Wild ape face recognition using artificial intelligence opens the way for fully automated analysis of large-scale video datasets.

Deep learning using audiovisual data from chimpanzee percussive behaviors enables action recognition in the wild.

Longitudinal video archives of behaviour are crucial for examining how sociality shifts over the lifespan in wild animals. New approaches adopting computer vision technology hold serious potential to capture interactions and associations between individuals in video at large scale; however, such approaches need a priori validation, as methods of sampling and defining edges for social networks can substantially impact results. Here, we apply a deep learning face recognition model to generate association networks of wild chimpanzees using 17 years of a video archive from Bossou, Guinea. Using 7 million detections from 100 h of video footage, we examined how varying the size of fixed temporal windows (i.e. aggregation rates) for defining edges impact individual‐level gregariousness scores. The highest and lowest aggregation rates produced divergent values, indicating that different rates of aggregation capture different association patterns. To avoid any potential bias from false positives and negatives from automated detection, an intermediate aggregation rate should be used to reduce error across multiple variables. Individual‐level network‐derived traits were highly repeatable, indicating strong inter‐individual variation in association patterns across years and highlighting the reliability of the method to capture consistent individual‐level patterns of sociality over time. We found no reliable effects of age and sex on social behaviour and despite a significant drop in population size over the study period, individual estimates of gregariousness remained stable over time. We believe that our automated framework will be of broad utility to ethology and conservation, enabling the investigation of animal social behaviour from video footage at large scale, low cost and high reproducibility. We explore the implications of our findings for understanding variation in sociality patterns in wild ape populations. Furthermore, we examine the trade‐offs involved in using face recognition technology to generate social networks and sociality measures. Finally, we outline the steps for the broader deployment of this technology for analysis of large‐scale datasets in ecology and evolution.

Object manipulation can be used as a comparative scale of cognitive development among primates, including humans. Combinatory object manipulation is a precursor of tool-using behavior that indicates material intelligence in primates. However, developmental data on it regarding the great apes other than chimpanzees is insufficient. We conducted a longitudinal investigation of humans and chimpanzees as well as a cross-sectional examination of other great-ape infants (two bonobos, three gorillas, and four orangutans) in captive settings by using two kinds of tasks that required either inserting or stacking combinatory action. The four species of great apes and humans demonstrated both types of combinatory object manipulation during infancy. However, the order of development in different types of combinatory object manipulations varied among the great apes. Furthermore, we applied a nesting-cup task to examine the hierarchical complexity in the combinatory strategies of human children and adult chimpanzees. Both of them exhibited highly hierarchical combinations in the nesting-cup task and employed the subassembly strategy, indicating that an action merge may exist not only in human children but also in adult chimpanzees. The results were discussed with reviews of the tool-use literature from the wild great apes. The early acquisition of an inserting action in the chimpanzees may explain the tool utilization commonality reported in wild chimpanzees. The combinatory object manipulation may have worked as an external enhancer to achieve an additional hierarchical complexity in cognition and behavior, eventually leading humans to develop a language system.