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.