Biological Sciences Division
Published December 26, 2023 | Version v1
Journal article Open

Nighthawk: Acoustic monitoring of nocturnal bird migration in the Americas

Creators

  • 1. Cornell University
  • 2. MPG Ranch
  • 3. New Mexico State University
  • 4. University of Chicago

Description

  1. Animal migration is one of nature's most spectacular phenomena, but migratory animals and their journeys are imperilled across the globe. Migratory birds are among the most well-studied animals on Earth, yet relatively little is known about in-flight behaviour during nocturnal migration. Because many migrating bird species vocalize during flight, passive acoustic monitoring shows great promise for facilitating widespread monitoring of bird migration.
  2. Here, 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.
  3. Our 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). It achieves an average precision score above 0.80 for 50 species and a mean average precision of 0.96 across 4 orders. The model accurately quantified nightly nocturnal migration intensity (80% variation explained) and species phenology (78% variation explained) 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 (average precision on order Passeriformes >0.99).
  4. By 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 behaviours. 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.

Data availability

The Nighthawk model, trained on the Core dataset presented here, is archived on Zenodo (Van Doren, Mills, et al., 2023) and available for download on GitHub (https://github.com/bmvandoren/Nighthawk/), along with Python code demonstrating its proper use. We welcome questions, feedback and collaboration inquiries by email to the corresponding author. Users can also use Nighthawk by installing the program Vesper (https://github.com/HaroldMills/Vesper) and using a plugin (https://github.com/HaroldMills/vesper-nighthawk). Vesper is designed for the management and processing of audio recordings for nocturnal bird migration monitoring and is maintained by Harold Mills (https://github.com/HaroldMills).

Files

Methods Ecol Evol - 2023 - Van Doren - Nighthawk Acoustic monitoring of nocturnal bird migration in the Americas.pdf

Files (47.9 MB)

Additional details

Identifiers

DOI
10.1111/2041-210X.14272
Other
oai:uchicago.tind.io:14060

Funding

Cornell Lab of Ornithology, Cornell University
Cornell University
Presidential Postdoctoral Fellowship
University of Chicago
Steiner Fund
Actions@EBMF
Field Museum of Natural History
H. B. Conover Fund

UChicago Information

Division(s)
Biological Sciences Division
Department(s)
Evolutionary Biology