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Animal behavior spans many timescales, from short, seconds-scale actions to daily rhythms over many hours to life-long changes during aging. To access longer timescales of behavior, we continuously recorded individualDrosophila melanogasterat 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 dataset 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 daily patterns in all stereotyped behaviors, adding specific information about trends in different grooming modalities, proboscis extension duration, and locomotion speed to what is known about theD. melanogastercircadian cycle. Using our holistic measurements of behavior, we find that the hour after dawn is a unique time point in the flies’ daily pattern of behavior, and that the behavioral composition of this hour tracks well with other indicators of health such as locomotion speed and the fraction of time spend moving vs. resting. The method, data, and analysis presented here give us a new and clearer picture ofD. melanogasterbehavior across timescales, revealing novel features that hint at unexplored underlying biological mechanisms. 

Summary Dominant individuals often structure group organization, but less is known about how social networks differ in their absence or how variation among subordinates contributes to collective outcomes. Bumble bees (Bombus impatiens) provide an ideal system to study how individual behavior shapes colony organization: queens typically monopolize reproduction, but in some contexts individual workers can adopt queen-like social roles. We asked how this process shapes the collective phenotype. Using multi-animal pose tracking to quantify social behaviors, we compared matched queenright and queenless partitions from the same source colonies. Queenless colonies were more interactive and contained a subset of behaviorally extreme queen-like workers with higher movement, spatial centrality, and reproductive potential. Such variation, absent in queenright colonies, coincided with a shift to decentralized, efficient network structures. These results demonstrate how social context shapes the expression of individual phenotypes, revealing a mechanism by which seemingly hierarchical societies can retain latent social flexibility and underscoring the link between individual variation and collective organization. 

Abstract 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.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).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.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.