Data from: Sharing is caring? measurement error and the issues arising from combining 3D morphometric datasets

Geometric morphometrics is routinely used in ecology and evolution and morphometric datasets are increasingly shared among researchers, allowing for more comprehensive studies and higher statistical power (as a consequence of increased sample size). However, sharing of morphometric data opens up the question of how much nonbiologically relevant variation (i.e., measurement error) is introduced in the resulting datasets and how this variation affects analyses. We perform a set of analyses based on an empirical 3D geometric morphometric dataset. In particular, we quantify the amount of error associated with combining data from multiple devices and digitized by multiple operators and test for the presence of bias. We also extend these analyses to a dataset obtained with a recently developed automated method, which does not require human-digitized landmarks. Further, we analyze how measurement error affects estimates of phylogenetic signal and how its effect compares with the effect of phylogenetic uncertainty. We show that measurement error can be substantial when combining surface models produced by different devices and even more among landmarks digitized by different operators. We also document the presence of small, but significant, amounts of nonrandom error (i.e., bias). Measurement error is heavily reduced by excluding landmarks that are difficult to digitize. The automated method we tested had low levels of error, if used in combination with a procedure for dimensionality reduction. Estimates of phylogenetic signal can be more affected by measurement error than by phylogenetic uncertainty. Our results generally highlight the importance of landmark choice and the usefulness of estimating measurement error. Further, measurement error may limit comparisons of estimates of phylogenetic signal across studies if these have been performed using different devices or by different operators. Finally, we also show how widely held assumptions do not always hold true, particularly that measurement error affects inference more at a shallower phylogenetic scale and that automated methods perform worse than human digitization.几何形态测量学（Geometric morphometrics）如今已被广泛应用于生态学与进化生物学研究领域，且形态测量学数据集在研究者间的共享也日益频繁，这使得研究的综合性得以提升，同时因样本量扩大而获得更强的统计效力。然而，形态测量学数据的共享也引出了新的问题：最终得到的数据集中会引入多少与生物学无关的变异（即测量误差），以及这类变异会对分析结果产生何种影响。本研究基于一套实证性3D几何形态测量学数据集开展了一系列分析。具体而言，我们量化了整合多设备采集的数据以及多名操作者数字化地标点所带来的误差总量，并检验了偏差的存在性。此外，我们还将此类分析拓展至一套通过新近开发的无需人工数字化地标点的自动化方法获取的数据集。进一步地，我们探究了测量误差对系统发育信号估计值的影响，并对比了其与系统发育不确定性所产生效应的差异。本研究结果显示，整合不同设备生成的表面模型时，测量误差可能较为显著；而由多名操作者数字化地标点所带来的误差则更为突出。同时本研究还证实了存在少量但显著的非随机误差（即偏差）。剔除难以数字化的地标点可大幅降低测量误差。本研究测试的自动化方法若配合降维流程使用，误差水平极低。相较于系统发育不确定性，测量误差对系统发育信号估计值的影响可能更为显著。本研究结果总体上凸显了地标点选择的重要性，以及估算测量误差的实用价值。此外，若不同研究采用不同设备或由不同操作者开展数据数字化，测量误差可能会阻碍跨研究的系统发育信号估计值比较。最后，本研究还证实了一些广为接受的假设并不总是成立，具体而言：测量误差对较浅系统发育尺度的推断影响更大，且自动化方法的表现逊于人工数字化操作。