  How predictable is the next move of an animal? Specifically, which factors
govern the short- and long-term motion patterns and the overall dynamics of
landbound, plant-eating animals and ruminants in particular? To answer this
question, we here study the movement dynamics of springbok antelopes Antidorcas
marsupialis. We propose complementary statistical analysis techniques combined
with machine learning approaches to analyze, across multiple time scales, the
springbok motion recorded in long-term GPS-tracking of collared springboks at a
private wildlife reserve in Namibia. As a new result, we are able to predict
the springbok movement within the next hour with a certainty of about 20\%. The
remaining 80\% are stochastic in nature and are induced by unaccounted factors
in the modeling algorithm and by individual behavioral features of springboks.
We find that directedness of motion contributes approximately 17\% to this
predicted fraction. We find that the measure for directedeness is strongly
dependent on the daily cycle. The previously known daily affinity of springboks
to their water points, as predicted from our machine learning algorithm,
overall accounts for only 3\% of this predicted deterministic component of
springbok motion. Moreover, the resting points are found to affect the motion
of springboks at least as much as the formally studied effects of water points.
The generality of these statements for the motion patterns and their underlying
behavioral reasons for other ruminants can be examined on the basis of our
statistical analysis tools.
