Context Roads are a widespread feature of landscapes worldwide, and road traffic sound potentially makes nearby habitat unsuitable for acoustically communicating organisms. It is important to understand the influence of roads at the soundscape level to mitigate negative impacts of road sound on individual species as well as subsequent effects on the surrounding landscape. Objectives We seek to characterize the relationship between anthropogenic and biological sounds in western New York and assess the extent to which available traffic data explains variability in anthropogenic noise. Methods Recordings were obtained in the spring of 2016 at 18 sites throughout western New York. We used the Welch Power Spectral Density (PSD) at low frequencies (0.5–2 kHz) to represent anthropogenic noise and PSD values at higher frequencies (2–11 kHz) to represent biological sound. Relationships were modeled using a novel two-stage hierarchical Bayesian model utilizing beta regression and basis splines. Results Model results and map predictions illustrate that anthropogenic noise and biological sound have an inverse relationship, and anthropogenic noise is greatest in close proximity to high traffic volume roads. The predictions have large uncertainty, resulting from the temporal coarseness of public road data used as a proxy for traffic sound. Conclusions Results suggest that finer temporal resolution traffic sound data, such as crowd-sourced time-indexed traffic data from geographic positioning systems, might better account for observed temporal changes in the soundscape. The use of such data, in combination with the proposed modeling framework, could have important implications for the development of sound management policies.

中文翻译：

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表征人为和生物声音之间的功能关系：纽约州西部音景案例研究

背景道路是世界各地景观的一个普遍特征，道路交通声音可能会使附近的栖息地不适合进行声学交流的生物。重要的是要了解道路在声景层面的影响，以减轻道路声音对单个物种的负面影响以及对周围景观的后续影响。目标 我们试图描述纽约西部人为声音和生物声音之间的关系，并评估可用交通数据在多大程度上解释了人为噪音的变化。方法 2016 年春季在纽约西部的 18 个地点获得了录音。我们在低频 (0. 5–2 kHz) 代表人为噪声，更高频率 (2–11 kHz) 的 PSD 值代表生物声音。使用利用 beta 回归和基础样条的新型两阶段分层贝叶斯模型对关系进行建模。结果 模型结果和地图预测表明，人为噪声和生物声呈反比关系，靠近高交通量道路的人为噪声最大。由于用作交通声音代理的公共道路数据的时间粗糙性，预测具有很大的不确定性。结论 结果表明，更精细的时间分辨率交通声音数据，例如来自地理定位系统的众包时间索引交通数据，可能更好地解释观察到的音景时间变化。使用此类数据，