{"title":"通过新型记录器技术和自动物种识别推进鸟类调查工作","authors":"Matthew Toenies, L. Rich","doi":"10.51492/cfwj.107.5","DOIUrl":null,"url":null,"abstract":"Recent advances in acoustic recorder technology and automated species identification hold great promise for avian monitoring efforts. Assessing how these innovations compare to existing recorder models and traditional species identification techniques is vital to understanding their utility to researchers and managers. We carried out field trials in Monterey County, California, to compare bird detection among four acoustic recorder models (AudioMoth, Swift Recorder, and Wildlife Acoustics SM3BAT and SM Mini) and concurrent point counts, and to assess the ability of the artificial neural network BirdNET to correctly identify bird species from AudioMoth recordings. We found that the lowest-cost unit (AudioMoth) performed comparably to higher-cost units and that on average, species detections were higher for three of the five recorder models (range 9.8 to 14.0) than for point counts (12.8). In our assessment of BirdNET, we developed a subsetting process that enabled us to achieve a high rate of correctly identified species (96%). Using longer recordings from a single recorder model, BirdNET identified a mean of 8.5 verified species per recording and a mean of 16.4 verified species per location over a 5-day period (more than point counts conducted in similar habitats). We demonstrate that a combination of long recordings from low-cost recorders and a conservative method for subsetting automated identifications from BirdNET presents a process for sampling avian community composition with low misidentification rates and limited need for human vetting. These low-cost and automated tools may greatly improve efforts to survey bird communities and their ecosystems, and consequently, efforts to conserve threatened indigenous biodiversity.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Advancing bird survey efforts through novel recorder technology and automated species identification\",\"authors\":\"Matthew Toenies, L. Rich\",\"doi\":\"10.51492/cfwj.107.5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recent advances in acoustic recorder technology and automated species identification hold great promise for avian monitoring efforts. Assessing how these innovations compare to existing recorder models and traditional species identification techniques is vital to understanding their utility to researchers and managers. We carried out field trials in Monterey County, California, to compare bird detection among four acoustic recorder models (AudioMoth, Swift Recorder, and Wildlife Acoustics SM3BAT and SM Mini) and concurrent point counts, and to assess the ability of the artificial neural network BirdNET to correctly identify bird species from AudioMoth recordings. We found that the lowest-cost unit (AudioMoth) performed comparably to higher-cost units and that on average, species detections were higher for three of the five recorder models (range 9.8 to 14.0) than for point counts (12.8). In our assessment of BirdNET, we developed a subsetting process that enabled us to achieve a high rate of correctly identified species (96%). Using longer recordings from a single recorder model, BirdNET identified a mean of 8.5 verified species per recording and a mean of 16.4 verified species per location over a 5-day period (more than point counts conducted in similar habitats). We demonstrate that a combination of long recordings from low-cost recorders and a conservative method for subsetting automated identifications from BirdNET presents a process for sampling avian community composition with low misidentification rates and limited need for human vetting. These low-cost and automated tools may greatly improve efforts to survey bird communities and their ecosystems, and consequently, efforts to conserve threatened indigenous biodiversity.\",\"PeriodicalId\":1,\"journal\":{\"name\":\"Accounts of Chemical Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":16.4000,\"publicationDate\":\"2021-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Accounts of Chemical Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.51492/cfwj.107.5\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.51492/cfwj.107.5","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Advancing bird survey efforts through novel recorder technology and automated species identification
Recent advances in acoustic recorder technology and automated species identification hold great promise for avian monitoring efforts. Assessing how these innovations compare to existing recorder models and traditional species identification techniques is vital to understanding their utility to researchers and managers. We carried out field trials in Monterey County, California, to compare bird detection among four acoustic recorder models (AudioMoth, Swift Recorder, and Wildlife Acoustics SM3BAT and SM Mini) and concurrent point counts, and to assess the ability of the artificial neural network BirdNET to correctly identify bird species from AudioMoth recordings. We found that the lowest-cost unit (AudioMoth) performed comparably to higher-cost units and that on average, species detections were higher for three of the five recorder models (range 9.8 to 14.0) than for point counts (12.8). In our assessment of BirdNET, we developed a subsetting process that enabled us to achieve a high rate of correctly identified species (96%). Using longer recordings from a single recorder model, BirdNET identified a mean of 8.5 verified species per recording and a mean of 16.4 verified species per location over a 5-day period (more than point counts conducted in similar habitats). We demonstrate that a combination of long recordings from low-cost recorders and a conservative method for subsetting automated identifications from BirdNET presents a process for sampling avian community composition with low misidentification rates and limited need for human vetting. These low-cost and automated tools may greatly improve efforts to survey bird communities and their ecosystems, and consequently, efforts to conserve threatened indigenous biodiversity.
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.