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.

从声景生态学视角出发，声学交互对于解析鸣禽群落的种内与种间通讯机制至关重要。已有研究表明，鸟类可通过划分声域提升通讯效率，具体表现为鸣唱时倾向于规避与其他个体的鸣声发生重叠。我们旨在阐明这一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开展研究。然而，仅依靠标准单通道录音采集多物种同时鸣唱时的声学事件时空模式，在自然生境中手动完成此类数据收集极具挑战性。本研究的核心目标是探究大苇莺（Acrocephalus arundinaceus）的精细尺度时空声学交互行为。我们借助面向机器人听觉的开源软件HARK（Honda Research Institute Japan Audition for Robots with Kyoto University），以及三台部署于鸣禽生境中、命名为DACHO的新型16通道独立防水麦克风阵列，对多只佩戴彩色脚环的鸣唱大苇莺的时空鸣唱模式展开了调查。本研究首先验证了系统的定位精度：对于两只佩戴彩色脚环个体的鸣唱站位，其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唱时长与人类观察者标注的时长进行比对，评估了定位鸣唱的时间模式交错程度；对于停留在单一鸣唱站位的个体，其平均准确率得分为0.89%。我们借助传递熵分析发现，两只鸣唱个体的鸣声存在显著的时间重叠规避行为，且二者的鸣唱模式呈现非对称关联。我们认为，本研究采用的系统与分析方法，将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。