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.
Where and when animals forage depends on the spatio-temporal distribution and catchability of their prey. In dynamic environments, animals can repeatedly target areas that provide predictable availability of prey or may search for ephemeral conditions of high prey availability. However, how foraging behaviour is initiated in response to static versus dynamic environmental conditions is difficult to study, since both environmental data sources are often lacking. In this study, central-place foraging Sandwich terns were tracked using GPS loggers during foraging. Hidden Markov models showed that the probability of switching between transit and foraging was most strongly affected by the static variable sediment type. Wave period (a dynamic variable related to weather), salinity (a dynamic variable) and water depth (another static variable) affected the transition probability to a lesser extent. Cloud cover, wind speed and current speed were only included in lower ranked models. Air and water temperature were not included in any model. Consistent with the greater importance of static versus dynamic abiotic conditions, consistency between foraging trips of the same individual varied irrespective of tidal, diurnal or seasonal cycles, although trips made close in time within a season were slightly more similar than trips with a larger time gap. We suggest that Sandwich terns target broad areas with coarser sediments, where sandeels (Ammodytidae) are more common, and that weather variables may be related to prey visibility. Our study suggests that even in highly dynamic environments, static environmental variables may more strongly affect foraging behaviour of coastal seabirds than dynamic variables.
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