Unveiling the unknown of Mexican ants: sampling bias, knowledge gaps and priority survey areas

-sensitivity_completeness_20_30_60arcmin.pdf This plot summarises the sensitivity analyses conducted at the three arcmin sizes and considering 2, 5, 10, and 20 records within each cell to calculate inventory completeness. Includes the rasters that were used in all analyses (min_10_20, min_10_30, and min_10_60 arcmin). -grids.zip Contains the three arcmin raster cell sizes used for all analyses. -Kernel_density_20_30_60arcmin.zip Contains the output raster of the KDE analysis conducted in the ArcMap software. Those rasters were subsequently used for conducting sampling bias analysis with the variables included in the pred_var_sampling -Completeness_class_min10rec.zip Output rasters with the classification categories: well-surveyed (>0.8), moderately surveyed (0.5–0.8), low surveyed (0–0.5), insufficient data (=0), and no records (NA). -Priority_classes.zip Contains the rasters across the three cell sizes after being categorized depending on their priority for future sampling. -mex_eck4.zip Contains the shapefile of Mexico projected to the Eckert IV, in which all analyses were conducted. -records.zip Occurrence files in which several pieces of information are provided according to their incorporation/exclusion into analyses: 1- occurrences_included_wo_duplicates.csv = This database contains the 52,689 ant records compiled for Mexico after removing the duplicates of the species within the same coordinate. 2.-Ocurrences_excluded.csv = This database contains the 85,403 duplicated records removed after the cleaning pipeline (see methods section). 3.-full_records_with_status.csv = This database contains the 137,093 ant records compiled from the Kass data, AntWeb data, and Dattilo et al. (2020) data, and a column with the status whether the record was included or excluded for the analyses. -scripts.zip This folder contains the instructions to calculate the inventory completeness at different arcmin sizes (inventory_completeness_supp_material.R), and further calculate the uniqueness geographical and climatic distances to obtain the priority maps (priority_maps.R). Finally, the R script for running the spatial autoregressive (SAR) error models is also provided (SM_SAR_model.R)