Using GPS tracking data to assess collision risk in an operational wind farm

Abstract

**Abstract:** Assessing avian collision risk is a key part of the Environmental Impact Assessment (EIA) process for offshore wind farms. Whilst a number of collision risk models are available, these all typically combine an estimate of the probability of a bird which passes through a turbine's rotor sweep colliding with a blade and the number of birds passing through this collision risk window in any given time period (as estimated from pre-construction data). As the size and extent of offshore wind farms increase as part of attempts to reach net zero carbon emissions by 2050, accurately assessing collision risk in order to minimise any negative environmental impacts becomes a pressing concern. However, the simplistic assumptions made by these models in relation to the flight behaviour of the species concerned - speed, height and direction - have raised questions about how realistic predictions from these models are. A lack of validation of existing models further calls into question whether these are able to accurately assess collision risk. Tracking data are increasingly being used to investigate potential impacts of wind farms and offer the potential to more accurately describe the behaviour of birds within any given site. These data demonstrate how seabird behaviour varies spatially, highlighting that the summarised information used by many existing models may not accurately reflect spatial and temporal variation in collision risk. Using multi-year tracking data, we modelled the speed, flight height and distribution of lesser black-backed gulls in relation to operational offshore wind farms. We combined these data with real time estimates of turbine rotational speed and the height of the turbine rotor above sea-level in order to estimate collision risk over the course of a breeding season. We further discuss how combining tracking data with ongoing camera-radar studies of collision risk at operational wind farms offers the potential to validate estimates of collision risk and reduce uncertainty in the EIA process. **Authors:** Aonghais Cook¹, Chris Thaxter², Jennifer Border², Philipp Boersch-Supan², Elizabeth Humphreys³, Elizabeth Masden⁴, Niall Burton² ¹Dr, ²British Trust for Ornithology, ³British Trust for Ornithology Scotland, ⁴University of Highlands and Islands Environmental Research Institute