Authors: Benjamin M. Van Doren, Vincent Lostanlen, Aurora Cramer, Justin Salamon, Adriaan Dokter, Steve Kelling, Juan Pablo Bello, Andrew Farnsworth
Year: 2023
Publication: Journal of Applied Ecology
Publication Link: https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/1365-2664.14342
Keywords: acoustic monitoring, bird migration, ecology, machine learning, remote sensing
Abstract:
1. Monitoring small, mobile organisms is crucial for science and conservation, but is
technically challenging. Migratory birds are prime examples, often undertaking
nocturnal movements of thousands of kilometres over inaccessible and inhospitable
geography. Acoustic technology could facilitate widespread monitoring of
nocturnal bird migration with minimal human effort. Acoustics complements existing
monitoring methods by providing information about individual behaviour
and species identities, something generally not possible with tools such as radar.
However, the need for expert humans to review audio and identify vocalizations
is a challenge to application and development of acoustic technologies.
2. Here, we describe an automated acoustic monitoring pipeline that combines
acoustic sensors with machine listening software (BirdVoxDetect). We monitor 4
months of autumn migration in the northeastern United States with five acoustic
sensors, extracting nightly estimates of nocturnal calling activity of 14 migratory
species with distinctive flight calls. We examine the ability of acoustics to
inform two important facets of bird migration: (1) the quantity of migrating birds
aloft and (2) the migration timing of individual species. We validate these data
with contemporaneous observations from Doppler radars and a large community
of citizen scientists, from which we derive independent measures of migration
passage and timing.
3. Together, acoustic and weather data produced accurate estimates of the number
of actively migrating birds detected with radar. A model combining acoustic
data, weather and seasonal timing explained 75% of variation in radar-derived
migration intensity. This model outperformed models that lacked acoustic data.
Including acoustics in the model decreased prediction error by 33%. A model
with only acoustic information outperformed a model comprising weather and
date (57% vs. 48% variation explained, respectively).
4. Acoustics also successfully measured migration phenology: species-specific
timing estimated by acoustic sensors explained 71% of variation in timing derived
from citizen science observations.
5. Synthesis and applications. Our results demonstrate that cost-effective
acoustic sensors can monitor bird migration at species resolution at the landscape scale
and should be an integral part of management toolkits. Acoustic monitoring
presents distinct advantages over radar and human observation, especially in
inaccessible and inhospitable locations, and requires significantly less expense.
Managers should consider using acoustic tools for monitoring avian movements
and identifying and understanding dangerous situations for birds. These recommendations
apply to a variety of conservation and policy applications, including
mitigating the impacts of light pollution, siting energy infrastructure (e.g. wind
turbines) and reducing collisions with structures and aircraft.