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.