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，声学交互对于理解鸣禽群落内的种内与种间交流至关重要。已有研究表明，鸟类可通过划分声域提升交流效率，具体表现为鸣唱时倾向于规避与其他鸟类的鸣声重叠。本研究旨在阐明该过程背后的动力学机制，将其作为基于短期行为可塑性的复杂系统案例展开探究。然而，若采用标准单通道录音采集多种鸟类同时鸣唱的声学事件时空模式，在自然生境中手动收集此类数据存在极大挑战。 本研究的核心目标为探究大苇莺（Acrocephalus arundinaceus）的精细尺度时空声学交互行为。我们依托机器人听觉开源软件HARK（Honda Research Institute Japan Audition for Robots with Kyoto University），以及部署于鸟类生境中的三款新型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模式展开了调查。 我们首先证实，本系统对两只佩戴彩色脚环个体的鸣唱站位的定位结果，与实际观测到的鸣唱站位之间的平均误差距离为5.5±4.5米。随后，我们将定位得到的鸣唱时长与人工标注的鸣唱时长进行对比，以此评估定位鸣唱的时序模式匹配度；针对停留在单一鸣唱站位的个体，其平均准确率得分为0.89%。借助传递熵（transfer entropy）分析，我们发现这两只鸣唱个体的鸣唱存在显著的时序重叠规避现象，且二者的鸣唱间存在不对称关联。 我们认为，本研究的系统搭建与分析方法，有助于更深入地理解鸟类群落中精细尺度的时空声学交互行为。