Soundscape components inform acoustic index patterns and refine estimates of bird species richness

Colin A. Quinn, P. Burns, C. Hakkenberg, Leonardo Salas, B. Pasch, S. Goetz, M. Clark
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Abstract

Ecoacoustic monitoring has proliferated as autonomous recording units (ARU) have become more accessible. ARUs provide a non-invasive, passive method to assess ecosystem dynamics related to vocalizing animal behavior and human activity. With the ever-increasing volume of acoustic data, the field has grappled with summarizing ecologically meaningful patterns in recordings. Almost 70 acoustic indices have been developed that offer summarized measurements of bioacoustic activity and ecosystem conditions. However, their systematic relationships to ecologically meaningful patterns in varying sonic conditions are inconsistent and lead to non-trivial interpretations. We used an acoustic dataset of over 725,000 min of recordings across 1,195 sites in Sonoma County, California, to evaluate the relationship between 15 established acoustic indices and sonic conditions summarized using five soundscape components classified using a convolutional neural network: anthropophony (anthropogenic sounds), biophony (biotic sounds), geophony (wind and rain), quiet (lack of emergent sound), and interference (ARU feedback). We used generalized additive models to assess acoustic indices and biophony as ecoacoustic indicators of avian diversity. Models that included soundscape components explained acoustic indices with varying degrees of performance (avg. adj-R2 = 0.61 ± 0.16; n = 1,195). For example, we found the normalized difference soundscape index was the most sensitive index to biophony while being less influenced by ambient sound. However, all indices were affected by non-biotic sound sources to varying degrees. We found that biophony and acoustic indices combined were highly predictive in modeling bird species richness (deviance = 65.8%; RMSE = 3.9 species; n = 1,185 sites) for targeted, morning-only recording periods. Our analyses demonstrate the confounding effects of non-biotic soundscape components on acoustic indices, and we recommend that applications be based on anticipated sonic environments. For instance, in the presence of extensive rain and wind, we suggest using an index minimally affected by geophony. Furthermore, we provide evidence that a measure of biodiversity (bird species richness) is related to the aggregate biotic acoustic activity (biophony). This established relationship adds to recent work that identifies biophony as a reliable and generalizable ecoacoustic measure of biodiversity.
声景观成分为声学指数模式提供信息,并改进鸟类物种丰富度的估计
随着自主录音装置(ARU)的普及,生态声学监测也越来越普及。ARUs提供了一种非侵入性的被动方法来评估与发声动物行为和人类活动相关的生态系统动力学。随着声学数据量的不断增加,该领域一直在努力总结录音中有生态意义的模式。近70种声学指标已经开发出来,提供了生物声学活动和生态系统条件的总结测量。然而,在不同的声波条件下,它们与生态意义模式的系统关系是不一致的,并导致非琐碎的解释。我们使用了加利福尼亚州索诺玛县1195个地点超过72.5万分钟的录音声学数据集,以评估15种已建立的声学指数与声音条件之间的关系,这些声学指数与使用卷积神经网络分类的五种声景成分总结而成:anthropophony(人为声音)、biophony(生物声音)、geophony(风和雨)、quiet(缺乏意外声音)和interference (ARU反馈)。采用广义加性模型评价了鸟类多样性的声学指标和生物声学指标。包含声景成分的模型解释了不同表现程度的声学指标(average . aj - r2 = 0.61±0.16;N = 1195)。例如,我们发现归一化差音景指数是对生物噪声最敏感的指数,而受环境声的影响较小。但各指标均不同程度地受到非生物声源的影响。研究发现,生物声学和声学指标组合对鸟类物种丰富度的预测效果较好(偏差值为65.8%;RMSE = 3.9种;N = 1185个站点),用于定向的、仅限上午的记录时段。我们的分析证明了非生物声景观成分对声学指标的混淆效应,我们建议应用基于预期的声音环境。例如,在存在广泛的雨和风的情况下,我们建议使用受地磁影响最小的指数。此外,我们提供的证据表明,生物多样性(鸟类物种丰富度)的测量与生物声学活动(生物蜂蜜)有关。这种已建立的关系为最近的工作增添了新的内容,即确定生物声学是生物多样性的可靠和可推广的生态声学测量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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