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Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals?

Animal social network is the key to understand many ecological and epidemiological processes. We used real-time location system (RTLS) to accurately track cattle position, analyze their proximity networks, and tested the hypothesis of temporal stationarity and spatial homogeneity in these networks d...

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Autores principales: Chen, Shi, Ilany, Amiyaal, White, Brad J., Sanderson, Michael W., Lanzas, Cristina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4479463/
https://www.ncbi.nlm.nih.gov/pubmed/26107251
http://dx.doi.org/10.1371/journal.pone.0129253
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author Chen, Shi
Ilany, Amiyaal
White, Brad J.
Sanderson, Michael W.
Lanzas, Cristina
author_facet Chen, Shi
Ilany, Amiyaal
White, Brad J.
Sanderson, Michael W.
Lanzas, Cristina
author_sort Chen, Shi
collection PubMed
description Animal social network is the key to understand many ecological and epidemiological processes. We used real-time location system (RTLS) to accurately track cattle position, analyze their proximity networks, and tested the hypothesis of temporal stationarity and spatial homogeneity in these networks during different daily time periods and in different areas of the pen. The network structure was analyzed using global network characteristics (network density), subgroup clustering (modularity), triadic property (transitivity), and dyadic interactions (correlation coefficient from a quadratic assignment procedure) at hourly level. We demonstrated substantial spatial-temporal heterogeneity in these networks and potential link between indirect animal-environment contact and direct animal-animal contact. But such heterogeneity diminished if data were collected at lower spatial (aggregated at entire pen level) or temporal (aggregated at daily level) resolution. The network structure (described by the characteristics such as density, modularity, transitivity, etc.) also changed substantially at different time and locations. There were certain time (feeding) and location (hay) that the proximity network structures were more consistent based on the dyadic interaction analysis. These results reveal new insights for animal network structure and spatial-temporal dynamics, provide more accurate descriptions of animal social networks, and allow more accurate modeling of multiple (both direct and indirect) disease transmission pathways.
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spelling pubmed-44794632015-06-29 Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals? Chen, Shi Ilany, Amiyaal White, Brad J. Sanderson, Michael W. Lanzas, Cristina PLoS One Research Article Animal social network is the key to understand many ecological and epidemiological processes. We used real-time location system (RTLS) to accurately track cattle position, analyze their proximity networks, and tested the hypothesis of temporal stationarity and spatial homogeneity in these networks during different daily time periods and in different areas of the pen. The network structure was analyzed using global network characteristics (network density), subgroup clustering (modularity), triadic property (transitivity), and dyadic interactions (correlation coefficient from a quadratic assignment procedure) at hourly level. We demonstrated substantial spatial-temporal heterogeneity in these networks and potential link between indirect animal-environment contact and direct animal-animal contact. But such heterogeneity diminished if data were collected at lower spatial (aggregated at entire pen level) or temporal (aggregated at daily level) resolution. The network structure (described by the characteristics such as density, modularity, transitivity, etc.) also changed substantially at different time and locations. There were certain time (feeding) and location (hay) that the proximity network structures were more consistent based on the dyadic interaction analysis. These results reveal new insights for animal network structure and spatial-temporal dynamics, provide more accurate descriptions of animal social networks, and allow more accurate modeling of multiple (both direct and indirect) disease transmission pathways. Public Library of Science 2015-06-24 /pmc/articles/PMC4479463/ /pubmed/26107251 http://dx.doi.org/10.1371/journal.pone.0129253 Text en © 2015 Chen et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Chen, Shi
Ilany, Amiyaal
White, Brad J.
Sanderson, Michael W.
Lanzas, Cristina
Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals?
title Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals?
title_full Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals?
title_fullStr Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals?
title_full_unstemmed Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals?
title_short Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals?
title_sort spatial-temporal dynamics of high-resolution animal networks: what can we learn from domestic animals?
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4479463/
https://www.ncbi.nlm.nih.gov/pubmed/26107251
http://dx.doi.org/10.1371/journal.pone.0129253
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