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Passive acoustic monitoring has proven effective for broad-scale population surveys of acoustically active species, making it a valuable tool for conserving threatened species. However, successful automated classification of anuran vocalisations in large audio datasets has been limited. We deployed five autonomous recording units at three known breeding areas of the Yosemite toad (Anaxyrus canorus), which is threatened and relatively uncommon, and the sympatric Pacific chorus frog (Pseudacris regilla), which is widespread and more common, to test the viability of bioacoustics as a means of supplementing ongoing, human survey efforts. We analysed the audio data with the BirdNET algorithm, which was originally developed for birds but has been expanded to include both species. We achieved efficient and accurate identification of both species in 2,756 h of audio, which yielded high-resolution phenological data about seasonal and daily vocal activity as well as daily detection counts. These findings demonstrate that a newly expanded machine learning detector, BirdNET, can effectively process passive acoustic surveys for these species. Further exploration of how passive acoustic monitoring may complement existing survey techniques for these and other Anurans is warranted. [ABSTRACT FROM AUTHOR]

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. Highlights: The BirdNET algorithm was developed to identify birds by sound but was recently expanded to include non‐avian taxa; we demonstrate that it can identify an endangered primate, the Yucatán black howler monkey (Alouatta pigra).BirdNET‐based howler monkey detections enabled us to use an occupancy model to inform further survey efforts—and to generate data on up to 286 co‐occurring bird species, illustrating the value of multispecies classifiers for biodiversity surveys.Combining free and easy‐to‐use tools like BirdNET, passive acoustic surveys, and occupancy modeling can facilitate rapid, broad‐scale assessments of primate population trends and distributions. [ABSTRACT FROM AUTHOR]

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