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.