Between Wires and Wings: What Are the Impacts of Power Transmission Lines on the Diversity of Insectivorous Bats?

Abstract

Energy consumption in the world is growing every year, and there is an increasing demand on the energy system to meet the increase in consumption, resulting in the installation of new power transmission lines. The understanding of how power transmission lines affect biodiversity is predominantly focused on birds, with limited information available on other organisms. In this study, we assessed the potential effect of power transmission lines on bat communities in a locality in the Cerrado biome in Brazil. More specifically, we used a paired sample design and acoustically sampled bats in locations near and far from the transmission lines. Our findings suggest that power transmission lines do not have a generally positive or negative effect on insectivorous bat communities in the study area. However, their presence seems to be associated with increased diversity in specific functional groups and changes in the activity patterns of some bat species and families. We believe that this information is of particular importance for establishing appropriate programs during the environmental licensing process, assisting in the development of projects in the different stages of construction as well as in monitoring programs during operation.

1. Introduction

Energy consumption in the world is growing every year and is expected to increase by around 50% by the year 2050 [1]. As a result, there is a growing demand for the energy system to meet the increase in consumption, resulting in the installation of new power transmission lines (PTLs). However, these linear structures can cause significant environmental impacts during both the installation and operation phases [2]. The most apparent impact is the clearing of vegetation to create the right of way situated just beneath the conductor cables. The removal directly changes the landscape, leading to negative effects like heightened edge impact and habitat fragmentation, and acting as a barrier for various organisms [3,4]. In addition, other impacts of PTLs on fauna during the operation phase have been pointed out because, in addition to direct collisions (e.g., birds and bats), some less noticeable effects, such as the effects of the electromagnetic field, noise, and corona discharges, can also generate negative impacts [4,5].

The understanding of how power transmission lines affect biodiversity is predominantly focused on birds, with limited information available on other organisms [4]. Although little is known about the responses of bat communities to transmission line effects, just like birds, they also have a high probability of suffering these impacts induced by PTLs. Bats constitute one of the most diverse and widely distributed groups of living mammals [6]. Their great taxonomic and functional diversity make many groups of bats sensitive to human-induced changes to ecosystems and render them highly suitable as bioindicators [7]. Within this group, insectivorous bats are particularly likely to suffer negatively with PTLs given that they fly at high altitudes (over 50 m), equivalent to those of transmission lines [8], and have many species that occur all over the world and form large colonies that can reach up to 20 million individuals [9]. These features make insectivorous bats more vulnerable than other guilds since the area of use and high densities can favor collision with PTLs and other impacts, as cited previously.

To date, the few studies on the effect of PTLs on bats have presented different results. In Asia, for example, studies have been reported from the collision and electrocution in Pteropus giganteus [10] to flight avoidance at open areas near power lines by Tadarida teniotis [11]. Recently a pioneering study carried out in Europe shows a strong influence of humidity on the foraging and activity of bats on transmission lines [5]. One of the main results pointed out by the authors was that bats were attracted to high-voltage power lines in areas with high humidity, one of the possible causes being corona discharges [5]. On the other hand, areas with low humidity seem to keep bats away, perhaps due to the presence of towers and cables as well as the effect of electromagnetic fields. Corona discharges are electric discharges resulting from the ionization of the atmospheric air around the conductors, occurring primarily in wet conditions with minimal wind speed [12]. Although it is recognized, the impact of corona discharges is still little explored and, to date, no studies have been found addressing this effect in the Neotropical region, which is the region of the globe with the highest concentration of biodiversity [13,14]. Therefore, evaluating the possibility of this impact on the winged fauna present in this region of the globe is essential.

Although Brazil has the second greatest diversity of bats in the world, it also has alarming levels of environmental impacts, affecting the entire national territory and especially the most vulnerable biomes, such as the Cerrado, considered a “hotspot” for biodiversity conservation [15]. The Cerrado is one of the richest tropical savannas, with a high diversity and endemism of species [16], and, as far as bats are concerned, it is home to almost 70% of the country’s diversity [17]. Furthermore, in relation to power line installations, the Cerrado also retains a large part (approximately 30%) of the more than 185,000 km of high-voltage transmission lines (>220 kV) of Brazil, which is growing every year as new projects are built [18]. This rapid growth, together with a lack of information about the impacts of PTLs on bats, has led to misguided environmental licensing studies, often not applicable to bats (FF pers. obs.).

Our aim was to assess the potential effect of power transmission lines on bat communities in a locality in the Cerrado biome in Brazil. If the observations made by Froidevaux et al. (2023) [5] are applicable to tropical bat communities, we would expect to observe greater bat diversity in areas near PTLs compared to areas away from them. Furthermore, given that the installation of PTLs is associated with a loss of vegetation cover, we would expect to see changes in species composition between areas close to PTLs and control areas. We anticipate that open-area bats would be less affected than forest bats by the installation of PTLs and that bats whose life history predominantly occurs in forests would be more impacted by the installation of PTLs for the same reason. Due to the association between the installation of PTLs and an increase in open areas, along with greater light exposure (both natural and artificial), we would expect a positive association of light-tolerant species (e.g., Emballonuridae) with the areas where PTLs are installed. Lastly, we expect the disturbance caused by the installation of PTLs to be reflected in changes in bat activity within the bat communities between areas near the PTLs and control areas.

2. Materials and Methods

2.1. Study Area and Sampling Design

This study was conducted in the APA (Environmental Protection Area) of the Nascentes do Rio Vermelho (APA-NRV), located in Goiás state, central region of Brazil. The APA-NRV is in the Cerrado domain, with an area of approximately 176,000 ha (ICMBio, Mambaí, Brazil, 2021). The area is intercepted by the 500 kV Rio das Éguas–Arinos 2–Pirapora 2 Power Line—PTL (Figure 1), which was used to assess possible impacts on insectivorous bats.

The sampling of insectivorous bat activity was carried out in eight areas along the PTL, separated by a minimum distance of 1000 m from each other. We divided these areas into two phytophysiognomies: four areas in open areas (OAs) and the other four areas in riparian forest (RF). The OAs were characterized by the presence of grasses and sparse trees, without the formation of a canopy or the presence of bodies of water. On the other hand, the RF areas were wetter and had a formed canopy, with the presence of streams inside. Two sampling points were established in each area: one in the service strip, located less than 5 m from the PTL (treatment), and the other one at least 500 m away from the PTL (control). Each pair always maintained the same phytophysiognomy.

2.2. Bat Sampling and Identification

Bats were sampled by acoustic monitoring using the full-spectrum ultrasound recorders Song Meter SM2BAT+ and SM4BAT (Wildlife Acoustics Inc.^®, Maynard, MA, USA), both with omnidirectional microphones sensitive to frequencies up to 192 kHz. All sites were sampled in two periods (first period—survey 1: between 30 September and 3 October 2019; second period—survey 2: between 31 July and 6 August 2021). During the first period, the transmission lines were being installed, while, in the second period, they were already in operation. Recording devices were configured with the same specifications (to record sounds at frequencies over 10 kHz with a 384 kHz sampling rate and 16-bit resolution) and programming (continuous recording in 1 min files, for 12 consecutive hours, starting at dusk—around 6 pm—and ending at 6 am). At each sampling point, one of the recorders was used for 24 h (2 points × 12 h), totaling 192 h of recordings (8 areas × 24 h) during the period, adding up to 384 h for the two periods.

To identify each recording to species level, we visually inspected the spectrograms of all recordings using Avisoft—SAS Lab Lite (Avisoft Bioacoustics^®, Glienicke, Germany) and Kaleidoscope 4.3.2 (Wildlife Acoustics^®), comparing it based on our call library and the classification already described for Neotropical bats [19,20,21,22,23,24]. When species-level identification was not achievable, we categorized the sound recordings as sonotypes. Following the identification sound records, we quantified the activity level of different species at each sampling site using the relative activity index (AI) proposed [25]. This index consists of evaluating the presence/absence of species in each minute of recording, and the files were then grouped by sampling area. This approach is used as a proxy for bat activity (relative abundance) and was chosen to minimize problems related to the direct counting of passes, since the number of passes does not represent the absolute number of individuals recorded [26].

We adopted the approach proposed by Estrada-Villegas et al. (2010) [27], which classifies the species or sonotypes identified into functional groups, taking into account their foraging strategies: those species that forage in wider areas, free of obstacles or vegetation, were classified as open-area species, while species that forage close to vegetation were classified as forest species. The sonotypes could be classified by similarity down to family level based on the properties of their calls.

2.3. Data Analysis

To assess variations in the alpha diversity of bat communities between survey 1 (installation) and survey 2 (operation), as well as the comparison between control and treatment points, functional groups, and phytophysionomies within surveys, we estimated diversity as the effective number of species or diversity of order q (^qD) [28,29,30]. This approach divides diversity into three orders: when q = 0, species richness is obtained, when q ≈ 1, the Shannon exponential or the effective number of equally common (frequent) species is obtained, understood as the diversity when all abundances have equal weight (without bias for rare or hyper abundant species, i.e., typical diversity), and when q = 2, the inverse of Simpson’s concentration or the effective number of hyper-abundant (dominant) species (or highly frequent species) is obtained [28,30]. For the analysis, due to the inability to individualize the calls, we used an approach based on frequency matrices, where the weight of each species (or sonotype) was the number of sampling points where it was recorded. The comparison of communities was carried out using the interpolation/extrapolation protocol proposed by Chao et al. (2014) [29] and the R language 4.4.1 package “iNEXT” [31,32].

To assess the compositional dissimilarity of bat communities between surveys and controls/treatments, we used the Jaccard dissimilarity index (β_cc), which can be decomposed to understand the contribution of species turnover (β₋₃) and differences in species richness (β_rich) to the observed beta diversity [33]. The compositional dissimilarity was represented through a cluster, constructed using the UPGMA method [34]. We estimated the number of clusters using the silhouette method, and the support for those clusters using bootstrapping (100 rep.) [34,35]. The entire process was conducted in the R language, using the “BAT” package for beta diversity estimation and “pvclust”, “factoextra”, and “fpc” for cluster construction, cluster evaluation, and their support [32,35,36,37]. We evaluated the possible association of families and/or species with surveys and treatments through non-metric multidimensional scaling (NMDS) using the Bray–Curtis index as the distance measure [34,38,39]. Possible configurations for each NMDS were evaluated 100 times, retaining the configuration with the lowest stress level [39]. The analysis was conducted using the “vegan” package in the R language 4.4.1 [32,40].

To evaluate variations in the activity of bat communities between control and treatment for each survey, we used Fisher’s exact test, simulating the p-value using the Monte Carlo algorithm with 10,000 repetitions [34,39,41]. The test was carried out using the ‘stats’ package in the R language 4.4.1 [32] and was supplemented with rank–abundance curves, focusing on the position of the top five species.

The nightly activity of bat families in the controls/treatments by sampling was compared using circular bar plots. Two-hour intervals were used on the X-axis, and the relative frequency of vocalization activity was used on the Y-axis. The plots were created using “ggplot2” in the R language 4.4.1 [32,42].

3. Results

At the end of the two surveys, 23 species/sonotypes were recorded, distributed in four families: Emballonuridae, Molossidae, Mormoopidae, and Vespertilionidae (Table S1). All the species recorded have an insectivorous feeding habit and are representatives of the aerial insectivore guild, which forages at high altitudes in search of its prey, consisting of insects of varying sizes. A total of 10,717 records were obtained, of which 5434 were in the impact areas and 5283 in the control areas.

3.1. Alpha Diversity

For all the alpha diversity comparisons conducted, we found an overlap in the 95% confidence intervals for the sampling coverage (SC) of the elements involved in each case. Therefore, comparisons were made directly between the observed diversities. All comparisons of diversity in numerical terms with their respective confidence intervals, both for the estimation and the SC, are presented in Table S2.

We did not find significant differences in the species richness of bat communities between controls and treatments. However, we found a significant difference in species richness between samplings 1 and 2, with the first being lower (Figure 2—⁰D). Regarding the effective number of frequent species and highly frequent species (¹D and ²D, respectively), we did not find significant differences between controls and treatments, nor among the surveys (Figure 2).

For comparisons at the community level within the vegetation covers, we did not find significant differences in any of the diversity orders, either for the riparian forest (Figure 3—riparian) or for the open area (Figure 3—open).

Regarding the comparisons of bat communities by functional group, we found that species richness was significantly higher in the control than in the treatment for survey 1. For the remaining comparisons between control and treatment for both surveys, no significant differences were found. When comparing the surveys for forest bats, we found a significantly lower species richness and effective number of frequent species in survey 1 compared to survey 2 (Figure 4—forest). As for bat communities in open areas, we found no significant differences in any comparison, neither between control and treatments nor between surveys (Figure 4—open).

3.2. Beta Diversity

We found a dissimilarity of about 40% between the bat communities observed in surveys 1 (S1) and 2 (S2) (Figure 5A). Most of this dissimilarity was explained by species turnover (Figure 5B), which was around 35%, while dissimilarity due to differences in species richness (Figure 5C) was less than 15%. The lowest dissimilarity was between the control and impact of survey 1 (S1.c and S1.t, respectively), which showed less than 10% dissimilarity, primarily explained by differences in species richness. Secondly, dissimilarity was about 20% between the control and impact of survey 2 (S2.c and S2.t, respectively), mainly explained by species turnover and, to a lesser extent, by differences in species richness.

Regarding the NMDS, we found no differentiation between controls and treatments for surveys 1 and 2, neither at the species level (Figure 6A) nor at the family level (Figure 6B). Similarly, we did not find any associations of any species or family with specific treatments or surveys.

3.3. Communities Acoustic Activity

We found variations in the acoustic activity of species in the control and treatment communities for surveys 1 and 2. The identity and position of the five most active species varied between control and treatment for both surveys (Figure 7). This variation was significant in the analysis for all species in both surveys, conducted using Fisher’s exact test (p value < 0.00).

Subtle variations in nightly activity, as well as in the start and end times of bat family activity, were observed across the surveys and treatments (Figure 8).

4. Discussion

Our results do not support the idea that insectivorous bat communities are affected by the presence or installation of power transmission lines (PTLs). We did not find variations in bat diversity between control areas and treatment areas (with PTLs). Similarly, we did not observe any changes in species composition that would suggest variations caused by PTL installation; instead, the observed variations in composition seemed to be due to temporal changes between different surveys. Contrary to our expectations, we found no evidence that PTLs installed in riparian forest zones had a greater impact on bat diversity than those installed in open areas, nor did we find an association of specific species or families with PTL areas. However, we did find that forest bats exhibited an increase in species richness and typical diversity between survey 1 (installation) and survey 2 (operation), which could support the idea that PTLs might be attractive to this group. Finally, we also observed changes in the activity of the most dominant species between control and treatment (PTL) areas, as well as shifts in activity patterns among some families. Our findings suggest that power transmission lines do not have a generally positive or negative effect on insectivorous bat communities in the study area. However, their presence seems to be associated with increased diversity in specific functional groups and changes in the activity patterns of some bat species and families. More studies are needed in the Neotropical region to determine whether the observed results can be generalized to the insectivorous bats communities occurring there. If our results are not generalizable, it is crucial to identify which ecosystems are most sensitive to the installation and operation of PTLs, as the increasing energy demand will require more transmission lines installation.

We found lower bat richness between survey 1, which occurred during the installation phase, and survey 2, which occurred during the operation phase. This decrease in the number of species could be explained by the noise generated by machinery during the installation process. In fact, studies show that anthropogenic noise affects the natural behavior of bats, even reducing their foraging efficiency [43,44]. However, considering that the variation in the number of species between surveys was five species from different families, and that these species exhibited a relatively low number of calls during the sampling period, along with the fact that most bat species have demonstrated high resilience to both natural and anthropogenic noise in laboratory studies [45], we could suggest that only the most sensitive species might be affected by installation noise. This would explain why there were no variations in other orders of diversity, as these species, having low activity, do not significantly impact the overall structure of bat communities in the study area.

We did not find greater bat diversity in treatment areas compared to control areas; in fact, we did not find any significant differences, neither during the installation period nor during the operation. If the pattern observed by Froidevaux et al. (2023) [5] for European bats were consistent with the bat communities in the Cerrado, it would be logical to expect higher diversity in areas with PTLs. However, studies conducted in other parts of the world have shown that, in bat communities, most species maintain their activity similarly to areas without the presence of PTLs [11]. It is necessary to evaluate other areas in the Cerrado to confirm whether the bat communities in this region are less sensitive due to their natural history to the presence of PTLs compared to the bats studied by Froidevaux et al. (2023) [5].

Contrary to our expectations, we found no evidence that bat communities in forested areas were more affected by the installation of PTLs than those in open areas. In fact, we did not observe any response in any order of diversity in any of the communities within these types of vegetation cover. Given that the installation of PTLs often leads to a loss of vegetation cover [3,4], we expected to find a difference. However, studies such as that by Dos Santos et al. (2016) [46] indicate that, in the specific case of Cerrado bats, species richness and abundance seem to be more closely related to temporal variations than to changes in vegetation cover. This could suggest that both types of vegetation cover behave in a similar way, consistent with the general pattern observed, which might explain our results.

We found that, between survey 1 and survey 2, the richness and typical diversity of forest bats increased. This observation contradicts our initial predictions, where we expected that forest species would be more affected by the installation of PTLs compared to open-area species. This prediction was since the installation of these lines typically leads to a decrease in vegetation cover [3,4]. However, the same loss of vegetation cover may be contributing to the “moonlight effect” [47] by increasing both artificial and natural light, which promotes an increase in species activity. Additionally, it has been documented that power lines and their surroundings can provide an alternative food source [10], which may be utilized by forest species and which could explain our results.

The results of the beta diversity analysis suggest that the variation observed in the species composition of the communities is explained mainly by temporal changes between surveys and not necessarily by the impact of the transmission lines. Other studies that have analyzed environmental changes in forest species of insectivorous bats have found little or no variation in alpha diversity parameters, such as richness and abundance, but have pointed to differences in species composition between affected areas. For example, in a study carried out in Panama, Estrada-Villegas et al. (2010) [27] found no response in the activity of forest species to habitat fragmentation in a land-bridge island system but found differences in species composition between the parameters assessed. However, studies like that by Dos Santos et al. (2016) [46] suggest that, in the Cerrado biome, the composition changes (measured in their study as turnover) of bat communities primarily respond to temporal variations, which supports our observations. Thus, the data available for insectivorous bats indicate that the responses in relation to landscape structure (open area vs. riparian forest) should be at the species level and not at the community level.

We did not find associations of specific bat species or families with any survey, control areas, or PTL areas. Likewise, species composition did not vary significantly between control zones and areas with PTLs (treatment). However, the position (rank) of the most dominant species did change significantly between control areas and PTL areas, suggesting that some factor is influencing the shift in the identity of the dominant species, even though this does not significantly alter the overall composition or structure of the community. In both surveys, Peropteryx macrotis was the most dominant species in treatment zones, followed by Pteronotus rubiginosus, both of which have been recognized as light-tolerant species [48,49]. It is important to note that the most dominant species in control areas are Mol1 (an unidentified Molossidae and the third most dominant species in the treatment zones) and Pteronotus rubiginosus. Given that Molossidae are also known for their light tolerance [50], we can see that both the dominant species in control areas and those in PTL areas are light-tolerant species, and perhaps the changes in species rankings as well as variations in their proportions could be explained by the degree of light tolerance of the species and the increase in luminosity in areas with PTLs.

We found variations in the night-time activity period of bat communities. While, in the control points, all activity seems to be concentrated in the early hours of the night (between 18:00 and 00:00), in the treatment areas, a different pattern is observed, where activity (especially in survey 1) appears to extend throughout the night (or past midnight in survey 2). This is particularly evident in changes in the start and end times of activity for families such as Mormoopidae and Vespertilionidae. Seewagen (2021) [51] found that other species of the Vespertilionidae family in North America changed their nocturnal activity patterns when light was applied to their environments. Appel et al. (2019) [47] observed markedly different activity patterns on nights with abundant moonlight for Pteronotus rubiginosus, the most abundant Mormoopidae in our study. Thus, we might suggest that the increased light associated with the installation and operation of PTLs could lead to changes in the nocturnal activity of species within the studied bat communities.

5. Conclusions

Considering the relevance of the APA-NRV for bats, both because it is in the threatened Cerrado biome and because it has a large number of caves in its area, the absence of significant impacts on bats is a relief, but it is worth pointing out some limitations of our study. For example, we did not have access to the region during the pre-installation period of the PTL, which could have allowed us to see if there were any changes in both diversity and species during and after the installation. Obtaining other data could also shed light on some questions that remained open in this study. Measuring humidity at the exact locations of the recorders, as well as checking the occurrence and intensity of corona discharges, could also shed more light on the real reasons for why some species, especially Emballonuridae, were attracted to the transmission lines. It is important to emphasize that our observations are based on a “snapshot” of the Brazilian Cerrado insectivorous bats, given that our spatial and temporal scope is relatively limited. Therefore, future studies encompassing other areas of the Brazilian Cerrado, as well as different biomes in Brazil and the broader Neotropical region, are necessary for understanding how PTLs impact the diversity of insectivorous bats. These studies will help to determine whether our findings are generalizable or if there are specific regions within the Cerrado, or other regions, where bat fauna might respond differently, potentially making PTLs a threat to their survival and the maintenance of their ecosystem functions.

To our knowledge, our study is the first to examine the effects of power lines on Neotropical aerial insectivorous bats in detail at the functional group and species level. Our results allow for an analysis involving different types of impacts of power lines on different dimensions of insectivorous bat diversity, contributing to a better understanding of these potential impacts. We believe that this information is of particular importance for establishing appropriate programs during the environmental licensing process, assisting in the development of projects in the different stages of construction as well as in monitoring programs during operation. The lack of awareness of the potential impacts of these projects encourages long and often innocuous studies, when they should focus on precise and direct approaches to identify potential impacts [4]. Bearing in mind that, in general, the parameters of alpha diversity did not register major variations as a result of the impact of the installations, and we registered differences in some species and families activity (e.g., Peropteryx macrotis, Mormoopidae—Molossidae), we suggest that programmes should carefully evaluate the choice of keystone species identified in previous robust studies so that they can be analyzed for impacts from the pre-construction period onwards. We therefore encourage the deployment of more effective practices, ranging from baseline studies to monitoring programs, with the aim of improving the sustainability of these types of linear developments.

Author Contributions

Conceptualization, F.F.; methodology, F.F., J.M.H.-L., and C.V.d.M.-M.; formal analysis, J.M.H.-L.; investigation, F.F., J.M.H.-L., and C.V.d.M.-M.; resources, F.F.; data curation, F.F.; writing—original draft preparation, F.F., J.M.H.-L., and C.V.d.M.-M.; writing—review and editing, F.F., J.M.H.-L., and C.V.d.M.-M.; visualization, F.F., J.M.H.-L., and C.V.d.M.-M.; supervision, F.F.; project administration, F.F.; funding acquisition, F.F. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The datasets generated during the current study are available from the authors on reasonable request. The R code used to perform the analyses and graphs presented in this work is available at: https://gitlab.com/data-availability-ms/powerlines_and_bats (accessed 25 June 2024).

Acknowledgments

We would like to thank the Instituto Chico Mendes de Conservação da Biodiversidade—ICMBio, especially Raoni Merisse for providing the logistical support crucial for developing our study. We also wish to thank Marcelo Sena do Nascimento, Tiago Lisboa, Vanilson Barbosa, and Igor Inforzato for their help during field work. We would also like to thank Veredas—Transmissora de Eletricidade and Biodinâmica for allowing us to use the data. This study was carried out under IBAMA Process No. 02001.001104/2017-11.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures

Figure 1. Location map of the sampling points within the limits of the APA Nascentes do Rio Vermelho, in Goiás. MC: riparian forest, AB: open area, a: treatment, b: control.

Figure 2. Comparison of insectivorous bat diversity between control and treatment for each survey and comparison between surveys. S1: survey 1, S2: survey 2, S1.c: control for survey 1, S1.t: treatment for survey 1, S2.c: control for survey 2, S2.t: treatment for survey 2. The blue asterisk indicates a significant difference in the comparison.

Figure 3. Comparison of the diversity of insectivorous bat communities within the vegetation covers and across surveys. (Right) open areas, (Left) riparian forest. S1: survey 1, S2: survey 2, S1.c: control for survey 1, S1.t: treatment for survey 1, S2.c: control for survey 2, S2.t: treatment for survey 2. 0D: species richness, 1D: effective number of frequent species, and 2D: effective number of highly frequent species.

Figure 4. Comparison of the diversity of insectivorous bat communities within functional groups and across surveys. (Right) open-area bats, (Left) forest bats. S1: survey 1, S2: survey 2, S1.c: control for survey 1, S1.t: treatment for survey 1, S2.c: control for survey 2, S2.t: treatment for survey 2. 0D: species richness, 1D: effective number of frequent species, and 2D: effective number of highly frequent species. The blue asterisk indicates a significant difference in the comparison.

Figure 5. Comparison of beta diversity in insectivorous bats between the evaluated surveys and treatments. (A) Total beta diversity (Jaccard dissimilarity—βcc), Pearson cophenetic correlation (Pcc) with distance matrix: 0.97, (B) Beta diversity from species turnover (β−3), Pcc with distance matrix: 0.88, (C) Beta diversity due to differences in species richness (βrep), Pcc with distance matrix: 0.93. S1.c: control for survey 1, S1.t: treatment for survey 1, S2.c: control for survey 2, S2.t: treatment for survey 2. The tree support was provided by Bootstrap (100 reps).

Figure 6. Non-metric multidimensional scaling (NMDS) for the studied insectivorous bat communities. (A) Species level, stress: 0.16, (B) family level, stress: 0.11. S1.c: control for survey 1, S1.t: treatment for survey 1, S2.c: control for survey 2, S2.t: treatment for survey 2.

Figure 7. Rank–abundance curves (rank–acoustic activity curves) for insectivorous bat communities in control and treatment for the different surveys.

Figure 8. Nightly activity of insectivorous bat families across the evaluated surveys and treatments. Relative frequency is presented as the number of records (acoustic index) from each family in a two-hour period, divided by the total number of calls (acoustic index) for that family. S1.c: control for survey 1, S1.t: treatment for survey 1, S2.c: control for survey 2, S2.t: treatment for survey 2.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su16135639/s1, Table S1: Sampling points for acoustic monitoring of bats APA (Environmental Protection Area) of the Nascentes do Rio Vermelho. OA—Open area; RF—Riparian forest; Table S2: Diversity comparisons for the studied bat communities. Estimates were made using the iNEXT package in the R language (see data analysis section). ^qD: Diversity value, SC: Sampling coverage, lower: lower confidence interval (95%), upper: upper confidence interval (95%).

References

1. EIA International Energy Outlook—U.S. Energy Information Administration (EIA). Available online: https://www.eia.gov/outlooks/ieo/tables_side_xls.php (accessed on 30 April 2024).

2. Bagli, S.; Geneletti, D.; Orsi, F. Routeing of Power Lines through Least-Cost Path Analysis and Multicriteria Evaluation to Minimise Environmental Impacts. Environ. Impact Assess. Rev.; 2011; 31, pp. 234-239. [DOI: https://dx.doi.org/10.1016/j.eiar.2010.10.003]

3. Richardson, M.L.; Wilson, B.A.; Aiuto, D.A.S.; Crosby, J.E.; Alonso, A.; Dallmeier, F.; Golinski, G.K. A Review of the Impact of Pipelines and Power Lines on Biodiversity and Strategies for Mitigation. Biodivers. Conserv.; 2017; 26, pp. 1801-1815. [DOI: https://dx.doi.org/10.1007/s10531-017-1341-9]

4. Biasotto, L.D.; Kindel, A. Power Lines and Impacts on Biodiversity: A Systematic Review. Environ. Impact Assess. Rev.; 2018; 71, pp. 110-119. [DOI: https://dx.doi.org/10.1016/j.eiar.2018.04.010]

5. Froidevaux, J.S.P.; Jones, G.; Kerbiriou, C.; Park, K.J. Acoustic Activity of Bats at Power Lines Correlates with Relative Humidity: A Potential Role for Corona Discharges. Proc. R. Soc. B Biol. Sci.; 2023; 290, 20222510. [DOI: https://dx.doi.org/10.1098/rspb.2022.2510]

6. Hutson, A.M.; Mickleburgh, S.P.; Racey, P.A. Microchiropteran Bats: Global Status Survey and Conservation Action Plan; IUCN/SSC action plans for the conservation of biological diversity IUCN: Gland, Switzerland, Cambridge, UK, 2001; ISBN 978-2-8317-0595-8

7. Jones, G.; Jacobs, D.; Kunz, T.; Willig, M.; Racey, P. Carpe Noctem: The Importance of Bats as Bioindicators. Endanger. Species Res.; 2009; 8, pp. 93-115. [DOI: https://dx.doi.org/10.3354/esr00182]

8. Jung, K.; Kalko, E.K.V. Adaptability and Vulnerability of High Flying Neotropical Aerial Insectivorous Bats to Urbanization. Divers. Distrib.; 2011; 17, pp. 262-274. [DOI: https://dx.doi.org/10.1111/j.1472-4642.2010.00738.x]

9. Arita, H.T. Conservation Biology of the Cave Bats of Mexico. J. Mammal.; 1993; 74, pp. 693-702. [DOI: https://dx.doi.org/10.2307/1382291]

10. Tella, J.L.; Hernández-Brito, D.; Blanco, G.; Hiraldo, F. Urban Sprawl, Food Subsidies and Power Lines: An Ecological Trap for Large Frugivorous Bats in Sri Lanka?. Diversity; 2020; 12, 94. [DOI: https://dx.doi.org/10.3390/d12030094]

11. Kahnonitch, I.; Lubin, Y.; Korine, C. Insectivorous Bats in Semi-Arid Agroecosystems—Effects on Foraging Activity and Implications for Insect Pest Control. Agric. Ecosyst. Environ.; 2018; 261, pp. 80-92. [DOI: https://dx.doi.org/10.1016/j.agee.2017.11.003]

12. Matthews, J.C. The Effect of Weather on Corona Ion Emission from AC High Voltage Power Lines. Atmospheric Res.; 2012; 113, pp. 68-79. [DOI: https://dx.doi.org/10.1016/j.atmosres.2012.03.016]

13. Antonelli, A.; Ariza, M.; Albert, J.; Andermann, T.; Azevedo, J.; Bacon, C.; Faurby, S.; Guedes, T.; Hoorn, C.; Lohmann, L.G. et al. Conceptual and Empirical Advances in Neotropical Biodiversity Research. PeerJ; 2018; 6, e5644. [DOI: https://dx.doi.org/10.7717/peerj.5644] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30310740]

14. Bogoni, J.A.; Peres, C.A.; Ferraz, K.M.P.M.B. Extent, Intensity and Drivers of Mammal Defaunation: A Continental-Scale Analysis across the Neotropics. Sci. Rep.; 2020; 10, 14750. [DOI: https://dx.doi.org/10.1038/s41598-020-72010-w] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32934299]

15. Myers, N.; Mittermeier, R.A.; Mittermeier, C.G.; Da Fonseca, G.A.B.; Kent, J. Biodiversity Hotspots for Conservation Priorities. Nature; 2000; 403, pp. 853-858. [DOI: https://dx.doi.org/10.1038/35002501] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10706275]

16. Strassburg, B.B.N.; Brooks, T.; Feltran-Barbieri, R.; Iribarrem, A.; Crouzeilles, R.; Loyola, R.; Latawiec, A.E.; Oliveira Filho, F.J.B.; Scaramuzza, C.A.D.M.; Scarano, F.R. et al. Moment of Truth for the Cerrado Hotspot. Nat. Ecol. Evol.; 2017; 1, 0099. [DOI: https://dx.doi.org/10.1038/s41559-017-0099] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28812670]

17. Silva, D.C.; Vieira, T.B.; Da Silva, J.M.; De Cassia Faria, K. Biogeography and Priority Areas for the Conservation of Bats in the Brazilian Cerrado. Biodivers. Conserv.; 2018; 27, pp. 815-828. [DOI: https://dx.doi.org/10.1007/s10531-017-1464-z]

18. MME Boletim de Monitoramento do Sistema Elétrico. Available online: https://www.gov.br/mme/pt-br/assuntos/secretarias/secretaria-nacional-energia-eletrica/publicacoes/boletim-de-monitoramento-do-sistema-eletrico/boletim-de-monitoramento-do-sistema-eletrico (accessed on 30 May 2024).

19. Rydell, J.; Arita, H.T.; Santos, M.; Granados, J. Acoustic Identification of Insectivorous Bats (Order Chiroptera) of Yucatan, Mexico. J. Zool.; 2002; 257, pp. 27-36. [DOI: https://dx.doi.org/10.1017/S0952836902000626]

20. Jung, K.; Kalko, E.K.V.; Von Helversen, O. Echolocation Calls in Central American Emballonurid Bats: Signal Design and Call Frequency Alternation. J. Zool.; 2007; 272, pp. 125-137. [DOI: https://dx.doi.org/10.1111/j.1469-7998.2006.00250.x]

21. Jung, K.; Molinari, J.; Kalko, E.K.V. Driving Factors for the Evolution of Species-Specific Echolocation Call Design in New World Free-Tailed Bats (Molossidae). PLoS ONE; 2014; 9, e85279. [DOI: https://dx.doi.org/10.1371/journal.pone.0085279] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24454833]

22. Barataud, M.; Giosa, S.; Leblanc, F.; Rufray, V.; Disca, T.; Tillon, L.; Delaval, M.; Haquart, A.; Dewynter, M. Identification et écologie acoustique des chiroptères de Guyane française. Rhinolophe; 2013; 19, pp. 103-145.

23. Falcão, F.; Dodonov, P.; Caselli, C.B.; Dos Santos, J.S.; Faria, D. Landscape Structure Shapes Activity Levels and Composition of Aerial Insectivorous Bats at Different Spatial Scales. Biodivers. Conserv.; 2021; 30, pp. 2545-2564. [DOI: https://dx.doi.org/10.1007/s10531-021-02210-x]

24. Arias-Aguilar, A.; Hintze, F.; Aguiar, L.M.S.; Rufray, V.; Bernard, E.; Pereira, M.J.R. Who’s Calling? Acoustic Identification of Brazilian Bats. Mammal Res.; 2018; 63, pp. 231-253. [DOI: https://dx.doi.org/10.1007/s13364-018-0367-z]

25. Miller, B.W. A Method for Determining Relative Activity of Free Flying Bats Using a New Activity Index for Acoustic Monitoring. Acta Chiropterol.; 2001; 3, pp. 93-105.

26. Williams-Guillén, K.; Perfecto, I. Ensemble Composition and Activity Levels of Insectivorous Bats in Response to Management Intensification in Coffee Agroforestry Systems. PLoS ONE; 2011; 6, e16502. [DOI: https://dx.doi.org/10.1371/journal.pone.0016502]

27. Estrada-Villegas, S.; Meyer, C.F.J.; Kalko, E.K.V. Effects of Tropical Forest Fragmentation on Aerial Insectivorous Bats in a Land-Bridge Island System. Biol. Conserv.; 2010; 143, pp. 597-608. [DOI: https://dx.doi.org/10.1016/j.biocon.2009.11.009]

28. Jost, L. Entropy and Diversity. Oikos; 2006; 113, pp. 363-375. [DOI: https://dx.doi.org/10.1111/j.2006.0030-1299.14714.x]

29. Chao, A.; Chiu, C.-H.; Jost, L. Unifying Species Diversity, Phylogenetic Diversity, Functional Diversity, and Related Similarity and Differentiation Measures Through Hill Numbers. Annu. Rev. Ecol. Evol. Syst.; 2014; 45, pp. 297-324. [DOI: https://dx.doi.org/10.1146/annurev-ecolsys-120213-091540]

30. Hill, M.O. Diversity and Evenness: A Unifying Notation and Its Consequences. Ecology; 1973; 54, pp. 427-432. [DOI: https://dx.doi.org/10.2307/1934352]

31. Hsieh, T.C.; Ma, K.H.; Chao, A. iNEXT: An R Package for Rarefaction and Extrapolation of Species Diversity (Hill Numbers). Methods Ecol. Evol.; 2016; 7, pp. 1451-1456. [DOI: https://dx.doi.org/10.1111/2041-210X.12613]

32. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2024.

33. Carvalho, J.C.; Cardoso, P.; Borges, P.A.V.; Schmera, D.; Podani, J. Measuring Fractions of Beta Diversity and Their Relationships to Nestedness: A Theoretical and Empirical Comparison of Novel Approaches. Oikos; 2013; 122, pp. 825-834. [DOI: https://dx.doi.org/10.1111/j.1600-0706.2012.20980.x]

34. Legendre, P.; Legendre, L. Numerical Ecology; 3rd English ed. Developments in Environmental Modelling Elsevier: Amsterdam, The Netherlands, 2012; ISBN 978-0-444-53868-0

35. Suzuki, R.; Terada, Y.; Shimodaira, H. Pvclust: Hierarchical Clustering with P-Values via Multiscale Bootstrap Resampling. 2019; Available online: https://cran.r-project.org/web/packages/pvclust/index.html (accessed on 25 June 2024).

36. Kassambara, A.; Mundt, F. Factoextra: Extract and Visualize the Results of Multivariate Data Analyses. 2020; Available online: https://cran.r-project.org/web/packages/factoextra/index.html (accessed on 25 June 2024).

37. Hennig, C. Fpc: Flexible Procedures for Clustering. 2023; Available online: https://cran.r-project.org/web/packages/fpc/index.html (accessed on 25 June 2024).

38. Krebs, C.J. Ecological Methodology; 2nd ed. Benjamin/Cummings: Menlo Park, CA, USA, 1999; ISBN 978-0-321-02173-1

39. Zar, J.H. Biostatistical Analysis: Pearson New International Edition; 5th ed. Always Learning Pearson: Harlow, UK, 2014; ISBN 978-1-292-02404-2

40. Oksanen, J.; Simpson, G.L.; Blanchet, F.G.; Kindt, R.; Legendre, P.; Minchin, P.R.; Solymos, P.; Stevens, M.H.H.; Szoecs, E.; Wagner, H. et al. Vegan: Community Ecology Package; GitHub: San Francisco, CA, USA, 2022.

41. Quinn, G.P.; Keough, M.J. Experimental Design and Data Analysis for Biologists; 1st ed. Cambridge University Press: Cambridge, UK, 2002; ISBN 978-0-521-81128-6

42. Wickham, H. Ggplot2: Elegant Graphics for Data Analysis; Springer: New York, NY, USA, 2016; ISBN 978-3-319-24277-4

43. Luo, J.; Siemers, B.M.; Koselj, K. How Anthropogenic Noise Affects Foraging. Glob. Chang. Biol.; 2015; 21, pp. 3278-3289. [DOI: https://dx.doi.org/10.1111/gcb.12997]

44. Allen, L.C.; Hristov, N.I.; Rubin, J.J.; Lightsey, J.T.; Barber, J.R. Noise Distracts Foraging Bats. Proc. R. Soc. B Biol. Sci.; 2021; 288, 20202689. [DOI: https://dx.doi.org/10.1098/rspb.2020.2689] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33563124]

45. Smotherman, M.S.; Simmons, A.M.; Simmons, J.A. How Noise Affects Bats and What It Reveals About Their Biosonar Systems. 50 Years of Bat Research; Lim, B.K.; Fenton, M.B.; Brigham, R.M.; Mistry, S.; Kurta, A.; Gillam, E.H.; Russell, A.; Ortega, J. Fascinating Life Sciences Springer International Publishing: Cham, Switzerland, 2021; pp. 61-76. ISBN 978-3-030-54726-4

46. Dos Santos, A.J.; Vieira, T.B.; Faria, K.D.C. Effects of Vegetation Structure on the Diversity of Bats in Remnants of Brazilian Cerrado Savanna. Basic Appl. Ecol.; 2016; 17, pp. 720-730. [DOI: https://dx.doi.org/10.1016/j.baae.2016.09.004]

47. Appel, G.; López-Baucells, A.; Magnusson, W.E.; Bobrowiec, P.E.D. Temperature, Rainfall, and Moonlight Intensity Effects on Activity of Tropical Insectivorous Bats. J. Mammal.; 2019; 100, pp. 1889-1900. [DOI: https://dx.doi.org/10.1093/jmammal/gyz140]

48. Arita, H.T. The Conservation of Cave-Roosting Bats in Yucatan, Mexico. Biol. Conserv.; 1996; 76, pp. 177-185. [DOI: https://dx.doi.org/10.1016/0006-3207(95)00105-0]

49. Yee, D.A. Peropteryx macrotis. Mamm. Species; 2000; 643, pp. 1-4. [DOI: https://dx.doi.org/10.1644/1545-1410(2000)643<0001:PM>2.0.CO;2]

50. Avila-Flores, R.; León-Madrazo, R.; Perez-Perez, L.; Aguilar-Rodríguez, A.; Campuzano-Romero, Y.Y.; Rodas-Martínez, A.Z. Are Molossid Bats Behaviourally Preadapted to Urban Environments? Insights from Foraging, Echolocation, Social, and Roosting Behaviour. Urban Bats; Moretto, L.; Coleman, J.L.; Davy, C.M.; Fenton, M.B.; Korine, C.; Patriquin, K.J. Fascinating Life Sciences Springer International Publishing: Cham, Switzerland, 2022; pp. 33-42. ISBN 978-3-031-13172-1

51. Seewagen, C.L.; Adams, A.M. Turning to the Dark Side: LED Light at Night Alters the Activity and Species Composition of a Foraging Bat Assemblage in the Northeastern United States. Ecol. Evol.; 2021; 11, pp. 5635-5645. [DOI: https://dx.doi.org/10.1002/ece3.7466]