Critical phase transition in bee movement dynamics can be modeled using a two-dimensional cellular automaton

The collective behavior of numerous animal species, including insects, exhibits scale-free behavior indicative of the critical (second-order) phase transition. Previous research uncovered such phenomena in the behavior of honeybees, most notably the long-range correlations in space and time. Furthermore, it was demonstrated that the bee activity in the hive manifests the hallmarks of the jamming process. We follow up by presenting a discrete model of the system that faithfully replicates some of the key features found in the data, such as the divergence of correlation length and scale-free distribution of jammed clusters. The dependence of the correlation length on the control parameter, density, is demonstrated for both the real data and the model. We conclude with a brief discussion on the contribution of the insights provided by the model to our understanding of the insects' collective behavior.

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Publication Details

Journal
Physical review. E
Published
2026-01-29
DOI
https://doi.org/10.1103/dc35-lmgd
Primary Topic
Ecosystem dynamics and resilience
Type
article
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article

Critical phase transition in bee movement dynamics can be modeled using a two-dimensional cellular automaton

Tom Froese, Anonymous
Physical review. E
Ecosystem dynamics and resilience
article

Critical phase transition in bee movement dynamics can be modeled using a two-dimensional cellular automaton

Tom Froese, Anonymous
article en

Abstract

The collective behavior of numerous animal species, including insects, exhibits scale-free behavior indicative of the critical (second-order) phase transition. Previous research uncovered such phenomena in the behavior of honeybees, most notably the long-range correlations in space and time. Furthermore, it was demonstrated that the bee activity in the hive manifests the hallmarks of the jamming process. We follow up by presenting a discrete model of the system that faithfully replicates some of the key features found in the data, such as the divergence of correlation length and scale-free distribution of jammed clusters. The dependence of the correlation length on the control parameter, density, is demonstrated for both the real data and the model. We conclude with a brief discussion on the contribution of the insights provided by the model to our understanding of the insects' collective behavior.

Physical review. EVol. 113(2)
Okinawa Institute of Science and Technology Graduate University (JP)
Openalex Percentile: Top 82%
Ecosystem dynamics and resilience
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