Spatial-Temporal Dynamics of High-Resolution Animal Social Networks:   What Can We Learn from Domestic Animals?
  Recent studies of animal social networks have significantly increased our understanding of animal behavior, social interactions, and many important ecological and epidemiological processes. However, most of the studies are at low temporal and spatial resolution due to the difficulty in recording accurate contact information. Domestic animals such as cattle have social behavior and serve as an excellent study system because their position can be explicitly and continuously tracked, allowing their social networks to be accurately constructed. We used radio-frequency tags to accurately track cattle position and analyze high-resolution cattle social networks. We tested the hypothesis of temporal stationarity and spatial homogeneity in these high-resolution networks and demonstrated substantial spatial-temporal heterogeneity during different daily time periods (feeding and non-feeding) and in different areas of the pen (grain bunk, water trough, hay bunk, and other general pen area). The social network structure is analyzed using global network characteristics (network density, exponential random graph model structure), subgroup clustering (modularity), triadic property (transitivity), and dyadic interactions (correlation coefficient from a quadratic assignment procedure). Cattle tend to have the strongest and most consistent contacts with others around the hay bunk during the feeding time. These results cannot be determined from data at lower spatial (aggregated at entire pen level) or temporal (aggregated at daily level) resolution. These results reveal new insights for real-time animal social network structure dynamics, providing more accurate descriptions that allow more accurate modeling of multiple (both direct and indirect) disease transmission pathways. 
Shi Chen, Amiyaal Ilany, Brad J. White, Michael W. Sanderson, and   Cristina Lanzas
q-bio.PE cs.SI physics.soc-ph