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.

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.

North American bats have experienced catastrophic population declines from white-nose syndrome (WNS), a fungal disease caused by Pseudogymnoascus destructans (Pd). Although Pd can infect many hibernating bat species, population-level impacts of WNS vary by host species. Microbial skin assemblages, including the fungal component (mycobiome), can influence host resistance to infectious diseases; however, little is known about the influence the skin mycobiome of bats may have on susceptibility to WNS. We sampled ten bat species in the eastern United States that are known to be either susceptible, tolerant, or resistant to WNS by swabbing their wing skin. We then cultured fungi from the swabs, isolated morphologically distinct colonies of fungi, and identified the fungi through DNA sequencing. Using this culture-based approach, we compared skin mycobiome characteristics. The mycobiomes of WNS-susceptible bat species had significantly lower alpha diversity and abundance compared to WNS-tolerant species. Overall mycobiome structure did not vary based on WNS-susceptibility, but several yeast species were differentially abundant on WNS-tolerant bat species. Multi-locus phylogenies and scanning electron microscopy suggest that some yeasts likely represent novel taxa which may be adapted to colonizing bat skin. Further exploration of interactions between Pd and components of the mycobiome may prove useful for predicting susceptibility of bat populations and for developing effective mitigation strategies for WNS.

White-nose syndrome (WNS), a fungal disease that has caused catastrophic population declines of bats in eastern North America, is rapidly spreading across the continent and now threatens previously unexposed bat species in western North America. The causal agent of WNS, Pseudogymnoascus destructans, can infect many species of hibernating bats, but susceptibility to WNS varies by host species. Predicting which western bat species will be most susceptible to WNS would be of great value for establishing conservation priorities. We previously reported that certain traits of the skin microbiome of bat species in eastern North America were strongly associated with tolerance to WNS. Using these traits, we developed a model to predict WNS susceptibility of 13 species of western North American bats. Based on the model, only two bat species, Myotis velifer and Eptesicus fuscus, were predicted to be WNS-tolerant. If accurate, a greater proportion of western bat species will be susceptible to the disease compared to eastern bat species, indicating that WNS may pose a significant conservation threat in western North America.

False positive occupancy analysis predictions with model uncertainty based on summertime data provided to support the three bat species status assessment (SSA) for Myotis lucifigus (MYLU), Myotis septentrionalis (MYSE), and Perimyotis subflavus (PESU). The objectives outlined by the Fish and Wildlife Service’s SSA team were to estimate summertime distributions across the entire species range. Statistical analysis included five types of response data requested from the North American Bat Monitoring Program database (NABat): automatically identified stationary acoustic calls, manually vetted stationary acoustic calls, automatically identified mobile acoustic calls, manually vetted mobile acoustic calls, and capture records. Statistical analysis was for the summertime distribution modeling, data collected between June 1 and Sept 1 during 2010 until 2019 were only included.

False positive occupancy analysis predictions with model uncertainty based on summertime data provided to support the three bat species status assessment (SSA) for Myotis lucifigus (MYLU), Myotis septentrionalis (MYSE), and Perimyotis subflavus (PESU). The objectives outlined by the Fish and Wildlife Service’s SSA team were to estimate summertime distributions across the entire species range. Statistical analysis included five types of response data requested from the North American Bat Monitoring Program database (NABat): automatically identified stationary acoustic calls, manually vetted stationary acoustic calls, automatically identified mobile acoustic calls, manually vetted mobile acoustic calls, and capture records. Statistical analysis was for the summertime distribution modeling, data collected between June 1 and Sept 1 during 2010 until 2019 were only included.

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).