东北虎豹国家公园森林声景的昼夜和季节变化

doi:10.17520/biods.2022523

生物多样性 ›› 2023, Vol. 31 ›› Issue (1): 22523. DOI: 10.17520/biods.2022523

• 中国野生脊椎动物鸣声监测与生物声学研究专题 • 上一篇下一篇

东北虎豹国家公园森林声景的昼夜和季节变化

孙翊斐¹^,²^,³, 王士政¹^,²^,³, 冯佳伟¹^,²^,³, 王天明¹^,²^,³^,^*()

1.东北虎豹国家公园保护生态学国家林业和草原局重点实验室, 北京 100875
2.生物多样性与生态工程教育部重点实验室, 北京 100875
3.北京师范大学生命科学学院, 北京 100875

收稿日期:2022-09-13 接受日期:2023-01-08 出版日期:2023-01-20 发布日期:2023-01-30
通讯作者: *王天明, E-mail: wangtianming@bnu.edu.cn
基金资助:
国家科技基础资源调查专项(2019FY101700);国家科技基础资源调查专项(2021FY100702)

Diel and seasonal variability of the forest soundscape in the Northeast China Tiger and Leopard National Park

Yifei Sun¹^,²^,³, Shizheng Wang¹^,²^,³, Jiawei Feng¹^,²^,³, Tianming Wang¹^,²^,³^,^*()

1. National Forestry and Grassland Administration Key Laboratory for Conservation Ecology of Northeast Tiger and Leopard, Beijing 100875
2. Ministry of Education Key Laboratory for Biodiversity Science and Engineering, Beijing 100875
3. College of Life Sciences, Beijing Normal University, Beijing 100875

Received:2022-09-13 Accepted:2023-01-08 Online:2023-01-20 Published:2023-01-30
Contact: *Tianming Wang, E-mail: wangtianming@bnu.edu.cn

摘要/Abstract

摘要：

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1-21 kHz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显著不同于其他时段; 白天声景的复杂度和多样性以及生物声的强度更高, 但夏季的夜晚比白天有更高的声音复杂度; 春季的黎明时段由于强烈的鸟类和鸣而具有较高的声音多样性和生物声强度。此外, 声景和声学成分具有显著的季节性差异, 其中春、夏、秋等3个季节(主要是5-10月)具有高的声音复杂度、多样性和生物声强度, 但每个声学指数峰值出现的时间具有高度的异质性。本研究为东北虎豹国家公园声景资源的恢复和保护提供了基础数据, 未来需要进一步结合非声学变量深入探讨区域声景形成的驱动因素, 揭示人类干扰和气候变化的影响。

关键词: 生态声学, 森林声景, 声学指数, 东北虎豹国家公园, 生物多样性监测

Abstract

Aims: Passive acoustic monitoring (PAM) and acoustic indices could provide an effective method for monitoring changes in forest soundscapes. However, annual dynamics of soundscapes remain poorly understood. We studied the diel, seasonal patterns, and shifts of the biotic component of the acoustic environment in the Northeast China Tiger and Leopard National Park.

Methods: We sampled recordings at 52 sites from June 2020 to June 2021 within the park. For each recording, we calculated the acoustic complexity index (ACI), acoustic diversity index (ADI), acoustic evenness index (AEI), bioacoustic index (BIO), normalized difference soundscape index (NDSI), acoustic entropy (H), and power spectral density (PSD) of each 1 kHz frequency bin within the 1-21 kHz frequency band. We divided a diel cycle into 4 phases (dawn, day, dusk, and night) according to the times the sun rose and set, and utilized generalized additive models (GAMs) to fit the curves of the diel and annual patterns of acoustic indices.

Results: The soundscapes demonstrated a marked distinction among all four phases, especially between night and other time periods. The soundscapes typically had more acoustic complexity, diversity, and biophony intensity during the daytime. However, night soundscapes showed more acoustic complexity throughout the summer season. In spring, the acoustic diversity index and bioacoustic index were higher at dawn due to intensive bird choruses. Acoustic complexity, diversity, and intensity of biophony demonstrate varied annual dynamics and peaked from May to October.

Conclusion: This study identified measurements that effectively summarize baseline soundscape attributes and prioritizes future opportunities for integrating non-acoustic and acoustic variables in research in order to inform area-specific management questions within the Northeast China Tiger and Leopard National Park.

Key words: ecoacoustics, forest soundscape, acoustic indices, Northeast China Tiger and Leopard National Park, biodiversity monitoring

孙翊斐, 王士政, 冯佳伟, 王天明 (2023) 东北虎豹国家公园森林声景的昼夜和季节变化. 生物多样性, 31, 22523. DOI: 10.17520/biods.2022523.

Yifei Sun, Shizheng Wang, Jiawei Feng, Tianming Wang (2023) Diel and seasonal variability of the forest soundscape in the Northeast China Tiger and Leopard National Park. Biodiversity Science, 31, 22523. DOI: 10.17520/biods.2022523.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2022523

https://www.biodiversity-science.net/CN/Y2023/V31/I1/22523

图/表 12

图1 东北虎豹国家公园东部研究区位置和52个声景监测样点

Fig. 1 Location of the study area in the eastern part of the Northeast China Tiger and Leopard National Park and 52 sampling sites

图2 东北虎豹国家公园森林声景中主要声学元素的3D声谱图

Fig. 2 3D spectrograms of major acoustic elements from the forest soundscape in the Northeast China Tiger and Leopard National Park

图3 一天内不同时间段声景的非度量多维尺度法(NMDS)排序图。A、B、C和D分别为春季、夏季、秋季和冬季, 椭圆表示围绕聚类质心的95%置信区间。

Fig. 3 Non-metric multidimensional scaling (NMDS) ordination plots showing the diel divisions between soundscapes in spring (A), summer (B), autumn (C) and winter (D). Group centroids are shown with 95% confidence interval ellipses.

图4 一天内不同时间段基于功率谱密度(PSD)的声学成分非度量多维尺度法(NMDS)排序图。A、B、C和D分别为春季、夏季、秋季和冬季, 椭圆表示围绕聚类质心的95%置信区间。

Fig. 4 Non-metric multidimensional scaling (NMDS) ordination plots showing the diel divisions between acoustic components using power spectral density (PSD) in spring (A), summer (B), autumn (C) and winter (D). Group centroids are shown with 95% confidence interval ellipses.

图5 不同季节中每个声学指数昼夜变化趋势的广义加性模型(GAM)拟合曲线。时间已由时钟时转换为太阳时, 橙色垂直虚线表示日出和日落时间, 黄色、橙色、灰色阴影和空白区域分别代表黎明、黄昏、夜晚和白天。

Fig. 5 Fitted curves from generalized additive models (GAMs) showing clear diel patterns of each acoustic index in different seasons. Time of day was converted from clock time to sun time. Dashed lines in orange show sunrise and sunset. Yellow, orange, grey shades and blank represent dawn, dusk, night and day, respectively.

表1 不同季节各声学指数昼夜差异的Kruskal-Wallis检验结果统计表

Table 1 Results of Kruskal-Wallis tests examining significance of acoustic index differences among diel phases in different seasons

季节 Season	声学指数 Acoustic index	黎明 Dawn	白天 Day	黄昏 Dusk	夜晚 Night	χ2	P
春季 Spring	声音复杂度指数 ACI	908.99 ± 1.36^ab	912.70 ± 1.02^a	903.49 ± 0.89^b	895.68 ± 1.02^c	97.453	< 0.001^***
	声音多样性指数 ADI	1.38 ± 0.05^a	1.09 ± 0.04^b	0.93 ± 0.05^b	0.58 ± 0.05^c	80.140	< 0.001^***
	声音均匀度指数 AEI	0.84 ± 0.01^a	0.87 ± 0.01^b	0.88 ± 0.01^b	0.91 ± 0.01^c	78.321	< 0.001^***
	生物声学指数 BIO	12.79 ± 0.63^a	9.79 ± 0.45^ab	8.38 ± 0.40^b	5.89 ± 0.31^c	78.187	< 0.001^***
	标准化声景差异指数 NDSI	0.57 ± 0.03^a	0.39 ± 0.04^b	0.30 ± 0.04^b	0.22 ± 0.05^b	35.158	< 0.001^***
	声音熵指数 H	0.37 ± 0.01^a	0.38 ± 0.01^a	0.36 ± 0.01^ab	0.34 ± 0.01^b	15.694	< 0.001^***
夏季 Summer	声音复杂度指数 ACI	905.12 ± 1.25^a	898.86 ± 0.85^b	897.38 ± 0.61^b	908.09 ± 2.04^a	43.277	< 0.001^***
	声音多样性指数 ADI	1.61 ± 0.04^ab	1.78 ± 0.05^a	1.64 ± 0.05^a	1.40 ± 0.06^b	27.909	< 0.001^***
	声音均匀度指数 AEI	0.80 ± 0.01^a	0.75 ± 0.01^b	0.78 ± 0.01^ab	0.81 ± 0.01^a	25.499	< 0.001^***
	生物声学指数 BIO	13.27 ± 0.46^a	12.03 ± 0.36^a	11.64 ± 0.34^a	8.81 ± 0.28^b	79.432	< 0.001^***
	标准化声景差异指数 NDSI	0.58 ± 0.04^a	0.67 ± 0.03^a	0.63 ± 0.03^a	0.42 ± 0.05^b	20.688	< 0.001^***
	声音熵指数 H	0.42 ± 0.01^ab	0.51 ± 0.01^c	0.47 ± 0.01^ac	0.41 ± 0.01^b	32.107	< 0.001^***
秋季 Autumn	声音复杂度指数 ACI	902.78 ± 1.06^a	906.87 ± 1.02^b	901.52 ± 0.94^a	897.82 ± 0.86^c	47.306	< 0.001^***
	声音多样性指数 ADI	1.32 ± 0.05^a	1.58 ± 0.05^b	1.37 ± 0.05^ab	0.95 ± 0.05^c	53.534	< 0.001^***
	声音均匀度指数 AEI	0.84 ± 0.01^a	0.79 ± 0.01^b	0.82 ± 0.01^ab	0.87 ± 0.01^c	50.750	< 0.001^***
	生物声学指数 BIO	10.65 ± 0.38^a	9.45 ± 0.24^ab	8.81 ± 0.24^b	7.43 ± 0.40^c	44.403	< 0.001^***
	标准化声景差异指数 NDSI	0.40 ± 0.04^ab	0.52 ± 0.03^a	0.40 ± 0.04^ab	0.25 ± 0.05^b	14.445	0.002^**
	声音熵指数 H	0.40 ± 0.01^a	0.46 ± 0.01^b	0.42 ± 0.01^ab	0.38 ± 0.01^a	21.746	< 0.001^***
冬季 Winter	声音复杂度指数 ACI	900.38 ± 0.78^a	909.17 ± 1.72^b	901.30 ± 1.12^a	894.93 ± 0.81^c	67.327	< 0.001^***
	声音多样性指数 ADI	0.87 ± 0.04^ab	0.98 ± 0.05^a	0.74 ± 0.04^b	0.47 ± 0.04^c	57.509	< 0.001^***
	声音均匀度指数 AEI	0.88 ± 0.01^a	0.86 ± 0.01^a	0.89 ± 0.01^a	0.91 ± 0.01^b	38.324	< 0.001^***
	生物声学指数 BIO	6.05 ± 0.16^ab	6.54 ± 0.19^a	5.49 ± 0.16^b	4.61 ± 0.11^c	64.825	< 0.001^***
	标准化声景差异指数 NDSI	0.60 ± 0.02^a	0.63 ± 0.02^a	0.60 ± 0.02^a	0.63 ± 0.02^a	3.510	0.320
	声音熵指数 H	0.31 ± 0.01^ab	0.34 ± 0.01^a	0.32 ± 0.01^ab	0.30 ± 0.01^b	8.840	0.031^*

表1 不同季节各声学指数昼夜差异的Kruskal-Wallis检验结果统计表

Table 1 Results of Kruskal-Wallis tests examining significance of acoustic index differences among diel phases in different seasons

季节 Season	声学指数 Acoustic index	黎明 Dawn	白天 Day	黄昏 Dusk	夜晚 Night	χ2	P
春季 Spring	声音复杂度指数 ACI	908.99 ± 1.36^ab	912.70 ± 1.02^a	903.49 ± 0.89^b	895.68 ± 1.02^c	97.453	< 0.001^***
	声音多样性指数 ADI	1.38 ± 0.05^a	1.09 ± 0.04^b	0.93 ± 0.05^b	0.58 ± 0.05^c	80.140	< 0.001^***
	声音均匀度指数 AEI	0.84 ± 0.01^a	0.87 ± 0.01^b	0.88 ± 0.01^b	0.91 ± 0.01^c	78.321	< 0.001^***
	生物声学指数 BIO	12.79 ± 0.63^a	9.79 ± 0.45^ab	8.38 ± 0.40^b	5.89 ± 0.31^c	78.187	< 0.001^***
	标准化声景差异指数 NDSI	0.57 ± 0.03^a	0.39 ± 0.04^b	0.30 ± 0.04^b	0.22 ± 0.05^b	35.158	< 0.001^***
	声音熵指数 H	0.37 ± 0.01^a	0.38 ± 0.01^a	0.36 ± 0.01^ab	0.34 ± 0.01^b	15.694	< 0.001^***
夏季 Summer	声音复杂度指数 ACI	905.12 ± 1.25^a	898.86 ± 0.85^b	897.38 ± 0.61^b	908.09 ± 2.04^a	43.277	< 0.001^***
	声音多样性指数 ADI	1.61 ± 0.04^ab	1.78 ± 0.05^a	1.64 ± 0.05^a	1.40 ± 0.06^b	27.909	< 0.001^***
	声音均匀度指数 AEI	0.80 ± 0.01^a	0.75 ± 0.01^b	0.78 ± 0.01^ab	0.81 ± 0.01^a	25.499	< 0.001^***
	生物声学指数 BIO	13.27 ± 0.46^a	12.03 ± 0.36^a	11.64 ± 0.34^a	8.81 ± 0.28^b	79.432	< 0.001^***
	标准化声景差异指数 NDSI	0.58 ± 0.04^a	0.67 ± 0.03^a	0.63 ± 0.03^a	0.42 ± 0.05^b	20.688	< 0.001^***
	声音熵指数 H	0.42 ± 0.01^ab	0.51 ± 0.01^c	0.47 ± 0.01^ac	0.41 ± 0.01^b	32.107	< 0.001^***
秋季 Autumn	声音复杂度指数 ACI	902.78 ± 1.06^a	906.87 ± 1.02^b	901.52 ± 0.94^a	897.82 ± 0.86^c	47.306	< 0.001^***
	声音多样性指数 ADI	1.32 ± 0.05^a	1.58 ± 0.05^b	1.37 ± 0.05^ab	0.95 ± 0.05^c	53.534	< 0.001^***
	声音均匀度指数 AEI	0.84 ± 0.01^a	0.79 ± 0.01^b	0.82 ± 0.01^ab	0.87 ± 0.01^c	50.750	< 0.001^***
	生物声学指数 BIO	10.65 ± 0.38^a	9.45 ± 0.24^ab	8.81 ± 0.24^b	7.43 ± 0.40^c	44.403	< 0.001^***
	标准化声景差异指数 NDSI	0.40 ± 0.04^ab	0.52 ± 0.03^a	0.40 ± 0.04^ab	0.25 ± 0.05^b	14.445	0.002^**
	声音熵指数 H	0.40 ± 0.01^a	0.46 ± 0.01^b	0.42 ± 0.01^ab	0.38 ± 0.01^a	21.746	< 0.001^***
冬季 Winter	声音复杂度指数 ACI	900.38 ± 0.78^a	909.17 ± 1.72^b	901.30 ± 1.12^a	894.93 ± 0.81^c	67.327	< 0.001^***
	声音多样性指数 ADI	0.87 ± 0.04^ab	0.98 ± 0.05^a	0.74 ± 0.04^b	0.47 ± 0.04^c	57.509	< 0.001^***
	声音均匀度指数 AEI	0.88 ± 0.01^a	0.86 ± 0.01^a	0.89 ± 0.01^a	0.91 ± 0.01^b	38.324	< 0.001^***
	生物声学指数 BIO	6.05 ± 0.16^ab	6.54 ± 0.19^a	5.49 ± 0.16^b	4.61 ± 0.11^c	64.825	< 0.001^***
	标准化声景差异指数 NDSI	0.60 ± 0.02^a	0.63 ± 0.02^a	0.60 ± 0.02^a	0.63 ± 0.02^a	3.510	0.320
	声音熵指数 H	0.31 ± 0.01^ab	0.34 ± 0.01^a	0.32 ± 0.01^ab	0.30 ± 0.01^b	8.840	0.031^*

图6 不同季节中每个功率谱密度(PSD)昼夜变化趋势的广义加性模型(GAM)拟合曲线。时间已由时钟时转换为太阳时, 橙色垂直虚线表示日出和日落时间, 黄色、橙色、灰色阴影和空白区域分别代表黎明、黄昏、夜晚和白天。

Fig. 6 Fitted curves from generalized additive models (GAMs) showing clear diel patterns of each power spectral density (PSD) in different seasons. Time of day was converted from clock time to sun time. Dashed lines in orange shows sunrise and sunset. Yellow, orange, grey shades and blank represent dawn, dusk, night and day, respectively.

图7 不同季节整体声景(A)和基于功率谱密度(PSD)的声学成分非度量多维尺度法(NMDS) (B)排序图。椭圆表示围绕聚类质心的95%置信区间。

Fig. 7 Non-metric multidimensional scaling (NMDS) ordination plots showing the seasonal divisions between soundscapes (A) and acoustic components using power spectral density (PSD) (B). Group centroids are shown with 95% confidence interval ellipses.

图8 每个声学指数季节变化趋势的广义加性模型(GAM)拟合曲线

Fig. 8 Fitted curves from generalized additive models (GAMs) showing clear seasonal patterns of each acoustic index

表2 各声学指数季节差异的Kruskal-Wallis检验结果统计表

Table 2 Results of Kruskal-Wallis tests examining significance of acoustic index differences among seasons

声学指数 Acoustic index	春季 Spring	夏季 Summer	秋季 Autumn	冬季 Winter	χ2	P
声音复杂度指数 ACI	903.41 ± 0.82^a	902.39 ± 0.85^a	900.74 ± 0.77^ab	898.72 ± 0.94^b	27.325	< 0.001^***
声音多样性指数 ADI	0.88 ± 0.04^a	1.61 ± 0.04^b	1.18 ± 0.05^c	0.63 ± 0.04^d	115.990	< 0.001^***
声音均匀度指数 AEI	0.89 ± 0.01^a	0.78 ± 0.01^b	0.84 ± 0.01^c	0.90 ± 0.01^a	109.720	< 0.001^***
生物声学指数 BIO	8.32 ± 0.40^a	10.77 ± 0.29^b	8.40 ± 0.31^a	5.19 ± 0.11^c	113.100	< 0.001^***
标准化声景差异指数 NDSI	0.33 ± 0.04^a	0.57 ± 0.03^b	0.36 ± 0.04^a	0.62 ± 0.01^b	56.311	< 0.001^***
声音熵指数 H	0.36 ± 0.01^a	0.46 ± 0.01^b	0.41 ± 0.01^b	0.31 ± 0.01^c	80.463	< 0.001^***

图9 不同频段功率谱密度(PSD)季节变化趋势的广义加性模型(GAM)拟合曲线

Fig. 9 Fitted curves from generalized additive models (GAMs) showing clear seasonal patterns of each power spectral density (PSD)

图10 东北虎豹国家公园不同季节主要声学元素变化示意图

Fig. 10 Seasonal variation of major acoustic elements in the Northeast China Tiger and Leopard National Park

参考文献 70

[1]	Alcocer I, Lima H, Sugai LSM, Llusia D (2022) Acoustic indices as proxies for biodiversity: A meta-analysis. Biological Reviews of the Cambridge Philosophical Society, 97, 2209-2236. DOI PMID
[2]	Barbaro L, Sourdril A, Froidevaux JSP, Cauchoix M, Calatayud F, Deconchat M, Gasc A (2022) Linking acoustic diversity to compositional and configurational heterogeneity in mosaic landscapes. Landscape Ecology, 37, 1125-1143. DOI URL
[3]	Bertucci F, Parmentier E, Lecellier G, Hawkins AD, Lecchini D (2016) Acoustic indices provide information on the status of coral reefs: An example from Moorea Island in the South Pacific. Scientific Reports, 6, 33326. DOI PMID
[4]	Boelman NT, Asner GP, Hart PJ, Martin RE (2007) Multi-trophic invasion resistance in Hawaii: Bioacoustics, field surveys, and airborne remote sensing. Ecological Applications, 17, 2137-2144. DOI URL
[5]	Bradfer-Lawrence T, Gardner N, Bunnefeld L, Bunnefeld N, Willis SG, Dent DH (2019) Guidelines for the use of acoustic indices in environmental research. Methods in Ecology and Evolution, 10, 1796-1807. DOI
[6]	Burivalova Z, Purnomo, Orndorff S, Truskinger A, Roe P, Game ET (2021) The sound of logging: Tropical forest soundscape before, during, and after selective timber extraction. Biological Conservation, 254, 108812.
[7]	Butler J, Pagniello CMLS, Jaffe JS, Parnell PE, Širović A (2021) Diel and seasonal variability in kelp forest soundscapes off the southern California coast. Frontiers in Marine Science, 8, 629643.
[8]	Buxton RT, Agnihotri S, Robin VV, Goel A, Balakrishnan R (2018) Acoustic indices as rapid indicators of avian diversity in different land-use types in an Indian biodiversity hotspot. Journal of Ecoacoustics, 2, 1-17.
[9]	Buxton RT, Pearson AL, Allou C, Fristrup K, Wittemyer G (2021) A synthesis of health benefits of natural sounds and their distribution in national parks. Proceedings of the National Academy of Sciences, USA, 118, e2013097118.
[10]	Chen YF, Luo YH, Mammides C, Cao KF, Zhu SD, Goodale E (2021) The relationship between acoustic indices, elevation, and vegetation, in a forest plot network of Southern China. Ecological Indicators, 129, 107942.
[11]	Depraetere M, Pavoine S, Jiguet F, Gasc A, Duvail S, Sueur J (2012) Monitoring animal diversity using acoustic indices: Implementation in a temperate woodland. Ecological Indicators, 13, 46-54. DOI URL
[12]	Derryberry EP, Phillips JN, Derryberry GE, Blum MJ, Luther D (2020) Singing in a silent spring: Birds respond to a half-century soundscape reversion during the COVID-19 shutdown. Science, 370, 575-579.
[13]	Devi RR, Pugazhenthi D (2016) Ideal sampling rate to reduce distortion in audio steganography. Procedia Computer Science, 85, 418-424. DOI URL
[14]	Diepstraten J, Willie J (2021) Assessing the structure and drivers of biological sounds along a disturbance gradient. Global Ecology and Conservation, 31, e01819.
[15]	Dinerstein E, Loucks C, Wikramanayake E, Ginsberg J, Sanderson E, Seidensticker J, Forrest J, Bryja G, Heydlauff A, Klenzendorf S, Leimgruber P, Mills J, O’Brien TG, Shrestha M, Simons R, Songer M (2007) The fate of wild tigers. BioScience, 57, 508-514. DOI URL
[16]	Dirzo R, Young HS, Galetti M, Ceballos G, Isaac NJB, Collen B (2014) Defaunation in the anthropocene. Science, 345, 401-406. DOI PMID
[17]	Dooley JM, Brown MT (2020) The quantitative relation between ambient soundscapes and landscape development intensity in North Central Florida. Landscape Ecology, 35, 113-127. DOI
[18]	Doser JW, Finley AO, Kasten EP, Gage SH (2020) Assessing soundscape disturbance through hierarchical models and acoustic indices: A case study on a shelterwood logged northern Michigan forest. Ecological Indicators, 113, 106244.
[19]	Eldridge A, Guyot P, Moscoso P, Johnston A, Eyre-Walker Y, Peck M (2018) Sounding out ecoacoustic metrics: Avian species richness is predicted by acoustic indices in temperate but not tropical habitats. Ecological Indicators, 95, 939-952. DOI URL
[20]	Elise S, Urbina-Barreto I, Pinel R, Mahamadaly V, Bureau S, Penin L, Adjeroud M, Kulbicki M, Bruggemann JH (2019) Assessing key ecosystem functions through soundscapes: A new perspective from coral reefs. Ecological Indicators, 107, 105623.
[21]	Farina A (2013) Soundscape Ecology: Principles, Patterns, Methods and Applications. Springer, Cham.
[22]	Feng RN, Lü XY, Xiao WH, Feng JW, Sun YF, Guan Y, Feng LM, Smith JLD, Ge JP, Wang TM (2021) Effects of free-ranging livestock on sympatric herbivores at fine spatiotemporal scales. Landscape Ecology, 36, 1441-1457. DOI URL
[23]	Fuller S, Axel AC, Tucker D, Gage SH (2015) Connecting soundscape to landscape: Which acoustic index best describes landscape configuration? Ecological Indicators, 58, 207-215. DOI URL
[24]	Gage SH, Axel AC (2014) Visualization of temporal change in soundscape power of a Michigan lake habitat over a 4-year period. Ecological Informatics, 21, 100-109. DOI URL
[25]	Gage SH, Towsey M, Kasten EP (2017) Analytical methods in ecoacoustics. In: Ecoacoustics: The Ecological Role of Sounds (eds Farina A, Gage SH), pp. 273-296. John Wiley & Sons, Oxford.
[26]	Gasc A, Francomano D, Dunning JB, Pijanowski BC (2017) Future directions for soundscape ecology: The importance of ornithological contributions. The Auk, 134, 215-228. DOI URL
[27]	Gasc A, Sueur J, Jiguet F, Devictor V, Grandcolas P, Burrow C, Depraetere M, Pavoine S (2013) Assessing biodiversity with sound: Do acoustic diversity indices reflect phylogenetic and functional diversities of bird communities? Ecological Indicators, 25, 279-287. DOI URL
[28]	Gibb R, Browning E, Glover-Kapfer P, Jones KE (2019) Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring. Methods in Ecology and Evolution, 10, 169-185. DOI URL
[29]	Gómez WE, Isaza CV, Daza JM (2018) Identifying disturbed habitats: A new method from acoustic indices. Ecological Informatics, 45, 16-25. DOI URL
[30]	Hao ZZ, Wang C, Sun ZK, Zhao DX, Sun BQ, Wang HJ, van den Bosch CK (2021) Vegetation structure and temporality influence the dominance, diversity, and composition of forest acoustic communities. Forest Ecology and Management, 482, 118871.
[31]	Haver SM, Rand Z, Hatch LT, Lipski D, Dziak RP, Gedamke J, Haxel J, Heppell SA, Jahncke J, McKenna MF, Mellinger DK, Oestreich WK, Roche L, Ryan J, Van Parijs SM (2020) Seasonal trends and primary contributors to the low-frequency soundscape of the Cordell Bank National Marine Sanctuary. The Journal of the Acoustical Society of America, 148, 845-858. DOI URL
[32]	Jantz SM, Barker B, Brooks TM, Chini LP, Huang QY, Moore RM, Noel J, Hurtt GC (2015) Future habitat loss and extinctions driven by land-use change in biodiversity hotspots under four scenarios of climate-change mitigation. Conservation Biology, 29, 1122-1131. DOI PMID
[33]	Kasten EP, Gage SH, Fox J, Joo W (2012) The remote environmental assessment laboratory’s acoustic library: An archive for studying soundscape ecology. Ecological Informatics, 12, 50-67.
[34]	Lawson J, Whitworth A, Banks-Leite C (2022) Soundscapes show disruption across the diel cycle in human modified tropical landscapes. Ecological Indicators, 144, 109413.
[35]	Li JW, Shi J, Xue Y, Mao HB, Luo YQ (2014) Major physiological adjustments in freezing-tolerant grey tiger longicorn beetle (Xylotrechus rusticus) during overwintering period. Journal of Forestry Research, 25, 653-659. DOI URL
[36]	Li ZW, Wu JG, Kou XJ, Tian Y, Wang TM, Mu P, Ge JP (2009) Land use pattern and its dynamic changes in Amur tiger distribution region. Chinese Journal of Applied Ecology, 20, 713-724. (in Chinese with English abstract)
	[李钟汶, 邬建国, 寇晓军, 田瑜, 王天明, 牟溥, 葛剑平 (2009) 东北虎分布区土地利用格局与动态. 应用生态学报, 20, 713-724.]
[37]	Maysenhölder W, Heggli M, Zhou X, Zhang T, Frei E, Schneebeli M (2012) Microstructure and sound absorption of snow. Cold Regions Science and Technology, 83/84, 3-12. DOI URL
[38]	Metcalf OC, Lees AC, Barlow J, Marsden SJ, Devenish C (2020) hardRain: An R package for quick, automated rainfall detection in ecoacoustic datasets using a threshold-based approach. Ecological Indicators, 109, 105793.
[39]	Metcalf OC, Barlow J, Devenish C, Marsden S, Berenguer E, Lees AC (2021) Acoustic indices perform better when applied at ecologically meaningful time and frequency scales. Methods in Ecology and Evolution, 12, 421-431. DOI URL
[40]	Morrison CA, Auniņš A, Benkő Z, Brotons L, Chodkiewicz T, Chylarecki P, Escandell V, Eskildsen DP, Gamero A, Herrando S, Jiguet F, Kålås JA, Kamp J, Klvaňová A, Kmecl P, Lehikoinen A, Lindström, Moshøj C, Noble DG, Øien IJ, Paquet JY, Reif J, Sattler T, Seaman BS, Teufelbauer N, Trautmann S, Vořišek P, Butler SJ (2021) Bird population declines and species turnover are changing the acoustic properties of spring soundscapes. Nature Communications, 12, 6217. DOI PMID
[41]	Mullet TC, Gage SH, Morton JM, Huettmann F (2016) Temporal and spatial variation of a winter soundscape in south-central Alaska. Landscape Ecology, 31, 1117-1137. DOI URL
[42]	Newbold T, Hudson LN, Hill SLL, Contu S, Lysenko I, Senior RA, Börger L, Bennett DJ, Choimes A, Collen B, Day J, De Palma A, Díaz S, Echeverria-Londoño S, Edgar MJ, Feldman A, Garon M, Harrison MLK, Alhusseini T, Ingram DJ, Itescu Y, Kattge J, Kemp V, Kirkpatrick L, Kleyer M, Laginha Pinto Correia D, Martin CD, Shai MR, Novosolov M, Yuan P, Phillips HRP, Purves DW, Robinson A, Simpson J, Tuck SL, Weiher E, White HJ, Ewers RM, Mace GM, Scharlemann JPW, Purvis A (2015) Global effects of land use on local terrestrial biodiversity. Nature, 520, 45-50. DOI URL
[43]	Nouvellet P, Rasmussen GSA, MacDonald DW, Courchamp F (2012) Noisy clocks and silent sunrises: Measurement methods of daily activity pattern. Journal of Zoology, 286, 179-184. DOI URL
[44]	Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Szoecs E, Wagner H (2020) vegan: Community Ecology Package. https://CRAN.R-project.org/package=vegan. (accessed on 2020-12-22)
[45]	Pieretti N, Farina A, Morri D (2011) A new methodology to infer the singing activity of an avian community: The acoustic complexity index (ACI). Ecological Indicators, 11, 868-873. DOI URL
[46]	Pijanowski BC, Farina A, Gage SH, Dumyahn SL, Krause BL (2011a) What is soundscape ecology? An introduction and overview of an emerging new science. Landscape Ecology, 26, 1213-1232. DOI URL
[47]	Pijanowski BC, Villanueva-Rivera LJ, Dumyahn SL, Farina A, Krause BL, Napoletano BM, Gage SH, Pieretti N (2011b) Soundscape Ecology: The science of sound in the landscape. BioScience, 61, 203-216. DOI URL
[48]	Pohlert T (2021) PMCMRplus: Calculate Pairwise Multiple Comparisons of Mean Rank Sums Extended. R package version 1.9.6. https://CRAN.R-project.org/package=PMCMRplus. (accessed on 2021-03-16)
[49]	Reed J, Deakin L, Sunderland T (2015) What are ‘Integrated Landscape Approaches’ and how effectively have they been implemented in the tropics: A systematic map protocol. Environmental Evidence, 4, 1-7. DOI URL
[50]	Ridout MS, Linkie M (2009) Estimating overlap of daily activity patterns from camera trap data. Journal of Agricultural, Biological, and Environmental Statistics, 14, 322-337. DOI URL
[51]	Rocha PC, Romano PSR (2021) The shape of sound: A new R package that crosses the bridge between Bioacoustics and Geometric Morphometrics. Methods in Ecology and Evolution, 12, 1115-1121. DOI URL
[52]	Roe P, Eichinski P, Fuller RA, McDonald PG, Schwarzkopf L, Towsey M, Truskinger A, Tucker D, Watson DM (2021) The Australian acoustic observatory. Methods in Ecology and Evolution, 12, 1802-1808. DOI URL
[53]	Ross SRPJ, Friedman NR, Dudley KL, Yoshimura M, Yoshida T, Economo EP (2018) Listening to ecosystems: Data-rich acoustic monitoring through landscape-scale sensor networks. Ecological Research, 33, 135-147.
[54]	Sousa-Lima RS, Ferreira LM, Oliveira EG, Lopes LC, Brito MR, Baumgarten J, Rodrigues FH (2018) What do insects, anurans, birds, and mammals have to say about soundscape indices in a tropical savanna. Journal of Ecoacoustics, 2, PVH6YZ.
[55]	Stowell D, Sueur J (2020) Ecoacoustics: Acoustic sensing for biodiversity monitoring at scale. Remote Sensing in Ecology and Conservation, 6, 217-219. DOI URL
[56]	Sueur J, Krause B, Farina A (2019) Climate change is breaking earth’s beat. Trends in Ecology & Evolution, 34, 971-973. DOI URL
[57]	Sueur J, Pavoine S, Hamerlynck O, Duvail S (2008) Rapid acoustic survey for biodiversity appraisal. PLoS ONE, 3, e4065.
[58]	Sugai LSM, Silva TSF, Ribeiro JW, Llusia D (2019) Terrestrial passive acoustic monitoring: Review and perspectives. BioScience, 69, 15-25. DOI
[59]	Sun YJ, Yen SC, Lin TH (2022) soundscape_IR: A source separation toolbox for exploring acoustic diversity in soundscapes. Methods in Ecology and Evolution, 13, 2347-2355. DOI URL
[60]	van der Lee GH, Desjonquères C, Sueur J, Kraak MHS, Verdonschot PFM (2020) Freshwater ecoacoustics: Listening to the ecological status of multi-stressed lowland waters. Ecological Indicators, 113, 106252.
[61]	Villanueva-Rivera LJ, Pijanowski BC (2018) soundecology: Soundscape Ecology. R package version 1.3.3. https://CRAN.R-project.org/package=soundecology. (accessed on 2020-09-11)
[62]	Villanueva-Rivera LJ, Pijanowski BC, Doucette J, Pekin B (2011) A primer of acoustic analysis for landscape ecologists. Landscape Ecology, 26, 1233-1246. DOI URL
[63]	Wang TM, Andrew Royle J, Smith JLD, Zou L, Lü XY, Li T, Yang HT, Li ZL, Feng RN, Bian YJ, Feng LM, Ge JP (2018) Living on the edge: Opportunities for Amur tiger recovery in China. Biological Conservation, 217, 269-279. DOI URL
[64]	Wang TM, Feng LM, Mou P, Wu JG, Smith JLD, Xiao WH, Yang HT, Dou HL, Zhao XD, Cheng YC, Zhou B, Wu HY, Zhang L, Tian Y, Guo QX, Kou XJ, Han XM, Miquelle DG, Oliver CD, Xu RM, Ge JP (2016) Amur tigers and leopards returning to China: Direct evidence and a landscape conservation plan. Landscape Ecology, 31, 491-503. DOI URL
[65]	Welch P (1967) The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE Transactions on Audio and Electroacoustics, 15, 70-73. DOI URL
[66]	Wickham H (2009) Ggplot2:Elegant Graphics for Data Analysis, 2nd edn. Springer, New York.
[67]	Yang HT, Zhao XD, Han BY, Wang TM, Mou P, Ge JP, Feng LM (2018) Spatiotemporal patterns of Amur leopards in Northeast China: Influence of tigers, prey, and humans. Mammalian Biology, 92, 120-128. DOI URL
[68]	Yen SC, Shieh BS, Wang YT, Wang Y (2013) Rutting vocalizations of Formosan sika deer Cervus nippon taiouanus—Acoustic structure, seasonal and diurnal variations, and individuality. Zoological Science, 30, 1025-1031. DOI URL
[69]	Zhao Y, Shen XL, Li S, Zhang YY, Peng RH, Ma KP (2020) Progress and outlook for soundscape ecology. Biodiversity Science, 28, 806-820. (in Chinese with English abstract) DOI
	[赵莹, 申小莉, 李晟, 张雁云, 彭任华, 马克平 (2020) 声景生态学研究进展和展望. 生物多样性, 28, 806-820.] DOI
[70]	Znidersic E, Towsey M, Roy WK, Darling SE, Truskinger A, Roe P, Watson DM (2020) Using visualization and machine learning methods to monitor low detectability species—The least bittern as a case study. Ecological Informatics, 55, 101014.

东北虎豹国家公园森林声景的昼夜和季节变化

Diel and seasonal variability of the forest soundscape in the Northeast China Tiger and Leopard National Park

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