Date Awarded

2021

Document Type

Thesis

Degree Name

Master of Science (M.Sc.)

Department

Biology

Advisor

John P. Swaddle

Committee Member

Daniel A. Cristol

Committee Member

Matthias Leu

Abstract

Collisions with human-made structures are responsible for billions of bird deaths each year, resulting in ecological damage as well as regulatory and financial burdens to many industries. Acoustic warning signals can alert birds to obstacles in their flight paths in order to mitigate collisions, but these signals should be tailored to the sensory ecology of birds in flight. The effectiveness of various acoustic warning signals likely depends on the influence of background noise and the relative ability of various sound types to propagate within a landscape. I evaluated the ability of four sound signals to elicit collision-avoidant flight behaviors from birds released into a flight corridor containing a physical obstacle. I selected signals to test two frequency ranges (4-6 kHz or 6-8 kHz) and two temporal modulation patterns (broadband or frequency-modulated oscillating) to determine whether any particular combination of sound attributes elicited the strongest collision avoidance behaviors. I found that, relative to control flights, all sound treatments caused birds to maintain a greater distance from hazards and to adjust their flight trajectories before coming close to obstacles. There were no statistical differences among different sound treatments, but consistent trends within the data could suggest that the 4-6 kHz frequency-modulated oscillating signal elicited the strongest avoidance behaviors, followed by the 6-8 kHz broadband signal. I conclude that acoustic warning signals could be an effective avian collision deterrent in several contexts, and that the particular sound used in a warning signal may impact the detectability of the signal as well as the type of flight behaviors used to evade a collision. These findings can be used to design more effective warning signals and demonstrate the value of using behavioral data to assess collision risk.

Rights

© The Author

Available for download on Saturday, August 27, 2022

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