A framework for improving treatment of uncertainty in offshore wind assessments for protected marine birds

Governments worldwide are setting ambitious targets for offshore renewable energy development (ORD). However, deployment is constrained by a lack of understanding of the environmental consequences of ORD, with impacts on protected birds forming a key environmental consenting challenge. Assessing the impacts of ORD on marine birds is challenging, utilizing interlinked approaches to understand complex behavioural, energetic, and demographic processes. Consequently, there is considerable uncertainty associated with ORD assessments for marine birds, with current methods failing to quantify uncertainty in a scientifically robust, evidence-based manner. This leads to a high degree of precaution and a lack of confidence in the evidence used to inform ORD consenting decisions. We review the methods used to estimate ornithological ORD impacts in the UK, a country at the forefront of ORD. We identify areas in which uncertainty quantification could be improved through statistical modelling, data collection, or adaptation of the assessment process. We develop a framework for end-to-end quantification of uncertainty, integrating uncertainty estimates from individual stages of the assessment process. Finally, we provide research recommendations to better quantify and reduce uncertainty, to lower future ORD consenting risk. These recommendations extend beyond the UK and could improve impact assessments in other countries with different legislative frameworks

Flight heights obtained from GPS versus altimeters influence estimates of collision risk with offshore wind turbines in Lesser Black-backed Gulls <i>Larus fuscus</i>

The risk posed by offshore wind farms to seabirds through collisions with turbine blades is greatly influenced by species-specific flight behaviour. Bird-borne telemetry devices may provide improved measurement of aspects of bird behaviour, notably individual and behaviour specific flight heights. However, use of data from devices that use the GPS or barometric altimeters in the gathering of flight height data is nevertheless constrained by a current lack of understanding of the error and calibration of these methods. Uncertainty remains regarding the degree to which errors associated with these methods can affect recorded flight heights, which may in turn have a significant influence on estimates of collision risk produced by Collision Risk Models (CRMs), which incorporate flight height distribution as an input. Using GPS/barometric altimeter tagged Lesser Black-backed Gulls Larus fuscus from two breeding colonies in the UK, we examine comparative flight heights produced by these devices, and their associated errors. We present a novel method of calibrating barometric altimeters using behaviour characterised from GPS data and open-source modelled atmospheric pressure. We examine the magnitude of difference between offshore flight heights produced from GPS and altimeters, comparing these measurements across sampling schedules, colonies, and years. We found flight heights produced from altimeter data to be significantly, although not consistently, higher than those produced from GPS data. This relationship was sustained across differing sampling schedules of five minutes and of 10 s, and between study colonies. We found the magnitude of difference between GPS and altimeter derived flight heights to also vary between individuals, potentially related to the robustness of calibration factors used. Collision estimates for theoretical wind farms were consequently significantly higher when using flight height distributions generated from barometric altimeters. Improving confidence in telemetry-obtained flight height distributions, which may then be applied to CRMs, requires sources of errors in these measurements to be identified. Our study improves knowledge of the calibration processes for flight height measurements based on telemetry data, with the aim of increasing confidence in their use in future assessments of collision risk and reducing the uncertainty over predicted mortality associated with wind farms.</p

Investigating avoidance and attraction responses in lesser black-backed gulls Larus fuscus to offshore wind farms

Movements through or use of offshore wind farms by seabirds while commuting or foraging may increase the potential for collision with turbine blades. Collision risk models provide a method for estimating potential impacts of wind farms on seabird populations, but are sensitive to input parameters, including avoidance rates (ARs). Refining understanding of avoidance through the use of high-resolution empirical movement data has the potential to inform assessments of the collision impacts of offshore wind farms on seabird populations. We assessed the movements of GPS-tagged lesser black-backed gulls Larus fuscus from a breeding colony in northwest England to estimate the species' AR and avoidance/attraction index (AAI) to nearby offshore wind farms. To investigate both macro- (0−4 km) and meso-scale (0−200 m) responses to wind turbines, we used calculations of AR and AAI based on simulated vs. observed tracks. We found that birds exhibited an AR of −0.15 (95% CI: −0.44 to 0.06), indicating a degree of attraction within 4 km of the wind farms. However, AAI values varied with distance from wind farm boundaries, with a degree of avoidance displayed between 3 and 4 km, which weakened as distance bands approach wind farm boundaries. Meso-scale avoidance/attraction was assessed with regard to the nearest individual turbine, and flight height relative to the rotor height range (RHR) of the nearest turbine. We found attraction increased below the RHR at distances <70 m, while avoidance increased within the RHR at distances approaching the turbine. We explore how high-resolution tracking data can be used to improve our knowledge of L. fuscus avoidance/attraction behaviour to established wind farms, and so inform assessments of collision impacts

Investigating avoidance and attraction responses in lesser black-backed gulls Larus fuscus to offshore wind farms

Movements through or use of offshore wind farms by seabirds while commuting or foraging may increase the potential for collision with turbine blades. Collision risk models provide a method for estimating potential impacts of wind farms on seabird populations, but are sensitive to input parameters, including avoidance rates (ARs). Refining understanding of avoidance through the use of high-resolution empirical movement data has the potential to inform assessments of the collision impacts of offshore wind farms on seabird populations. We assessed the movements of GPS-tagged lesser black-backed gulls Larus fuscus from a breeding colony in northwest England to estimate the species' AR and avoidance/attraction index (AAI) to nearby offshore wind farms. To investigate both macro- (0−4 km) and meso-scale (0−200 m) responses to wind turbines, we used calculations of AR and AAI based on simulated vs. observed tracks. We found that birds exhibited an AR of −0.15 (95% CI: −0.44 to 0.06), indicating a degree of attraction within 4 km of the wind farms. However, AAI values varied with distance from wind farm boundaries, with a degree of avoidance displayed between 3 and 4 km, which weakened as distance bands approach wind farm boundaries. Meso-scale avoidance/attraction was assessed with regard to the nearest individual turbine, and flight height relative to the rotor height range (RHR) of the nearest turbine. We found attraction increased below the RHR at distances <70 m, while avoidance increased within the RHR at distances approaching the turbine. We explore how high-resolution tracking data can be used to improve our knowledge of L. fuscus avoidance/attraction behaviour to established wind farms, and so inform assessments of collision impacts