声学指数在城市森林鸟类多样性评估中的应用

doi:10.17520/biods.2022080

生物多样性 ›› 2023, Vol. 31 ›› Issue (1): 22080. DOI: 10.17520/biods.2022080

• 中国野生脊椎动物鸣声监测与生物声学研究专题 • 下一篇

声学指数在城市森林鸟类多样性评估中的应用

边琦¹^,², 王成¹^,²^,^*(), 程贺¹^,²^,³, 韩丹¹^,², 赵伊琳¹^,², 殷鲁秦¹^,²

1.中国林业科学研究院林业研究所, 北京 100091
2.国家林业和草原局城市森林研究中心, 北京 100091
3.北京土人城市规划设计股份有限公司, 北京 100080

收稿日期:2022-02-18 接受日期:2022-06-01 出版日期:2023-01-20 发布日期:2022-06-23
通讯作者: 王成
作者简介:*E-mail: wch8361@163.com
基金资助:
中央级公益性科研院所基本科研业务费专项资金(CAFYBB2020ZB008)

Exploring the application of acoustic indices in the assessment of bird diversity in urban forests

Qi Bian¹^,², Cheng Wang¹^,²^,^*(), He Cheng¹^,²^,³, Dan Han¹^,², Yilin Zhao¹^,², Luqin Yin¹^,²

1. Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091
2. Urban Forest Research Center, National Forestry and Grassland Administration, Beijing 100091
3. Turenscape Company Limited, Beijing 100080

Received:2022-02-18 Accepted:2022-06-01 Online:2023-01-20 Published:2022-06-23
Contact: Cheng Wang

1. 附录1 东郊森林公园4–6月鸟类综合调查名录.pdf(179KB)

摘要/Abstract

摘要：

鸣声是鸟类之间进行沟通和传递信息的重要方式, 这为通过声学监测评估鸟类多样性提供了独特的机会。利用声学指数快速评估生物多样性是一种新兴的调查方法, 但城市森林中的复杂声环境可能会导致声学指数的指示结果出现偏差。为了解声学指数在城市森林中应用的可行性, 本研究在北京市东郊森林公园设置了50个矩阵式调查样点, 于2021年4-6月每月进行1次鸟类传统观测和同步鸣声采集, 通过比较两种方法的结果来探究声学监测的有效性。采用Spearman相关分析和广义线性混合模型评估6个常用声学指数与鸟类丰富度和多度的关系, 并衡量了每个指数的性能。结果表明: (1)本研究共记录到鸟类10目23科35种, 通过声学监听识别的总物种数与传统鸟类观测相等, 但具体鸟种存在差异; (2)不同月份间声学指数与鸟类丰富度和多度的相关性有明显差别, 声学复杂度指数(ACI)和标准化声景差异指数(NDSI)优于其他指数, 是评估鸟类多样性的关键变量; (3)声学指数对鸟类多度的预测能力(R²m = 0.32, R²c = 0.80)要高于丰富度(R²m = 0.12, R²c = 0.18)。声学指数为快速评估生物多样性提供了有前景的分析手段, 但仍需继续探讨改进。随着方法的逐步完善和处理技术的提升, 声学监测在城市生物多样性保护和跟踪管理方面的潜力也越来越大。

关键词: 城市森林, 鸟类多样性, 声学指数, 声学监测

Abstract

Aims: Calling is an important way for birds to communicate and transmit information to each other. This provides a unique opportunity to assess bird diversity through acoustic monitoring. The use of acoustic indices for the rapid assessment of biodiversity is an emerging survey method, but the complex sonic environment in urban forests may lead to bias. The feasibility of using acoustic indices to assess bird diversity in urban forests still needs to be further explored.

Methods: To understand the effectiveness of acoustic indices in urban forests, we set up 50 matrix survey sample sites in Beijing Eastern Suburb Forest Park. Bird sample point observations and simultaneous acoustic data collection were conducted monthly from April to June 2021. In order to verify the effectiveness of acoustic monitoring, we compared the results of the two methods. Spearman correlation analysis and generalized linear mixed models were used to assess the relationship between six commonly used acoustic indices and bird richness and abundance. The performance of each acoustic index was subsequently measured.

Results: (1) A total of 35 species, comprising 10 orders and 23 families, were recorded in this experiment. Although the total number of species identified through acoustic monitoring was equal to bird observations, there were discrepancies between which specific bird species were observed. (2) The correlation between acoustic indices and bird richness and abundance varied significantly in different months. The acoustic complexity index (ACI) and normalized difference sound index (NDSI) outperformed others were key variables for assessing bird diversity. (3) Acoustic indices had higher predictive power for bird abundance (R²m = 0.32, R²c = 0.80) than richness (R²m = 0.12, R²c = 0.18).

Conclusion: Acoustic monitoring provides a promising tool for urban biodiversity assessment, but there are still many areas that need to be explored. With the gradual improvement of methods and technology, acoustic monitoring has great potential in the tracking and conservation management of urban biodiversity.

Key words: urban forest, bird diversity, acoustic indices, acoustic monitoring

边琦, 王成, 程贺, 韩丹, 赵伊琳, 殷鲁秦 (2023) 声学指数在城市森林鸟类多样性评估中的应用. 生物多样性, 31, 22080. DOI: 10.17520/biods.2022080.

Qi Bian, Cheng Wang, He Cheng, Dan Han, Yilin Zhao, Luqin Yin (2023) Exploring the application of acoustic indices in the assessment of bird diversity in urban forests. Biodiversity Science, 31, 22080. DOI: 10.17520/biods.2022080.

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链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2022080

https://www.biodiversity-science.net/CN/Y2023/V31/I1/22080

图/表 8

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声学指数 Acoustic indices	详细信息 Details	参考文献 Reference
声熵指数 Acoustic entropy index (H)	将音频划分为多个频段, 使用Shannon指数来计算时间熵(Ht)和频谱熵(Hf), 两者相乘得到声熵指数, 体现声学信号在时域和频域上的复杂度。The audio is divided into multiple frequency bands and the Shannon index is used to calculate the temporal entropy (Ht) and spectral entropy (Hf), which are multiplied to obtain the acoustic entropy index, reflecting the complexity of the acoustic signal in the time and frequency domains.	Sueur et al, 2008
声学复杂度指数 Acoustic complexity index (ACI)	该指数用于测量相邻频率带之间振幅的变化, 反映声强的可变性和不规律性, 特别是鸟鸣声。该指数相对不受恒定强度或持续声音的影响。本研究中使用以下参数计算: min_freq = 2000, max_freq = 11000, j = 5, fft_w = 512。This index is used to measure changes in amplitude between adjacent frequency bands, reflecting the variability and irregularity of sound intensity, especially bird calls. The index is relatively unaffected by a constant intensity of a sustained sound. The following parameters are used in this study: min_freq = 2000, max_freq = 11000, j = 5, fft_w = 512.	Pieretti et al, 2011
声学多样性指数 Acoustic diversity index, (ADI)	将频谱图划分为多个频段(默认为10), 并使用Shannon指数计算每个频段中超过阈值(默认为-50 dBFS)的声音所占比例。本研究中使用默认参数计算。The spectrogram is divided into frequency bands (default 10) and the percentage of sounds in each band that exceed the threshold (default -50 dBFS) is calculated using the Shannon index. The default parameters are used for calculation in this study.	Villanueva-Rivera et al, 2011
声学均匀度指数 Acoustic evenness index (AEI)	将频谱图划分为多个频段(默认为10), 并使用Gini指数计算每个频段中超过阈值(默认为-50 dBFS)的声音所占比例。本研究中使用默认参数计算。The spectrogram is divided into frequency bands (default 10) and the Gini index is used to calculate the proportion of sounds in each band that exceeded the threshold (default -50 dBFS). The default parameters are used for calculation in this study.	Villanueva-Rivera et al, 2011
生物声学指数 Bioacoustic index (BIO)	测量指定频率范围内的声音强度, 频谱中超过阈值部分的面积与大多数鸟类的鸣叫频率区间和声强有关, 本研究中使用以下参数计算: min_freq = 2000, max_freq = 11000, fft_w = 512。The sound intensity in the specified frequency range was measured and the area of the above-threshold portion of the spectrum was related to the frequency range and sound intensity of most birds’ calls. The following parameters were used in this study to calculate: min_freq = 2000, max_freq = 11000, fft_w = 512.	Boelman et al, 2007
标准化声景差异指数 Normalized difference sound index (NDSI)	通过计算人工声(1-2 kHz)与生物声(2-11 kHz)的比值来评估人类活动对声景观的影响程度。范围从-1到1, 本研究中使用默认参数计算。NDSI was assessed by calculating the ratio of anthrophony (1-2 kHz) to biophony (2-11 kHz). The range is from -1 to 1 and is calculated using the default parameters in this study.	Kasten et al, 2012

声学指数 Acoustic indices	详细信息 Details	参考文献 Reference
声熵指数 Acoustic entropy index (H)	将音频划分为多个频段, 使用Shannon指数来计算时间熵(Ht)和频谱熵(Hf), 两者相乘得到声熵指数, 体现声学信号在时域和频域上的复杂度。The audio is divided into multiple frequency bands and the Shannon index is used to calculate the temporal entropy (Ht) and spectral entropy (Hf), which are multiplied to obtain the acoustic entropy index, reflecting the complexity of the acoustic signal in the time and frequency domains.	Sueur et al, 2008
声学复杂度指数 Acoustic complexity index (ACI)	该指数用于测量相邻频率带之间振幅的变化, 反映声强的可变性和不规律性, 特别是鸟鸣声。该指数相对不受恒定强度或持续声音的影响。本研究中使用以下参数计算: min_freq = 2000, max_freq = 11000, j = 5, fft_w = 512。This index is used to measure changes in amplitude between adjacent frequency bands, reflecting the variability and irregularity of sound intensity, especially bird calls. The index is relatively unaffected by a constant intensity of a sustained sound. The following parameters are used in this study: min_freq = 2000, max_freq = 11000, j = 5, fft_w = 512.	Pieretti et al, 2011
声学多样性指数 Acoustic diversity index, (ADI)	将频谱图划分为多个频段(默认为10), 并使用Shannon指数计算每个频段中超过阈值(默认为-50 dBFS)的声音所占比例。本研究中使用默认参数计算。The spectrogram is divided into frequency bands (default 10) and the percentage of sounds in each band that exceed the threshold (default -50 dBFS) is calculated using the Shannon index. The default parameters are used for calculation in this study.	Villanueva-Rivera et al, 2011
声学均匀度指数 Acoustic evenness index (AEI)	将频谱图划分为多个频段(默认为10), 并使用Gini指数计算每个频段中超过阈值(默认为-50 dBFS)的声音所占比例。本研究中使用默认参数计算。The spectrogram is divided into frequency bands (default 10) and the Gini index is used to calculate the proportion of sounds in each band that exceeded the threshold (default -50 dBFS). The default parameters are used for calculation in this study.	Villanueva-Rivera et al, 2011
生物声学指数 Bioacoustic index (BIO)	测量指定频率范围内的声音强度, 频谱中超过阈值部分的面积与大多数鸟类的鸣叫频率区间和声强有关, 本研究中使用以下参数计算: min_freq = 2000, max_freq = 11000, fft_w = 512。The sound intensity in the specified frequency range was measured and the area of the above-threshold portion of the spectrum was related to the frequency range and sound intensity of most birds’ calls. The following parameters were used in this study to calculate: min_freq = 2000, max_freq = 11000, fft_w = 512.	Boelman et al, 2007
标准化声景差异指数 Normalized difference sound index (NDSI)	通过计算人工声(1-2 kHz)与生物声(2-11 kHz)的比值来评估人类活动对声景观的影响程度。范围从-1到1, 本研究中使用默认参数计算。NDSI was assessed by calculating the ratio of anthrophony (1-2 kHz) to biophony (2-11 kHz). The range is from -1 to 1 and is calculated using the default parameters in this study.	Kasten et al, 2012

声学指数 Acoustic indices	四月 April		五月 May		六月 June		4-6月 April-June
声学指数 Acoustic indices	丰富度 Richness	多度 Abundance	丰富度 Richness	多度 Abundance	丰富度 Richness	多度 Abundance	丰富度 Richness	多度 Abundance
声熵指数 Acoustic entropy index (H)	0.371^*	0.345^*	0.022	0.035	-0.11	0.175	0.16	0.288^**
声学复杂度指数 Acoustic complexity index (ACI)	0.614^**	0.591^**	0.349^*	0.437^**	0.456^**	0.316^*	0.425^**	0.369^**
声学多样性指数 Acoustic diversity index (ADI)	0.282	0.175	0.166	0.194	0.164	0.327^*	0.323^**	0.373^**
声学均匀度指数 Acoustic evenness index (AEI)	-0.365^*	-0.259	-0.181	-0.191	-0.153	-0.325^*	-0.339^**	-0.387^**
生物声学指数 Bioacoustic index (BIO)	0.228	0.300^*	0.034	0.007	-0.257	0.095	0.076	0.213^*
标准化声景差异指数 Normalized difference sound index (NDSI)	0.517^**	0.671^**	0.111	0.186	-0.044	0.423^**	0.269^**	0.502^**

声学指数 Acoustic indices	四月 April		五月 May		六月 June		4-6月 April-June
声学指数 Acoustic indices	丰富度 Richness	多度 Abundance	丰富度 Richness	多度 Abundance	丰富度 Richness	多度 Abundance	丰富度 Richness	多度 Abundance
声熵指数 Acoustic entropy index (H)	0.371^*	0.345^*	0.022	0.035	-0.11	0.175	0.16	0.288^**
声学复杂度指数 Acoustic complexity index (ACI)	0.614^**	0.591^**	0.349^*	0.437^**	0.456^**	0.316^*	0.425^**	0.369^**
声学多样性指数 Acoustic diversity index (ADI)	0.282	0.175	0.166	0.194	0.164	0.327^*	0.323^**	0.373^**
声学均匀度指数 Acoustic evenness index (AEI)	-0.365^*	-0.259	-0.181	-0.191	-0.153	-0.325^*	-0.339^**	-0.387^**
生物声学指数 Bioacoustic index (BIO)	0.228	0.300^*	0.034	0.007	-0.257	0.095	0.076	0.213^*
标准化声景差异指数 Normalized difference sound index (NDSI)	0.517^**	0.671^**	0.111	0.186	-0.044	0.423^**	0.269^**	0.502^**

响应变量 Response variable	解释变量 Explanatory variables	自由度 df	Loglink	AICc	ΔAICc	权重 Weight
多度 Abundance	ACI + ADI + H + NDSI	7	-562.41	1,139.65	0.00	0.29
	ACI + ADI + NDSI	6	-564.15	1,140.92	1.27	0.15
	ACI + ADI + BIO + H + NDSI	8	-562.15	1,141.39	1.74	0.12
	ACI + ADI + AEI + H + NDSI	8	-562.34	1,141.77	2.12	0.10
	ACI + ADI + BIO + NDSI	7	-563.69	1,142.22	2.56	0.08
	ACI + AEI + H + NDSI	7	-563.74	1,142.33	2.68	0.08
	ACI + AEI + NDSI	6	-565.05	1,142.72	3.07	0.06
	ACI + ADI + AEI + NDSI	7	-564.11	1,143.06	3.40	0.05
	ACI + ADI + AEI + BIO + H + NDSI	9	-562.08	1,143.54	3.88	0.04
	ACI + AEI + BIO + H + NDSI	8	-563.52	1,144.13	4.48	0.03

声学指数在城市森林鸟类多样性评估中的应用

Exploring the application of acoustic indices in the assessment of bird diversity in urban forests

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响应变量 Response variable	解释变量 Explanatory variables	参数估计 Parameter estimate	标准误 SE	P
多度 Abundance	截距 Intercept	2.68	0.10	< 0.001^***
	ACI	0.16	0.03	< 0.001^***
	ADI	0.18	0.07	0.01^**
	H	-0.10	0.06	0.07
	NDSI	0.28	0.07	< 0.001^***
	BIO	-0.05	0.06	0.44
	AEI	-0.13	0.14	0.37

响应变量 Response variable	解释变量 Explanatory variables	参数估计 Parameter estimate	标准误 SE	P
丰富度 Richness	截距 Intercept	1.250	0.054	0.000
	H	-0.044	0.073	0.512
	ACI	0.138	0.039	< 0.001^***
	ADI	0.200	0.118	0.093
	AEI	-0.026	0.161	0.873
	BIO	-0.112	0.070	0.080
	NDSI	0.064	0.073	0.344

	样点法 Point counts	声学监测 Acoustic monitoring	参考文献 Reference
优势Advantage	(1)最常采用的鸟类调查方法之一, 易于实施、易于做到随机化或系统化。One of the most commonly used bird survey methods, easy to implement and easy to randomize or systematize. (2)观测过程中能够直接获得鸟的种类、数量、行为活动等信息, 具有高效性和灵活性。The observation process is efficient and flexible as it provides direct access to information on bird species, numbers, behavioral activities, etc. (3)适用于复杂、斑块化的生境。相较于样线法, 观察员有更多的时间观察鸟类, 减弱行走速度带来的影响。Suitable for complex habitats, observers have more time to observe birds and attenuate the effects of walking speed compared to the sample line method.	(1)快速发展的调查方法之一。操作简单易重复, 支持同时进行大尺度、长期、连续的动态监测与跟踪。One of the rapidly developing survey methods, simple and repeatable in operation, supports simultaneous large-scale, long-term, continuous dynamic monitoring. (2)不侵入自然环境, 尽可能地减少现场调查对动植物的影响。有利于对偏远地区、稀有物种的监测。It does not invade the natural environment, minimizes the impact of field surveys on flora and fauna, and facilitates the monitoring of remote areas and rare species. (3)录音永久保存、可反复监听, 降低识别偏差。声学指数和机器学习的不断发展有助于快速评估生物多样性的动态变化。Recordings can be permanently stored and repeatedly monitored to reduce identification bias. The development of acoustic indices and machine learning helps to rapidly assess dynamic changes in biodiversity.	吴飞和杨晓君, 2008; Zhang et al, 2018; Darras et al, 2019
劣势Weakness	(1)观测次数有限, 不能保证多样点间同步进行, 观测周期影响结果的可靠性。The limited number of observations does not guarantee the synchronization of multiple points, and the period affects the reliability of the results. (2)对观察员的专业能力要求较高。在地形复杂、植被密集的森林中, 因视线容易受阻导致记录不完全。The professional competence of observers is required. In forests with complex terrain and dense vegetation, it is easy to have incomplete records due to obstructed vision. (3)人类的接近引入了惊飞或回避效应, 尤其是多名调查人员带来的干扰可能会使鸟类远离观察者的视线, 影响调查结果。Human proximity introduces a startle or avoidance effect, especially as disturbance from multiple investigators may keep birds away and affect findings.	(1)应对大数据的挑战。长期录制产生庞大的音频文件, 需要考虑大数据的存储与处理, 电池容量及更换成本等。The challenge of big data. Long-term recording generates huge audio files, requiring consideration of big data storage and processing, battery capacity, and replacement costs, etc. (2)人工识别需要大量的时间精力, 目前自动识别的技术还易受到背景噪声和声音重叠的干扰导致结果偏差。 Manual recognition takes a lot of time, and current automatic recognition techniques are still subject to interference from background noise and sound overlap, leading to biased results. (3)天气条件或人为破坏对录音设备的灵敏度造成影响, 长期监测中数据丢失可能会在很长一段时间内不被注意到, 需要定期查看维护。Weather conditions or vandalism affect the sensitivity of recording equipment, and data loss during long-term monitoring may unnoticed and require regular maintenance.	Prabowo et al, 2016; Priyadarshani et al, 2018; Darras et al, 2019

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