phylogenetic signal than parametric likelihood ratio tests, especially for small trees. The analytical framework we present extends the phylogenetic comparative methods toolkit, allowing for statistical comparison of phylogenetic signal in multiple traits. Future studies could also consider this framework for the comparison of different evolutionary models, especially in light of different null processes.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据