Comparing the strength of modular signal, and evaluating alternative modular hypotheses, using covariance ratio effect sizes with morphometric data
Comparing the strength of modular signal, and evaluating alternative modular hypotheses, using covariance ratio effect sizes with morphometric data
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DOI：
10.1111/evo.13867
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发表时间：
2019-11-11
期刊：
影响因子：
3.3
通讯作者：
Collyer, Michael L.
中科院分区：
文献类型：
--
作者：
Adams, Dean C.;Collyer, Michael L.
模块性的研究是至关重要的了解表型进化的趋势，并确定在何种程度上的协同变异模式是跨类群和生物组织水平的保守。然而，生物学家目前缺乏定量方法来统计比较不同数据集的模块化信号的强度，以及评估相同数据集的替代模块化假设的稳健方法。作为这些挑战的解决方案，我们提出了一个效果大小的措施（ZCR）来自协方差比，并开发假设检验程序进行比较。计算机模拟表明，ZCR显示适当的统计特性和低水平的错误规范，这意味着它正确地识别模块化信号，当存在。相比之下，基于似然（EMMLi）和拟合优度（MINT）的替代方法遭受高假阳性率和高模型误指定率。在sigmodontine啮齿动物下颌骨的实证例子来说明ZCR比较模块化假设的效用。总的来说，我们发现协方差比效应大小对于比较数据集之间的模块化信号模式或评估相同数据集的替代模块化假设是有用的。最后，使用效应量的成对模型比较的统计学原理应适应任何未来的分析发展，以表征模块化信号。
The study of modularity is paramount for understanding trends of phenotypic evolution, and for determining the extent to which covariation patterns are conserved across taxa and levels of biological organization. However, biologists currently lack quantitative methods for statistically comparing the strength of modular signal across datasets, and a robust approach for evaluating alternative modular hypotheses for the same dataset. As a solution to these challenges, we propose an effect size measure (ZCR) derived from the covariance ratio, and develop hypothesis-testing procedures for their comparison. Computer simulations demonstrate that ZCR displays appropriate statistical properties and low levels of mis-specification, implying that it correctly identifies modular signal, when present. By contrast, alternative methods based on likelihood (EMMLi) and goodness of fit (MINT) suffer from high false positive rates and high model mis-specification rates. An empirical example in sigmodontine rodent mandibles is provided to illustrate the utility of ZCR for comparing modular hypotheses. Overall, we find that covariance ratio effect sizes are useful for comparing patterns of modular signal across datasets or for evaluating alternative modular hypotheses for the same dataset. Finally, the statistical philosophy for pairwise model comparisons using effect sizes should accommodate any future analytical developments for characterizing modular signal.