TY - JOUR
AU - Zollner, Patrick, A.
AB - Abstract Severe population declines in numerous North American bat species makes population monitoring increasingly difficult. We tested the effectiveness of an acoustic lure at increasing capture success of bats in mist nets. Increasing detection rate is especially relevant for species that have been heavily affected by white-nose syndrome, such as the Indiana bat (Myotis sodalis), little brown bat (M. lucifugus), and northern long-eared bat (M. septentrionalis). We conducted our study at 3 properties in southern Indiana during summer 2014. We set up 7 mist-netting sites at each property, netting 2 times at each site, with and without the use of an UltraSoundGate Player BL acoustic lure. The lure played recordings of various social calls from Myotis, Eptesicus, and Lasiurus spp. recorded in Europe and North America on a loop throughout the mist-net night. A total of 21 bats were caught using the lure, while 46 bats were caught without the use of the lure. We ran a series of zero-inflated Poisson generalized linear models on number of bats captured per trapping night to test whether the lure produced a difference in bat captures overall and for each genus while accounting for additional sources of variability. Using an information theoretical approach, we determined the most parsimonious models for each species grouping. For bats overall and those in the genera Myotis and Eptesicus, the top performing models contained an effect for use of the lure. This effect was positive and significant (P = 0.007) for our Myotis model, while the Eptesicus model showed a marginally significant and negative effect of the lure. We conclude that level of sociality in bat species influences the effectiveness of an acoustic lure on bat capture success. Understanding this distinction can inform when and where the use of an acoustic lure may enhance conservation goals. acoustic lure, bats, Chiroptera, endangered species, mist-netting, population monitoring, sociality Understanding population numbers of eastern North American bats is increasingly vital due to the progressive spread of white-nose syndrome (WNS—Wilder et al. 2015); the increased use and development of wind energy (Arnett et al. 2016); and continued habitat fragmentation and loss (Mickleburgh et al. 2002). WNS has had a detrimental effect on eastern cave-dwelling North American bat populations, causing more than 5.5 million deaths of eastern species of bats from 2006 to 2012 (USFWS 2012). As the fatal fungal pathogen continues to spread west into previously uninfected hibernacula (Wilder et al. 2015), WNS is causing widespread changes in the conservation status of once-common bat species (e.g., the recent listing of Myotis septentrionalis as federally threatened—Guertin 2015). Wind energy development is a threat to tree-roosting bat species (e.g., those in the genus Lasiurus) in North America with current fatality estimates reaching from the 10,000s to the 100,000s annually (Kunz et al. 2007; Arnett et al. 2008; Hayes 2013; Arnett et al. 2016). Both of these stressors are further exacerbated by the continued loss of key roosting and foraging habitats for both cave-dwelling and tree-roosting bats in North America (Russo and Ancillotto 2015). Several techniques are common in modern bat sampling including both live-capture methods (mist nets, harp traps) and passive methods (roost monitoring and bat detectors—Thomas and West 1989). Passive methods are less invasive and can provide important information on bat activity levels in an area; however, determining abundance or species presence (especially between closely related species such as the endangered Myotis sodalis and the common Myotis lucifugus—Britzke et al. 2011) via passive methods is difficult and controversial (O’Farrell and Gannon 1999). Additionally, important demographic information vital to predicting population growth and stability is not available through acoustic detection. Thus, mist-netting and harp traps are the most commonly used sampling methods to determine species presence and collect demographic information (Kunz and Parsons 2009). Because of the cryptic nature of bats, live-capture methods are often inherently biased regarding which habitats are sampled and the resultant capture rates of specific species (Larsen et al. 2007). As population densities of bats continue to decline, these biases could be exacerbated and mist-netting efforts could fail to detect species present on the landscape. Microchiropteran bats, such as those in North America, exhibit social behavior in the form of vocal communication (Guo et al. 2015). Sociality in bats is often tied to colony cohesion through roost selection and fidelity, including those that facilitate communication between a mother and her pups (Kerth 2008). Social calls have been investigated in several bat species and growing evidence suggests that social calls of bats can be individual and species specific, indicating communication is an important aspect of bat sociality (Pfalzer and Kusch 2003). In fact, bats are known to communicate foraging information and recognize conspecifics in addition to maintaining day-roosting colonies (Fenton 2003). Because bats clearly communicate using vocalizations, it should be possible to utilize these calls to increase captures of target bat species. One way to take advantage of bat social structures to improve live-capture surveys is through the use of acoustic lures. Acoustic lures work by replaying recorded ultrasonic bat calls that alter bat behavior. For example, distress calls in the pipistrelle bat (Pipistrellus pipistrellus) attracted conspecifics in a mobbing behavior used as an antipredatory response (Russ et al. 1998). The use of an acoustic lure has effectively increased sampling success in Europe (Hill and Greenaway 2005; Lintott et al. 2014). Lintott et al. (2014) found that an acoustic lure could increase bat capture rates by 2- to 12-fold in the United Kingdom. Despite the possibility that an acoustic lure could increase capture rates, the use of a lure is absent from United States sampling protocols. To date, only one species response to an acoustic lure has been investigated in North America (Loeb and Britzke 2010). This study found that Rafinesque’s big-eared bat (Corynorhinus rafinesquii) were not attracted to conspecific calls. Clearly, additional investigations are needed on the effectiveness of acoustic lures for other bat species in North America. The objective of our study was to compare mist-netting captures using an acoustic lure versus a traditional mist-netting protocol. We hypothesized that the bats in our study area would respond to social calls played by the lure in accordance with their level of sociality. Thus, our prediction was that use of the acoustic lure would increase our overall capture success of highly social bats (Myotis spp.) but have a minor impact on more solitary bats (Lasiurus spp.). Materials and Methods Study area. We conducted our study at 3 Agricultural Centers owned by Purdue University (PACs [Purdue Agricultural Centers]), located in southern Indiana: Southeast Purdue Agriculture Center (SEPAC), Southern Indiana Purdue Agriculture Center (SIPAC), and Feldun Purdue Agricultural Center (FPAC; Fig. 1). All 3 properties contain suitable habitat to support bat populations in the form of central hardwoods and oak-hickory forests. SEPAC is located approximately 10 km from North Vernon in Jennings County, Indiana. The property is the largest of the PACs and consists of 983 ha of farmland and forest. Out of the 983 ha, 647 ha consist of hardwood forest that is managed and periodically harvested via uneven-aged management techniques for timber sale. The remaining 336 ha are primarily used for row crop agriculture. SIPAC is located near the Patoka Reservoir in Dubois County. It is the 2nd largest PAC with 534 ha. The property consists of 304 ha of hardwood forest that is periodically harvested via uneven-aged management techniques for timber sales and 230 ha that are dedicated to open pastureland for goat and cattle. FPAC is located approximately 8 km northwest of Bedford, Indiana in Lawrence County. The property consists of 364 ha with 121 ha dedicated to hardwood forest that is managed and harvested via uneven-aged management techniques for timber sale and 242 ha dedicated to pasture. Fig. 1. Open in new tabDownload slide Locations of the 3 study sites in southern Indiana, United States. Fig. 1. Open in new tabDownload slide Locations of the 3 study sites in southern Indiana, United States. Field methods. We selected 7 mist-netting sites at each property nonrandomly based on forest cover and habitat composition in the surrounding landscape. Each mist-netting site was netted a total of 2 times. For each 1st netting night, we randomly selected whether or not to use the acoustic lure. At least 7 days after the 1st netting night, we repeated netting efforts at the same site for a 2nd time. The use of the acoustic lure on the 1st night determined if we used the acoustic lure the 2nd night. Mist nets were deployed at sunset and remained open for 5h. Within each PAC, we netted all 7 sites without replacement and then repeated netting efforts at that PAC. We placed the mist nets strategically to intercept bat flight paths, such as across creeks and travel corridors (Kunz and Parsons 2009). Travel corridors included unpaved vehicular roads, ATV trails, and walking trails. We set up 2 double or triple high mist-net systems (Bat Conservation and Management Inc., Carlisle, Pennsylvania) for each trapping occasion. We used 38-mm polyester mesh mist nets (Avinet Inc., Dryden, New York), ranging in length from 6 to 18 m. Nets were checked every 10min for captures and all captured bats were identified to species, sexed, weighed, and aged. Research performed on bats in-hand followed the guidelines of the American Society of Mammalogists (Sikes et al., 2016) and was approved by Purdue University’s Animal Care and Use Committee (PACUC Protocol #: 1404001055). We used an UltraSoundGate Player BL (Avisoft Bioacoustics, Glienicke, Germany) as an acoustic lure, which was capable of playing bat distress and social calls via an ultrasonic speaker. We connected this device to a laptop and deployed the lure at sunset, and left the lure playing for the duration of the 5-h netting session. Due to the strategic placement of nets to intercept bat pathways, the lure was placed between the 2 nets 1 m above the ground at a volume of 120 dB at 10cm (100 dB at 1 m) to ensure playback was present at both nets. In order to minimize the risk of habituation, calls were played for 10 successive minutes followed by 5min of silence while net was checked throughout the 5-h netting night. We played European and North American calls from the genera Lasiurus, Myotis, and Eptesicus. European calls were used because we did not have access to a large call library of North American bat social or distress calls. Thus, we used social calls provided by Avisoft Bioacoustics (http://batcalls.com) and supplemented with calls recorded by the authors. The playlists were comprised of mostly distress calls with a few social calls obtained through Avisoft Bioacoustics (further detail on the species, call type, and recording location can be found in the Supporting Information S1). We generated 7 different playlists each containing all of the same calls but presented in different 10-min block sequences. In all cases, the 10-min blocks were comprised of calls from a single genus. We randomly selected an individual playlist without replacement for each night that we used the lure. Data analysis. We tabulated bat captures per netting night and ran a series of zero-inflated generalized linear models with a Poisson distribution in Program R (package “pscl” in R—Zeileis et al. 2008; R Development Core Team 2012; Jackman 2015). These models compared numbers of bats captured with and without the use of the lure for overall bat captures and among genus. We analyzed data at the genus level due to small sample sizes at the species level. Additional models were run with the added predictors of Julian day and net area (m2) to account for other sources of variation in capture success. We used these predictors because net area varied considerably across our net sites and Julian day reflected changes in bat abundance at sites where pups began to forage. Playlist number was not used as a predictor because our analysis focused on the scale of a net night and each playlist played the same calls but in varying order. Sample size constrained our ability to use additional predictors in the analysis. The best model was chosen using Akaike’s information criterion corrected for small sample sizes and their associated weights (Bozdogan 1987). We conducted model averaging on competing models making up 95% of the model weights for each genus to obtain model-averaged beta estimates of the effect of our predictor variables (Symonds and Moussalli 2011). Results Throughout the season, we captured a total of 67 bats over 74 trap nights (0.90 bats per trap night), which included members from 5 different species (19 Eptesicus fuscus, 37 Lasiurus borealis, 6 M. lucifugus, 3 M. septentrionalis, and 2 M. sodalis). Figure 2 shows the frequency of trap nights in which 0, 1, 2, or > 2 bats were captured for each genus. For overall bats and E. fuscus, the best model contained the covariate for the lure and Julian day (Table 1). For these models, the use of the lure had a negative effect on bat captures, but this effect was not significant for the averaged model (Table 2). Julian day, however, was significant and positive for the E. fuscus model. The best model for bats in the genus Myotis contained only the covariate for the acoustic lure (Table 1). The lure had a significant positive effect on captures in the averaged best model (Table 2). Results for our L. borealis models showed the best model to be the Null model, indicating that the predictors we chose were not strong predictors of L. borealis captures (Table 1). Fig. 2. Open in new tabDownload slide Number of net nights where 0, 1, 2, or > 2 bats were captured with and without the use of a lure during mist-net surveys in southern Indiana (May–August 2014). Myotis spp. include captures of Myotis lucifugus, Myotis septentrionalis, and Myotis sodalis. Fig. 2. Open in new tabDownload slide Number of net nights where 0, 1, 2, or > 2 bats were captured with and without the use of a lure during mist-net surveys in southern Indiana (May–August 2014). Myotis spp. include captures of Myotis lucifugus, Myotis septentrionalis, and Myotis sodalis. Table 1. Model selection results for zero-inflated Poisson generalized linear models of bat captures in southern Indiana (May–August, 2014). AICc = Akaike’s information criterion corrected for small sample sizes. Species . Model . AICc . ΔAICc . wi . All bats LUREa + JULb 141.68 0.00 0.35 LURE 142.19 0.52 0.27 NULL 142.77 1.09 0.20 LURE + JUL + NETc 143.03 1.35 0.18 Myotis spp. LURE 46.77 0.00 0.65 LURE + JUL 48.83 2.06 0.23 LURE + JUL + NET 51.45 4.69 0.06 NULL 51.46 4.69 0.06 Eptesicus fuscus LURE + JUL + NET 82.00 0.00 0.52 LURE + JUL 82.88 0.88 0.34 NULL 85.83 3.83 0.08 LURE 86.15 4.15 0.07 Lasiurus borealis NULL 101.56 0.00 0.51 LURE 102.67 1.10 0.29 LURE + JUL 103.91 2.35 0.16 LURE + JUL + NET 106.51 4.95 0.04 Species . Model . AICc . ΔAICc . wi . All bats LUREa + JULb 141.68 0.00 0.35 LURE 142.19 0.52 0.27 NULL 142.77 1.09 0.20 LURE + JUL + NETc 143.03 1.35 0.18 Myotis spp. LURE 46.77 0.00 0.65 LURE + JUL 48.83 2.06 0.23 LURE + JUL + NET 51.45 4.69 0.06 NULL 51.46 4.69 0.06 Eptesicus fuscus LURE + JUL + NET 82.00 0.00 0.52 LURE + JUL 82.88 0.88 0.34 NULL 85.83 3.83 0.08 LURE 86.15 4.15 0.07 Lasiurus borealis NULL 101.56 0.00 0.51 LURE 102.67 1.10 0.29 LURE + JUL 103.91 2.35 0.16 LURE + JUL + NET 106.51 4.95 0.04 a Acoustic lure (used or not used). b Julian date of net night. c Net area in m2. Open in new tab Table 1. Model selection results for zero-inflated Poisson generalized linear models of bat captures in southern Indiana (May–August, 2014). AICc = Akaike’s information criterion corrected for small sample sizes. Species . Model . AICc . ΔAICc . wi . All bats LUREa + JULb 141.68 0.00 0.35 LURE 142.19 0.52 0.27 NULL 142.77 1.09 0.20 LURE + JUL + NETc 143.03 1.35 0.18 Myotis spp. LURE 46.77 0.00 0.65 LURE + JUL 48.83 2.06 0.23 LURE + JUL + NET 51.45 4.69 0.06 NULL 51.46 4.69 0.06 Eptesicus fuscus LURE + JUL + NET 82.00 0.00 0.52 LURE + JUL 82.88 0.88 0.34 NULL 85.83 3.83 0.08 LURE 86.15 4.15 0.07 Lasiurus borealis NULL 101.56 0.00 0.51 LURE 102.67 1.10 0.29 LURE + JUL 103.91 2.35 0.16 LURE + JUL + NET 106.51 4.95 0.04 Species . Model . AICc . ΔAICc . wi . All bats LUREa + JULb 141.68 0.00 0.35 LURE 142.19 0.52 0.27 NULL 142.77 1.09 0.20 LURE + JUL + NETc 143.03 1.35 0.18 Myotis spp. LURE 46.77 0.00 0.65 LURE + JUL 48.83 2.06 0.23 LURE + JUL + NET 51.45 4.69 0.06 NULL 51.46 4.69 0.06 Eptesicus fuscus LURE + JUL + NET 82.00 0.00 0.52 LURE + JUL 82.88 0.88 0.34 NULL 85.83 3.83 0.08 LURE 86.15 4.15 0.07 Lasiurus borealis NULL 101.56 0.00 0.51 LURE 102.67 1.10 0.29 LURE + JUL 103.91 2.35 0.16 LURE + JUL + NET 106.51 4.95 0.04 a Acoustic lure (used or not used). b Julian date of net night. c Net area in m2. Open in new tab Table 2. Model-averaged (all models up to 95% of the weight) beta estimates for covariates present in the most parsimonious model by genus. Species model . Covariate . β . SE . P . All bats INTERCEPT 1.17 LURE [USED]a −0.45 0.27 0.09 JULb 0.26 0.16 0.10 Myotis spp. INTERCEPT −0.02 LURE [USED] 1.90 0.71 0.007 Eptesicus fuscus INTERCEPT 0.03 LURE [USED] −0.84 0.52 0.10 JULD 0.56 0.25 0.03 NETc −0.39 0.24 0.10 Species model . Covariate . β . SE . P . All bats INTERCEPT 1.17 LURE [USED]a −0.45 0.27 0.09 JULb 0.26 0.16 0.10 Myotis spp. INTERCEPT −0.02 LURE [USED] 1.90 0.71 0.007 Eptesicus fuscus INTERCEPT 0.03 LURE [USED] −0.84 0.52 0.10 JULD 0.56 0.25 0.03 NETc −0.39 0.24 0.10 a Acoustic lure (used or not used). b Julian date of net night. c Net area in m2. Open in new tab Table 2. Model-averaged (all models up to 95% of the weight) beta estimates for covariates present in the most parsimonious model by genus. Species model . Covariate . β . SE . P . All bats INTERCEPT 1.17 LURE [USED]a −0.45 0.27 0.09 JULb 0.26 0.16 0.10 Myotis spp. INTERCEPT −0.02 LURE [USED] 1.90 0.71 0.007 Eptesicus fuscus INTERCEPT 0.03 LURE [USED] −0.84 0.52 0.10 JULD 0.56 0.25 0.03 NETc −0.39 0.24 0.10 Species model . Covariate . β . SE . P . All bats INTERCEPT 1.17 LURE [USED]a −0.45 0.27 0.09 JULb 0.26 0.16 0.10 Myotis spp. INTERCEPT −0.02 LURE [USED] 1.90 0.71 0.007 Eptesicus fuscus INTERCEPT 0.03 LURE [USED] −0.84 0.52 0.10 JULD 0.56 0.25 0.03 NETc −0.39 0.24 0.10 a Acoustic lure (used or not used). b Julian date of net night. c Net area in m2. Open in new tab Discussion The use of an acoustic lure had significant effects on the capture of some, but not all species. We expected the use of the lure to increase captures of bats for each genera and for overall bats. Instead, the use of the lure marginally decreased captures for all bats and Eptesicus, increased capture rates for Myotis, and had no significant effect on Lasiurus. Our results point to a conclusion that the level of sociality in specific bat species will influence a species’ level of response to an acoustic lure. The marginal decrease in Eptesicus captures hint at a deterrent effect of the acoustic lure, which has also been observed in a previous study in North America (Loeb and Britzke 2010). In behavioral studies of E. fuscus, some social calls are used by males to claim food resources, indicating to conspecifics to move away from the area (Wright et al. 2014). Eptesicus spp. social calls played by the lure may have deterred individuals due to the species’ aggressive territoriality, which has also been observed in females of that genus (Kilgour and Brigham 2013). The lack of any impact of the lure on captures of Lasiurus spp. may be because these bats are solitary animals (Shump and Shump 1982; Koehler and Barclay 2000) where the only known social calls are those between the mother and pup (Schmidt-French et al. 2006). Thus, social calls played by the lure may not have had any impact on the behavior of Lasiurus spp. Most notably, the acoustic lure had a significant positive impact on the captures of bats in the genus Myotis. Species within the genus Myotis are known to be highly colonial, with females congregating in maternity colonies of thousands of individuals (Silvis et al. 2014). Female Myotis spp. bats exhibit colony roost fidelity and strong fission–fusion social dynamics. Because of this, it is not surprising that Myotis spp. responded strongly to the social and distress calls played by the lure, as these social vocalizations are used to communicate a diverse array of information between conspecifics. In fact, Fenton et al. (1976) found M. lucifugus to respond to distress calls of other bats, including those from other species. We also note that all Myotis spp. individuals captured with the acoustic lure in our study were female, while those captured without the use of the lure were male, strengthening the assertion that those individuals with increased sociality (colonial females) would exhibit a greater response to social calls played on a lure. Our conclusions represent the 2nd time the effectiveness of an acoustic lure has been investigated in North America. Loeb and Britzke (2010) concluded that Rafinesque’s big-eared bat, a highly colonial species, was not attracted to social calls of their conspecifics. However, the social calls played by the lure in this study were recorded at the roosts that were surveyed and at additional colonies within the species range. In our study, we used playback of social calls largely from European bats and these sounds may have been novel to the individuals at our study sites. Thus, it may be important for acoustic lures to play calls from members of different areas than those being sampled to ensure adequate attraction. Similarly, in studies where social call playback increased capture success, the calls were not recorded locally at the study sites (Hill and Greenaway 2005; Lintott et al. 2014). As bat populations continue to decline, mist-netting will become less successful at determining species presence, population size and reproductive condition, and habitat preferences. These measures, however, are essential to understand if conservation of bats is to be successful (O’Shea et al. 2003). As we show here, the use of an acoustic lure can increase captures of bats, especially of those that are highly social. These highly social bats (Myotis spp.) are at the greatest risk for population decline from WNS (Frick et al. 2010), thus increasing the potential of an acoustic lure to provide researchers with vital information. We recommend that additional investigations of bat social call type (e.g., distress, contact) should be tested to further improve our understanding of how acoustic lures can impact capture rates of bat species. Supporting Information The Supporting Information documents are linked to this manuscript and are available at Journal of Mammalogy online (jmammal.oxfordjournals.org). The materials consist of data provided by the author that are published to benefit the reader. The posted materials are not copyedited. The contents of all supporting data are the sole responsibility of the authors. Questions or messages regarding errors should be addressed to the author. Supporting Information S1.—Table of species, call type, location recorded, and source of recording used to generate playlists for the acoustic lure. Acknowledgments This work was funded by Purdue University’s College of Agriculture Mary S. 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TI - Testing the efficacy of an acoustic lure on bat mist-netting success in North American central hardwood forests
JF - Journal of Mammalogy
DO - 10.1093/jmammal/gyw125
DA - 2016-12-05
UR - https://www.deepdyve.com/lp/oxford-university-press/testing-the-efficacy-of-an-acoustic-lure-on-bat-mist-netting-success-IX6basSu68
SP - 1617
VL - 97
IS - 6
DP - DeepDyve
ER -