Capturing continuous, long timescale behavioral changes in Drosophila melanogaster postural data.

Animal behavior spans many timescales, from short, seconds-scale actions to circadian rhythms over many hours to life-long changes during aging. Most quantitative behavior studies have focused on short-timescale behaviors such as locomotion and grooming. Analysis of these data suggests there exists a hierarchy of timescales; however, the limited duration of these experiments prevents the investigation of the full temporal structure. To access longer timescales of behavior, we continuously recorded individual Drosophila melanogaster at 100 frames per second for up to 7 days at a time in featureless arenas on sucrose-agarose media. We use the deep learning framework SLEAP to produce a full-body postural data set for 47 individuals resulting in nearly 2 billion pose instances. We identify stereotyped behaviors such as grooming, proboscis extension, and locomotion and use the resulting ethograms to explore how the flies' behavior varies across time of day and days in the experiment. We find distinct circadian patterns in all of our stereotyped behavior and also see changes in behavior over the course of the experiment as the flies weaken and die.

NAPS: Integrating pose estimation and tag-based tracking.

1. Significant advances in computational ethology have allowed the quantification of behaviour in unprecedented detail. Tracking animals in social groups, however, remains challenging as most existing methods can either capture pose or robustly retain individual identity over time but not both. 2. To capture finely resolved behaviours while maintaining individual identity, we built NAPS (NAPS is ArUco Plus SLEAP), a hybrid tracking framework that combines state-of-the-art, deep learning-based methods for pose estimation (SLEAP) with unique markers for identity persistence (ArUco). We show that this framework allows the exploration of the social dynamics of the common eastern bumblebee (Bombus impatiens). 3. We provide a stand-alone Python package for implementing this framework along with detailed documentation to allow for easy utilization and expansion. We show that NAPS can scale to long timescale experiments at a high frame rate and that it enables the investigation of detailed behavioural variation within individuals in a group. 4. Expanding the toolkit for capturing the constituent behaviours of social groups is essential for understanding the structure and dynamics of social networks. NAPS provides a key tool for capturing these behaviours and can provide critical data for understanding how individual variation influences collective dynamics.