$Rheomergy$: Collective behavior mediated by active flow-based recruitment

TL;DR

This paper develops a theoretical framework for understanding how active flow-based recruitment influences collective patterning in swarms, extending classical models to capture complex behaviors driven by signal communication.

Contribution

It introduces a generalized model that incorporates active flow dynamics into chemotactic aggregation, revealing new patterning behaviors and phase space complexity.

Findings

The model predicts diverse migration and aggregation patterns.

Pattern formation depends on two key dimensionless parameters.

Results align with observed behaviors in bee swarms.

Abstract

The physics of signal propagation in a collection of organisms that communicate with each other both enables and limits how active excitations at the individual level reach, recruit and lead to collective patterning. Inspired by the patterns in a planar swarm of bees that release pheromones, and use fanning flows to recruit additional bees, we develop a theoretical framework for patterning via active flow-based recruitment. Our model generalizes the well-known Patlak-Keller-Segel model of diffusion-dominated aggregation and leads to more complex phase space of patterns spanned by two dimensionless parameters that measure the scaled stimulus/activity and the scaled chemotactic response. Together these determine the efficacy of signal communication that leads to a variety of migration and aggregation patterns consistent with observations.