Quantifying Raptors’ Flight Behavior to Assess Collision Risk and Avoidance Behavior to Wind Turbines

• Published in: Symmetry
• Main Authors: Linder, Anne Cathrine, Lyhne, Henriette, Laubek, Bjarke, Bruhn, Dan, Pertoldi, Cino
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2022
• Subjects: IdentiFlight; avoidance response; flight behavior; flight symmetry; golden eagle; red kite; white-tailed eagle; wind turbine curtailment; Haliaeetus albicilla; White-tailed eagle; Aquila chrysaetos
• Online Access: https://vbn.aau.dk/da/publications/79395df1-3a53-45d9-8c74-ad91209c8e73; https://doi.org/10.3390/sym14112245; https://vbn.aau.dk/ws/files/508434159/symmetry_14_02245_v2.pdf; http://www.scopus.com/inward/record.url?scp=85141735804&partnerID=8YFLogxK

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.