Wind power generation is a major source of renewable energy and has gained increasing attention due to lower greenhouse gases emissions (Jacobson, 2009). The installation of hundreds of wind farms in different parts of the world has brought the need to assess the impact of wind turbines on bats (e.g. Kunz et al., 2007a; Rodrigues et al., 2008). Such studies indicated the occurrence of collisions with blades and towers, causing the death of animals in several countries (e.g. Arnett et al., 2008; Baerwald and Barclay, 2009; Hayes, 2013). Towers can reach heights equivalent to 30-story buildings, blades cover large areas when moving, and larger turbines can reach the airspace of migratory bats (Barclay et al., 2007; Voigt et al., 2012). Some studies have shown that while bats collide with other man-made structures, the frequency and magnitude of these collisions are minor when compared to collisions associated with wind turbines (Arnett et al., 2008).

The current knowledge about the causes of bat collisions with wind turbines is unsatisfactory (Kunz et al., 2007a), but the cumulative effects of this mortality can have significant impacts on long-term populations of species affected (Kunz et al., 2007b; Arnett et al., 2008). Bats are long-living, low reproductive rate organisms, projecting a slow population growth and limited ability to recover from population declines, increasing the risk of local extinctions (Arnett et al., 2008). In fact, the American Society of Mammalogists points to the imperative need of scientific studies pre- and post-installation of wind farms (Arnett et al., 2008).

Wind energy production is now booming in some biodiversity rich tropical countries (WWEA, 2013). This is the case of Brazil. Although the country relies mainly on hydroelectricity, the Atlas do Potencial Eólico do Brasil (Amarante et al., 2001) indicates more than 71,000km2 with wind speeds suitable for power generation, with an estimated potential of 143gigawatts (GW). Currently, wind energy accounts for only 2% of the electricity produced in Brazil, with a vast potential for growth in the country. The Brazilian government has been promoting the installation of new wind farms, and the construction of new parks is in full speed. Currently in Brazil there are at least 119 wind farms in operation, producing about 2.8 GW (ABEE, 2013). The prediction of wind power installed for the country is 8.7GW in 2017 (ABEE, 2013), indicating that the number of farms and turbines in Brazil will triple in the next five years.

The knowledge on the impacts of wind turbines on bats in Brazil is very scarce, usually restricted to gray literature (Sovernigo, 2009; Rui and Barros, 2012). With a rich and diversified bat fauna (nearly 180 species – Paglia et al., 2012), the interaction with wind turbines is already considered one of the 10 most relevant issues for the conservation of bats in Brazil (Bernard et al., 2012). To document the existence and patterns of bat fatalities associated with wind farms is critical to (1) better understand this interaction and classify its environmental impacts as neutral or negative, (2) quantify and qualify environmental impacts so far little measured in the country, (3) contribute to the local and cumulative mitigation of impacts on the flying wildlife, and (4) generate quantitative and qualitative data useful for improving the environmental licensing of future wind projects in the country.

We analyzed the status of bat species richness and occurrence in areas with high wind potential in Brazil. We address data gaps, suggest priority areas for research on the interaction of bats and wind farms in the country, and discuss caveats in the environmental licensing, contributing to the discussion about the impacts of electricity generation in Brazil, a megadiverse country.

We used a data bank on the occurrence of bats in Brazil (see Bernard et al., 2011) and plotted coordinates on a map, grouping them in 1° latitude×1° of longitude grid cells. Using ArcGIS v10 (www.esri.com), we summarized data sets into the grid cells, with the respective number of species inside. We used the “Spatial Join” command to overlap the maps containing single records and grid cells, and the program RStatistical Package (R Core Team, 2013) to calculate the total number of species inside each cell (Fig. 1).

Fig. 1.

Bat species richness in Brazil grouped in cells with 0.5° of latitude×0.5° of longitude.

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We then considered the atlas with the wind potential for the entire country (Amarante et al., 2001), which presents the average wind speed for Brazil. Since the original atlas used a finer scale, we adopted the maximum wind speed detected in each cell for that entire cell, producing a map at the same scale of that with species richness (Fig. 2). We then sorted the cells with a potential ≥300W/m2, and considered them as with the highest wind potential in the country (hereafter, CHP).

Fig. 2.

Estimated annual wind energy potential (in W/m2) in Brazil, based on data from Atlas do Potencial Eólico do Brasil (Amarante et al., 2001). Data presented in cells with 0.5° of latitude×0.5° of longitude.

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We crossed data from both maps and evaluated the current status of the knowledge on bat records along the CHP. We considered as data gaps the CHP with no data for bats, and as hotspots the CHP with richness ≥10spp., a very conservative threshold considering that nearly 180 species of bats are known in the country.

We considered 5502 records of bats in Brazil which resulted in data for 330 of the 804 cells covering the country. Species-rich areas are scattered along the country, most as single cells (Fig. 1). South-eastern parts of Brazil had the highest density of records. Considering the total number of cells within each of the terrestrial Brazilian biomes, there were records for 80% of the Atlantic Forest, 67% of Caatinga, 47% of Pantanal, 41% of Cerrado, 40% of Pampa, and 24% of Amazonia. However, <10% of the country is minimally surveyed, and for nearly 60% of Brazil there is not a single record of bat species (for more details see Bernard et al., 2011). The wind potential is basically concentrated along a diagonal crossing the country from its Southern cone, through Central Brazil, up to the Northeastern coast (Fig. 2).

Based on the wind potential, we identified 321 CHP, mainly located in the southern cone of Brazil, and along the central portion of Bahia and northern Minas Gerais, along the northeastern coast – especially in Rio Grande do Norte and Ceará – and in the extreme north of Roraima (Fig. 2). Crossing species richness data against CHP indicated 21 hotspots and 226 data gap areas (Figs. 3 and 4). Overall, 70.4% of the CHP are data gaps for bats in Brazil.

Fig. 3.

Hotspots and data gap areas for bats and wind farms in Brazil. Cells with the highest wind potential (≥300W/m2) but with no data for bat species richness were classified as data gaps (gray); those with richness ≥10 species were considered hotspots (black). Data presented in cells with 0.5° of latitude×0.5° of longitude.

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Fig. 4.

Bat species richness in 321 areas with the highest wind potential in Brazil. Areas consisted of a cell with 0.5° of latitude×0.5° of longitude.

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Our analysis indicates that 70% of the areas with the greatest potential for wind energy generation in Brazil are data gaps for bats, with a complete lack of elementary information about their species richness and occurrence. In those areas are being built the largest wind farms in the country and due to such striking absence of data we recommend that data gap areas here identified should be listed as priorities for bat inventories in Brazil. This is the case of parts of the states of Rio Grande do Sul, Bahia, Rio Grande do Norte and Ceará.

Even within the data gap areas there is a need for prioritization. The strip of coastal dunes of Rio Grande do Norte and Ceará have a tendency of lower bat species richness. The situation in Bahia and Rio Grande do Sul raises special concern. In Bahia, about 20 species of bats have been recorded in the Chapada Diamantina (Oliveira and Pessôa, 2005), but the vegetation of the region, a mosaic of campo rupestre, hillside forests, semi-deciduous seasonal forest, cerrados, caatinga and gallery forests points to a higher species potential. The real local bats richness may be underestimated, requiring more inventories (Bernard et al., 2011).

The chiropterofauna of Rio Grande do Sul, dominated by insectivorous from Vespertilionidade and Molossidae families, resembles the bat fauna affected by wind farms in other temperate regions (Barclay et al., 2007; Arnett et al., 2008). Further, the possibility of migratory species in southern Brazil – and the lack of data about it – need to be considered. In Germany, bats killed in wind farms in the summer and autumn were originated from Scandinavia, the Baltic countries, Belarus or Russia, showing that wind turbines kill bats not only of sedentary local populations but also of distant populations (Voigt et al., 2012).

There are no conflicts of interest.