Discriminating and classifying odontocete echolocation clicks in the Hawaiian Islands using machine learning methods
Passive acoustic monitoring (PAM) has proven a powerful tool for the study of marine mammals, allowing for documentation of biologically relevant factors such as movement patterns or animal behaviors while remaining largely non-invasive and cost effective. From 2008-2019, a set of PAM recordings cov...
Gespeichert in:
| Veröffentlicht in: | PloS one 2022-04, Vol.17 (4), p.e0266424-e0266424 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | e0266424 |
|---|---|
| container_issue | 4 |
| container_start_page | e0266424 |
| container_title | PloS one |
| container_volume | 17 |
| creator | Ziegenhorn, Morgan A Frasier, Kaitlin E Hildebrand, John A Oleson, Erin M Baird, Robin W Wiggins, Sean M Baumann-Pickering, Simone |
| description | Passive acoustic monitoring (PAM) has proven a powerful tool for the study of marine mammals, allowing for documentation of biologically relevant factors such as movement patterns or animal behaviors while remaining largely non-invasive and cost effective. From 2008-2019, a set of PAM recordings covering the frequency band of most toothed whale (odontocete) echolocation clicks were collected at sites off the islands of Hawai'i, Kaua'i, and Pearl and Hermes Reef. However, due to the size of this dataset and the complexity of species-level acoustic classification, multi-year, multi-species analyses had not yet been completed. This study shows how a machine learning toolkit can effectively mitigate this problem by detecting and classifying echolocation clicks using a combination of unsupervised clustering methods and human-mediated analyses. Using these methods, it was possible to distill ten unique echolocation click 'types' attributable to regional odontocetes at the genus or species level. In one case, auxiliary sightings and recordings were used to attribute a new click type to the rough-toothed dolphin, Steno bredanensis. Types defined by clustering were then used as input classes in a neural-network based classifier, which was trained, tested, and evaluated on 5-minute binned data segments. Network precision was variable, with lower precision occurring most notably for false killer whales, Pseudorca crassidens, across all sites (35-76%). However, accuracy and recall were high (>96% and >75%, respectively) in all cases except for one type of short-finned pilot whale, Globicephala macrorhynchus, call class at Kaua'i and Pearl and Hermes Reef (recall >66%). These results emphasize the utility of machine learning in analysis of large PAM datasets. The classifier and timeseries developed here will facilitate further analyses of spatiotemporal patterns of included toothed whales. Broader application of these methods may improve the efficiency of global multi-species PAM data processing for echolocation clicks, which is needed as these datasets continue to grow. |
| doi_str_mv | 10.1371/journal.pone.0266424 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2649552202</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A700233410</galeid><doaj_id>oai_doaj_org_article_7d808eb0eb724dee894445b31a8cced3</doaj_id><sourcerecordid>A700233410</sourcerecordid><originalsourceid>FETCH-LOGICAL-c692t-462ef1c094d4a2d7a322def94e424f3f147b6771655b57d2b310dab2b844273e3</originalsourceid><addsrcrecordid>eNqNk9tu1DAQhiMEoqXwBggiISG42MWnOMkNUlUOXalSJU63lmNPNl689jZ2gL49TjetNqgXJBdxJt__T2bsybLnGC0xLfG7jR96J-1y5x0sEeGcEfYgO8Y1JQtOEH14sD7KnoSwQaigFeePsyNaMEwRr46z-MEE1ZutcTIat86l07myMgTTXo_vXnsXvYIIOajOW68S511ijPoZcuPy2EF-Ln9LY6TLV8Emh5APYRRvpeqMg9yC7N1NAGLndXiaPWqlDfBsep5k3z99_HZ2vri4_Lw6O71YKF6TuGCcQIsVqplmkuhSUkI0tDWDVGpLW8zKhpcl5kXRFKUmDcVIy4Y0FWOkpEBPspd73531QUwNC4JwVhcFIYgkYrUntJcbsUuNkP218NKIm4Dv10L20SgLotQVqqBB0JSEaYCqZowVKaeslAJNk9f7KdvQbEErcLGXdmY6_-JMJ9b-l6gRYiUvksGbyaD3VwOEKLZpc8CmloIf9v-dLl7VCX31D3p_dRO1lqkA41qf8qrRVJyWKAGUYZSo5T1UujVsjUqnqzUpPhO8nQkSE-FPXMshBLH6-uX_2csfc_b1AduBtLEL3g7jgQtzkO1B1fsQemjvmoyRGIfjthtiHA4xDUeSvTjcoDvR7TTQv5USCtc</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2649552202</pqid></control><display><type>article</type><title>Discriminating and classifying odontocete echolocation clicks in the Hawaiian Islands using machine learning methods</title><source>Public Library of Science (PLoS) Journals Open Access</source><source>Open Access: PubMed Central</source><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Free E-Journal (出版社公開部分のみ）</source><source>Free Full-Text Journals in Chemistry</source><creator>Ziegenhorn, Morgan A ; Frasier, Kaitlin E ; Hildebrand, John A ; Oleson, Erin M ; Baird, Robin W ; Wiggins, Sean M ; Baumann-Pickering, Simone</creator><creatorcontrib>Ziegenhorn, Morgan A ; Frasier, Kaitlin E ; Hildebrand, John A ; Oleson, Erin M ; Baird, Robin W ; Wiggins, Sean M ; Baumann-Pickering, Simone</creatorcontrib><description>Passive acoustic monitoring (PAM) has proven a powerful tool for the study of marine mammals, allowing for documentation of biologically relevant factors such as movement patterns or animal behaviors while remaining largely non-invasive and cost effective. From 2008-2019, a set of PAM recordings covering the frequency band of most toothed whale (odontocete) echolocation clicks were collected at sites off the islands of Hawai'i, Kaua'i, and Pearl and Hermes Reef. However, due to the size of this dataset and the complexity of species-level acoustic classification, multi-year, multi-species analyses had not yet been completed. This study shows how a machine learning toolkit can effectively mitigate this problem by detecting and classifying echolocation clicks using a combination of unsupervised clustering methods and human-mediated analyses. Using these methods, it was possible to distill ten unique echolocation click 'types' attributable to regional odontocetes at the genus or species level. In one case, auxiliary sightings and recordings were used to attribute a new click type to the rough-toothed dolphin, Steno bredanensis. Types defined by clustering were then used as input classes in a neural-network based classifier, which was trained, tested, and evaluated on 5-minute binned data segments. Network precision was variable, with lower precision occurring most notably for false killer whales, Pseudorca crassidens, across all sites (35-76%). However, accuracy and recall were high (>96% and >75%, respectively) in all cases except for one type of short-finned pilot whale, Globicephala macrorhynchus, call class at Kaua'i and Pearl and Hermes Reef (recall >66%). These results emphasize the utility of machine learning in analysis of large PAM datasets. The classifier and timeseries developed here will facilitate further analyses of spatiotemporal patterns of included toothed whales. Broader application of these methods may improve the efficiency of global multi-species PAM data processing for echolocation clicks, which is needed as these datasets continue to grow.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0266424</identifier><identifier>PMID: 35413068</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Acoustic tracking ; Acoustics ; Algorithms ; Animal behavior ; Animals ; Aquatic mammals ; Automation ; Biology and Life Sciences ; Cetacea ; Classification ; Classifiers ; Clustering ; Data processing ; Datasets ; Dolphins ; Earth Sciences ; Echolocation ; Echolocation (Physiology) ; Engineering and Technology ; Environmental aspects ; Fin Whale ; Fisheries ; Frequencies ; Globicephala macrorhynchus ; Hawaii ; Islands ; Learning algorithms ; Machine Learning ; Marine mammals ; Methods ; Neural networks ; Oceanography ; Odontoceti ; Physical Sciences ; Pseudorca crassidens ; Recall ; Social Sciences ; Sound Spectrography ; Species ; Species classification ; Steno bredanensis ; Vocalization, Animal ; Whales & whaling</subject><ispartof>PloS one, 2022-04, Vol.17 (4), p.e0266424-e0266424</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication: https://creativecommons.org/publicdomain/zero/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-462ef1c094d4a2d7a322def94e424f3f147b6771655b57d2b310dab2b844273e3</citedby><cites>FETCH-LOGICAL-c692t-462ef1c094d4a2d7a322def94e424f3f147b6771655b57d2b310dab2b844273e3</cites><orcidid>0000-0001-8205-9345</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9004765/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9004765/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79343,79344</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35413068$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ziegenhorn, Morgan A</creatorcontrib><creatorcontrib>Frasier, Kaitlin E</creatorcontrib><creatorcontrib>Hildebrand, John A</creatorcontrib><creatorcontrib>Oleson, Erin M</creatorcontrib><creatorcontrib>Baird, Robin W</creatorcontrib><creatorcontrib>Wiggins, Sean M</creatorcontrib><creatorcontrib>Baumann-Pickering, Simone</creatorcontrib><title>Discriminating and classifying odontocete echolocation clicks in the Hawaiian Islands using machine learning methods</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Passive acoustic monitoring (PAM) has proven a powerful tool for the study of marine mammals, allowing for documentation of biologically relevant factors such as movement patterns or animal behaviors while remaining largely non-invasive and cost effective. From 2008-2019, a set of PAM recordings covering the frequency band of most toothed whale (odontocete) echolocation clicks were collected at sites off the islands of Hawai'i, Kaua'i, and Pearl and Hermes Reef. However, due to the size of this dataset and the complexity of species-level acoustic classification, multi-year, multi-species analyses had not yet been completed. This study shows how a machine learning toolkit can effectively mitigate this problem by detecting and classifying echolocation clicks using a combination of unsupervised clustering methods and human-mediated analyses. Using these methods, it was possible to distill ten unique echolocation click 'types' attributable to regional odontocetes at the genus or species level. In one case, auxiliary sightings and recordings were used to attribute a new click type to the rough-toothed dolphin, Steno bredanensis. Types defined by clustering were then used as input classes in a neural-network based classifier, which was trained, tested, and evaluated on 5-minute binned data segments. Network precision was variable, with lower precision occurring most notably for false killer whales, Pseudorca crassidens, across all sites (35-76%). However, accuracy and recall were high (>96% and >75%, respectively) in all cases except for one type of short-finned pilot whale, Globicephala macrorhynchus, call class at Kaua'i and Pearl and Hermes Reef (recall >66%). These results emphasize the utility of machine learning in analysis of large PAM datasets. The classifier and timeseries developed here will facilitate further analyses of spatiotemporal patterns of included toothed whales. Broader application of these methods may improve the efficiency of global multi-species PAM data processing for echolocation clicks, which is needed as these datasets continue to grow.</description><subject>Acoustic tracking</subject><subject>Acoustics</subject><subject>Algorithms</subject><subject>Animal behavior</subject><subject>Animals</subject><subject>Aquatic mammals</subject><subject>Automation</subject><subject>Biology and Life Sciences</subject><subject>Cetacea</subject><subject>Classification</subject><subject>Classifiers</subject><subject>Clustering</subject><subject>Data processing</subject><subject>Datasets</subject><subject>Dolphins</subject><subject>Earth Sciences</subject><subject>Echolocation</subject><subject>Echolocation (Physiology)</subject><subject>Engineering and Technology</subject><subject>Environmental aspects</subject><subject>Fin Whale</subject><subject>Fisheries</subject><subject>Frequencies</subject><subject>Globicephala macrorhynchus</subject><subject>Hawaii</subject><subject>Islands</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Marine mammals</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Oceanography</subject><subject>Odontoceti</subject><subject>Physical Sciences</subject><subject>Pseudorca crassidens</subject><subject>Recall</subject><subject>Social Sciences</subject><subject>Sound Spectrography</subject><subject>Species</subject><subject>Species classification</subject><subject>Steno bredanensis</subject><subject>Vocalization, Animal</subject><subject>Whales & whaling</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNk9tu1DAQhiMEoqXwBggiISG42MWnOMkNUlUOXalSJU63lmNPNl689jZ2gL49TjetNqgXJBdxJt__T2bsybLnGC0xLfG7jR96J-1y5x0sEeGcEfYgO8Y1JQtOEH14sD7KnoSwQaigFeePsyNaMEwRr46z-MEE1ZutcTIat86l07myMgTTXo_vXnsXvYIIOajOW68S511ijPoZcuPy2EF-Ln9LY6TLV8Emh5APYRRvpeqMg9yC7N1NAGLndXiaPWqlDfBsep5k3z99_HZ2vri4_Lw6O71YKF6TuGCcQIsVqplmkuhSUkI0tDWDVGpLW8zKhpcl5kXRFKUmDcVIy4Y0FWOkpEBPspd73531QUwNC4JwVhcFIYgkYrUntJcbsUuNkP218NKIm4Dv10L20SgLotQVqqBB0JSEaYCqZowVKaeslAJNk9f7KdvQbEErcLGXdmY6_-JMJ9b-l6gRYiUvksGbyaD3VwOEKLZpc8CmloIf9v-dLl7VCX31D3p_dRO1lqkA41qf8qrRVJyWKAGUYZSo5T1UujVsjUqnqzUpPhO8nQkSE-FPXMshBLH6-uX_2csfc_b1AduBtLEL3g7jgQtzkO1B1fsQemjvmoyRGIfjthtiHA4xDUeSvTjcoDvR7TTQv5USCtc</recordid><startdate>20220412</startdate><enddate>20220412</enddate><creator>Ziegenhorn, Morgan A</creator><creator>Frasier, Kaitlin E</creator><creator>Hildebrand, John A</creator><creator>Oleson, Erin M</creator><creator>Baird, Robin W</creator><creator>Wiggins, Sean M</creator><creator>Baumann-Pickering, Simone</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-8205-9345</orcidid></search><sort><creationdate>20220412</creationdate><title>Discriminating and classifying odontocete echolocation clicks in the Hawaiian Islands using machine learning methods</title><author>Ziegenhorn, Morgan A ; Frasier, Kaitlin E ; Hildebrand, John A ; Oleson, Erin M ; Baird, Robin W ; Wiggins, Sean M ; Baumann-Pickering, Simone</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-462ef1c094d4a2d7a322def94e424f3f147b6771655b57d2b310dab2b844273e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Acoustic tracking</topic><topic>Acoustics</topic><topic>Algorithms</topic><topic>Animal behavior</topic><topic>Animals</topic><topic>Aquatic mammals</topic><topic>Automation</topic><topic>Biology and Life Sciences</topic><topic>Cetacea</topic><topic>Classification</topic><topic>Classifiers</topic><topic>Clustering</topic><topic>Data processing</topic><topic>Datasets</topic><topic>Dolphins</topic><topic>Earth Sciences</topic><topic>Echolocation</topic><topic>Echolocation (Physiology)</topic><topic>Engineering and Technology</topic><topic>Environmental aspects</topic><topic>Fin Whale</topic><topic>Fisheries</topic><topic>Frequencies</topic><topic>Globicephala macrorhynchus</topic><topic>Hawaii</topic><topic>Islands</topic><topic>Learning algorithms</topic><topic>Machine Learning</topic><topic>Marine mammals</topic><topic>Methods</topic><topic>Neural networks</topic><topic>Oceanography</topic><topic>Odontoceti</topic><topic>Physical Sciences</topic><topic>Pseudorca crassidens</topic><topic>Recall</topic><topic>Social Sciences</topic><topic>Sound Spectrography</topic><topic>Species</topic><topic>Species classification</topic><topic>Steno bredanensis</topic><topic>Vocalization, Animal</topic><topic>Whales & whaling</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ziegenhorn, Morgan A</creatorcontrib><creatorcontrib>Frasier, Kaitlin E</creatorcontrib><creatorcontrib>Hildebrand, John A</creatorcontrib><creatorcontrib>Oleson, Erin M</creatorcontrib><creatorcontrib>Baird, Robin W</creatorcontrib><creatorcontrib>Wiggins, Sean M</creatorcontrib><creatorcontrib>Baumann-Pickering, Simone</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Opposing Viewpoints Resource Center</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>ProQuest Nursing and Allied Health Journals</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>ProQuest - Health & Medical Complete保健、医学与药学数据库</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database (ProQuest Medical & Health Databases)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>Biological Sciences</collection><collection>Agriculture Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>ProQuest Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest advanced technologies & aerospace journals</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ziegenhorn, Morgan A</au><au>Frasier, Kaitlin E</au><au>Hildebrand, John A</au><au>Oleson, Erin M</au><au>Baird, Robin W</au><au>Wiggins, Sean M</au><au>Baumann-Pickering, Simone</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Discriminating and classifying odontocete echolocation clicks in the Hawaiian Islands using machine learning methods</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2022-04-12</date><risdate>2022</risdate><volume>17</volume><issue>4</issue><spage>e0266424</spage><epage>e0266424</epage><pages>e0266424-e0266424</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Passive acoustic monitoring (PAM) has proven a powerful tool for the study of marine mammals, allowing for documentation of biologically relevant factors such as movement patterns or animal behaviors while remaining largely non-invasive and cost effective. From 2008-2019, a set of PAM recordings covering the frequency band of most toothed whale (odontocete) echolocation clicks were collected at sites off the islands of Hawai'i, Kaua'i, and Pearl and Hermes Reef. However, due to the size of this dataset and the complexity of species-level acoustic classification, multi-year, multi-species analyses had not yet been completed. This study shows how a machine learning toolkit can effectively mitigate this problem by detecting and classifying echolocation clicks using a combination of unsupervised clustering methods and human-mediated analyses. Using these methods, it was possible to distill ten unique echolocation click 'types' attributable to regional odontocetes at the genus or species level. In one case, auxiliary sightings and recordings were used to attribute a new click type to the rough-toothed dolphin, Steno bredanensis. Types defined by clustering were then used as input classes in a neural-network based classifier, which was trained, tested, and evaluated on 5-minute binned data segments. Network precision was variable, with lower precision occurring most notably for false killer whales, Pseudorca crassidens, across all sites (35-76%). However, accuracy and recall were high (>96% and >75%, respectively) in all cases except for one type of short-finned pilot whale, Globicephala macrorhynchus, call class at Kaua'i and Pearl and Hermes Reef (recall >66%). These results emphasize the utility of machine learning in analysis of large PAM datasets. The classifier and timeseries developed here will facilitate further analyses of spatiotemporal patterns of included toothed whales. Broader application of these methods may improve the efficiency of global multi-species PAM data processing for echolocation clicks, which is needed as these datasets continue to grow.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>35413068</pmid><doi>10.1371/journal.pone.0266424</doi><tpages>e0266424</tpages><orcidid>https://orcid.org/0000-0001-8205-9345</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2022-04, Vol.17 (4), p.e0266424-e0266424 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2649552202 |
| source | Public Library of Science (PLoS) Journals Open Access; Open Access: PubMed Central; MEDLINE; DOAJ Directory of Open Access Journals; Free E-Journal (出版社公開部分のみ）; Free Full-Text Journals in Chemistry |
| subjects | Acoustic tracking Acoustics Algorithms Animal behavior Animals Aquatic mammals Automation Biology and Life Sciences Cetacea Classification Classifiers Clustering Data processing Datasets Dolphins Earth Sciences Echolocation Echolocation (Physiology) Engineering and Technology Environmental aspects Fin Whale Fisheries Frequencies Globicephala macrorhynchus Hawaii Islands Learning algorithms Machine Learning Marine mammals Methods Neural networks Oceanography Odontoceti Physical Sciences Pseudorca crassidens Recall Social Sciences Sound Spectrography Species Species classification Steno bredanensis Vocalization, Animal Whales & whaling |
| title | Discriminating and classifying odontocete echolocation clicks in the Hawaiian Islands using machine learning methods |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-01T08%3A43%3A44IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Discriminating%20and%20classifying%20odontocete%20echolocation%20clicks%20in%20the%20Hawaiian%20Islands%20using%20machine%20learning%20methods&rft.jtitle=PloS%20one&rft.au=Ziegenhorn,%20Morgan%20A&rft.date=2022-04-12&rft.volume=17&rft.issue=4&rft.spage=e0266424&rft.epage=e0266424&rft.pages=e0266424-e0266424&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0266424&rft_dat=%3Cgale_plos_%3EA700233410%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2649552202&rft_id=info:pmid/35413068&rft_galeid=A700233410&rft_doaj_id=oai_doaj_org_article_7d808eb0eb724dee894445b31a8cced3&rfr_iscdi=true |