BackgroundGastrointestinal bacteria play a central role in the health of animals. The bacteria that individuals acquire as they age may therefore have profound consequences for their future fitness. However, changes in microbial community structure with host age remain poorly understood. We characterised the cloacal bacteria assemblages of chicks and adults in a natural population of black-legged kittiwakes (Rissa tridactyla), using molecular methods.ResultsWe show that the kittiwake cloaca hosts a diverse assemblage of bacteria. A greater number of total bacterial OTUs (operational taxonomic units) were identified in chicks than adults, and chicks appeared to host a greater number of OTUs that were only isolated from single individuals. In contrast, the number of bacteria identified per individual was higher in adults than chicks, while older chicks hosted more OTUs than younger chicks. Finally, chicks and adults shared only seven OTUs, resulting in pronounced differences in microbial assemblages. This result is surprising given that adults regurgitate food to chicks and share the same nesting environment.ConclusionsOur findings suggest that chick gastrointestinal tracts are colonised by many transient species and that bacterial assemblages gradually transition to a more stable adult state. Phenotypic differences between chicks and adults may lead to these strong differences in bacterial communities. These data provide the framework for future studies targeting the causes and consequences of variation in bacterial assemblages in wild birds.

Individual identification is a crucial step to answer many questions in evolutionary biology and is mostly performed by marking animals with tags. Such methods are well‐established, but often make data collection and analyses time‐consuming, or limit the contexts in which data can be collected. Recent computational advances, specifically deep learning, can help overcome the limitations of collecting large‐scale data across contexts. However, one of the bottlenecks preventing the application of deep learning for individual identification is the need to collect and identify hundreds to thousands of individually labelled pictures to train convolutional neural networks (CNNs). Here we describe procedures for automating the collection of training data, generating training datasets, and training CNNs to allow identification of individual birds. We apply our procedures to three small bird species, the sociable weaver Philetairus socius, the great tit Parus major and the zebra finch Taeniopygia guttata, representing both wild and captive contexts. We first show how the collection of individually labelled images can be automated, allowing the construction of training datasets consisting of hundreds of images per individual. Second, we describe how to train a CNN to uniquely re‐identify each individual in new images. Third, we illustrate the general applicability of CNNs for studies in animal biology by showing that trained CNNs can re‐identify individual birds in images collected in contexts that differ from the ones originally used to train the CNNs. Finally, we present a potential solution to solve the issues of new incoming individuals. Overall, our work demonstrates the feasibility of applying state‐of‐the‐art deep learning tools for individual identification of birds, both in the laboratory and in the wild. These techniques are made possible by our approaches that allow efficient collection of training data. The ability to conduct individual recognition of birds without requiring external markers that can be visually identified by human observers represents a major advance over current methods.

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