Collision Risk in White-Tailed Eagles Modelling Kernel-Based Collision Risk Using Satellite Telemetry Data in Smøla Wind-Power Plant

Report

Title: Collision Risk in White-Tailed Eagles Modelling Kernel-Based Collision Risk Using Satellite Telemetry Data in Smøla Wind-Power Plant
Publication Date:
May 01, 2011
Document Number: NINA Report 692
Pages: 26
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Citation

May, R.; Nygård, T.; Dahl, E.; Reitan, O.; Bevanger, K. (2011). Collision Risk in White-Tailed Eagles Modelling Kernel-Based Collision Risk Using Satellite Telemetry Data in Smøla Wind-Power Plant. Report by Norwegian Institute for Nature Research (NINA). pp 26.
Abstract: 

Large soaring birds of prey, such as the white-tailed eagle, are recognized to be perhaps the most vulnerable bird group regarding risk of collisions with turbines in wind-power plants. Their mortalities have called for methods capable of modelling collision risks in connection with the planning of new wind-power developments. The so-called “Band model” estimates collision risk based on the number of birds flying through the rotor swept zone and the probability of being hit by the passing rotor blades. In the calculations for the expected collision mortality a correction factor for avoidance behaviour is included. The overarching objective of this study was to use satellite telemetry data and recorded mortality to back-calculate the correction factor for white-tailed eagles. The Smøla wind-power plant consists of 68 turbines, over an area of approximately 18 km2. Since autumn 2006 the number of collisions has been recorded on a weekly basis. The analyses were based on satellite telemetry data from 28 white-tailed eagles equipped with backpack transmitters since 2005. The correction factor (i.e. “avoidance rate”) including uncertainty levels used within the Band collision risk model for white-tailed eagles was 99% (94-100%) for spring and 100% for the other seasons. The year-round estimate, irrespective of season, was 98% (95-99%). Although the year-round estimate was similar, the correction factor for spring was higher than the correction factor of 95% derived earlier from vantage point data. The satellite telemetry data may provide an alternative way to provide insight into relative risk among seasons, and help identify periods or areas with increased risk either in a pre- or post-construction situation.

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