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Artificial Life and Robotics

, Volume 24, Issue 4, pp 439–444 | Cite as

Complex systems approaches to temporal soundspace partitioning in bird communities as a self-organizing phenomenon based on behavioral plasticity

  • Reiji SuzukiEmail author
  • Martin L. Cody
Invited Article
  • 14 Downloads

Abstract

This paper introduces our several preliminary approaches toward understanding temporal soundspace partitioning in bird communities as a self-organizing phenomenon based on behavioral plasticity. First, we describe this phenomenon from our recordings, and show there are asymmetric relationships and the diversity in the temporal avoidance behaviors among the species, using transfer entropy analysis. Then, we consider the evolutionary significance of such a diversity using a computational experiment of the coevolution of the temporal overlap avoidance of singing behaviors among sympatric species with different species-specific song lengths, implying that diversity in the behavioral plasticity in bird communities can contribute to the more efficient establishment of the soundspace partitioning. Finally, we introduce our preliminary works on extracting the temporal dynamics of interaction processes among multiple birds from recordings with a microphone array using an open-source software system for robot audition called HARK.

Keywords

Temporal soundspace partitioning Bird songs Agent-based model Sound source localization HARK Artificial Life 

Notes

Acknowledgements

The authors thank Charles E. Taylor and Takaya Arita for valuable comments and suggestions, Hiroshi G. Okuno and HARK developer team for supports for localization trials with HARK, and Iain McCowan for supports for the use of Microcone. This work was supported in part by JSPS/MEXT KAKENHI: JP24220006, JP18K11467 and JP17H06383 in #4903 (Evolinguistics).

References

  1. 1.
    Cody ML, Brown JH (1969) Song asynchrony in neighbouring bird species. Nature 222:778–780CrossRefGoogle Scholar
  2. 2.
    Ficken RW, Ficken MS, Hailman JP (1974) Temporal pattern shifts to avoid acoustic interference in singing birds. Science 183(4126):762–763CrossRefGoogle Scholar
  3. 3.
    Popp JW, Ficken RW, Reinartz JA (1985) Short-term temporal avoidance of interspecific acoustic interference among forest birds. Auk 102:744–748Google Scholar
  4. 4.
    Brumm H (2006) Signalling through acoustic windows: nightingales avoid interspecific competition by short-term adjustment of song timing. J Comp Physiol A Neuroethol 192:1279–1285CrossRefGoogle Scholar
  5. 5.
    Planqué R, Slabbekoorn H (2008) Spectral overlap in songs and temporal avoidance in a Peruvian bird assemblage. Ethology 114:262–271CrossRefGoogle Scholar
  6. 6.
    Suzuki R, Taylor CE, Cody ML (2012) Soundscape partitioning to increase communication efficiency in bird communities. Artif Life Robot 17(1):30–34CrossRefGoogle Scholar
  7. 7.
    Yang X, Ma X, Slabbekoorn H (2014) Timing vocal behaviour: experimental evidence for song overlap avoidance in Eurasian wrens. Behav Process 103:84–90CrossRefGoogle Scholar
  8. 8.
    Schwartz JJ, Wells KD (1984) Interspecific acoustic interactions of the neotropical treefrog hyla ebraccata. Behav Ecol Sociobiol 14(3):211–224CrossRefGoogle Scholar
  9. 9.
    Aihara I, Mizumoto T, Otsuka T, Awano H, Nagira K, Okuno HG, Aihara K (2014) Spatio-temporal dynamics in collective frog choruses examined by mathematical modeling and field observations. Sci Rep 4:3891CrossRefGoogle Scholar
  10. 10.
    Greenfield MD (1988) Interspecific acoustic interactions among katydids neoconocephalus: inhibition-induced shifts in diel periodicity. Anim Behav 36(3):684–695CrossRefGoogle Scholar
  11. 11.
    Degesys J, Rose I, Patel A, Nagpal R (2007) DESYNC: self-organizing desynchronization and TDMA on wireless sensor networks. In: International conference on information processing in sensor networks (IPSN). IEEE Press, pp 11–20Google Scholar
  12. 12.
    Tobias JA, Planqué R, Cram DL, Seddon N (2014) Species interactions and the structure of complex communication networks. Proc Natl Acad Sci 111(3):1020–1025CrossRefGoogle Scholar
  13. 13.
    West-Eberhard MJ (2003) Developmental plasticity and evolution. Oxford University Press, OxfordGoogle Scholar
  14. 14.
    Gilbert SF, Epel D (2009) Ecological developmental biology: integrating epigenetics, medicine, and evolution. Sinauer Associates, SunderlandGoogle Scholar
  15. 15.
    Hinton GE, Nowlan SJ (1987) How learning can guide evolution. Complex Syst 1:495–502zbMATHGoogle Scholar
  16. 16.
    Suzuki R, Arita T (2013) A simple computational model of the evolution of a communicative trait and its phenotypic plasticity. J Theor Biol 330(7):37–44CrossRefGoogle Scholar
  17. 17.
    Suzuki R, Arita T (2014) Emergence of a dynamic resource partitioning based on the coevolution of phenotypic plasticity in sympatric species. J Theor Biol 352:51–59CrossRefGoogle Scholar
  18. 18.
    Nakadai K, Takahashi T, Okuno HG, Nakajima H, Hasegawa Y, Tsujino H (2010) Design and implementation of robot audition system ’HARK’—open source software for listening to three simultaneous speakers. Adv Robot 24:739–761CrossRefGoogle Scholar
  19. 19.
    Catchpole CK, Slater PJB (2008) Bird song: biological themes and variations. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  20. 20.
    Schreiber T (2000) Measuring information transfer. Phys Rev Lett 85:461–464CrossRefGoogle Scholar
  21. 21.
    Marschinski R, Kantz H (2002) Analysing the information flow between financial time series: an improved estimator for transfer entropy. Eur Phys J B 30:275–281MathSciNetCrossRefGoogle Scholar
  22. 22.
    Suzuki R, Sumitani S, Naren Matsubayashi S, Arita T, Nakadai K, Okuno HG (2018) Field observations of ecoacoustic dynamics of a Japanese bush warbler using an open-source software for robot audition HARK. J Ecoacoust 2:EYAJ46 (11 pages) CrossRefGoogle Scholar
  23. 23.
    Sumitani S, Suzuki R, Matsubayashi S, Arita T, Nakadai K, Okuno HG (2019) An integrated framework for field recording, localization, classification and annotation of birdsongs using robot audition techniques—HARKBird 2.0. In: Proceedings of IEEE international conference on acoustics, speech, and signal processing, pp 8246–8250Google Scholar
  24. 24.
    Collier TC, Kirschel ANG, Taylor CE (2010) Acoustic localization of antbirds in a Mexican rainforest using a wireless sensor network. J Acoust Soc Am 128:182–189CrossRefGoogle Scholar
  25. 25.
    Cai S, Collier T, Girod L, Hudson RE, Yao K, Taylor CE, Bao M (2013) Voxnet acoustic array for multiple bird source separation by beamforming using measured data. In: Proceedings of the 12th international conference on information processing in sensor networks (IPSN’13), pp 355–356Google Scholar

Copyright information

© International Society of Artificial Life and Robotics (ISAROB) 2019

Authors and Affiliations

  1. 1.Graduate School of InformaticsNagoya UniversityNagoyaJapan
  2. 2.Department of Ecology and Evolutionary BiologyUniversity of California, Los AngelesLos AngelesUSA

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