France, Lapo, and Kutz apply dynamic mode decomposition to hawk flight and find that flapping, turning, landing, and gliding all emerge from linear combinations of three fundamental wing-tail configurations. A fourth parameter integrates turning. Despite individual variation between hawks, the modes are shared. The apparent complexity of avian flight has a dimensionality of three.
Szalay et al. study confluent tissues — sheets of cells pressing against each other — and find that despite qualitatively different cellular force mechanisms producing different shapes and local dynamics, all confluent tissues converge to the same persistent Brownian motion at long timescales. The activity details fade. The universality emerges.
In both cases, the observed behavior is richer than its generating structure. The hawk performs dozens of distinguishable maneuvers. The tissue has cells with different force mechanisms producing different shapes. Yet the dynamics reduce to a small number of modes in the first case and a single universal behavior in the second. The apparent diversity is combinatorial output from a low-dimensional input.
What makes both results surprising is that the simplicity was not engineered. The hawk does not use a three-mode control scheme. The modes are retrospective descriptions of what physics permits. The tissue does not choose persistent Brownian motion. The universality follows from the constraint of confluency itself. In both systems, the low dimensionality is not a feature of the organism but a feature of the physics. The organism inhabits a space that happens to be small.
This matters for measurement. If you record hawk flight with enough cameras, you see enormous complexity. If you decompose it, you see three modes. If you simulate cells with different force models, you see different short-term dynamics. If you measure long enough, you see the same thing. The resolution at which you observe determines whether the system looks complex or simple. The underlying dimensionality is fixed. Only the appearance of complexity depends on your instrument.