Biases of acoustic indices measuring biodiversity in urban areas
Biases of acoustic indices measuring biodiversity in urban areas
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DOI：
10.1016/j.ecolind.2017.07.064
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发表时间：
2017-12-01
影响因子：
6.9
通讯作者：
Jones, Kate E.
中科院分区：
文献类型：
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作者：
Fairbrass, Alison J.;Rennett, Peter;Jones, Kate E.
城市绿色基础设施、GI（例如公园、花园、绿色屋顶）是潜在的重要生物多样性栖息地，但人们对它们的全部生态能力知之甚少，部分原因是监测城市野生动物种群的困难。生态声学测量是监测栖息地的一种有用方法，其中声学指数（AI）用于通过总结生物声音的活动或多样性来测量生物多样性。然而，在以人为噪音为主的声学复杂的城市栖息地中，人工智能引入的偏差尚不清楚。在此，我们根据录音的声学和视觉分析，测量了 2013 年 6 月至 10 月英国大伦敦地区 15 个地点的 2452 小时声学记录的低频率（0-12 kHz，（l））和高频率（12-96 kHz，（h））生物、人为和地声成分的活动水平和多样性。我们使用混合效应模型将这些测量值与四种常用的 Ms 测量值进行比较：声学复杂性指数 (ACI)、声学多样性指数 (ADI)、生物声学指数 (BI) 和归一化差异声景指数 (NDSI)。我们发现三个AI（ACI（l）、BIl、NDSIl）与我们的生物（l）活性和多样性测量显着正相关。然而，所有三者也与人类活动相关，并且BIl和NDSIl与人类(l)多样性相关。所有低频 Ms 均与地声 (l) 声的存在相关。对于高频记录，只有一项AI（ACI（h））与测量的生物（h）活动呈正相关，但也与人为（h）活动呈正相关，并且没有指数与生物（h）多样性相关。因此，如果不事先从录音中消除偏差声音，这里测试的人工智能不适合在人为主导的栖息地中对生物多样性进行声学监测。然而，随着进一步的方法学研究克服此处确定的一些局限性，生态声学具有巨大的潜力，可以在未来管理城市所需的规模上促进城市生物多样性和生态系统监测。
Urban green infrastructure, GI (e.g., parks, gardens, green roofs) are potentially important biodiversity habitats, however their full ecological capacity is poorly understood, in part due to the difficulties of monitoring urban wildlife populations. Ecoacoustic surveying is a useful way of monitoring habitats, where acoustic indices (AIs) are used to measure biodiversity by summarising the activity or diversity of biotic sounds. However, the biases introduced to AIs in acoustically complex urban habitats dominated by anthropogenic noise are not well understood. Here we measure the level of activity and diversity of the low (0-12 kHz, (l)) and high (12-96 kHz, (h)) frequency biotic, anthropogenic, and geophonic components of 2452 h of acoustic recordings from 15 sites across Greater London, UK from June to October 2013 based on acoustic and visual analysis of recordings. We used mixed-effects models to compare these measures to those from four commonly used Ms: Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Bioacoustic Index (BI), and Normalised Difference Soundscape Index (NDSI). We found that three AIs (ACI(l), BIl, NDSIl) were significantly positively correlated with our measures of biotic(l) activity and diversity. However, all three were also correlated with anthropogenict activity, and BIl and NDSIl were correlated with anthropogenic(l) diversity. All low frequency Ms were correlated with the presence of geophonic(l) sound. Regarding the high frequency recordings, only one AI (ACI(h)) was positively correlated with measured biotic(h) activity, but was also positively correlated with anthropogenic(h) activity, and no index was correlated with biotic(h) diversity. The AIs tested here are therefore not suitable for monitoring biodiversity acoustically in anthropogenically dominated habitats without the prior removal of biasing sounds from recordings. However, with further methodological research to overcome some of the limitations identified here, ecoacoustics has enormous potential to facilitate urban biodiversity and ecosystem monitoring at the scales necessary to manage cities in the future.