These data are the collection of generalized linear mixed models run for AIC comparison of the pre- and post-White-nose Syndrome bat mist-net captures and percent juveniles in capture by year, time since White-nose Syndrome at collection set, U.S Fish and Wildlife Service designated geographic units, states or NABAT grid cell, collection site mean temperature, collection site temperature range and collection site elevation. Models are inclusive of data from 1999-2019 for the little brown bat (Myotis lucifugus), northern long-eared bat (Myotis septentrionalis) and the tri-colored bat (Perimyotis subflavus).

Through the North American Bat Monitoring Program, Bat Conservation International and U.S Geological Survey (USGS) provided technical and science support to assistance in U.S. Fish and Wildlife Service Species Status Assessment (“SSA”) for the northern long-eared bat (Myotis septentrionalis), little brown bat (Myotis lucifugus), and tri-colored bat (Perimyotis subflavus). USGS facilitated the SSA data call providing data archival for repeatable and transparent analyses, provided statistical support to assess the historical, current, an future population status for each of the three species, and developed a demographic projection tool to evaluate future viability of each species under multiple threat scenarios. We assessed population trends from count surveys of wintering colonies at hibernacula for these three bat species. Winter colony counts were downloaded from the database of the North American Bat Monitoring Program (U.S. Geological Survey North American Bat Monitoring Program. Accessed 2020-12-01. NABat Request Number 12. Database Version v5.4.0).

Our model is a full-annual-cycle population model {hostetler2015full} that tracks groups of bat surviving through four seasons: breeding season/summer, fall migration, non-breeding/winter, and spring migration. Our state variables are groups of bats that use a specific maternity colony/breeding site and hibernaculum/non-breeding site. Bats are also accounted for by life stages (juveniles/first-year breeders versus adults) and seasonal habitats (breeding versus non-breeding) during each year, This leads to four states variable (here depicted in vector notation): the population of juveniles during the non-breeding season, the population of adults during the non-breeding season, the population of juveniles during the breeding season, and the population of adults during the breeding season, Each vector's elements depict a specific migratory pathway, e.g., is comprised of elements, {non-breeding sites}, {breeding sites}The variables may be summed by either breeding site or non-breeding site to calculate the total population using a specific geographic location. Within our code, we account for this using an index column for breeding sites and an index column for non-breeding sides within the data table. Our choice of state variables caused the time step (i.e. \(t\)) to be 1 year. However, we recorded the population of each group during the breeding and non-breeding season as an artifact of our state-variable choice. We choose these state variables partially for their biological information and partially to simplify programming. We ran our simulation for 30 years because the USFWS currently issues Indiana Bat take permits for 30 years. Our model covers the range of the Indiana Bat, which is approximately the eastern half of the contiguous United States (Figure \ref{fig:BatInput}). The boundaries of our range was based upon the United States boundary, the NatureServe Range map, and observations of the species. The maximum migration distance was 500-km, which was based upon field observations reported in the literature \citep{gardner2002seasonal, winhold2006aspects}. The landscape was covered with approximately 33,000, 6475-ha grid cells and the grid size was based upon management considerations. The U.S.~Fish and Wildlife Service considers a 2.5 mile radius around a known maternity colony to be its summer habitat range and all of the hibernaculum within a 2.5 miles radius to be a single management unit. Hence the choice of 5-by-5 square grids (25 miles\(^2\) or 6475 ha). Each group of bats within the model has a summer and winter grid cell as well as a pathway connecting the cells. It is possible for a group to be in the cell for both seasons, but improbable for females (which we modeled). The straight line between summer and winter cells were buffered with different distances (1-km, 2-km, 10-km, 20-km, 100-km, and 200-km) as part of the turbine sensitivity and uncertainty analysis. We dropped the largest two buffer sizes during the model development processes because they were biologically unrealistic and including them caused all populations to go extinct all of the time. Note a 1-km buffer would be a 2-km wide path. An example of two pathways are included in Figure \ref{fig:BatPath}. The buffers accounts for bats not migrating in a straight line. If we had precise locations for all summer maternity colonies, other approaches such as Circuitscape \citep{hanks2013circuit} could have been used to model migration routes and this would have reduced migration uncertainty.

This data release contains the North American Bat Monitoring Program (NABat) Master Sampling Grid at the 5 km x 5 km scale with biologically relevant covariates for NABat analyses attributed to each cell of the 5 km x 5 km grid frame for the continental United States. It was created using ArcPro and the 'sf', 'tidyverse', 'dplyr' and 'exactextractr' packages in R to extract covariates from multiple data sources following the 10 km x 10 km attributed grid process as well as adding additional covariates. These covariates include the habitat characteristics such as percent of wetlands, forest, deciduous and coniferous forest, dominant and subdominant oak types, the number of tree and oak species, topographic features such as physiographic diversity, elevation, and the presence of karst terrain features or water feature, climate variables such as mean temperature and precipitation, and subterranean human structures such as the number and length of culverts. This layer provides the predictive covariates used in the integrated species distribution model for tricolored bats (Perimyotis subflavus, see External Related Resources). The attributed grid can also support future modeling efforts and data visualizations.

Our model is a full-annual-cycle population model {hostetler2015full} that tracks groups of bat surviving through four seasons: breeding season/summer, fall migration, non-breeding/winter, and spring migration. Our state variables are groups of bats that use a specific maternity colony/breeding site and hibernaculum/non-breeding site. Bats are also accounted for by life stages (juveniles/first-year breeders versus adults) and seasonal habitats (breeding versus non-breeding) during each year, This leads to four states variable (here depicted in vector notation): the population of juveniles during the non-breeding season, the population of adults during the non-breeding season, the population of juveniles during the breeding season, and the population of adults during the breeding season, Each vector's elements depict a specific migratory pathway, e.g., is comprised of elements, {non-breeding sites}, {breeding sites}The variables may be summed by either breeding site or non-breeding site to calculate the total population using a specific geographic location. Within our code, we account for this using an index column for breeding sites and an index column for non-breeding sides within the data table. Our choice of state variables caused the time step (i.e. \(t\)) to be 1 year. However, we recorded the population of each group during the breeding and non-breeding season as an artifact of our state-variable choice. We choose these state variables partially for their biological information and partially to simplify programming. We ran our simulation for 30 years because the USFWS currently issues Indiana Bat take permits for 30 years. Our model covers the range of the Indiana Bat, which is approximately the eastern half of the contiguous United States (Figure \ref{fig:BatInput}). The boundaries of our range was based upon the United States boundary, the NatureServe Range map, and observations of the species. The maximum migration distance was 500-km, which was based upon field observations reported in the literature \citep{gardner2002seasonal, winhold2006aspects}. The landscape was covered with approximately 33,000, 6475-ha grid cells and the grid size was based upon management considerations. The U.S.~Fish and Wildlife Service considers a 2.5 mile radius around a known maternity colony to be its summer habitat range and all of the hibernaculum within a 2.5 miles radius to be a single management unit. Hence the choice of 5-by-5 square grids (25 miles\(^2\) or 6475 ha). Each group of bats within the model has a summer and winter grid cell as well as a pathway connecting the cells. It is possible for a group to be in the cell for both seasons, but improbable for females (which we modeled). The straight line between summer and winter cells were buffered with different distances (1-km, 2-km, 10-km, 20-km, 100-km, and 200-km) as part of the turbine sensitivity and uncertainty analysis. We dropped the largest two buffer sizes during the model development processes because they were biologically unrealistic and including them caused all populations to go extinct all of the time. Note a 1-km buffer would be a 2-km wide path. An example of two pathways are included in Figure \ref{fig:BatPath}. The buffers accounts for bats not migrating in a straight line. If we had precise locations for all summer maternity colonies, other approaches such as Circuitscape \citep{hanks2013circuit} could have been used to model migration routes and this would have reduced migration uncertainty.

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 spread of white-nose syndrome (WNS) across the eastern United States has raised conservation concerns and provided motivation for efforts to monitor the impacts of this disease. Currently, WNS has not yet been detected in Montana, or any other western state besides Washington, and it is unknown how severe it will impact species in this region once it arrives. Within an occupancy model framework, we analyzed mist netting and acoustic records for eight bat species in Montana to estimate baseline distributions across the state prior to the arrival of WNS. Heterogeneity in the probabilities of occupancy for each species was explained with covariates for forest cover (%), elevation, ruggedness, and average degree days. Our analysis provided no evidence of spatial correlation among occupancy probabilities within each species, but did suggest spatial correlation among detection probabilities likely related to timing of surveys. We incorporated spatially-correlated random effects in the model for detection probabilities to account for these patterns.

The spread of white-nose syndrome (WNS) across the eastern United States has raised conservation concerns and provided motivation for efforts to monitor the impacts of this disease. Currently, WNS has not yet been detected in Montana, or any other western state besides Washington, and it is unknown how severe it will impact species in this region once it arrives. Within an occupancy model framework, we analyzed mist netting and acoustic records for eight bat species in Montana to estimate baseline distributions across the state prior to the arrival of WNS. Heterogeneity in the probabilities of occupancy for each species was explained with covariates for forest cover (%), elevation, ruggedness, and average degree days. Our analysis provided no evidence of spatial correlation among occupancy probabilities within each species, but did suggest spatial correlation among detection probabilities likely related to timing of surveys. We incorporated spatially-correlated random effects in the model for detection probabilities to account for these patterns.