Authors: Nilsson, Cecilia et al.
Publication: Journal of Applied Ecology
Publication Link: https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2664.13971
Keywords: bird migration, bird strikes, citizen science, eBird, flight safety, weather surveillance radar, wildlife management
Abstract: 1. Aircraft collisions with birds span the entire history of human aviation, including
fatal collisions during some of the first powered human flights. Much effort
has been expended to reduce such collisions, but increased knowledge about bird
movements and species occurrence could dramatically improve decision support
and proactive measures to reduce them. Migratory movements of birds pose a
unique, often overlooked, threat to aviation that is particularly difficult for individual
airports to monitor and predict the occurrence of birds vary extensively in
space and time at the local scales of airport responses.
2. We use two publicly available datasets, radar data from the US NEXRAD network
characterizing migration movements and eBird data collected by citizen scientists to
map bird movements and species composition with low human effort expenditures but
high temporal and spatial resolution relative to other large-scale
bird survey methods.
As a test case, we compare results from weather radar distributions and eBird species
composition with detailed bird strike records from three major New York airports.
3. We show that weather radar-based
estimates of migration intensity can accurately
predict the probability of bird strikes, with 80% of the variation in bird
strikes across the year explained by the average amount of migratory movements
captured on weather radar. We also show that eBird-based
estimates of species
occurrence can, using species’ body mass and flocking propensity, accurately predict
when most damaging strikes occur.
4. Synthesis and applications. By better understanding when and where different bird
species occur, airports across the world can predict seasonal periods of collision
risks with greater temporal and spatial resolution; such predictions include potential
to predict when the most severe and damaging strikes may occur. Our results
highlight the power of federating datasets with bird movement and distribution data for developing better and more taxonomically and ecologically tuned models of likelihood of strikes occurring and severity of strikes.