Stefan Heinänen

Waterbird Populations and Pressures in the Baltic Sea

This report outlines the results of the internationally coordinated census of wintering waterbirds in the Baltic Sea 2007-2009 undertaken under the SOWBAS project (Status of wintering Waterbird pop ...

High-resolution sea duck distribution modeling: Relating aerial and ship survey data to food resources, anthropogenic pressures, and topographic variables

ABSTRACT Anthropogenic developments in marine coastal zones potentially overlap with areas of conservation interest, including important areas for birds. Ideally, spatial patterns of species abundance should be considered at ecologically relevant spatial resolutions (high resolutions) to inform spatial planning and environmental assessments. Most planning so far, however, has relied on coarse-resolution distribution maps from atlas projects or models often based on limited datasets (few surveys), and relationships with environmental variables have rarely been taken into account, leaving many studies and recommendations vulnerable to criticism. We therefore combined the strengths of a detailed database of spatially explicit aerial and ship surveys with high-resolution environmental predictors and species distribution models to predict detailed density patterns for 3 sea duck species, as part of an environmental impact assessment (EIA) in the southern Baltic Sea. We also compared the results from 2 different survey platforms to assess potential differences. We related survey data for Common Eiders (Somateria mollissima), Long-tailed Ducks (Clangula hyemalis), and Common Scoters (Melanitta nigra) to topographic variables, food resources, and anthropogenic pressures using 2-step generalized additive models accounting for zero inflation, nonnormality, and nonlinearity. We accurately predicted distribution patterns (the area under the receiver operating characteristic curve [AUC]: 0.79–0.84) and abundances (Spearmans correlation: 0.36–0.62) at a resolution of 750 m. However, abundance predictions based on aerial survey data differed in magnitude in comparison with predictions from ship survey data, particularly for the frequently diving Long-tailed Duck. We suggest that the main source of the differing abundance estimates was differences in the input data collected using different survey platforms, rather than the modeling approach. A correction factor for birds missed during surveys due to diving activity would therefore increase the accuracy of abundance estimates. Our results show that it is possible to fit ecologically interpretable relationships between species and environmental variables, allowing for the creation of high-resolution predictions useful for management and conservation.

Patterns of migrating soaring migrants indicate attraction to marine wind farms

Monitoring of bird migration at marine wind farms has a short history, and unsurprisingly most studies have focused on the potential for collisions. Risk for population impacts may exist to soaring migrants such as raptors with K-strategic life-history characteristics. Soaring migrants display strong dependence on thermals and updrafts and an affinity to land areas and islands during their migration, a behaviour that creates corridors where raptors move across narrow straits and sounds and are attracted to islands. Several migration corridors for soaring birds overlap with the development regions for marine wind farms in NW Europe. However, no empirical data have yet been available on avoidance or attraction rates and behavioural reactions of soaring migrants to marine wind farms. Based on a post-construction monitoring study, we show that all raptor species displayed a significant attraction behaviour towards a wind farm. The modified migratory behaviour was also significantly different from the behaviour at nearby reference sites. The attraction was inversely related to distance to the wind farm and was primarily recorded during periods of adverse wind conditions. The attraction behaviour suggests that migrating raptor species are far more at risk of colliding with wind turbines at sea than hitherto assessed.

Marine habitat modelling for harbour porpoises in the German Bight

The harbour porpoise habitat modelling project aims to synthesise the data by long-term analysis and modelling of harbour porpoise distribution in the German Bight, i.e. the alpha ventus area and adjacent waters. This study uses predictive distribution models – a recent development in habitat modelling – to estimate harbour porpoise distributions in the German Bight during the summer season (June to August). The statistical models link observations from aerial surveys to oceanographic conditions and pressures. Oceanographic conditions during porpoise surveys were estimated using hydrodynamic models capable of describing patterns and dynamics of feeding habitats. As the modelling depends on the availability of coherent oceanographic and pressure data for the entire German Bight, the approach used combines hydrodynamic and noise models designed for the purpose.

Predicting the Weather-Dependent Collision Risk for Birds at Wind Farms

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.