In urban environments, anthropogenic noise may mask bird song, especially the notes occurring at lower frequencies (1–2 kHz). Birds living in urban environments may modify their songs, particularly the low-frequency portions, to minimize masking by anthropogenic noise. Such modifications have been observed in Great Tits (Parus major) in The Netherlands, as well as in some mammals. We studied Song Sparrows (Melospiza melodia), which are common in both urban and rural environments in much of North America, and recorded the songs of 28 free- living males in Portland, Oregon. We also measured the amplitude and spectrum of ambient noise at singing locations. Song Sparrows singing at noisier locations exhibited higher-frequency low notes and had relatively less energy (amplitude) in the low-frequency range of their songs (1–4 kHz), where most anthropogenic noise also occurred. Although the mechanism(s) producing the correlation are as yet undetermined, the observed match between song and noise may result from behavioral plasticity. We discuss explanations for these patterns and how to test them.
Le Chant de Melospiza melodia Varie avec le Bruit Urbain
The ability to generate variable movements is essential for learning and adjusting complex behaviours. This variability has been linked to the temporal irregularity of neuronal activity in the central nervous system. However, how neuronal irregularity actually translates into behavioural variability is unclear. Here we combine modelling, electrophysiological and behavioural studies to address this issue. We demonstrate that a model circuit comprising topographically organized and strongly recurrent neural networks can autonomously generate irregular motor behaviours. Simultaneous recordings of neurons in singing finches reveal that neural correlations increase across the circuit driving song variability, in agreement with the model predictions. Analysing behavioural data, we find remarkable similarities in the babbling statistics of 5–6-month-old human infants and juveniles from three songbird species and show that our model naturally accounts for these ‘universal' statistics.
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