Some wind farms have implemented automated camera\textendash based monitoring systems e.g. IdentiFlight to mitigate the impact of wind turbines on protected raptors. These systems have effectuated the collection of large amounts of data that can be used to describe flight behavior in a novel way. This data uniquely provides both flight trajectories and images of individual birds throughout their flight trajectories. The aim of this study was to evaluate how this unique data could be used to create a robust quantitative behavioral analysis, that could be used to identify risk prone flight behavior and avoidance behavior thereby in the future assess collision risk. This was attained through a case study at a wind farm on the Swedish island Gotland, where golden eagles (Aquila chrysaetos), white-tailed eagles (Haliaeetus albicilla), and red kites (Milvus milvus), were chosen as the selected bird species. The results demonstrate that flight trajectories and bird images can be used to identify high risk flight behavior and thereby also used to evaluate collision risk and avoidance behavior. This study presents a promising framework for future research, demonstrating how data from camera\textendash based monitoring systems can be utilized to quantitatively describe risk prone behavior and thereby assess collision risk and avoidance behavior.

Behavioural instability is a newly coined term used for measuring asymmetry of bilateral behavioural traits as indicators of genetic or environmental stress. However, this concept might also be useful for other types of data than bilateral traits. In this study, behavioural instability indices of expected behaviour were evaluated as an indicator for environmental stress through the application of aerial photos of foraging flocks of geese. It was presumed that geese would increase anti-predator behaviour through the dilution effect when foraging near the following landscape elements: wind turbines, hedgerows, and roads. On this presumption, it was hypothesized that behavioural instability of spatial distribution in flocks of geese could be used as indicators of environmental stress. Asymmetry in spatial distribution was measured for difference in flock density across various distances to disturbing landscape elements through the following indices; behavioural instability of symmetry and behavioural instability of variance. The behavioural instability indices showed clear tendencies for changes in flock density and variance of flock density for geese foraging near wind turbines, hedgerows, and roads indicating increasing environmental stress levels. Thus, behavioural instability has proven to be a useful tool for monitoring environmental stress that does not need bilateral traits to estimate instability but can be applied for indices of expected behaviour.

Some wind farms have implemented automated camera-based monitoring systems, e.g., IdentiFlight to mitigate the impact of wind turbines on protected birds. These systems have promoted the collection of large amounts of unique data that can be used to describe flight behavior in a novel way. The aim of this study was to evaluate how this unique data can be used to create a robust quantitative behavioral analysis, that can be used to identify risk-prone flight behavior and avoidance behavior and thereby used to assess collision risk in the future. This was achieved through a case study at a wind farm on the Swedish island Gotland, where golden eagles (Aquila chrysaetos), white-tailed eagles (Haliaeetus albicilla), and red kites (Milvus milvus), were chosen as the bird species. These three species are generally rare breeds in Europe and have also been shown to be particularly vulnerable to collisions with wind turbines. The results demonstrate that data from the IdentiFlight system can be used to identify risk-prone flight behaviors, e.g., tortuous flight and foraging behavior. Moreover, it was found that these flight behaviors were affected by both weather conditions, but also their distance to the nearest wind turbine. This data can, thus, be used to evaluate collision risk and avoidance behavior. This study presents a promising framework for future research, demonstrating how data from camera-based monitoring systems can be utilized to quantitatively describe risk-prone behavior and thereby assess collision risk and avoidance behavior.

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.

SummaryUnmanned aerial vehicles (UAVs) are useful tools in ornithological studies. Importantly, though, UAV-caused disturbance has been noted to vary among species. This study evaluated guidelines for UAVs as a tool for researching geese. Twenty-four flocks of foraging geese were approached at an altitude of 50–100 m with a quadcopter UAV and disturbance effects were analysed across different horizontal distances between the UAV and the flocks. Geese were increasingly disturbed when approached by a UAV, with birds showing increased vigilance behaviour within approximately 300 m. Increasing UAV flight altitude as well as increasing take-off distance from the flocks both decreased the risk of bird flocks flushing. In conclusion, when monitoring geese using UAVs, flight altitudes of 100 m and take-off distances of ideally ∼500 m are recommended, to minimise initial disturbance and reducing the risk of birds flushing.