Phase Coexistence in Insect Swarms.

Animal aggregations are visually striking, and as such are popular examples of collective behavior in the natural world. Quantitatively demonstrating the collective nature of such groups, however, remains surprisingly difficult. Inspired by thermodynamics, we applied topological data analysis to laboratory insect swarms and found evidence for emergent, material-like states. We show that the swarms consist of a core "condensed" phase surrounded by a dilute "vapor" phase. These two phases coexist in equilibrium, and maintain their distinct macroscopic properties even though individual insects pass freely between them. We further define a pressure and chemical potential to describe these phases, extending theories of active matter to aggregations of macroscopic animals and laying the groundwork for a thermodynamic description of collective animal groups.

[1]  Nicholas T Ouellette,et al.  On the tensile strength of insect swarms , 2016, Physical biology.

[2]  Nir S. Gov,et al.  Long-range acoustic interactions in insect swarms: an adaptive gravity model , 2015, 1510.07259.

[3]  Mikael Vejdemo-Johansson,et al.  javaPlex: A Research Software Package for Persistent (Co)Homology , 2014, ICMS.

[4]  James G. Puckett,et al.  Intrinsic fluctuations and driven response of insect swarms. , 2015, Physical review letters.

[5]  Stephen Hailes,et al.  Upwash exploitation and downwash avoidance by flap phasing in ibis formation flight , 2014, Nature.

[6]  L. Edelstein-Keshet,et al.  Complexity, pattern, and evolutionary trade-offs in animal aggregation. , 1999, Science.

[7]  D. Sumpter,et al.  Inferring the rules of interaction of shoaling fish , 2011, Proceedings of the National Academy of Sciences.

[8]  J. Urry.  Complexity , 2006, Interpreting Art.

[9]  Brittany Terese Fasy,et al.  Exploring persistent local homology in topological data analysis , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[10]  J. Fewell,et al.  Models of division of labor in social insects. , 2001, Annual review of entomology.

[11]  W. Bialek,et al.  Statistical mechanics for natural flocks of birds , 2011, Proceedings of the National Academy of Sciences.

[12]  M. Kardar,et al.  Pressure is not a state function for generic active fluids , 2014, Nature Physics.

[13]  Douglas H. Kelley,et al.  Emergent dynamics of laboratory insect swarms , 2013, Scientific Reports.

[14]  I. Couzin,et al.  Collective memory and spatial sorting in animal groups. , 2002, Journal of theoretical biology.

[15]  David Hu,et al.  Mechanics of fire ant aggregations. , 2016, Nature materials.

[16]  T. Vicsek,et al.  Hierarchical group dynamics in pigeon flocks , 2010, Nature.

[17]  Eberhard Bodenschatz,et al.  A quantitative study of three-dimensional Lagrangian particle tracking algorithms , 2006 .

[18]  Lena Osterhagen.  Adaptation And Natural Selection A Critique Of Some Current Evolutionary Thought , 2016 .

[19]  James G. Puckett,et al.  Determining asymptotically large population sizes in insect swarms , 2014, Journal of The Royal Society Interface.

[20]  Tom Halverson,et al.  Topological Data Analysis of Biological Aggregation Models , 2014, PloS one.

[21]  C. Tsallis.  Entropy , 2022, Thermodynamic Weirdness.

[22]  E. Sober,et al.  Reviving the superorganism. , 1989, Journal of theoretical biology.

[23]  I. Couzin.  Collective cognition in animal groups , 2009, Trends in Cognitive Sciences.

[24]  Andrea Cavagna,et al.  Collective Behaviour without Collective Order in Wild Swarms of Midges , 2013, PLoS Comput. Biol..

[25]  Alan M. Wilson,et al.  Flying in a flock comes at a cost in pigeons , 2011, Nature.

[26]  I. Couzin,et al.  Inferring the structure and dynamics of interactions in schooling fish , 2011, Proceedings of the National Academy of Sciences.

[27]  J. Brady,et al.  Towards a thermodynamics of active matter. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  Samir Okasha,et al.  Emergence, hierarchy and top-down causation in evolutionary biology , 2012, Interface Focus.

[29]  R. Ghrist.  Barcodes: The persistent topology of data , 2007 .

[30]  L. Cugliandolo.  The effective temperature , 2011, 1104.4901.

[31]  J. Brady,et al.  Swim pressure: stress generation in active matter. , 2014, Physical review letters.

[32]  I. Couzin,et al.  Self-Organization and Collective Behavior in Vertebrates , 2003 .

[33]  I. Couzin,et al.  Emergent Sensing of Complex Environments by Mobile Animal Groups , 2013, Science.