Meeting Abstract
The city is a complex environment to navigate as a bird; the wind interacts with buildings and other urban infrastructure to create areas of strong up- and down-drafts. During the breeding season, urban nesting gulls spend 40% of their time in flight, flying to and from foraging locations through these complex wind-scapes. Choosing appropriate flight paths has the potential to substantially reduce their energetic flight costs which could be key for breeding success. We used GPS backpacks to track 11 Lesser Black-backed Gulls (Larus fuscus) over two breeding seasons in the city of Bristol. The loggers collected a GPS fix and a one-second burst of 20 Hz three-axis accelerometer data up to every 4 seconds, allowing us to measure the flight paths of the gulls and characterise their flight modes. Meteorological forecasting data and machine learning techniques were used to identify the most commonly used flight strategies and associated environmental predictors. It was found that the gulls used a combination of orographic soaring and thermalling strategies; adapting their flight paths in response to local conditions to make energy savings of up to 66%. Computational fluid dynamics models of the wind in the city were then used to characterise the complex aerodynamic environment available to the gulls, and path planning optimization techniques were used to understand the potential energetic savings available to the birds and to investigate the trade-offs involved between energy expenditure and flight time. There is potential for using these flight strategies in the design of path planning algorithms for small unmanned air vehicles operating in similar environments.
