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Comparative genomic studies of social insects suggest that changes in gene regulation are associated with evolutionary transitions in social behavior, but the activity of predicted regulatory regions has not been tested empirically. We used STARR-seq, a high-throughput enhancer discovery tool, to identify and measure the activity of enhancers in the socially variable sweat bee,Lasioglossum albipes. We identified over 36,000 enhancers in theL. albipesgenome from three social and three solitary populations. Many enhancers were identified in only a subset ofL. albipespopulations, revealing rapid divergence in regulatory regions within this species. Population-specific enhancers were often proximal to the same genes across populations, suggesting compensatory gains and losses of regulatory regions may preserve gene activity. We also identified 1182 enhancers with significant differences in activity between social and solitary populations, some of which are conserved regulatory regions across species of bees. These results indicate that social trait variation inL. albipesis driven both by the fine-tuning of ancient enhancers as well as lineage-specific regulatory changes. Combining enhancer activity with population genetic data revealed variants associated with differences in enhancer activity and identified a subset of differential enhancers with signatures of selection associated with social behavior. Together, these results provide the first empirical map of enhancers in a socially flexible bee and highlight links between cis-regulatory variation and the evolution of social behavior. 

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 Natural variation can provide important insights into the genetic and environmental factors that shape social behaviour and its evolution. The sweat bee,Lasioglossum baleicum, is a socially flexible bee capable of producing both solitary and eusocial nests. We demonstrate that within a single nesting aggregation, soil temperatures are a strong predictor of the social structure of nests. Sites with warmer temperatures in the spring have a higher frequency of social nests than cooler sites, perhaps because warmer temperatures provide a longer reproductive window for those nests. To identify the molecular correlates of this behavioural variation, we generated a de novo genome assembly forL. baleicum, and we used transcriptomic profiling to compare adults and developing offspring from eusocial and solitary nests. We find that adult, reproductive females have similar expression profiles regardless of social structure in the nest, but that there are strong differences between reproductive females and workers from social nests. We also find substantial differences in the transcriptomic profiles of stage‐matched pupae from warmer, social‐biased sites compared to cooler, solitary‐biased sites. These transcriptional differences are strongly predictive of adult reproductive state, suggesting that the developmental environment may set the stage for adult behaviours inL. baleicum. Together, our results help to characterize the molecular mechanisms shaping variation in social behaviour and highlight a potential role of environmental tuning during development as a factor shaping adult behaviour and physiology in this socially flexible bee. 

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.