Abstract
The risk of collision with wind turbines remains a critical issue for bird conservation. Undoubtedly, for the continued development of wind farms to increase the energy capacity, wind farm locations must be carefully chosen going forward. This can be achieved not only by avoiding areas with higher bird densities but also by avoiding installations at sensitive distances from their ecologically important land‐use types. Through analyses of the Euclidean distances to the different land‐use types, we utilized the random forest (RF) machine learning algorithm to model the distance‐based impacts of wind turbine locations on detected bird collisions for the frequently‐hit groups of birds at WTs. Although, the predicted areas with potential collision risk in total had a small but highly dispersed expanse of ~2,130 km2 across the vast 29,479 km2 area of the federal state. Our results further segregated these assessed areas based on their different probabilities of collision thresholds (between 0 and 1) to only detect the areas with collision probabilities <.05, which were interpreted as the actual “no risk areas”. These “no risk areas” summed to a total of merely 754 km2 of the land space in Brandenburg, suggesting that any further planned additions of wind energy farms in the state that is, the proposed wind turbines, to be suitably positioned only in these safer areas. Additionally, the study also enabled the identification of any existing wind turbines already installed in the remaining less safe 28,725 km2 area of the state. These areas are also essential to be include in the collision detection surveys and bird population dynamic studies. This would further our understanding regarding the deleterious consequences of collisions at the population levels of birds, eventually helping in the formulation of adequate mitigation measures.