Automatic identification of bird females using egg phenotype

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.