Drucker, J. | bioRxiv Science

bioRxiv2024

Analysis of mixtures of birds and insects in weather radar data

Weather radars are increasingly used to study the spatial-temporal dynamics of airborne birds and insects. These two taxa often co-occur and separating their contributions remains a major analytical challenge. Most studies have restricted analyses to locations, seasons, and periods when one or the other taxa dominates. In this study, we describe an analytical method to estimate the proportion of birds and insects from vertical profiles of biological reflectivities, using a minimal number of assumptions on the airspeeds of birds and insects. We evaluated our method on understudied regions where airborne insect density is too high for existing approaches of studying bird migration with weather radars: the tropics (Colombia) and the southern temperate zone (Southeast Australia). Our method estimates that bird and insect signals routinely reach similar magnitudes in these regions. Retrieved patterns across daily and annual cycles reflected expected biological patterns that are indicative of migratory and non-migratory movements in both climates and migration systems. Compared to fixed airspeed thresholding, we obtain finer separation and retain more spatial-temporal complexity that is crucial to revealing aerial habitat use of both taxa. Our analytical procedure is readily implemented into existing software, empowering ecologists to explore aerial ecosystems outside the northern temperate zone, as well as diurnal migration of birds and insects that remains heavily understudied. Lay summaryO_LIWe developed a new method to differentiate between birds and insects in weather radar data. C_LIO_LIThis method uses minimal assumptions about the flight speeds of birds and insects. C_LIO_LIWe tested the method in regions with high insect density: the tropics (Colombia) and southern temperate zone (Southeast Australia). C_LIO_LIOur method estimated proportions of birds and insects that captured expected patterns of daily and annual movements, which were indicative of migratory and non-migratory movement of both taxa. C_LIO_LIUnlike fixed airspeed criteria for bird and insect separation, our approach provides a more detailed understanding of aerial habitat use by both birds and insects. C_LIO_LIThis method can be easily added to existing software, helping ecologists study bird and insect movements in less-studied areas and ecosystems. C_LI

ecology↗

bioRxiv2023

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↗