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Aerial imagery and video recordings of animals are used for many areas of research such as animal behaviour, behavioural neuroscience and field biology. Many automated methods are being developed to extract data from such high-resolution videos. Most of the available tools are developed for videos taken under idealised laboratory conditions. Therefore, the task of animal detection and tracking for videos taken in natural settings remains challenging due to heterogeneous environments. Methods that are useful for field conditions are often difficult to implement and thus remain inaccessible to empirical researchers. To address this gap, we present an open-source package called Multi-Object Tracking in Heterogeneous environments (MOTHe), a Python-based application that uses a basic convolutional neural network for object detection. MOTHe offers a graphical interface to automate the various steps related to animal tracking such as training data generation, animal detection in complex backgrounds and visually tracking animals in the videos. Users can also generate training data and train a new model which can be used for object detection tasks for a completely new dataset. MOTHe doesn't require any sophisticated infrastructure and can be run on basic desktop computing units. We demonstrate MOTHe on six video clips in varying background conditions. These videos are from two species in their natural habitat-wasp colonies on their nests (up to 12 individuals per colony) and antelope herds in four different habitats (up to 156 individuals in a herd). Using MOTHe, we are able to detect and track individuals in all these videos. MOTHe is available as an open-source GitHub repository with a detailed user guide and demonstrations at: https://github.com/tee-lab/MOTHe-GUI.

Individuals of many species utilise social information whilst making decisions. While many studies have examined social information in making large scale decisions, there is increasing interest in the use of fine scale social cues in groups. By examining the use of these cues and how they alter behaviour, we can gain insights into the adaptive value of group behaviours. We investigated the role of social information in choosing when and where to dive in groups of socially foraging European shags. From this we aimed to determine the importance of social information in the formation of these groups. We extracted individuals’ surface trajectories and dive locations from video footage of collective foraging and used computational Bayesian methods to infer how social interactions influence diving. Examination of group spatial structure shows birds form structured aggregations with higher densities of conspecifics directly in front of and behind focal individuals. Analysis of diving behaviour reveals two distinct rates of diving, with birds over twice as likely to dive if a conspecific dived within their visual field in the immediate past. These results suggest that shag group foraging behaviour allows individuals to sense and respond to their environment more effectively by making use of social cues.