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Bat Occurrence and Habitat Preference on the Delmarva Peninsula
Andrew T. McGowan and Aaron S. Hogue

Northeastern Naturalist, Volume 23, Issue 2 (2016): 259–276

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Northeastern Naturalist Vol. 23, No. 2 A.T. McGowan and A.S. Hogue 2016 259 2016 NORTHEASTERN NATURALIST 23(2):259–276 Bat Occurrence and Habitat Preference on the Delmarva Peninsula Andrew T. McGowan1 and Aaron S. Hogue1,* Abstract - White-nose syndrome (WNS) and wind-turbine facilities on the Delmarva Peninsula are emerging threats to the peninsula’s current bat fauna. However, until our study, there had been no assessment of bat populations or their habitats in that region. The purpose of our research was to fill this gap by using 28 road-based transects and 24 passive-monitoring sites to acoustically monitor bats across the peninsula. In total, we recorded 4432 bat-call sequences and documented the presence of at least 6 species: Lasiurus borealis (Eastern Red Bat), Eptesicus fuscus (Big Brown Bat), Nycticeius humeralis (Evening Bat), L. cinereus (Hoary Bat), Perimyotis subflavus (Tri-colored Bat), 1 or more species in the genus Myotis, and potentially Lasionycteris noctivagans (Silverhaired Bat). Given the similarity in call structure between Silver-haired and Big Brown Bats, we cannot say with certainty the former were present. Eastern Red Bats, Evening Bats, and Hoary Bats were relatively widespread and abundant; Tri-colored Bat and Myotis were not. Of the species for which adequate sample sizes were available, all but the Hoary Bat (and possibly the Silver-haired Bat) showed strong preferences for forest edges, demonstrating the importance of these landscape features for maintaining healthy bat populations. Point-counts along road transects and stationary-monitoring sites yielded similar results, suggesting that road-based transects are a valuable tool for surveying bat populations across large geographic areas. Introduction Bats are important yet often underappreciated animals. Across the globe, they play valuable roles in regulating insect populations, pollinating plants, and dispersing seeds (Galindo-González et al. 2000, Kalka et al. 2008). The critical nature of their interaction with many ecosystems has led some to propose their use as bioindicator taxa (Jones et al. 2009). In the mid-Atlantic region of the US, all bats are obligate insectivores (Voigt et al. 2011, Webster et al. 1985). Although the ecological significance of these animals extends beyond their diet, bats’ consumption of aerial insects exerts a powerful control on insect populations, which has ripple effects throughout ecosystems, such as reducing plant damage from insect herbivory (Kalka et al. 2008). Bats consume a variety of agricultural pests and can do so in large numbers (Carter et al. 2004, Whitaker 1995), saving the US agricultural industry an estimated average of $22.9 billion per year (Boyles et al. 2011). Thus, it is important to monitor and protect these animals in regions that have large amounts of native habitat and/or agricultural landscapes that rely on the ecosystem services provided by bats. One such area is the Delmarva Peninsula. 1Department of Biological Sciences, Salisbury University, Salisbury, MD 21801. *Corresponding author - Manuscript Editor: Jacques Veilleux Northeastern Naturalist 260 A.T. McGowan and A.S. Hogue 2016 Vol. 23, No. 2 Little is known about bats on Delmarva, which, as a peninsula, may a have different community structure compared to surrounding areas. Of the handful of studies conducted here, all were significantly restricted in geographic and ecological scope (Fox 2007, Johnson and Gates 2008, Limpert et al. 2007, Sjollema et al. 2014, Wolcott and Vulinec 2012). The lack of data at present is particularly disconcerting due to the recent emergence of 2 significant threats to bats on the peninsula: White-nose syndrome (WNS) and wind turbines. WNS has killed an estimated 5.5 million bats in the eastern US (Reeder et al. 2012), and has recently spread onto the northern portion of the peninsula (DNREC 2014). According to various models, wind turbines in the nearby Appalachian Mountains are projected to kill 33,000 to 111,000 bats annually by 2020, particularly during fall migration (Kunz et al. 2007). In recent years, there have been several proposals for wind-turbine facilities both on the peninsula and off its shores; however, few studies aimed at assessing the potential impact of these proposed threats to peninsular bat populations have been conducted. A baseline assessment of the local bat fauna was needed to help clarify the current situation and as a basis of comparison with future studies. Given that habitat use and availability significantly affect the abundance and distribution of bats, it was also necessary to identify which elements of the landscape are most often utilized. The purpose of our study was to fill these gaps by determining which species are present on the peninsula during the summer months, as well as their habitat preferences at the local and landscape level. Field-Site Description The Delmarva Peninsula lies between the Atlantic Ocean to the east and the nation’s largest estuary, the Chesapeake Bay, to the west (Fig. 1). The peninsula covers about 15,500 km2 and is located entirely within the Coastal Plain Physiographic Province (Denver et al. 2004). Findings of studies conducted in the Choptank watershed suggest that prior to European colonization, roughly 92–94% of the peninsula was covered in forests, principally mixed hardwood–Pinus (pine) forests (Benitez and Fisher 2004, Smith and Barbour 1986). Much of the remainder consisted of marshes and Native American settlements in small clearings (Benitez and Fisher 2004). Today, the landscape consists of a mosaic of agriculture (48%), forests (37%), marshes, and various human-modified areas (Denver et al. 2004). The forests and marshlands are extremely important because of the many ecological services they offer, including providing valuable habitat to local and migrating wildlife (Hogue and Hayes 2015, McCann et al. 1993). Methods Sampling Sites We used ArcGIS 10.2 (ESRI 2014) and satellite imagery provided by Maryland iMap and Delaware Geospatial Data Exchange to lay out transects. We created 28 non-overlapping 18-km transects on the peninsula. We first established 23 idealized (perfectly straight) east–west transects spaced 10 km apart north to south. Where Northeastern Naturalist Vol. 23, No. 2 A.T. McGowan and A.S. Hogue 2016 261 the peninsula was wide enough to prevent overlap, we organized the transects into 3 non-overlapping columns: a western column, which started on the western shore of the peninsula and terminated inland; an eastern column, which started along the eastern edge of the peninsula and terminated inland; and a middle column, which was centrally positioned. In some areas, the peninsula could only accommodate 2 non-overlapping eastern and western columns or just a single centrally located column. We selected this approach to transect design because it ensured that the full Figure 1. Map of the Delmarva Peninsula. Forests are shown in grey, and wetlands are indicated by areas with diagonal lines. Black dots represent transect sampling sites and black triangles indicate passivemonitoring sites. DE = Delaware, MD = Maryland, NJ = New Jersey, and VA = Virginia. Northeastern Naturalist 262 A.T. McGowan and A.S. Hogue 2016 Vol. 23, No. 2 range of habitats from the Chesapeake Bay to the Atlantic Ocean was represented in a systematic and unbiased manner. When space and resources permitted additional transects, we created an additional 5 north–south transects to fill gaps that were present between columns, bringing the total number of transects to 28 (Fig. 1). We planned to access sample points via automobile; thus, we used ArcGIS 10.2 (ESRI 2014) to trace modified transects along 2-lane roads located as close as possible to the idealized transects. We placed 10 sampling points (sites) 2 km apart (in linear distance, not driving distance) on each modified transect for a total of 280 sites (Fig. 1). We chose to space the sites 2 km apart to minimize the potential for recording the same bats at multiple sites, and to avoid overlap in the surrounding habitat during analyses of habitat use. We sampled at the nearest safe roadside location to each point. In addition to active monitoring along transects, we also conducted passive monitoring overnight at 24 sites (Fig. 1). We selected sites by generating 24 randomly placed points across the Maryland portion of the peninsula using ArcGIS 10.2 (ESRI 2014). If the point fell on public land, we attempted to obtain permission to access these points through the agency responsible for the property. If access was granted, we set up a detector as close as possible to the coordinates of that point for our sampling site. When the point did not fall on public land, we worked with local land trusts (Eastern Shore Land Conservancy, Easton, MD; Lower Shore Land Trust, Berlin, MD), land conservation organizations (The Nature Conservancy, Snow Hill, MD), and natural resource agencies (Maryland Department of Natural Resources, Annapolis, MD; US Fish and Wildlife Service, Cambridge, MD) to gain access to the nearest such property with similar surrounding habitat. We restricted sampling to the Maryland portion of the peninsula because the land trusts and land conservation organizations listed above had agreed to facilitate access to a large number of private properties. Sampling We used RStudio (2014) to randomly select driving direction and transects for sampling without replacement on a single night between June and August 2014 using. Bat-activity levels are typically bimodal in distribution—the largest peak in activity usually occurs within the first several hours after sunset and there is often a smaller peak just before sunrise (Hayes 1997). In order to document the abundance and diversity of bats recorded, we began sampling 30 min after sunset and terminated once all 10 sites on that transect were sampled ~3 hours later. In order to limit variation in bat activity between sampling nights, and because bat activity is significantly affected by precipitation, temperature, and wind, we restricted sampling to nights with no rain, temperatures above 10 °C, and wind speeds less than 20 km/h (5.6 m/s; Johnson and Gates 2008, Johnson et al. 2008, Jung et al. 1999). We assessed weather variables at the start of sampling using the National Weather Service’s forecast for the town closest to the starting location. To record bats, we employed a miniMIC ultrasonic microphone (Binary Acoustic Technology, Tucson, AZ) connected by a USB cable to a Dell Venue tablet running Northeastern Naturalist Vol. 23, No. 2 A.T. McGowan and A.S. Hogue 2016 263 SPECT’R Software (Binary Acoustic Technology). We set the miniMIC to a trigger threshold of 20 kHz to minimize false triggers by insect noise, with a maximumtime length of 3 sec to keep the full-spectrum file recordings small enough to easily be processed by the call-analysis software. We set a threshold amplitude of 18 db in order to prevent distant noises from triggering the detector. Upon arrival at each sampling site, we extended the miniMIC 4 m into the air along a telescoping pole and recorded for 12 min. The 12-min sampling period represented a compromise between needing to cover a large geographic area in the 3 h of peak bat-activity, and adequately capturing bat activity at each site. At the end of each period, we turned the detector off, collapsed the telescoping pole, and drove to the next sampling point to repeat the process until all 10 sites had been sampled. For passive survey sites, we monitored bats with a D500X bat detector (Pettersson Elektronik AB, Uppsala, Sweden) set to record from sunset to sunrise the following morning. If the property being sampled was an agricultural field or other open, dry-land site, we placed the detector in a weather-proof box and extended an external microphone (Pettersson Elektronik) at the tip of a 4-m telescoping pole held in place by strapping it to a metal stake in the ground. If the sample site was a marsh, we followed the same procedure but secured the weather-proof box containing the detector to a floating platform that was tethered to a metal stake. If the property being sampled was a forest or forest edge, we placed the detector in a tree strap (Bat Conservation and Management, Carlisle, PA) and attached it to the nearest tree trunk at a height of 4 m and angled 45° away from the trunk to minimize interference from the trunk. We set the detector to auto record, a sampling frequency of 500 kHz, recording length of 3 s, low trigger-sensitivity, no pre-trigger, enabled HP filter, input gain of 80, trigger level of 120, and an interval of 0. As with the transects, we restricted sampling to nights with no rain, temperatures above 10 °C, and wind speeds less than 20 km/h (Johnson and Gates 2008, Johnson et al. 2008, Jung et al. 1999). We assessed weather variables when we deployed the stationary detector, using the National Weather Service’s forecast for the town closest to the location. Call analysis and species identification We attempted to identify each recorded bat pass to species with SonoBat 3.2 automated classifier (Arcada, CA; Szewczak 2015). As recommended by SonoBat, we set a probability threshold of 90% for accurate species identification. We defined a bat pass (henceforth referred to as a call sequence) as a series of 1 or more echolocation pulses with less than 1 s between sequential pulses (Fenton 1970). We visually inspected call sequences with a questionable species allocation to verify proper species identification by comparison to the call library and call-identification sheet provided by sonobat. We tallied the total number of call sequences per species at each point and imported these data into RStudio (2014) for statistical analyses. The absolute number of bats at each site cannot be measured accurately from the call sequences recorded by acoustic detectors (Kunz et al. 2007); thus, we used the number of bat-call sequences as an overall measure of bat activity, and compared activity levels between habitats (Vaughan et al. 1997). Northeastern Naturalist 264 A.T. McGowan and A.S. Hogue 2016 Vol. 23, No. 2 Predicted habitat associations Local level. We tested habitat associations separately at the local and landscape levels. We defined the local level as habitat variables within 30 m of the sampling point, which is the approximate range of detection for the ultrasonic microphone. Thus, we assumed that any recorded call sequences originated in the immediate presence of these habitat variables. We used data from previous ecological research on bats, as well as theoretical work on wing morphology to predict which elements of the landscape would influence activity for each species at the local level. Wing morphology is thought to influence habitat use by bats because differences in wing form affect flight speed and maneuverability, and therefore which habitat best suits each species for navigation and foraging (Aldridge and Rautenbach 1987, Fenton 1990, Norberg and Rayner 1987). Specifically, species with low wing-loadings typically forage in cluttered forested environments, while species with high wing-loadings utilize open environments (Norberg and Rayner 1987). At least 7 bat species have been documented on Delmarva: Lasiurus borealis Müller (Eastern Red Bat), Eptesicus fuscus (Palisot de Beauvois) (Big Brown Bat), Lasionycteris noctivagans (Le Conte) (Silver-haired Bat), Nycticeius humeralis (Rafinesque) (Evening Bat), Perimyotis subflavus (Cuvier) (Tri-colored Bat), Lasiurus cinereus (Palisot de Beauvois) (Hoary Bat), Myotis lucifugus (Le Conte) (Little Brown Bat), and potentially other Myotis species (collections of the Smithsonian Institution National Museum of Natural History, Washington, DC, and Delaware Museum of Natural History, Wilmington, DE; Fox 2007, Johnson and Gates 2008, Johnson et al. 2011, Limpert et al. 2007, Paradiso 1969). Of these species, only the Hoary Bat is considered to be an open-habitat forager (Jantzen and Fenton 2013). The remaining species have wings more suited to foraging in closed-forest or forest-edge habitats (Farney and Fleharty 1969, Jantzen and Fenton 2013, Norberg and Rayner 1987). Additionally, edges provide protection from predators and shelter from wind, which reduces the energetic costs of flight in windy conditions, and may be important to bats for navigation (Verboom and Huitema 1997, Verboom and Spoelstra 1999). Edges also have higher insect abundance than open fields (Gruebler et al. 2008). Based on these factors and previous studies which have shown especially high activity along forest edges (Frey-Ehrenbold et al. 2013, Jantzen and Fenton 2013, Morris et al. 2010, Walsh and Harris 1996, Wolcott and Vulinec 2012), we predicted that every species except the Hoary Bat would exhibit significantly more activity along forest edges than in open environments. Our transect sites were at roadsides; thus, all forested sites were at edges. In instances where forest was on both sides of the road, 2 forest edges were present. We predicted that sites with 2 forest edges would have higheractivity levels than those with forest on only one side, and both of these would have higher activity than sites with only open habitat for all species except for the Hoary Bat. We define open habitat as marsh (non-forested wetlands), agriculture, open water (rivers, ponds, bays, etc.), and developed land (defined as any area with buildings, cut grass, pavement, or similar landscape features maintained for human use). We classified developed areas as open habitat because they consisted largely of open spaces with sparsely scattered vertical structures. Northeastern Naturalist Vol. 23, No. 2 A.T. McGowan and A.S. Hogue 2016 265 Forests in this study usually fell into 1 of 2 types: native mixed hardwood–pine forests and pine plantations. Pine plantations have a lower diversity of tree species than mixed hardwood–pine forests, and this feature may negatively impact diversity and abundance of other plants and insects. If insect populations are reduced in pine forests, these habitats should have lower bat-abundance than mixed hardwood–pine forests. However, previous studies have found that many of the bat species native to Delmarva utilize both logged and unmanaged pine forests (Jung et al. 1999, Morris et al. 2010) and roost in both forest types (Limpert et al. 2007, Menzel et al. 2001). To test whether forest-adapted bats prefer mixed forests over pines, we conducted a secondary analysis to compare bat-habitat use and activity levels between mixed hardwood–pine and pine-forest sites. Previous studies have also found that natural water bodies and riparian areas are important to foraging bats (Lookingbill et al. 2010, Menzel et al. 2005). At the local level, none of the transects or passive sites fell into these categories. Therefore, we were not able to assess bat use of these habitats. To assess whether bats showed habitat preferences beyond those predicted above, we compared habitat use between all possible habitat combinations (from each side of the road). Specifically, we compared activity between sites in the following 10 categories: forest only, marsh only, agriculture (ag) only, developed only, forest–marsh, forest–ag, forest–developed, marsh–ag, marsh–developed, and ag–developed. Landscape level. We defined the landscape level as habitat characteristics in the 1-km radius around the site for area and distance measures. We chose a 1-km radius because a previous study on the peninsula used this scale to investigate which elements of the landscape are important for bat-roost selection (Limpert et al. 2007). We used ArcGIS 10.2 to measure all landscape-level habitat variables from satellite imagery. For distance measures, we quantified the distance from the sampling point to the edge of the nearest forest, mixed hardwood–pine forest, pine forest, natural tree-lined water body, and water feature. We defined natural tree-lined water bodies as streams, creeks, rivers, bays, and ponds with at least some natural, treelined riparian areas (3 or more trees along edge). Water features included all of the above types of natural water bodies as well as human-created ones such as drainage ponds, provided they held water April through September. For area measures, we drew a 1-km-radius circle around each sampling point using the buffer tool and calculated the total area of forest and agriculture, and the total length of all forest edge within each circle. All species except the Hoary Bat are thought to be forest- and edge-adapted species (Farney and Fleharty 1969, Jantzen and Fenton 2013, Norberg and Rayner 1987); thus, we predicted that activity would increase for each species, except the Hoary Bat, as distance to the nearest forest, mixed hardwood–pine forest, and pine forest decreased. Based on the established relationships between bats and natural freshwater bodies, especially riparian areas (Lookingbill et al. 2010, Menzel et al. 2005), we predicted an increase in activity for all species as the distance to the nearest natural Northeastern Naturalist 266 A.T. McGowan and A.S. Hogue 2016 Vol. 23, No. 2 water-feature decreased. We tested the prediction that bat activity would be greater closer to both natural and human-made water bodies because drainage ponds and other anthropogenic water bodies may also be used by bats. Forest and forest edge in the surrounding landscape not only provide foraging area for forest-adapted bats, but also potential roosting sites because many of these animals use trees as day roosts (Limpert et al. 2007, Menzel et al. 2001). Thus, we predicted a positive correlation between total bat activity at a site and total percent of forest and amount of forest edge within a 1-km radius for all species except the Hoary Bat. To assess the impact of agriculture on bats, we looked at the correlation between bat activity and percent area of agriculture in the surrounding 1-km radius. We predicted a significant positive correlation between Hoary Bat activity levels and percent area of agriculture because this species is thought to be an open-habitat forager. For all other bat species, we predicted a negative correlation. Passive monitoring sites. One major drawback of our roadside-sampling approach was that we did not sample forest-interior habitats. The addition of passive sampling allowed us to monitor forest-interior habitats to determine if our roadbased method missed any species and to compare bat activity along forest edges to interior-forest conditions. Given previous research suggesting that forest-adapted bats native to the Delmarva Peninsula prefer forest edges to interior-forest conditions (Frey-Ehrenbold et al. 2013, Jantzen and Fenton 2013, Morris et al. 2010, Walsh and Harris 1996, Wolcott and Vulinec 2012), we predicted we would find higher levels of bat activity at forest edges compared to forest interiors. Statistical analyses We employed RStudio (2014), which runs R statistical software, to perform all analyses (R Core Team 2014). On the local level, we tested habitat predictions by comparing differences in total bat activity (median number of bat-call sequences per site) between habitat categories. A Shapiro-Wilk test (Gross and Ligges 2012, Meyer et al. 2014) revealed that the data were not normally distributed; thus, we used a non-parametric Kruskal Wallis test followed by a 1-tailed Mann- Whitney U test (α = 0.05) to test predictions that species would exhibit greater activity in some habitats than others. We restricted our analyses to species that had a sufficient number of call sequences recorded: Eastern Red Bat, Big Brown Bat, Evening Bat, and Hoary Bat. Sonobat also identified a large number of call sequences as belonging to Silver-haired Bat. This species had previously been found on the peninsula only during periods thought to coincide with spring and fall migrations (Johnson et al. 2011, Sturgis 2013). Given the surprising nature of our finding (discussed further in the Discussion section), and the similarity of this species’ call structure with the more common Big Brown Bat, we analyzed both sets of calls separately and combined. For predictions in which we compared 3 or more groups of sites (e.g., forest on both sides of road vs. forest on only one side vs. no forest), we used a multiple-comparison Kruskal Wallis test with a Bonferroni correction (Giraudoux 2014). Northeastern Naturalist Vol. 23, No. 2 A.T. McGowan and A.S. Hogue 2016 267 At the landscape level, we tested predicted correlations between total bat activity at each site to distance and area measurements using a Kendall’s correlation test for the species that returned a sufficient number of passes. Results Species detected In total, we recorded 3690 call sequences at transect sites during 56 h (28 nights) of recording. Of these calls, we identified 65.5% to 1 of at least 7 species: 669 as Big Brown Bat, 621 as Eastern Red Bat, 528 as Silver-haired Bat, 503 as Evening Bat, 74 as Hoary Bat, 25 as Tri-colored Bat, and 7 as Myotis sp., which were not identified to species due to challenges in discriminating among species based on calls (Corcoran 2007). At passive monitoring sites, we recorded 742 call sequences over 216 h. Of these, we identified 68.4% to the same 7 species: 210 as Big Brown Bat, 194 as Eastern Red Bat, 14 as Silver-haired Bat, 83 as Evening Bat, 1 as Hoary Bat, 6 as Tri-colored Bat, and 9 as Myotis sp. Given the strong similarity between Silver-haired Bat and Big Brown Bat calls, and the lack of definitive evidence of Silver-haired bats on the peninsula during summer months (suggesting calls identified as Silver-haired Bat may be Big Brown Bat), we performed habitat analyses on both sets of calls separately and lumped together. Habitat analyses Local level. As predicted, the Eastern Red Bat (W = 11,807, P < 0.001), Big Brown Bat (W = 12,362, P < 0.001), Evening Bat (W = 12,188.5, P < 0.001), and Big Brown/Silver-haired Bat combined (W = 11,460, P < 0.01) had significantly higher-activity levels at sites with forest edge compared to sites without forest edge (Table 1). Analyzed separately, calls attributed to the Silver-haired Bat did not exhibit this preference (W = 9970, P = 0.37). As predicted, the Hoary Bat had significantly higher-activity levels at open sites (W = 9075, P < 0.05; Table 1). Our predicted gradient of activity levels—forest on both sides of the road > forest only on one side > open on both sides—was partially supported. Activity levels at sites with forest on both sides of the road were significantly greater than sites with open habitat on both sides for the Eastern Red Bat (P < 0.05), Big Brown Bat (P < 0.05), and Evening Bat (P less than 0.05), and activity levels at sites with forest on one side of the road were significantly greater than open-habitat only sites for the Big Brown Bat (P < 0.05) and Evening Bat (P < 0.05) (Table 1). However, there was no significant difference between forest on both sides and forest on one side only for any species (Table 1). Our prediction that sites with only mixed hardwood–pine forest edges would have significantly higher-activity levels than sites with only pine-forest edges was supported by results for the Evening Bat (W = 552.5, P < 0.05) and calls attributed to the Silver-haired Bat (W = 493, P < 0.05) (Table 1). While all other species showed greater activity in mixed hardwood–pine forests, these differences were not significant. Additionally, we recorded all 7 bat species in the 40 sites that only contained mixed hardwood–pine forest; whereas, the Hoary Bat and Myotis were not recorded at the Northeastern Naturalist 268 A.T. McGowan and A.S. Hogue 2016 Vol. 23, No. 2 Table 1. Comparison of mean (SD) number of call sequences recorded for each species at sites with the indicated habitat variable on the local level. Asterisks represent a significant difference in median values (* = P < 0.05,** = P < 0.01, *** = P < 0.001), as assessed by either a Kruskal Wallis and 1-tailed Mann-Whitney U test (α = 0.05) or multiple comparison Kruskal Wallis test with a Bonferroni correction. Comparison n Eastern Red Bat Evening Bat Big Brown Bat Big Brown/Silver-haired Bat Silver-haired Bat Hoary Bat Forest edge 148 3.32 (12.73)*** 2.62 (4.94)*** 3.14 (6.87)*** 5.01 (9.61)** 1.87 (4.06) 0.14 (0.72) Open 132 0.97 (4.11) 0.86 (3.29) 1.54 (5.33) 3.44 (7.25) 1.90 (3.64) 0.39 (1.28)* Forest onlyA 64 5.87 (18.93)*B 3.90 (6.30)*B 3.28 (6.58)*B 4.78 (9.03) 1.50 (3.94) 0.25 (1.02) Forest–openA 84 1.38 (2.52) 1.65 (3.28)*C 3.03 (7.11)*C 5.19 (10.09) 2.15 (4.15) 0.07 (0.30) Open onlyA 132 0.97 (4.11) 0.86 (3.29) 1.54 (5.33) 3.44 (7.25) 1.90 (3.64) 0.39 (1.28) Mixed only 40 6.65 (22.63) 4.62 (6.91)* 3.70 (7.79) 6.10 (8.99) 1.32 (2.77)* 0.40 (1.27) Pine only 19 4.21 (10.89) 2.31 (4.71) 2.89 (4.21) 3.63 (4.07) 0.94 (2.99) 0.00 (0.00) Forest edge 8 22.5 (46.00)* 6.62 (9.60)** 35.75 (36.38)** 19.5 (30.84)** 1.50 (1.29) 0.13 (0.35) Forest interior 9 0.22 (0.66) 0.00 (0.00) 0.22 (0.66) 0.22 (0.66) 0.00 (0.00) 0.00 (0.00) AForest only = forest on both sides of the road, Forest–open = forest on one side of the road and open on the other, Open only = open habitat on both sides of the road. BForest only > open only. CForest–open > open only. Table 2. Mean (SD) number of call sequences for each species in each habitat category at the local level. * = forest-only sites had significantly higher bat activity than agriculture-only sites (P < 0.05) for these species. Habitat n Eastern Red Bat Evening Bat Big Brown Bat Big Brown/Silver-haired Bat Silver-haired Bat Hoary Bat Forest only 64 5.87 (18.93) 3.90 (6.30)* 3.28 (6.58)* 4.78 (9.03) 1.50 (3.94) 0.25 (1.02) Forest–marsh 1 5.00 (0.00) 3.00 (0.00) 3.00 (0.00) 3.00 (0.00) 0.00 (0.00) 0.00 (0.00) Forest-ag 68 1.44 (2.62) 1.75 (3.49) 3.14 (7.73) 5.39 (10.97) 2.25 (4.26) 0.08 (0.33) Forest–developed 15 0.86 (1.92) 1.13 (2.26) 2.53 (3.70) 4.40 (5.11) 1.86 (3.83) 0.00 (0.00) Marsh only 3 1.33 (1.15) 1.00 (1.00) 0.00 (0.00) 3.33 (5.77) 3.33 (5.77) 0.00 (0.00) Marsh–ag 3 0.33 (0.57) 0.33 (0.57) 6.00 (7.93) 8.00 (11.35) 2.00 (3.46) 0.33 (0.57) Marsh–developed 1 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 1.00 (0.00) 1.00 (0.00) 0.00 (0.00) Ag only 85 0.82 (4.48) 0.49 (1.50) 0.88 (2.52) 2.36 (4.40) 1.48 (3.27) 0.36 (1.32) Ag–developed 20 1.10 (2.33) 1.15 (3.18) 1.15 (2.90) 3.20 (6.60) 2.05 (4.04) 0.30 (1.12) Developed only 20 1.60 (4.66) 2.25 (7.18) 4.40 (11.75) 7.75 (13.71) 3.35 (4.45) 0.70 (1.45) Northeastern Naturalist Vol. 23, No. 2 A.T. McGowan and A.S. Hogue 2016 269 19 sites which contained only pine forest, suggesting that the former habitat may be more biodiverse. Our fine-scale analysis of more-detailed habitat classifications (i.e., marsh, developed, agriculture) revealed that the Big Brown Bat and Evening Bat had significantly higher activity along 2 forest edges than in agriculture only (P < 0.05; Table 2). We detected no other significant differences. Landscape level. In line with our predictions, activity levels of the Eastern Red Bat (t = -0.1690901, z = -3.4833, P < 0.001), Big Brown Bat (t = -0.205658, z = -4.0099, P < 0.001), Evening Bat (t = -0.198889, z = -3.7046, P < 0.001), and Big Brown/Silver-haired Bat (t = -0.1139518, z = -2.6305, P < 0.01) were significantly negatively correlated with distance to forest as a whole and mixed forests specifically (Table 3). Neither the Hoary Bat nor the calls attributed to the Silver-haired Bat showed a significant correlation between activity level and distance to any forest type (Table 3). Our distance to water-edge predictions were weakly supported. The Big Brown/ Silver-haired Bat (t = -0.1030338, z = -2.3826, P < 0.05) had significantly higher activity as distance to tree-lined water features decreased. No other species demonstrated a significant relationship with proximity to either natural or human-made water features (Table 3). Our predictions regarding total percent forest-cover and forest-edge were largely unsupported. We expected activity levels for all species except the Hoary Bat to be significantly positively correlated with total percent forest-cover. However, although Hoary Bat activity levels were significantly negatively correlated with total forest-cover (t = -0.09602988, z = -1.9377, P < 0.05), none of the other species showed the predicted significant positive relationship. Moreover, when analyzed separately, calls attributed to the Silver-haired Bat actually showed a significant negative correlation with percent forest-cover (t = -0.100843, z = -2.0914, P < 0.05, Table 3). Similarly, total forest-edge was not significantly correlated with activity level for any species (Table 3). We expected that total agricultural land-cover would be positively correlated with Hoary Bat activity levels and negatively correlated with activity of all other bats. No species demonstrated a significant relationship (Table 3). Table 3. Kendall’s Tau correlations between number of call sequences at each site for each species and distance to indicated habitat variable at the landscape level or amount of habitat variable within a 1-km radius. Asterisks represent significance (* = P < 0.05, ** = P < 0.01 *** = P < 0.001). Big Brown/ Eastern Evening Big Brown Silver-haired Silver-haired Hoary Variable Red Bat Bat Bat Bat Bat Bat Distance to tree edge -0.186*** -0.235*** -0.192*** -0.1240** -0.019 0.044 Distance to forest -0.169*** -0.199*** -0.206*** -0.1139** -0.006 0.072 Distance to mixed forest -0.147** -0.159*** -0.178*** -0.1370** -0.042 0.018 Distance to pine forest -0.029 -0.067 -0.038 -0.0005 0.026 0.057 Distance to natural water bodies -0.024 -0.030 -0.073 -0.1030* -0.045 0.019 Distance to water -0.012 -0.007 -0.009 -0.0429 -0.013 0.040 Percent forest-cover 0.066 0.055 0.051 -0.0329 -0.101* -0.096* Total forest-edge 0.026 0.041 -0.028 -0.0196 -0.028 -0.006 Percent agricultural land -0.186 -0.235 -0.192 -0.0371 -0.001 -0.013 Northeastern Naturalist 270 A.T. McGowan and A.S. Hogue 2016 Vol. 23, No. 2 Passive sites. In line with our predictions, forest-edge sites had significantly higher activity than forest-interior sites for the Eastern Red Bat (W = 56, P < 0.05), Big Brown/Silver-haired Bat (W = 60, P < 0.01), and Evening Bat (W = 63, P less than 0.01) (Table 1). We did not analyze activity levels for the Hoary Bat and calls attributed to the Silver-haired Bat due to small sample sizes. Discussion We documented all 6 of the bat species known to occur on the Delmarva Peninsula during the summer, assuming the Myotis call sequences can be attributed to the Little Brown Bat. A comparison of the results we obtained using the road-based transect technique versus traditional passive monitoring revealed both techniques obtained similar results, suggesting that survey effort beyond the first 3 hours of nightly activity is not necessary. In fact, the passive data revealed that only 10 call sequences were recorded in 9 nights of sampling at interior forest sites, whereas over 500 call sequences were recorded in just 8 nights at edge sites, indicating that, during our sampling period, little activity was missed by not being able to sample interior forest conditions at transect sites, though this may not be true during other seasons. Moreover, we documented 5 times as many call sequences in a quarter of the recording time at a larger and more diverse array of sites over a larger geographic area using driving transects compared to passive sites. These findings suggest point-count road transects are an excellent tool for surveying summer bat populations in the region. Caution should be used when extrapolating these results to other seasons and areas where forest-interior species are present or where roads are few and far between. Of the species documented, the most surprising is the Silver-haired Bat. Few previous local studies recorded this species. Both Sturgis (2013) and Johnson et al. (2011) found Silver-haired Bats on the Peninsula, but only during times presumed to coincide with migratory periods for the species. This finding prompted Johnson et al. (2011) to speculate that Silver-haired Bats may not utilize the area during summer months. However, most previous studies were extremely limited in geographic scope and documented far fewer species as a result. The large number of calls attributed to this species, as well as the greater number of species found in our peninsula-wide study suggest at least some of these calls may be from Silverhaired bats. This finding is consistent with data from museum records, which show that, while these bats are largely absent from much of the southeastern US during the summer, their warm-season range appears to extend as far south as Delmarva in the eastern US (Cryan 2003). The failure of calls attributed to this species to follow predicted patterns of habitat use adds to the uncertainty of their proper attribution. Future work incorporating mist-net captures are needed to verify the presence of this species. Although we sampled for 248 h at 304 sites, we could confidently classify to the genus Myotis only 16 of 4432 call sequences. Fox (2007) and Limpert et al. (2007) only encountered a single Myotis bat in their studies. Sturgis (2013) collected Little Brown Bats from 2 large colonies on the Delmarva Peninsula in 2010, but Northeastern Naturalist Vol. 23, No. 2 A.T. McGowan and A.S. Hogue 2016 271 only 2 of 200 bats returned in 2011 to 1 colony, likely due to die-offs from WNS. The structure housing the second colony was destroyed, thus its fate is unclear (Kevina Vulinec, Delaware State University, Dover, DE, pers. comm.). However, of the 27 bat specimens in the US National Museum of Natural History collections from Delmarva, all but 5 are Little Brown Bats. These specimens were collected between 1899 and 1977 at several locations in the northern part of the peninsula. Field sites in all recent studies, including our own, were located south of where the museum specimens were collected; thus, it remains unclear if the low number of Myotis found lately represents a longstanding regional difference between the north and central portions of the peninsula, a failure to adequately sample areas with larger Myotis populations, or a recent decline in these populations. The mass die-off of Myotis species caused by WNS (Reeder et al. 2012) has likely reduced the number of these bats traveling from infected hibernacula to Delmarva, and may explain why so few call sequences were recorded for this genus. However, many of these hibernacula are located in caves, mines, and tunnels in the Appalachian Mountains, and activity levels may be influenced by proximity to hibernacula (Furlonger et al. 1987, Gates et al. 1984). Most locations on the peninsula are located in excess of 100 km from the Appalachian Mountains, and this may help explain the low numbers recorded. Still, the large decline of the colonies on the peninsula noted above suggests WNS is at least partly to blame. Our predictions about habitat selection were largely supported by both local and landscape-level analyses. Hoary Bats generally preferred open areas, whereas the Eastern Red Bat, Big Brown Bat, and Evening Bat all showed higher activity levels at forested sites and as the distance to forests decreased. These findings are consistent with previous theoretical and empirical studies (Frey-Ehrenbold et al. 2013, Jantzen and Fenton 2013, Morris et al. 2010, Walsh and Harris 1996, Wolcott and Vulinec 2012) and suggest that the preservation of forests, especially forest edges, is of crucial importance to these species. Our results indicate that bats chose mixed hardwood–pine forests over pine plantations, possibly because of their higher floral diversity and greater structural complexity compared to pine plantations. This preference raises concerns because many of the large tracts of preserved forest on the peninsula are single-age pine plantations that are regularly logged. Factors such as forest age, canopy cover, average diameter at breast height, and other details of forest composition may influence bat roosting and foraging behavior (Jung et al. 1999, Limpert et al. 2007, Menzel et al. 2001). Future studies to evaluate bat activity in light of these details at the local and landscape level are needed to determine which variables are most important to local bats, and to what extent bats prefer mixed forest over pine forests when controlling for these variables. Our failure to detect a positive relationship with total forest-cover for most forest-adapted species may indicate that the 1-km radius we selected to determine landscape effects of forest on activity was not sufficient, or that finer-grained habitat variables are more important for bats. While a recent study on the peninsula used a similar scale to assess roost availability (Limpert et al. 2007), the species examined Northeastern Naturalist 272 A.T. McGowan and A.S. Hogue 2016 Vol. 23, No. 2 in that study has a smaller foraging distance than some of the species found in our study. Additionally, other studies examining landscape features and their effects on bats have used larger scales (Erickson and West 2003, Medlin et al. 2010). Bats can cover large distances when foraging; thus, it is possible that the 1-km scale was simply not large enough to adequately assess the effects of landscape forest-cover on activity (Gorresen et al. 2005). Future studies in the region should consider sampling designs that permit analyzing landscape variables on larger scales than we examined in our study. Our prediction that bat activity would be positively correlated with the total length of forest edge within a 1-km radius was also not supported. Given that batactivity levels were higher at sites adjacent to forest edges (local level) and with proximity to forest edges (landscape level) for 3 of the 4 forest-edge species, it is clear that forest edges are important features of the landscape for these species. The fact that amounts of edge in the landscape were not correlated with bat activity suggests that while bats are attracted to forest edges, if the sampling site is not near one of these edges, bats will not be detected in large numbers at that site regardless of how much total edge is in the area. All local bat species require access to surface water for drinking. It is unclear why only the combined Big Brown/Silver-haired Bat calls showed significantly higher activity levels with greater proximity to water bodies given that the importance of water to foraging bats is well established regionally (Lookingbill et al. 2010, Menzel et al. 2005). One possible explanation is that selection of foraging areas may be weakly dictated by proximity to water bodies, particularly artificial water bodies. Second, water systems in this study were not separated based on salinity, and it is possible that some water bodies were too saline to provide bats with viable drinking water. Both a large-scale radio-tracking study, and a study which compares water sources of differing salinities would help answer these questions. In conclusion, this study showed that road-based point-count transects are a valuable technique for sampling large geographic areas over a single season. We documented the presence of every species previously known to inhabit the peninsula in the summer, and recorded relatively high levels of activity for all but the Tri-colored Bat and Myotis sp. While the reasons for the low activity levels of these species are not clear, they likely reflect the mass die-offs associated with WNS in other regions, as well as the considerable distance from suitable hibernacula to the peninsula. We found that most species strongly prefer forest edges above all other measured features of the landscape. Thus, our research showed that in an area dominated by agriculture, it is essential to retain forest patches and corridors in order to maintain healthy bat populations. Future research should focus on uncovering the reasons for the low numbers of the Tri-colored Bat and Myotis sp., confirming the presence as well as habitat preferences of the Silver-haired Bat, and a more detailed examination of habitat use along mixed hardwood-pine forests versus pine plantations. Northeastern Naturalist Vol. 23, No. 2 A.T. McGowan and A.S. Hogue 2016 273 Acknowledgments We thank Dr. P. Anderson for help with statistics and programming and Dr. K. Vulinec for helpful editorial comments and input on the project. Thanks to M. Schofield, A. Clark, and J. Moulis (Maryland Department of Natural Resources), M. Whitbeck (US Fish and Wildlife Service), K. Patton (Lower Shore Land Trust), M. 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