Abstract
Collision risk for birds remains a potential conservation issue and environmental barrier to the development of wind farms on land as well as at sea. Baseline and post-construction studies in Denmark carried out at coastal and marine wind farms during 2010–2012 have aimed at developing prediction tools which could pave the way for improved planning and siting of wind farms in relation to movements of birds. Detection of flight trajectories by means of visual observations is severely constrained, and thus field campaigns were undertaken using a combination of visual observations and radar- and rangefinder-based tracking. The collection of two- and three-dimensional track data was necessary to obtain useful information on the responses of migrating bird species to the wind farms, and on flight altitudes of the birds during different weather conditions and in relation to landscape components. To be able to assess general patterns in the migration behaviour of birds, we developed statistical models capable of explaining the differences in altitude based on relationships with wind and weather conditions and distance to coast. As these relationships in many cases were non-linear, the error structure of the data non-normally distributed, and the track data spatially and temporally auto-correlated we chose to use a generalized additive mixed modelling (GAMM) framework. The resulting models of the migration altitude of raptors and other groups of landbirds made it possible to assess the weather-dependent flight altitude at the wind farm sites. The studies provided strong indications that wind speed and direction as well as humidity, air clarity and air pressure are important predictors in general for all species in addition to distance to land and wind farm, and the birds favour tail winds and decreasing wind speed. Collision models display a variety of specific trends with rates of collisions of landbirds increasing during periods of head winds and reduced visibility, while the collision rates of seabirds typically increase during periods of tail winds and increased visibility. Our studies have shown that birds across a wide range of species show clear weather-dependent movements which can be predicted for specific spatial settings using statistical models. These findings stress the potential for intensifying the strategic planning processes related to wind farms.
This chapter is a conference paper presented in the book Wind and Wildlife: Proceedings from the Conference on Wind Energy and Wildlife Impacts, October 2012, Melbourne, Australia.