Predicting burrowing owl flight trajectories in urban environments

It is important to understand how animals respond to changes in landscape structure, especially when considering habitat alteration and urbanization. Using the burrowing owl (Athene cunicularia) as an ecological model we tested two hypotheses: (1) the landscape of urban areas influences trajectories of the burrowing owl, which responds changing its displacement patterns, and (2) models incorporating species-specific traits, such as stochasticity and landscape perception, better predict flight trajectories than least-cost path models. Thirty owls were captured at the Brasilia International Airport, fitted with VHF transmitters, and released in random locations within 30 km of the airport. We generated a habitat suitability map for use as a resistance raster to model flight trajectories: one model was based on the least-cost path, and four models included stochasticity and variations of the owls' landscape perception and dependency on natural ecosystems. Observed trajectories were compared with those predicted by models using the following metrics extracted from the trajectories: mean habitat suitability values, sinuosity, and length. The best generalized linear models were selected using the Akaike information criterion. The owls dispersed through areas with a lower cost than expected by chance (z = 104.65, p < 0.05). More complex models performed better than the least-cost path model for suitability (r(2) = 0.18, p > 0.05), sinuosity (r(2) = 0.03, p > 0.05), and length (r(2) = 0.37, p > 0.05). Our results demonstrate that landscape information influences flight trajectories, and models incorporating landscape perception and stochasticity better predict burrowing owl flight trajectories.

Automated summary

This study using the burrowing owl as a model demonstrated the influence of landscape on flight trajectories, with models incorporating landscape perception and stochasticity showing better predictive ability.