A new model to quantify the probability of collision between birds and aircraft: applications for onboard lighting

Globally, bird and aircraft collisions are a major safety hazard and monetary expense for the aviation industry. Empirical evidence suggests that the behavioral response of the animal just prior to a collision is a critical factor in determining whether a collision occurs. However, no theoretical framework exists to predict the probability of a collision based on the escape response of the animal to an approaching vehicle. We adapted concepts from existing predator-prey theoretical frameworks to develop a novel model to quantify the outcome of an animal-vehicle interaction. Specifically, our model consists of two distinct phases. Phase one determines if a collision is even possible based on the amount of time the animal has available to clear the trajectory of the approaching vehicle. If the animal does not have enough time, then phase two of the model estimates the probability of collision based on the surface area of the vehicle given the location of the animal within the trajectory. We demonstrate the utility of the model by estimating the probability of collision between a Canada goose and an approaching Boeing-737 aircraft with the absence and presence of onboard lights of different wavelength, a technological intervention aimed at minimizing bird strikes. Our model predicts that when a Canada goose is within the trajectory of a Boeing-737, the average probability of collision is approximately 0.43; however, onboard lights with wavelengths tuned to the visual system of the species can reduce that probability on average by either 19% (red-light onboard) or 32% (blue-light onboard). The highest probability of collision occurred when the animal was in the center of the trajectory of the vehicle. The behaviors with the largest effect on reducing the probability of collision were an increase in flight-initiation distance and an increase in escape speed. Our approach provides a framework to quantitatively predict how the probability of collision might change across different species, vehicles, and situations, which could be used in forecasting the impacts of present and future transportation projects on wildlife populations.