Biology subjects

Wolf, S. W.

Publications and source records attributed to Wolf, S. W..

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

pipemake: A pipeline creation tool using Snakemake for reproducible analysis of biological datasets

The exponential growth in biological data generation has created an urgent need for efficient, reproducible computational analysis workflows. Here, we present pipemake, a computational platform designed to streamline the development and implementation of efficient and reproducible Snakemake workflows. pipemake creates modular pipelines that can be seamlessly integrated or removed from the platform without requiring reconfiguration of the core system, enabling flexible adaptation of workflows to different analytical needs across diverse fields. To demonstrate the platforms capabilities, we created and implemented pipelines to reanalyze two distinct biological datasets. First, we recreated a population genomics analysis of the socially flexible halictid bee, Lasioglossum albipes, using pipemake-generated workflows for de novo genome annotation, processing of variant data, dimensionality reduction, and a genome-wide association study (GWAS). We then used pipemake to analyze behavioral tracking data from the common eastern bumble bee, Bombus impatiens. In both cases, pipemake workflows produced results consistent with published findings while substantially reducing hands-on analysis time. Overall, pipemakes modular design allows researchers to easily modify existing pipelines or develop new ones without software development expertise. Beyond streamlining workflow creation, pipemake leverages the full Snakemake ecosystem to enable parallel processing, automated error recovery, and comprehensive analysis documentation. These features make pipemake an efficient and accessible solution for analyzing complex biological datasets. pipemake is freely available as a conda package or direct download at https://github.com/kocherlab/pipemake

bioinformatics↗

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↗