Genetic Approaches Are Necessary to Accurately Understand Bat-Wind Turbine Impacts

diversity

Article Genetic Approaches Are Necessary to Accurately Understand Bat-Wind Turbine Impacts

Austin S. Chipps 1,* , Amanda M. Hale 1 , Sara P. Weaver 2,3 and Dean A. Williams 1

1 Department of Biology, Texas Christian University, Fort Worth, TX 76219, USA; [email protected] (A.M.H.); [email protected] (D.A.W.) 2 Biology Department, Texas State University, San Marcos, TX 78666, USA; [email protected] 3 Bowman Consulting Group, San Marcos, TX 78666, USA * Correspondence: [email protected]

Received: 26 May 2020; Accepted: 9 June 2020; Published: 11 June 2020

Abstract: Bats are killed at wind energy facilities worldwide and we must improve our understanding of why this is happening and implement effective strategies to minimize impacts. To this end, we need accurate assessments of which individuals from which bat species are being killed at individual wind projects and at regional and range-wide scales. Traditional fatality searches have relied on physical characteristics to ascertain species and sex of bat carcasses collected at wind turbines; however, the resulting data can be incomplete and inaccurate. In contrast, the use of readily available and low-cost molecular methods improves both the quality and quantity of available data. We applied such methods to a bat fatality dataset (n = 439 bats) from far-south Texas, USA. Using DNA barcoding, we increased accurate species identification from 83% to 97%, and discovered the presence of 2 bat species outside of their known geographic ranges. Using a PCR-based approach to determine sex, the number of carcasses with correct sex assignment increased from 35% to 94%, and we documented a female-biased sex ratio for all species combined and for Dasypterus ega. We recommend that molecular methods be used during future survey efforts to accurately assess the impacts of wind energy on bats.

Keywords: bats; conservation genetics; Dasypterus; DNA barcoding; Lasiurus; mortality; wind energy; wind turbine; yellow bats

1. Introduction Bats currently face a multitude of threats worldwide, making their study and conservation essential for the preservation of species and the important ecosystem services that they provide [1–4]. Many aspects of bat biology are poorly understood, and fear of bats has led to the extermination of entire colonies and roost destruction, which are major drivers of species decline [3–5]. Changes in land use, driven by increased agriculture and urbanization, have eliminated valuable bat habitats, while also accelerating the effects of climate change, which together threaten both bats and their ecosystem services [6]. Efforts being made to reduce the effects of climate change have altered the way we produce energy in favor of renewable processes, like wind energy. Mitigating the impacts of climate change by reducing greenhouse gas emissions through an increase in electricity generation from renewable sources like wind power has the potential to benefit wildlife conservation [7,8]. Nonetheless, bat mortality at wind energy facilities is an unanticipated consequence that unfortunately may simultaneously threaten the persistence of bat populations [4,9,10]. Several studies have identified the potential for wind energy development to cause population declines in bat species from the northern hemisphere, yet few studies have provided quantitative estimates of bat-wind fatality and population viability [11,12]. In order to minimize the detrimental

Diversity 2020, 12, 236; doi:10.3390/d12060236 www.mdpi.com/journal/diversity Diversity 2020, 12, 236 2 of 11 effects wind development could have on bats, there is a pressing need to establish conservation strategies protecting bats from this source of mortality. Operational minimization, smart curtailment, and acoustic deterrents have reduced bat fatalities where implemented [13–16], but the application of these strategies is not yet widespread, and operational minimization incurs a loss in power generation. The U.S. Department of Energy, technology vendors, and wind project owners have therefore invested considerable financial resources in the further development and testing of impact reduction technologies that minimize power generation loss. However, to fully assess the effectiveness of these strategies, stakeholders must know which individuals of which bat species are being impacted, both before and after the implementation of a specific mitigation method. Accurate species identification of bat carcasses collected during turbine searches is not always possible and worsens with increased estimated time since death [17]. Furthermore, sex assignment based on external morphology can generate inaccurate sex ratio estimates, that could in turn provide a misleading representation of which individuals are being killed by wind-turbines [17,18]. Therefore, DNA barcoding and molecular sexing are viable means, by which species identification and sex determination can be obtained from bat carcasses collected at wind energy facilities [17,19]. Recent wind energy expansion into the Lower Rio Grande Valley of Texas has led to two additional bat species, the northern yellow bat (Dasypterus intermedius) and the southern yellow bat (Dasypterus ega), as being identified as collision fatalities at wind turbines [20]. Dasypterus species are morphologically similar and poorly studied, but the potential for impacts from wind-related mortality is high, given the level of annual mortality in other closely related tree bats, Lasiurus borealis and Aeorestes cinereus [21,22]. (Note: Recent phylogenetic analyses of the lasiurine bats [23,24] suggest several taxonomic revisions, including the recognition of three separate genera from what was formerly the genus Lasiurus: Aeorestes for hoary bats, Lasiurus for red bats, and Dasypterus for yellow bats. We have elected to use the new nomenclature here.) Thus, wind energy is a new source of mortality for these yellow bats, and as both species have a limit to their respective geographic ranges in Texas [25,26], these fatalities provide a unique opportunity to understand how populations may be impacted at the edge of their geographic range due to wind energy development. Therefore, the objective of our study was to demonstrate the effectiveness of mitochondrial DNA sequencing (DNA barcoding) and molecular sex methods when applied to carcasses collected from a region with closely related and morphologically similar bat species. By comparing both the quality and quantity of data obtained from field-based and molecular methods, we provide a quantitative estimate of the gains possible from applying these techniques to post-construction fatality datasets from wind energy development in: 1) new regions where species impacts may be unknown; 2) at sites with diverse, yet morphologically similar, bat species; and 3) at sites with bat species of conservation concern where correctly identifying protected bat species is a higher priority.

2. Materials and Methods We obtained wing tissue samples from bat carcasses collected during post-construction fatality surveys at wind energy facilities in Starr and Hidalgo Counties (TX, USA) in the Lower Rio Grande Valley region near the USA-Mexico border, from March through November of 2017 and 2018 (n = 439 carcasses). These facilities are in the Texas-Tamaulipan Thornscrub Level IV ecoregion [27], and the habitat includes scrub and shrub woodlands, cultivated crops, pasture and hay fields, and some developed lands. Bat carcasses were collected in accordance with the Texas State University Institutional Animal Care and Use Committee (IACUC: permit number 20171185494, to S. Weaver) and Texas Parks and Wildlife Department (TPWD: permit number SPR-0213-023, to S. Weaver). The climate, habitats, and bat community at this study site are similar to other regions in the southwestern USA and northwestern Mexico [25,28], for which data on the impacts of wind energy development on bats are largely unknown. Time since death (< 1 day, 2–3 days, 4 days) was estimated based on ≥ the state of decomposition and decay as well as the body tissues present [29]. We assigned species identifications (D. ega or D. intermedius) based on pelage color, presence of fur on anterior half of tail Diversity 2020, 12, 236 3 of 11 membrane, and/or forearm length [25], combined with epiphyseal-diaphyseal fusion status [30] when these structures were available to observe. Sex was assigned to only those carcasses with an estimated time since death < 1 day (the remaining carcasses were assigned sex unknown, due to uncertainty associated with rapid decomposition using external morphology) [29]. Wing tissue samples were taken from carcasses and stored in vials containing 95% ethanol. In addition to yellow bats, Brazilian free-tailed bat (Tadarida brasiliensis) fatalities have been documented in high numbers at this site [16,28], but we do not include them here, because they are morphologically distinct and better studied than the Dasypterus species [31–33]. We extracted DNA from the preserved Dasypterus tissue samples following the methods detailed in Korstian et al. [19] We sequenced the DNA samples at a 550 bp section of the mitochondrial cytochrome c oxidase I (COI) gene. To amplify the COI gene using a polymerase chain reaction (PCR), we used an M13-tailed primer cocktail [34], cocktail 2 in [35]. PCR reactions (10 µL) contained 10–50 ng DNA, 0.2 µM of the primer cocktail, 1X BSA, and 1X AccuStart™ II PCR SuperMix. PCR reactions were completed using an ABI 2720 thermal cycler with parameters: one cycle at 94 ◦C for 15 min, followed by 30 cycles of 30 s at 94 ◦C, 90 s at 57 ◦C, 90 s at 72 ◦C, and then a final extension of 5 min at 72 ◦C. Negative PCR controls were included in each set of amplifications. Products were sequenced in both directions using ABI Big Dye Terminator Cycle Sequencing v3.1 Chemistry (Applied Biosystems, USA), with the M13 primers. DNA sequences were analyzed on an ABI 3130XL Genetic Analyzer (Applied Biosystems, USA); trimmed, edited, and assembled into contigs using Sequencher v5.1 (Gene Codes, USA); and then aligned in MEGA v10 [36]. Aligned sequences were translated to verify the absence of stop codons, after which, they were compared to GenBank voucher sequences compiled by Korstian et al. [17], to generate a species ID. Only sequences > 400 bp in length were used and our criterion to accept a molecular species identification required an identity value > 98% in BLAST. Unique sequence haplotypes were detected using GenAlEx v6.5 [37]. We used Chi-square contingency tests to determine if correct species identification was independent of estimated time since death (< 1 day, 2–3 days, 4 days; α = 0.05). All unique sequences have been deposited in GenBank (accession numbers: ≥ MT510982–MT510998). We compiled sequences from this study, Korstian et al. [17], and GenBank, including sequences for D. i. floridanus (MN326026) and D. i. intermedius (MF990089). We constructed an unrooted maximum-likelihood tree in MEGA X, to visualize relationships among haplotypes [36]. The tree was inferred using the HKY + G model of base substitution with the highest value of the Bayesian information criterion (BIC) and the pairwise deletion option for missing data. Bootstrap values for the branching pattern were calculated using 1000 replicates. Using PCR, we amplified the zfx and zfy introns found in the sex chromosomes using the primers in Korstian et al. [19]. PCR reactions deviated from the mitochondrial sequencing protocol, as 0.5 µM of X-primer and 0.35 µM of Y-primer were used in the 10 µL reaction. PCR cycling parameters were: one cycle at 95 ◦C for 15 min, followed by 30 cycles of 30 s at 94 ◦C, 15 s at 57 ◦C, 30 s at 72 ◦C. PCR products were fluorescently stained with Gel Red, electrophoresed at 200 volts in a 1% agarose gel for 20 min, and visualized using UV light. Sex was determined by the presence of one band in females corresponding to the X-chromosome intron (245 bp), whereas males had two bands, one for the X-chromosome intron and one for the Y-chromosome intron (80 bp). We used Fisher’s exact tests to compare the proportions of males to females, using both molecular and field sex identification (α = 0.05). We used Chi-square contingency tests to determine if correct sex determination was independent of estimated time since death (< 1 day, 2–3 days, 4 days; α = 0.05). ≥ 3. Results

3.1. DNA Barcoding Of the 439 tissue samples collected during surveys, 83% of carcasses (n = 366) were correctly identiﬁed to species based on DNA barcoding analysis (Figure1). DNA barcoding improved the overall Diversity 2020, 12, 236 4 of 11

Diversityproportion 2020, 1 of2, carcassesx FOR PEER with REVIEW accurate species identification to 97% (n = 425; Figure1). Correct species4 of 11 identificationDiversity 2020, 12, in x FOR the PEER field REVIEW was independent of time since death, suggesting that decomposition4 andof 11 barcodingscavenging revealed did not the interfere presence with of species two additional identification species (χ2 =in 1.45,the dataset: df = 2, p D=. 0.484).xanthinus DNA (n = barcoding 36) and Lasiurusrevealedbarcoding blossevillii the revealed presence (n the= 3). of presence twoOf the additional 295 of twcarcasseso additional species that in were species the dataset:field in- identifiedtheD. dataset: xanthinus as DD.. xanthinusintermedius(n = 36) and(n, 88%=Lasiurus 36) (n and = 261)blossevilliiLasiurus were blossevillii correctly(n = 3). Of (nidentifiedthe = 3). 295 Of carcasses asthe D 295. intermedius carcasses that were, thatwhereas field-identified were 5%field were-identified as D.D. intermediusega as(n D= .14), intermedius, 88% and (n 4%= , 261) were88% were (nD .= xanthinuscorrectly261) were (n identified correctly = 11). Of as theidentifiedD. 144 intermedius carcasses as D., intermedius whereasthat were 5% field, whereas were-identifiedD. ega5%( n wereas= D14),. egaD.and ,ega 73% 4%(n (n = were =14), 105) D.and were xanthinus 4% correctly were(n D= . identified11).xanthinus Of the as(n 144 D= .11).carcasses ega Of, whereas the that 144 were17carcasses% were field-identified that D. xanthinus were field as D. (n-identified ega= 25),, 73% 4% ( nas were= D105). ega D were., intermedius73% correctly (n = 105) (n identifiedwere = 6), correctlyand as2%D. wereegaidentified, whereasD. blossevillii as 17%D. ega were (n, whereas =D. 3; xanthinus Fig 17ure% were1).(n Due= D25),. xanthinus to 4% DNA were degradation,(nD. = intermedius 25), 4% were we(n = wereD6),. intermedius and unable 2% were to (n obtain D.= 6), blossevillii and DNA 2% barcodes(weren = 3; D Figure. for blossevillii 3%1). (n Due = (n9) to of = DNA 3;field Fig- degradation,identifiedure 1). Due D. intermedius towe DNA were unabledegradation, and 4% to obtain(n = we5) DNAof were field barcodes unable-identified to for obtain D 3%. ega ( .n DNAThe= 9) maximumofbarcodes field-identified -forlikelihood 3% (nD. = tree9) intermedius of using field- identifiedtheand 20 4%unique( nD.= intermedius haplotypes5) of field-identified and we 4%found (n D.= in 5) egathis of. field Thestudy- maximum-likelihoodidentified and from D Korstian. ega. The ettreemaximum al. [17] using, has- thelikelihood clades 20 unique corresponding tree haplotypes using the to 20 weeach unique found of the haplotypes in three this Dasypterus study we and found species from in Korstian this (Fig studyure et2). and al. Bootstrap [17 from], has Korstian values clades separatingcorrespondinget al. [17], hasspecies clades to each are corresponding in of excess the three of Dasypterus95 to. D. each intermedius ofspecies the three was (Figure Dasypterus split2). into Bootstrap twospecies well values (Fig supportedure separating 2). Bootstrap monophyletic species values are cladesinseparating excess that of corresponded species 95. D. intermedius are in toexcess eachwas of putative split 95. D. into intermedius subspecies two well supported ,was D. i. split floridanus into monophyletic two and well D. i.supported clades intermedius that monophyletic corresponded. toclades each that putative corresponded subspecies, to D.each i. floridanusputative subspeciesand D. i. intermedius., D. i. floridanus and D. i. intermedius.

Figure 1. Number of individual bats that were identified to each species, based on either external Figure 1. Number of individual bats that were identiﬁed to each species, based on either external morphologyFigure 1. Number in the field of individual or DNA barcoding. bats that were identified to each species, based on either external morphology in the ﬁeld or DNA barcoding. morphology in the field or DNA barcoding.

13 4 4 13 1 4 100 1 1 Dasypterus intermedius floridanus 1

100 1 1 Dasypterus intermedius floridanus 1 29 95 1 1 29 1 95 1 2 1 89 90 2 100 110

89 90 1 Dasypterus intermedius intermedius 10078 110 1 1 5 Dasypterus intermedius intermedius 7876 1 3 5 100 76 5 3 Dasypterus xanthinus 32

100 5 116 Dasypterus xanthinus Dasypterus ega 32 100 1 116 Dasypterus ega 100 1

0.020

Figure 2. Maximum0.020 likelihood tree of unique cytochrome c oxidase I (COI) haplotypes from Dasypterus Figure 2. Maximum likelihood tree of unique cytochrome c oxidase I (COI) haplotypes from DasypterusFigure 2. samplesMaximum in thislikelihood study, treeas well of uniqueas samples cytochrome obtained c from oxidase Korstian I (COI et )al. haplotypes [17] and from from GenBank.Dasypterus Numbers samples at in the this nodes study, indicate as well bootstrap as samples support obtained following from Korstian1000 replicates, et al. [17] and and branch from lengthGenBank. represents Numbers genetic at the distance. nodes Numbersindicate bootstrap at branch supporttips represent following the number 1000 replicates, of individuals and branch with thatlength unique represents haplotype. genetic distance. Numbers at branch tips represent the number of individuals with that unique haplotype.

Diversity 2020, 12, x FOR PEER REVIEW 5 of 11 Diversity 2020, 12, 236 5 of 11 3.2. Molecular Sex Determination

Surveysamples efforts in this study,in the as field well assigned as samples sex obtained to just from 35% Korstian (n = 153) et al. of [ 17the] and total from carcasses, GenBank. of Numbers which 94% had a atcorrect the nodes sex indicateassignment bootstrap that support was confirmed following 1000using replicates, molecular and methods branch length (Fig representsure 3). Of genetic carcasses identifieddistance. as either Numbers male at or branch female tips based represent on external the number morphology, of individuals there with was that no unique significant haplotype. deviation from a 50:50 male:female sex ratio (Fisher’s exact test: p = 0.87). Using molecular methods, 94% (n = 411)3.2. of Molecular the DNA Sex samples Determination amplified at the sex chromosome introns. With all species pooled together, 56% ofSurvey bats were efforts female in the (n field = 230), assigned with a sex significant to just 35% deviation (n = 153) from of thea 50:50 total sex carcasses, ratio (Fisher’s of which exact 94% test:had p a = correct 0.016; sexFigure assignment 3) when thatusing was molecular confirmed sex using determination. molecular All methods three (FigureL. blossevillii3). Of bats carcasses were female.identified Of asthe either 112 D. male ega orwith female successful based onsex external chromosome morphology, intron amplification, there was no significant 62% (n = 69) deviation were femalefrom a, representing 50:50 male:female a significant sex ratio deviation (Fisher’s from exact a test:50:50p sex= 0.87). ratio (Fisher’s Using molecular exact test: methods, p = 0.018; 94% Figure (n = 4411)). Of ofthe the 254 DNA D. intermedius samples amplified with successful at the sexsex chromosomechromosome introns.intron amplification, With all species 53% pooled (n = 134) together, were female,56% of batswhich were was female not significantly (n = 230), with different a significant from deviationa 50:50 sex from ratio a 50:50 (Fisher’s sex ratio exact (Fisher’s test: p exact= 0.415; test: Figp =ure0.016; 4). Of Figure the 343) whenD. xant usinghinus molecular with successful sex determination. sex chromosome All three intronL. amplification, blossevillii bats 62% were (n female. = 21) wereOf the female, 112 D. which ega with was successfulalso not significantly sex chromosome different intron from amplification, a 50:50 sex ratio 62% (Fisher’s (n = 69) exact were test: female, p = 0.229;representing Figure 4 a). significant deviation from a 50:50 sex ratio (Fisher’s exact test: p = 0.018; Figure4). Of the 254In contrastD. intermedius to specieswith identification, successful sex chromosomecorrect sex determinat intron amplification,ion based 53%on external (n = 134) morphology were female, 2 waswhich dependent was not significantlyupon time since different death from (χ a= 50:50162.1, sex df ratio= 2, p (Fisher’s < 0.001); exact fresh test: carcassesp = 0.415; ( < 1 Figure day) 4were). Of significantlythe 34 D. xanthinus more likelywith successfulto be correctly sex chromosome identified as intronmale or amplification, female, compared 62% (n =to 21)carcasses were female, with longerwhich estimated was also not times significantly since death. diff erent from a 50:50 sex ratio (Fisher’s exact test: p = 0.229; Figure4).

300 * 250

s 200

r 150

e 286

m 230

N 100 181

50 78 75

28 0 Unknown Male Female Field sex Molecular sex

Figure 3. Number of individual bats from all species combined that were assigned the sex of unknown, Figuremale, or 3. femaleNumber based of individual on external bats morphology from all in species the ﬁeld combined or by using that molecular were assigned methods the ( n sex= 439). of unknown,The star above male, theor female female based category on external indicates morphology a signiﬁcant in deviationthe field or from by usin a 50:50g molecular sex ratio met (Fisher’shods (nexact = 439 test:). Thep = star0.016). above the female category indicates a significant deviation from a 50:50 sex ratio (Fisher’s exact test: p = 0.016).

Diversity 2020, 12, 236 6 of 11 Diversity 2020, 12, x FOR PEER REVIEW 6 of 11

160 Female 140 134 Male 120 Unknown 120

100

80 69 *

60 Number of bats 43 40 21 20 13 13 7 2 3 0 0 0 D. intermedius D. ega D. xanthinus L. blossevillii

Figure 4. Number of individual bats from each species identified as female, male, or sex unknown Figure 4. Number of individual bats from each species identified as female, male, or sex unknown using molecular methods. The star above D. ega indicates a significant deviation from a 50:50 sex ratio using molecular methods. The star above D. ega indicates a significant deviation from a 50:50 sex ratio (Fisher’s exact test: p = 0.018). (Fisher’s exact test: p = 0.018). In contrast to species identification, correct sex determination based on external morphology was4. Discussion dependent upon time since death (χ2 = 162.1, df = 2, p < 0.001); fresh carcasses ( < 1 day) were significantlyThe error more rate likely for field to be identification correctly identified of Dasypterus as male orspecies female, (14% compared misidentified to carcasses overall) with included longer estimatedin this study times was since higher death. than what was reported for L. borealis (2.6% misidentified) and A. cinereus (0.4% misidentified), killed at a north-central Texas wind energy facility [17]. Species 4.misidentifications Discussion in this study likely occurred at a higher rate due to the morphological similarity betweenThe errorthe two rate focal for field species identification and the presence of Dasypterus of twospecies additional (14% bat misidentified species that overall) were outside included their in thisknown study geographic was higher ranges than what(L. blossevilli was reported and forD. L.xanthinus borealis (2.6%; [25]). misidentified) Field personnel and wereA. cinereus unaware(0.4% of misidentified),the potential presence killed at of a north-centralthese additional Texas species. wind energyFor the facilityD. ega [samples17]. Species for which misidentifications we obtained in a thisDNA study barcode, likely 24% occurred of the atbats a higher had been rate misidentifie due to the morphologicald in the field, perhaps similarity due between to the presence the two focalof D. speciesxanthinus and, thewhich presence is morphologically of two additional very bat speciessimilar thatto D. were ega outside [25] and their comprised known geographic 74% of rangesD. ega (L.misidentifications.blossevilli and D. xanthinus;[25]). Field personnel were unaware of the potential presence of these additionalA recent species. study For by the DeckerD. ega sampleset al. [26] for provides which we updated obtained geographic a DNA barcode, ranges, 24% including of the bats range had beenextensions misidentified at the county in the field,level, perhaps for all three due to Dasypterus the presence species of D. xanthinusin Texas;, whichbut it does is morphologically not, however, verydifferentiate similar to betweenD. ega [25 the] and pu comprisedtative subspecies 74% of D.of egaD. intermediusmisidentifications. (see Figure 5 for the study site locationA recent and range study maps by Decker of D. etega al., D. [26 intermedius] provides updated, D. xanthinus geographic, and L. ranges, blossevillii including within range the extensionscontinental at USA the [38 county–41]). level, Although for all only three D. Dasypterusi. intermediusspecies was insuspected Texas; but to occur it does in not, this however,region of disouthfferentiate Texas between[25], the theDNA putative barcoding subspecies results of ofD. this intermedius study clearly(see Figureindicate5 for that the both study D. sitei. floridanus location andand rangeD. i. intermedius maps of D. are ega present, D. intermedius at our study, D. site. xanthinus Additional, and supportL. blossevillii for the presencewithin the of both continental species USAis also [38 provided–41]). Although by Decker only [42].D. i. intermediusThis finding,was in suspectedcombination to occur within the this abundance region of southof D. Texasxanthinus [25], thesamples DNA in barcoding our dataset, results recent of this evidence study clearly of D. xanthinus indicate that range both expansionD. i. floridanus in Newand MexicoD. i. intermedius [43], and areclose present proximity at our of studyour study site. site Additional to a new supportcounty record for the for presence D. xanthinus of both inspecies Texas [26], is also suggests provided that bymore Decker research [42]. is This needed finding, to full iny combinationunderstand the with distributions the abundance of Dasypterus of D. xanthinus species,samples especially in our in dataset,light of on-going recent evidence land use of modificationD. xanthinus andrange climat expansione change. in NewSimilarly, Mexico recent [43 ],studies and close of capture proximity and ofspecimen our study records site to for a newLasiurus county bats record from fortheD. western xanthinus USAin and Texas Canada [26], suggestshave documented that more the research range isexpansion needed to for fully L. borealis understand in North the distributionsAmerica [44,45], of Dasypterus and clarifiedspecies, the eastern especially and northern in light ofrange on-going limits landof L. use blossevilli modification in southwestern and climate USA change. [45]; Similarly, although, recent there studies is still ofmuch capture uncertainty and specimen regarding records the forextentLasiurus of sympatrybats from for thethese western two Lasiurus USA and species Canada in far have western documented and southern the range Texas expansion [45]. In light for L.of these recent findings, we will continue to underestimate the number of bat species and subspecies that may be impacted by wind energy, as development continues to expand into new areas if we rely

Diversity 2020, 12, 236 7 of 11

borealis in North America [44,45], and clariﬁed the eastern and northern range limits of L. blossevilli in southwestern USA [45]; although, there is still much uncertainty regarding the extent of sympatry for these two Lasiurus species in far western and southern Texas [45]. In light of these recent ﬁndings, we Diversitywill continue 2020, 12, x to FOR underestimate PEER REVIEW the number of bat species and subspecies that may be impacted7 of 11 by wind energy, as development continues to expand into new areas if we rely solely on historic range rangemaps maps andcounty-level and county- recordslevel records to make to make inferences inferences regarding regarding potential potential impacts. impacts. The presence The presence of these ofpreviously these previously undocumented undocumented species in species the region in the also region has implications also has implications for bat acoustic for bat monitoring acoustic monitoringstudies if biologists studies if arebiologists not considering are not considering their potential their for potential occurrence. for occurrence.

FigureFigure 5. 5. IUCNIUCN (International (International Union Union for for the the Conservation Conservation of of Nature) Nature) Red Red List List range range map mapss for for L.L. blossevilliiblossevillii [38][38,], D.D. intermedius intermedius [39][39],, D. ega [[40]40],, andandD. D. xanthinus xanthinus[ 41[41]] within within the the continental continental USA. USA. The The star starindicates indicates the the location location of theof the wind wind energy energy facilities facilities included included in thisin this study. study.

InIn addition addition to to errors errors with with species species identification, identification, errors errors or or missing missing data data for for sex sex assignment assignment also also introduceintroduce uncertainty uncertainty regarding regarding the the impacts impacts of wind of wind energy energy development development on bats. on Variation bats. Variation in post- in constructionpost-construction fatality fatality monitoring monitoring protocols protocols (e.g., (e.g.,search search interval interval length, length, carcass carcass persistence persistence times) times),, as wellas well as how as how field field technicians technicians are are trained trained,, introduce introduce variation variation in in the the quantity quantity and and quality quality of of se sexx assignmentassignment data data that that can can be be obtained obtained in in the the field. field. For For example, example, in in this this study, study, personnel personnel only only assigned assigned sexsex to to 35% 35% of of the the DasypterusDasypterus carcassescarcasses they they collected collected in in the the field field (i.e., (i.e., those those with with estimated estimated time time since since deathdeath < <1 1day), day), meaning meaning that that any any inferences inferences related related to to a apotential potential sex sex-bias-bias or or a alack lack of of sex sex-bias-bias in in mortalitymortality would would be be limited limited to to only only a asmall small subset subset of of carcasses. carcasses. By By applying applying molecular molecular methods, methods, we we werewere able able to to increase increase both both the the quantity quantity and and quality quality of of the the sex sex data data for for DasypteruDasypteruss killedkilled at at these these win windd energyenergy facilities: facilities: the the percentage percentage of of bat bat carcasses carcasses with sex informationinformation waswas increasedincreased to to 94% 94% ( n(n= =411 411 of of439 439 bats), bats), and and we we improved improved accuracy accuracy by by identifying identifying errors errors in sexin sex assignment assignment of 6% of of6% the of batsthe bats that that were wereassigned assigned sex insex the in field.the field. In a In similar a similar study study from from north-central north-central Texas, Texas, Korstian Korstian et al. et [al.19 ][19] showed showed that thatsex sexdetermination determination based based on external on external morphology morphology had a had10% aerror 10% rate, error and rate, that and by using that by molecular using molecularmethods, methods they increased, they increased the number the of number bats with ofsex bats data with from sex 554data to from 867, representing554 to 867, representing a 56% increase a 56%in available increase data.in available data. SomeSome previous previous studies studies have reportedreported male-biasedmale-biased sex sex ratios ratios for for migratory migratory tree tree bats bats killed killed at wind at windenergy energy facilities facilities [46,47 [4];6 however,,47]; however, it is unknown it is unknown if this if observed this observed bias is bias real is or real if it or reflects if it reflects the greater the greateruncertainty uncertainty of assigning of assigning sex to femalesex to female bat carcasses, bat carcasses, especially especially when they when may they have may been have partially been partiallyscavenged scavenged and decomposing. and decomposing. Using molecular Using molecular methods, methods, sex ratios forsex L.ratios borealis forkilled L. borealis at wind killed energy at windfacilities energy did facilities not differ did from not 50:50 differ [18 from,19], 50:50 whereas [18, sex19], ratioswhereas for sexA. cinereus ratios foreither A. cinereus did not e diitherffer fromdid not50:50 differ [19 from] or were 50:50 female-biased [19] or were female [18]. In-biased this study, [18]. In sex this identification study, sex identification based on external based morphologyon external morphologysuggested a suggested 50:50 sex ratio,a 50:50 whereas sex ratio, molecular whereas methods molecular revealed methods a female-biased revealed a female sex ratio-biased across sex all ratiospecies across combined all species and combined for D. ega analyzedand for D. separately. ega analyzed We doseparately. not know We if the do female-biased not know if the sex female ratio we- biased sex ratio we observed is due to an overall female bias in Dasypterus populations, or if the behavior of female bats increases their collision risk relative to males. Sex-specific conservation strategies for female Dasypterus may be warranted, as females are responsible for providing all offspring care and their mortality could lead to population declines over time [48]. In contrast to molecular species identification, which would be required only under certain conditions (i.e., in new regions exposed to wind energy development or in areas with morphologically similar species or

Diversity 2020, 12, 236 8 of 11 observed is due to an overall female bias in Dasypterus populations, or if the behavior of female bats increases their collision risk relative to males. Sex-specific conservation strategies for female Dasypterus may be warranted, as females are responsible for providing all offspring care and their mortality could lead to population declines over time [48]. In contrast to molecular species identification, which would be required only under certain conditions (i.e., in new regions exposed to wind energy development or in areas with morphologically similar species or species of conservation concern where correct species identification is paramount), we recommend molecular sex determination be more broadly utilized in bat-wind turbine fatality studies. Assigning sex based on morphology is simply inadequate to accurately characterize which sex of any given bat species is most susceptible to wind turbine mortality. Moving forward, there is a need to gather more accurate data on sex ratios of bat-wind turbine fatalities to determine if sex-biases are consistent across species and geographic areas before making range-wide management decisions. Like previous studies, we found that reliance on morphology alone to identify species and determine sex of bats killed at wind energy facilities can be inaccurate and produce biased sex ratio estimates [17–19]. Accurate species identification and sex determination have a powerful influence on the ways in which we create, implement, and evaluate conservation strategies [49,50]. Uncertainty regarding species presence or probable presence makes it difficult to make informed siting decisions or habitat conservation plans to minimize the impacts of future development. Furthermore, minimization and mitigation strategies designed to reduce the number of bats killed at wind energy facilities require population-level knowledge of the impacted species to fully assess the effectiveness of these management practices [13,15]. The application of inexpensive and reliable molecular methods, such as those employed in this study, increases the number of individuals with sex and species identifications and reduces identification errors made during fatality surveys. Our in-house cost per sample (including DNA extraction, sequencing the barcode region, and determining sex) is USD $ 7.35. Sex determination alone, from DNA extraction to PCR, would be USD $ 1.00. The work described here could be conducted by a commercial research lab or alternatively at academic or governmental research labs with access to the necessary equipment, software (for analyzing the DNA barcoding results), and expertise. Numerous individuals and institutions already have the skills and expertise in place to generate these types of data. Enhancing our knowledge of which individual bats are being impacted adversely by wind energy development will allow for the implementation of effective sex- and species-specific management strategies.

Author Contributions: Conceptualization, A.S.C., A.M.H., S.P.W. and D.A.W.; methodology, A.S.C., S.P.W. and D.A.W.; statistical analyses, A.S.C., A.M.H. and D.A.W.; writing—original draft preparation, A.S.C., A.M.H. and D.A.W.; writing—review and editing, A.S.C., A.M.H., S.P.W. and D.A.W.; funding acquisition, A.S.C., A.M.H., S.P.W. and D.A.W. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by a TCU College of Science & Engineering SERC Graduate Student Grant (G 190301) and a TCU Biology Department Adkins Fellowship to A.S.C. Supplies were funded by a Texas State University College of Science & Engineering Dorothy Coker Fellowship to S.P.W. and a Texas State University Graduate College Doctoral Research Support Fellowship to S.P.W. Acknowledgments: We thank NextEra Energy Resources for their continued interest and support of our wind-wildlife research efforts. We thank Duke Energy and EDP Renewables for allowing access to their wind energy facilities, and the many field technicians who collected tissue samples. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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