The expanding use of wind farms as a source of renewable energy can impact bird populations due to collisions and other factors. Globally, seabirds are one of the avian taxonomic groups most threatened by anthropogenic disturbance; adequately assessing the potential impact of offshore wind farms (OWFs) is important for developing strategies to avoid or minimize harm to their populations. We estimated avoidance rates of OWFs — the degree to which birds show reduced utilization of OWF areas — by Sandwich Terns Thalasseus sandvicensis at two breeding colonies in western Europe: Scolt Head (United Kingdom) and De Putten (the Netherlands). We modeled GPS tracking data using integrated Step Selection Functions (iSSFs) to estimate the relative selection of habitats at the scale of time between successive GPS relocations – in our case 10 minutes, in which terns traveled ca. 2 km on average. The foraging ranges of birds from each colony overlapped with multiple OWFs. iSSFs considered distance from the colony and habitat characteristics (water depth and sediment grain size) and movement characteristics. Macro-avoidance rates, where 1 means complete avoidance, were estimated at 0.54 (95% CrI = 0.35, 0.7) for birds originating from Scolt Head and 0.41 (95% CrI = 0.21, 0.56) for those from De Putten. Estimates for individual OWFs also indicated avoidance but were associated with considerable uncertainty. Our results were inconclusive with regard to the behavioral response to the areas directly surrounding OWFs (within 1.5 km); estimates suggested indifference and avoidance and were associated with large uncertainty. Avoidance rate of OWFs significantly increased with turbine density, suggesting OWF design may help to reduce the impact of OWFs on Sandwich Terns. The partial avoidance of OWFs by Sandwich Terns implies that the species will experience risks of collision and habitat loss due to OWFs constructed within their foraging ranges.

Fieldwork

Adult Sandwich Terns were captured on the nest, using walk-in traps, in the colonies of De Putten, Camperduin, the Netherlands (N52° 44’ E4° 39) in 2019–2021 and at Scolt Head, Norfolk, United Kingdom (N52° 59’ E0° 40) in 2016–2019 (figure 1), during the second or third week of incubation. Individuals weighing >220g were selected for GPS-logger deployment. In total, 63 individuals were tagged at De Putten and 43 at Scolt Head. Additional data were included from two individuals GPS-tagged at the Slijkplaat, Zuid-Holland, the Netherlands (N51° 48’ E4° 09) in 2021 that relocated for a second breeding attempt at De Putten in that year (Fijn and Bemmelen 2023). In addition, data were obtained from 7 individuals tagged in 2021 in the Netherlands that bred at De Putten in 2022.

Ecotone GPS-UHF loggers with solar panels (Ecotone, model PICA, ~4.5 g, 35 × 12 × 8 mm) were attached using a full body harness constructed from fishing elastic (Preston Innovations Slip Elastic, diameter: 1.4−2.2 mm), which disintegrated over ca. 1.5 months, or, in 2021, from 2 mm wide Teflon, which is relatively permanent. The combined weight of the logger, harness and rings was 6.3 g, which represented 2.3 to 2.8% of the body mass, thus staying below the generally accepted limit of 3% (Phillips et al. 2003, Vandenabeele 2013). GPS loggers were pre-set to record positions between 5:00 and 21:00 local time, taking positions at intervals of 5, 10 or 15 min, depending on year, location and the battery voltage. GPS loggers automatically transmitted the tracking data to base stations positioned at each colony.

Avoidance of and attraction to OWFs

Integrated Step Selection Functions were used to assess the degree to which Sandwich Terns avoided or were attracted to offshore wind farms because they provide unbiased and robust parameter estimates and can be fitted using freely available and open-source software (Avgar et al. 2016a, Fieberg et al. 2021, Mercker et al. 2021). Discrete-time Step Selection Functions require regular time intervals between subsequent positions. Selecting an appropriate time interval involves balancing the number of interpolated positions with the spatial and temporal resolution. Here, we regularized tracking data to time intervals between positions of 10 min (with each set of two subsequent positions called a ‘step’), considering most data were collected at intervals of 5 or 10 min (15 min data concerned only some loggers at De Putten in 2019), with linear interpolation of positions across time gaps no longer than 35 min (thus, a maximum of 2 positions). In addition, 10-minute intervals were chosen because at this time interval, most step lengths were < 5 km, and OWFs will – under most weather circumstances – be easily visible to the birds at this range. For each used step, random steps from the first position were generated from the sea (as none of the used positions were on land) using the random_steps function from the amt package (Signer et al. 2019), which first fits a gamma distribution to the observed step lengths and a von Mises distribution to the observed turning angles and subsequently samples from these distributions. Whereas usually ca. 10-20 random steps per used step suffices in SSFs, estimating the selection strength of relatively rare habitats requires larger samples (Thurfjell et al. 2014). We generated 50 random steps per used step, which produced parameter estimates that were stable across model runs. At the endpoint of each used or available step, water depth (EMODnet website, www.emodnet.eu, data from 2018, spatial resolution of 1/16 * 1/16 arc minutes or ca. 115 * 115 m), median grain size (um) of the bottom sediment (Bockelmann et al. (2018), spatial resolution of 1852 * 1852 m, obtained from, hereafter referred to as ‘sediment’) and the distance to the colony (avoiding overland routes) were extracted. Sediment was heavily left-skewed and therefore log-transformed. Water depth and sediment were included in the iSSFs following the study by Fijn et al. (2022), which indicated that these were the most important factors in explaining the switch to foraging behavior in Sandwich Terns; we, therefore, expect they also drive movement patterns in the terns in our study. Water depth, sediment and distance to the colony were standardized within the data sets for each colony to allow comparison of effect sizes within models. To avoid overfitting of models, no additional environmental variables were added other than our primary interest, the presence in or proximity to OWF. Wind turbine positions were obtained from Zhang et al. (2021) and turbines from Lincs, Lynn and Inner Dowsing OWFs were combined for statistical analyses as these OWFs border each other and buffers would overlap. Around the outer row of turbines of each OWF, a convex hull was drawn, as well as a distance band of 0–1.5 km around the convex hull. The distance of 1.5 km was selected considering the scale at which turbulence around turbines may attract terns (10s to 100s of meters; Lieber et al. (2019); Schultze et al. (2020)) and to obtain a large enough sample size of bird positions within the distance band. OWFs were only included when tracking data positions fell within their perimeters (Figure 1. These were Eneco Luchterduinen, Prinses Amaliawindpark and Egmond aan Zee near De Putten and Sheringham Shoal, Race Bank, Dudgeon and Lincs/Lynn/Inner Dowsing wind farms near Scolt Head. Other operational OWFs were not considered as they were far outside the foraging ranges of the two colonies (>100km) and no bird positions were recorded within these OWFs.

We aimed to quantify the overall avoidance/attraction rates of OWFs and their 1.5 km distance bands, as well as avoidance/attraction rates per OWF. Only steps were selected in which at least one of the used or available positions was within an OWF or distance band, thus where the OWF or its distance band were available.