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

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dc.contributor.author Chen, S.
dc.contributor.author Ilany, A.
dc.contributor.author White, Bradley J.
dc.contributor.author Sanderson, Michael W.
dc.contributor.author Lanzas, C.
dc.date.accessioned 2016-04-04T22:27:49Z
dc.date.available 2016-04-04T22:27:49Z
dc.date.issued 2015-06-24
dc.identifier.uri http://hdl.handle.net/2097/32268
dc.description Citation: Chen, S., Ilany, A., White, B. J., Sanderson, M. W., & Lanzas, C. (2015). Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals? Plos One, 10(6), 11. doi:10.1371/journal.pone.0129253
dc.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.
dc.relation.uri https://doi.org/10.1371/journal.pone.0129253
dc.rights Attribution 4.0 International (CC BY 4.0)
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.subject Social Networks
dc.subject Models
dc.subject Space
dc.subject Time
dc.subject Science & Technology - Other Topics
dc.title Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals?
dc.type Article
dc.date.published 2015
dc.citation.doi 10.1371/journal.pone.0129253
dc.citation.issn 1932-6203
dc.citation.issue 6
dc.citation.jtitle PLoS One
dc.citation.spage 11
dc.citation.volume 10
dc.contributor.authoreid whiteb
dc.contributor.authoreid sandersn


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