Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The physics of cooperative transport in groups of ants

Abstract

Anyone who has moved furniture together with friends will appreciate that cooperative transport requires some non-trivial communication. Yet ants are adept at collectively moving objects several times their size. How they do so has long been a subject of research, but recent advances have suggested that this communication occurs through the forces the ants exert on the load. This implies that the collective transport problem can be mapped to an Ising model, in which decisions by individual ants are described by spin flips. Within this framework, the group is poised in the vicinity of the transition between uncoordinated and coordinated motion. It thus profits from both internal coordination and maximal responsiveness to external information, mediated by temporarily informed leader ants. Here, we review the implications of these findings for cooperative transport, and discuss the way in which a more complete multiscale understanding of such systems would require the development of a new formalism that combines statistical physics of interacting particles with the cognitive capabilities of individuals.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Cooperative transport.
Fig. 2: Empirical findings and theoretical model.
Fig. 3: Criticality, susceptibility and leadership.
Fig. 4: Oscillatory motion under constrained conditions.
Fig. 5: Increasing system size transits the system between order and disorder in experiment and simulation.
Fig. 6: Phase transitions under constrained motion.
Fig. 7: Oscillatory motion in a different species.

References

  1. 1.

    Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I. & Shochet, O. Novel type of phase transition in a system of self-driven particles. Phys. Rev. Lett. 75, 1226 (1995).

    ADS  MathSciNet  Google Scholar 

  2. 2.

    Toner, J. & Tu, Y. Long-range order in a two-dimensional dynamical XY model: how birds fly together. Phys. Rev. Lett. 75, 4326–4329 (1995).

    ADS  Google Scholar 

  3. 3.

    Toner, J. & Tu, Y. Flocks, herds, and schools: A quantitative theory of flocking. Phys. Rev. E 58, 4828–4858 (1998).

    ADS  MathSciNet  Google Scholar 

  4. 4.

    Cavagna, A. et al. Scale-free correlations in starling flocks. Proc. Natl Acad. Sci. USA 107, 11865–11870 (2010).

    ADS  Google Scholar 

  5. 5.

    Vicsek, T. Universal patterns of collective motion from minimal models of flocking. In Proc. 2nd IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2008 3–11 (2008).

  6. 6.

    Vicsek, T. & Zafeiris, A. Collective motion. Phys. Rep. 517, 71–140 (2012).

    ADS  Google Scholar 

  7. 7.

    Ariel, G. & Ayali, A. Locust collective motion and its modeling. PLoS Comput. Biol. 11, e1004522 (2015).

    ADS  Google Scholar 

  8. 8.

    Procaccini, A. et al. Propagating waves in starling, Sturnus vulgaris, flocks under predation. Anim. Behav. 82, 759–765 (2011).

    Google Scholar 

  9. 9.

    Buhl, J. et al. From disorder to order in marching locusts. Science 312, 1402–1406 (2006).

    ADS  Google Scholar 

  10. 10.

    Parrish, J. K., Viscido, S. V. & Grunbaum, D. Self-organized fish schools: an examination of emergent properties. Biol. Bull. 202, 296–305 (2002).

    Google Scholar 

  11. 11.

    Tunstrøm, K. et al. Collective states, multistability and transitional behavior in schooling fish. PLoS Comput. Biol. 9, e1002915 (2013).

    MathSciNet  Google Scholar 

  12. 12.

    Pearce, D. J., Miller, A. M., Rowlands, G. & Turner, M. S. Role of projection in the control of bird flocks. Proc. Natl Acad. Sci. USA 111, 10422–10426 (2014).

    ADS  Google Scholar 

  13. 13.

    Rosenthal, S. B., Twomey, C. R., Hartnett, A. T., Wu, H. S. & Couzin, I. D. Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion. Proc. Natl Acad. Sci. USA 112, 4690–4695 (2015).

    ADS  Google Scholar 

  14. 14.

    Dussutour, A., Fourcassie, V., Helbing, D. & Deneubourg, J.-L. Optimal traffic organization in ants under crowded conditions. Nature 428, 70–73 (2004).

    ADS  Google Scholar 

  15. 15.

    Bazazi, S. et al. Collective motion and cannibalism in locust migratory bands. Curr. Biol. 18, 735–739 (2008).

    Google Scholar 

  16. 16.

    Liao, J. C., Beal, D. N., Lauder, G. V. & Triantafyllou, M. S. Fish exploiting vortices decrease muscle activity. Science 302, 1566–1569 (2003).

    ADS  Google Scholar 

  17. 17.

    Morgan, E. D. Trail pheromones of ants. Physiol. Entomol. 34, 1–17 (2009).

    Google Scholar 

  18. 18.

    Ben-Jacob, E. et al. Generic modelling of cooperative growth patterns in bacterial colonies. Nature 368, 46 (1994).

    ADS  Google Scholar 

  19. 19.

    Darmon, M., Brachet, P. & Da Silva, L. Chemotactic signals induce cell differentiation in dictyostelium discoideum. Proc. Natl Acad. Sci. USA 72, 3163–3166 (1975).

    ADS  Google Scholar 

  20. 20.

    Cvikel, N. et al. Bats aggregate to improve prey search but might be impaired when their density becomes too high. Curr. Biol. 25, 206–211 (2015).

    Google Scholar 

  21. 21.

    Gorbonos, D. et al. Long-range acoustic interactions in insect swarms: an adaptive gravity model. New J. Phys. 18, 073042 (2016).

    ADS  Google Scholar 

  22. 22.

    Ballerini, M. et al. Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study. Proc. Natl Acad. Sci. USA 105, 1232–1237 (2008).

    ADS  Google Scholar 

  23. 23.

    Czaczkes, T. J. & Ratnieks, F. L. W. Cooperative transport in ants (Hymenoptera: Formicidae) and elsewhere. Myrmecol. News 18, 1–11 (2013).

    Google Scholar 

  24. 24.

    McCreery, H. & Breed, M. Cooperative transport in ants: a review of proximate mechanisms. Insect Soc. 61, 99–110 (2014).

    Google Scholar 

  25. 25.

    Moffett, M. W. Cooperative food transport by an Asiatic ant. Natl Geogr. Res. 4, 386–394 (1988).

    Google Scholar 

  26. 26.

    Hölldobler, B. & Wilson, E. O. The Ants (Harvard Univ. Press, Cambridge, MA, 1990).

  27. 27.

    Sudd, J. H. The transport of prey by ants. Behaviour 25, 234–271 (1965).

    Google Scholar 

  28. 28.

    Buffin, A. & Pratt, S. Cooperative transport by the ant Novomessor cockerelli. Insectes Soc. 63, 429–438 (2016).

    Google Scholar 

  29. 29.

    Franks, N. R. Teams in social insects: group retrieval of prey by army ants (Eciton burchellii, Hymenoptera: Formicidae). Behav. Ecol. Sociobiol. 18, 425–429 (1986).

    Google Scholar 

  30. 30.

    Moffett, M. W. Sociobiology of the Ants of the Genus Pheidologeton (Harvard Univ. Press, Cambridge, MA, 1988).

  31. 31.

    Czaczkes, T. & Ratnieks, F. L. Simple rules result in the adaptive turning of food items to reduce drag during cooperative food transport in the ant Pheidole oxyops. Insectes Soc. 58, 91–96 (2011).

    Google Scholar 

  32. 32.

    Berman, S., Lindsey, Q., Sakar, M. S., Kumar, V. & Pratt, S. Study of group food retrieval by ants as a model for multi-robot collective vtransport strategies. Robot. Proc. https://doi.org/10.15607/RSS.2010.VI.033 (2010).

  33. 33.

    Gelblum, A. et al. Ant groups optimally amplify the effect of transiently informed individuals. Nat. Commun. 6, 7729 (2015).

    ADS  Google Scholar 

  34. 34.

    Gelblum, A., Pinkoviezky, I., Fonio, E., Gov, N. S. & Feinerman, O. Emergent oscillations assist obstacle negotiation during ant cooperative transport. Proc. Natl Acad. Sci. USA 113, 14615–14620 (2016).

    ADS  Google Scholar 

  35. 35.

    McCreery, H. A comparative approach to cooperative transport in ants: individual persistence correlates with group coordination. Insectes Soc. 64, 535–547 (2017).

    Google Scholar 

  36. 36.

    Berman, S., Lindsey, Q., Sakar, M. S., Kumar, V. & Pratt, S. C. Experimental study and modeling of group retrieval in ants as an approach to collective transport in swarm robotic systems. Proc. IEEE 99, 1470–1481 (2011).

    Google Scholar 

  37. 37.

    Bialek, W. et al. Statistical mechanics for natural flocks of birds. Proc. Natl Acad. Sci. USA 109, 4786–4791 (2012).

    ADS  Google Scholar 

  38. 38.

    Mora, T. & Bialek, W. Are biological systems poised at criticality? J. Stat. Phys. 144, 268–302 (2011).

    ADS  MathSciNet  MATH  Google Scholar 

  39. 39.

    Bialek, W. et al. Social interactions dominate speed control in poising natural flocks near criticality. Proc. Natl Acad. Sci. USA 111, 7212–7217 (2014).

    ADS  Google Scholar 

  40. 40.

    Hidalgo, J. et al. Information-based fitness and the emergence of criticality in living systems. Proc. Natl Acad. Sci. USA 111, 10095–10100 (2014).

    ADS  Google Scholar 

  41. 41.

    Attanasi, A. et al. Finite-size scaling as a way to probe near-criticality in natural swarms. Phys. Rev. Lett. 113, 238102 (2014).

    ADS  Google Scholar 

  42. 42.

    Sumpter, D., Buhl, J., Biro, D. & Couzin, I. Information transfer in moving animal groups. Theory Biosci. 127, 177–186 (2008).

    Google Scholar 

  43. 43.

    Peeters, C. & De Greef, S. Predation on large millipedes and self-assembling chains in Leptogenys ants from Cambodia. Insectes Soc. 62, 471–477 (2015).

    Google Scholar 

  44. 44.

    Czaczkes, T. J., Vollet-NetoA. & Ratnieks, F. L. Prey escorting behavior and possible convergent evolution of foraging recruitment mechanisms in an invasive ant. Behav. Ecol. 24, 1177–1184 (2013).

    Google Scholar 

  45. 45.

    Trager, J. C. A revision of the genus Paratrechina (Hymenoptera: Formicidae) of the continental united states. Sociobiology 8, 49–162 (1984).

  46. 46.

    McCreery, H. F., Dix, Z. A., Breed, M. D. & Nagpal, R. Collective strategy for obstacle navigation during cooperative transport by ants. J. Exp. Biol. 219, 3366–3375 (2016).

    Google Scholar 

  47. 47.

    Fonio, E. et al. A locally-blazed ant trail achieves efficient collective navigation despite limited information. eLife 5, e20185 (2016).

    Google Scholar 

  48. 48.

    Simons, A. M. Many wrongs: the advantage of group navigation. Trends Ecol. Evol. 19, 453–455 (2004).

    Google Scholar 

  49. 49.

    Galton, F. Vox populi (the wisdom of crowds). Nature 75, 450–451 (1907).

    ADS  MATH  Google Scholar 

  50. 50.

    Faria, J. J., Codling, E. A., Dyer, J. R., Trillmich, F. & Krause, J. Navigation in human crowds; testing the many-wrongs principle. Anim. Behav. 78, 587–591 (2009).

    Google Scholar 

  51. 51.

    Hancock, W. O. Bidirectional cargo transport: moving beyond tug of war. Nat. Rev. Mol. Cell Biol. 15, 615–628 (2014).

    Google Scholar 

  52. 52.

    Hendricks, A. G. et al. Motor coordination via a tug-of-war mechanism drives bidirectional vesicle transport. Curr. Biol. 20, 697–702 (2010).

    Google Scholar 

  53. 53.

    Mobilia, M. Does a single zealot affect an infinite group of voters? Phys. Rev. Lett. 91, 028701 (2003).

    ADS  Google Scholar 

  54. 54.

    Hartnett, A. T., Schertzer, E., Levin, S. A. & Couzin, I. D. Heterogeneous preference and local nonlinearity in consensus decision making. Phys. Rev. Lett. 116, 038701 (2016).

    ADS  Google Scholar 

  55. 55.

    Wehner, R. Desert ant navigation: how miniature brains solve complex tasks. J. Comp. Physiol. A 189, 579–588 (2003).

    ADS  Google Scholar 

  56. 56.

    Razin, N., Eckmann, J.-P. & Feinerman, O. Desert ants achieve reliable recruitment across noisy interactions. J. R. Soc. Interface 10, 20130079 (2013).

    Google Scholar 

  57. 57.

    Robson, S. K. & Traniello, J. F. Transient division of labor and behavioral specialization in the ant Formica schaufussi. Naturwissenschaften 89, 128–131 (2002).

    ADS  Google Scholar 

  58. 58.

    Feinerman, O. in Landscapes of Collectivity in the Life Sciences (eds Gissis, S. et al.) Ch. 4 (MIT Press, Cambridge, MA, 2018).

  59. 59.

    Ludwig, M. & Marquardt, F. Quantum many-body dynamics in optomechanical arrays. Phys. Rev. Lett. 111, 073603 (2013).

    ADS  Google Scholar 

  60. 60.

    Chan, C.-K., Lee, T. E. & Gopalakrishnan, S. Limit-cycle phase in driven-dissipative spin systems. Phys. Rev. A 91, 051601 (2015).

    ADS  Google Scholar 

  61. 61.

    D’Ettorre, P. & Heinze, J. Sociobiology of slave-making ants. Acta Ethol. 3, 67–82 (2001).

    Google Scholar 

  62. 62.

    Ward, P. S. & Branstetter, M. G. The acacia ants revisited: convergent evolution and biogeographic context in an iconic ant/plant mutualism. Proc. R. Soc. B 284, 1850 (2017).

  63. 63.

    Deneubourg, J.-L., Pasteels, J. M. & Verhaeghe, J.-C. Probabilistic behaviour in ants: a strategy of errors? J. Theor. Biol. 105, 259–271 (1983).

    Google Scholar 

  64. 64.

    Müller, M. & Wehner, R. Path integration in desert ants, Cataglyphis fortis. Proc. Natl Acad. Sci. USA 85, 5287–5290 (1988).

    ADS  Google Scholar 

  65. 65.

    Rauch, E. M., Millonas, M. M. & Chialvo, D. R. Pattern formation and functionality in swarm models. Phys. Lett. A 207, 185–193 (1995).

    ADS  MathSciNet  MATH  Google Scholar 

  66. 66.

    Daniels, B. C., Krakauer, D. C. & Flack, J. C. Control of finite critical behaviour in a small-scale social system. Nat. Commun. 8, 14301 (2017).

  67. 67.

    Langton, C. G. Computation at the edge of chaos: phase transitions and emergent computation. Phys. D 42, 12–37 (1990).

    MathSciNet  Google Scholar 

  68. 68.

    Kabla, A. J. Collective cell migration: leadership, invasion and segregation. J. R. Soc. Interface 9, 3268–3278 (2012).

    Google Scholar 

  69. 69.

    Szabo, B. et al. Phase transition in the collective migration of tissue cells: experiment and model. Phys. Rev. E 74, 061908 (2006).

    ADS  Google Scholar 

  70. 70.

    Goldberg, J. A., Rokni, U. & Sompolinsky, H. Patterns of ongoing activity and the functional architecture of the primary visual cortex. Neuron 42, 489–500 (2004).

    Google Scholar 

  71. 71.

    Green, J. et al. A neural circuit architecture for angular integration in Drosophila. Nature 546, 101–106 (2017).

    ADS  Google Scholar 

  72. 72.

    Kube, C. R. & Bonabeau, E. Cooperative transport by ants and robots. Robot. Auton. Syst. 30, 85–101 (2000).

    Google Scholar 

  73. 73.

    Iqbal, T., Rack, S. & Riek, L. D. Movement coordination in human–robot teams: A dynamical systems approach. IEEE Trans. Robot. 32, 909–919 (2016).

    Google Scholar 

  74. 74.

    Wilson, S. et al. Design of ant-inspired stochastic control policies for collective transport by robotic swarms. Swarm Intell. 8, 303–327 (2014).

    Google Scholar 

  75. 75.

    Wang, Z. & Schwager, M. in Distributed Autonomous Robotic Systems (eds Chong, N.-Y., Cho, Y.-J.) 135–149 (Springer, Berlin, Heidelberg, 2016).

  76. 76.

    Wang, Z. & Schwager, M. Kinematic multi-robot manipulation with no communication using force feedback. In Proc. 2016 IEEE International Conference on Robotics and Automation (ICRA) 427–432 (IEEE, 2016).

  77. 77.

    Realpe-Gómez, J., Andrighetto, G., Nardin, G. & Montoya, J. A. Balancing selfishness and norm conformity can explain human behavior in large-scale Prisoner's Dilemma games and can poise human groups near criticality. Preprint at https://arxiv.org/abs/1608.01291 (2016).

  78. 78.

    Lehmann, O. F. Situational Project Management: The Dynamics of Success and Failure (CRC Press, Boca Raton, FL, 2016).

  79. 79.

    Puranam, P. When will we stop studying innovations in organizing, and start creating them? Innovation 19, 5–10 (2017).

    Google Scholar 

  80. 80.

    Detrain, C. & Deneubourg, J.-L. Self-organized structures in a superorganism: do ants “behave” like molecules? Phys. Life Rev. 3, 162–187 (2006).

    ADS  Google Scholar 

  81. 81.

    Gillespie, D. T. Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81, 2340–2361 (1977).

    Google Scholar 

Download references

Acknowledgements

We would like to thank Ehud Altman for useful discussions. N.S.G. is the incumbent of the Lee and William Abramowitz Professorial Chair of Biophysics and is supported by the Israel Science Foundation (ISF) (grant no. 580/12), and Minerva Foundation research grant no. 712601. O.F. is the incumbent of the Shloimo and Michla Tomarin Career Development Chair and was supported by the Israeli Science Foundation grant 833/15, and the European Research Council under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 770964), and the Clore Duffield Foundation. E.F. is the incumbent of the Tom Beck Research Fellow Chair in the Physics of Complex Systems.

Author information

Affiliations

Authors

Contributions

All authors have contributed to the writing and figure preparation of this review.

Corresponding author

Correspondence to Ofer Feinerman.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Feinerman, O., Pinkoviezky, I., Gelblum, A. et al. The physics of cooperative transport in groups of ants. Nature Phys 14, 683–693 (2018). https://doi.org/10.1038/s41567-018-0107-y

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing