  How do we capture the breadth of behavior in animal movement, from rapid body
twitches to aging? Using high-resolution videos of the nematode worm $C.
elegans$, we show that a single dynamics connects posture-scale fluctuations
with trajectory diffusion, and longer-lived behavioral states. We take short
posture sequences as an instantaneous behavioral measure, fixing the sequence
length for maximal prediction. Within the space of posture sequences we
construct a fine-scale, maximum entropy partition so that transitions among
microstates define a high-fidelity Markov model, which we also use as a means
of principled coarse-graining. We translate these dynamics into movement using
resistive force theory, capturing the statistical properties of foraging
trajectories. Predictive across scales, we leverage the longest-lived
eigenvectors of the inferred Markov chain to perform a top-down subdivision of
the worm's foraging behavior, revealing both ``runs-and-pirouettes'' as well as
previously uncharacterized finer-scale behaviors. We use our model to
investigate the relevance of these fine-scale behaviors for foraging success,
recovering a trade-off between local and global search strategies.
