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Osterhaus, D. M.

Publications and source records attributed to Osterhaus, D. M..

2 recordsLinked to original sources

Nighthawk: acoustic monitoring of nocturnal bird migration in the Americas

O_LIAnimal migration is one of natures most spectacular phenomena, but migratory animals and their journeys are imperiled across the globe. Migratory birds are among the most well-studied animals on Earth, yet relatively little is known about in-flight behavior during nocturnal migration. Because many migrating bird species vocalize during flight, passive acoustic monitoring shows great promise for facilitating widespread monitoring of bird migration. C_LIO_LIHere, we present Nighthawk, a deep learning model designed to detect and identify the vocalizations of nocturnally migrating birds. We trained Nighthawk on the in-flight vocalizations of migratory birds using a diverse dataset of recordings from across the Americas. C_LIO_LIOur results demonstrate that Nighthawk performs well as a nocturnal flight call detector and classifier for dozens of avian taxa, both at the species level and for broader taxonomic groups (e.g., orders and families). The model accurately quantified nightly nocturnal migration intensity and species phenology and performed well on data from across North America. Incorporating modest amounts of additional annotated audio (50-120 h) into model training yielded high performance on target datasets from both North and South America. C_LIO_LIBy monitoring the vocalizations of actively migrating birds, Nighthawk provides a detailed window onto nocturnal bird migration that is not presently attainable by other means (e.g., radar or citizen science). Scientists, managers, and practitioners could use acoustic monitoring with Nighthawk for a number of applications, including: monitoring migration passage at wind farms; studying airspace usage during migratory flights; monitoring the changing migrations of species susceptible to climate change; and revealing previously unknown migration routes and behaviors. Overall, this work will empower diverse stakeholders to efficiently monitor migrating birds across the Western Hemisphere and collect data in aid of science and conservation. Nighthawk is freely available at https://github.com/bmvandoren/Nighthawk. C_LI

ecology↗

Comparison of sampling methods for small oxbow wetland fish communities

Throughout the world, wetlands have experienced degradation and declines in areal coverage. Fortunately, recognition of the value of wetlands has generated interest in preserving and restoring them. Post-restoration monitoring is necessary to analyze success or failure, thereby informing subsequent management decisions. Restoration of oxbow wetlands has become the focus of targeted restoration efforts to promote recovery of biodiversity and sensitive species, and to enhance ecosystem services. The fish communities of oxbows have been the subject of many monitoring studies. However, an optimal method for monitoring the fish communities of oxbows has not been described, thereby limiting our capacity to effectively manage these ecosystems. We compared four sampling methodologies (backpack electrofishing, fyke netting, minnow trapping, and seining) for fish community data collection with a primary objective of determining an optimal method for sampling fish communities in small oxbow wetlands. Seining and fyke netting were determined to be optimal methods for sampling oxbow fish communities. Backpack electrofishing and minnow trapping produced lower total catch and taxonomic richness values than seining and fyke netting. Although seining and fyke netting produced similar taxonomic diversity and abundance values, qualitative analysis revealed that seining caused greater habitat disturbance and potential stress to fish. Therefore, consideration must be given to how species present (especially sensitive species) within the wetland could be impacted by sampling disturbance when choosing between seining and fyke netting.

ecology↗