Collective movement is a fundamental process affecting the survival and reproductive success of group-living animals. Many of the hypothesized benefits of grouping such as predation evasion and foraging efficiency require the individuals in a group to move in a coordinated way. While moving in groups, animals are not only responding to the environment but also interacting with each other. These interactions give rise to emergent collective movement and behavioural patterns. A novel aspect of emergent behaviour is that a group can exhibit properties that no individual displays on its own.
Most studies on emergent properties of collective behaviour are conducted in controlled conditions. However, in natural settings, habitat is heterogeneous in terms of resource distribution, availability of hiding places and substrate for movement. Empirical studies have rarely investigated such fine-scale interactions (e.g. alignment, attraction among individuals) in their natural habitat. One reason for the dearth of such studies is the difficulty of data collection. Recent advances in techniques of aerial imagery allow us to observe and record such fine-scale data. For my PhD project, I studied the collective behaviour of blackbuck herds in their natural habitat. More specifically, I investigated the collective response of blackbuck herds during predation-like events. By analyzing multiple interactions among group members simultaneously, I aimed to understand the role of social interactions in shaping the collective response of blackbuck herds when faced with predation-like threats.
First, we overcome the difficulty of observing fine-scale interactions in animal groups (in their natural habitat) by using UAVs. We recorded blackbuck herding behaviour at high spatio-temporal resolutions (30 frames per second). Using this technique we were able to record the blackbuck herd’s collective escape behaviour in the context of predation using controlled-simulated threats.
Tracking animals in the videos recorded in natural habitat is extremely difficult due to varying background and light conditions and clutter in the background. Relatively basic image processing methods and default tools don’t perform satisfactorily in such a scenario.
Hence, we developed a machine learning method and GUI tool to extract the spatial locations and movement trajectories of all the individuals in a group from the videos recorded in natural field conditions.
Once we were able to obtain the movement trajectories from the videos, I then analysed these trajectories and interactions between individuals to explore - how the information about predatory risk spreads through a group in natural conditions. Broadly, our results suggest that transient leader-follower relationships emerge in these groups while performing the high-speed coordinated movement. Also, males and females respond differently to the threat scenario: adult females are more likely to be the response initiators whereas adult breeding males are more likely to influence the group movement during the escape response. Our results indicate that in fission-fusion groups associations are likely to last for short time scales and spatial positions of the individuals only affect their response-time (vigilance behaviour) but not their influence on the group.