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Measuring agreement among experts in classifying camera images of similar species

机译:专家对相似物种的相机图像进行分类时的测量协议

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Camera trapping and solicitation of wildlife images through citizen science have become common tools in ecological research. Such studies collect many wildlife images for which correct species classification is crucial; even low misclassification rates can result in erroneous estimation of the geographic range or habitat use of a species, potentially hindering conservation or management efforts. However, some species are difficult to tell apart, making species classification challenging—but the literature on classification agreement rates among experts remains sparse. Here, we measure agreement among experts in distinguishing between images of two similar congeneric species, bobcats ( Lynx rufus ) and Canada lynx ( Lynx canadensis ). We asked experts to classify the species in selected images to test whether the season, background habitat, time of day, and the visible features of each animal (e.g., face, legs, tail) affected agreement among experts about the species in each image. Overall, experts had moderate agreement (Fleiss’ kappa = 0.64), but experts had varying levels of agreement depending on these image characteristics. Most images (71%) had ≥1 expert classification of “unknown,” and many images (39%) had some experts classify the image as “bobcat” while others classified it as “lynx.” Further, experts were inconsistent even with themselves, changing their classifications of numerous images when they were asked to reclassify the same images months later. These results suggest that classification of images by a single expert is unreliable for similar‐looking species. Most of the images did obtain a clear majority classification from the experts, although we emphasize that even majority classifications may be incorrect. We recommend that researchers using wildlife images consult multiple species experts to increase confidence in their image classifications of similar sympatric species. Still, when the presence of a species with similar sympatrics must be conclusive, physical or genetic evidence should be required.
机译:通过公民科学捕获照相机和诱捕野生生物图像已成为生态研究中的常用工具。这些研究收集了许多野生生物图像,对于这些图像而言,正确的物种分类至关重要。甚至低的误分类率也可能导致对物种的地理范围或栖息地使用的错误估计,从而有可能阻碍保护或管理工作。但是,有些物种很难区分,这使物种分类具有挑战性,但是有关专家之间的分类协议率的文献仍然很少。在这里,我们衡量专家之间的一致性,以区分两种相似的同类物种山猫(山猫rufus)和加拿大山猫(Lynx canadensis)的图像。我们要求专家对所选图像中的物种进行分类,以测试季节,背景栖息地,一天中的时间以及每只动物的可见特征(例如脸,腿,尾巴)是否会影响专家对每张图像中物种的一致性。总体而言,专家的同意程度中等(Fleiss的kappa = 0.64),但是专家的同意程度取决于这些图像特征。大多数图像(71%)的专家分类为“未知”≥1,许多图像(39%)的专家将图像分类为“山猫”,而其他专家则将图像分类为“山猫”。此外,专家甚至连自己都不一致,当几个月后要求他们对同一图像进行重新分类时,他们改变了许多图像的分类。这些结果表明,由单个专家对图像进行分类对于看上去相似的物种是不可靠的。大多数图像的确从专家那里获得了明确的多数分类,尽管我们强调即使多数分类也可能是错误的。我们建议使用野生生物图像的研究人员请教多个物种专家,以提高他们对类似同居物种的图像分类的信心。但是,当必须确定具有相似同胞的物种的存在时,则需要物理或遗传证据。

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