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

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          Abstract

          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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          Most cited references11

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          Modularity and community structure in networks

          M. Newman (2006)
          Many networks of interest in the sciences, including a variety of social and biological networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure has attracted considerable recent attention. One of the most sensitive detection methods is optimization of the quality function known as "modularity" over the possible divisions of a network, but direct application of this method using, for instance, simulated annealing is computationally costly. Here we show that the modularity can be reformulated in terms of the eigenvectors of a new characteristic matrix for the network, which we call the modularity matrix, and that this reformulation leads to a spectral algorithm for community detection that returns results of better quality than competing methods in noticeably shorter running times. We demonstrate the algorithm with applications to several network data sets.
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            Policing stabilizes construction of social niches in primates.

            All organisms interact with their environment, and in doing so shape it, modifying resource availability. Termed niche construction, this process has been studied primarily at the ecological level with an emphasis on the consequences of construction across generations. We focus on the behavioural process of construction within a single generation, identifying the role a robustness mechanism--conflict management--has in promoting interactions that build social resource networks or social niches. Using 'knockout' experiments on a large, captive group of pigtailed macaques (Macaca nemestrina), we show that a policing function, performed infrequently by a small subset of individuals, significantly contributes to maintaining stable resource networks in the face of chronic perturbations that arise through conflict. When policing is absent, social niches destabilize, with group members building smaller, less diverse, and less integrated grooming, play, proximity and contact-sitting networks. Instability is quantified in terms of reduced mean degree, increased clustering, reduced reach, and increased assortativity. Policing not only controls conflict, we find it significantly influences the structure of networks that constitute essential social resources in gregarious primate societies. The structure of such networks plays a critical role in infant survivorship, emergence and spread of cooperative behaviour, social learning and cultural traditions.
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              Reality mining of animal social systems.

              The increasing miniaturisation of animal-tracking technology has made it possible to gather exceptionally detailed machine-sensed data on the social dynamics of almost entire populations of individuals, in both terrestrial and aquatic study systems. Here, we review important issues concerning the collection of such data, and their processing and analysis, to identify the most promising approaches in the emerging field of 'reality mining'. Automated technologies can provide data sensing at time intervals small enough to close the gap between social patterns and their underlying processes, providing insights into how social structures arise and change dynamically over different timescales. Especially in conjunction with experimental manipulations, reality mining promises significant advances in basic and applied research on animal social systems. Copyright © 2013 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                24 June 2015
                2015
                : 10
                : 6
                : e0129253
                Affiliations
                [1 ]Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, 27607, United States of America
                [2 ]Department of Biology, University of Pennsylvania, Philadelphia, PA, 19104, United States of America
                [3 ]College of Veterinary Medicine, Kansas State University, Manhattan, KS, 66506, United States of America
                University of Namur, BELGIUM
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: BW MS. Performed the experiments: BW MS. Analyzed the data: SC. Contributed reagents/materials/analysis tools: SC AI. Wrote the paper: SC CL.

                Article
                PONE-D-14-49886
                10.1371/journal.pone.0129253
                4479463
                26107251
                d54a4089-b448-4f86-b8a5-0c58f8810c59
                Copyright @ 2015

                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 credited

                History
                : 9 November 2014
                : 6 May 2015
                Page count
                Figures: 4, Tables: 0, Pages: 11
                Funding
                This work was conducted with partial funding provided at NIMBioS, an institute sponsored by the U.S. National Science Foundation, the U.S. Department of Homeland Security, and the U.S. Department of Agriculture through NSF Award # EF-0832858, with additional support from the University of Tennessee, Knoxville. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                Relevant data are available at Figshare. DOI: http://dx.doi.org/10.6084/m9.figshare.1416302.

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