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Recent methodological advances in studying large scale animal movements have let researchers gather rich datasets from behaving animals. Often collected in small sample sizes due to logistical constraints, these datasets are however, ideal for multivariate explorations into behavioral complexity. In behavioral studies of domestic dogs, although automated data loggers have recently seen increasing use, a comprehensive framework to identify complex behavioral axes is lacking. Dog behavioral studies frequently rely on subjective ratings, despite demonstrable evidence that these are insufficient for identifying behavioral variables. Taking advantage of dogs’ innate running abilities and readily available GPS data loggers, we extracted latitude-longitude coordinates from running dogs in a large field setup. By extracting multiple variables from each logged coordinate, we generated a complex dataset from limited numbers of dog runs. Individual variables were successful in classifying aerobic competence, social awareness, and different exploratory patterns of dogs. Multivariate analyses identified latent features in movement patterns of dogs which were primarily comprised of two behavioral axes: spatial acuity and social awareness. Individual dogs were then behaviorally classified into independent clusters through unsupervised learning. Interestingly, even though field dogs clustered primarily with each other in varying degrees of energetic exploration and handler focus, some house pets displayed moderately high exploration abilities as well. We expect our proof of principle quantitative pipeline to provide a robust framework for behavioral classification, generating case-control clusters based solely on complex behavioral axes, and greatly benefiting genetic association studies of dog behavior.


This article was originally published in Frontiers in Behavioral Neuroscience, available at

This work is distributed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).



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