{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T08:02:39Z","timestamp":1761897759538,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T00:00:00Z","timestamp":1659916800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000202","name":"U.S. Fish and Wildlife Service, Division of Bird Habitat Conservation","doi-asserted-by":"publisher","award":["F19AP00330"],"award-info":[{"award-number":["F19AP00330"]}],"id":[{"id":"10.13039\/100000202","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Reliable and efficient avian monitoring tools are required to identify population change and then guide conservation initiatives. Autonomous recording units (ARUs) could increase both the amount and quality of monitoring data, though manual analysis of recordings is time consuming. Machine learning could help to analyze these audio data and identify focal species, though few ornithologists know how to cater this tool for their own projects. We present a workflow that exemplifies how machine learning can reduce the amount of expert review time required for analyzing audio recordings to detect a secretive focal species (Sora; Porzana carolina). The deep convolutional neural network that we trained achieved a precision of 97% and reduced the amount of audio for expert review by ~66% while still retaining 60% of Sora calls. Our study could be particularly useful, as an example, for those who wish to utilize machine learning to analyze audio recordings of a focal species that has not often been recorded. Such applications could help to facilitate the effective conservation of avian populations.<\/jats:p>","DOI":"10.3390\/rs14153816","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T04:16:55Z","timestamp":1660018615000},"page":"3816","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Ornithologist\u2019s Guide for Including Machine Learning in a Workflow to Identify a Secretive Focal Species from Recorded Audio"],"prefix":"10.3390","volume":"14","author":[{"given":"Ming","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Computer Science, Central Michigan University, Mt. Pleasant, MI 48859, USA"}]},{"given":"Qiyu","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Central Michigan University, Mt. Pleasant, MI 48859, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8868-9997","authenticated-orcid":false,"given":"Dustin E.","family":"Brewer","sequence":"additional","affiliation":[{"name":"Department of Biology, Institute for Great Lakes Research, Central Michigan University, Mt. Pleasant, MI 48859, USA"}]},{"given":"Thomas M.","family":"Gehring","sequence":"additional","affiliation":[{"name":"Department of Biology, Institute for Great Lakes Research, Central Michigan University, Mt. Pleasant, MI 48859, USA"}]},{"given":"Jesse","family":"Eickholt","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Central Michigan University, Mt. Pleasant, MI 48859, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1038\/nature11148","article-title":"Biodiversity loss and its impact on humanity","volume":"486","author":"Cardinale","year":"2012","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1126\/science.aaw1313","article-title":"Decline of the North American avifauna","volume":"366","author":"Rosenberg","year":"2019","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s13157-016-0835-7","article-title":"Standardized measures of coastal wetland condition: Implementation at a Laurentian Great Lakes basin-wide scale","volume":"37","author":"Uzarski","year":"2017","journal-title":"Wetlands"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"50","DOI":"10.2307\/4088504","article-title":"Observer differences in the North American breeding bird survey","volume":"111","author":"Sauer","year":"1994","journal-title":"Auk"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1007\/s13157-011-0155-x","article-title":"Summary of intrinsic and extrinsic factors affecting detection probability of marsh birds","volume":"31","author":"Conway","year":"2011","journal-title":"Wetlands"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"14","DOI":"10.5751\/ACE-00974-120114","article-title":"Autonomous recording units in avian ecological research: Current use and future applications","volume":"12","author":"Shonfield","year":"2017","journal-title":"Avian Conserv. 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