These data contain the supplementary results corresponding with the journal article: Using mobile acoustic monitoring and false-positive N-mixture models to estimate bat abundance and population trends by Udell et al. (2024) in Ecological Monographs. These results contain the findings from the North American Bat Monitoring Program's (NABat) "Summer Abundance Status and Trends" analyses which used mobile transect acoustic data for three species (tricolored bat, little brown bat, and big brown bat). Data from the entire summer season (May 1–Aug 31) were used in the modeling process. Here, tabular data for each species include predictions (with uncertainty) of relative abundance (and trends over time) in the summer maternity season (May1 - July 16) from 2012-2020. Predictions for status and trends are provided for each species at four different spatial resolutions: 1) across the modeled species ranges, 2) at the state or province level, 3) at the NABat grid cell (10km x 10km scale) level, and 4) for each sampled transect. Predictions were produced using an analytical pipeline supported by web-based infrastructure, Bayesian hierarchical modeling, and 'false-positive N-mixture model' framework which analyzed mobile transect acoustics to model the relative abundance distribution (and trends over time) of each species while accounting for imperfect detection and species misclassification. Tabular files provided include: 1) range-wide average relative abundance predictions by year for each species, 2) range-wide trends in average relative abundance for each species, 3) regional (state/province) average relative abundance by year for each species, 4) regional (state/province) trends in average relative abundance for each species, 5) grid cell-level predictions of relative abundance by year for each species, 6) grid cell-level trends (overall change from 2012-2020) for each species (one file per species), and 7) transect-level estimates of relative abundance by year for each species. Estimates include means, medians, standard deviations, and the 95% Bayesian credible intervals. These data can be cross-referenced to the 'knitted' NABat master sample for CONUS, Canada, and Alaska (NABat_grid_covariates.shp, which is available on ScienceBase).

These data contain supplemental results and model code from the North American Bat Monitoring Program's (NABat) integrated species distribution model (iSDM) for the tricolored bat (Perimyotis subflavus). These data also serve as supplemental results, source data used to create figures, and model code to the companion manuscript: "Integrated distribution modeling resolves asynchrony between bat population impacts and occupancy trends through latent abundance " published in Communications Biology. Predictions were produced using an analytical pipeline supported by web-based infrastructure, Bayesian hierarchical modeling, and multi-scale integrated species distribution modeling (MS-iSDM) framework which integrated stationary acoustic, mobile transect acoustic, and live-capture data to model the recent summer distribution of the species while accounting for imperfect detection and species misclassification. The provided tabular data include predictions (with uncertainty) for tricolored bat summer distributions (relative abundance and occupancy probability) based on data from the entire summer season (May 1–Aug 31), for each from 2012-2022. Predictions represent relative abundances and occupancy probabilities in the pre-volancy season in the summer (May 1 – July 15), i.e., the period of time before juveniles can fly and become detectable. Results are summarized at 4 different spatial scales (Range-wide, state-level, 10 kilometer (km) x 10 km grid-cells, and 5 km x 5 km quadrants). At the grid-cell level, predictions (with uncertainty) are provided for relative abundance each year (2012-2022), and the overall proportional change in relative abundance between 2012-2022. At the quadrant level, predictions (with uncertainty) are provided for occupancy probabilities (i.e., probability of presence) each year (2012-2022), and for the overall proportional change in occupancy probability between 2012-2022. At the state-level, average relative abundance (across all grid cells) and average occupancy probability (across all quadrants) is provided for each state and year. Trend estimates for total proportional change between 2012-2022 are also provided for each state for average relative abundance and average occupancy probability, while additional trend metrics (absolute change) between 2012-2022 are provided for average occupancy probability. At the range-wide scale, average relative abundance (across all grid cells) and average occupancy probability (across all quadrants) is provided for each year, along with the overall trends in both metrics from 2012-2022. Predictions at the grid cell (10km x 10km) and quadrant (5km x 5km) can be cross-referenced to the NABat CONUS 5km master sample and/or NABat CONUS 10km master sample for analytical or visualization purposes (see related products). Model code was provided to document the JAGS model used to produce the results. Parameter estimates from the final model and model comparisons used to make figures in the manuscript are also provided.

The dataset is comprised of historical observations and predictions of winter colony counts at known sites for three bat species (little brown bat, Myotis lucifugus; tricolored bat, Perimyotis subflavus; and big brown bat, Eptesicus fuscus). The dataset consists of two separate but related data files in tabular format (comma-separated values [.csv]). Each data set consists of predicted winter counts derived using winter status and trends modeling methods developed by the North American Bat Monitoring Program (NABat). These two predicted winter count data sets were used to inform NABat summertime status and trends analysis: 1) modeled abundance predictions for all hibernacula for all three species from 2010-2021, and 2) modeled abundance predictions for P. subflavus from 2010-2023 using updated monitoring data. Abundance predictions were derived with a combined modeling approach that applied an exponential linear interpolation model (when there were less than 4 observations per location) and a Bayesian hierarchical model (where there were 4 or more data points per location).

The dataset is comprised of historical observations and predictions of winter colony counts at known sites for three bat species (little brown bat, Myotis lucifugus; tricolored bat, Perimyotis subflavus; and big brown bat, Eptesicus fuscus). The dataset consists of two separate but related data files in tabular format (comma-separated values [.csv]). Each data set consists of predicted winter counts derived using winter status and trends modeling methods developed by the North American Bat Monitoring Program (NABat). These two predicted winter count data sets were used to inform NABat summertime status and trends analysis: 1) modeled abundance predictions for all hibernacula for all three species from 2010-2021, and 2) modeled abundance predictions for P. subflavus from 2010-2023 using updated monitoring data. Abundance predictions were derived with a combined modeling approach that applied an exponential linear interpolation model (when there were less than 4 observations per location) and a Bayesian hierarchical model (where there were 4 or more data points per location).

We analyzed detection/non-detection data from acoustic surveys of bat species in Colorado during the summers of 2016 and 2017. The goal of this analysis is to create species distribution maps estimating the probability of occupancy across the state for each species. We fit a community occupancy model using both years of data from all the available species. Spatially explicit covariates were included to explain heterogeneity in the probabilities of occupancy and nightly covariates were used to model detection. We also allowed for spatial patterns in the probability of occupancy for each species in order to account for the ranges of many species including only a portion of Colorado. This is also useful for explaining additional spatial variation in the probabilities of occupancy that are not captured with the available covariates. The results of this analysis are provided as a shapefile including the estimates and associated uncertainty in the occupancy predictions across the state.

We analyzed detection/non-detection data from acoustic surveys of bat species in Colorado during the summers of 2016 and 2017. The goal of this analysis is to create species distribution maps estimating the probability of occupancy across the state for each species. We fit a community occupancy model using both years of data from all the available species. Spatially explicit covariates were included to explain heterogeneity in the probabilities of occupancy and nightly covariates were used to model detection. We also allowed for spatial patterns in the probability of occupancy for each species in order to account for the ranges of many species including only a portion of Colorado. This is also useful for explaining additional spatial variation in the probabilities of occupancy that are not captured with the available covariates. The results of this analysis are provided as a shapefile including the estimates and associated uncertainty in the occupancy predictions across the state.

These data contain the results from the North American Bat Monitoring Program's (NABat) integrated species distribution model (iSDM) for tricolored bats (Perimyotis subflavus). The provided tabular data include predictions (with uncertainty) for tricolored bat occupancy probabilities (i.e., probability of presence) based on data from the entire summer season (May 1–Aug 31), averaged from 2017-2022, in each NABat grid cell (5km x 5km scale) across the range of the species. Specifically, predictions represent occupancy probabilities in the pre-volancy season in the summer (May 1 – July 15), i.e., the period of time before juveniles can fly and become detectable. Predictions were produced using an analytical pipeline supported by web-based infrastructure, Bayesian hierarchical modeling, and iSDM framework which integrated stationary acoustic, mobile transect acoustic), and live-capture data to model the recent summer distribution of the species while accounting for imperfect detection and species misclassification. A tabular file is included detailing the average occupancy probability predictions (from 2017-2022) of each 5 km x 5 km grid cell in the species range, including means, standard deviations, and the 95% Bayesian credible intervals. These data can be cross-referenced to the NABat CONUS 5km master sample for analytical or visualization purposes.

This dataset is a backup of the data stored on NABat.org, for NABat stationary acoustic monitoring on national wildlife refuges in the Pacific Region for years 2016-2022. It was directly downloaded from NABat to update FWSpecies lists on refuges. Significant post-processing was required in order to verify data and locations, and correct errors. This post-processing resulted in an enhanced dataset specific for region 1 refuges.

These data contain the results from the North American Bat Monitoring Program's (NABat) species distribution model (SDM) for the northern long-eared bat (Myotis septentrionalis). The provided tabular data includes predictions (with upper and lower confidence intervals) for northern long-eared bat occupancy probabilities (which represent the probability of presence) based on data from the entire summer season (May 1-August 31), averaged from 2017-2022, in each NABat grid cell (5km x 5km scale) across the range of the species. Specifically, predictions represent occupancy probabilities in the pre-volancy season in the summer (May 1–July 15), i.e., the period of time before juveniles can fly and become detectable by capture and/or acoustic surveys. Predictions were produced using an analytical pipeline supported by web-based infrastructure, Bayesian hierarchical modeling, and an SDM framework which integrated stationary acoustic and live-capture data to model the recent summer distribution of the species while accounting for imperfect detection and species misclassification. A tabular file is included detailing the average occupancy probability predictions (from 2017-2022) of each 5 km x 5 km grid cell in the species range, including means, standard deviations, and the 95% Bayesian credible intervals. These data can be cross-referenced to the NABat CONUS 5km master sample for analytical or visualization purposes. We also provide a text file containing the JAGS model used to estimate these occupancy probabilities for reference.

Microsoft Access database contains mobile acoustic bat detections along road-based transects on NWRs and several Ecological Service Field Offices. Species identification was based on BCID version 2.7c software. Call id was constrained to a minimum of five pulses and a time between calls of 1 second. Routes were not created based on the NABat grid priority. Database contains survey data, call classifications, point locations and route metrics. Each route used a site level filter to minimize false positive and negative detections.