Eisenring Elena, Eens Marcel, Pradervand Jean-Nicolas, Jacot Alain, Baert Jan, Ulenaers Eddy, Lathouwers Michiel, Evens Ruben
Department of Biology Behavioural Ecology and Ecophysiology Group University of Antwerp Wilrijk Belgium.
Swiss Ornithological Institute Field Station Valais Sion Switzerland.
Ecol Evol. 2022 Jan 23;12(1):e8446. doi: 10.1002/ece3.8446. eCollection 2022 Jan.
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 (). 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, = 10, = 21.7, = .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.
为了对动物交流有一个基本的了解，需要在自然环境中对个体进行持续观察。虽然在声乐研究中使用动物携带的声学记录器仍然具有挑战性，但当个体的发声与身体振动同时发生时，轻质加速度计有可能记录下其发声输出。我们使用轻质标签收集了一种自由生活的晨昏性鸟类——欧夜鹰（Caprimulgus europaeus）的一维加速度计数据。我们开发了一个分类模型，用于从加速度计数据中识别四种行为（休息、唱歌、飞行和跳跃），并且为了本研究的目的，验证了唱歌行为的分类。雄性欧夜鹰在静止的歌唱柱上休息时会发出独特的“呼呼”声。我们预计这种呼呼声会与身体振动（即中等幅度的身体加速度）相关，我们认为这很容易与休息（即低幅度的身体加速度）区分开来。我们使用同步GPS跟踪数据（即关于个体移动以及与音频记录器接近程度的信息）和来自一个被跟踪个体已知歌唱柱处的固定音频记录器的声乐录音，验证了唱歌行为的分类。分类模型检测到唱歌活动的准确率为92%。在距离音频记录器超过20米的阈值之外，只有8%的分类唱歌时段被记录下来。检测到的唱歌活动（即加速度数据）的持续时间与同时记录的唱歌时段的持续时间高度相关（相关系数 = 0.87，n = 10，r² = 21.7，p = 0.001）。我们表明，基于加速度计的发声识别可以作为研究自由生活的小型鸟类交流的一种有前景的工具，并证明了音频记录器在研究基于个体的唱歌行为变化方面可能存在的局限性。
