DOI: 10.5061/dryad.hx3ffbgsz
title: Digital distribution maps of the bats of Texas

description: Of all the terrestrial mammals in Texas, bats (order Chiroptera) are the
 most imperiled with 23 species (72% of species in the order that occur in
 Texas) listed by the Texas Parks and Wildlife Department as Species of
 Greatest Conservation Need (SGCN). Despite so many bat species categorized
 as SGCN, we have only a course understanding of their distribution and
 little quantitative understanding of the relative imperilment of species
 stemming from a variety of anthropogenic threats. High resolution estimate
 of distribution would aid much in directing conservation efforts in the
 state and could identify species needing the greatest conservation
 attention. Recent advances in ecological niche modeling allow construction
 of digital distribution maps that are much more resolved and provide
 estimates of habitat suitability that reflect the probability of
 occurrence of species. We generated ecological niche models (ENM’s) for 34
 species of bats occurring in the state of Texas. These data represent the
 data we used to create these models and the results we obtained.
description: # Digital distribution maps of the bats of Texas Dataset DOI:
 [10.5061/dryad.hx3ffbgsz](https://doi.org/10.5061/dryad.hx3ffbgsz) ##
 Description of the data and file structure We submitted here our data
 (**Data** **folder**) around which Texas species we analyzed (**Texas
 species from grant.csv**), the standardized names we used for each
 species (**Species name code.csv**), and the smaller Texas collections we
 requested and received data from (**Distribution Data from Smaller Texas
 Collections** folder). We also submitted our results from our MaxEnt
 models of each Texas bat species (**Results** **folder**) as geotiffs
 (**MaxEnt raster predictions sub-folder**) and tables (**MaxEnt
 Tables** **sub-folder**) including MaxEnt tuning results
 (**final_results.csv**), variable importance measures
 (**permutation_importance.csv**), the total number of thinned coordinates
 per species (**species_thinrec_counts.csv**), and a master table
 containing the values from our environmental variables and species record
 data for each raster cell (**master_values_table.csv**). Additionally, we
 submitted our R codes (**Codes** **folder**) for preparing the
 environmental layers (**Layer prep** **sub-folder**), preparing background
 points and occurrence records (**Occ record and background
 prep** **sub-folder**), MaxEnt tuning and modeling (**Maxent tuning and
 models** **sub-folder**), and creating final maps and variable response
 curve plots (**Maps, final plots, and master table** **sub-folder**). In
 addition, our raw data can be found in a Supplementary folder which
 contains our environmental raster layers as geotiffs (**Environmental
 rasters** **folder**), a shapefile for our study area (**study_area**
 **folder**), and our occurrence records (**Occurrences folder**) including
 our filtered, but non-thinned museum occurrence records
 (**Final_Chiroptera_records.csv**), and the thinned version of these
 records  (**total_thin_coords.csv**).  ### Files and variables #### File:
 Digital_Distribution_Maps_for_the_Bats_of_Texas.zip ### Data folder
 **Texas species from grant.csv**  * List of all the species from this
 project in the order they appear in our final report  **Species name
 code.csv**  * Verbatim Name = GBIF field and data which can vary widely
 for the same species  * Standardized = our name for the species to
 standardize all the GBIF species names  #### Distribution Data from
 Smaller Texas Collections sub-folder **Museum Request Status.csv**  *
 Which museums we requested data from, their response, and the contact at
 the institutions that responded  * 'N/A' represents not
 applicable as no contact is provided for collections with no bats in
 collection **Sul Ross Bats.csv**  * The bat records including their
 catalog number, taxonomy, locality, date, collector, preservation, and sex
 from Sul Ross State University   * 'N/A' represents not
 available and data not provided in the species record by the collection
 **Tarleton Bats.csv**  * The bat records including taxonomy, locality,
 catalogue number, sex, and date from Tarleton State University   ###
 Results folder #### MaxEnt raster predictions sub-folder * The cloglog
 prediction output from each species final tuned MaxEnt model as geotiff
 rasters * Predictions are in the same projection, North America Albers
 Equal Area Conic projection (ESRI:102008), and resolution (19x19km) as our
 environmental rasters * Values range from 0-1 along representing the
 probability of suitable habitat #### MaxEnt Tables sub-folder 
 **final_results.csv**  * Represents the optimal parameters and evaluation
 metrics of the final MaxEnt model after tuning, given by the ENMeval R
 package * Species = the species that was modeled * fc = feature class * rm
 = regularization multiplier * tune.args = number of argument combinations,
 all 1 here as only 1 combination was provided after filtering
 non-contributing variables to the final MaxEnt model  * auc.train = AUC
 calculated on the full dataset * cbi.train = Continuous Boyce Index
 calculated on the full dataset * auc.diff = average and standard deviation
 of the difference between auc.train and auc.val * auc.val = average and
 standard deviation of the AUC calculated on the validation datasets *
 cbi.val = average and standard deviation of the Continuous Boyce Index
 calculated on the validation datasets * or.10p = average and standard
 deviation of the omission rate with threshold as the minimum suitability
 value across occurrence records after removing the lowest 10 percent *
 or.mtp = average and standard deviation of the omission rate with
 threshold as the minimum suitability value across occurrence records *
 AICc = AIC corrected for small sample sizes * delta.AICc = highest AICc
 value across all models minus this model's AICc value, zero here as
 only the final model was entered per species * w.AIC = AIC weights, all 1
 here as only the final model was entered per species * ncoef = the number
 of non-zero coefficients in the final MaxEnt model
 **permutation_importance.csv**  * Permutation values for each
 environmental variable for each species final MaxEnt model.  * \--- =
 variable was not included in that species final MaxEnt model. 
 **species_thinrec_counts.csv**  * Number of thinned records (one record
 per 19x19km raster cell) that went into each species final MaxEnt model 
 **master_value_table.csv**  * Cell ID = id number given to each raster
 cell with data in our study area * x,y = center coordinates for each
 raster cell with data in our study area * Bio1 = annual mean temperature
 values given in C * Bio2 = mean diurnal range (mean of monthly (max temp -
 min temp)) given in C * Bio3 = isothermality (Bio2/Bio7) x 100 given in C
 * Bio4 = temperature seasonality (standard deviation x 100) given in C *
 Bio5 = max temperature of warmest month given in C * Bio6 = min
 temperature of coldest month given in C * Bio7 = temperature annual range
 (Bio5 - Bio6) given in C * Bio8 = mean temperature of wettest
 quarter given in C * Bio9 = mean temperature of driest quarter given in C
 * Bio10 = mean temperature of warmest quarter given in C * Bio11 = mean
 temperature of coldest quarter given in C * Bio12 = annual precipitation
 given in mm * Bio13 = precipitation of wettest month given in mm * Bio14 =
 precipitation of driest month given in mm * Bio15 = precipitation
 seasonality (coefficient of variation) given in mm * Bio16 = precipitation
 of wettest quarter given in mm * Bio17 = precipitation of driest
 quarter given in mm * Bio18 = precipitation of warmest quarter given in mm
 * Bio19 = precipitation of coldest quarter given in mm * Cropland =
 proportion of raster cell that is designated as cropland  * DEM_elev =
 elevation given in m * Evgrn Broadleaf = proportion of raster cell that is
 designated as evergreen broadleaf forest * Evgrn Needleleaf = proportion
 of raster cell that is designated as evergreen needleleaf forest * Decds
 Broadleaf = proportion of raster cell that is designated as deciduous
 broadleaf forest * Grass/Shrubland = proportion of raster cell that is
 designated as grass/shrubland * Igneous = proportion of raster cell that
 is designated as above ground igneous rock * Metamorphic = proportion of
 raster cell that is designated as above ground metamorphic rock *
 Sedimentary = proportion of raster cell that is designated as above ground
 sedimentary rock * Pasture = proportion of raster cell that is designated
 as pasture * Surface Water = proportion of raster cell that is designated
 as open surface water * Prob of = the probability of each species in the
 raster cell ranging from 0 to 1 (0 to 100%) * P/A = the presence (1) or
 absence (0) of each species ### Codes folder  R code files here are in
 their workflow order #### Layer prep sub-folder * R codes to prepare the
 environmental data by masking it down to North America and to reproject
 them from the WGS84 projection (EPSG:4326) to North America Albers Equal
 Area Conic projection (ESRI:102008)  * Start with **wc_layer_prep.R**,
 then **lithology_layer_prep.R**, **surface_water_layer_prep.R**,
 **landcover_layer_prep.R**, and **aridity_layer_prep.R**  #### Occ record
 and background prep sub-folder ###### *Background points sub-folder*  * R
 code, **background_points.R**  that prepares background points for MaxEnt
 models  ###### *Occurrence records sub-folder* * R code,
 **Coords_cleaner.R**, that prepare the occurrences for MaxEnt models by
 highlighting dubious records for further review   * R code,
 **Coords_NA.R** after cleaning out final dubious records that snaps
 records outside the raster data to the nearest raster cell with data  ####
 Maxent tuning and models sub-folder * R codes that tune and find optimized
 MaxEnt models for each species  * Most species processed through **MaxEnt
 models full.R** which tunes full set of feature class combinations and
 regularization multiplier values  * For species with more covariates than
 occurrences in complex models, here *Diphylla ecuadata* and
 *Nyctinomops femorosaccus*, tuning only with L and LQ through **MaxEnt
 models reduced.R** to avoid overfitting  #### Maps, final plots, and
 master table sub-folder * R codes that gather results and make final
 figures  * R code, **final_maps.R**, that creates final figures for each
 species with a North American distribution map, North American cloglog
 MaxEnt prediction map, and Texas cloglog MaxEnt prediction map with state
 (level 1) and county boundaries (level 2)  * R code,
 **variable_response_curves.R**, to plot all the marginal response curves
 for each species  * R code, **master_values_table.R**, to create the
 master table which contains all the environmental raster,
 presence/absences for each species, and predicted probabilities of
 suitable habitat for each species in table format  #### File:
 Supplementary.zip ### Occurrences folder **Final_Chiroptera_records.csv** 
 * x and y = coordinates in North America Albers Equal Area Conic
 projection (ESRI:102008) * gbifID  = occurrence record ID on GBIF  *
 species = field from GBIF  * verbatimSpecies = standardized scientific
 names for species of each record  * year, month, and day = fields from
 GBIF describing the collection date of the specimen  * countryCode,
 county, locality, and verbatimLocality = fields from GBIF describing the
 collection locality of the specimen * 'N/A' represents not
 available and data not provided in the GBIF record downloaded for this
 species **total_thin_coords.csv**  * Thinned coordinates, x and y, for
 each species that went into the MaxEnt models  ### Environmental rasters
 folder * All rasters as geotiffs   * Layers have been trimmed from global
 extents to North America and are in North America Albers Equal Area Conic
 projection (ESRI:102008)  * Layers include measures of aridity, elevation,
 and 19 bioclimatic variables measuring temperature and precipitation as
 well as the proportion of above ground lithology, available surface water,
 and landcover type in each raster cell  * Landcover forest layers (forest)
 divided by tree type, coniferous (con) and deciduous (dec), and leaf type,
 broadleaf (broad) and needleleaf (needle)  * Above ground lithological
 layers (litho) divided into igneous, metamorphic, and sedimentary rock in
 each cell  ### Study area folder **study_area_whole.shp**   * Shapefile
 with country (0) and state levels (1)  * Read in .shp file, but .shx,
 .dbf, and .prj files are needed in the same folder to ensure it works
 properly  * NAME_0 country names and NAME_1 state/province names  *
 geometry = shape and was used to trim environmental data  ##
 Code/software  * All data was prepared and analyses run using the R
 statistical software  ## Access information Key Information Sources
 Occurrence data gathered from:  * GBIF  Environmental raster data gathered
 from:  * WorldClim, historical climate, dataset of 19 bioclimatic
 variables at 10 arcminutes   * Version 3 of the Global Aridity Index and
 Potential Evapotranspiration Database | Scientific Data, figshare link in
 paper  * World Ecological Land Units (ELUs) 2015 from USGS Science Data
 Catalog  * HydroRIVERS and HydroLAKES from HydroSHEDS project and Global
 Lakes and Wetlands Database accessed through World Wildlife Fund  *
 Winkler, K. et al. (2020): HILDA+ Global Land Use Change between 1960 and
 2019 data found on PANGEA.  Shapefiles for country, state/province, and
 county boundaries from:  * Global Administrative Database (GADM)