title: Proximity data-loggers increase the quantity and quality of social network data.
abstract: Social network analysis is an ideal quantitative tool for advancing our understanding of complex social behaviour. However, this approach is often limited by the challenges of accurately characterizing social structure and measuring network heterogeneity. Technological advances have facilitated the study of social networks, but to date, all such work has focused on large vertebrates. Here, we provide proof of concept for using proximity data-logging to quantify the frequency of social interactions, construct weighted networks and characterize variation in the social behaviour of a lek-breeding bird, the wire-tailed manakin, Pipra filicauda. Our results highlight how this approach can ameliorate the challenges of social network data collection and analysis by concurrently improving data quality and quantity.
authors.affiliation: Smithsonian Conservation Biology Institute, PO Box 37012 MRC5503, Washington DC 20008, USA. rydert@si.edu
authors.forename: Thomas B
authors.initials: TB
authors.lastname: Ryder
authors.forename: Brent M
authors.initials: BM
authors.lastname: Horton
authors.forename: Mike
authors.initials: M
authors.lastname: van den Tillaart
authors.forename: Juan De Dios
authors.initials: Jde D
authors.lastname: Morales
authors.forename: Ignacio T
authors.initials: IT
authors.lastname: Moore
mesh_terms: D000818:Animals; D003625:Data Collection / Q000379:methods*; D004484:Ecuador; D036103:Nanotechnology; D046109:Passeriformes / Q000502:physiology*; D012919:Social Behavior; D013037:Spatial Behavior / Q000502:physiology*