Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals?
| 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.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.contributor.authoreid | whiteb | |
| dc.contributor.authoreid | sandersn | |
| dc.date.accessioned | 2016-04-04T22:27:49Z | |
| dc.date.available | 2016-04-04T22:27:49Z | |
| dc.date.issued | 2015-06-24 | |
| dc.date.published | 2015 | |
| 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.identifier.uri | http://hdl.handle.net/2097/32268 | |
| 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 |
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