Quantifying song behavior in a free‐living, light‐weight, mobile bird using accelerometers

Abstract

To acquire a fundamental understanding of animal communication, continuous observations in a natural setting and at an individual level are required. Whereas the use of animal‐borne acoustic recorders in vocal studies remains challenging, light‐weight accelerometers can potentially register individuals’ vocal output when this coincides with body vibrations. We collected one‐dimensional accelerometer data using light‐weight tags on a free‐living, crepuscular bird species, the European Nightjar (Caprimulgus europaeus). We developed a classification model to identify four behaviors (rest, sing, fly, and leap) from accelerometer data and, for the purpose of this study, validated the classification of song behavior. Male nightjars produce a distinctive “churring” song while they rest on a stationary song post. We expected churring to be associated with body vibrations (i.e., medium‐amplitude body acceleration), which we assumed would be easy to distinguish from resting (i.e., low‐amplitude body acceleration). We validated the classification of song behavior using simultaneous GPS tracking data (i.e., information on individuals’ movement and proximity to audio recorders) and vocal recordings from stationary audio recorders at known song posts of one tracked individual. Song activity was detected by the classification model with an accuracy of 92%. Beyond a threshold of 20 m from the audio recorders, only 8% of the classified song bouts were recorded. The duration of the detected song activity (i.e., acceleration data) was highly correlated with the duration of the simultaneously recorded song bouts (correlation coefficient = 0.87, N = 10, S = 21.7, p = .001). We show that accelerometer‐based identification of vocalizations could serve as a promising tool to study communication in free‐living, small‐sized birds and demonstrate possible limitations of audio recorders to investigate individual‐based variation in song behavior.

Not your private tête-à-tête: leveraging the power of higher-order networks to study animal communication

“…A key challenge to applying higher-order network approaches to study animal vocal communication will be collecting appropriate data, something that has only recently become possible with the advancement of acoustic and biologging technology. We envision an ideal data collection protocol as gathering both movement and vocalization data of tracked individuals simultaneously, for example, using acoustic location systems [80] or animal-borne microphones [81] or accelerometers [82]. In general, it will be important to obtain longitudinal data, akin to recent efforts in human social networks [83], whose spatial and temporal extent go beyond more traditional data collection procedures.…”

Multimodal system for recording individual-level behaviors in songbird groups

“…In general, limitations due to sight occlusions and sound superpositions can be overcome with animalborne sensors such as accelerometers 16,17 , gyroscopes, microphones 18 , and global positioning systems (GPS) 17 . In combination with wireless transmitters 18 and loggers 16 , these sensors enable the detection of behaviors such as walking, grooming, eating, drinking, and flying, for example, in birds 19 , cats 20 , and dogs 21 , though often with low reliability because of noisy and ambiguous sensor signals 12 .…”

Multimodal system for recording individual-level behaviors in songbird groups

“…In general, limitations due to sight occlusions and sound superpositions can be overcome with animal-borne sensors such as accelerometers 22,23 , gyroscopes, microphones 24 , and global positioning systems (GPS) 23 . In combination with wireless transmitters 24 and loggers 22 , these sensors enable the detection of behaviors such as walking, grooming, eating, drinking, and flying, for example, in birds 25 , cats 26 , and dogs 27 , though often with low reliability due to noisy and ambiguous sensor signals 15 .…”