Animal movement, search strategies and behavioural ecology: a cross‐disciplinary way forward

Abstract

A long historical tradition exists among mathematicians, biologists and theoretical ecologists on the topic of quantifying, up-scaling and modelling the statistical properties of animal movement. Nevertheless, new paradigms and technical challenges have recently pervaded the field, attracting renewed interest, as evidenced by the large body of literature on the topic, and by the recent series of international workshops and conferences on the ecology of animal movement(*). As the need of new approaches to study movement and search aspects of behavioural ecology is becoming increasingly evident, we identify those cross-disciplinary concepts and ideas that have been burgeoning over the last few years which have the potential to drive fundamental advances in animal movement research. Three factors can be recognized as the genesis of the exciting scientific advances that have contributed to where movement research is at present. The first one is the extensive use of experimental devices of lighter weight and higher resolution, such as GPS and bio-loggers (e.g. Cooke et al. 2004; Hays et al. 2006; Wilson & McMahon 2006; Wilson et al. 2006), which allow recording of the intimate details of animal lives. New computational tools to analyse and understand such details, gathered from a constantly growing number of species, have become a necessity. The second factor, much related to the first one, is the increased awareness of the need of a unifying theory and an integrative paradigm for organismmovement (Nathan et al. 2008). A framework that places animal motion as the central theme would help identify and focus the efforts for developing general empirical and analytical tools to fully understand movement patterns. The third factor is the relatively recent interpretation of animal movement with new tools and concepts borrowed from the field of statistical physics, i.e. Levy statistics and anomalous diffusion (see the review articles: Montroll & West 1979; Schlesinger, Zaslavsky & Klafter 1993; Klafter & Sokolov 2005), and optimal stochastic search (e.g. Viswanathan et al. 1999; Bartumeus & Levin 2008). All these aspects have contributed to enrich the biological discussion on the classical topics of animal ecology such as dispersal, optimal foraging theory, landscape behavioural ecology, animal decision-making and learning. These recent developments clearly indicate the presence of a productive platform for theoreticians and empiricists to help each other answer fundamental questions in animal ecology. In this Forum, we discuss how we believe cross-disciplinary efforts should be brought to the fore in trying to move the field forward. We do so by considering the statistical modelling of: (i) animal trajectories, (ii) search strategies, and (iii) behavioural ecology.