## 2 Materials and Methods
### Acoustic Data  
From 2012 to 2019, we collected acoustic data at the five ESVA sites named Assateague Island, Cedar Island, Hog Island, Smith Island, and Silver Beach (Figures 1 and 2). We used frequency division/zero-crossing acoustic detectors (Anabat SD1 and SD2, Titley Scientific, New Ballina, NSW (any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government) that record high frequency (15-150 kHz) echolocation pulses of bats. We placed the detectors on existing structures (lighthouses or similar) at heights of approximately 10-40 m. We collected data annually typically across three seasons--beginning in early spring and through late fall and recorded during the winter season at least once per site (Figure 3). We considered acoustic recordings on a nightly basis from sunset to sunrise.  
Figure 1: Acoustic detector sites (black points) on barrier islands and a light tower off the Eastern Shore of Virginia, USA, 2012–2019. Weather data was obtained from the nearest available National Oceanic and Atmospheric Administration (NOAA) weather stations (starred points) from the National Climatic Data Center (NCDC) online tool (Climate data online; [[https://www.ncdc.noaa.gov/cdo-web/](https://www.ncdc.noaa.gov/cdo-web/)]([https://www.ncdc.noaa.gov/cdo-web/](https://www.ncdc.noaa.gov/cdo-web/)); accessed on 9 November 2019).  
Figure 3: Site specific acoustic recording effort by ordinal date (day of the year). Effort (number of years monitored) is indicated as a heatmap of site vs. ordinal date such that lighter shades indicate lower effort (min = 1 year) and darker shades indicate higher effort (max = 8 years). Monitoring was conducted on the Eastern Shore of Virginia, USA, 2012-2019.  
Figure 2: Lighthouses and structures serving as infrastructure for acoustic bat detector deployment on Eastern Shore of Virginia, USA, 2012-2019. Acoustic detector microphone locations are indicated by the red circles. Labels correspond to the locations given in Figure 1: (**a**) Assateague Island (Assateague Lighthouse); (**b**) Cedar Island (inactive United States Coast Guard station); (**c**) Hog Island (inactive United States Coast Guard station); (**d**) Silver Beach (offshore navigation light structure); (**e**) Smith Island (Cape Charles Lighthouse).  
The post processing data structure was composed of timestamped individual echolocation sequences of bats (hereafter \"bat passes\" or \"passes\"). Bat passes is defined as a distinct series of echolocation pulses, or \"clicks\", which is identified to one bat as they pass within range of the detector [51]. We used Kaleidoscope 4.5.0 Bats of North America--4.2.0 classifier (Wildlife Acoustic, Inc., Maynard, MA, USA) to identify passes to species, unidentified bat passes (\"no ID\"), or noise. We tallied nightly pass counts by individual species, no ID, and noise. To minimize false positives, we manually inspected subsets of passes identified to species to confirm identification. Due to the context of the problem and realization that >85% of identified passes were tree bats, we placed particular emphasis on correct identification of eastern red bats (_Lasiurus borealis_), silver-haired bats (_Lasiomycteris noctivgans_), and hoary bats (_Lasiurus cinerea_), and only used tree bat pass data in our analysis. In wind turbine collision risk studies, recent evidence suggests that the hourly or nightly passage rates of bats pre-construction are poor predictors of fatality rates post-construction [52] so to account for this, instead of using hourly or nightly tallies of bat passes as our response variable, we restructured the data to consider only the binary occurrence (or non-occurrence) of tree bat(s) on a nightly basis.