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Evaluation of Mobile Acoustic Techniques for Bat Population MonitoringAuthor(s): Michael D. Whitby, Timothy C. Carter, Eric R. Britzke and Scott M. BergesonSource: Acta Chiropterologica, 16(1):223-230. 2014.Published By: Museum and Institute of Zoology, Polish Academy of SciencesDOI: http://dx.doi.org/10.3161/150811014X683417URL: http://www.bioone.org/doi/full/10.3161/150811014X683417

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INTRODUCTION

Population monitoring is an essential part ofwildlife management. Understanding populationtrends and distribution of a species allows managersand policy makers to evaluate management actionsand provide appropriate legal protections. However,designing and implementing statistically robustmonitoring programs is often resource intensive andbeyond the capacity of many budgets (Field et al.,2005). As a result, few species have a reliable mon-itoring program in place.

While the value has been regularly recognized,monitoring of North American bat populations hasbeen limited primarily to protected cave dwellingspecies. Monitoring efforts for other species areplagued by inconsistent methodologies, inadequateresources, and sampling biases leading participantsin a 1999 monitoring workshop to conclude that newmethodologies are needed in order to reduce bias,increase consistency, and achieve monitoring goals

(O’Shea et al., 2003). One reason for these difficul-ties is that bat populations exhibit extremely highspatial and temporal variation in activity levels, adjusting their foraging area across the landscape inresponse to unknown or unpredictable variables(e.g., insect abundances, weather, etc.; Hayes,1997). There fore historical sampling methodologies(both acoustic and capture) have been unable tomeet statistical assumptions for rigorous populationmonitoring.

Emergence of two large scale threats to bat pop-ulations in the mid-2000’s has underscored the needfor national bat population monitoring in the UnitedStates. White-nose syndrome (WNS), a fungal in-fection discovered in 2006, has killed approximate-ly 5.7–6.7 million hibernating bats across at least 22 states (U.S. Fish and Wildlife Service, 2012),threat ening once abundant species with extinction(Frick et al., 2010). Additionally, bats are increas-ingly threatened by wind energy facilities where0.1–69.6 bats are killed per turbine per year (Arnett

Acta Chiropterologica, 16(1): 223–230, 2014PL ISSN 1508-1109 © Museum and Institute of Zoology PAS

doi: 10.3161/150811014X683417

Evaluation of mobile acoustic techniques for bat population monitoring

MICHAEL D. WHITBY1, 3, TIMOTHY C. CARTER1, ERIC R. BRITZKE2, and SCOTT M. BERGESON1

1Department of Biology, Ball State University, Muncie, IN, 47303, USA2US Army Engineer Research and Development Center, Vicksburg, MS 39180, USA

3Corresponding author: E-mail: Michael.Whitby@gmail.com

Understanding population trends of any species is essential for conservation and management. However, due to difficulty insampling some species, population status of many bat species is poorly understood. In an effort to resolve this issue, especially inlight of emerging threats (e.g., white-nose syndrome and wind energy), a national mobile acoustic monitoring protocol, modeledafter European programs, was developed to survey summer bat populations in the United States. While the program calls forconducting transects along roadways, some have suggested that waterways may allow for the gathering of more information.Therefore, we quantified species richness and abundance along car and boat transects to identify the most efficient mobile method.Furthermore, to compare the capabilities of mobile acoustic transects to a more traditional and better understood survey method, wecompared species density along transects to stationary acoustic detectors. Using sample-based rarefaction, there was no differenceat the 95% confidence level in species density (species/sample) between methods, however stationary points accumulated speciesmore quickly than mobile methods. Of the mobile transect methods, car transects had higher diversity indices than boat transectsand tended to show slightly higher species density. While over 1.5 times as many calls were recorded and identified along boattransects, there were no clear advantages of boat transects for monitoring bats except for Myotis grisescens. Additionally, cartransects were least time consuming, leading us to conclude that car transects are the most efficient mobile acoustic method tomonitor species. Mobile acoustic transects can likely monitor 2–4 species in the Eastern United States, including species with nocurrent population monitoring methodology.

Key words: active sampling, Anabat, bats, mobile acoustic monitoring, passive sampling, population monitoring, sampling methods

et al., 2008). Poorly understood diffuse threats suchas habitat destruction and environmental contamina-tion could further affect populations on a large scale(Weller et al., 2009).

In response to the urgent need to increase ourability to detect population declines, a national mo-bile acoustic monitoring program was established inthe US in 2009 (Britzke and Herzog, 2009).Modeled after European monitoring programs, theUS program calls for constant recording of batecholocation calls from a vehicle moving at 32 kphalong a predetermined 48 km route. Because the vehicle is moving faster than most bats (9–32 kphdepending on species — Hayward and Davis 1964;Pat terson and Hardin 1969), each call sequence isassumed to represent one bat, thus providing an index to abundance (Roche et al., 2011). Mobileacoustic transects were designed to help account forthe high spatial, temporal, and inter-specific varia-tion in hab itat use by sampling diverse habitats overlarge areas with few resources (equipment and per-sonnel — Roche et al., 2005). The program hasspread across the United States and is currently im-plemented in at least five statewide programs, threeNational Parks, and 20 National Forests.

While road transects have been shown to be ef-fective in monitoring some species in Ireland, abi-lity to detect small trends (0.9–1.6% per year) in less abundant species is masked by variability in detections (Roche et al., 2011). Furthermore, certainbat species may avoid roadways due to issues suchas the perceived threat of traffic or increased light-ing (Stone et al., 2009; Zurcher et al., 2010). There -fore, placement of transects on or near certain road-ways may not allow the monitoring of all species occupying an area (Roche et al., 2011; Jones et al.,2013).

As bat activity is generally believed to be higherabove water (Grindal, 1999; Ellison, 2005), it hasbeen suggested that transects along rivers could in-crease the number of species that can be monitored.The advantages are especially likely for species thatprefer these habitats for foraging (e.g., Myotis gri-sescens, M. lucifugus, Perimyotis subflavus). How -ever, assumed advantages of sampling along rivershave not been examined.

Therefore, we compared the results of paired carand boat based monitoring efforts across a landscapeto test if one method provides the opportunity tomonitor more species. We hypothesized that boat-based mobile acoustic monitoring along riverswill provide the opportunity to monitor more spe-cies than the traditional car-based mobile acoustic

sampling. Because mobile transects are a fairly new sampling technique, we further compared bothmobile methods to stationary point sampling, prob-ably the most commonly utilized acoustic sampl-ing technique. Results of the stationary points willprovide a reference for bat species assemblages in the area and of the cost of traditional acoustic surveys.

MATERIALS AND METHODS

Study Area

Our study site is located in southern Illinois, within oraround the Shawnee National Forest (SNF). Fourteen species ofbats occur within this portion of Illinois. Eight are common inthe study area: Eptesicus fuscus, Lasiurus borealis, L. cine re us,Myotis grisescens, M. lucifugus, M. septentrionalis, M. sodalis,Nyc ti ceius humeralis, and P. subflavus. Three occur in isolatedareas and lower abundances: Corynorhinus rafinesquii, M. aus-troriparius, and M. leibii. Lasionycteris noctivagans and Tada -rida brasiliensis have been observed infrequently in the studyarea during migration periods (T. Carter, unpublished data).

Study Design

A comparison of car and boat transects was designed aroundthree car-based mobile acoustic transects established by SNF in2009 under the nationwide monitoring program. Each car tran-sect was paired with the nearest navigable river (Big MuddyRiver, Saline and Ohio Rivers, and Lusk Creek and Ohio River).Car transects at the three sites ranged from 45–65 km and cor-responding boat transects from 24–57 km. Rivers varied greatlyin size. Lusk Creek was the smallest, ranging from 20–40 mwide with nearly closed canopy. The Big Muddy and Salinerivers were open canopy and ranged from 35–100 m and 20–30m wide respectively. Car and boat transects were conducted onthe same night, starting at the same time in May–July 2010 (n = 2) and 2011 (n = 4). Five stationary locations along eachtransect (30 total) were selected a priori for their likely high batactivity levels (stream crossings, field edges, etc.).

When each transect pair was sampled, we randomly select-ed, without replacement, two stationary sites along each tran-sect and sampled for four consecutive nights (sunset to sunrise), a standard application of stationary sampling regimes.Stationary detectors were powered by an external battery for theentire sampling period and deployed in a weatherproof contain-er with a PVC microphone opening and mounted on a tripod(Britzke et al., 2010). Detectors were oriented perpendicular to the selected travel corridor (primarily the transect itself, oroccasionally along a corridor (road/stream) leading into thetransect and within 100 m of transect) with an unobstructed fieldof detection. All detectors were calibrated throughout the seasonto assure similar sensitivities (Larson and Hayes, 2000).

We recorded the total person-hours for each method to com-pare cost effectiveness. All field work (travel time, equipmentestablishment and removal time, boat prep and launching, etc.)was timed to the nearest 15 min. Because our stationary pointswere along roads/rivers with mostly easy access, placementtime was negligible and deploying sites further from roadswould add considerable time.

224 M. D. Whitby, T. C. Carter, E. R. Britzke, and S. M. Bergeson

Acoustic Sampling

Data collectionWe sampled transects using Anabat SD1 and SD2 detectors

(Titley Electronics, Ballina, NSW, Australia). Recording wasconducted while moving approximately 32 kph along a roughly48 km route. The detector was mounted vertically on the carroof and was placed on a tripod at a 30° angle on the front of theboat. For safety, we reduced the boat transect speed to ap prox -imately 20 kph and used docking lights similar to car headlights.We sampled the river and road simultaneously, beginning 30 min after sunset on nights with low wind. Stationary siteswere deployed on the day of the transect and ran four consecu-tive nights.

Call identificationCalls were downloaded and analyzed using ANALOOK

(version 4.7j). We used a screening filter to eliminate noise anda second filter to identify sequences with one or more high-qua lity calls (Britzke and Murray, 2000). Parameters from sequences with three or more calls were then identified using a mixture discriminate function analysis trained with a 12 spe -cies call library collected across the eastern United States(Britzke et al., 2011). Because both C. rafinesquii and T. bra -siliensis are infrequently encountered in Illinois and the call library lacked reference calls for these species, they were ex-cluded from analysis. Species with less than two sequencesidentified in a night were not considered present due to the pos-sibility of identification error (Britzke et al., 2002). Since stationary bat detectors do not provide abundance data (Barclay,1999), we converted those sequence counts to presence-absencedata. For mobile acoustic transects, we assume that each se-quence represents an individual bat and therefore number of se-quences recorded provides an index of abundance; however, toaccount for differences in sampling speed, time, and distance,the number of sequences was divided by the duration of record-ing (Roche et al., 2011).

Species density and diversityWe used the program EstimatesS (version 8.2, http://purl.

oclc.org/estimates, accessed 6 Mar 2012) to compare speciesdensity and diversity (Shannon-Weaver and Simpson’s index)among sampling methods. We used computational sample-based rarefaction to interpolate species density (species/sam-ple). Because sample-based rarefaction uses presence-absencedata to account for non-random association of species occur-rence (Colwell et al., 2004) we were able to compare both mo-bile and stationary methods using this analysis. Estimates ofspecies density do not account for differences in sample effortor area (Colwell et al., 2004); therefore, there is an inherent biastowards stationary points where one sampling unit equals fournights of recording, while one transect unit is only approximate-ly two hours of recording. Because of this bias, stationary detec-tor information was only used as a reference point for thespecies assemblage of our study area. For example, if a specieshad not been detected by stationary recorders, then it should notbe counted as being ‘missed’ by transect methods.

We further compared transect methods using rarefied diver-sity indices (Shannon-Weaver [SW] and Simpson’s index [SI])over 100 repetitions with replacement. The exponentialShannon-Weaver and inverse Simpson’s expressions of thesediversity indices represent the number of species required at

even abundances to reach the observed index value and can beinterpreted as the number of abundant and very abundant spe -cies in a sample, respectively (Hill, 1973). Rare species are given less weight with species density (rarefaction), Shannon-Weaver, and Simpson’s index and combined are the useful indi-cators of community diversity (Hill, 1973).

Activity ratesTo test if there was an overall difference in the transect sam-

pling methodologies we used a permutation-based nonparamet-ric multivariate analysis of variance (perMANOVA; Anderson,2001) in PC-ORD version 5 (MjM Software, Gleneden Beach,Oregon) using Sorensen distances. Following a significantperMANOVA, we compared within species abundance withWil coxon sign-rank tests in program PAST (version 2.14,http://folk.uio.no/ohammer/past/, accessed 23 Jan 2012). Tocon trol for false discovery rates, we converted all P-values to Q-values using QVALUE (version 1.0, http://genomics.princeton.edu/storeylab/qvalue/, accessed 23 Jan 2012) in theprogram R (version 14.2, http://www.r-project.org/, accessed 23 Jan 2012). All tests were two-tailed and were consideredsignificant if Q ≤ 0.1.

RESULTS

Each transect was sampled two times in 2010 and four times in 2011 (total 18 paired car and boat tran-sects). We sampled 650 km of river (37 h 6 min ofrecording) and 933 km of roadways (34 h 43 min ofrecording). Along the boat transects, 4,233 and3,528 (83%) sequences were recorded and identi-fied, respectively. On the car transects, 2,490 and1,779 (71%) sequences were recorded and identi-fied. Stationary detectors recorded and identified89,303 and 66,485 (74%) sequences over 180 detec-tor nights at 50 detector sites.

Species Richness and Diversity

All 12 species were identified along the car transects and at stationary locations while boat tran-sects detected nine species (Table 1). Rarefiedspecies density was not different at the 95% confi-dence interval (Fig. 1 and Table 2). However, sta-tionary acoustic locations indicated that all 12 spe -cies were in the area with three sampling events,while cars and boats took nearly all 18 events to de-tect the maximum number of species (Fig. 1). None -theless, because one stationary sampling event is similar in time to almost 18 transect events there may be no difference in species recorded perunit time. Based on the Shannon-Weaver and Simp -son’s index, 1.63 times more abundant (SW) and1.56 times more very abundant (SI) species were detected along car transects than boat transects (Table 2).

Acoustic bat monitoring techniques 225

Activity Rates

Based on the perMANOVA, there was a signifi-cant difference in bat communities between sam-pling methods (P = 0.04) but not between sites (P = 0.61). Because there was no difference in sites,we pooled data by method to test mean speciesabundance. We did not compare transect abundancesto stationary data, as there is no way to estimateabundances from the latter. L. noctivagans, L. cine -reus, M. austroriparius, M. leibii, M. septentrion-alis, and M. sodalis were excluded from pairwisecomparisons because all occurred along less thanhalf the transects (< 9) within both methods (Table3). Only L. borealis and P. subflavus were encoun-tered every time car and boat transects were sam-pled (Table 3). Only M. grisescens was encounteredmore frequently along the boat transect (Table 3).Total activity was 0.6 (±0.45) sequences per mingreater along the boat transect (Q = 0.006). Twospecies were more abundant on the boat transect,while three were more abundant on the car transect(Table 3).

226 M. D. Whitby, T. C. Carter, E. R. Britzke, and S. M. Bergeson

TABLE 1. Number of sequences identified per species for mobileacoustic transects (car and boat) and stationary detectors acrossShawnee National Forest, IL, USA, May–July 2010–2011

Species Boat Car Stationary1

E. fuscus 30 112 3,079L. borealis 260 432 8,493L. cinereus 10 68 947L. noctivagans 26 30 1,715M. austroriparius 0 2 519M. grisescens 604 53 7,224M. leibii 0 15 962M. lucifugus 22 28 2,004M. septentrionalis 0 2 424M. sodalis 4 15 2,026N. humeralis 64 94 2,516P. subflavus 2,508 928 36,576

Total2 4,233 2,490 89,303

1 — Number of sequences for stationary detectors can only beused as an index to activity and does not represent number ofindividuals; 2 — Total represents all sequences of one or more pulsesthat were recorded, but not necessarily identified

Efficiency

Car transects required the least total time (travel,preparation, transect sampling, clean-up) to survey(mean 4.8 hr, range 3.5–5.5hr). Per kilo meter sam-pled, car transects (5.6 min/km) took less than halfthe time of boat transects (13.0 min/km). Four sta-tionary detectors required 1.77 and 1.42 times aslong to establish and remove per session than carand boat transects respectively (mean 8.5 hr, range5.4–11.7 hr). Additional advantages and disadvan-tages exist for all methods (Table 4).

DISCUSSION

It is well established that stationary acoustic de-tectors quickly accumulate and detect bat speciesrichness (Ford et al., 2005; Murray et al., 1999). Ourresults are similar, with all 12 species identified af-ter sampling four stationary locations for four nightseach across Shawnee National Forest. Both mobileac oustic transect methods did not show similar abil-ity to detect species. Car transects were able to de-tect all 12 species, but took more sampling eventsthan stationary detectors to do so, and boat transectsfailed to detect all 12 species within our study area.Overall, observed species density (species per sam-ple) decreased from stationary locations to car-basedtransects to boat-based transects. The higher numberof species detected by stationary detectors and thehigher species density observed is expected con-sidering one stationary night equals approximatelynine hours of recording (36 hours per samplingevent) while transects were sampled for approxi-mately two hours/event. Furthermore, the usefulnessof stationary detectors in determining species pres-ence/ absence is demonstrated by our ability to sam-ple four stationary points in a similar amount of timeas two car transects.

Steeper initial rarefaction curves also suggestthat stationary detectors more consistently and evenly detect species than both mobile acoustic transect methods (Colwell et al., 2004). The wideconfidence intervals along both transect types fur-ther demonstrate the infrequent detection of many

TABLE 2. Species density and diversity indices (Shannon-Weaver and Simpson’s) for stationary dectors and mobile acoustic transects(Car and Boat) across Shawnee National Forest, Illinois, USA May–July 2010–2011

Method Species density Shannon-Weaver Simpson’s index

Car transect 12 ± 1.84 4.22 ± 0.65 2.90 ± 0.43Boat transect 9 ± 2.94 2.59 ± 0.27 1.86 ± 0.22Stationary detectors 12 ± 0.00 – –

species, especially myotids, with mobile methods.However, overall variation in bat activity that weobserved for both the boat and car (coefficient ofvariation [CV] 54% and 55%, respectively) was be-low the CV of our stationary site activity (115%)and the average variation for bat studies (CV 95%— Gibbs et al., 1998), suggesting that mobileacoustic transects may be able to lower the highvariation that typifies historical bat monitoring ef-forts (stationary acoust ics and mist-netting) whilealso providing an index to abundance not providedby stationary detectors. Additionally, car transectsprovide this information with the least investment(both time and equipment — Table 4).

Acoustic bat monitoring techniques 227

FIG. 1. Species density and 95% confidence intervals for cartransects (short dash), boat transects (solid line), and stationarydetector sites (long dash) in Shawnee National Forest, Illinois,

USA, May–July 2010–2011

TABLE 3. Number of transects species detected along (max 18) and mean sequences per minute sampling of 12 bat species alongthree mobile acoustic transects conducted by boat and car in Shawnee National Forest, Illinois, USA May–July 2010–2011

SpeciesTransects present Sequences/minute

Boat Car Boat Car

E. fuscus 6 11 0.012 0.051**

L. noctivagans1 5 6 0.012 0.013L. borealis 18 18 0.119 0.213**

L. cinereus1 4 5 0.005 0.036M. austroriparius1 0 1 0.000 0.001M. grisescens 14 8 0.263** 0.026M. leibii1 0 4 0.000 0.007M. lucifugus 8 9 0.010 0.014M. septentrionalis1 0 1 0.000 0.001M. sodalis1 2 6 0.002 0.007N. humeralis 12 16 0.029 0.043*

P. subflavus 18 18 1.068*** 0.457

Total – – 1.825** 1.224

* — Q < 0.10, ** — Q < 0.01, *** — Q < 0.001; 1 — Gray rows indicate species that were detected < 50% of samples and were excluded from comparison

As expected, overall bat activity was greateralong boat transects than car transects. This could bea result of the lower boat speed. However, the re-duced boat speed (20 kph) is still greater than the average speed of most bat species in the area and isonly slightly lower than the car speed used in theIrish Bat Monitoring Program (24 kph — Roche etal., 2005). Only the flight speed of E. fuscus (33km/hr — Patterson and Hardin, 1969) is greater.Furthermore, because species density did not followthe same trend as activity we feel that the reducedboat speed had minimal impacts. In fact, car tran-sects detected more species and continuously higherspecies density than boat transects, although the dif-ference was not significant. Additionally, higher di-versity indices along car transects indicates that cartransects detected a greater number of abundantspecies than boat transects. Bell (1980) similarly ob-served decreasing richness and diversity over water,while other studies indicate that these measures aregreater at water sites compared to land sites (Ellison,2005; Winhold and Kurta, 2008). However, none ofthese studies account for differences in sample size(i.e., rarefaction).

Reports from Europe have shown that mobile-acoustic transects can provide useful information ontrends and distribution of bats (see Roche et al.,2011 and Jones et al., 2013). In Ireland where theprogram has been implemented since 2004, three ofnine species were encountered frequently enough (> approximately 0.1 sequences per min) for sta-tistical analysis of small changes in populations(0.69–1.64%/yr; Roche et al., 2011). Assuming sim-ilar requirements in the United States, L. borealis,

Sampling events

Specie

s d

ensity

P. subflavus, and M. grisescens were encountered atsufficient rates for similar trend analysis. AlthoughP. subflavus was nearly twice as abundant on boattransects then car transects, both P. subflavus and L. borealis are still more abundant along U.S. roadsthan the most common Irish species (Roche et al.,2011). Considering, the added effort required to con-duct boat transects, car-based monitoring is the mostefficient way to monitor both species. Additionally,larger declines in E. fuscus and N. humeralis may be able to be detected quicker from cars than boats.M. grisescens would likely have to be monitored viaboat transects to detect trends; however, summerand winter cave sampling likely provides adequatepopulation monitoring (Tuttle, 1979).

Habitat and time partitioning among bat speciesmay account for low encounter rates of some species(Kunz, 1973; Aldridge and Rautenbach, 1987).Extending transects or including replicates that startlater in the evening could increase encounters ofspecies such as L. cinereus and L. noctivagans whichmay not reach peak foraging activity until 4–8 hoursafter sunset (Kunz, 1973). Additionally, assuringtran sects are designed to stratify available habitatand therefore target certain species may add to mon-itoring ability. However, transects must still be spa-tially distributed across the landscape, otherwise observed changes along these routes may not be in-dicative of overall population trends (Buckland etal., 2005; Rodhouse et al., 2011).

Mobile acoustic sampling from the car requiredthe least time-investment. Preparing for and con-ducting these routes was also the simplest of allmethods and can be conducted by volunteers (Joneset al., 2013). Besides requiring added preparationtime, boat transects introduced sampling variationthat could cause problems for the long term analysis.Log-jams caused two transects to have to be alteredthroughout the 2011 season and access to rivers wasinconsistent due to flooding. Furthermore, inherentdangers of operating a boat at night required twopeople to participate in sampling and a third personto drive the boat trailer to the end of the transect.Therefore, while the boat transects took about onehour longer than car transects, over three times thetime investment in total person hours was required.Additionally, conducting boat transects adds fueland maintenance costs and assumes that resourcesand access to waterways are available. Stationarydetectors require a great deal of initial investment(multiple detectors, weatherproofing, batteries, etc.)and the greatest amount of attention — batteriesmust be exchanged and charged regularly, detectors

228 M. D. Whitby, T. C. Carter, E. R. Britzke, and S. M. Bergeson

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can fail, or even be vandalized/stolen. Additionally,our stationary sites were easy to access and still took almost twice the time as car transects. Weather -proofing and placement can greatly affect call qual-ity and identification accuracy (Britzke et al., 2010),and varies depending on the experience level of the person placing the detector (Britzke et al., 2013).In mobile acoustic transects, these issues are eithernot present or can immediately be remedied by anattentive operator. Additionally, because of the rela-tive ease, many mobile transects have been conduct-ed by volunteers with little training, further reducingcosts.

The great diversity in bats, even in temperate cli-mates, makes a single universal monitoring protocolunlikely (Flaquer et al., 2007). Decisions about batmonitoring have to be made based on project goalsand resource availability as each method has its ben-efits and drawbacks. Additionally, it has been sug-gested that walking transects in homogenous habitatmay not account for spatial variation (Stahlschmidtand Brül, 2012). However, because mobile transectsare designed to sample a wide variety of habitatsacross a landscape, they may account for landscapescale spatial variation. Additionally, use of GPS referenced calls may increase the utility of transectsto account for spatial variation. Boat transects how-ever, may not account for spatial variation as theyprimarily sample a single riparian habitat type. Allthese factors need to be incorporated when develop-ing bat monitoring efforts.

If simply establishing species presence/absenceat sites is the goal, then traditional stationaryacoustic detectors may be the best approach.However, if goals include monitoring abundancelevels, then car based mobile acoustic transects pro-vide an index to abundance for the most speciesacross a landscape with the least amount of effort.Boat-based surveys did not offer the clear advan-tages to monitoring that we hypothesized and in-clude greater investment, risk, and potential bias(i.e., boat/fuel, person hours, irregular sampling,etc); nonetheless, if monitoring species that arehighly associated with water (e.g., M. grisescens)boat based transects may be necessary. Furthermore,stationary sampling cannot be eliminated as a mon-itoring method, especially if statistical methods thatdo not rely on abundance data (e.g. occupancy mod-eling) are used. Nonetheless, the bias of stationarysite selection across the landscape would likely haveto be overcome with a large and variety of samplingpoints selected by experienced personnel — thus increasing costs.

Mobile acoustic transects may provide an oppor-tunity to monitor populations of L. borealis and P. subflavus (and likely E. fuscus and N. humeralis),species not able to be monitored using traditionalmethods and greatly threatened by wind energyand/or WNS. However, variability of activity withinsampling areas and low encounter rates for some im-portant species with mobile acoustic transects makeit clear that this is not a universal approach to batmonitoring.

ACKNOWLEDGEMENTS

A special thanks the many field technicians who assistedwith the project. Funding was provided by Shawnee NationalForest and Bat Conservation International. Comments from twoanonymous reviewers greatly improved the manuscript.

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Received 25 March 2013, accepted 01 February 2014