Data from: Just Google it: assessing the use of Google Images to describe geographical variation in visible traits of organisms

Describing spatial patterns of phenotypic traits can be important for evolutionary and ecological studies. However, traditional approaches, such as fieldwork, can be time-consuming and expensive. Information technologies, such as Internet search engines, could facilitate the collection of these data. Google Images is one such technology that might offer an opportunity to rapidly collect information on spatial patterns of phenotypic traits.
We investigated the use of Google Images in extracting data on geographical variation in phenotypic traits visible from photographs. We compared the distribution of visual traits obtained from Google Images with four previous studies: colour morphs of black bear (Ursus americanus); colouration and spottiness in barn owl (Tyto alba); colour morphs of black sparrowhawk (Accipiter melanoleucus) and the distribution of hooded (Corvus corone) and carrion crows (Corvus cornix) across their European hybrid zone. Additionally, we develop and present a web application (Morphic), which facilitates the human data capture process of this method.
We found good agreement between fieldwork data and Google Images data across all studies. Indeed, there was strong agreement between the data obtained from the original study and from the Google Images method for the colour morphs of black bear (R2 = 80%) and for two barn owl plumage traits (R2 = 64% and 53%). Our approach also successfully matched the clinal variation of black sparrowhawks morphs across South Africa. Our method also gave a good agreement between the distribution of hooded and carrion crows (with 86% placed on the correct side of the hybrid zone line).
Our results suggest that this method can work well for visible traits of common and widespread species that are objective, binary, and easy to see irrespective of angle. The Google Images method is cost-effective and rapid and can be used with some confidence when investigating patterns of geographical variation, as well as a range of other applications. In many cases, it could therefore supplement or replace fieldwork.

对表型性状（phenotypic traits）空间分布模式的描述，在进化生物学与生态学研究中具有重要意义。然而，野外调查等传统研究方法往往耗时且成本高昂。互联网搜索引擎等信息技术可为这类数据的采集提供助力，谷歌图片（Google Images）便是其中之一，有望帮助研究人员快速获取表型性状空间分布模式的相关信息。
我们探究了利用谷歌图片提取照片中可见表型性状地理变异数据的可行性。我们将从谷歌图片获取的可视性状分布数据，与四项已发表的研究进行对比：美洲黑熊（Ursus americanus）的体色型；仓鸮（Tyto alba）的羽色与斑点特征；黑雀鹰（Accipiter melanoleucus）的体色型，以及冠小嘴乌鸦（Corvus corone）和小嘴乌鸦（Corvus cornix）在欧洲杂交带的分布情况。此外，我们还开发并推出了一款名为Morphic的网页应用工具，可简化该方法中的人工数据采集流程。
我们发现，在所有对比研究中，野外调查数据与谷歌图片数据均呈现出良好的一致性。具体而言，在美洲黑熊体色型（决定系数R²=80%）以及仓鸮的两项羽色性状（决定系数R²分别为64%和53%）的相关数据上，原始研究与谷歌图片方法获取的结果具有极强的一致性。我们的方法还成功还原了南非地区黑雀鹰体色型的渐变变异模式。对于冠小嘴乌鸦和小嘴乌鸦的分布情况，该方法也取得了不错的匹配效果——86%的样本被准确划分至杂交带的对应一侧。
研究结果表明，该方法对于常见广布物种的客观、二元且不受拍摄角度影响的可视性状具有良好的适用性。谷歌图片方法兼具成本效益与高效性，在探究地理变异模式及其他一系列相关应用场景中均可放心使用。在多数情况下，该方法可作为野外调查的补充手段，甚至替代部分野外调查工作。