Skip to main content
Log in

Multifractal fluctuations in zebrafish (Danio rerio) polarization time series

  • Regular Article - Flowing Matter
  • Published:
The European Physical Journal E Aims and scope Submit manuscript

Abstract

In this work, we study the polarization time series obtained from experimental observation of a group of zebrafish (Danio rerio) confined in a circular tank. The complex dynamics of the individual trajectory evolution lead to the appearance of multiple characteristic scales. Employing the Multifractal Detrended Fluctuation Analysis (MF-DFA), we found distinct behaviors according to the parameters used. The polarization time series are multifractal at low fish densities and their average scales with \(\rho ^{-1/4}\). On the other hand, they tend to be monofractal, and their average scales with \(\rho ^{-1/2}\) for high fish densities. These two regimes overlap at critical density \(\rho _c\), suggesting the existence of a phase transition separating them. We also observed that for low densities, the polarization velocity shows a non-Gaussian behavior with heavy tails associated with long-range correlation and becomes Gaussian for high densities, presenting an uncorrelated regime.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

Data will be made available on request.

References

  1. G. Popkin, The physics of life. Nature 529(7584), 16–18 (2016). https://doi.org/10.1038/529016a

    Article  ADS  Google Scholar 

  2. C. Bechinger, R. Di Leonardo, H. Löwen, C. Reichhardt, G. Volpe, G. Volpe, Active particles in complex and crowded environments. Rev. Mod. Phys. 88, 045006 (2016). https://doi.org/10.1103/RevModPhys.88.045006

    Article  ADS  MathSciNet  Google Scholar 

  3. M.J. Bowick, N. Fakhri, M.C. Marchetti, S. Ramaswamy, Symmetry, thermodynamics, and topology in active matter. Phys. Rev. X. 12, 010501 (2022). https://doi.org/10.1103/PhysRevX.12.010501

    Article  Google Scholar 

  4. M.C. Marchetti, J.F. Joanny, S. Ramaswamy, T.B. Liverpool, J. Prost, M. Rao, R.A. Simha, Hydrodynamics of soft active matter. Rev. Mod. Phys. 85, 1143–1189 (2013). https://doi.org/10.1103/RevModPhys.85.1143

    Article  ADS  Google Scholar 

  5. M.L. Manning, Essay: Collections of deformable particles present exciting challenges for soft matter and biological physics. Phys. Rev. Lett. 130, 130002 (2023). https://doi.org/10.1103/PhysRevLett.130.130002

    Article  ADS  Google Scholar 

  6. S. Ramaswamy, The mechanics and statistics of active matter. Annu. Rev. Condens. Matter Phys. 1(1), 323–345 (2010). https://doi.org/10.1146/annurev-conmatphys-070909-104101

    Article  ADS  Google Scholar 

  7. S.P. Thampi, R. Golestanian, J.M. Yeomans, Velocity correlations in an active nematic. Phys. Rev. Lett. 111, 118101 (2013). https://doi.org/10.1103/PhysRevLett.111.118101

    Article  ADS  Google Scholar 

  8. E. Fodor, C. Nardini, M.E. Cates, J. Tailleur, P. Visco, F. Wijland, How far from equilibrium is active matter? Phys. Rev. Lett. 117, 038103 (2016). https://doi.org/10.1103/PhysRevLett.117.038103

    Article  ADS  MathSciNet  Google Scholar 

  9. C. Becco, N. Vandewalle, J. Delcourt, P. Poncin, Experimental evidences of a structural and dynamical transition in fish school. Phys. A 367, 487–493 (2006). https://doi.org/10.1016/j.physa.2005.11.041

    Article  Google Scholar 

  10. N. Miller, R. Gerlai, From schooling to shoaling: Patterns of collective motion in zebrafish (danio rerio). PLOS ONE 7(11), 1–6 (2012). https://doi.org/10.1371/journal.pone.0048865

    Article  Google Scholar 

  11. J. Gautrais, F. Ginelli, R. Fournier, S. Blanco, M. Soria, H. Chaté, G. Theraulaz, Deciphering interactions in moving animal groups. PLoS Comput. Biol. 8(9), 1–11 (2012). https://doi.org/10.1371/journal.pcbi.1002678

    Article  MathSciNet  Google Scholar 

  12. L. Lei, R. Escobedo, C. Sire, G. Theraulaz, Computational and robotic modeling reveal parsimonious combinations of interactions between individuals in schooling fish. PLoS Comput. Biol. 16(3), 1–45 (2020). https://doi.org/10.1371/journal.pcbi.1007194

    Article  Google Scholar 

  13. G. Li, D. Kolomenskiy, H. Liu, R. Godoy-Diana, B. Thiria, Intermittent versus continuous swimming: An optimization tale. Phys. Rev. Fluids 8, 013101 (2023). https://doi.org/10.1103/PhysRevFluids.8.013101

    Article  ADS  Google Scholar 

  14. S. Butail, V. Mwaffo, M. Porfiri, Model-free information-theoretic approach to infer leadership in pairs of zebrafish. Phys. Rev. E 93, 042411 (2016). https://doi.org/10.1103/PhysRevE.93.042411

    Article  ADS  Google Scholar 

  15. T. Niizato, K. Sakamoto, Y.-I. Mototake, H. Murakami, T. Tomaru, T. Hoshika, T. Fukushima, Finding continuity and discontinuity in fish schools via integrated information theory. PLOS ONE 15(2), 1–29 (2020). https://doi.org/10.1371/journal.pone.0229573

    Article  Google Scholar 

  16. M. Porfiri, P. Zhang, S.D. Peterson, Hydrodynamic model of fish orientation in a channel flow. eLife 11, 75225 (2022). https://doi.org/10.7554/eLife.75225

    Article  Google Scholar 

  17. H. Murakami, T. Niizato, T. Tomaru, Y. Nishiyama, Y.-P. Gunji, Inherent noise appears as a lévy walk in fish schools. Sci. Rep. 5, 10605 (2015). https://doi.org/10.1038/srep10605

    Article  ADS  Google Scholar 

  18. J. Múgica, J. Torrents, J. Cristín, A. Puy, M.C. Miguel, R. Pastor-Satorras, Scale-free behavioral cascades and effective leadership in schooling fish. Sci. Rep. 12, 10783 (2022). https://doi.org/10.1038/s41598-022-14337-0

    Article  ADS  Google Scholar 

  19. W. Bialek, A. Cavagna, I. Giardina, T. Mora, E. Silvestri, M. Viale, A.M. Walczak, Statistical mechanics for natural flocks of birds. PNAS 109(13), 4786–4791 (2012). https://doi.org/10.1073/pnas.1118633109

    Article  ADS  Google Scholar 

  20. J. Dunkel, S. Heidenreich, K. Drescher, H.H. Wensink, M. Bär, R.E. Goldstein, Fluid dynamics of bacterial turbulence. Phys. Rev. Lett. 110, 228102 (2013). https://doi.org/10.1103/PhysRevLett.110.228102

    Article  ADS  Google Scholar 

  21. K.-A. Liu, I. Lin, Multifractal dynamics of turbulent flows in swimming bacterial suspensions. Phys. Rev. E 86, 011924 (2012). https://doi.org/10.1103/PhysRevE.86.011924

    Article  ADS  Google Scholar 

  22. K.V. Kiran, A. Gupta, A.K. Verma, R. Pandit, Irreversibility in bacterial turbulence: Insights from the mean-bacterial-velocity model. Phys. Rev. Fluids 8, 023102 (2023). https://doi.org/10.1103/PhysRevFluids.8.023102

    Article  ADS  Google Scholar 

  23. B. Szabó, G.J. Szöllösi, B. Gönci, Z. Jurányi, D. Selmeczi, T. Vicsek, Phase transition in the collective migration of tissue cells: experiment and model. Phys. Rev. E 74, 061908 (2006). https://doi.org/10.1103/PhysRevE.74.061908

    Article  ADS  Google Scholar 

  24. T. Vicsek, A. Czirók, E. Ben-Jacob, I. Cohen, O. Shochet, Novel type of phase transition in a system of self-driven particles. Phys. Rev. Lett. 75, 1226–1229 (1995). https://doi.org/10.1103/PhysRevLett.75.1226

    Article  ADS  MathSciNet  Google Scholar 

  25. O. Dauchot, V. Démery, Dynamics of a self-propelled particle in a harmonic trap. Phys. Rev. Lett. 122, 068002 (2019). https://doi.org/10.1103/PhysRevLett.122.068002

    Article  ADS  Google Scholar 

  26. L.H. Miranda-Filho, T.A. Sobral, A.J.F. Souza, Y. Elskens, A.R.C. Romaguera, Lyapunov exponent in the vicsek model. Phys. Rev. E (2022). https://doi.org/10.1103/physreve.105.014213

    Article  MathSciNet  Google Scholar 

  27. K. Tunstrøm, Y. Katz, C.C. Ioannou, C. Huepe, M.J. Lutz, I.D. Couzin, Collective states, multistability and transitional behavior in schooling fish. PLoS Comput. Biol. 9(2), 1002915 (2013). https://doi.org/10.1371/journal.pcbi.1002915

    Article  ADS  MathSciNet  Google Scholar 

  28. H.-S. Niwa, School size statistics of fish. J. Theor. Biol. 195(3), 351–361 (1998). https://doi.org/10.1006/jtbi.1998.0801

    Article  ADS  Google Scholar 

  29. T.J. Pitcher, Heuristic definitions of fish shoaling behaviour. Anim. Behav. 31(2), 611–613 (1983). https://doi.org/10.1016/s0003-3472(83)80087-6

    Article  Google Scholar 

  30. H. Kunz, C.K. Hemelrijk, Artificial fish schools: collective effects of school size, body size, and body form. Artif. Life 9(3), 237–253 (2003). https://doi.org/10.1162/106454603322392451. (Cited by: 113; All Open Access, Green Open Access)

    Article  Google Scholar 

  31. A. Czirók, T. Vicsek, Collective behavior of interacting self-propelled particles. Phys. A 281(1), 17–29 (2000). https://doi.org/10.1016/S0378-4371(00)00013-3

    Article  Google Scholar 

  32. J. Zhang, M. Small, Complex network from pseudoperiodic time series: topology versus dynamics. Phys. Rev. Lett. 96, 238701 (2006). https://doi.org/10.1103/PhysRevLett.96.238701

    Article  ADS  Google Scholar 

  33. A. Vespignani, Modelling dynamical processes in complex socio-technical systems. Nat. Phys. 8(1), 32–39 (2011). https://doi.org/10.1038/nphys2160

    Article  Google Scholar 

  34. H.E. Stanley, V. Afanasyev, L.A.N. Amaral et al., Anomalous fluctuations in the dynamics of complex systems: from DNA and physiology to econophysics. Phys. A 224(1–2), 302–321 (1996). https://doi.org/10.1016/0378-4371(95)00409-2

    Article  Google Scholar 

  35. L. Zhao, W. Li, C. Yang, J. Han, Z. Su, Y. Zou, Multifractality and network analysis of phase transition. PLOS ONE 12(1), 1–23 (2017). https://doi.org/10.1371/journal.pone.0170467

    Article  Google Scholar 

  36. B.B. Mandelbrot, Intermittent turbulence in self-similar cascades: divergence of high moments and dimension of the carrier. J. Fluid Mech. 62(2), 331–358 (1974). https://doi.org/10.1017/S0022112074000711

    Article  ADS  Google Scholar 

  37. A.S.A. da Silva, T. Stosic, I. Arsenić, R.S.C. Menezes, B. Stosic, Multifractal analysis of standardized precipitation index in northeast brazil. Chaos Solit. Fract. 172, 113600 (2023). https://doi.org/10.1016/j.chaos.2023.113600

    Article  MathSciNet  Google Scholar 

  38. S.A. Nejad, T. Stosic, B. Stosic, Multifractal analysis of the gold market. Fractals 29(01), 2150010 (2021). https://doi.org/10.1142/S0218348X21500109

    Article  ADS  Google Scholar 

  39. F. Evers, A.D. Mirlin, Anderson transitions. Rev. Mod. Phys. 80, 1355–1417 (2008). https://doi.org/10.1103/RevModPhys.80.1355

    Article  ADS  Google Scholar 

  40. A.L.R. Barbosa, T.H.V. Lima, I.R.R. González, N.L. Pessoa, A.M.S. Macêdo, G.L. Vasconcelos, Turbulence hierarchy and multifractality in the integer quantum hall transition. Phys. Rev. Lett. 128, 236803 (2022). https://doi.org/10.1103/PhysRevLett.128.236803

    Article  ADS  Google Scholar 

  41. K.R. Amin, R. Nagarajan, R. Pandit, A. Bid, Multifractal conductance fluctuations in high-mobility graphene in the integer quantum hall regime. Phys. Rev. Lett. 129, 186802 (2022). https://doi.org/10.1103/PhysRevLett.129.186802

    Article  ADS  Google Scholar 

  42. H.J. Tanna, K.N. Pathak, Multifractality due to long-range correlation in the l-band ionospheric scintillation s 4 index time series. Astrophys. Space Sci. 350(1), 47–56 (2013). https://doi.org/10.1007/s10509-013-1742-5

    Article  ADS  Google Scholar 

  43. N.B. Padhan, R. Pandit, Activity-induced droplet propulsion and multifractality. Phys. Rev. Res. 5, 032013 (2023). https://doi.org/10.1103/PhysRevResearch.5.L032013

    Article  Google Scholar 

  44. J. Kwapień, P. Blasiak, S. Drożdż, P. Oświecimka, Genuine multifractality in time series is due to temporal correlations. Phys. Rev. E (2023). https://doi.org/10.1103/PhysRevE.107.034139

    Article  MathSciNet  Google Scholar 

  45. H. Suyari, Mathematical structures derived from the q-multinomial coefficient in tsallis statistics. Phys. A 368(1), 63–82 (2006). https://doi.org/10.1016/j.physa.2005.12.061

    Article  MathSciNet  Google Scholar 

  46. J.W. Kantelhardt, S.A. Zschiegner, E. Koscielny-Bunde, S. Havlin, A. Bunde, H.E. Stanley, Multifractal detrended fluctuation analysis of nonstationary time series. Phys. A 316(1), 87–114 (2002). https://doi.org/10.1016/S0378-4371(02)01383-3

    Article  Google Scholar 

  47. G.J. Lieschke, P.D. Currie, Animal models of human disease: zebrafish swim into view. Nat. Rev. Genet. 8(5), 353–367 (2007). https://doi.org/10.1038/nrg2091

    Article  Google Scholar 

  48. S. Shishis, B. Tsang, R. Gerlai, The effect of fish density and tank size on the behavior of adult zebrafish: a systematic analysis. Front. Behav. Neurosci. (2022). https://doi.org/10.3389/fnbeh.2022.934809

    Article  Google Scholar 

  49. S. Macrì, D. Neri, T. Ruberto, V. Mwaffo, S. Butail, M. Porfiri, Three-dimensional scoring of zebrafish behavior unveils biological phenomena hidden by two-dimensional analyses. Sci. Rep. (2017). https://doi.org/10.1038/s41598-017-01990-z

    Article  Google Scholar 

  50. A. Laan, R. Sagredo, G.G. Polavieja, Signatures of optimal control in pairs of schooling zebrafish. Proc. R. Soc. B Biol. Sci. 284(1852), 20170224 (2017). https://doi.org/10.1098/rspb.2017.0224

    Article  Google Scholar 

  51. OECD: Education at a Glance 2013: OECD Indicators. OECD Publishing (2013). https://doi.org/10.1787/eag-2013-en

  52. A. Pérez-Escudero, J. Vicente-Page, R.C. Hinz, S. Arganda, G.G. Polavieja, idtracker: tracking individuals in a group by automatic identification of unmarked animals. Nat. Methods. 11, 743 (2014). https://doi.org/10.1038/nmeth.2994

    Article  Google Scholar 

  53. B.L. Partridge, Internal dynamics and the interrelations of fish in schools. J. Comp. Physiol. 144(3), 313–325 (1981). https://doi.org/10.1007/bf00612563

    Article  Google Scholar 

  54. K. Kremer, J.W. Lyklema, Monte Carlo series analysis of irreversible self-avoiding walks. I. the indefinitely-growing self-avoiding walk (IGSAW). J. Phys. A Math. Gener. 18(9), 1515 (1985). https://doi.org/10.1088/0305-4470/18/9/031

    Article  ADS  Google Scholar 

  55. V. Pipiras, M.S. Taqqu, Long-Range Dependence and Self-Similarity (Cambridge University Press, Cambridge, 2017). https://doi.org/10.1017/cbo9781139600347

    Book  Google Scholar 

  56. N.L. Pessoa, A.L.R. Barbosa, G.L. Vasconcelos, A.M.S. Macedo, Multifractal magnetoconductance fluctuations in mesoscopic systems. Phys. Rev. E 104, 054129 (2021). https://doi.org/10.1103/PhysRevE.104.054129

    Article  ADS  Google Scholar 

  57. Y. Wang, W. Liu, J. Yang, F. Wang, Y. Sima, Z.-M. Zhong, H. Wang, L.-F. Hu, C.-F. Liu, Parkinson’s disease-like motor and non-motor symptoms in rotenone-treated zebrafish. NeuroToxicology 58, 103–109 (2017). https://doi.org/10.1016/j.neuro.2016.11.006

    Article  Google Scholar 

  58. A.L. Andrade, R. Silva, P. Soares, T. Santos, R. Padilha, P. Bastos, P. Cadena, Evaluation of toxicity and non-motor symptoms of parkinson-like induced by rotenone in zebrafish animal model (2023) https://doi.org/10.21203/rs.3.rs-2440652/v1

Download references

Acknowledgements

This work was supported by CAPES (Coordenação de Aper-feiçoamento de Pessoal de Nível Superior), CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) and FACEPE (Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco).

Author information

Authors and Affiliations

Authors

Contributions

A. R. de C. Romaguera conceived the experimental procedure, analyzed the data, and wrote the manuscript. J. V. A. Vasconcelos and Luis G. Negreiros-Neto conducted the experiments and analyzed the data. N. L. Pessoa analyzed the data and contributed to manuscript writing. J. F. da Silva and P. G. Cadena provided the animals and oversaw the ethical committee for experiments involving living animals. Adauto J. F. de Souza, Viviane M. de Oliveira, and Anderson L. R. Barbosa analyzed the data and contributed to manuscript writing.

Corresponding author

Correspondence to Antonio R. de C. Romaguera.

Ethics declarations

Conflict of interest

The author declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Romaguera, A.R.d.C., Vasconcelos, J.V.A., Negreiros-Neto, L.G. et al. Multifractal fluctuations in zebrafish (Danio rerio) polarization time series. Eur. Phys. J. E 47, 29 (2024). https://doi.org/10.1140/epje/s10189-024-00423-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1140/epje/s10189-024-00423-w

Navigation