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 examined ‘ōpe‘ape‘a, or Hawaiian hoary bat (Lasiurus semotus; Chiroptera: Vespertilionidae), acoustic activity and surveyed potential roost trees at the Ford Island Chief Petty Officer’s (CPO) Bungalows Neighborhood of Pearl Harbor National Memorial (PERL) on the island of O‘ahu. We established four acoustic monitoring stations (PERL1–PERL4) that operated nightly to survey for ‘ōpe‘ape‘a echolocation activity from June to December 2024. Tree physiognomy data were collected at 12 potential roost trees and included tree species, tree height, diameter at breast height (DBH), percent canopy cover, distance to nearest tree, elevation, and canopy geometry. We collected 357,802 acoustic files over 672 station-nights across the four acoustic monitoring stations. ‘Ōpe‘ape‘a echolocation activity was detected over a total of 26 (4%) station-nights and during each month of the survey period except June. Echolocation activity peaked in September and October 2024, which is during the post-lactation and fledging period. These results represent a baseline for assessing ‘ōpe‘ape‘a acoustic activity and potential roosting resources in PERL.

These data were compiled to improve our understanding of bat foraging along the Colorado River in Grand Canyon. Objectives of our study were to determine whether bat activity was influenced primarily by variation in prey availability relative to other environmental or geomorphic factors. These data represent 1,428 paired samples of bat activity and insect abundance calculated as catch rates collected on 611 sampling nights at 410 sampling sites throughout a 470 km segment of river. These data were collected from April to October in 2017-2020 at recreational camps along the Colorado River in Grand Canyon between Glen Canyon Dam and Pearce Ferry. These data were collected through a U.S. Geological Survey led community science program that recruited recreational river runners to sample insects using light traps and to record bat activity using handheld acoustic recorders. Sampling was conducted for one hour at dusk during each night of the river expedition. These data can be used to interpret changes in total bat activity and insect catch rates over time and space relative to environmental variables.

These data were compiled to improve our understanding of bat foraging along the Colorado River in Grand Canyon. Objectives of our study were to determine whether bat activity was influenced primarily by variation in prey availability relative to other environmental or geomorphic factors. These data represent 1,428 paired samples of bat activity and insect abundance calculated as catch rates collected on 611 sampling nights at 410 sampling sites throughout a 470 km segment of river. These data were collected from April to October in 2017-2020 at recreational camps along the Colorado River in Grand Canyon between Glen Canyon Dam and Pearce Ferry. These data were collected through a U.S. Geological Survey led community science program that recruited recreational river runners to sample insects using light traps and to record bat activity using handheld acoustic recorders. Sampling was conducted for one hour at dusk during each night of the river expedition. These data can be used to interpret changes in total bat activity and insect catch rates over time and space relative to environmental variables.

In 2022, an estimated 1.6 million people visited New River Gorge (NPS 2023b). To date, there are over 1,600 established rock climbs documented in the New River Gorge area (Mountain Project 2022, Williams 2019). As visitation has increased, NERI has become interested in documenting the presence of bat species and their habitat on and near the cliff face, rim, and base areas to support a park management plan. A total of 51 routes were selected to survey three times throughout the summer of 2022. Cliff line surveys included personnel on-rope rappelling down each selected cliff face to look for bats or bat sign and to collect data on the physical attributes and record habitat information. In addition, a simultaneous acoustic survey for bats was conducted at the base of each survey route. Although no bats or bat sign were documented, analysis of the acoustic data in concert with the environmental factors revealed that bats were detected at more highly climbed walls versus unclimbed walls, at least northern long-eared bats (Myotis septentrionalis) were more likely to be detected near less vegetated walls, and activity of eastern small-footed bats (Myotis leibii) was higher as the summer progressed. Bats that roost on rock walls use cracks and crevices, which are also an important feature for climbers. Opportunistic conversations with climbers revealed that bats are observed on the cliffs. Colorado State University has a Climbers for Bat Conservation group (CSU 2023) that could be a resource for engaging climbers in a citizen science campaign.

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).

From June 23-28 and on August 6, 2002, Mount Rainier Wildlife Program staff conducted a small mammal inventory, a rapid assessment of bat species, and documented the presence of other mammals in the area at Fort Vancouver National Historical Site (FOVA) and parts of the Vancouver National Historic Reserve (Reserve). Our small mammal inventory focused on live trapping in seven habitat types for 600 trap-nights. We captured 74 unique individuals of five different species (+ two unknown) and documented two additional species incidentally. The most frequently-trapped species was the deer mouse (Peromyscus maniculatus). We collected eight voucher specimens to include at least one of each of the five species trapped. We were unable to capture bats in one night of mist-netting in the Reserve but did identify big brown bat (Eptesicus fuscus) calls using an electronic bat detector and visually documented likely two different Myotis species.