CONCLUSIONS AND NEW HORIZONS
Traditionally, studies of animal learning and movement have taken place
in controlled laboratory environments or small-scale field studies.
Thanks to animal tracking technologies, increasingly detailed
observations of how free-ranging animals move and interact are possible
leading to opportunities to formulate and test new ideas about learning
and movement. However, potential pitfalls accompany this exciting
development. Alternative explanations to learning must be considered,
and if these alternatives cannot be ruled out, then we can only infer
that observations are consistent with learning (Table 2).
There are two possible approaches to solving this problem. First, field
observations can be transformed into controlled experiments via
manipulations, as in the hummingbird example in Table 2. While allowing
for incisive analysis, this approach limits the scientific questions to
those where such experiments can be set up. A second possible solution
is to collect more direct data on the individual experiences over a
life-time, including the environmental features of locations animals
visit, physiological measurements, and sensory data as made possible by
daylight sensors and collar cameras.
Exciting approaches to studying learning and animal movement arise from
“uncontrolled” experiments, specifically translocations,
reintroductions, aversive conditioning and rapid environmental change.
Understanding learning in the context of relocations and environmental
change may ultimately help with understanding how animals can adapt to
an increasingly complex world, driven by elevated levels of
anthropogenic impact from environmental change, habitat degradation, and
habitat fragmentation.
The emergence of machine learning as a dominant paradigm for solving
human problems provides fertile ground for modeling and understanding
learning from animal movement patterns. Here, processes such as
reinforcement learning have close natural ties to animals learning to
move so as to maximize fitness (e.g., optimal foraging). As machine
learning algorithms are currently improving and evolving, we expect this
field to shed light on further possible models for learning and animal
movement.
Overall, the subject of learning and animal movement is at a crucial
point in development and a host of new possibilities are on the horizon.
Our goal in this review has been to set the context for these new
possibilities and point out some future directions.