US Geological Survey and National Park Service researchers designed and implemented an acoustic monitoring program for bats in the three large parks of Western Washington, all part of the North Coast and Cascades Network (NCCN) of National Parks.This work was part of the larger NCCN bat population monitoring and white-nose syndrome (WNS) surveillance program, designed to understand bat distribution, activity, and disease dynamics on the leading edge of WNS spread in the state. Data were collected throughout each park along elevational, precipitation, and seasonal gradients. This monitoring program examines questions of interest at the national, regional, and park scale to increase the understanding of bat distribution, occurrence, and seasonal/annual dynamics before extensive spread of WNS in the state. This data release contains acoustic detections of bat species in Mount Rainier National Park during 2019-2020 monitoring.

These data were compiled as part of an effort to compare the relative data quality and modeling performance of bat data recorded with two different acoustic detectors. The objective of our study was to construct models evaluating the relative predictive quality of data collected by these two detectors. These data represent 48 paired sampling events where acoustic bat data was collected using a Song Meter SM4 Acoustic Recorder and with an Echo Meter Touch 2 bat detector in parallel with sampling insect prey using a light trap. These data were collected in Grand Canyon National Park between April 17 and October 10, 2022. These data were collected by several river guides through a community science program organized by the U.S. Geological Survey, Southwest Biological Science Center, Grand Canyon Monitoring and Research Center. These data can be used to compare the two acoustic methods to each other and relative to insect prey catch rates.

These data were compiled as part of an effort to compare the relative data quality and modeling performance of bat data recorded with two different acoustic detectors. The objective of our study was to construct models evaluating the relative predictive quality of data collected by these two detectors. These data represent 48 paired sampling events where acoustic bat data was collected using a Song Meter SM4 Acoustic Recorder and with an Echo Meter Touch 2 bat detector in parallel with sampling insect prey using a light trap. These data were collected in Grand Canyon National Park between April 17 and October 10, 2022. These data were collected by several river guides through a community science program organized by the U.S. Geological Survey, Southwest Biological Science Center, Grand Canyon Monitoring and Research Center. These data can be used to compare the two acoustic methods to each other and relative to insect prey catch rates.

Bat Acoustical Monitoring data collected at four National Park units in 2019. Colonial National Historical Park, Petersburg National Battlefield, Richmond National Battlefield Park, Shenandoah National Park were surveyed using Acoustic recording equipment and analyzed using Kaleidoscope Pro Software by staff at the USGS Virginia Cooperative Fish and Wildlife Research Unit and Virginia Polytechnic Institute and State University.

Bat Acoustical data were collected at Gettysburg National Battlefield in 2018. Staff at the USGS Cooperative Fish and Wildlife Research Unit and Virginia Polytechnic Institute and State University collected data using recording equipment and analyzed using Kaleidoscope Pro Analysis Software.

The excel sheet contains the Stations (Refuges and ES Offices) conducted mobile acoustic bat monitoring and a site level filter of potentially occurring species during the June 1- July 15 sampling Period. The table only includes species for which the Bat Call ID Version 2.7 can classify and not all species which might be on the route. Specifically, Brazilian free-tailed, Northern Yellow, Seminole Bat do not have classifiers in the software, therefore, any call of these species will be classified to the nearest similar species (e.g., Seminole bat classified as Eastern Red Bats). Use of the same filer and software provides a standard classification approach for each route and survey year as differing autoclassification software programs often have significant disagreements of call classification.

The excel sheet contains the Stations (Refuges and ES Offices) conducted mobile acoustic bat monitoring and a site level filter of potentially occurring species during the June 1- July 15 sampling Period. The table only includes species for which the Bat Call ID Version 2.7 can classify and not all species which might be on the route. Specifically, Brazilian free-tailed, Northern Yellow, Seminole Bat do not have classifiers in the software, therefore, any call of these species will be classified to the nearest similar species (e.g., Seminole bat classified as Eastern Red Bats). Use of the same filer and software provides a standard classification approach for each route and survey year as differing autoclassification software programs often have significant disagreements of call classification.

Through the North American Bat Monitoring Program, Bat Conservation International and U.S. Geological Survey (USGS) collaborated with the U.S. Fish and Wildlife Service to provided technical and science support to assistance in U.S. Fish and Wildlife Services’ Species Status Assessment (“SSA”) for the northern long-eared bat (Myotis septentrionalis), little brown bat (Myotis lucifugus), and tri-colored bat (Perimyotis subflavus). We conducted analyses to estimate changes in bat echolocation activity recorded during mobile transect surveys. Bat activity recorded during mobile acoustic transects provide an index of abundance and can be used to determine changes in populations over time (Roche et al. 2011, Jones et al. 2013). We hypothesized that mobile transect surveys would detect changes in populations for Myotis lucifugus, Myotis septentrionalis, and Perimyotis subflavus over the past decade related to two main stressors on North American bat populations: the emergence of White-nose Syndrome (WNS) and increases in installed wind energy facilities. We obtained data stored in the North American Bat Monitoring Program (NABat) (U.S. Fish and Wildlife Service, 3-Species Status Assessment - Mobile Transect Acoustic Monitoring Data Accessed 2020-11-23. NABat Request Number 11. Database Version v5.3.0), West Virginia (West Virginia Division of Natural Resources), and New York (New York State Department of Environmental Conservation). West Virginia and New York have mobile acoustic sampling programs that began in 2009 but their mobile acoustic data have not been contributed to the NABat Program database. These data were joined with stressor and habitat covariates (year of Pd arrival, wind energy risk index, habitat composition) with SSAmobile_04_combineData.R. A dataset for each species was created by filtering for grid cells within a species range (as defined by the USFWS). The following data were removed from final analyses: • Data from Canada were removed due to our inability to calculate a comparable wind energy index in Canada (see below) • Data collected from September to April as this does not represent the summer maternity season • Data where no observations of a species were recorded on any run at a site (i.e., all zeros) were removed to prevent zero inflation • Sites with only one run were removed due to the lack of information they provide for trend analysis. Note: Sites with multiple runs within a single year were retained for analysis because these data provide information on the effect of day of year and sampling variability. To determine changes in bat populations, we first modeled bat activity as counts of echolocation call sequences recorded along mobile acoustic transects. We used three categories of variables to model the count of call sequences along a transect: 1) Stressors to populations — We examined the influence of WNS and wind energy development over time 2) Spatial variation in activity — We used latitude, longitude, and habitat covariates to account for changes in activity across landscapes 3) Sampling variation — We accounted for day of year, sampled transect length, detector type, and ID software used. We then predicted the number of call sequences at each spatial scale and year. Finally, we derived the rate of change in population from the change in the predicted number of call sequences.

Hawaiian hoary bats (Lasiurus cinereus semotus) were captured at multiple locations on the east side of Hawaii Island from May 2018 through September 2019. Radio transmitters were affixed to captured bats and, when possible, radio telemetry was used to locate bats in trees used for day-roosts. In 2019, three maternity roosts were identified however only two were suitable for acoustic recording. Acoustic detectors were used to record acoustic activity (i.e., echolocation pulses) at two maternity roosts. Song Meter SM4BAT FS ultrasonic recorders (Wildlife Acoustics, Maynard, MA) with SMX-US ultrasonic microphones (Wildlife Acoustics, Maynard, MA) were deployed within 5-m of each maternity roost tree and configured for continuous (24-hr) data collection. Upon detection of a vocalizing bat, recording was triggered, and a call file was stored with the corresponding date and time. Recordings were analyzed with Kaleidoscope Pro version 5.1.9 (Wildlife Acoustics, Maynard, MA). All files were aurally and visually inspected for bat acoustic activity. A total of 2791 call files were proofed positive for bat calls. A total of 2657 bat call files were recorded from 18:00 to 05:59 (>95%). Less than 5% of call files containing bat calls were recorded from 06:00 to 17:59.