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Computational methods for the characterization of Apis mellifera comb architecture

The architecture of honey bee combs embodies a range of expressions associated with swarm intelligence, emergent behaviors, and social organization, which has drawn scientists to study them as a model of collective construction processes. Until recently, however, the development of models to charact...

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Autores principales: Bader, Christoph, Costa, João, Lee, Nic, Smith, Rachel, Ri, Ren, Weaver, James C., Oxman, Neri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9110387/
https://www.ncbi.nlm.nih.gov/pubmed/35577930
http://dx.doi.org/10.1038/s42003-022-03328-6
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author Bader, Christoph
Costa, João
Lee, Nic
Smith, Rachel
Ri, Ren
Weaver, James C.
Oxman, Neri
author_facet Bader, Christoph
Costa, João
Lee, Nic
Smith, Rachel
Ri, Ren
Weaver, James C.
Oxman, Neri
author_sort Bader, Christoph
collection PubMed
description The architecture of honey bee combs embodies a range of expressions associated with swarm intelligence, emergent behaviors, and social organization, which has drawn scientists to study them as a model of collective construction processes. Until recently, however, the development of models to characterize comb-building behavior has relied heavily on laborious manual observations and measurements. The use of high-throughput multi-scale analyses to investigate the geometric features of Apis mellifera comb therefore has the potential to vastly expand our understanding of comb-building processes. Inspired by this potential, here we explore connections between geometry and behavior by utilizing computational methods for the detailed examination of hives constructed within environments designed to observe how natural building rule sets respond to environmental perturbations. Using combs reconstructed from X-ray micro-computed tomography source data, we introduce a set of tools to analyze geometry and material distributions from these scans, spanning from individual cells to whole-hive-level length scales. Our results reveal relationships between cell geometry and comb morphology, enable the generalization of prior research on build direction, demonstrate the viability of our methods for isolating specific features of comb architecture, and illustrate how these results may be employed to investigate hive-level behaviors related to build-order and material distributions.
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spelling pubmed-91103872022-05-18 Computational methods for the characterization of Apis mellifera comb architecture Bader, Christoph Costa, João Lee, Nic Smith, Rachel Ri, Ren Weaver, James C. Oxman, Neri Commun Biol Article The architecture of honey bee combs embodies a range of expressions associated with swarm intelligence, emergent behaviors, and social organization, which has drawn scientists to study them as a model of collective construction processes. Until recently, however, the development of models to characterize comb-building behavior has relied heavily on laborious manual observations and measurements. The use of high-throughput multi-scale analyses to investigate the geometric features of Apis mellifera comb therefore has the potential to vastly expand our understanding of comb-building processes. Inspired by this potential, here we explore connections between geometry and behavior by utilizing computational methods for the detailed examination of hives constructed within environments designed to observe how natural building rule sets respond to environmental perturbations. Using combs reconstructed from X-ray micro-computed tomography source data, we introduce a set of tools to analyze geometry and material distributions from these scans, spanning from individual cells to whole-hive-level length scales. Our results reveal relationships between cell geometry and comb morphology, enable the generalization of prior research on build direction, demonstrate the viability of our methods for isolating specific features of comb architecture, and illustrate how these results may be employed to investigate hive-level behaviors related to build-order and material distributions. Nature Publishing Group UK 2022-05-16 /pmc/articles/PMC9110387/ /pubmed/35577930 http://dx.doi.org/10.1038/s42003-022-03328-6 Text en © The Author(s) 2022, corrected publication 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Bader, Christoph
Costa, João
Lee, Nic
Smith, Rachel
Ri, Ren
Weaver, James C.
Oxman, Neri
Computational methods for the characterization of Apis mellifera comb architecture
title Computational methods for the characterization of Apis mellifera comb architecture
title_full Computational methods for the characterization of Apis mellifera comb architecture
title_fullStr Computational methods for the characterization of Apis mellifera comb architecture
title_full_unstemmed Computational methods for the characterization of Apis mellifera comb architecture
title_short Computational methods for the characterization of Apis mellifera comb architecture
title_sort computational methods for the characterization of apis mellifera comb architecture
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9110387/
https://www.ncbi.nlm.nih.gov/pubmed/35577930
http://dx.doi.org/10.1038/s42003-022-03328-6
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