Figure 1. A conceptualization of learning in the context of
animal movement. The information gathering pathway appears inside the
animal’s brain. Note that an individuals’ internal state and its
environment can influence both the onset of information gathering and
how well memory maps onto movement decisions.
Figure 2: A schematic representation of a forager’s movement
rules in a heterogeneous landscape, how a stable set of rules might be
applied, and how landscape disturbance could force an update to the
movement rules via learning. In a pre-disturbance world (left three
columns), the forager (denoted by the white elk symbol) occupies a
landscape with three depletable and renewable resource patches and a
water body. The ‘real world’ is represented in the top row, with all of
its complexity. The second row represents the forager’s model of that
world, which distills the complexity to the most relevant information.
Shapes indicate different landscape elements, while colors reflect a
quantitative score: darker greens are regenerated, paler greens are
depleted. The forager has two movement rules in this landscape (bottom
row): 1) move from depleted resource patch to a regenerated resource
patch and 2) avoid the water body. The pre-disturbance movements rely on
a dynamically updated spatial memory, as the forager learns about a
changing environment. Post-disturbance, the forager’s world model
changes after it gains information about the loss of a potential
foraging area, e.g. a new oil well destroys one of the patches.
Accordingly, the forager’s world model is refined to include a novel
categorical element (orange triangle), with its own avoidance rule for
movement (dynamic learning).