Data from: On the comparison of the strength of morphological integration across morphometric datasets

Evolutionary morphologists frequently wish to understand the extent to which organisms are integrated, and whether the strength of morphological integration among subsets of phenotypic variables differ among taxa or other groups. However, comparisons of the strength of integration across datasets are difficult, in part because the summary measures that characterize these patterns (RV and rPLS) are dependent both on sample size and on the number of variables. As a solution to this issue we propose a standardized test statistic (a z-score) for measuring the degree of morphological integration between sets of variables. The approach is based on a partial least squares analysis of trait covariation, and its permutation-based sampling distribution. Under the null hypothesis of a random association of variables, the method displays a constant expected value and confidence intervals for datasets of differing sample sizes and variable number, thereby providing a consistent measure of integration suitable for comparisons across datasets. A two-sample test is also proposed to statistically determine whether levels of integration differ between datasets, and an empirical example examining cranial shape integration in Mediterranean wall lizards illustrates its use. Some extensions of the procedure are also discussed.进化形态学家常希望阐明生物体的整合程度，以及表型变量子集间的形态整合强度在不同类群或其他分组间是否存在差异。然而，跨数据集的整合强度比较颇具挑战，部分原因在于表征此类模式的汇总统计量RV和rPLS同时依赖于样本量与变量数量。针对这一问题，我们提出一种标准化检验统计量z分数（z-score），用于衡量变量集之间的形态整合程度。该方法基于性状协变的偏最小二乘（Partial Least Squares）分析及其基于置换的抽样分布。在变量随机关联的原假设下，无论数据集的样本量与变量数各异，该方法的期望取值与置信区间均保持恒定，从而可提供一致的整合程度度量指标，适用于跨数据集的比较。我们还提出了双样本检验，用于统计学上判断不同数据集间的整合水平是否存在差异，并以地中海壁蜥颅部形态整合的实证案例展示了该方法的应用。本文还讨论了该流程的若干拓展方向。