do wind turbines affect bird migration

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Wind energy generation has increased immensely over the last decades, and this growth is expected to continue in the forthcoming years, with a predicted annual increase of 5% of the installed capacity until 2020 (GWEC, 2015; IPCC, 2011). Despite the immediate benefits for climate change mitigation, negative interactions between wind energy production and wildlife, mainly birds and bats, have been widely reported (Saidur, Rahim, Islam, & Solangi, 2011). Soaring birds, including most raptors, storks and other large birds, are among the groups of highest concern, as their movement corridors have been populated by wind farms (Cabrera-Cruz & Villegas-Patraca, 2016; Katzner et al., 2012; Martín, Perez-Bacalu, Onrubia, De Lucas, & Ferrer, 2018) leading to high fatality rates through collisions with turbines (e.g., Barrios & Rodriguez, 2004; Ferrer et al., 2012; Smallwood & Thelander, 2008).

Soaring flight allows large birds to travel long distances with a reduced energetic cost (Duriez et al., 2014; Pennycuick, 1975). However, soaring depends on updrafts, which are relatively scarce and scattered across the landscape (Horvitz et al., 2014; Katzner et al., 2015). Two types of updrafts are commonly used by terrestrial soaring birds: (a) orographic uplift that results from the deflection of horizontal winds by sloping terrain and (b) thermal uplift that is formed during the day due to the heating of the land surface by solar radiation (Kerlinger, 1989). Soaring birds use orographic uplift either to gain altitude and glide downwards in a desired direction, or to travel along uplift-rich areas such as mountain ranges (Bohrer et al., 2012; Katzner et al., 2015). Orographic uplift is particularly useful when generated from mountain ranges oriented in the migration direction (Dennhardt, Duerr, Brandes, & Katzner, 2015; Kerlinger, 1989). In the case of thermal uplift, soaring birds typically climb in thermals using a circular trajectory from which they glide linearly towards the next thermal in the desired direction (Katzner et al., 2015; Kerlinger, 1989; Santos et al., 2017). Due to such specific requirements, soaring birds tend to move along areas with high uplift potential, often named corridors (sensu Dennhardt et al., 2015). Besides the physical requirements for soaring, the importance of different corridors may vary dramatically depending on their geographical position relative to migration routes of soaring birds. For example, areas in the vicinity of narrow sea crossings may experience higher traffic during migrations, as soaring birds avoid crossing large bodies of water (Newton, 2008).

Soaring birds and wind energy developments may compete for the same areas both at the local and regional scales. At local scales, wind turbines are frequently installed along the top of mountain ranges, in order to maximize exposure to horizontal winds, and these areas also tend to have high orographic uplift potential for birds (Katzner et al., 2012). At a broader scale, migratory bottlenecks of soaring birds often correspond to narrow sea crossings or mountain passes where the topography favours high wind speeds, thus also well suited for wind-power production (Hilgerloh, Michalik, & Raddatz, 2011; Martín et al., 2018; Villegas-Patraca, Cabrera-Cruz, & Herrera-Alsina, 2014). Therefore, understanding how wind turbines impact movement corridors of migratory soaring birds is of utmost importance to better reconcile the production of wind power with wildlife conservation.

In general, birds tend to avoid wind turbines through evasive movements and changes in space use (May, 2015). Empirical evidence published on soaring birds has been showing they change their flight trajectories to avoid turbines (de Lucas, Janss, & Ferrer, 2004; Villegas-Patraca et al., 2014) and that their numbers decrease in the close proximity of the turbines (Barrios & Rodriguez, 2004; Pearce-Higgins, Stephen, Langston, Bainbridge, & Bullman, 2009). Similarly, comparisons between the pre- and post-construction phases showed that soaring birds reduce their use of the areas where turbines are installed and their trajectories become more scattered in nearby areas (Cabrera-Cruz & Villegas-Patraca, 2016; Farfan et al., 2017; Garvin, Jennelle, Drake, & Grodsky, 2011; Johnston, Bradley, & Otter, 2014). While these avoidance behaviours suggest that soaring birds are to some extent able to cope with the presence of wind turbines (Marques et al., 2014), they may also cause functional habitat loss (i.e., loss of aerospace in movement corridors; Diehl, 2013), which is a potentially important, though largely neglected, impact of wind-power generation (Davy, Ford, & Fraser, 2017).

In this study, we investigated the footprint of wind turbines on movement corridors of migratory soaring birds using high-frequency GPS tracking (1-min temporal resolution or higher). GPS tracking is a powerful tool to investigate direct interactions between birds and wind turbines at multiple spatiotemporal scales, but it was only recently introduced in this field of study (e.g., Garthe, Markones, & Corman, 2017; Thaxter et al., 2015, 2018). We tracked 130 black kites (Milvus migrans) during the post-breeding migration in an area highly populated by wind turbines in the region of Tarifa, Spain. Black kites and other soaring birds concentrate in this region to cross the Strait of Gibraltar during their migration to Africa (MIGRES, 2009). Birds were captured and tracked during periods of strong crosswinds at the Strait of Gibraltar, which forced them to roam around Tarifa while waiting for conditions favouring the sea crossing. Bird movements were used to map space-use intensity using Brownian bridge movement models. The influence of the wind turbines on the birds’ use of the landscape was then modelled taking into account the main predictors of soaring flight, orographic and thermal uplift (Bohrer et al., 2012; Kerlinger, 1989). We hypothesized that (a) birds will use areas with greater uplift (orographic and thermal) more frequently, and (b) the area in the proximity of the wind turbines will be less frequented regardless of its uplift potential.

Pinpointing collision hotspots in Europe

51 scientists from 15 different nations, including Germany’s Max Planck Institute of Animal Behavior, worked together to determine the regions in which these birds would be more vulnerable to onshore wind turbine or power line development. Using GPS location data from 65 bird tracking studies, the study—which was published in the Journal of Applied Ecology—determined where birds fly more frequently at danger height, which is defined as 10 to 60 meters above ground for power lines and 15 to 135 meters for wind turbines. “Direct observation is not able to provide very accurate data on location and flight height, especially over long distances,” says Martin Wikelski, co-author of the study and director of the Max Planck Institute of Animal Behavior. This study is the first to combine GPS data from so many different species to produce a thorough map of bird risk areas.

The ensuing vulnerability maps show that the collision hotspots are mostly concentrated along coastlines, close to breeding grounds, and along significant migration routes. These include Eastern Romania, the Sinai Peninsula, the German Baltic coast, the Western Mediterranean coast of France, Southern Spain, and Morocco (including the area around the Strait of Gibraltar). The GPS information gathered covered 1,454 birds from 27 species, the majority of which were large, soaring birds like white storks. Different species were exposed to different levels of risk; among those that routinely soar to heights where they run the risk of colliding are the Eurasian spoonbill, European eagle owl, whooper swan, Iberian imperial eagle, and white stork. According to the authors, there should be as little development of new wind turbines and transmission power lines in these highly sensitive areas as possible. If development does occur, it will probably need to be accompanied by precautions against endangering birds.

2 MATERIALS AND METHODS

This study was conducted in the region of Tarifa (36. 0132°N, 5. 6027°W), on the Spanish side of the Strait of Gibraltar. The main migratory bottleneck for soaring birds traveling along the Western European–West African Flyway is the Strait, a narrow sea crossing that separates Europe and Africa (Newton, 2008). The wind energy industry places a lot of importance on the Cádiz region, which includes Tarifa. More than 1,300 MW of installed wind power capacity and 70 wind farms (IECA, 2015) There were 160 operational wind turbines in seven wind farms within our focal area, generating 132 MW of electricity (Figure 1, Supporting Information Table S1). The majority of these turbines were positioned in rows, running north to south (Figure 1)

2.3 Estimation of orographic and thermal uplift

To investigate our first study hypothesis, we employed estimates of orographic and thermal uplift. The technique used by Bohrer et al. was modified to estimate the orographic and thermal uplift velocities. (2012) and the high-resolution spatial data described in Santos et al. by Brandes and Ombalski (2004) (2017). Orographic uplift is estimated using wind (direction and speed) and local topography (terrain aspect and slope) parameters. The 30-m spatial resolution digital elevation model that can be found at http://gdex.org provided the local topography. cr. usgs. gov/gdex/ (NASA JPL, 2009). At a Tarifa weather station, the direction and speed of the wind were recorded (36 0138°N, 5. 5988°W). Two main wind conditions were found in the wind measurements taken in 2012 and 2013 for the entire black kite migration season (mid-July to mid-September; MIGRES, 2009): (a) strong Levanter winds (wind direction from 80 to 120°; speed from 4 to 15 m/s), which could last for up to a week; and (b) western breezes (wind direction from 270 to 310°; speed from 1 to 6 m/s), which usually occurred in between Levanters (Supporting Information Figure S1). These wind conditions coincide with the summertime conditions at the Strait of Gibraltar that are generally described (Dorman et al. , 1995). In this regard, we constructed three distinct orographic uplift models, the first of which represented uplift for typical wind conditions at the time our tracking dataset was gathered (direction = 97). 8°, speed = 8. 8 m/s), with the remaining two models corresponding to the typical Levanter wind conditions (direction = 100°, speed = 7 7 m/s) and a western breeze with a 290° direction and 4 mph speed 1 m/s) recorded throughout the black kite migration season in 2012 and 2013. The estimates of the remaining two uplift models were used to calculate general scenarios of habitat loss during Levanter wind and western breeze (shown in Figure 5), while the uplift estimated from the first model was used as a predictor in bird space-use models (detailed in the section below).

The thermal uplift velocity estimate given by Santos et al. (2017) uses land surface temperature derived from Landsat ry. If no significant changes in land use are noticed, satellite images taken during the same season typically exhibit a strong correlation in reflectance values (Zhu, 2017). Therefore, models of thermal uplift constructed from those data are also anticipated to exhibit high correlation. Santos et al. (2017) verified the strong correlation (r ) between uplift models constructed for the study area on various days in the summers of 2012 and 2013. 77). Thus, using land surface temperature estimated from a Landsat 8 OLI/TIRS acquired on July 17, 2013, which is accessible at http://earthexplorer, we chose to create a single thermal uplift model. usgs. gov/ (NASA Landsat Program, 2015). The model’s spatial resolution was 100 m, which corresponds to the Landsat 8 OLI/TIRS thermal band, and it was representative of uplift at 225 m height, which is the mean flight height of birds in our tracking dataset.

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