Kareemah Chopra

<jats:p>Movement ecology is important for advancing our comprehension of animal behavior, but its application is yet to be applied to farm dogs. This pilot study uses combined GPS and accelerometer technology to explore the spatial patterns and activity levels of free roaming farm dogs, <jats:italic>Canis familiaris</jats:italic> (<jats:italic>n</jats:italic> = 3). Space-use distributions and range sizes were determined to compare locations visited across days and between individuals, as well as in relation to specific areas of interest. Individual activity levels were analyzed and compared within and between dogs. Space-use patterns and range sizes showed variation among the dogs, although substantial similarity in overall spatial distributions were observed between each pair. Among the dogs, the extent of spatial distribution overlap between days varied, with some individuals exhibiting more overlap than others. The dogs allocated different amounts of their time close to landscape features, and to slow-, medium-, and fast movements. This study demonstrates the potential of using automated tracking technology to monitor space-use and interactions between dogs, livestock, and wildlife. By understanding and managing the free ranging behavior of their farm dogs, farmers could potentially take steps to improve the health and wellbeing of both their dogs and their livestock, limiting disease spread, and reducing the possibility of related economic losses.</jats:p>

<jats:p>Understanding the herd structure of housed dairy cows has the potential to reveal preferential interactions, detect changes in behavior indicative of illness, and optimize farm management regimes. This study investigated the structure and consistency of the proximity interaction network of a permanently housed commercial dairy herd throughout October 2014, using data collected from a wireless local positioning system. Herd-level networks were determined from sustained proximity interactions (pairs of cows continuously within three meters for 60 s or longer), and assessed for social differentiation, temporal stability, and the influence of individual-level characteristics such as lameness, parity, and days in milk. We determined the level of inter-individual variation in proximity interactions across the full barn housing, and for specific functional zones within it (feeding, non-feeding). The observed networks were highly connected and temporally varied, with significant preferential assortment, and inter-individual variation in daily interactions in the non-feeding zone. We found no clear social assortment by lameness, parity, or days in milk. Our study demonstrates the potential benefits of automated tracking technology to monitor the proximity interactions of individual animals within large, commercially relevant groups of livestock.</jats:p>