Wild nightingales flexibly match whistle pitch in real time

Interactive vocal communication, similar to a human conversation, requires flexible and real-time changes to vocal output in relation to preceding auditory stimuli. These vocal adjustments are essential to ensuring both the suitable timing and content of the interaction. Precise timing of dyadic vocal exchanges has been investigated in a variety of species, including humans. In contrast, the ability of non-human animals to accurately adjust specific spectral features of vocalization extemporaneously in response to incoming auditory information is less well studied. One spectral feature of acoustic signals is the fundamental frequency, which we perceive as pitch. Many animal species can discriminate between sound frequencies, but real-time detection and reproduction of an arbitrary pitch have only been observed in humans. Here, we show that nightingales in the wild can match the pitch of whistle songs while singing in response to conspecifics or pitch-controlled whistle playbacks. Nightingales matched whistles across their entire pitch production range indicating that they can flexibly tune their vocal output along a wide continuum. Prompt whistle pitch matches were more precise than delayed ones, suggesting the direct mapping of auditory information onto a motor command to achieve online vocal replication of a heard pitch. Although nightingales' songs follow annual cycles of crystallization and deterioration depending on breeding status, the observed pitch-matching behavior is present year-round, suggesting a stable neural circuit independent of seasonal changes in physiology. Our findings represent the first case of non-human instantaneous vocal imitation of pitch, highlighting a promising model for understanding sensorimotor transformation within an interactive context. VIDEO ABSTRACT.

Community engagement and data quality: best practices and lessons learned from a citizen science project on birdsong

Citizen Science (CS) is a research approach that has become popular in recent years and offers innovative potential for dialect research in ornithology. As the scepticism about CS data is still widespread, we analysed the development of a 3-year CS project based on the song of the Common Nightingale (Luscinia megarhynchos) to share best practices and lessons learned. We focused on the data scope, individual engagement, spatial distribution and species misidentifications from recordings generated before (2018, 2019) and during the COVID-19 outbreak (2020) with a smartphone using the 'Naturblick' app. The number of nightingale song recordings and individual engagement increased steadily and peaked in the season during the pandemic. 13,991 nightingale song recordings were generated by anonymous (64%) and non-anonymous participants (36%). As the project developed, the spatial distribution of recordings expanded (from Berlin based to nationwide). The rates of species misidentifications were low, decreased in the course of the project (10-1%) and were mainly affected by vocal similarities with other bird species. This study further showed that community engagement and data quality were not directly affected by dissemination activities, but that the former was influenced by external factors and the latter benefited from the app. We conclude that CS projects using smartphone apps with an integrated pattern recognition algorithm are well suited to support bioacoustic research in ornithology. Based on our findings, we recommend setting up CS projects over the long term to build an engaged community which generates high data quality for robust scientific conclusions.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10336-022-02018-8.