Methods for collecting animal behaviour data in natural environments, such as direct observation and biologging, are typically limited in spatiotemporal resolution, the number of animals that can be observed and information about animals' social and physical environments. Video imagery can capture rich information about animals and their environments, but image‐based approaches are often impractical due to the challenges of processing large and complex multi‐image datasets and transforming resulting data, such as animals' locations, into geographical coordinates. We demonstrate a new system for studying behaviour in the wild that uses drone‐recorded videos and computer vision approaches to automatically track the location and body posture of free‐roaming animals in georeferenced coordinates with high spatiotemporal resolution embedded in contemporaneous 3D landscape models of the surrounding area. We provide two worked examples in which we apply this approach to videos of gelada monkeys and multiple species of group‐living African ungulates. We demonstrate how to track multiple animals simultaneously, classify individuals by species and age–sex class, estimate individuals' body postures (poses) and extract environmental features, including topography of the landscape and animal trails. By quantifying animal movement and posture while reconstructing a detailed 3D model of the landscape, our approach opens the door to studying the sensory ecology and decision‐making of animals within their natural physical and social environments.

Comparative studies of nonhuman communication systems could provide insights into the origins and evolution of a distinct dimension of human language: intentionality. Recent studies have provided evidence for intentional communication in different species but generally in captive settings. We report here a novel behaviour of food requesting from humans displayed by wild bonnet macaques Macaca radiata, an Old World cercopithecine primate, in the Bandipur National Park of southern India. Using both natural observations and field experiments, we examined four different behavioural components—coo-calls, hand-extension gesture, orientation, and monitoring behaviour—of food requesting for their conformity with the established criteria of intentional communication. Our results suggest that food requesting by bonnet macaques is potentially an intentionally produced behavioural strategy as all the food requesting behaviours except coo-calls qualify the criteria for intentionality. We comment on plausible hypotheses for the origin and spread of this novel behavioural strategy in the study macaque population and speculate that the cognitive precursors for language production may be manifest in the usage of combination of signals of different modalities in communication, which could have emerged in simians earlier than in the anthropoid apes.

1. Methods for collecting animal behavior data in natural environments, such as direct observation and bio-logging, are typically limited in spatiotemporal resolution, the number of animals that can be observed, and information about animals' social and physical environments. 2. Video imagery can capture rich information about animals and their environments, but image-based approaches are often impractical due to the challenges of processing large and complex multi-image datasets and transforming resulting data, such as animals' locations, into geographic coordinates. 3. We demonstrate a new system for studying behavior in the wild that uses drone-recorded videos and computer vision approaches to automatically track the location and body posture of free-roaming animals in georeferenced coordinates with high spatiotemporal resolution embedded in contemporaneous 3D landscape models of the surrounding area. 4. We provide two worked examples in which we apply this approach to videos of gelada monkeys and multiple species of group-living African ungulates. We demonstrate how to track multiple animals simultaneously, classify individuals by species and age-sex class, estimate individuals' body postures (poses), and extract environmental features, including topography of the landscape and game trails. 5. By quantifying animal movement and posture, while simultaneously reconstructing a detailed 3D model of the landscape, our approach opens the door to studying the sensory ecology and decision-making of animals within their natural physical and social environments.