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Monitoring of a Nearshore Small Dolphin Species Using Passive Acoustic Platforms and Supervised Machine Learning Techniques

文献类型：期刊论文

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作者Caruso, Francesco1; Dong, Lijun1; Lin, Mingli1; Liu, Mingming1,2; Gong, Zining1,3; Xu, Wanxue1,3; Aionge, Giuseppe4; Li, Songhai1
刊名FRONTIERS IN MARINE SCIENCE ; FRONTIERS IN MARINE SCIENCE
出版日期2020-04-28 ; 2020-04-28
卷号7页码:19
关键词passive acoustic monitoring passive acoustic monitoring Indo-Pacific humpback dolphin spatiotemporal patterns distribution acoustic behavior coastal waters Indo-Pacific humpback dolphin spatiotemporal patterns distribution acoustic behavior coastal waters
DOI10.3389/fmars.2020.00267 ; 10.3389/fmars.2020.00267
通讯作者Li, Songhai
英文摘要Passive acoustic monitoring (PAM) is increasingly being adopted as a non-invasive method for the assessment of ocean ecological dynamics. PAM is an important sampling approach for acquiring critical information about marine mammals, especially in areas where data are lacking and where evaluations of threats for vulnerable populations are required. The Indo-Pacific humpback dolphin (IPHD, Sousa chinensis) is a coastal species which inhabits tropical and warm-temperate waters from the eastern Indian Ocean throughout Southeast Asia to central China. A new population of this species was recently discovered in waters southwest of Hainan Island, China. An array of passive acoustic platforms was deployed at depths of 10-20 m (the preferred habitat of humpback dolphins), across sites covering more than 100 km of coastline. In this study, we explored whether the acoustic data recorded by the array could be used to classify IPHD echolocation clicks, with the aim of investigating the spatiotemporal patterns of distribution and acoustic behavior of this species. A number of supervised machine learning algorithms were trained to automatically classify echolocation clicks from the different types of short-broadband pulses recorded. The best performance was reported by a cubic support vector machine (Cubic SVM), which was applied to 19,215 5-min recordings (similar to 4.2 TB), collected over a period of 75 days at six locations. Subsequently, using spectrogram visualization and audio listening, human operators confirmed the presence of clicks within the selected files. Additionally, other dolphin vocalizations (including whistles, buzzes, and burst pulses) and different sound sources (soniferous fishes, snapping shrimps, human activities) were also reported. The detection range of IPHD clicks was estimated using a transmission loss (IL) model and the performance of the trained classifier was compared with data synchronously collected by an acoustic data logger (A-tag). This study demonstrates that the distribution and habitat use of a coastal and resident dolphin species can be monitored over a large spatiotemporal scale, using an array of passive acoustic platforms and a data analysis protocol that includes both machine learning techniques and spectrogram inspection.; Passive acoustic monitoring (PAM) is increasingly being adopted as a non-invasive method for the assessment of ocean ecological dynamics. PAM is an important sampling approach for acquiring critical information about marine mammals, especially in areas where data are lacking and where evaluations of threats for vulnerable populations are required. The Indo-Pacific humpback dolphin (IPHD, Sousa chinensis) is a coastal species which inhabits tropical and warm-temperate waters from the eastern Indian Ocean throughout Southeast Asia to central China. A new population of this species was recently discovered in waters southwest of Hainan Island, China. An array of passive acoustic platforms was deployed at depths of 10-20 m (the preferred habitat of humpback dolphins), across sites covering more than 100 km of coastline. In this study, we explored whether the acoustic data recorded by the array could be used to classify IPHD echolocation clicks, with the aim of investigating the spatiotemporal patterns of distribution and acoustic behavior of this species. A number of supervised machine learning algorithms were trained to automatically classify echolocation clicks from the different types of short-broadband pulses recorded. The best performance was reported by a cubic support vector machine (Cubic SVM), which was applied to 19,215 5-min recordings (similar to 4.2 TB), collected over a period of 75 days at six locations. Subsequently, using spectrogram visualization and audio listening, human operators confirmed the presence of clicks within the selected files. Additionally, other dolphin vocalizations (including whistles, buzzes, and burst pulses) and different sound sources (soniferous fishes, snapping shrimps, human activities) were also reported. The detection range of IPHD clicks was estimated using a transmission loss (IL) model and the performance of the trained classifier was compared with data synchronously collected by an acoustic data logger (A-tag). This study demonstrates that the distribution and habitat use of a coastal and resident dolphin species can be monitored over a large spatiotemporal scale, using an array of passive acoustic platforms and a data analysis protocol that includes both machine learning techniques and spectrogram inspection.
WOS关键词PACIFIC HUMPBACK DOLPHINS ; PACIFIC HUMPBACK DOLPHINS ; BOTTLE-NOSED DOLPHINS ; PEARL RIVER ESTUARY ; SOUSA-CHINENSIS ; ECHOLOCATION SIGNALS ; OCEAN MEASUREMENTS ; FORAGING ACTIVITY ; SNAPPING SHRIMP ; MARINE MAMMALS ; SANNIANG BAY ; BOTTLE-NOSED DOLPHINS ; PEARL RIVER ESTUARY ; SOUSA-CHINENSIS ; ECHOLOCATION SIGNALS ; OCEAN MEASUREMENTS ; FORAGING ACTIVITY ; SNAPPING SHRIMP ; MARINE MAMMALS ; SANNIANG BAY
资助项目major science and technology project in Hainan Province[ZDKJ2016009-1-1] ; major science and technology project in Hainan Province[ZDKJ2016009-1-1] ; National Natural Science Foundation of China[41422604] ; National Natural Science Foundation of China[41306169] ; Biodiversity Investigation, Observation and Assessment Program (2019-2023) of Ministry of Ecology and Environment of China ; Indian Ocean Ninety-east Ridge Ecosystem and Marine Environment Monitoring and Protection ; China Ocean Mineral Resources RD Association[DY135-E2-4] ; National Natural Science Foundation of China[41422604] ; National Natural Science Foundation of China[41306169] ; Biodiversity Investigation, Observation and Assessment Program (2019-2023) of Ministry of Ecology and Environment of China ; Indian Ocean Ninety-east Ridge Ecosystem and Marine Environment Monitoring and Protection ; China Ocean Mineral Resources RD Association[DY135-E2-4]
WOS研究方向Environmental Sciences & Ecology ; Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Marine & Freshwater Biology
语种英语 ; 英语
WOS记录号WOS:000529218200001 ; WOS:000529218200001
出版者FRONTIERS MEDIA SA ; FRONTIERS MEDIA SA
资助机构major science and technology project in Hainan Province ; major science and technology project in Hainan Province ; National Natural Science Foundation of China ; Biodiversity Investigation, Observation and Assessment Program (2019-2023) of Ministry of Ecology and Environment of China ; Indian Ocean Ninety-east Ridge Ecosystem and Marine Environment Monitoring and Protection ; China Ocean Mineral Resources RD Association ; National Natural Science Foundation of China ; Biodiversity Investigation, Observation and Assessment Program (2019-2023) of Ministry of Ecology and Environment of China ; Indian Ocean Ninety-east Ridge Ecosystem and Marine Environment Monitoring and Protection ; China Ocean Mineral Resources RD Association
源URL[http://ir.idsse.ac.cn/handle/183446/7625]  
专题深海科学研究部_深海生物学研究室_海洋哺乳动物与海洋生物声学研究组
通讯作者Li, Songhai
作者单位1.Chinese Acad Sci, Inst Deep Sea Sci & Engn, Marine Mammal & Marine Bioacoust Lab, Sanya, Peoples R China
2.Univ Chinese Acad Sci, Dept Earth Sci, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Dept Elect Elect & Commun Engn, Beijing, Peoples R China
4.Italian Natl Agcy New Technol, Observat & Measures Environm & Climate, Energy & Sustainable Econ Dev ENEA, Palermo, Italy
推荐引用方式
GB/T 7714		 Caruso, Francesco,Dong, Lijun,Lin, Mingli,et al. Monitoring of a Nearshore Small Dolphin Species Using Passive Acoustic Platforms and Supervised Machine Learning Techniques, Monitoring of a Nearshore Small Dolphin Species Using Passive Acoustic Platforms and Supervised Machine Learning Techniques[J]. FRONTIERS IN MARINE SCIENCE, FRONTIERS IN MARINE SCIENCE,2020, 2020,7, 7:19, 19.
APA Caruso, Francesco.,Dong, Lijun.,Lin, Mingli.,Liu, Mingming.,Gong, Zining.,...&Li, Songhai.(2020).Monitoring of a Nearshore Small Dolphin Species Using Passive Acoustic Platforms and Supervised Machine Learning Techniques.FRONTIERS IN MARINE SCIENCE,7,19.
MLA Caruso, Francesco,et al."Monitoring of a Nearshore Small Dolphin Species Using Passive Acoustic Platforms and Supervised Machine Learning Techniques".FRONTIERS IN MARINE SCIENCE 7(2020):19.

入库方式： OAI收割

来源：深海科学与工程研究所

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