Artificial intelligence (BirdNET) supplements manual methods to maximize bird species richness from acoustic datasets generated from regional monitoring
{"title":"Artificial intelligence (BirdNET) supplements manual methods to maximize bird species richness from acoustic datasets generated from regional monitoring","authors":"L. Ware, C. L. Mahon, Logan McLeod, J. Jetté","doi":"10.1139/cjz-2023-0044","DOIUrl":null,"url":null,"abstract":"Processing methods that maximize species richness from acoustic recordings obtained from regional monitoring programs can increase detections of uncommon, rare, and cryptic species and provide key information on species status and distribution. Using data from a regional bird monitoring in Yukon, Canada, we (1) compared the number of bird species detected (species richness) and the cost associated with four acoustic processing methods (Listening, Visual Scanning, Recognizer, Recognizer with Validation); and (2) combined Listening and Recognizer with Validation information to increase detections of all bird species at the ecoregion scale. We used comprehensive Visual Scanning to detect all bird species on the recordings. We processed ~1% of the recordings using Listening and detected 56% of the bird community with 71.5 hours of human effort. We used Recognizer (multispecies recognizer BirdNET) with Validation and detected 89% of the bird community with ~22% of the effort required for Visual Scanning (56 and 257 hours respectively). As an application of our approach, we combined Listening and Recognizer with Validation to process recordings from five northern ecoregions and found a 23-63% increase in the number of bird species detected with little additional effort. Combining Listening and Recognizer with Validation can maximize species detections from large passive acoustic monitoring (PAM) datasets.","PeriodicalId":9484,"journal":{"name":"Canadian Journal of Zoology","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Canadian Journal of Zoology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1139/cjz-2023-0044","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ZOOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Processing methods that maximize species richness from acoustic recordings obtained from regional monitoring programs can increase detections of uncommon, rare, and cryptic species and provide key information on species status and distribution. Using data from a regional bird monitoring in Yukon, Canada, we (1) compared the number of bird species detected (species richness) and the cost associated with four acoustic processing methods (Listening, Visual Scanning, Recognizer, Recognizer with Validation); and (2) combined Listening and Recognizer with Validation information to increase detections of all bird species at the ecoregion scale. We used comprehensive Visual Scanning to detect all bird species on the recordings. We processed ~1% of the recordings using Listening and detected 56% of the bird community with 71.5 hours of human effort. We used Recognizer (multispecies recognizer BirdNET) with Validation and detected 89% of the bird community with ~22% of the effort required for Visual Scanning (56 and 257 hours respectively). As an application of our approach, we combined Listening and Recognizer with Validation to process recordings from five northern ecoregions and found a 23-63% increase in the number of bird species detected with little additional effort. Combining Listening and Recognizer with Validation can maximize species detections from large passive acoustic monitoring (PAM) datasets.
期刊介绍:
Published since 1929, the Canadian Journal of Zoology is a monthly journal that reports on primary research contributed by respected international scientists in the broad field of zoology, including behaviour, biochemistry and physiology, developmental biology, ecology, genetics, morphology and ultrastructure, parasitology and pathology, and systematics and evolution. It also invites experts to submit review articles on topics of current interest.