Spatial proximity loggers for recording animal social networks: consequences of inter-logger variation in performance

Abstract

Social network analysis has become an increasingly popular method to link individual behaviour to population level patterns (and vice versa). Technological advances of recent years, such as the development of spatial proximity loggers, have enhanced our abilities to record contact patterns between animals. However, loggers are often deployed without calibration which may lead to sampling biases and spurious results. In particular, loggers may differ in their performance (i.e., some loggers may over-sample and other loggers may under-sample social associations). However, the consequences of inter-logger variation in logging performance has not been thoroughly considered or quantified. In this study, proximity loggers made by Sirtrack Ltd. were fitted to 20 dairy cows over a 3-week period. Contact records resulting from field deployment demonstrated variability in logger performance when recording contact duration, which was highly consistent for each logger over time. Testing loggers under standardised conditions suggested that inter-logger variation observed in the field was due to a combination of intrinsic variation in devices, and environmental/behavioural effects. We demonstrate the potential consequences that inter-logger variation in logging performance can have for social network analysis; particularly how measures of connectivity can be biased by logging performance. Finally, we suggest some approaches to correct data generated by proximity loggers with imperfect performance, that should be used to improve the robustness of future analyses.