Biology subjects

Ruttenberg, D. M.

Publications and source records attributed to Ruttenberg, D. M..

3 recordsLinked to original sources

bioRxiv2025

Queen loss unmasks cryptic worker influence and decentralizes the bumble bee social network

Dominant individuals often structure group organization, but less is known about how social networks reorganize in their absence and how variation among subordinates contributes to collective outcomes. Bumble bees (Bombus impatiens) provide an ideal system to study these dynamics: queens typically monopolize reproduction, but in some contexts individual workers can adopt queenlike social roles. Using multi-animal pose tracking, we compared matched queenright and queenless partitions from the same source colonies, quantifying over 80 million social interactions. Queen-less colonies exhibited increased behavioral variation and contained a subset of highly influential workers with elevated movement, spatial centrality, and reproductive activity that was absent in queen-right conditions. The emergence of these individuals coincided with a shift from centralized to decentralized, efficient network architectures. These results demonstrate that queen presence constrains latent worker variation, revealing how individual behavioral differences can scale up to reshape collective social organization in hierarchical societies.

animal behavior and cognition↗

bioRxiv2024

Variation in season length and development time is sufficient to drive the emergence and coexistence of social and solitary behavioral strategies

Season length and its associated variables can influence the expression of social behaviors, including the occurrence of eusociality in insects. Eusociality can vary widely across environmental gradients, both within and between different species. Numerous theoretical models have been developed to examine the life history traits that underlie the emergence and maintenance of eusociality, yet the impact of seasonality on this process is largely uncharacterized. Here, we present a theoretical model that incorporates season length and offspring development time into a single, individual-focused model to examine how these factors can shape the costs and benefits of social living. We find that longer season lengths and faster brood development times are sufficient to favor the emergence and maintenance of a social strategy, while shorter seasons favor a solitary one. We also identify a range of season lengths where social and solitary strategies can coexist. Moreover, our theoretical predictions are well-matched to the natural history and behavior of two flexibly-eusocial bee species, suggesting our model can make realistic predictions about the evolution of different social strategies. Broadly, this work reveals the crucial role that environmental conditions can have in shaping social behavior and its evolution and underscores the need for further models that explicitly incorporate such variation to study evolutionary trajectories of eusociality.

evolutionary biology↗

bioRxiv2022

NAPS: Integrating pose estimation and tag-based tracking

O_LISignificant advances in computational ethology have allowed the quantification of behavior 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. C_LIO_LITo capture finely resolved behaviors 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). C_LIO_LIWe 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 behavioral variation within individuals in a group. C_LIO_LIExpanding the toolkit for capturing the constituent behaviors of social groups is essential for understanding the structure and dynamics of social networks. NAPS provides a key tool for capturing these behaviors and can provide critical data for understanding how individual variation influences collective dynamics. C_LI

animal behavior and cognition↗