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Individual-Level Discrimination in Song Characteristics of White-Rumped Shama, Copsychus malabaricus


Affiliations
1 Zoological Survey of India, Northern Regional Centre, Kaulagarh Road, Dehradun 248 195, India
2 Forest Research Institute, Kaulagarh Road, Dehradun 248 195, India
3 Department of Biology, University of Turuku, Fl-20014, Turuku, Finland
 

Passerines exhibit considerable variations in song characteristics, with crucial ecological, behavioural and conservation implications. We documented the individual variations and level of distinctness in the song characteristics of white-rumped shama, Copsychus malabaricus. Bioacoustic analysis revealed that songs were composed of repeated strophes (2–5 types per individual) with occasional whistles/trills or orphan syllables, made up of 3–13 elements, with dissimilar structures. Data analysis using 373 strophes from 22 males showed that all 8 variables (minimum, maximum, dominant and range of frequency, strophe duration, song rate, number of elements per strophe and type of element per strophe) differed significantly (P < 0.001). Song variations were higher among individuals compared to within individuals. The principal component analysis explained 56.87% variance. Discriminant function analysis exhibited 69.41% cumulative variance with 40.37% classification accuracy. The present study may be useful for further research on some other attributes of a song such as repertoire size, microstructure and seasonal/diurnal variations.

Keywords

Bioacoustic Analysis, Copsychus malabaricus, Discriminate Function Analysis, Principal Component Analysis, Song Distinctness.
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  • Linhart, P. and Šálek, M., The assessment of biases in the acoustic discrimination of individuals. PLoS ONE, 2017, 12, e0177206; https://doi.org/10.1371/journal.pone.0177206

  • Ranjard, L., Withers, S. J., Brunton, D. H., Parsons, S. and Ross, H. A., Geographic patterns of song variation reveal timing of song acquisition in wild avian population. Behav. Ecol., 2017, 28, 1085–1092.

  • Thompson, M. J., Pearse, K. A. and Foote, J. R., Seasonal and diel plasticity of song type use in individual ovenbirds (Seiurus aurocapilla). Ethology, 2020, 126, 824–838.

  • Chen, G., Xia, C. and Zhang, Y., Individual identification of birds with complex songs: the case of green-backed flycatchers Ficedula elisae. Behav. Process., 2020, 173, 104063.

  • Axelrod, R. and Hamilton, W. D., The evolution of cooperation. Science, 1981, 211, 1390–1396.

  • Godard, R., Tit for tat among neighbouring hooded warblers. Behav. Ecol. Sociobiol., 1993, 33, 45–50.

  • Naguib, M., Diehl, J., van Ores, K. and Sniders, L., Repeatability of signalling traits in the avian dawn chorus. Front. Zool., 2019, 16, 1–11.

  • Terry, A. M. R., Peake, T. M. and McGregor, P. K., The role of vocal individuality in conservation. Front. Zool., 2005, 2, 10–25; doi:10. 1186/1742-9994-2-10.

  • Beecher, M. D., Bird song learning as a social process. Anim. Behav., 2017, 106, 233–246.

  • Xia, C., Huang, R., Wei, C., Nie, P. and Zhang, Y., Individual identification on the basis of the songs of the Asian stubtail (Urosphena sqameiceps). Chin. Birds, 2011, 2, 132–139.

  • Xia, C., Xiao, H. and Zhang, Y., Individual variation in brownish-flanked bush warbler songs. Condor, 2010, 112, 591–595.

  • Rabiner, L. R. and Juang, B. H., An introduction to hidden Markov models. IEEE ASSP Mag., 1986, 3, 4–16.

  • Potter, J. R., Mellinger, D. K. and Clark, C. W., Marine mammal call discrimination using artificial neural networks. J. Acoust. Soc. Am., 1994, 96, 1255–1262.

  • Fox, E. J. S., A new perspective on acoustic individual recognition in animals with limited call sharing or changing repertoires. Anim. Behav., 2008, 75, 1187–1194.

  • Petrusková, T., Osiejuk, T. S. and Petrusek, A., Geographic variation in songs of the tree pipit (Anthus trivialis) at two spatial scales. Auk, 2010, 127, 274–282.

  • Charrier, I., Mathevon, N. and Jouventin P., Individuality in the voice of fur seal females: an analysis study of the pup attraction call in Arctocephalus tropicalis. Mar. Mamm. Sci., 2003, 19, 161–172.

  • Li, Y., Xia, C., Lloyd, H., Li, D. and Zhang, Y., Identification of vocal individuality in male cuckoos using different analytical techniques. Avian Res., 2017, 8, 1–7.

  • Deng, Z., Lloyd, H., Xia, C., Li, D. and Zhang, Y., Within-season decline in call consistency of individual male common cuckoos (Cuculus canorus). J. Ornithol., 2019, 160, 317–327.

  • Aguon, C. F. and Conant, S., Breeding biology of the white-rumped shama on Oahu, Hawaii. Wilson Bull., 1994, 106, 311–328.

  • Ali, S. and Ripley, S. D., Handbook of the Birds of India and Pakistan, Robins to Wagtails, Oxford University Press, Bombay, 1973, vol. 9.

  • Rasmussen, P. C. and Anderton, J. C., Birds of South Asia. The Ripley Guide. Vol. 1, Smithsonian Institution and Lynx Edicions, Washington DC, USA and Barcelona, Spain, 2005.

  • Fan, M.-W., Lin, R. S., Fang, W. and Lin, Y.-H., The distribution and abundance of the alien invasive white-rumped shama (Copsychus malabaricus) in Taiwan. Taiwania, 2009, 54, 248–254.

  • Kumar, A., Physical characteristics, categories and functions of song in Indian robin Saxicoloides fulicata (Aves: Muscicapidae). J. Threat. Taxa, 2011, 3, 1909–1918.

  • Champion, H. G. and Seth, S. K., A Revised Forest Types of India, Manager of Publications, Delhi, 1968.

  • R Core Team, R., A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2020; https://www.R-project.org/.

  • Kassambara, A., rstatix: Pipe-friendly framework for basic statistical tests. R package version 0.7.0, 2021; https://CRAN.R-project.org/package=rstatix

  • Ben-Shachar, M., Lüdecke, D. and Makowski, D., Effect size: estimation of effect size indices and standardized parameters. J. Open Source Softw., 2020, 5, 2815; doi:10.21105/joss.02815.

  • Venables, W. N. and Ripley, B. D., Modern Applied Statistics with S, Springer, New York, USA, 2002, 4th edn.

  • Wallschlager, D., Correlation of song frequency and body weight in passerine birds. Experientia, 1980, 36, 412.

  • Ryan, M. J. and Brenowitz, E. A., The role of body size, phylogeny, and ambient noise in the evolution of bird song. Am. Nat., 1985, 126, 87–100.

  • Mikula, P. et al., A global analysis of song frequency in passerines provides no support for the acoustic adaptation hypothesis but suggests a role for sexual selection. Ecol. Lett., 2021, 24, 477–486.

  • Morton, E. S., Ecological sources of selection on avian sounds. Am. Nat., 1975, 109, 17–34.

  • Ey, E. and Fischer, J., The ‘acoustic adaptation hypothesis’ – a review of the evidence from birds, anurans and mammals. Bioacoustics, 2009, 19, 21–48.

  • Pearse, W. D., Morales-Castilla, I., James, L. S., Farrell, M., Boivin, F. and Davies, T. J., Global macroevolution and macroecology of passerine song. Evolution, 2018, 72, 944–960.

  • Price, J. J., Earnshaw, S. M. and Webster, M. S., Montezuma oropendolas modify a component of song constrained by body size during vocal contests. Anim. Behav., 2006, 71, 799–807.

  • Lein, M. R., Song variation in buff-breasted flycatchers (Empidonax fulvifrons). Wilson J. Ornithol., 2008, 120, 256–267.

  • Tao, J., Johnson, M. T. and Osiejuk, T. S., Acoustic model adaptation for ortolan bunting (Emberiza hortulana L.) song-type classification. J. Acoust. Soc. Am., 2008, 123, 1582–1590.

  • Foote, J. R., Palazzi, E. and Mennill, D. J., Songs of the Eastern Phoebe, a suboscine songbird, are individually distinctive but do not vary geographically. Bioacoustics, 2013, 22, 137–151.

  • Bee, M. A. and Gerhardt, H. C., Individual voice recognition in a territorial frog (Rana catesbeiana). Proc. R. Soc. London, Ser. B, 2002, 269, 1443–1448.

  • Yovel, Y., Melcon, M. L., Franz, M. O., Denzinger, A. and Schnitzler, H.-U., The voice of bats: how greater mouse-eared bats recognize individuals based on their echolocation calls. PLOS Comput. Biol., 2009, 5, e1000400; doi:org/10.1371/journal.pcbi.1000400.

  • Deoniziak, K. and Osiejuk, T. S., Habitat-related differences in song structure and complexity in a songbird with a large repertoire. BMC Ecol., 2019, 19, 1–11; https://doi.org/10.1186/s12898-019-0255-7

  • Najar, N. and Benedict, L., The relationship between latitude, migration and the evolution of bird song complexity. Ibis, 2019, 161, 1–12.


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  • Individual-Level Discrimination in Song Characteristics of White-Rumped Shama, Copsychus malabaricus

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Authors

Anil Kumar
Zoological Survey of India, Northern Regional Centre, Kaulagarh Road, Dehradun 248 195, India
Himanshu
Forest Research Institute, Kaulagarh Road, Dehradun 248 195, India
Prakhar Rawal
Department of Biology, University of Turuku, Fl-20014, Turuku, Finland

Abstract


Passerines exhibit considerable variations in song characteristics, with crucial ecological, behavioural and conservation implications. We documented the individual variations and level of distinctness in the song characteristics of white-rumped shama, Copsychus malabaricus. Bioacoustic analysis revealed that songs were composed of repeated strophes (2–5 types per individual) with occasional whistles/trills or orphan syllables, made up of 3–13 elements, with dissimilar structures. Data analysis using 373 strophes from 22 males showed that all 8 variables (minimum, maximum, dominant and range of frequency, strophe duration, song rate, number of elements per strophe and type of element per strophe) differed significantly (P < 0.001). Song variations were higher among individuals compared to within individuals. The principal component analysis explained 56.87% variance. Discriminant function analysis exhibited 69.41% cumulative variance with 40.37% classification accuracy. The present study may be useful for further research on some other attributes of a song such as repertoire size, microstructure and seasonal/diurnal variations.

Keywords


Bioacoustic Analysis, Copsychus malabaricus, Discriminate Function Analysis, Principal Component Analysis, Song Distinctness.

References





DOI: https://doi.org/10.18520/cs%2Fv125%2Fi1%2F59-65