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Acoustic indices perform better when applied at ecologically meaningful time and frequency scales
Methods in Ecology and Evolution ( IF 6.6 ) Pub Date : 2020-10-29 , DOI: 10.1111/2041-210x.13521
Oliver C. Metcalf 1 , Jos Barlow 2, 3, 4 , Christian Devenish 1 , Stuart Marsden 1 , Erika Berenguer 3, 5 , Alexander C. Lees 1, 6
Affiliation  

  1. Acoustic indices are increasingly employed in the analysis of soundscapes to ascertain biodiversity value. However, conflicting results and lack of consensus on best practices for their usage has hindered their application in conservation and land‐use management contexts. Here we propose that the sensitivity of acoustic indices to ecological change and fidelity of acoustic indices to ecological communities are negatively impacted by signal masking. Signal masking can occur when acoustic responses of taxa sensitive to the effect of interest are masked by less‐sensitive acoustic groups, or target taxa sonification is masked by non‐target noise. We argue that by calculating acoustic indices at ecologically appropriate time and frequency bins, masking effects can be reduced and the efficacy of indices increased.
  2. We test this on a large acoustic dataset collected in Eastern Amazonia spanning a disturbance gradient of undisturbed, logged, burned, logged‐and‐burned and secondary forests. We calculated values for two acoustic indices: the Acoustic Complexity Index and the Bioacoustic Index, across the entire frequency spectrum (0–22.1 kHz), and four narrower subsets of the frequency spectrum; at dawn, day, dusk and night.
  3. We show that signal masking has a large impact on the sensitivity of acoustic indices to forest disturbance classes. Calculating acoustic indices at a range of narrower time–frequency bins substantially increases the classification accuracy of forest classes by random forest models. Furthermore, signal masking led to misleading correlations, including spurious inverse correlations, between biodiversity indicator metrics and acoustic index values compared to correlations derived from manual sampling of the audio data.
  4. Consequently, we recommend that acoustic indices are calculated either at a range of time and frequency bins, or at a single narrow bin, predetermined by a priori ecological understanding of the soundscape.


中文翻译:

当在对生态有意义的时间和频率范围内应用时,声学指数会表现更好

  1. 在声景分析中越来越多地采用声学指标来确定生物多样性的价值。但是,结果矛盾和对最佳做法的使用尚无共识,这阻碍了它们在保护和土地利用管理中的应用。在这里,我们提出,信号掩蔽对声学指数对生态变化的敏感性和对生态群落的声学指数的保真度产生了负面影响。当对敏感效果敏感的分类单元的声学响应被较不敏感的声学组所掩盖,或者目标分类单元的声波被非目标噪声所掩盖时,可能会发生信号掩蔽。我们认为,通过在生态上适当的时间和频率范围内计算声学指数,可以降低掩蔽效果,并提高指数的功效。
  2. 我们在东部亚马逊地区收集的一个大型声学数据集上进行了测试,该数据集的分布范围为未扰动,砍伐,燃烧,砍伐和燃烧以及次生森林的扰动梯度。我们计算了两个声学指数的值:整个频谱(0-22.1 kHz)的声学复杂度指数和生物声学指数,以及频谱的四个较窄子集。在黎明,白天,黄昏和夜晚。
  3. 我们表明,信号掩蔽对声学指数对森林干扰类别的敏感性有很大的影响。在较窄的时频区间范围内计算声学指数,可通过随机森林模型显着提高森林类别的分类准确性。此外,与从音频数据的手动采样获得的相关性相比,信号掩蔽导致生物多样性指标指标和声学指数值之间的误导性相关性,包括虚假的逆相关性。
  4. 因此,我们建议在时间和频率范围的范围内,或者在单个狭窄的范围内,通过对音景的先验生态学理解来确定声学指数。
更新日期:2020-10-29
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