1. Museum specimens are the main source of information on organisms' morphological features. Although access to this information was commonly limited to researchers able to visit collections, it is now becoming freely available thanks to the digitization of museum specimens. With these images, we will be able to collectively build large‐scale morphological datasets, but these will only be useful if the limits to this approach are well‐known. To establish these limits, we used two‐dimensional images of plant specimens to test the precision and accuracy of image‐based data and analyses.
2. To test measurement precision and accuracy, we compared leaf measurements taken from specimens and images of the same specimens. Then, we used legacy morphometric datasets to establish differences in the quality of datasets and multivariate analyses between specimens and images. To do so, we compared the multivariate space based on original legacy data to spaces built with datasets simulating image‐based data.
3. We found that trait measurements made from images are as precise as those obtained directly from specimens, but as traits diminish in size, the accuracy drops as well. This decrease in accuracy, however, has a very low impact on dataset and analysis quality. The main problem with image‐based datasets comes from missing observations due to image resolution or organ overlapping. Missing data lowers the accuracy of datasets and multivariate analyses. Although the effect is not strong, this decrease in accuracy suggests caution is needed when designing morphological research that will rely on digitized specimens.
4. As highlighted by images of plant specimens, 2D images are reliable measurement sources, even though resolution issues lower accuracy for small traits. At the same time, the impossibility of observing particular traits affects the quality of image‐based datasets and, thus, of derived analyses. Despite these issues, gathering phenotypic data from two‐dimensional images is valid and may support large‐scale studies on the morphology and evolution of a wide diversity of organisms.

中文翻译：

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薛定er的表型：标本室标本显示二维图像是形态数据的好坏来源（不是）

1. 博物馆标本是有关生物形态特征的主要信息来源。尽管通常只允许能够访问收藏品的研究人员访问此信息，但是由于博物馆标本的数字化，现在可以免费使用。有了这些图像，我们将能够共同建立大规模的形态数据集，但是只有在这种方法的局限性众所周知的情况下，这些数据才有用。为了建立这些限制，我们使用了植物标本的二维图像来测试基于图像的数据和分析的准确性和准确性。
2. 为了测试测量的准确性和准确性，我们比较了从标本和相同标本图像中获取的叶片测量值。然后，我们使用传统形态计量数据集来建立样本和图像之间数据集质量和多元分析的差异。为此，我们将基于原始遗留数据的多元空间与使用模拟基于图像的数据的数据集构建的空间进行了比较。
3. 我们发现，通过图像进行的特征测量与直接从标本获得的测量一样精确，但是随着特征尺寸的减小，准确性也会下降。但是，准确性的降低对数据集和分析质量的影响很小。基于图像的数据集的主要问题是由于图像分辨率或器官重叠而导致观测值丢失。数据丢失会降低数据集和多元分析的准确性。尽管效果不强，但准确性下降表明设计依赖数字化标本的形态学研究时需要谨慎。
4. 正如植物标本图像所突出显示的那样，即使分辨率对于小特征而言精度较低，二维图像也是可靠的测量来源。同时，无法观察到特定特征会影响基于图像的数据集的质量，从而影响衍生分析的质量。尽管存在这些问题，但从二维图像中收集表型数据仍然是有效的，并且可能支持对各种生物的形态和演化进行大规模研究。