Proximity loggers: data handling and classification for quality control

Abstract

Proximity loggers are a novel biotelemetry device used for quantifying animal-animal interactions in a non-invasive way. Such data has been used for studying a range of interactions from disease spread among badgers and cattle to quantifying cow-calf interactions. Such quantitative behavioral traits could be used for the purpose of selective breeding in domesticated animals. With the use of real data, from the study of oestrus behavior in cattle populations raised in an extensive grazing system, we have identified poor reciprocal agreement (RA) as a source of variation. To date, RA has not been adequately considered or addressed and can have serious implications for further analyses aimed at correctly quantifying and interpreting social behavior. We provide a database schema for storing the millions of contact records produced by proximity loggers and programming functions, for use in the statistical programming language R, for performing raw data quality control, data import, database queries and classification of RA between proximity logger pairs. Poor RA leads to a lack of confidence in the data recorded by a pair of loggers. At best, substantial noise is added to the data set and at worst can lead to over or under estimation of contacts between pairs of animals. Over successive deployments, the identification and removal of loggers consistently involved in poor RA pairs can improve the level of agreement and confidence in the recorded data. This is a necessity for the accurate estimation of genetic parameters based on proximity logger data.