DOI: 10.5061/dryad.j0zpc86mr
title: Avoidance of offshore wind farms by Sandwich Terns increases with turbine density

description: 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.
description: 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 &gt;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
 <em>ca.</em> 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 &lt; 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 <em>ca.</em> 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 <em>ca.</em> 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 (&gt;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.
description: # Avoidance of offshore wind farms by Sandwich Terns increases with
 turbine density
 [https://doi.org/10.5061/dryad.j0zpc86mr](https://doi.org/10.5061/dryad.j0zpc86mr) ## Description of the data and file structure The file is a comma separated file (.csv) containing both the actual GPS-tracking data (y = 1) and the randomly generated 'availability positions' (y = 0). The "ringnr" provides an identifier for individual terns. Columns "x1\_", "x2\_", "y1\_", "y2\_" are UTM31 N coordinates and represent the start (1) and end (2) of each 'step'. Start and end date/time of each step can be found in "t1\_" and "t2\_". The difference between t2\_ an t1\_ can be found in "dt\_", which is 10 minutes for the entire dataset as the original tracking data was regularized to this rate. Movement parameters are "sl\_" = step length, "log\_sl\_" = log of step length, "ta\_" = turning angle, "cos\_ta" = cosinus of turning angle and have been calculated using the 'amt' package in R. Used and available steps belonging to the same step have the same "step\_id". Column "onland" denotes whether the endpoint of a step is on land (versus sea). Column "home" denotes the colony, with "dist\_col" = distance to that colony in kilometers. Environmental variables include "depth" = water depth in meters, "sediment" = sediment median grain size in um. Columns "depth.stnd", "sedim.stnd", "distc.stnd" are standardized variables of water depth, sediment and distance to the colony, respectively. Column "inAny" denotes whether endpoints of steps are in an offshore wind farm, in a distance band of 1.5 around the offshore wind farm or outside. Column "inWhich" is similar to "inAny", but it includes info on in which offshore wind farm or accompanying distance band the step ends. ## Sharing/Access information The raw tracking data is available upon request via the Wozep datacentre via [https://www.noordzeeloket.nl](https://www.noordzeeloket.nl).