Pairing a user‐friendly machine‐learning animal sound detector with passive acoustic surveys for occupancy modeling of an endangered primate

Bibliographic Data

ID19386589
AuthorsConnor M Wood (0000-0002-0235-5214, K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology Cornell University Ithaca New York USA, corresponding author), Alicia Barceinas Cruz (0000-0001-8636-2341, Nelson Institute for Environmental Studies, Department of Geography University of Wisconsin‐Madison Madison Wisconsin USA), Stefan Kahl (0000-0002-2411-8877, K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology Cornell University Ithaca New York USA)
Year2023
Volume85
Issue8
Pagese23507-e23507
Publication date2023-08-01
Open AccessYes
TypeARTICLE
VenueAmerican Journal of Primatology (JOURNAL)
Journal identifiersISSN: 0275-2565 • E-ISSN: 1098-2345
PublisherWiley (PUBLISHER • GB)
DOI10.1002/ajp.23507
PMID37211970
OpenAlexW4377220452
LanguageEN
Citations received2
References cited51

Population declines and range contractions due to habitat loss are pervasive among nonhuman primates, with 60% of species threatened with extinction. However, the extensive vocal activity displayed by many primates makes them excellent candidates for passive acoustic surveys. Passive acoustic survey data is increasingly being used to support occupancy models, which have proven to be an efficient means of estimating both population trends and distributions. Passive acoustic surveys can be conducted relatively quickly and at broad scales, but efficient audio data processing has long proven elusive. The machine learning algorithm BirdNET was originally developed for birds but was recently expanded to include nonavian taxa. We demonstrate that BirdNET can accurately and efficiently identify an endangered primate, the Yucatán black howler monkey ( Alouatta pigra ), by sound in passive acoustic survey data (collected in southeastern Chiapas, Mexico), enabling us to use a single‐season occupancy model to inform further survey efforts. Importantly, we also generated data on up to 286 co‐occurring bird species, demonstrating the value of integrated animal sound classification tools for biodiversity surveys. BirdNET is freely available, requires no computer science expertise to use, and can readily be expanded to include more species (e.g., its species list recently tripled to >3000), suggesting that passive acoustic surveys, and thus occupancy modeling, for primate conservation could rapidly become much more accessible. Importantly, the long history of bioacoustics in primate research has yielded a wealth of information about their vocal behavior, which can facilitate appropriate survey design and data interpretation

Bioacoustics · Biology · Data science · Endangered species · Habitat · Occupancy · Population · Primate · Telecommunications · Threatened species · Animal Vocal Communication and Behavior · Computer Science · Engineering · Marine animal studies overview · Primate Behavior and Ecology · Ecology

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