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A modeling algorithm for exploring the architecture and construction of bird nests

The wide variety of nest architectural designs exhibited by passerine birds allowed them to diversify into a wide variety of ecological niches and terrestrial habitats. At present, very little is known about the mechanics of building these structures. Digitizing natural biological structures such as...

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Autores principales: Jessel, Hadass R., Aharoni, Lior, Efroni, Sol, Bachelet, Ido
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6791838/
https://www.ncbi.nlm.nih.gov/pubmed/31611582
http://dx.doi.org/10.1038/s41598-019-51478-1
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author Jessel, Hadass R.
Aharoni, Lior
Efroni, Sol
Bachelet, Ido
author_facet Jessel, Hadass R.
Aharoni, Lior
Efroni, Sol
Bachelet, Ido
author_sort Jessel, Hadass R.
collection PubMed
description The wide variety of nest architectural designs exhibited by passerine birds allowed them to diversify into a wide variety of ecological niches and terrestrial habitats. At present, very little is known about the mechanics of building these structures. Digitizing natural biological structures such as bird nests provides the opportunity to explore their structural properties and behavior under specific conditions by means of computational manipulations, simulations, and analyses. This study describes a generic algorithm for the digitization and exploration of complex interlocked bird nests, and validates it on nests built by the Dead-Sea Sparrow (Passer moabiticus) in branches of trees using stiff dry branches. This algorithm takes as input computerized tomographic scans of the nest, identifies and isolates each branch entity within the three-dimensional data, and finally extracts the characteristics of each branch. The result is a reliable three-dimensional digital model of the nest that contains a complete geometric dataset per each of its components, e.g. dimensions and contact points with neighboring components, as well as global properties, e.g. density distribution and network structure. Based on these, we were able to simulate various models of the nest construction process. Altogether, the described algorithm and possible derivatives thereof could be a valuable tool in studying the structure-function relationships of similarly complex biological objects, and may provide further insights into the potential selective mechanisms underlying historical evolution of this distinct nest form.
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spelling pubmed-67918382019-10-21 A modeling algorithm for exploring the architecture and construction of bird nests Jessel, Hadass R. Aharoni, Lior Efroni, Sol Bachelet, Ido Sci Rep Article The wide variety of nest architectural designs exhibited by passerine birds allowed them to diversify into a wide variety of ecological niches and terrestrial habitats. At present, very little is known about the mechanics of building these structures. Digitizing natural biological structures such as bird nests provides the opportunity to explore their structural properties and behavior under specific conditions by means of computational manipulations, simulations, and analyses. This study describes a generic algorithm for the digitization and exploration of complex interlocked bird nests, and validates it on nests built by the Dead-Sea Sparrow (Passer moabiticus) in branches of trees using stiff dry branches. This algorithm takes as input computerized tomographic scans of the nest, identifies and isolates each branch entity within the three-dimensional data, and finally extracts the characteristics of each branch. The result is a reliable three-dimensional digital model of the nest that contains a complete geometric dataset per each of its components, e.g. dimensions and contact points with neighboring components, as well as global properties, e.g. density distribution and network structure. Based on these, we were able to simulate various models of the nest construction process. Altogether, the described algorithm and possible derivatives thereof could be a valuable tool in studying the structure-function relationships of similarly complex biological objects, and may provide further insights into the potential selective mechanisms underlying historical evolution of this distinct nest form. Nature Publishing Group UK 2019-10-14 /pmc/articles/PMC6791838/ /pubmed/31611582 http://dx.doi.org/10.1038/s41598-019-51478-1 Text en © The Author(s) 2019 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/.
spellingShingle Article
Jessel, Hadass R.
Aharoni, Lior
Efroni, Sol
Bachelet, Ido
A modeling algorithm for exploring the architecture and construction of bird nests
title A modeling algorithm for exploring the architecture and construction of bird nests
title_full A modeling algorithm for exploring the architecture and construction of bird nests
title_fullStr A modeling algorithm for exploring the architecture and construction of bird nests
title_full_unstemmed A modeling algorithm for exploring the architecture and construction of bird nests
title_short A modeling algorithm for exploring the architecture and construction of bird nests
title_sort modeling algorithm for exploring the architecture and construction of bird nests
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6791838/
https://www.ncbi.nlm.nih.gov/pubmed/31611582
http://dx.doi.org/10.1038/s41598-019-51478-1
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