Northeastern Naturalist Vol. 21, No. 3 L.S. Coleman, W.M. Ford, C.A. Dobony, and E.R. Britzke 2014 431 2014 NORTHEASTERN NATURALIST 21(3):431–445 Comparison of Radio-telemetric Home-Range Analysis and Acoustic Detection for Little Brown Bat Habitat Evaluation Laci S. Coleman1, 2, W. Mark Ford3,*, Christopher A. Dobony4, and Eric R. Britzke5 Abstract - With dramatic declines of bat populations due to mortality caused by Pseudogymnoascus destructans (White-nose Syndrome), assessing habitat preferences of bats in the northeastern US is now critical to guide the development of regional conservation efforts. In the summer of 2012, we conducted fixed-station simultaneous telemetry to determine nocturnal spatial use and fixed-kernel home-range estimates of available habitat of a Myotis lucifugus (Le Conte) (Little Brown Bat) maternity colony in an artificial bat house. In summers of 2011 and 2012, we also deployed a 52-ha grid of 4 x 4 Anabat acoustic detectors over five 6–8-day sampling periods in various riparian and non-riparian environments in close proximity to the same bat house. The mean telemetry home range of 143 ha for bats (n = 7) completely overlapped the acoustic grid. Rankings of habitats from telemetry data for these 7 bats and 5 additional bats not included in home-range calculations but added for habitat-use measures (n = 13) revealed a higher proportional use of forested riparian habitats than other types at the landscape scale. Pair-wise comparisons of habitats indicated that bats were found significantly closer to forested riparian habitats and forests than to open water, developed areas, fields, shrublands, or wetland habitats at the landscape scale. Acoustic sampling showed that naïve occupancy was 0.8 and 0.6 and mean nightly detection probabilities were 0.23 and 0.08 at riparian and non-riparian sites, respectively. Our findings suggest that Little Brown Bats select forested riparian and forested habitats for foraging at the landscape scale but may be most easily detected acoustically at riparian sites when a simple occupancy determination for an area is required. Introduction Although there are a number of studies—albeit limited relative to other wildlife species—that report on home-range estimates of various species of bats in the northeastern US and Canada (Broders et al. 2006, Henry et al. 2002, Owen et al. 2003, Watrous et al. 2006), there is no research reporting on the foraging home range and associated habitat use of Myotis lucifugus (Le Conte) (Little Brown Bat) in the context of a landscape impacted by Pseudogymnoascus destructans (Whitenose Syndrome [WNS])-associated mortality. An emerging fungal disease, WNS was first described in the United States in 2006 (Blehert et al. 2009), and much of the eastern US and Canada are now affected 1Department of Fisheries and Wildlife Conservation, Virginia Tech, 106 Cheatham Hall, Blacksburg, VA 24061. 2Current address - Eco-Tech Consultants, Inc., 1220 Kennestone Circle Suite 100, Marietta, GA 30066. 3US Geological Survey, Virginia Cooperative Fish and Wildlife Research Unit, 106 Cheatham Hall, Blacksburg, VA 24061. 4Fort Drum Military Installation, Natural Resources Branch, 85 First Street West, IMNE-DRM-PWE, Fort Drum, NY 13602. 5US Army Engineer Research and Development Center, 3909 Halls Ferry Road, Vicksburg, MS 39180. *Corresponding author - wmford@vt.edu. Manuscript Editor: Jacques Veilleux Northeastern Naturalist 432 L.S. Coleman, W.M. Ford, C.A. Dobony, and E.R. Britzke 2014 Vol. 21, No. 3 by the fungus associated with WNS (USFWS 2013). This pathogen is known to affect 7 species of eastern cave-hibernating bats: the federally endangered Myotis sodalis Miller and Allen (Indiana Bat) and M. grisescens Howell (Gray Bat), as well as M. septentrionalis (Trouessart) (Northern Long-eared Bat), M. leibii (Audubon & Bachman) (Eastern Small-footed Bat), the Little Brown Bat, Eptesicus fuscus (Beauvois) (Big Brown Bat), and Perimyotis subflavus (Cuvier) (Tri-colored Bat). Since its onset, WNS has caused the death of >6 million bats (USFWS 2013), decreasing many winter hibernacula-colony counts by 75–100% from pre-WNS estimates (Blehert et al. 2009; Frick et al. 2010; R. Reynolds, Virginia Department of Game and Inland Fisheries, Verona, VA, pers. comm.; Turner et al. 2011) and has led to the proposed rule to list the Northern Long-eared Bat as endangered (Federal Register § 78:61045–61080). The Little Brown Bat was among the most common insectivorous bat species in North America (Fenton and Barclay 1980). Little Brown Bats use a variety of forested and open habitats near bodies of water for foraging (Fenton and Barclay 1980), and both forests and human-made structures as summer roosting sites (Davis and Hitchcock 1965, Fenton and Barclay 1980). As a species formerly found in summer colonies of hundreds to thousands of individuals (Davis and Hitchcock 1965), Little Brown Bats are now rarely observed in the northeastern US because most known colonies have decreased precipitously in number or disappeared (Dobony et al. 2011, Dzal et al. 2011, Ford et al. 2011, Frick et al. 2010, Turner et al. 2011). These declines have resulted in Little Brown Bats facing potential local to regional extirpation from WNS- associated mortality (Frick et al. 2010). As populations continue to shrink, understanding spatial and temporal use of the landscape by Little Brown Bats will be important for conservation of remaining populations and for potential recovery efforts in the future. The objective of our study was to determine the congruency between nocturnal spatial use of foraging habitat and acoustic monitoring locations for Little Brown Bats by describing foraging home ranges, conducting habitat analyses, and developing occupancy and detection estimates. Such data can help optimize detectordeployment sites for implementing an effective acoustic-sampling protocol for myotine species. Herein, we report on and compare the nocturnal spatial use of adult females from a Little Brown Bat maternity colony roosting in an artificial bat house at the Fort Drum Military Installation (hereafter, Fort Drum), in northwestern New York, using radio-telemetric and acoustical methods. Field-site Description We conducted our study at Fort Drum, a US Army installation of approximately 43,000 ha in Jefferson and Lewis counties in northern New York (44°00'N, 75°49'W; Fig. 1). The installation lies at the intersection of the St. Lawrence–Great Lakes Lowlands, foothills of the Adirondack Mountains, and Tug Hill Plateau ecoregions within the Black River and Indian River drainages. The nearby Niagara Escarpment (10–15 km west of Fort Drum) contains karst formations and numerous caves used as overwintering sites for bats. Approximately 57% of Fort Drum consists of forested habitat dominated by northern hardwoods. Wetland systems including wet meadows Northeastern Naturalist Vol. 21, No. 3 L.S. Coleman, W.M. Ford, C.A. Dobony, and E.R. Britzke 2014 433 and Castor canadensis Kuhl (North American Beaver)-impacted streams and ponds make up 20% of the installation’s area. Development is concentrated in a cantonment area, with the remainder of the installation consisting of 18 training areas, an airfield, and a large, centralized ordinance-impact zone that are all largely undeveloped. Methods In June and July 2012, we captured Little Brown Bats day-roosting in a bat house in the cantonment area using double-stacked mesh mist nets (5.1 m high and 3–12 m in width; Avinet, Inc., Dryden, NY). We placed nets along edges between open fields and forests, and along forested corridors in close proximity to the bat house occupied by Little Brown Bats. For each captured bat, we recorded age, sex, reproductive condition, mass, and right forearm length. We attached 0.34-g radio transmitters (LB 2XT Holohil Systems Ltd., Carp, ON, Canada) to the interscapular region of adult females using Skin Bond (Smith and Nephew, Largo, FL) or Perma-Type (Perma-Type Company Inc., Plainville, CT) medical adhesive. The transmitter to body-mass ratios were less than 5% of the average body mass of adult female Little Brown Bats as suggested by Aldridge and Brigham (1988). We released bats near the site of capture, and waited to begin foraging telemetry until the following night to avoid including unusual behaviors Figure 1. Fort Drum Military Installation, Jefferson and Lewis counties, NY; cantonment area site of the 4x4 grid of passive acoustic sampling for Little Brown Bats, during summers 2011–2012 is demarcated by black line. Northeastern Naturalist 434 L.S. Coleman, W.M. Ford, C.A. Dobony, and E.R. Britzke 2014 Vol. 21, No. 3 resulting from handling. Bat capture and handling procedures were approved by the Virginia Polytechnic Institute and State University Institutional Animal Care and Use Committee (Protocol # 11-059-FIW). We used fixed-station telemetry to conduct simultaneous bi-angulation (Menzel et al. 2005a, Owen et al. 2003) within or adjacent to the expected foraging area near the cantonment from emergence (approximately 2100 hours) until bats returned to the bat box, roosted elsewhere, or could no longer be found. We used Wildlife Materials TRX-2000S (Murphysboro, IL) telemetry receivers and five-element Yagi antennas to estimate the azimuths of foraging bats in 5–10-minute intervals (Menzel et al. 2005a). We monitored signals in synchronization between two observers at an approximately 90° position. We tracked individual bats nightly for the duration of transmitter operability. We entered coordinates of each fixed telemetry station and all azimuth readings into LOCATE III software (Pacer Computing, Tatamagouche, NS, Canada) to obtain universal transverse mercator (UTM) coordinates of each foraging location (Menzel et al. 2005a, Owen et al. 2003). We calculated home-range estimates for bats with ≥30 location estimations (Seaman et al. 1999) using the habitat-analysis tool in Biotas (Ecological Software Solutions, LLC, Hegymagas, Hungary). We used the fixedkernel density estimator with the least-squares cross-validation smoothing factor based on a 95% confidence interval to exclude outliers. In addition to the bats with calculated home-range metrics, we also incorporated telemetry data from 3 female bats in 2012 (1 pregnant and 2 post-lactating) and 2 females in 2010 (2 post-lactating) from the same colony to increase overall sample size for habitat-use analyses. Tracking methodologies were the same for all bats included in the study. We created a habitat map of the study area using 2006 land-cover data provided by the Fort Drum natural resources branch for areas inside the installation and the 2006 National Land Cover Database (Fry et al. 2011) for areas outside its boundary. We reclassified habitat types from both sources into 7 categories: open water, forests, developed areas, fields, shrublands, forested riparian areas, and wetlands (i.e., various emergent wetland and wetland meadow complexes). We categorized open water, forests, developed areas, fields, and shrublands directly from our landcover sources. We derived forested riparian zones and wetland zones by creating 15-m-wide buffer zones around mapped forested and open-water riparian areas and wetlands to match the width of suggested streamside management zones for New York (NYDEC 2011). We exported home-range polygons from Biotas for use in ArcMap 9.3 (Environmental Systems Research Institute, Redlands, CA) for area calculations and habitat analysis. We used the Euclidean distance approach that analyzes habitat use linearly, thereby readily capturing the use of ecotones or edges (Conner and Plowman 2001). Without requiring explicit error modeling or equal sampling of individuals, Euclidean distance can be adapted to multiple spatial scales, which for our data, were the home-range and landscape scales. To define the landscape scale, we buffered all points by the greatest distance observed among all points and merged those polygons. At both scales, we used the distance tool in ArcMap to calculate the Euclidean distance between each location and the closest representative polygon of each habitat Northeastern Naturalist Vol. 21, No. 3 L.S. Coleman, W.M. Ford, C.A. Dobony, and E.R. Britzke 2014 435 type (Conner and Plowman 2001). We used the Create Random Points tool in Arc- Map to create random locations to pair with bat locations within individual bat home ranges and across the entire landscape to represent the two scales, respectively. We created a vector of ratios between distances to habitat types from foraging and random locations, and used a multivariate analysis of variance (MANOVA) to determine if ratios were different from 1.0, indicating nonrandom habitat use. We then used a paired t-test to determine whether habitats were used in proportion with their availability at each scale and a series of t-tests to determine habitat preference rankings. We defined statistical significance as P ≤ 0.05 and used SAS statistical software (SAS Institute 2012, Cary, NC) to perform all statistical analyses. During the summers of 2011 and 2012, we collected acoustic data on Little Brown Bats as part of an effort that was initiated to modify pre-WNS bat-monitoring protocols initially established in 2003 at Fort Drum. We focused sampling efforts to specifically target the putative Little Brown Bat foraging area near the known maternity-roost structure. We deployed a 52-ha grid of 4 x 4 Anabat acoustic detectors (Titley Scientific, Columbia, MO) over 6–8-day sampling periods in various riparian (n = 11) and non-riparian (n = 5) environments near the bat house. Sampling occurred during 5 periods: 25 July–1 August, in 2011 and 30 May–5 June, 20 June–27 June, 6 July–13 July, and 23 July–30 July, in 2012. We collected acoustic data using Anabat II detectors connected to a compact flash-storage zero-crossings analysis-interface module (ZCAIM), as well as SD1 and SD2 units (Titley Electronics, Ballina, NSW, Australia). Before deployment, we calibrated all units using an ultrasonic insect-deterrent device following the methods of Larson and Hayes (2000). We placed Anabat units in weatherproof boxes with polyvinyl chloride (PVC) tubes that contained a small weep hole in the bottom for water drainage according to the methods of O’Farrell (1998). We placed boxes on 1.5-m tripods aligned to allow sound to enter the PVC tubes at a 45° reflective angle to be received perpendicularly by Anabat transducers (Britzke et al. 2010). To ensure that more than one Anabat did not collect data on the same bat simultaneously, we installed detectors so that ~200–250 m separated each placement site. At each sample site, we chose deployment location and microphone direction to maximize call quality. For example, we targeted sites with uncluttered openings such as canopy gaps, forested trails with open corridors, or open water. We set Anabats to record data continuously from approximately 1900–0700 h for 6–8 days during each sampling period. We changed batteries and memory cards as needed and downloaded data onto a laptop computer using CFCread software (Titley Electronics Ballina, NSW, Australia). We used the automated analysis program EchoClass version 1.1 (US Army Engineer Research and Development Center, Vicksburg, MS) to identify bat calls to species level. Although accuracy rates using automated software to identify individual species are <100% (Britzke et al. 2011), EchoClass provides a maximum likelihood estimate which allows the user to determine the probable presence or absence of a species with predetermined levels of accuracy as modified by the presence of other species with overlapping echolocation-pulse characteristics (Britzke 2002). We considered Little Brown Bats to be present at a site if the maximum Northeastern Naturalist 436 L.S. Coleman, W.M. Ford, C.A. Dobony, and E.R. Britzke 2014 Vol. 21, No. 3 likelihood value estimate was ≥90%. We visually examined a subset of bat calls from 2011 and found no discrepancy with the automated classifer (L.S. Coleman, unpubl. data). Moreover, by the summer of 2012, presence of Indiana Bats and Northern Long-eared Bats at Fort Drum (as determined by mist-net survey efforts) was much reduced relative to the remaining Little Brown Bats, further strengthening our confidence in correct identification of Little Brown Bat echolocation calls (Coleman et al. 2013). We created a nightly presence–absence detection history from the acoustic data (Gorresen et al. 2008). We considered each survey night independent due to the separation of sites and the break in sampling during daylight hours. We used program PRESENCE (version 2.4; Hines and Mackenzie 2008) to attempt to fit a candidate set of a priori models that incorporated broad habitat categorizations as a site covariate, and several time parameters as sampling covariates that could affect the probability of detection or occupancy (Table 1). We eliminated any models from the candidate set that included illogical parameter estimates and models with parameter estimates that did not converge. We ranked models using Akaike’s information criterion (AICc) corrected for small sample size and compared them with Akaike weights (Burnham and Anderson 2002). We compared occupancy estimates between habitat types using multi-season models that assumed changing detection probabilities based on site and sampling covariates. We then extracted the 95% confidence set of models (Burnham and Anderson 2002) to recalculate model weights (Weller 2008). Results We tracked and determined fixed-kernel density home-range estimates for 7 adult female Little Brown Bats during June–August 2012. Our sample included 1 pregnant, 2 lactating, 3 post-lactating, and 1 non-reproductive bat; to maintain Table 1. Multiple-season occupancy models explaining the influence of habitat and time on occupancy and detection estimates of Little Brown Bats at a grid of acoustic echolocation detectors at Fort Drum Military Installation, NY, summers of 2011–2012; Ψ = occupancy, γ = colonization, ε = extinction, p = detection, habitat = riparian versus non-riparian, day = day of the year, day2 = day of the year squared, year = 2011 versus 2012, day*year = interaction term of day and year, full = full identity, and “.” = constant. Model K AICc ΔAICc ωi Ψ(habitat), γ(full), ε(full), p (habitat + day + day2) 14 201.02 0.00 0.4292 Ψ (habitat), γ(full), ε(full), p (habitat + day*year + day2) 14 201.27 0.25 0.3787 Ψ (habitat), γ(full), ε(full), p (habitat + day + day2 + year) 15 202.95 1.93 0.1635 Ψ(habitat), γ(.), ε(.), p (habitat) 6 207.43 6.41 0.0174 Null 4 210.65 9.63 0.0035 Ψ(habitat + year), γ(.), ε(.), p (habitat + year) 8 210.97 9.95 0.0030 Ψ(habitat), γ(day*year), ε(year), p (habitat) 8 211.43 10.41 0.0024 Ψ(habitat), γ(full), ε(full), p (habitat + year) 13 213.48 12.46 0.0008 Ψ(habitat), γ(full), ε(full), p (habitat + day*year) 13 213.54 12.52 0.0008 Ψ(habitat + year), γ(full), ε(full), p (habitat + year) 14 215.48 14.46 0.0003 Ψ(habitat + day*year), γ(full), ε(full), p (habitat + year) 14 215.48 14.46 0.0003 Global 24 217.92 16.90 0.0001 Ψ(habitat), γ(full), ε(full), p (habitat) 12 233.31 32.29 0.0000 Northeastern Naturalist Vol. 21, No. 3 L.S. Coleman, W.M. Ford, C.A. Dobony, and E.R. Britzke 2014 437 sample size, bats were not separated by reproductive status in our analyses. Mean number of locations retained to calculate each bat’s home range was 63 (SE = 9.9, range = 33–102). The mean area of Little Brown Bat home ranges was 143.0 ha (n = 7, SE = 71.0). For these bats, and the additional 5 females also tracked in 2012 (1 pregnant and 2 post-lactating) or 2010 (1 pregnant and 1 non-reproductive), mean distances from bat locations to habitat types (Table 2) were not different from random at the home-range scale (F7,5 = 3.88, P = .0775) but were nonrandom at the landscape scale (F7,5 = 379.37, P < 0.0001). At the landscape scale, we found bats significantly closer to open water (t = -32.00, P < 0.0001), developed areas (t = -5.20, P = 0.0003), forests (t = -29.61, P < 0.0001), shrublands (t = -6.02, P < 0.0001), forested riparian areas (t = -35.32, P < 0.0001), and wetlands (t = -21.39, P < 0.0001) than expected, but further from fields (t = 10.31, P < 0.0001) than expected. A ranking of habitats showed that forested riparian zones and forests were used at a similar proportion to each other based on their availability, followed by open-water habitats (Table 3). Pairwise comparisons of the distances between Little Brown Bat foraging locations and habitat types indicated that bats foraged significantly closer to forested riparian zones and forests than to any other habitat type, followed by open water and wetland zones, but there was not a significant difference between the distance to forested riparian zones or fore sts (Table 3). We collected acoustic data during 5 sampling periods totaling 40 sampling nights at a grid of detectors placed in the vicinity of the Little Brown Bat maternity colony Figure 2. Location of acoustical detector grid at Fort Drum Military Installation, NY, summers 2011–2012 relative to 95% adaptive kernel home ranges for female Little Brown Bats (n = 7), summer 2012. MYLU = Myotis lucifugus. All home-ranges (demarcated by colored lines) encompassed most or all of the detector grid. See text for additional details. Northeastern Naturalist 438 L.S. Coleman, W.M. Ford, C.A. Dobony, and E.R. Britzke 2014 Vol. 21, No. 3 Table 4. Ninety-five percent confidence set of models and associated mean occupancy (ψ) estimates for Little Brown Bats at a grid of acoustic echolocation detectors at Fort Drum Military Installation, NY, summers 2011–2012. γ = colonization, ε + extinction, p = detection, habitat = riparian versus non-riparian, day = day of the year, day2 = day of the year squared, year = 2011 versus 2012, day*year = interaction term of day and year, and full = full identity. Model K AIC ΔAIC ωi ψRiparian (SE) ψNonriparian (SE) 1. Ψ(habitat) γ(full) ε(full) p (habitat + day + day2) 14 201.02 0.00 0.4418 0.6031 (0.5922) 1.0 (0.0) 2. Ψ(habitat) γ(full) ε(full) p (habitat + day*year + day2) 14 201.27 0.25 0.3899 0.6137 (0.2167) 1.0 (0.0) 3. Ψ(habitat) γ(full) ε(full) p (habitat + day + day2 + year) 15 202.95 1.93 0.1683 0.6168 (0.5699) 1.0 (0.0) Table 3. Ranking matrix of Little Brown Bat habitat use at the landscape scale derived from foraging telemetry conducted at Fort Drum Military Installation, June–August 2010 and 2012. Numbers are t-statistics associated with pairwise comparisons of corrected distances to habitat. Rankings interpreted as relative magnitudes, i.e., larger values associated with higher proportional use. *P < 0.05, **P < 0.01, ***P < 0.0001. Open Water Developed areas Fields Forests Shrublands Forested riparian areas Wetlands Open water -7.24*** -19.35*** 4.04** -12.85*** 15.57*** -24.74*** Developed areas 7.24*** -7.66*** 7.83*** -2.25* 8.80*** 2.98* Fields 19.35*** 7.66*** 30.40*** 8.93*** 22.68*** 14.50*** Forests -4.04** -7.83*** -30.40*** -12.66*** 1.35 (0.2029) -14.35*** Shrublands 12.85*** 2.25* -8.93*** 12.66*** 14.57*** 6.48*** Forested riparian areas -15.57*** -8.80*** -22.68*** -1.35 (0.2029) -14.57*** -36.90*** Wetlands 24.74*** -2.98* -14.50*** 14.35*** -6.48*** 36.90*** Table 2. Mean (SE) Euclidean distances of bat and random locations to habitat types derived from foraging telemetry conducted at Fort Drum Military Installation, June–August 2012. Locations n Open water Developed areas Field Forest Shrublands Forested riparian Wetland Bats 523 92.4 (6.9) 166.3 (12.2) 233.1 (8.5) 21.4 (3.2) 153.0 (5.9) 38.6 (6.7) 187.4 (6.4) Random (home range) 523 112.5 (12.2) 146.7 (11.0) 234.9 (17.5) 23.7 (5.2) 138.5 (6.4) 55.2 (13.7) 185.0 (10.4) Random (landscape) 523 312.3 (13.8) 229.5 (11.0) 143.5 (11.9) 115.1 (7.5) 188.8 (9.4) 276.8 (13.9) 326.8 (13.5) Northeastern Naturalist Vol. 21, No. 3 L.S. Coleman, W.M. Ford, C.A. Dobony, and E.R. Britzke 2014 439 (Fig. 2); we detected Little Brown Bats at 12 of the 16 sites. However, to eliminate the high potential for sound distortion caused by multiple bats being detected simultaneously, we removed one of the 12 sites where Little Brown Bats were detected due to its close proximity (within < 50 m) to the bat house and along its exiting and entering flight corridor. Little Brown Bat home ranges largely encompassed the acoustic detector grid (Fig. 2). The 95% confidence set of models included 3 competing top models with high empirical support according to AIC weights (Table 4). These competing models presented occupancy estimates that varied by habitat and detection probabilities that varied by habitat and time-related covariates. Because our top and third model had large standard errors around the occupancy estimates and detection probabilities, we drew most inference from model 2 (Table 4). This model suggested higher occupancy of Little Brown Bats at non-riparian sites versus riparian sites but higher naïve occupancy estimates and probabilities of detection at riparian sites than non-riparian sites (Table 4; Fig. 3). Discussion There have been few published reports of efforts to assess Little Brown Bat home range and habitat use by telemetric methods, with work to date restricted to Acadian region boreal forests in eastern Canada (Broders et al. 2006, Henry et al. Figure 3. Naïve occupancy, mean occupancy (psi) and detection (p) probability estimates of Little Brown Bats at riparian/wetland and non-riparian sites at a 4 x 4 grid of acoustic echolocation detectors at Fort Drum Military Installation, NY, summers 2011–2012. Northeastern Naturalist 440 L.S. Coleman, W.M. Ford, C.A. Dobony, and E.R. Britzke 2014 Vol. 21, No. 3 2002) and the agricultural Midwest (Bergeson 2012). The Little Brown Bats in our study exhibited larger home-range sizes (143 ha) than 90% fixed-kernel estimates of pregnant (30 ha) or lactating (18 ha) females in Quebec, Canada (Henry et al. 2002) or 100% minimum convex polygons of males (52 ha) in New Brunswick, Canada (Broders et al. 2006). In Illinois, Bergeson (2012) reported a mean 95% fixed-kernel home-range size much larger than we observed (515 ha versus 143 ha). Unlike our research, these studies occurred before the onset of WNS in their respective regions. We pooled data for all bats in our study regardless of reproductive status and home-range sizes, and landscape position appeared similar among bats; however, our sample sizes were too small to definitively demonstrate this. Neither did we attempt to track male Little Brown Bats that were common in mistnetting surveys on the installation away from the bat house prior to the onset of WNS (C.A. Dobony, unpubl. data). Nonetheless, discrepancies between our homerange estimations and those of other studies may have been caused by differences in estimation method, differences between males and females, sample size, habitat quality, prey availability, and/or spatial arrangement. Moreover, the presence of WNS-associated declines and physiological changes to extant bats may also be factors impacting how bats interact with their environment and how that response is modified by the changed intra- and interspecific competition among bats at Fort Drum (Jachowski et al., in press). From a habitat-selection perspective, our findings were consistent with the results of Broders et al. (2006) and Bergeson (2012). Specifically, Broders et al. (2006) reported that bats select open water and deciduous forested sites, and Bergeson (2012) described that bats selected closed-canopy and open-water sites but avoided open-field sites. Our results suggest that, at the landscape scale, adult female Little Brown Bats prefer forested riparian zones and forests, followed by open water and wetlands. We expected to detect selection for aquatic habitats because Little Brown Bats are known to have a diet that is skewed towards a diverse variety of adult aquatic insects (Belwood and Fenton 1976, Edythe and Kunz 1977). It is also well documented that linear landscape features such as riparian zones and corridors are used by bats as foraging areas and as travel corridors between roosting and foraging sites (Menzel et al. 2005a, b; Rogers et al. 2006; Verboom and Huitema 1997). The Little Brown Bat foraging areas at Fort Drum contained an abundance of such corridor habitats, e.g., old logging roads and hiking trails. These corridors may have been especially well-used by pregnant or lactating females to facilitate travel between open water and riparian foraging habitats and their primary bat-box day-roost or other night roosts. At Fort Drum, acoustic surveys showed that occupancy and detection probability estimates varied by our two simple habitat designations. We expected high detection-probability estimates at riparian sites, but the discrepancy between occupancy and detection-probability estimates at riparian versus non-riparian sites was unforeseen because Little Brown Bats are known to preferentially forage near bodies of water (Fenton and Barclay 1980). Johnson et al. (2008) reported that where Little Brown Bats were the predominant species, overall bat activity was Northeastern Naturalist Vol. 21, No. 3 L.S. Coleman, W.M. Ford, C.A. Dobony, and E.R. Britzke 2014 441 highest at water sources within a rural–urban gradient of the mid-Atlantic region near Washington, DC. Similar research from the coastal plain of South Carolina confirms the importance of riparian and wetland areas as foraging habitats for most species of bats, and suggests that monitoring acoustically within forest canopies may underestimate activity levels of many bat species (Ford et al. 2006, Menzel et al. 2005b). Therefore, higher probability of detection at acoustic sites dominated by aquatic characteristics in our study is consistent with most acoustic studies. Accordingly, using acoustics simply for presence/absence monitoring of Little Brown Bats in the Northeast should preferentially target riparian areas because they avoid closed-canopy, upland forests (Brooks and Ford 2005). The bats in our study equally preferred forests and forested riparian zones according to our Euclidean-distance habitat analysis, so a high occupancy estimate at non-riparian sites is unsurprising. However, occupancy estimates very close to 1 should be cautiously interpreted if obtained when the detection probability is <0.15 (MacKenzie et al. 2002). Such estimates are based on small amounts of presence data, making it difficult for models to distinguish between genuine absences and non-detections. Clearly, Little Brown Bats occupy forested habitat at Fort Drum; however, accuracy of the wide occupancy estimate across these sites reflected in our findings is uncertain. In this instance, we believe the naïve occupancy estimates were likely more informative, indeed suggesting higher occupancy in riparian habitats. Schirmacher et al. (2007) found similar acoustic survey results in the New River Gorge area of West Virginia, with Little Brown Bats more likely to be present in openings in forested habitats than closed-canopy forests when water was present or nearby. Brooks (2011) reported overall declines in Myotis species activity since the onset of WNS, but greatest activity levels at forested roads rather than at other forested sites such as recent clear cuts, streams, and beaver meadows. Despite WNS, Little Brown Bats continue to utilize habitat in similar patterns acoustically, although overall populations are smaller. In our study, all acoustic detectors that were set in non-riparian sites were placed along trails or near canopy gaps where some recordable activity was predicted a priori. Future acoustics sampling aimed at answering ecological questions about habitat should recognize that lower detection probabilities in forested upland habitats will require expanded survey effort—more sites and longer duration—to compensate (Coleman et al. 2014). Previous research has suggested that cluttered habitats do not significantly impact the ability to detect bats acoustically when species are adapted to such environments (Menzel et al. 2005b, Patriquin et al. 2003), but a threshold probably exists whereby acoustic detection is compromised in forested habitats (i.e., call structure is altered). Some Little Brown Bat calls may have been misclassified as Indiana Bats or labeled as unknown Myotis even though we placed microphones in our perceived best possible collection circumstances. Although acoustic identification accuracy rates of >90% can be achieved for myotids in open habitats in the Northeast, impacts of clutter on accuracy rates are not well understood (Britzke et al. 2011). Alternatively, it is possible that bats used forested habitats primarily for travel and did not extensively echolocate for navigation in these familiar corridors, Northeastern Naturalist 442 L.S. Coleman, W.M. Ford, C.A. Dobony, and E.R. Britzke 2014 Vol. 21, No. 3 resulting in lower acoustic detection. Nonetheless, use of occupancy analyses rather than traditional measures of relative activity is robust to identification errors of both commission and omission, assuming that enough sites over sufficient duration have been sampled. Regardless of differences between optimum derived occupancy and detection estimates at different habitats, telemetry and acoustic results suggest congruent patterns in bat activity at the landscape scale. In the only similar matched technique effort, Morris et al. (2011) found that telemetry and acoustic sampling were qualitatively similar at the home-range scale for determining the importance of pine habitat for Nycticeius humeralis Rafinesque (Evening Bat) in southwestern Georgia. At a landscape scale, however, telemetry data indicated that hardwood forests were used preferentially, but this relationship was not detected using acoustics. Accordingly, it should not be assumed that acoustic sampling always provides similar conclusions to telemetry across scales, because acoustics are biased towards foraging behavior and may underrepresent habitats that are important for travel or day- and night-roosting. As research using both telemetry and acoustics in concert expands, understanding how telemetry-error biases and detection-probability biases among habitats drives conclusions in similar or disparate directions is needed. However, in our study, telemetry results successfully validated use of general habitat types at the landscape scale that were chosen for acoustic sampling where Little Brown Bats were assumed to be present and active. Volant species are very difficult to track accurately using telemetry, and bats in this study were rarely tracked for their entire foraging durations in any one night. Regardless, results presented herein provide a novel perspective for validating optimal conditions for acoustically monitoring Little Brown Bats in a post-WNS environment. Furthermore, acoustics provide an alternative monitoring tool that may deliver similar results to telemetry at some scales, is a simpler method to implement over wider temporal and spatial scales, and will likely be more successful than other traditional capture methodologies and monitoring techniques as bat population declines continue. In areas severely impacted by WNS, Little Brown Bat detection will likely be difficult in any habitat and at any scale. Despite these challenges, results of our study confirm that summer monitoring can be successfully accomplished by deploying acoustic detectors along riparian habitats in areas where Little Brown Bats were historically present and are still known to occur, as confirmed by telemetry. Employing monitoring programs such as these, sooner rather than later, will help managers understand bat spatial and temporal use of their properties and provide them the information they need to address potential new state or federal listings of Little Brown Bats (Kunz and Reicha rd 2010). Acknowledgments Funding for this study was provided by the Fort Drum Natural Resources Branch through National Park Service, Southern Appalachian Cooperative Ecosystem Study Unit contract W9126G-11-2-SOI-0029 and the US Geological Survey Cooperative Research Unit Research Work Order VA-RWO-142. We thank R. Rainbolt, A. Dale, S. Dedrick, Northeastern Naturalist Vol. 21, No. 3 L.S. Coleman, W.M. Ford, C.A. Dobony, and E.R. Britzke 2014 443 G. Luongo, and N. Grosse for field assistance. An earlier draft of this manuscript was reviewed by D. Stauffer. Literature Cited Aldridge, H.D.J.N., and R.M. Brigham. 1988. 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