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Assessing vocal performance in complex birdsong: a novel approach

Overview of attention for article published in BMC Biology, August 2014
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Title
Assessing vocal performance in complex birdsong: a novel approach
Published in
BMC Biology, August 2014
DOI 10.1186/s12915-014-0058-4
Pubmed ID
Authors

Nicole Geberzahn, Thierry Aubin

Abstract

BackgroundVocal performance refers to the ability to produce vocal signals close to physical limits. Such motor skills can be used by conspecifics to assess a signaller¿s competitive potential. For example it is difficult for birds to produce repeated syllables both rapidly and with a broad frequency bandwidth. Deviation from an upper-bound regression of frequency bandwidth on trill rate has been widely used to assess vocal performance. This approach is, however, only applicable to simple trilled songs, and even then may be affected by differences in syllable complexity.ResultsUsing skylarks (Alauda arvensis) as a birdsong model with a very complex song structure, we detected another performance trade-off: minimum gap duration between syllables was longer when the frequency ratio between the end of one syllable and the start of the next syllable (inter-syllable frequency shift) was large. This allowed us to apply a novel measure of vocal performance ¿ vocal gap deviation: the deviation from a lower-bound regression of gap duration on inter-syllable frequency shift. We show that skylarks increase vocal performance in an aggressive context suggesting that this trait might serve as a signal for competitive potential.ConclusionsWe suggest using vocal gap deviation in future studies to assess vocal performance in songbird species with complex structure.

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Geographical breakdown

Country Count As %
United States 3 3%
Brazil 2 2%
Canada 1 1%
Unknown 94 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 22%
Student > Master 19 19%
Researcher 14 14%
Student > Bachelor 10 10%
Student > Doctoral Student 8 8%
Other 14 14%
Unknown 13 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 56 56%
Environmental Science 11 11%
Psychology 6 6%
Neuroscience 2 2%
Biochemistry, Genetics and Molecular Biology 1 1%
Other 7 7%
Unknown 17 17%