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Area of Science:

  • Collective behavior
  • Statistical physics
  • Animal dynamics

Background:

  • Flocking in animal groups demonstrates global order arising from local interactions.
  • Active systems, unlike ferromagnetic systems, involve dynamic rearrangement of interaction networks.
  • The impact of this dynamic network on biological systems requires further experimental investigation.

Purpose of the Study:

  • To develop a novel dynamical inference technique for analyzing flocking behavior.
  • To overcome limitations of slow experimental sampling rates and network rearrangements.
  • To infer the strength and range of alignment forces in starling flocks.

Main Methods:

  • Introduced a novel dynamical inference technique based on the principle of maximum entropy.
  • Accommodated network rearrangements within the inference method.
  • Overcame challenges posed by slow experimental sampling rates.

Main Results:

  • Local bird alignment occurs on a significantly faster timescale than neighbor rearrangement.
  • Equilibrium inference, assuming a fixed network, yielded results consistent with dynamical inference.
  • Bird orientations were found to be in a state of local quasi-equilibrium.

Conclusions:

  • The rapid timescale of bird alignment supports the applicability of statistical physics to active systems.
  • Local quasi-equilibrium over the interaction length scale validates theoretical models.
  • This study provides a robust framework for analyzing dynamic interaction networks in biological collectives.