Sarah M. Gaulke, United States Geological Survey Frank C. Tousley, United States Geological Survey Bradley J. Udell, United States Geological Survey Bethany R. Straw, United States Geological Survey Brian E. Reichert, United States Geological Survey 20230901 vector digital data https://doi.org/10.5066/P9BPRLVL 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. The attributed grid was used to provide predictive covariates for tricolored bat distributions at the 5km scale. The same predictors could also be used in future NABat analyses as covariates on bat species occupancy and/or abundance. Finally, it can also be used to aid visualization of bat occupancy. Source data for covariate layers. 20120501 20220831 See Supplemental Info Complete None planned -125.0048 -66.8134 49.4789 24.4670 Continental United States ISO 19115 Topic Category biota USGS Thesaurus bats USGS Metadata Identifier USGS:64c05bd3d34e70357a3238b3 None Continental United States Master Sample North American Bat Monitoring Program Bats None. Please see 'Distribution Info' for details. None. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. Brian E Reichert U.S. Geological Survey, ROCKY MOUNTAIN REGION ECOTECH Branch Chief & NABat Coordinator mailing address

2150 Centre Avenue Bldg C

Fort Collins CO 80526 US 970-226-9245 970-226-9230 breichert@usgs.gov Funding support provided by USGS Ecosystems Mission Area (Species Management Research Program), U.S. Fish and Wildlife Service, and Bureau of Land Management. Tabular and geospatial data were produced on a Windows 10 Enterprise Version 1909 operating system, using program R version 4.2.2 (2022-10-31) and R studio desktop version 2023.01.1, and ArcPro version 2.9.2. The R package "sf" was used to produce the shapefile, and the “tidyverse”, "exactextractr", and "dplyr" were used for data manipulation and producing the tabular data sets. Software and versions used included: Program R, version 4.2.2. RStudio, version 2023.01.1 ArcPro, version 2.9.2 There are multiple files included in this data release that make up 1 geospatial file in ESRI shapefile format (shp). Geospatial ESRI shapefile: GRTS_5km_covariates.dbf - 363,458KB, GRTS_5km_covariates.prj - 1KB, GRTS_5km_covariates.shp - 43,938KB, GRTS_5km_covariates.shx - 2,585KB The citations for the R packages used are below: R package “sf”: Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009 R package “sf”: Pebesma, E., & Bivand, R. (2023). Spatial Data Science: With Applications in R (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9780429459016 R package "tidyverse": Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” _Journal of Open Source Software_, *4*(43), 1686. doi:10.21105/joss.01686 <https://doi.org/10.21105/joss.01686>. R package "exactextracr": Daniel Baston (2022). _exactextractr: Fast Extraction from Raster Datasets using Polygons_. R package version 0.9.1, <https://CRAN.R-project.org/package=exactextractr>. R package "dplyr": Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A Grammar of Data Manipulation_. R package version 1.1.1, <https://CRAN.R-project.org/package=dplyr>. Attributes of the merged master sampling grid, and all spatial data joined to the master sampling grid were plotted and visually compared to source data to ensure the quality and accuracy of data sets. Data were checked to ensure all data fell within expected ranges, and that there are no duplication and omission of data. The geospatial data has been loaded in multiple GIS programs including ArcPro and the 'sf' package in R to ensure integrity. Automatic topology tests were conducted in the sf package when spatially joining data sets that led to the final geospatial data. The spatial data set spans the entire extent of the 5 km NABat master sample grids which is the continental United States. US Department of Transportation Federal Highway Administration 20220615 vector digital data https://www.fhwa.dot.gov/bridge/nbi/ascii.cfm Digital and/or Hardcopy 2022 publication date Culvert and bridges The National Bridge Inventory dataset provides point locations and conditions for bridges, culverts, and other roadway structures across the United States. The data was filtered to only culverts and the total number and length of culverts was calculated for each 5 km x 5km grid cell. Will Petry Shawn Taylor 2022 publication n/a Zenodo https://doi.org/10.5281/zenodo.7445016 Digital and/or Hardcopy 2022 publication date Forest types (oak types USFAS) This dataset provides the ranges for each tree species in the United States. Two layers were rasterized and summed - one of all oak (Quercus) species and one of all tree species. The maximum number of tree species and oak species was extracted to each 5 km x 5 km cell. USDA Forest Service - Forest Inventory and Analysis (FIA) Program & Remote Sensing Applications Center 2008 raster digital data https://www.fia.fs.usda.gov/ Digital and/or Hardcopy 2002 2003 ground condition Forest types (deciduous and conifer) This dataset provides 141 forest types across the United States based on the US Forest Service Forest Inventory and Analysis data that is derived from MODIS composite images. These data were classified into deciduous or coniferous forest types and dominant or subdominant oak species and the percentages of each were extracted to the 5km x 5km grid cell. David M. Theobald Dylan Harrison-Atlas William B. Monahan Christine M. Albano 20151207 publication PLOS ONE vol. 10, issue 12 n/a Public Library of Science (PLoS) Data from this manuscript were originally published as 18 child items here: https://www.sciencebase.gov/catalog/item/564b4bb0e4b0ebfbef0d31d2. They were accessed as a single layer through google earth engine [Earth Engine Snippet: ee.Image("CSP/ERGo/1_0/Global/ALOS_topoDiversity")] at the following URL: https://developers.google.com/earth-engine/datasets/catalog/CSP_ERGo_1_0_Global_ALOS_topoDiversity. https://doi.org/10.1371/journal.pone.0143619 Digital and/or Hardcopy 20151207 publication date Physiographic diversity This data set was used to as the source of the 'physiographic diversity' grid cell level covariate in the occupancy analysis. It is also a measure of ecologically relevant landscape complexity based on the underlying physiographic conditions. Topographic diversity (D) is a surrogate variable that represents the variety of temperature and moisture conditions available to species as local habitats. This variable is calculated based on the multi-scale Topographic Position Index (mTPI), which is a dominant control of soil moisture and used as a measure of hillslope position. The mTPI was combined with the square-root transform for mTPI>0 and with the standard deviation of the Continuous Heat-Insolation Load Index calculated at multiple scales. It is also a measure of ecologically relevant landscape complexity based on the underlying physiographic conditions in a grid cell. Earth Resources Observation And Science (EROS) Center 2017 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/f7df6pqs Digital and/or Hardcopy 2017 publication date Elevation The max elevation of each NABat grid cell was calculated from the EROS data. Colin Talbert Brian E Reichert 2018 vector digital data https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p9m00p17 Digital and/or Hardcopy 2018 publication date NABat Master Sample 5km grid This source provides the 5 km x 5 km spatial sampling grid across North America used to represent spatial units of sampling, and spatial units for predicting species occupancy probabilities. Colin Talbert Brian E Reichert 2018 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p9o75ydv Digital and/or Hardcopy 2018 publication date NABat Master Sampling Grid 10km This source provide the 10 km x 10 km spatial sampling grid across North America used to nest the 5 km x 5km grid in and attribute the 10 km quadrants and 10 km sampling priorities. Bradley J Udell Bethany R Straw Tina Cheng Kyle D Enns Winfred Frick Benjamin Gotthold Kathryn M Irvine Cori Lausen Susan Loeb Jonathan Reichard Thomas Rodhouse Dane A Smith Christian Stratton Wayne E Thogmartin Ashton M Wiens Brian E Reichert 2022 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p92jgacb Digital and/or Hardcopy 2022 publication date Attributed 10km grid This data release outlines the data processing steps that were followed for several of the covariates of the 5 km x 5 km grid frame. Canada Centre for Remote Sensing (CCRS), Earth Sciences Sector, Natural Resources Canada Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO) Comisión Nacional Forestal (CONAFOR) Insituto Nacional de Estadística y Geografía (INEGI) U.S. Geological Survey (USGS) 2010 tabular digital data http://www.cec.org/north-american-environmental-atlas/land-cover-2010-modis-250m/ Digital and/or Hardcopy 2010 publication date MODIS 2010 250m Data from this source were previously used to derive NABat grid cell level percentages of land cover types and were available in the NABat database for use in occupancy analysis. These data were used as environmental predictors in the occupancy analyses. In particular, we used layers for wetlands and forest and calculated the proportion of each habitat type in each 10km and 5km grid cell for use in occupancy modeling. US Environmental Protection Agency 20130416 vector digital data https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states Digital and/or Hardcopy 2013 publication date Ecoregions The level I, II, and III ecoregions were extracted to each grid cell for the continental United States. Zhao Chen Augusto S. Auler Michel Bakalowicz David Drew Franziska Griger Jens Hartmann Guanghui Jiang Nils Moosdorf Andrea Richts Zoran Stevanovic George Veni Nico Goldscheider 20170113 publication Hydrogeology Journal vol. 25, issue 3 n/a Springer Science and Business Media LLC ppg. 771-785 https://doi.org/10.1007/s10040-016-1519-3 Digital and/or Hardcopy 20170113 publication date Karst layer These data were used to derive a karst indicator covariate (presence/absence) for each NABat grid cell. Victor Maus Stefan Giljum Jakob Gutschlhofer Dieison M da Silva Michael Probst Sidnei L B Gass Sebastian Luckeneder Mirko Lieber Ian McCallum 2020 publication n/a PANGAEA https://doi.org/10.1594/pangaea.910894 Digital and/or Hardcopy 20200908 publication date Mines These data were used to calculate the distance-to-nearest-mine variable in kilometers. Natural Resources Canada Insituto Nacional de Estadística y Geografía (INEGI) U.S. Geological Survey North American Commission for Environmental Cooperation 20120101 vector digital data https://www.sciencebase.gov/catalog/item/4fb55df0e4b04cb937751e02 Digital and/or Hardcopy 20120101 publication date Rivers and Lakes The North American Atlas - Hydrography data set depicts the shoreline, linear hydrographic features, and area hydrographic features in North America. It was used to calculate an indicator variable for each NABat grid cell representing whether or not a river, lake, or shoreline of a large body of water was present in each. Stephen E. Fick Robert J. Hijmans 201710 publication International Journal of Climatology vol. 37, issue 12 n/a Wiley ppg. 4302-4315 https://doi.org/10.1002/joc.5086 Digital and/or Hardcopy 1970 2000 temporal time span of climate data Precip and Temp The historical average temperature and precipitation were downloaded and averaged across each grid cell. Step 1: Most of the predictors used in the analysis were already attributed to the 10km NABat grid in a previous USGS data release (Udell et al. 2022), and details on their geoprocessing can be found in the corresponding metadata. These variables include the physiographic diversity, maximum elevation, percent forest and wetlands, karst, rivers and lakes, distance to mines, average temperature, average precipitation, state, and ecoregions. The remaining spatial covariates were downloaded from their respective data sources and re-projected to the WGS_1984 datum using the 'sf' package in R. The 5 km x 5 km NABat grid frame has also been published previously (Talbert and Reichert, 2018) and was used to extract these layers. Attributed 10km grid NABat Master Sample 5km grid Physiographic diversity Elevation MODIS 2010 250m Ecoregions Karst layer Mines Rivers and Lakes Precip and Temp NABat Master Sampling Grid 10km 20230516 Step 2: Spatial covariates were then summarized at the grid cell level. For the number of tree and oak species, each species range of the National Tree Atlas (2022) data was transformed from a polygon to a raster and they were summed to create a raster of oak species and tree species with each cell representing how many species ranges each cell overlapped across the United States using the 'raster' and 'sf' package in R. The maximum number of each was then extracted for each grid cell using the 'exactextractr' function in R. For the percent of deciduous, coniferous, dominant or sub-dominant oak species, we took the forest types from the USDA Forest Service (2008) and created a binary raster for each by re-classifying each forest type as deciduous, coniferous, dominant, or sub-dominant oak cover. The percent of each of these forest types was then extracted for each grid cell using the 'exactextractr' function in R. For the length and number of culverts, National Bridge Inventory point data (2022) was taken into ArcPro, filtered to culverts only, and the number and length of culverts in meters were summed in each grid cell. Forest types (oak types USFAS) Forest types (deciduous and conifer) Culvert and bridges 20230516 Step 3: All covariates were plotted for quality and accuracy compared to the original layers and the previously published 10km x 10 km attributed grid frame. For the percent wetland, percent forest, percent dominant oak, percent sub-dominant oak, percent coniferous and percent deciduous species, covariate was rounded to the nearest whole number. Attributed 10km grid 20230516 Vector G-polygon 330826 0.0197612307 0.0246185008 Decimal seconds WGS_1984 WGS 84 6378137.0 298.257223563 GRTS_5km_covariates.shp Attribute Table Table containing attribute information associated with the data set. Producer Defined FID Internal feature number. ESRI Sequential unique whole numbers that are automatically generated. Shape Feature geometry. ESRI Coordinates defining the Feature type. CONUS_GRID A unique identifier for each 5 km x 5 km NABat grid cell across the United States. Producer Defined 75.0 535076.0 GRTS_ID The priority 'GRTS' order draw of each NABat grid cell for the CONUS sampling frame. Note this value is not unique across sample frames (CONUS and Canada + Alaska), and was combined with 'frame' to create a unique grid cell. identifier. This field corresponds to the "grts_cell_id" field in the NABat data request outputs. Producer Defined 2.0 133807.0 ecor_III Ecoregion level III names for each grid cell. Producer Defined NA No Data Producer defined Ecoregion level III names based on Ecoregions of North America Level III (EPA 2010). ecor_II Ecoregion level II names for each grid cell. Producer Defined NA No Data Producer defined Ecoregion level II names based on Ecoregions of North America Level III (EPA 2010). ecor_I Ecoregion level I number for each grid cell. Producer Defined NA No Data Producer defined Ecoregion level I number based on Ecoregions of North America Level III (EPA 2010). dist_mine Distance in kilometers of each grid cell to the nearest mine, based on polygon data from Maus et al. 2020. Producer Defined 0.0 479.8791 kilometers karst A karst indicator (presence = 1/ absence = 0) for each grid cell based on the The World Karst Aquifer Mapping project data (2017). Producer Defined 0 Absence Producer defined 1 Presence Producer defined riverlake Indicator variable for the presence of shoreline or linear hydrographic features (e.g. rivers and lakes). Producer Defined 0.0 Absence Producer defined 1.0 Presence Producer defined temp Monthly mean temperature of each grid cell based on WorldClim2.0. Producer Defined -3.9793 24.6221 degrees celsius precip Average monthly precipitation of each grid cell based on WorldClim2.0. Producer Defined 0.0 280.0227 millimeter p_forest Percent forest of any kind in each grid cell based on MODIS (2010), rounded to the nearest whole percentage point. Producer Defined 0.0 100.0 p_wetlnd Percent wetland of any kind in each grid cell based on MODIS (2010), rounded to the nearest whole percentage point. Producer Defined 0.0 100.0 p_dom_oak Percent of dominant oak forest in each grid cell based on USDA Forest Service (2008), rounded to the nearest whole percentage point. Producer Defined 0.0 100.0 p_sub_oak Percent of sub-dominant oak forest in each grid cell based on USDA Forest Service (2008), rounded to the nearest whole percentage point. Producer Defined 0.0 100.0 p_conifer Percent of coniferous forest in each grid cell based on USDA Forest Service (2008), rounded to the nearest whole percentage point. Producer Defined 0.0 100.0 p_decid Percent of deciduous forest in each grid cell based on USDA Forest Service (2008), rounded to the nearest whole percentage point. Producer Defined 0.0 100.0 sp_oak Number of oak species ranges that covers each grid cell based on Petry and Taylor (2022). Producer Defined 0.0 19.0 max_elev The maximum elevation in each grid cell based on GTOPO30 DEM data (30 Arc-Second). Producer Defined -74.0 4328.0 meters sp_tree Number of tree species ranges that covers each grid cell based on Petry and Taylor (2022). Producer Defined 0.0 135.0 state The U.S. state that each NABat grid cell is in. Producer Defined Name of the state that each grid cell is in. phys_div Average physiographic diversity of each NABat grid cell based on raster data from Theobald et al. 2015. Producer Defined 0 Values of '0' correspond with values of NoData in the original source data. However, since every NoData value was completely over water (where physiographic diversity is technically '0' due to a lack of topology), these values have been reclassified as '0' for the purposes of modeling bat distributions. Producer defined 0.0 0.6399 num_culv The number of culverts in each NABat grid cell based on data from the National Bridge Inventory (2022). Producer Defined 0.0 2081.3 leng_culv The sum of the length of all culverts in each NABat grid cell based on data from the National Bridge Inventory (2022). Producer Defined 0.0 36.0 meters long The longitude value of the centroid of each NABat grid cell in decimal degrees. Producer Defined -124.9632 -66.8532 decimal degrees lat The latitude value of the centroid of each NABat grid cell in decimal degrees. Producer Defined 24.4931 49.4559 decimal degrees 10KM_quad A code identifying which quadrant of the parent 10 km x 10km grid cell that the 5km x 5km grid cell is derived from. Producer Defined Each value represents the geographic quadrant of the parent 10 km x 10 km grid cell that the 5 km x 5 km cell is derived from. 5KM_grts A unique identifier for each 5 km x 5 km NABat grid cell across the United States. Producer Defined A combination of 'frame' (the source sample frame of each grid cell after reclassifying Alaska as a distinct frame from Canada) and 5km x 5km GRTS_ID (the priority GRTS order draw for each grid cell). 10KM_grts A unique identifier for each 10 km x 10 km NABat grid cell across the United States. Producer Defined A combination of 'frame' (the source sample frame of each grid cell after reclassifying Alaska as a distinct frame from Canada) and 10 km x 10 km GRTS_ID (the priority GRTS order draw for each grid cell). GS ScienceBase U.S. Geological Survey mailing address

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Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Digital Data https://doi.org/10.5066/P9BPRLVL None 20250422 FORT Data Management U.S. Geological Survey, Fort Collins Science Center FORT Data Management mailing

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Fort Collins CO 80526 US 970-226-9100 970-226-9320 fortdatamanagement@usgs.gov FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata FGDC-STD-001.1-1999