Conservation Research Institute and Department of Plant Sciences, University of Cambridge, Cambridge, UK.
Centre for Biodiversity and Environment Research, University College London, London, UK.
Nat Ecol Evol. 2023 Sep;7(9):1373-1378. doi: 10.1038/s41559-023-02148-z. Epub 2023 Jul 31.
Although eco-acoustic monitoring has the potential to deliver biodiversity insight on vast scales, existing analytical approaches behave unpredictably across studies. We collated 8,023 audio recordings with paired manual avifaunal point counts to investigate whether soundscapes could be used to monitor biodiversity across diverse ecosystems. We found that neither univariate indices nor machine learning models were predictive of species richness across datasets but soundscape change was consistently indicative of community change. Our findings indicate that there are no common features of biodiverse soundscapes and that soundscape monitoring should be used cautiously and in conjunction with more reliable in-person ecological surveys.
尽管生态声学监测具有在广阔范围内提供生物多样性见解的潜力，但现有分析方法在不同研究中表现出不可预测性。我们整理了 8023 段带有鸟类点计数配对的音频记录，以调查声音景观是否可用于监测不同生态系统中的生物多样性。我们发现，无论是单变量指数还是机器学习模型都不能预测数据集的物种丰富度，但声音景观的变化始终表明群落的变化。我们的研究结果表明，生物多样性丰富的声音景观没有共同特征，因此应该谨慎使用声音景观监测，并结合更可靠的实地生态调查。
