城市鸟类多样性被动声学监测与评价技术应用

doi:10.17520/biods.2024123

生物多样性 ›› 2024, Vol. 32 ›› Issue (10): 24123. DOI: 10.17520/biods.2024123 cstr: 32101.14.biods.2024123

城市鸟类多样性被动声学监测与评价技术应用

郝泽周¹(), 张承云²(), 李乐¹(), 高丙涛¹(), 曾伟³, 王淳¹, 王梓炫², 黄万涛², 张悦², 裴男才¹^,^*()(), 肖治术⁴()

1.中国林业科学研究院热带林业研究所, 广州 510520
2.广州大学电子与通信工程学院, 广州 510006
3.深圳市仙湖植物园, 广东深圳 518004
4.中国科学院动物研究所农业虫害鼠害综合治理研究国家重点实验室, 北京 100101

收稿日期:2024-03-30 接受日期:2024-08-14 出版日期:2024-10-20 发布日期:2024-09-08
通讯作者: *E-mail: nancai.pei@gmail.com
基金资助:
中国林业科学研究院基本科研业务费专项资助(CAFYBB2023MA016);国家自然科学基金(32201338);深圳市南亚热带植物多样性重点实验室开放基金课题(2022-2024);林业生态监测网络平台运行项目(2023KYXM09)

Applications of passive acoustic monitoring and evaluation in urban bird research

Zezhou Hao¹(), Chengyun Zhang²(), Le Li¹(), Bingtao Gao¹(), Wei Zeng³, Chun Wang¹, Zixuan Wang², Wantao Huang², Yue Zhang², Nancai Pei¹^,^*()(), Zhishu Xiao⁴()

1. Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou 510520, China
2. School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
3. Shenzhen Fairy Lake Botanical Garden, Shenzhen, Guangdong 518004, China
4. State Key Laboratory of Integrated Management of Pest Insects and Rodents in Agriculture, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China

Received:2024-03-30 Accepted:2024-08-14 Online:2024-10-20 Published:2024-09-08
Contact: *E-mail: nancai.pei@gmail.com
Supported by:
Fundamental Research Funds of Chinese Academy of Forestry(CAFYBB2023MA016);National Natural Science Foundation of China(32201338);Open Fund of Shenzhen Key Laboratory of Southern Subtropical Plant Diversity(2022-2024);Operation of Forestry Ecological Observation Network Platform(2023KYXM09)

摘要/Abstract

摘要：

在当前城市化快速发展的背景下, 监测与评估城市鸟类多样性是城市生态学研究和生物多样性保护的关键技术。被动声学监测(passive acoustic monitoring, PAM)作为一种利用环境声音评估生物多样性的新兴技术, 能够提供城市鸟类种群的连续动态信息, 为洞察人类活动对生物多样性的影响提供了独特的视角。目前, 中国及全球范围内已经开展了许多基于被动声学监测的生物多样性研究案例。然而, 监测与评价技术的差异直接影响基于被动声学监测的城市鸟类多样性评估的有效性, 制约了城市生物多样性维持机制等科学问题的深入探讨。随着被动声学监测技术的广泛应用, 迫切需要制定一套城市鸟类鸣声被动声学监测与评价技术规范, 推动声学数据的规范化采集与处理, 并构建全国性的城市鸟类声学数据平台, 以高质量的大数据推动城市生态学研究和城市生物多样性保护。本文综述了城市环境下基于被动声学监测评估鸟类多样性的研究案例, 系统地总结了监测方案和评价技术, 梳理了存在的主要问题, 并对未来的研究进行了展望, 旨在为今后城市鸟类多样性被动声学监测与评价的理论研究、调查方案和技术应用提供参考。

关键词: 被动声学监测, 城市生态学, 鸟类多样性, 生态声学

Abstract

Background & Aims: Rapid urbanization has proved the importance of effectively monitoring and evaluating urban bird diversity, making it a crucial area of technique inquiry within urban ecology and biodiversity conservation. Passive acoustic monitoring (PAM), a technique that utilizes the environment to assess biodiversity, provides long-term and continuous data on urban avian population dynamics. This approach offers valuable insights into the influence of human activities on natural habitats. Although PAM technology has been adopted globally for urban biodiversity monitoring and has resulted in extensive acoustic datasets, variations in monitoring and assessment methodologies show significant challenges in effectively evaluating urban avian diversity.

Review Results: We synthesize representative cases of urban avian diversity research conducted using PAM technology, focusing on aspects such as spatio-temporal experimental design, recording device parameters, and quantification techniques of acoustic signals. The results indicate that current case studies exhibit general routines in experimental frameworks, parameter selection, and quantification methods. However, variability in monitoring and evaluation technologies, along with their effects on factors such as signal-to-noise ratio and representativeness of sound signals, remains a significant challenge that hinders the application of PAM in urban bird diversity research, yet this issue has not received adequate attention. Therefore, this paper advocates for a comprehensive examination of passive acoustic monitoring and evaluation techniques for urban bird sounds, which would facilitate the creation of eco-acoustic big data and address broader ecological questions.

Perspectives: Given the increasing prevalence of PAM applications, there is an urgent need for the development of technical standards for passive acoustic monitoring and evaluation of urban birdsong. Establishing these standards would promote the standardization of sound data collection and analysis, leading to the creation of a comprehensive urban bird sound database. Such advancements would enable the utilization of big data to elucidate the impacts of urbanization on birdsong diversity and response mechanisms, thereby enriching urban avian studies and supporting biodiversity conservation efforts in urban environments. This paper summarizes current monitoring schemes and technological applications, providing a foundation for future theoretical frameworks. Methodological approaches and technological implementations are proposed for future passive acoustic monitoring and evaluation of urban bird diversity in China.

Key words: passive acoustic monitoring, urban ecology, bird diversity, ecoacoustics

郝泽周, 张承云, 李乐, 高丙涛, 曾伟, 王淳, 王梓炫, 黄万涛, 张悦, 裴男才, 肖治术 (2024) 城市鸟类多样性被动声学监测与评价技术应用. 生物多样性, 32, 24123. DOI: 10.17520/biods.2024123.

Zezhou Hao, Chengyun Zhang, Le Li, Bingtao Gao, Wei Zeng, Chun Wang, Zixuan Wang, Wantao Huang, Yue Zhang, Nancai Pei, Zhishu Xiao (2024) Applications of passive acoustic monitoring and evaluation in urban bird research. Biodiversity Science, 32, 24123. DOI: 10.17520/biods.2024123.

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链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2024123

https://www.biodiversity-science.net/CN/Y2024/V32/I10/24123

图/表 5

参考文献 92

[1]	Abrahams C, Ashington B, Baker E, Bradfer-Lawrence T, Browning E, Carruthers-Jones J, Darby J, Dick J, Eldridge A, Elliott D, Heath B, Howden-Leach P, Johnston A, Lees A, Meyer C, Arana U, Smyth S (2023) Good Practice Guidelines for Long-Term Ecoacoustic Monitoring in the UK. UK Acoustics Network. https://www.researchgate.net/publication/368683386. (accessed on 2023-03-20)
[2]	Alcocer I, Lima H, Sugai LSM, Llusia D (2022) Acoustic indices as proxies for biodiversity: A meta-analysis. Biological Reviews, 97, 2209-2236.
[3]	Alquezar RD, Macedo RH, Sierro J, Gil D (2020) Lack of consistent responses to aircraft noise in dawn song timing of bird populations near tropical airports. Behavioral Ecology and Sociobiology, 74, 88.
[4]	Aodha OM, Martínez Balvanera S, Damstra E, Cooke M, Eichinski P, Browning E, Barataud M, Boughey K, Coles R, Giacomini G, Swiney G, Obrist MK, Parsons S, Sattler T, Jones KE (2022) Towards a general approach for bat echo-location detection and classification. BioRxiv, 520490.
[5]	Arneill GE, Critchley EJ, Wischnewski S, Jessopp MJ, Quinn JL (2020) Acoustic activity across a seabird colony reflects patterns of within-colony flight rather than nest density. Ibis, 162, 416-428. DOI
[6]	Azar JF, Bell BD (2016) Acoustic features within a forest bird community of native and introduced species in New Zealand. Emu - Austral Ornithology, 116, 22-31.
[7]	Barber JR, Crooks KR, Fristrup KM (2010) The costs of chronic noise exposure for terrestrial organisms. Trends in Ecology & Evolution, 25, 180-189.
[8]	Bhakti T, Pena JC, Moura ACM, Pujoni D, Saliba L, Rodrigues M (2024) Urban biodiversity suitability index: Decoding the relationships between cities and birds. Urban Ecosystems, 27, 305-319.
[9]	Boncoraglio G, Saino N (2007) Habitat structure and the evolution of bird song: A meta-analysis of the evidence for the acoustic adaptation hypothesis. Functional Ecology, 21, 134-142.
[10]	Boullhesen M, Vaira M, Barquez RM, Akmentins MS (2021) Evaluating the efficacy of visual encounter and automated acoustic survey methods in anuran assemblages of the Yungas Andean forests of Argentina. Ecological Indicators, 127, 107750.
[11]	Bradfer-Lawrence T, Bunnefeld N, Gardner N, Willis SG, Dent DH (2020) Rapid assessment of avian species richness and abundance using acoustic indices. Ecological Indicators, 115, 106400.
[12]	Bradfer-Lawrence T, Desjonqueres C, Eldridge A, Johnston A, Metcalf O (2023) Using acoustic indices in ecology: Guidance on study design, analyses and interpretation. Methods in Ecology and Evolution, 14, 2192-2204.
[13]	Brumm H, Slabbekoorn H (2005) Acoustic communication in noise. In: Advances in the Study of Behavior (ed. Slater PJ), pp.151-209. Elsevier, Amsterdam.
[14]	Budka M, Jobda M, Szałański P, Piórkowski H (2022) Acoustic approach as an alternative to human-based survey in bird biodiversity monitoring in agricultural meadows. PLoS ONE, 17, e0266557.
[15]	Campos-Cerqueira M (2023) Combining passive acoustic monitoring and species distribution models to monitor species responses to climate change. The Journal of the Acoustical Society of America, 153, A25.
[16]	Chace JF, Walsh JJ (2006) Urban effects on native avifauna: A review. Landscape and Urban Planning, 74, 46-69.
[17]	Chamberlain DE, Cannon AR, Toms MP, Leech DI, Hatchwell BJ, Gaston KJ (2009) Avian productivity in urban landscapes: A review and meta-analysis. Ibis, 151, 1-18.
[18]	Chen YX, Rasool MA, Hussain S, Meng S, Yao YP, Wang X, Liu YH (2023) Bird community structure is driven by urbanization level, blue-green infrastructure configuration and precision farming in Taizhou, China. Science of the Total Environment, 859, 160096.
[19]	Cohen Y, Nicholson DA, Sanchioni A, Mallaber EK, Skidanova V, Gardner TJ (2022) Automated annotation of birdsong with a neural network that segments spectrograms. eLife, 11, e63853.
[20]	Collins MK, Magle SB, Gallo T (2021) Global trends in urban wildlife ecology and conservation. Biological Conservation, 261, 109236.
[21]	Darras K, Batáry P, Furnas B, Celis-Murillo A, Van Wilgenburg SL, Mulyani YA, Tscharntke T (2018) Comparing the sampling performance of sound recorders versus point counts in bird surveys: A meta-analysis. Journal of Applied Ecology, 55, 2575-2586.
[22]	DeGregorio BA, Weatherhead PJ, Sperry JH (2014) Power lines, roads, and avian nest survival: Effects on predator identity and predation intensity. Ecology and Evolution, 4, 1589-1600. DOI PMID
[23]	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.
[24]	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.
[25]	Dröge S, Martin DA, Andriafanomezantsoa R, Burivalova Z, Fulgence TR, Osen K, Rakotomalala E, Schwab D, Wurz A, Richter T, Kreft H (2021) Listening to a changing landscape: Acoustic indices reflect bird species richness and plot-scale vegetation structure across different land-use types in north-eastern Madagascar. Ecological Indicators, 120, 106929.
[26]	Durden JM, Luo JY, Alexander H, Flanagan AM, Grossmann L (2017) Integrating “big data” into aquatic ecology: Challenges and opportunities. Limnology and Oceanography Bulletin, 26, 101-108.
[27]	Eldridge A, Casey M, Moscoso P, Peck M (2016) A new method for ecoacoustics? Toward the extraction and evaluation of ecologically-meaningful soundscape components using sparse coding methods. PeerJ, 4, e2108.
[28]	Fairbrass AJ, Firman M, Williams C, Brostow GJ, Titheridge H, Jones KE (2019) CityNet—Deep learning tools for urban ecoacoustic assessment. Methods in Ecology and Evolution, 10, 186-197. DOI
[29]	Fairbrass AJ, Rennert P, Williams C, Titheridge H, Jones KE (2017) Biases of acoustic indices measuring biodiversity in urban areas. Ecological Indicators, 83, 169-177.
[30]	Francis CD, Kleist NJ, Ortega CP, Cruz A (2012) Noise pollution alters ecological services:Enhanced pollination and disrupted seed dispersal. Proceedings of the Royal Society B: Biological Sciences, 279, 2727-2735.
[31]	French K, Major R, Hely K (2005) Use of native and exotic garden plants by suburban nectarivorous birds. Biological Conservation, 121, 545-559.
[32]	Frommolt KH (2017) Information obtained from long-term acoustic recordings: Applying bioacoustic techniques for monitoring wetland birds during breeding season. Journal of Ornithology, 158, 659-668.
[33]	Gallardo Cruz KV, Paxton KL, Hart PJ (2021) Temporal changes in songbird vocalizations associated with helicopter noise in Hawai’i’s protected natural areas. Landscape Ecology, 36, 829-843.
[34]	Gottesman BL, Olson JC, Yang S, Acevedo-Charry O, Francomano D, Martinez FA, Appeldoorn RS, Mason DM, Weil E, Pijanowski BC (2021) What does resilience sound like? Coral reef and dry forest acoustic communities respond differently to Hurricane Maria. Ecological Indicators, 126, 107635.
[35]	Hagstrum JT, McIsaac HP, Drob DP (2016) Seasonal changes in atmospheric noise levels and the annual variation in pigeon homing performance. Journal of Comparative Physiology A, 202, 413-424. DOI PMID
[36]	Hao ZZ, Wang C, Sun ZK, van den Bosch CK, Zhao DX, Sun BQ, Xu XH, Bian Q, Bai ZT, Wei KY, Zhao YL, Pei NC (2021) Soundscape mapping for spatial-temporal estimate on bird activities in urban forests. Urban Forestry & Urban Greening, 57, 126822.
[37]	Hao ZZ, Zhan HS, Zhang CY, Pei NC, Sun B, He JH, Wu RC, Xu XH, Wang C (2022) Assessing the effect of human activities on biophony in urban forests using an automated acoustic scene classification model. Ecological Indicators, 144, 109437.
[38]	Hao ZZ, Zhang CY, Li L, Gao BT, Wu RC, Pei NC, Liu Y (2024) Anthropogenic noise and habitat structure shaping dominant frequency of bird sounds along urban gradients. iScience, 27, 109056.
[39]	Holgate B, Maggini R, Fuller S (2021) Mapping ecoacoustic hot spots and moments of biodiversity to inform conservation and urban planning. Ecological Indicators, 126, 107627.
[40]	Ji T, Zhang YY (2011) Impacts of ambient noise on bird song and adaptation strategies of birds. Chinese Journal of Ecology, 30, 831-836. (in Chinese with English abstract)
	[季婷, 张雁云 (2011) 环境噪音对鸟类鸣声的影响及鸟类的适应对策. 生态学杂志, 30, 831-836.]
[41]	Kahl S, Wood CM, Eibl M, Klinck H (2021) BirdNET: A deep learning solution for avian diversity monitoring. Ecological Informatics, 61, 101236.
[42]	Krause BL (1987) Bioacoustics, habitat ambience in ecological balance. Whole Earth Review, 57, 14-18.
[43]	Lomolino MV (2000) Ecology’s most general, yet protean pattern: The species-area relationship. Journal of Biogeography, 27, 17-26.
[44]	Luo LY, Guo ST, Wang M, Qiu HB, Liu ZH (2023) Adaptive noise reduction algorithm based on SPP and NMF for environmental sound event recognition under low-SNR conditions. Wireless Communications and Mobile Computing, 2023, 6582296.
[45]	Luther D, Baptista L (2010) Urban noise and the cultural evolution of bird songs. Proceedings of the Royal Society B: Biological Sciences, 277, 469-473.
[46]	Ma KP (2011) Assessing progress of biodiversity conservation with monitoring approach. Biodiversity Science, 19, 125-126. (in Chinese) DOI
	[马克平 (2011) 监测是评估生物多样性保护进展的有效途径. 生物多样性, 19, 125-126.] DOI
[47]	MacArthur RH, Wilson EO (1969) The Theory of Island Biogeography. Princeton University Press, Princeton.
[48]	McKinney ML (2002) Urbanization, biodiversity, and conservation. BioScience, 52, 883-890.
[49]	McPhearson T, Pickett STA, Grimm NB, Niemelä J, Alberti M, Elmqvist T, Weber C, Haase D, Breuste J, Qureshi S (2016) Advancing urban ecology toward a science of cities. BioScience, 66, 198-212.
[50]	Mendes S, Colino-Rabanal VJ, Peris SJ (2011) Bird song variations along an urban gradient: The case of the European blackbird (Turdus merula). Landscape and Urban Planning, 99, 51-57.
[51]	Merchant ND, Fristrup KM, Johnson MP, Tyack PL, Witt MJ, Blondel P, Parks SE (2015) Measuring acoustic habitats. Methods in Ecology and Evolution, 6, 257-265. PMID
[52]	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
[53]	Miller-Rushing AJ, Lloyd-Evans TL, Primack RB, Satzinger P (2008) Bird migration times, climate change, and changing population sizes. Global Change Biology, 14, 1959-1972.
[54]	Mockford EJ, Marshall RC (2009) Effects of urban noise on song and response behaviour in great tits. Proceedings of the Royal Society B: Biological Sciences, 276, 2979-2985.
[55]	Morales G, Vargas V, Espejo D, Poblete V, Tomasevic JA, Otondo F, Navedo JG (2022) Method for passive acoustic monitoring of bird communities using UMAP and a deep neural network. Ecological Informatics, 72, 101909.
[56]	Moreno-Gómez FN, Bartheld J, Silva-Escobar AA, Briones R, Márquez R, Penna M (2019) Evaluating acoustic indices in the Valdivian rainforest, a biodiversity hotspot in South America. Ecological Indicators, 103, 1-8. DOI
[57]	Müller J, Mitesser O, Schaefer HM, Seibold S, Busse A, Kriegel P, Rabl D, Gelis R, Arteaga A, Freile J, Leite GA, de Melo TN, LeBien J, Campos-Cerqueira M, Blüthgen N, Tremlett CJ, Böttger D, Feldhaar H, Grella N, Falconí-López A, Donoso DA, Moriniere J, Buřivalová Z (2023) Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests. Nature Communications, 14, 6191. DOI PMID
[58]	Müller S, Gossner MM, Penone C, Jung K, Renner SC, Farina A, Anhäuser L, Ayasse M, Boch S, Haensel F, Heitzmann J, Kleinn C, Magdon P, Perović DJ, Pieretti N, Shaw T, Steckel J, Tschapka M, Vogt J, Westphal C, Scherer-Lorenzen M (2022) Land-use intensity and landscape structure drive the acoustic composition of grasslands. Agriculture, Ecosystems & Environment, 328, 107845.
[59]	Nemeth E, Pieretti N, Zollinger SA, Geberzahn N, Partecke J, Miranda AC, Brumm H (2013) Bird song and anthropogenic noise:Vocal constraints may explain why birds sing higher-frequency songs in cities. Proceedings of the Royal Society B: Biological Sciences, 280, 20122798.
[60]	Pataki DE (2015) Grand challenges in urban ecology. Frontiers in Ecology and Evolution, 3, 57.
[61]	Pekin BK, Jung J, Villanueva-Rivera LJ, Pijanowski BC, Ahumada JA (2012) Modeling acoustic diversity using soundscape recordings and LIDAR-derived metrics of vertical forest structure in a neotropical rainforest. Landscape Ecology, 27, 1513-1522.
[62]	Pickett STA, Cadenasso ML, Baker ME, Band LE, Boone CG, Buckley GL, Groffman PM, Grove JM, Irwin EG, Kaushal SS, LaDeau SL, Miller AJ, Nilon CH, Romolini M, Rosi EJ, Swan CM, Szlavecz K (2020) Theoretical perspectives of the Baltimore ecosystem study: Conceptual evolution in a social-ecological research project. BioScience, 70, 297-314. DOI PMID
[63]	Pijanowski BC, Villanueva-Rivera LJ, Dumyahn SL, Farina A, Krause BL, Napoletano BM, Gage SH, Pieretti N (2011) Soundscape ecology: The science of sound in the landscape. BioScience, 61, 203-216.
[64]	Quinn CA, Burns P, Gill G, Baligar S, Snyder RL, Salas L, Goetz SJ, Clark ML (2022) Soundscape classification with convolutional neural networks reveals temporal and geographic patterns in ecoacoustic data. Ecological Indicators, 138, 108831.
[65]	Rajan SC, Athira K, Jaishanker R, Sooraj NP, Sarojkumar V (2019) Rapid assessment of biodiversity using acoustic indices. Biodiversity and Conservation, 28, 2371-2383. DOI
[66]	Rasmussen JH, Stowell D, Briefer EF (2024) Sound evidence for biodiversity monitoring. Science, 385, 138-140. DOI PMID
[67]	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.
[68]	Ross SR, 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.
[69]	Sánchez-Giraldo C, Bedoya CL, Morán-Vásquez RA, Isaza CV, Daza JM (2020) Ecoacoustics in the rain: Understanding acoustic indices under the most common geophonic source in tropical rainforests. Remote Sensing in Ecology and Conservation, 6, 248-261.
[70]	Sangermano F (2022) Acoustic diversity of forested landscapes: Relationships to habitat structure and anthropogenic pressure. Landscape and Urban Planning, 226, 104508.
[71]	Sethi SS, Bick A, Ewers RM, Klinck H, Ramesh V, Tuanmu MN, Coomes DA (2023) Limits to the accurate and generalizable use of soundscapes to monitor biodiversity. Nature Ecology & Evolution, 7, 1373-1378.
[72]	Shonfield J, Bayne EM (2017) Autonomous recording units in avian ecological research: Current use and future applications. Avian Conservation and Ecology, 12, art14.
[73]	Slabbekoorn H, Peet M (2003) Birds sing at a higher pitch in urban noise. Nature, 424, 267.
[74]	Sugai LSM, Silva TSF, Ribeiro JW, Llusia D (2019) Terrestrial passive acoustic monitoring: Review and perspectives. BioScience, 69, 15-25. DOI
[75]	To AWY, Dingle C, Collins SA (2021) Multiple constraints on urban bird communication: Both abiotic and biotic noise shape songs in cities. Behavioral Ecology: Official Journal of the International Society for Behavioral Ecology, 32, 1042-1053.
[76]	Tolentino VC, Baesse CQ, Melo C (2018) Dominant frequency of songs in tropical bird species is higher in sites with high noise pollution. Environmental Pollution, 235, 983-992. DOI PMID
[77]	Towsey M, Planitz B, Nantes A, Wimmer J, Roe P (2012) A toolbox for animal call recognition. Bioacoustics, 21, 107-125.
[78]	Tu HM, Fan MW, Ko JCJ (2020) Different habitat types affect bird richness and evenness. Scientific Reports, 10, 1221.
[79]	Van Doren BM, Horton KG (2018) A continental system for forecasting bird migration. Science, 361, 1115-1118. DOI PMID
[80]	Van Doren BM, Lostanlen V, Cramer A, Salamon J, Dokter A, Kelling S, Bello JP, Farnsworth A (2023) Automated acoustic monitoring captures timing and intensity of bird migration. Journal of Applied Ecology, 60, 433-444.
[81]	Warren PS, Katti M, Ermann M, Brazel A (2006) Urban bioacoustics: It’s not just noise. Animal Behaviour, 71, 491-502.
[82]	Wood CM, Peery MZ (2022) What does ‘occupancy’ mean in passive acoustic surveys? Ibis, 164, 1295-1300.
[83]	Wu H, Xu XH, Feng XJ, Mi XC, Su YJ, Xiao ZS, Zhu CD, Cao L, Gao X, Song CY, Guo LD, Wu DH, Jiang JP, Shen H, Ma KP (2022) Progress and prospect of China biodiversity monitoring from a global perspective. Biodiversity Science, 30, 22434. (in Chinese with English abstract) DOI
	[吴慧, 徐学红, 冯晓娟, 米湘成, 苏艳军, 肖治术, 朱朝东, 曹垒, 高欣, 宋创业, 郭良栋, 吴东辉, 江建平, 沈浩, 马克平 (2022) 全球视角下的中国生物多样性监测进展与展望. 生物多样性, 30, 22434.] DOI
[84]	Xiao ZS, Cui JG, Wang DP, Wang ZT, Luo JH, Xie J (2023) Interdisciplinary development trends of contemporary bioacoustics and the opportunities for China. Biodiversity Science, 31, 22423. (in Chinese with English abstract) DOI
	[肖治术, 崔建国, 王代平, 王志陶, 罗金红, 谢捷 (2023) 现代生物声学的学科发展趋势及中国机遇. 生物多样性, 31, 22423.] DOI
[85]	Xu ZY, Chen L, Pijanowski BC, Zhao Z (2023) A frequency-dependent acoustic diversity index: A revision to a classic acoustic index for soundscape ecological research. Ecological Indicators, 155, 110940.
[86]	Yang J (2020) Big data and the future of urban ecology: From the concept to results. Scientia Sinica (Terrae), 50, 1339-1353. (in Chinese with English abstract)
	[杨军 (2020) 大数据与城市生态学的未来: 从概念到结果. 中国科学: 地球科学, 50, 1339-1353.]
[87]	Zhang CY, Jin NT, Xie J, Hao ZZ (2024a) CicadaNet: Deep learning based automatic cicada chorus filtering for improved long-term bird monitoring. Ecological Indicators, 158, 111423.
[88]	Zhang CY, Zhan HS, Hao ZZ, Gao XH (2023) Classification of complicated urban forest acoustic scenes with deep learning models. Forests, 14, 206.
[89]	Zhang CY, Zhang Y, Zheng XJ, Gao XH, Hao ZZ (2024b) Influence of recording devices and environmental noise on acoustic index scores: Implications for bird sound-based assessments. Ecological Indicators, 159, 111759.
[90]	Zhang J (2017) Biodiversity science and macroecology in the era of big data. Biodiversity Science, 25, 355-363. (in Chinese with English abstract) DOI
	[张健 (2017) 大数据时代的生物多样性科学与宏生态学. 生物多样性, 25, 355-363.] DOI
[91]	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
[92]	Zhao YL, Sheppard S, Sun ZK, Hao ZZ, Jin JL, Bai ZT, Bian Q, Wang C (2022) Soundscapes of urban parks: An innovative approach for ecosystem monitoring and adaptive management. Urban Forestry & Urban Greening, 71, 127555.

类型 Type	名称 Name	基本功能 Basic functions	网址 Website
声学指标计算 Acoustic indices calculate	Scikit-maad	基于Python平台 Based on the Python platform 提供预处理、声学指数和声压级计算模块 Providing preprocessing, acoustic indices and sound pressure level calculation modules 提供多样的音频处理工具集 Providing a diverse set of audio analysis utilities 支持大规模数据处理 Supporting large-scale data processing	https://github.com/scikit-maad/scikit-maad
	seewave	基于R语言平台 Based on the R language platform 支持音频信号处理和特征提取 Supporting audio signal processing and feature extraction 支持声学指数计算和可视化 Supporting the calculation and visualization of acoustic indices 并未针对大规模数据进行优化 Not optimized for large-scale data	https://github.com/cran/seewave
	soundecology	基于R语言平台 Based on the R language platform 支持声学指数计算 Supporting the calculation of acoustic indices 提供目标信息框选和栅格文件处理功能 Providing the functions of bounding box selection and raster file creation 支持大规模数据处理 Supporting large-scale data processing	https://github.com/ljvillanueva/soundecology
声场景分类模型 Acoustic scene classification model	AlexNet	深度卷积神经网络模型 Deep convolutional neural network model 以语谱图为输入特征 Using spectrogram as input feature 有效提取音频全局特征 Effectively extracting global audio features 适用于多种声音识别任务 Suitable for various sound recognition tasks	https://github.com/amir-saniyan/AlexNet
	GoogleNet	基于Inception的架构设计 Inception-based architecture design 采用1 × 1卷积核以优化效率 Utilizing 1 × 1 convolutional kernels for efficiency 适配语谱图输入进行音频分析 Adapting spectrogram inputs for audio analysis 轻量级网络, 计算需求低 Lightweight network with low computational demand	https://github.com/conan7882/GoogLeNet-Inception
	CityNet	基于卷积神经网络 Based on convolutional neural network CityBioNet: 专注于生物声音的识别 Specialized in recognizing biological sounds CityAnthroNet: 专门识别人为声音 Dedicated to identifying anthropogenic sounds 采用one-vs-one方法优化双模型性能 Using one-vs-one approach to enhance performance of dual models	https://github.com/mdfirman/CityNet
鸟鸣识别模型 Bird song recognition model	BirdNET	基于CNN模型 Based on the CNN framework 利用大规模的已标记鸟类声音数据进行训练 Trained by using a large dataset of labeled bird sounds 能够处理多种音频格式的输入, 提高模型适用性 Capable of processing multiple audio formats, enhancing model adaptability 具备迁移学习能力 Equipped with transfer learning capabilities 提供了GUI界面, 便于非专业用户使用 Providing a GUI interface, making it user-friendly for non-experts	https://github.com/kahst/BirdNET-Analyzer
	AudioMAE	基于Transformer架构 Based on the transformer architecture 利用大规模本地无标签数据完成自监督预训练 Utilizing large-scale local unlabeled data for self-supervised pre-training 有标注数据量要求低 Requiring a low amount of labeled data 支持大规模的音频数据训练 Supporting training with large-scale audio data	https://github.com/facebookresearch/AudioMAE
	Google Perch	基于CNN框架 Based on the CNN framework 在大量有标签生物声学数据上进行预训练 Pre-trained on a large amount of labeled bioacoustic data 对目标物种有标签数据的依赖小 Requiring minimal labeled data for the target species 具有迁移学习能力 Possessing transfer learning capabilities	https://github.com/google-research/perch

类型 Type	名称 Name	基本功能 Basic functions	网址 Website
声学指标计算 Acoustic indices calculate	Scikit-maad	基于Python平台 Based on the Python platform 提供预处理、声学指数和声压级计算模块 Providing preprocessing, acoustic indices and sound pressure level calculation modules 提供多样的音频处理工具集 Providing a diverse set of audio analysis utilities 支持大规模数据处理 Supporting large-scale data processing	https://github.com/scikit-maad/scikit-maad
	seewave	基于R语言平台 Based on the R language platform 支持音频信号处理和特征提取 Supporting audio signal processing and feature extraction 支持声学指数计算和可视化 Supporting the calculation and visualization of acoustic indices 并未针对大规模数据进行优化 Not optimized for large-scale data	https://github.com/cran/seewave
	soundecology	基于R语言平台 Based on the R language platform 支持声学指数计算 Supporting the calculation of acoustic indices 提供目标信息框选和栅格文件处理功能 Providing the functions of bounding box selection and raster file creation 支持大规模数据处理 Supporting large-scale data processing	https://github.com/ljvillanueva/soundecology
声场景分类模型 Acoustic scene classification model	AlexNet	深度卷积神经网络模型 Deep convolutional neural network model 以语谱图为输入特征 Using spectrogram as input feature 有效提取音频全局特征 Effectively extracting global audio features 适用于多种声音识别任务 Suitable for various sound recognition tasks	https://github.com/amir-saniyan/AlexNet
	GoogleNet	基于Inception的架构设计 Inception-based architecture design 采用1 × 1卷积核以优化效率 Utilizing 1 × 1 convolutional kernels for efficiency 适配语谱图输入进行音频分析 Adapting spectrogram inputs for audio analysis 轻量级网络, 计算需求低 Lightweight network with low computational demand	https://github.com/conan7882/GoogLeNet-Inception
	CityNet	基于卷积神经网络 Based on convolutional neural network CityBioNet: 专注于生物声音的识别 Specialized in recognizing biological sounds CityAnthroNet: 专门识别人为声音 Dedicated to identifying anthropogenic sounds 采用one-vs-one方法优化双模型性能 Using one-vs-one approach to enhance performance of dual models	https://github.com/mdfirman/CityNet
鸟鸣识别模型 Bird song recognition model	BirdNET	基于CNN模型 Based on the CNN framework 利用大规模的已标记鸟类声音数据进行训练 Trained by using a large dataset of labeled bird sounds 能够处理多种音频格式的输入, 提高模型适用性 Capable of processing multiple audio formats, enhancing model adaptability 具备迁移学习能力 Equipped with transfer learning capabilities 提供了GUI界面, 便于非专业用户使用 Providing a GUI interface, making it user-friendly for non-experts	https://github.com/kahst/BirdNET-Analyzer
	AudioMAE	基于Transformer架构 Based on the transformer architecture 利用大规模本地无标签数据完成自监督预训练 Utilizing large-scale local unlabeled data for self-supervised pre-training 有标注数据量要求低 Requiring a low amount of labeled data 支持大规模的音频数据训练 Supporting training with large-scale audio data	https://github.com/facebookresearch/AudioMAE
	Google Perch	基于CNN框架 Based on the CNN framework 在大量有标签生物声学数据上进行预训练 Pre-trained on a large amount of labeled bioacoustic data 对目标物种有标签数据的依赖小 Requiring minimal labeled data for the target species 具有迁移学习能力 Possessing transfer learning capabilities	https://github.com/google-research/perch

城市鸟类多样性被动声学监测与评价技术应用

Applications of passive acoustic monitoring and evaluation in urban bird research

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