**2.2. Sampling location**

The study was conducted in the states of Maryland and Delaware on the Delmarva Peninsula, which is located along the mid-Atlantic coast of the United States between the Atlantic Ocean and the Chesapeake Bay. The peninsula consists primarily of a mosaic of agriculture (48%) and forests (37%, mostly mixed hardwood-pine and loblolly pine—*Pinus taeda*—plantations) [37]. The remainder is comprised of coastal marshes and scattered suburban and urban developments [37].

#### **2.3. Sampling protocol**

We established 28 transects that were evenly spaced across the Delmarva Peninsula as described by McGowan and Hogue [38]. Each transect contained 10 sampling points spaced 2 km apart (in straight line distance) for a total of 280 sites. We restricted transects to 2 lane roads, and sampling points to the nearest safe roadside location to stop for sampling. We sampled each transect once between June and August of 2014, yielding a total of 28 sampling nights. Transects were selected randomly for sampling without replacement using the random number generator in R Statistical Software [39]. The direction of travel along each transect was also randomly chosen. Unfortunately, due to equipment failure, two transects had to be excluded from analyses, dropping our total sampling nights (and transects) to 26.

We sampled each transect for bats using two approaches: point count and continuous sampling. Point count sampling occurred for 12 min at each of the 10 sampling points along each transect. Continuous sampling was carried out by leaving the detectors to operate as we drove the vehicle at speeds of 32–48 km/h along the transect between point count sites. We sampled each transect during peak bat activity, beginning 30 min after sunset and continuing until the transect was completed several hours later. For the continuous approach, we allowed the EM3 and miniMIC to operate atop a telescoping pole connected to the vehicle at a height of 2 m while we drove between sampling points. This allowed the detectors to be at a moderately elevated height while preventing damage from overlying bridges and road signs. Upon arriving at sampling points, we stopped the vehicle and extended the pole to 4 m and recorded for 12 min. We then collapsed the pole and drove the transect until reaching the next sampling point, repeating the process until all 10 points were sampled. In all cases, the detectors were pointed toward the immediately adjacent habitat to the right the road. Following recommendations of previous studies, we restricted sampling to nights without rain, temperatures above 10°C, and wind speeds less than 20 km/h [21, 40]. Call sequences recorded while driving were allocated to the continuous sampling data pool. Those recorded within the 12 min at each site were allocated to the point count sample. In total, we logged 52 h of recording time at stationary sampling points and just over 27 h from continuous sampling.

#### **2.4. Analyses**

**2. Methods**

10 Bats

EM3.

**2.2. Sampling location**

**2.3. Sampling protocol**

opments [37].

**2.1. Bat detectors**

We recorded bats using an EM3 EchoMeter (Wildlife Acoustics Inc., Maynard, MA, USA) fitted with a Garmin GPS device that stamps all call sequence recordings with the coordinates. Since the calls of most aerial foraging insectivorous bats are above 20 kHz [36], we set the minimum frequency to begin recording (trigger threshold) to 20 kHz. This minimizes false triggers by insects, road noise and other sounds. As bat call sequences typically last only few seconds, maximum time length for individual recordings was set to 3 s to ensure file sizes of recordings were easily managed by the call-analysis software (see below). To minimize triggering by indiscernible, distant, low intensity sounds, we set the threshold amplitude to 18 db. Lastly, to determine if detector microphone sensitivity influences whether, and to what extent, background noise during driving adversely impacts the number and quality of bat passes recorded, we decided to add a second detector known for being highly sensitive. We selected the miniMIC ultrasonic microphone (Binary Acoustic Technology Inc. Tucson, Arizona, USA). The miniMIC was connected to a Dell Venue tablet via USB and call sequences were recorded using Spectral Analysis, digital Tuning and Recording Software (SPECT'R, Binary Acoustic Technology Inc. Tucson, Arizona, USA). Settings were as described for the

The study was conducted in the states of Maryland and Delaware on the Delmarva Peninsula, which is located along the mid-Atlantic coast of the United States between the Atlantic Ocean and the Chesapeake Bay. The peninsula consists primarily of a mosaic of agriculture (48%) and forests (37%, mostly mixed hardwood-pine and loblolly pine—*Pinus taeda*—plantations) [37]. The remainder is comprised of coastal marshes and scattered suburban and urban devel-

We established 28 transects that were evenly spaced across the Delmarva Peninsula as described by McGowan and Hogue [38]. Each transect contained 10 sampling points spaced 2 km apart (in straight line distance) for a total of 280 sites. We restricted transects to 2 lane roads, and sampling points to the nearest safe roadside location to stop for sampling. We sampled each transect once between June and August of 2014, yielding a total of 28 sampling nights. Transects were selected randomly for sampling without replacement using the random number generator in R Statistical Software [39]. The direction of travel along each transect was also randomly chosen. Unfortunately, due to equipment failure, two transects had to be excluded from analyses, dropping our total sampling nights (and transects) to 26. We sampled each transect for bats using two approaches: point count and continuous sampling. Point count sampling occurred for 12 min at each of the 10 sampling points along each transect. Continuous sampling was carried out by leaving the detectors to operate as we We defined a bat pass as a sequence of one or more echolocation calls with <1 s between sequential calls [24]. Based on currently available technology, researchers are not able to distinguish individual bats of the same species from their calls. As a result, it is not possible to determine the absolute number of bats at a given location with bat detectors [19, 41]. Instead, the number of bat passes may be viewed as a measure of overall bat activity rather than number of individuals [19, 41]. We attempted to identify all bat passes to species using Sonobat 3.2 automated classifier (SonoBat, Arcata, CA, USA). As recommended by official Sonobat Guidelines, a probability threshold of 90% was set for accurate species identification.

For comparisons between continuous versus point count methods (EM3 detector only, see Section 3), we tallied the total number of bat passes recorded along each transect while continuously sampling and separately for point count sampling. We then divided these numbers by the amount of time spent recording using each method to yield passes per minute. Since the data were not normally distributed, we compared passes per minute between the two methods at the 26 transects using a two-tailed Wilcoxon signed-rank test (N = 26, α = 0.05) in R Statistical Software [39].

For reasons discussed below (Section 3), comparisons between the two detectors were not possible using continuously sampled data. We therefore limited analyses to the point count data. Since these data were recorded at 260 discrete sampling points, each sampled simultaneously by both detectors for 12 min, we were able to treat each site as a separate data point. Specifically, we compared total bat passes recorded at each site between the two detectors. Since the data were not normally distributed, we used a two-tailed Wilcoxon signed-rank test (N = 260, α = 0.05) in R Statistical Software [39] to test for statistically significant differences. We also compared data on percent of bat passes identified to species and total number of species identified between the different detectors and sampling methodologies.
