Data from: A spatiotemporal analysis of acoustic interactions between great reed warblers (Acrocephalus arundinaceus) using microphone arrays and robot audition software HARK

Acoustic interactions are important for understanding intra- and interspecific communication in songbird communities from the viewpoint of soundscape ecology. It has been suggested that birds may divide up sound space to increase communication efficiency in such a manner that they tend to avoid overlap with other birds when they sing. We are interested in clarifying the dynamics underlying the process as an example of complex systems based on short-term behavioral plasticity. However, it is very problematic to manually collect spatiotemporal patterns of acoustic events in natural habitats using data derived from a standard single-channel recording of several species singing simultaneously. Our purpose here is to investigate fine-scale spatiotemporal acoustic interactions of the great reed warbler. We surveyed spatial and temporal patterns of several vocalizing color-banded great reed warblers (Acrocephalus arundinaceus) using an open source software for robot audition HARK (Honda Research Institute Japan Audition for Robots with Kyoto University) and three new 16-channel, stand-alone, and water-resistant microphone arrays, named DACHO spread out in the bird's habitat. We first show that our system estimated the location of two color-banded individuals' song posts with mean error distance of 5.5 ± 4.5 m from the location of observed song posts. We then evaluated the interdigitation of the temporal pattern of localized songs by comparing the duration of localized songs with those annotated by human observers, with an accuracy score of average 0.89% for one bird that stayed at one song post. We found significant temporal overlap avoidance and an asymmetric relationship between songs of the two singing individuals, using transfer entropy. We believe that our system and analytical approach contribute to a better understanding of fine-scale acoustic interactions in time and space in bird communities.

从声景生态学（soundscape ecology）的视角出发，声学互动对于理解鸣禽群落内的种内与种间交流具有关键意义。已有研究提出，鸟类或可通过划分声学空间提升交流效率，具体体现为鸣唱时倾向于规避与其他鸟类的鸣声重叠。我们希望以该过程为基于短期行为可塑性的复杂系统案例，阐明其背后的动力学机制。然而，若仅依托标准单通道录音来同步记录多个物种的同时鸣唱数据，人工采集自然生境中声学事件的时空模式将面临极大困难。本研究的核心目标是探究大苇莺（*Acrocephalus arundinaceus*）的精细时空尺度声学互动。我们采用面向机器人听觉的开源软件HARK（Honda Research Institute Japan Audition for Robots with Kyoto University），以及部署于鸣禽生境中的三台新型16通道独立防水麦克风阵列DACHO，对多只佩戴彩色脚环的鸣唱大苇莺的时空分布模式开展了调查。本研究首先验证了系统性能：本系统对两只佩戴彩色脚环个体的鸣唱站位的定位结果，与实际观测站位的平均误差距离为5.5±4.5米。随后，我们通过将定位得到的鸣声时长与人工标注的鸣声时长进行对比，评估了定位鸣声的时间模式的交互重叠情况；对于停留在单个鸣唱站位的个体，其定位结果的平均准确率得分为0.89%。我们借助传递熵（transfer entropy）分析发现，两只鸣唱个体的鸣声间存在显著的时间重叠回避现象，且二者的鸣声交互关系呈现不对称性。我们认为，本研究的系统与分析方法，将有助于更深入地理解鸟类群落中时空尺度下的精细声学互动。