Characterization of Animal Movement Patterns using Information Theory: a Primer

Understanding the movement patterns of animals across different spatio-temporal scales, conditions, habitats and contexts is becoming increasingly important for addressing a series of questions in animal behaviour studies, such as mapping migration routes, evaluating resource use, modelling epidemic spreading in a population, developing strategies for animal conservation as well as understanding several emerging patterns related to feeding, growth and reproduction. In recent times, information theory has been successfully applied in several fields of science, in particular for understanding the dynamics of complex systems and characterizing adaptive social systems, such as dynamics of entities as individuals and as part of groups. In this paper, we describe a series of non-parametric information-theoretic measures that can be used to derive new insights about animal behaviour with a specific focus on movement patterns, namely Shannon entropy, Mutual information, Kullback-Leibler divergence and Kolmogorov complexity. In particular, we believe that the metrics presented in this paper can be used to formulate new hypotheses that can be verified potentially through a set of different observations and be complementary to existing techniques. We show how these measures can be used to characterize the movement patterns of several animals across different habitats and scales. Specifically, we show the effectiveness in using Shannon entropy to characterize the movement of sheep with Batten disease, mutual information to measure association in pigeons, Kullback-Leibler divergence to study the flights of Turkey vulture, and Kolmogorov complexity to find similarities in the movement patterns of animals across different scales and habitats. Finally, we discuss the limitations of these methods and we outline the challenges in this research area.