Increasing evidence that bats actively forage at wind turbines

Study site

We conducted our study at Wolf Ridge Wind, LLC (hereafter Wolf Ridge), a utility-scale wind farm owned and operated by NextEra Energy Resources in the southern Great Plains of the US (N33°44′01.19″, W97°24′57.26″). The 48-km² wind resource area comprises 75 1.5-MW General Electric wind turbines (GE 1.5xle specifications: 80-m tower, three 42-m blades attached to the front of the nacelle, 84-m diameter rotor swept zone that reaches 122-m above ground) situated in a matrix of cattle-grazed pastures, hayfields, and some agricultural lands, with shrub-woodland habitat extending from the riverine valleys of the Red River escarpment to the north. Wolf Ridge has been operational since October 2008, and based on fatality monitoring surveys and acoustic surveys conducted from 2009 to 2014, six bat species are known to be present at this site: eastern red bat, hoary bat, silver-haired bat, tri-colored bat (Perimyotis subflavus), evening bat (Nycticeius humeralis), and Mexican free-tailed bat (Tadarida brasiliensis) (Bennett & Hale, 2014).

Acoustic monitoring

We conducted acoustic surveys at wind turbines to determine which bat species were in close proximity to wind turbine tower surfaces and to characterize bat activity at wind turbines. Over a seven year period in conjunction with other surveys (e.g., fatality searches or thermal camera studies), we recorded bat activity using two different acoustic monitoring systems in close proximity to wind turbine tower surfaces. Between 28 July 2010 and 22 July 2011, we deployed two ReBAT™ acoustic monitoring systems ∼85 m above ground on the rear of the nacelle of two operational wind turbines. Each ReBAT™ system comprised two acoustic detectors, one facing upward from the nacelle into the upper half of the rotor swept zone (RSZ) and the other facing downward into the lower half of the RSZ. From 14 May to 1 October 2012, 5 April to 24 October 2013, 9 July to 28 September 2015, and 1 July to 10 August 2016, we used Binary Acoustic Technology (BAT, Tucson, AZ) AR125-EXT ultrasonic receivers and BAT FR125 recorders mounted to tripods at the base of the wind turbines to record bat activity. We positioned this latter acoustic detector assembly on the gravel pad approximately 2 m from the turbine base with the AR125 receiver pointing 45° toward the tower surface to record bat activity within close proximity of the wind turbine tower surface.

Both types of acoustic detector assemblies recorded sound files as a standard .wav file. Furthermore, from 2010 to 2012 detectors were set up to begin recording from dusk until dawn, whereas in 2013, 2015, and 2016 detectors recorded data starting at dusk and continued recording for three hours, a time interval considered to be a primary foraging activity period of bats (Baerwald & Barclay, 2011).

We used SonoBat bat call analysis software (version 3.04) to analyze the recorded sound files. Each file containing a bat call was counted as one bat pass (Miller, 2001). Using full-spectrum spectrograms generated in Sonobat we manually determined each call to species and activity (where possible) using available call libraries. For the latter, we identified the following four distinct activities: commuting—consecutive calls (i.e., individual chirps) were synchronized with wing beats (Altringham, 2011) and were either constant, steadily decreasing, or steadily increasing in call strength (in addition any sound file with <2 calls was also categorized as commuting; e.g., a bat moving through the area from one foraging site to another); searching—consecutive calls were synchronized with wing beats, but varied in strength due to the bat turning its head from side to side while echolocating (Altringham, 2011); foraging or approach phase—call interval varied with multiple calls occurring in succession within a single wing beat, and call strength was constant, steadily decreasing, or steadily increasing (Altringham, 2011); and feeding or terminal buzz—interval between successive calls decreased rapidly and the frequency of these calls was higher or lower (depending on species) than calls representing the other three activities (Altringham, 2011). The latter two acoustic activities are generally associated with foraging behavior, as the foraging or approach phase of echolocation calls is an indication that bats are in pursuit of prey, and feeding or terminal buzzes suggest that bats are successfully capturing prey.

Insect surveys

We used light traps and malaise traps to survey insects at three pairs of turbines at Wolf Ridge in 2012 (Cochran, 2013), 2013 and 2015). Light traps use a UV light that attracts nearby insects and typically captures a large variety of species, whereas malaise traps have no attractant and typically catch a more limited sample with fewer individuals and fewer species represented (Hosking, 1979). Light trapping therefore provided us with a general characterization of the insect community at the wind turbines, whereas malaise trapping functioned as a passive control that would inform us if we were missing species with the light traps that were otherwise present at the study site.

We conducted insect surveys at two turbines a night, three nights a week, over a six week period in July and August during each year. We selected this time period as it coincides with peak bat fatality at our site (Bennett & Hale, 2014). We were able to light trap as long as wind speeds were <15 mph and there was no precipitation. We were able to employ malaise traps as long as wind speeds were <10 mph and there was no precipitation.

In 2012, surveys took place in two 3-hour periods, the first beginning at dusk and the second beginning 3 h before dawn, as these times have been shown to coincide with peak bat foraging activity (Baerwald & Barclay, 2011). In 2013, surveys began at dusk and ran continuously through the night until dawn. While in 2015, we streamlined the survey method and only sampled insects for a 3-hour period beginning at dusk.

We assembled our light traps on the gravel pad surrounding the turbine tower. Light traps consisted of a fluorescent black bulb shielded by opaque plastic on three sides: the side facing the turbine and the top were not shielded, illuminating the turbine tower. The light trap was placed on a white sheet on the gravel pad, which allowed us to see and collect insects more easily. We assembled malaise traps on the ground next to the gravel pad (≤5 m from the turbine) on the opposite site of the turbine from the light trap, where they would be shielded from the UV light. Because we left the traps out for different lengths of time and checked them at different time intervals each year, we cannot directly compare insect abundance and diversity among years. Instead, we use the results of the insect surveys to characterize the insect community at wind turbines at the time of year when bat fatality rates are the highest.

We collected and tallied each insect during the survey period by morpho-species in the field. In order to avoid counting individual insects multiple times, we collected the insects as we counted them and released them at the end of the survey period each night. For each survey night, unique specimens were photographed, identified to order, and preserved in either glycine bags or 100% ethanol.

Bat carcasses and assessment of bat stomach fullness

Although six bat species have been found in fatality monitoring surveys, we only included eastern red bat and hoary bat carcasses in this study because these species had the highest fatality rates at Wolf Ridge (Bennett & Hale, 2014), and experience high levels of fatality at wind facilities across North America (e.g., Arnett & Baerwald, 2013; Zimmerling & Francis, 2016). To select carcasses for analysis, we prioritized those in best overall condition (i.e., no obvious decay or damage by insect scavengers), including adult and juvenile males and females of both species that were collected between July and August of 2013 and 2014 and subsequently stored at −4 °C. Thus, we dissected 45 eastern red bat (27 females, 18 males; 40 adults, five juveniles) and 23 hoary bat carcasses (11 females, 12 males; 19 adults, four juveniles), removing their digestive systems (esophagus, stomach, and intestines) and storing them with their contents intact in 70% ethanol.

Before beginning the genetic analyses, we separated the bat stomachs from the esophagi and intestines and visually determined if the stomachs were full using the following definitions. A stomach was considered “full,” if it appeared taut from the outside (i.e., not folded or wrinkled), whereas a stomach with obvious extra space or folded membrane was considered “not full”. The stomach contents from each bat were then homogenized using a mortar and pestle and weighed. In some instances the stomach membrane was perforated and the contents had been exposed to ethanol, so we allowed the ethanol to evaporate for up to an hour in the extraction hood (described below) prior to homogenization and weighing. We conducted a two-sample t-test to determine if stomachs classified as “full” were significantly heavier than “not full” stomachs.

Genetic analysis of bat stomach contents

We extracted DNA from the homogenized stomach contents using DNeasy^® mericon Food Kits (Qiagen, Valencia, CA, USA; Zarzoso-Lacoste, Corse & Vidal, 2013). We included a negative control with each round of extraction (3 to 7 bat stomach samples) to ensure non-contamination of reagents. All extractions were completed in a dedicated extraction AirClean^® 600 PCR workstation to minimize contamination.

Samples were then amplified using arthropod-specific primers ZBJ-ArtF1c and ZBJ-ArtR2c developed by Zeale et al. (2011). We set up the polymerase chain reactions (PCR) in a dedicated PCR AirClean^® 600 PCR workstation in a different room from where the DNA extractions took place. Again, we included negative controls in our PCR reaction batches. PCR (10 µL) contained 2 µl DNA, 0.5 µM of each primer, 1X Qiagen Multiplex PCR Master Mix with HotStarTaq, Multiplex PCR buffer with 3 mM MgCl₂ pH 8.7, and dNTPs. Reactions were cycled in an ABI 2720 thermal cycler. PCR ran for one cycle at 95 °C for 15 min, followed by 40 cycles of 30 s at 94 °C, 90 s at 55 °C, 90 s at 72 °C, and then a final extension at 72 °C for 5 min. We purified the products on a gel, ligated them into pGEM-T vectors (Promega, Madison, WI, USA), and then transformed them into JM109 competent cells. We plated the transformed cells on ampicillin plates and left them in a 37 °C incubator overnight. The following day we selected colonies that had been successfully transformed (i.e., those in which the PCR product had been inserted) based on color (white colonies had been transformed, whereas blue colonies had not). Each clone was amplified using vector-specific primers (F: CGACTCACTATAGGGCGAATTG, R: CTCAAGCTATGCATCCAAGG). Unincorporated nucleotides and excess primers were removed from PCR products using ExoI and rSAP (New England Biolabs, Ipswich, MA, USA) according to manufacturer protocols. PCR products were then unidirectionally sequenced using the forward vector primers and ABI Big Dye Terminator Cycle Sequencing v3.1 Chemistry (Life Technologies, Carlsbad, CA, USA). We electrophoresed sequences on an ABI 3130XL Genetic Analyzer (Life Technologies, Carlsbad, CA, USA), edited and trimmed the sequences using Sequencher v5.0 (Gene Codes Ann Arbor, MI, USA), and then aligned the sequences in MEGA 6.0 using Muscle (Edgar, 2004; Tamura et al., 2013). We used the Barcode of Life Data System (BOLD), an online index of known DNA sequences, to identify sequences (Ratnasingham & Hebert, 2007; http://www.boldsystems.org). We assigned species for a 99–100% matched sequence, we assigned genus for a 95–98% match, assigned family for a 90–94% match, and assigned order for an 85–90% match according to the methods in Clare et al. (2009) and Zeale et al. (2011). From each stomach sample, we picked and sequenced at least 12 colonies containing recombinant clones since a preliminary study indicated that 10 clones was sufficient to detect all insect species present. In two stomachs, we were only able to sequence nine clones and in one stomach we were only able to sequence eight clones because fewer than 10 recombinant clones were present after cloning or some clones gave low quality sequence.

We created neighbor-joining trees using the Kimura Two-Parameter distance in MEGA to determine the number of OTUs (operational taxonomic units) that were present in the samples for which there was less than a 99% match in BOLD. We created separate trees for each order and classified samples as belonging to different species if they were >2% different and clearly clustered separately from other known species identified in BOLD. We used letters to distinguish unidentified species from one another (e.g., Lepidoptera A and Lepidoptera B).

Fecal collection, DNA extraction, amplification, and sequencing

From July to November 2011 and April to October 2012, we conducted weekly systematic searches for bat feces at all 75 wind turbines at Wolf Ridge (Bennett, Hale & Williams, 2017). Single fecal samples were collected from between the upper slats of the turbine door, between the gills of the transformer, on the frame beneath the gills of the transformer, and beneath the stairwell. Once found, we placed each fecal pellet in a 1.5 ml plastic tube and stored them at room temperature.

DNA extraction followed the protocol outlined in the QIAamp DNA Stool Mini-kit (Qiagen Genomics, Valencia, CA, USA). A negative control (i.e., a tube with no fecal sample in it) was included with each round of extraction to ensure that the extraction reagents used were not contaminated. Two successive PCR procedures were then undertaken. We first used PCR to identify fecal samples to bat species following the methods described in Korstian et al. (2015). As most of the fecal pellets (59% of 56 pellets) were from eastern red bats (<5 were from hoary bats; Bennett, Hale & Williams, 2017), we limited diet analysis from fecal pellets to eastern red bats only. Following bat species identification, we then used a second PCR to amplify the remnants of prey items in the fecal pellets from eastern red bats. PCR, cloning, sequencing, and arthropod identification protocols were identical to the ones used for bat stomach contents. For each fecal sample, we picked and sequenced at least 12 colonies containing recombinant clones since a preliminary study indicated that 10 clones was sufficient to detect all insect species present. For two fecal pellets, we were only able to sequence seven and nine clones because less than 10 recombinant clones were present after cloning or some clones gave low quality sequence.

For the clones generated from any given bat stomach or fecal sample, each insect species detected was counted only one time. We calculated the Simpson’s diversity index to summarize the number and abundance of prey items separately for eastern red bat stomachs, hoary bat stomachs, and eastern red bat fecal pellets. We present sampling curves for both the stomach and fecal samples using the number of clones successfully sequenced for all samples and the number of insects identified.

Comparing stomach and fecal contents to insect surveys

To determine whether insect species consistently found in bat stomachs and fecal pellets were also present near turbine towers, we compared all of the insect species identified in BOLD that were found in ≥5 stomach samples to the insect specimens that were collected during surveys in July and August of 2013 and 2015 and identified these species using the voucher specimens we collected. In all cases, the most common species found in bat stomachs were easy to identify morphologically from the insects we collected. If we found that bats were consistently eating insects that were not captured at turbines, it would suggest that bats were foraging elsewhere and not using the turbines as a foraging resource. Conversely, if the species that we consistently found in bat stomachs were present in insect surveys conducted at turbines, then this would provide support for the foraging attraction hypothesis.

Acoustic bat activity at wind turbines

Acoustic data were collected at wind turbine towers on 284 nights (93 in 2010, 42 in 2012, 90 in 2013, 38 in 2015, and 21 in 2016). We recorded a total of 3,606 bat passes and identified calls from all six bat species known to be in the study area (Fig. 1). Within this dataset, foraging and approach phase activity were recorded in 23% of the bat passes (n = 836) and feeding or terminal buzzes were recorded in 3.1% of the bat passes (n = 113). Of these feeding or terminal buzzes, 56% were recorded at detectors placed at the turbine nacelles. All six bat species exhibited foraging behavior at wind turbines (Fig. 1).

Insect surveys

We confirmed that the majority of the insect orders caught in the malaise traps did not differ from the insect orders collected with light trapping (Fig. S1, Fig. 2), therefore we only summarized the light trapping results to characterize the insect community near the turbine towers. In 2012, we light trapped a total of 17 nights between July and August, collecting 1,238 invertebrates belonging to nine orders. The three most abundant orders were Coleoptera (37.2%), Orthoptera (23.7%), and Lepidoptera (20.0%). In 2013, we light trapped a total of 13 nights between July and August, collecting 1,937 invertebrates belonging to 11 orders. The three most abundant orders were Lepidoptera (42.8%), Coleoptera (38.0%), and Hemiptera (9.1%). In 2015, we light trapped for 16 nights between July and August, collecting 7,479 invertebrates belonging to 13 orders. The three most abundant orders were Coleoptera (54.9%), Lepidoptera (14.7%), and Hemiptera (13.2%).

Due to differences in survey methods among years, we could not statistically compare the three years of insect surveys to determine if the insect community at Wolf Ridge changed over time. However, an informal comparison based on the average biweekly proportions of each order suggests that the insect community has remained relatively consistent between July and August of 2012, 2013, and 2015 (Fig. 2). Note that the confidence intervals are wide due to nightly variation in insect abundance among survey periods. For example, on one night we might have not caught any water beetles, whereas the next survey night might have coincided with an emergence of water beetles. Additionally, some species of insects were only captured on a single survey night during the season.

Assessment of bat stomach fullness

Of the 45 eastern red bats included in this study, 22 had full stomachs (three stomachs were not weighed). The mean ± SD weight of full stomach contents was 0.139 ± 0.076 g, whereas not full stomach contents weighed 0.043 ± 0.039 g (t = 5.04, df = 27, P < 0.001, n = 20 and 22 stomachs, respectively). Of the 23 hoary bats included in this study, 16 had full stomachs (1 stomach was not weighed). The mean ± SD weight of full stomach contents was 0.458 ± 0.252 g, whereas not full stomach contents weighed 0.131 ± 0.114 g (t = 4.19, df = 19, P < 0.001, n = 15 and seven stomachs, respectively). For both eastern red bats and hoary bats, the body masses of juveniles and adults were not significantly different (see Data S1), so we did not separate our analysis by age group.

Genetic analysis of bat stomach contents

Insect DNA was successfully extracted and amplified from all of the 68 bat stomachs included in this study. The average number of clones for the 68 bat stomachs was 13.4 (range: 8–21 clones). The sampling curve peaked at 10 clones indicating that 10 clones was sufficient to detect all prey species within a single stomach sample (Fig. S2A).

Collectively, the results of our stomach analysis yielded 153 insects representing 60 genetically distinct species. Based on the percentage match to known sequences in BOLD, 38 insects were identified to species, 10 were identified to genus, three were identified to family, and nine were identified to order (Tables S1 and S2). Individual bats in our study had a mean (±SE) of 2.26 ± 0.11 prey species in their stomachs (range: 1–6 species; Fig. 3). The majority of our samples consisted of only one or two species of insects (28 bats and 15 bats, respectively).

Eastern red bats (n = 45) had a mean (±SE) of 2.2 ± 0.19 individual prey species in their stomachs (range: 1–5 species; Fig. 3). We found 43 different species of insects from seven orders in eastern red bat stomachs (Fig. 4A; Table S1). Thirty-one of these species were detected in only one stomach (Fig. S3). We detected one species of moth (Spodoptera frugiperda) in 11 different stomachs and one species of cricket (Gryllus spp.) in 29 stomachs (Table S1). Lepidoptera comprised 55.1% and Orthoptera comprised 32.7% of the insect species identified in eastern red bat stomachs. The remainder belonged to Blattodea, Coleoptera, Diptera, Hemiptera, and Neuroptera. In addition to the species mentioned above, the following insects were detected in bats from both years: Parcoblatta spp., Achyra rantalis, Euchromius ocelleus, and Bleptina caradrinalis.

Hoary bats (n = 23) had a mean (±SE) of 2.4 ± 0.34 individual prey species in their stomachs (range: 1–6 species; Fig. 3). We found 25 different species of insects from three orders in hoary bat stomachs (Fig. 4B; Table S2). Eighteen of these species were found in only one stomach (Fig. S3). Similar to our eastern red bat stomach analysis, we found that Lepidoptera was the most abundant and diverse order in the stomachs; the most frequently detected moth, S. frugiperda, was found in seven stomachs. Gryllus spp. were the most frequently detected species and were found in 18 stomachs. Lepidoptera comprised 60.7% and Orthoptera comprised 32.1% of the species identified in hoary bat stomachs. The remainder consisted of four species of Coleoptera, each only detected once. In addition to the species mentioned above, only two other species of moths were detected in both years: E. ocelleus and Helicoverpa zea.

Genetic analysis of bat fecal pellets

Insect DNA was successfully extracted and amplified from 23 of the 33 eastern red bat fecal pellets collected from wind turbines (Bennett, Hale & Williams, 2017). The average number of clones for the fecal pellets was 13.5 (range: 7–18 clones), and the sampling curve appeared to level off at approximately 10 clones; however, a few samples were highly diverse causing the curve to continue to increase after 14 clones (Fig. S2B).

Collectively, the results of our fecal analysis yielded 57 insects representing 39 genetically distinct species. Based on the percentage match to known sequences in BOLD, 18 insects were identified to species, 10 were identified to genus, five were identified to family, and six were identified to order (Table S3). Individual fecal pellets in our study contained a mean ( ± SE) of 2.4 ± 0.34 prey species (range: 1–6 species; Fig. 3), with the majority (61%) of the fecal pellets containing only one or two species of insects. The 39 species of insects detected in fecal pellets belonged to nine insect orders (Fig. 4C; Table S3). Thirty-one of these species had a detection frequency of 1 (Fig. S3). We detected one species of cricket (Gryllus rubens) in 35% of the fecal samples (n = 8 pellets). The remaining species were detected in fewer than five pellets each. Lepidoptera comprised 32.1%, Orthoptera 17.8%, Diptera 21.4%, and Coleoptera 17.9% of the insect species identified in fecal pellets. The remainder belonged to Blattodea, Ephemeroptera, Hemiptera, Neuroptera, and Isopoda. The single fecal sample that contained Isopoda (Armadillium sp.) was probably due to environmental contamination since these species are terrestrial and have not been observed on the surface of the turbine tower where they might be exposed to bat predation.

Comparing diversity between species and sample types

Eastern red bat stomachs had a Simpson’s diversity index of 0.898, hoary bats 0.878, and eastern red bat fecal pellets 0.973, indicating that both species have similar diversity of insects in their stomach contents; however, fecal samples had slightly higher diversity than the stomach samples.

As we analyzed more bat stomachs and fecal pellets, we continued to identify insect species that we had not previously found in our study (Fig. 5). Our discovery rate suggests that we would have continued to discover more species of insects with larger sample sizes of bat stomachs and fecal pellets, suggesting that our analysis may only reveal a fraction of the insect species that bats are eating in a night at our study site.

Comparing stomach and fecal contents to insect surveys

To determine whether insects frequently found in bat stomachs were present at wind turbines, we compared the insect species detected most frequently in the stomach contents to the insect surveys. Because 2012 insect surveys did not incorporate species identification, we only compared our stomach content results to 2013 and 2015 insect surveys. We omitted any insects that were found in <5 stomachs because so many species from our genetic analysis were single-stomach detections. This left us with four species of moths (E. ocelleus, B. caradrinalis, H. zea, and S. frugiperda) and two unidentified species of cricket (Gryllus spp.). We found these insects in the stomachs of both eastern red bats and hoary bats (Table 1). Of these commonly eaten insect species, we documented most at wind turbines in 2013 and all of them at wind turbines in 2015 (Table 1). Furthermore, all of these species were found at wind turbines on multiple nights throughout the survey period in both 2013 and 2015 (Table 1).

Fecal analysis also had a high number of single-pellet detections, so we only compared insect species detected in ≥5 eastern red bat fecal pellets to the results of our insect surveys. Crickets (Gryllus spp.) were the only species of insect consistently detected in eastern red bat fecal pellets; crickets were also collected on multiple nights throughout the insect survey period in both 2013 and 2015.