Individual identification is crucial for studying animal ecology and evolution. In birds this is often achieved by capturing and tagging. However, these methods are insufficient for identifying individuals/species that are secretive or difficult to catch. Here, we employ an automatic analytical approach to predict the identity of bird females based on the appearance of their eggs, using the common cuckoo (Cuculus canorus) as a model species. We analysed 192 cuckoo eggs using digital photography and spectrometry. Cuckoo females were identified from genetic sampling of nestlings, allowing us to determine the accuracy of automatic (unsupervised and supervised) and human assignment. Finally, we used a novel analytical approach to identify eggs that were not genetically analysed. Our results show that individual cuckoo females lay eggs with a relatively constant appearance and that eggs laid by more genetically distant females differ more in colour. Unsupervised clustering had similar cluster accuracy to experienced human observers, but supervised methods were able to outperform humans. Our novel method reliably assigned a relatively high number of eggs without genetic data to their mothers. Therefore, this is a cost-effective and minimally invasive method for increasing sample sizes, which may facilitate research on brood parasites and other avian species.

中文翻译：

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使用卵表型自动识别鸟类雌性

个体识别对于研究动物生态学和进化至关重要。在鸟类中，这通常是通过捕获和标记来实现的。然而，这些方法不足以识别隐秘或难以捕捉的个体/物种。在这里，我们采用一种自动分析方法，使用普通杜鹃（Cuculus canorus）作为模型物种，根据卵的外观预测雌鸟的身份。我们使用数码摄影和光谱分析了 192 个杜鹃蛋。杜鹃雌性是从雏鸟的遗传采样中识别出来的，这使我们能够确定自动（无监督和监督）和人工分配的准确性。最后，我们使用了一种新的分析方法来识别未经基因分析的鸡蛋。我们的研究结果表明，个体杜鹃雌性产卵的外观相对稳定，而遗传距离较远的雌性产卵的颜色差异更大。无监督聚类与有经验的人类观察者具有相似的聚类准确性，但有监督的方法能够胜过人类。我们的新方法可靠地将相对大量的没有遗传数据的卵子分配给了它们的母亲。因此，这是一种增加样本量的成本效益和微创方法，可能有助于对育雏寄生虫和其他鸟类物种的研究。我们的新方法可靠地将相对大量的没有遗传数据的卵子分配给了它们的母亲。因此，这是一种增加样本量的成本效益和微创方法，可能有助于对育雏寄生虫和其他鸟类物种的研究。我们的新方法可靠地将相对大量的没有遗传数据的卵子分配给了它们的母亲。因此，这是一种增加样本量的成本效益和微创方法，可能有助于对育雏寄生虫和其他鸟类物种的研究。