Power-laws in dog behavior may pave the way to predictive models: A pattern analysis study.

Apparently random events in nature often reveal hidden patterns when analyzed using diverse and robust statistical tools. Power law distributions, for example, project diverse natural phenomenon, ranging from earthquakes to heartbeat dynamics into a common platform of self-similarity. Animal behavior in specific contexts has been shown to follow power law distributions. However, the behavioral repertoire of a species in its entirety has never been analyzed for the existence of such underlying patterns. Here we show that the frequency-rank data of randomly sighted behaviors at the population level of free-ranging dogs follow a scale-invariant power law behavior. It suggests that irrespective of changes in location of sightings, seasonal variations and observer bias, datasets exhibit a conserved trend of scale invariance. The data also exhibits robust self-similarity patterns at different scales which we extract using multifractal detrended fluctuation analysis. We observe that the probability of consecutive occurrence of behaviors of adjacent ranks is much higher than behaviors widely separated in rank. The findings open up the possibility of designing predictive models of behavior from correlations existing in true time series of behavioral data and exploring the general behavioral repertoire of a species for the presence of syntax.