INTRODUCTION
Animal movement takes many forms and is critical to ecological processes. This understanding has given rise to a relatively young but rapidly growing sub-discipline of ecology called movement ecology (Nathan 2008). At the same time, the subject of learning has been studied from the perspective of animal behavior, both in the context of ecological interactions and in the context of movement itself. Animal behavior has a well-established and celebrated history of understanding learning and there has been recent growth in connecting learning and memory to animal movement behavior (e.g., Fagan et al . 2013). At the same time, a recent explosion of ideas about learning in artificial intelligence is now reshaping the landscape of learning, and now the lines dividing the functioning of machines and living organisms are starting to blur.
In addition to these recent developments, the ability of ecologists to observe animal movements and behaviors remotely in the wild has been steadily increasing. The collection of massive amounts of data on animal movement patterns, primarily via remote sensing, is now possible at a scale and level of detail previously unimaginable and can be linked with similarly improving remotely sensed or modeled environmental data. Furthermore, more recent advances in bio-logging, including accelerometers, proximity measures, audio- and video-recording devices, provide direct information on some of the environmental, physiological, and social contexts of movements. This coupling of movement patterns with behavioral, social and environmental contexts has led to novel opportunities to make inferences about possible learning mechanisms and meld ideas from animal behavior, movement ecology, artificial intelligence, and remote sensing in the context of ecology of learning and animal movement. We develop such a synthesis here.
We start with a focus on learning as a means for acquiring information and making decisions. Employing two related definitions of learning, one from psychology and the other from computer science, we evaluate the benefits, costs and limitations of learning in the context of animal movement. Next, we address the modality of learning in animal movement, ranging from individual to social. We then develop links to related disciplines: psychology, animal cognition, and statistical learning. We close with an overview of approaches to studying the process of learning and animal movement, whether from experimental or observational studies, and discuss the role that models can play in this endeavour. Finally, we make some concluding remarks and suggest areas for future developments.