声景生态学研究进展和展望

声景生态学以景观中的声音为研究对象, 探讨其在不同时空维度上的分布和变化模式, 从而揭示自然环境、野生动物和人类活动的相互作用关系。本文通过系统检索声景生态学研究的相关文献, 回顾了该学科的研究框架和研究方法, 总结了目前常用的声学指标, 重点归纳了声景生态学的研究内容, 包括声景组成和各组分间的相互作用, 声景的时空格局, 以及声景生态学在生物多样性监测中的应用。目前, 声景监测中存在的问题主要包括监测的生态系统类型和物种类群有限、声学指标效力有待提高等。建议未来着重推进建立系统性的声景监测网络和数据管理平台, 开发和完善音频数据采集、分析方法和评估指标, 并重视声景数据的采集, 将声景视作一种资源进行研究和保护。     

东北虎豹国家公园森林声景的昼夜和季节变化

被动声学监测技术和声学指数通过对音频数据的时频域特征进行定量分析,可以反映声景的复杂度、多样性和健康程度等,已经成为评估生物多样性变化的重要手段。本研究从2020年6月至2021年6月在东北虎豹国家公园采集了52个点的声学数据,计算了春、夏、秋、冬4个季节和黎明、白天、黄昏、夜晚4个昼夜时间段的声音复杂度指数(acoustic complexity index,ACI)、声音多样性指数(acoustic diversity index,ADI)、声音均匀度指数(acoustic evenness index,AEI)、生物声学指数(bioacoustic index,BIO)、标准化声景差异指数(normalized difference soundscape index,NDSI)、声音熵指数(acoustic entropy index,H)和1–21k Hz共20个频段的功率谱密度(power spectral density,PSD)等声学指数,评价了声景构成和多样性的昼夜和季节性差异。结果表明,东北虎豹国家公园的声景随季节变化具有显著的昼夜节律差异,尤其是夜晚的声景和声学成分...     

海南长臂猿生境的声景观与声学活动评估

海南霸王岭自然保护区是中国唯一保护海南长臂猿及其生存环境的国家级自然保护区,具有海南长臂猿(Nomascus hainanus)声音、鸟类声音等丰富的声景资源。探究海南长臂猿生境内的声景构成及声学活动,旨在为该区域的生态保护提供技术支撑。利用被动声学监测技术采集该保护区的声音数据,通过分析不同频率范围内的声景功率分布,描述了保护区声景观的特征与日变化;基于广泛使用的4个声学指数ACI(acoustic complex index)、ADI(acoustic diversity index)、BI(bioacoustic index)、NDSI(normalized difference soundscape index)来评估海南长臂猿声音,并利用随机森林算法,以4个声学指数为预测变量,有无海南长臂猿声音为响应变量,对声音数据进行分类。结果表明：(1)不同频率范围内的声景功率反映了研究区不同的声学群落信息,具有显著的时间变化特征,表明了鸟类、海南长臂猿等动物的黎明和黄昏合唱活动,其中,1～2 kHz符合海南长臂猿的黎明合唱,2～5 kHz符合鸟类等动物的生活规律;(2)有海南长臂猿与无...     

生态型城市公园声景体验的影响因素研究

生态型城市公园是城市居民重要的游憩空间,其声景品质直接影响游客体验的满意程度。以福州金山公园为研究对象,通过定性与定量相结合的方法研究生态型城市公园中声景感知与游憩体验之间的关系及其影响因素。研究发现：生态型城市公园中人们更偏爱自然声,尤其是鸟叫声;交通声是主要影响公园景观美景度的声源类型;儿童嬉闹声及音乐声是影响声景安静度的显著因素,交通声、音乐声以及脚步声与总体满意度关系最为密切;年龄、性别和教育背景是社会、人口及行为学因素中影响典型声源感知最显著的3个因素,而不同游览动机的人群对声景感知也存在显著差异。研究结论可为生态型城市公园景观及声景设计提供理论依据。     

武陵源世界遗产地声景感知及影响感知的要素研究

保护地的声景具有重要价值。声景感知是衡量保护地声景质量的有效工具之一。声景感知受到声景环境、声源构成、主导音等要素影响,也会对人们环境感知的可接受度、情绪等方面产生影响。已有对城市和乡村环境中主导音、听觉对视觉及情绪影响等方面的研究,但对保护地中声景感知及影响感知的要素研究较为缺乏。以武陵源世界遗产地的声景为对象,通过听音实验,相关性分析、对比分析得出武陵源声景的总体特点。可知武陵源声环境中导游喇叭和谈话的声音是感知频率高且接受度最低的声源,因而在保护管理中应予以重视和控制。武陵源世界遗产地内乡村环境中的鸟鸣、流水等自然声源的可接受度要高于武陵源中纯自然环境中的同等声源的可接受度。在低噪声环境下,高感知频率的声源与主导音不一致,即被感知频率高的声源不一定是主导音。并通过相关性分析,得出声景与情绪感知与性格之间存在相关性,且不同声景环境中的自然声源均能使性格“内向安静”和“易焦虑的人”感到“放松”“舒适”和“多样丰富”。最后,基于上述结论,提出了对武陵源声景价值认知的策略和保护管理的建议。     

声景生态学数据分析与应用

声景生态学是研究景观中生物与非生物声音在多种时空尺度下的声学格局与过程,揭示声音与人类以及声音与自然之间关系的学科。基于声景生态学的研究内容,从声景元素解析、生物多样性评估及人类身心健康评价应用案例中,梳理了数据分析的前沿方法。结论表明,分析技术的发展,特别是人工智能技术的进步,使声景生态学的研究呈现从人工到机器、从单一特征计算到多维特征提取、从单学科研究到多学科联合分析的技术化发展趋势,不断拓展着声景生态学的研究深度与广度。同时这些分析技术的发展也急需优化和标准化,来提高方法的通用性和研究结果间的可比性。此外,需要融合生态学、计算机科学和心理学等交叉学科的理论和方法,进一步推动声景生态学数据分析技术方法体系的完善。     

基于语谱图特征信息分割提取的声景观中鸟类生物多样性分析

声学手段是监测和研究生态系统生物活动规律、评价生态系统健康状况的一种新方法,声景观生态学也是景观生态学的一个新兴研究领域。声景指数是描述复杂的音频数据生态学特征的有效方法,但是,单一的声景指数并不能有效的指示物种的真实丰度。在充分挖掘音频文件时频结构特征的基础上,将遥感领域常用的面向对象图像分割技术引入语谱图分割,并提出了适合于自然界鸟类生物多样性提取的知识规则和斑块统计分析方法。研究实验在杭州植物园的不同区域布点采集音频数据,研究结果表明:鸟类的多样性与地物景观类型和人类活动影响程度密切相关,鸟类叫声的中心频率集中分布在2.5—4.5k Hz之间,最低频率分布在0.67—2.1k Hz之间,最高频率分布在7.6—8.9k Hz之间,人类活动较多的区域,鸟类活动较少且叫声更短促。此外,该方法提取的斑块面积周长比参数,可以定量的反映鸟类叫声的婉转程度。     

鸟类迁徙对图们江下游湿地声景时间格局的影响

声景生态学是一个相对较新和快速发展的研究领域,被动声学监测技术和声学指数已经成为研究湿地鸟类和声景多样性的重要方法。本研究评价了鸟类迁徙对中国东北图们江流域下游湿地声景日、月和季节变化的影响。我们从2020年11月至2021年12月在图们江下游敬信湿地设置10个采样点,获得91,988条时长5min的有效音频,计算了声音复杂度指数(acoustic complexity index,ACI)、生物声学指数(bioacoustic index,BIO)、声音均匀度指数(acoustic evenness index,AEI)和标准化声景差异指数(normalized difference soundscape index,NDSI)以及1–11 k Hz频段的功率谱密度(power spectral density,PSD)。结果表明,声学指数对鸟类迁徙活动敏感,其中2个迁徙期声景(2–4月和10–11月)都以1–2k Hz雁类白天的叫声为主,NDSI显著降低,1–2k Hz的PSD显著升高,但雁类向北迁徙时几个声学指数变化更为敏感,有效地捕获了迁徙峰值,表明不同季节鸟类迁徙模式存在差异...     

基于音节聚类分析的被动声学监测技术及其在鸟类监测中的应用

被动声学监测通过分析鸟鸣声信息来实现物种识别,为鸟类多样性监测提供了一种切实可行的技术方案。由于鸟种的鸣声复杂多变,如何通过声纹快速准确辨别物种,分析鸟类丰度,降低对人工操作的需求等技术难题,成为基于声纹的鸟类多样性监测所面临的挑战。本文提出了基于音节聚类的鸟类鸣声监测框架:首先通过音高、频率平坦度等音频特征在声纹数据中提取音节,然后通过无监督表征学习与狄利克雷过程(Dirichlet process)混合模型对音节进行深度无监督聚类训练,完成音节聚类和自动音节种类推断。分析结果表明,本文提出的基于音节聚类的鸟类鸣声监测框架在处理开源数据集白腰文鸟(Lonchura striata)的曲目时可获得接近90%的聚类准确率。在此基础上,本研究对2022年4-5月在广州市白云山公园固定监测点所录制的10种鸟类鸣声进行了无监督的音节聚类分析,验证了本文所提出的基于音节聚类的鸟类鸣声监测框架的有效性:本技术不仅可以支持快速鸟类物种识别,还可以统计和分析不同物种鸟鸣在时间、频度、数量上的变化。这些结果表明,基于音节聚类的鸟类鸣声监测框架可以显著降低对人工标注训练数据的要求,克服传统鸟鸣物种识别框架...     

城市公园春季声景观与植被结构的关系

城市公园是城市生态系统的重要组成部分和城市生物多样性热点地区，具有丰富的声景观资源。由于声景观及声学方法具有信息量大，成本低，低侵入的特点，因此其研究和应用对生态系统健康及监测具有较高价值。声景观研究通过总结生物声的活动或多样性来衡量生物多样性。记录了北京20个城市公园的春季声景观，使用定量方法描述了声景观特征与变化；测试三种了已被证明与生物多样性相关并被广泛使用的声学指数（BIO、ADI、NDSI）与植被群落关系，完成了城市环境中声景观与环境关系的初步探究。研究结果表明：（1）声学指数能够有效表征城市公园声景观信息，具有显著的时间动态特性，能准确反映鸟类黎明合唱等重要生物生态活动；（2）声学强度指数也具有显著的时间动态变化和沿频率梯度的变化，不同的频率区间反映了不同声学群落的活动信息；（3）植被结构尤其是垂直结构对声景观起着重要作用，垂直异质性越大，声学多样性越高。发现支持声景观作为公园植被状况的度量，强调了其作为生物多样性和生态系统健康状况监测方法，用于城市管理和可持续发展的巨大潜力。     

钱江源国家公园体制试点区日间声景观的质量评价及其空间格局特征

声景观在现行国家公园景观评价体系中,是一个十分重要但又薄弱的环节,关系着国家公园的精细化管理水平。以钱江源国家公园体制试点区为例,采用调查问卷法对国家公园日间的53种声景观进行主观愉悦度评价,考察并选取描述声景观质量的客观心理物理学指标,通过多元线性回归方法建立二者主客观关系,并得到国家公园声景观质量评价模型。结果表明：（1）不同类型声景观表现为不同的声学特征,并在响度、尖锐度、粗糙度指标上具有显著性差异。虫鸣声响度最大（5.829）、尖锐度最大（2.667）、粗糙度最大（0.018）,鸟鸣声的尖锐度与其余六种声景观均显著差异。（2）采用响度、尖锐度、粗糙度、波动度作为描述声景观质量的客观心理物理学指标具有适宜性。其中,响度是影响公众对国家公园声景观主观愉悦度认知的主要因素,二者呈负相关关系。（3）采用K-均值聚类等级划分发现,国家公园声景观质量处于较高水平。其中,虫鸣声质量最高（3.86）、鸟鸣声其次（3.58）、交谈声最低（2.43）。自然声景观质量整体高于人工声景观。（4）国家公园声景观质量在空间上具有南部高、北部其次、中部低的格局特征,且与区域生态保护等级水平存在较强空间重叠性。研究结果为国家公园体制试点结束后,未来声景观科学管理与规划设计提供重要参考,同时为其它类型自然保护地的声景观评价提供方法借鉴。     

Contributions of MIR to soundscape ecology. Part 3: Tagging and classifying audio features using a multi-labeling k-nearest neighbor approach

Scientists are using acoustic monitoring to assess the impact of altered soundscapes on wildlife communities and human systems. In the soundscape ecology field, monitoring and analyses approaches rely on the interdisciplinary intersection of ecology, acoustics, and computer science. Combining theory and practice of each field in the context of Knowledge Discovery in Databases (KDD), soundscape ecologists provide innovative monitoring solutions for ecologically-driven research questions. We propose a soundscape content analysis framework for improved knowledge outcome with assistance of the new multi-label (ML) concept.Here, we investigated the effectiveness of a ML k-nearest neighbor algorithm (ML-kNN) for labeling concurrent soundscape components within a single recording. We manually labeled 1200 field recordings for the presence of soundscape components and extracted ecological acoustic features, audio profile features, and Gaussian-mixture model features for each recording. Then, we tested the ML-kNN algorithm accuracy with well-established metrics adapted to ML learning.We found that seventeen unique acoustic features could predict a set of biophonic, geophonic, and anthrophonic labels for a single field recording with average precision of 0.767. However, certain labels were predicted incorrectly depending on the time of day and co-occurrence of that label with another label, suggesting further refinement is needed to improve the accuracy of predicted labels.Overall, this ML classification approach could enable researchers to label field recordings more quickly and generate an “alert” system for monitoring changes in a specific sound class. Ultimately, the adaptation of the ML algorithm may provide soundscape ecologists with new metadata labels that are searchable in large databases of soundscape field recordings.     

Acoustic monitoring reveals diversity and surprising dynamics in tropical freshwater soundscapes

1. Freshwater systems are globally threatened and in need of enhanced monitoring and assessment. We applied soundscape recording and analysis—which presents an opportunity for long-term, high-resolution animal community monitoring and assessment—to a freshwater context to better understand the acoustic diversity and dynamics of these systems.
2. We recorded the aquatic soundscape of a Neotropical freshwater swamp in Costa Rica for 23 days in January and February 2015 during the dry season. We classified biological sound types in these recordings and developed measurements of richness and occupancy based on this classification. We also calculated six complementary acoustic indices to assess soundscape diversity and daily and longer-term soundscape dynamics, and we examined correlations between these acoustic indices and sound type metrics.
3. We found rich soundscapes in which biological sounds were almost always present, and we classified 18 sound types that we attribute to aquatic insects. These sound types showed distinct daily patterns and exhibited temporal and spectral acoustic niche partitioning. Sound type richness was most correlated with the number of peaks index (correlation = .36; p < .001), while sound type occupancy was most correlated with the Bioacoustic Index (correlation = .92; p < .001). In contrast to generally high levels of acoustic activity, there were brief (approximately 1 hr), unexpected quiet periods around dawn and dusk.
4. This study represents an early attempt to comprehensively describe tropical freshwater soundscapes in a systematic and quantitative manner. We demonstrate that sound type classification and the quantification of acoustic occupancy capture aspects of soundscape diversity and dynamics that are complementary to those assessed by acoustic indices. Our analyses reveal that the soundscapes of this tropical wetland were diverse and exhibited daily dynamics that differed from those found in other ecosystems.

     

Time-series forecasting offers novel quantitative measure to assess loud sound event in an urban park with restored prairie

Soundscape ecology and ecoacoustics study the spatiotemporal dynamics of a soundscape and how the dynamics reflect and influence ecological processes in the environment. Soundscape analysis methods employ acoustic recording units (ARUs) that collect acoustic data in study areas over time. Analyzing these data includes computation of several acoustic diversity indices developed to quantify species abundance, richness, or habitat condition through digital audio processing and algorithm adaptations for within-group populations. However, the success of specific indices is often dependent on habitat type and biota richness present and analyzing these data can be challenging due to temporal pseudo-replication. Time-series analytical methods address the inherent problems of temporal autocorrelation for soundscape analyses challenges. Animal population dynamics fluctuate in a variety of ways due to changes in habitat or natural patterns of a landscape and chronic noise exposure, with soundscape phenology patterns evident in terrestrial and aquatic environments. Historical phenological soundscape patterns have been used to predict expected soundscape patterns in long-term studies but limited work has explored how forecasting can quantify changes in short-term studies. We evaluate how forecasting from an acoustic index can be used to quantify change in an acoustic community response to a loud, acute noise. We found that the acoustic community of a Midwestern restored prairie had decreased acoustic community activity after a loud sound event (LSE), a Civil War Reenactment, mainly driven by observed changes in the bird community and quantified using two methods: an automated acoustic index and species richness. Time-series forecasting maybe considered an underutilized tool in analyzing acoustic data whose experimental design can be flawed with temporal autocorrelation. Forecasting using auto ARIMA with acoustic indices could benefit decision makers who consider ecological questions at different time scales.     

生物声音监测研究在生物多样性领域的应用

声音是生物之间交流的重要手段,对生物声音的监测与分析是描述和评估生物多样性的新兴方法。这种方法不侵入和破坏自然环境,通过声音记录生态信息,并有效反映生物多样性的相关特征,是一种重要的生态工具。从声音角度探讨生物多样性的变化拓宽了多学科交叉的新思路,因此近年来被越来越多地应用于生态学研究中。本文阐述了利用声音监测评估生物多样性的主要理论基础和研究方法,从发声动物的生物多样性、声景的时空多样性两个方面介绍了相关领域的研究进展,列举了声音监测在评估土地利用变化、气候变化和城市化对生物多样性影响的应用实例。最后,对未来研究方向进行了展望,希望能进一步挖掘声音调查的发展潜力,为生物多样性的监测评估提供有效的借鉴和参考。     

Vocalizations of bearded seals (Erignathus barbatus) and their influence on the soundscape of the western Canadian Arctic

The soundscape is a crucial habitat feature for marine mammals. This study investigates the contribution of bearded seal vocalizations to the soundscape in the western Canadian Arctic, and also the vocal characteristics of bearded seals relative to sea ice conditions. Passive acoustic data were recorded near Sachs Harbour between August 2015 and July 2016. Sound pressure levels (SPL) in the 50–1,000 Hz and 1–10 kHz bands increased as the total duration of all bearded seal vocalizations increased, and this relationship was moderated by sea ice concentration. Bearded seals in this region had an overlapping vocal repertoire with bearded seals in other areas of the Arctic, and had seven additional vocalizations that have not been previously documented for this region. This study is the first detailed assessment of the influence of bearded seal calls on SPL, which shows the high potential of bearded seals to influence underwater sound levels during the mating season. Bearded seals live in a changing Arctic seascape, and their influence on the soundscape may shift as sea ice continues to diminish. It is imperative that acoustic monitoring continues within the Arctic, and this study provides a baseline for future monitoring as the Arctic continues to change.     

Spatio‐temporal scaling of biodiversity in acoustic tropical bird communities

Automated analysis of acoustic communities is a rapidly emerging approach for the characterization and monitoring of biodiversity. To evaluate its utility, we should verify that such ‘bioacoustics’ can accurately detect ecological signal in spatiotemporal acoustic data. Targeting the ‘Biological Dynamics of Forest Fragments Project’ sites in Brazil, we ask: What is the relative contribution of the spatial, temporal and habitat dimension to variation in bird acoustic communities in a previously fragmented tropical rainforest? Does the functional diversity of bird communities scale similarly to space and time as does species diversity, when both are recorded by bioacoustics means? Overall, is the imprint of landscape fragmentation 30 years ago still audible in the present‐day soundscape? We sampled forty‐four sites in secondary forest and 107 sites in old‐growth forest, resulting in 11 000 h of audio recordings. We detected 60 bird species with satisfactory precision and recovered a linear log–log relation between sampling time and species diversity. Sites in primary forest host more species than sites in secondary forest, but the difference decreased with sampling time, as the slope was slightly higher in secondary than primary forests. Functional diversity, as exposed by vocalizing birds, accumulates faster than does species diversity. The similarity among local communities decreases with distance in both time and space, but stability in time is remarkably high: two acoustic samples from the same site one year (or more) apart prove more similar than two samples taken at the same time but from sites situated just a few hundred meters apart. These findings suggest that habitat modification can be heard as a long‐lasting imprint on the soundscape of regenerating habitats and identify soundscape–area and soundscape–time relations as a promising tool for biodiversity research, applied biomonitoring and restoration ecology.