Data from: Geographic and climatic constraints on bioregionalization of European ants Runxi Wang1, *, Jamie M. Kass2, Christophe Galkowski3, Federico Garcia4, Matthew T. Hamer1, Alexander Radchenko5, Sebastian Salata6, Enrico Schifani7, Zalimkhan M. Yusupov8, Evan P. Economo2,9 and Benoit Guénard1 1 School of Biological Sciences, The University of Hong Kong, Kadoorie Biological Sciences Building, Pok Fu Lam Road, Hong Kong SAR, China. 2 Biodiversity and Biocomplexity Unit, Okinawa Institution of Science and Technology Graduate University, Onna, Okinawa, Japan. 3 Société Linnéenne de Bordeaux, 1 place Bardineau, 33000, Bordeaux, France. 4 Iberian Myrmecological Association, Barcelona, Spain. 5 Schmalhausen Institute of Zoology of the National Academy of Sciences of Ukraine, 15 Bogdana Khmelnitskogo str., 01030, Kiev, Ukraine. 6 Department of Biodiversity and Evolutionary Taxonomy, University of Wroclaw, Przybyszewskiego 65, PL-51148 Wroclaw, Poland. 7 Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parco Area delle Scienze 11/a, 43124 Parma, Italy. 8 Tembotov Institute of Ecology of Mountain Territories of the Russian Academy of Science, I. Armand Street 37-A, Nalchik city, 360051, Kabardino-Balkarian Republic, Russia. 9 Radcliffe Institute of Advanced Study, Harvard University, Cambridge, MA, USA * Corresponding: runxiwg@connect.hku.hk This dataset includes: 1. Regional species lists of the western Palearctic ants (excel file): This database is derived from the Global Ant Biodiversity Informatics (GABI, Guénard et al., 2017) but features a higher spatial resolution for the region. Ultimately, we compiled native ant taxa occurrence information for each of the 207 geographic divisions (i.e., regional lists) for the western Palearctic realm, which was previously divided to 57 regions in GABI. Our definition of the western Palearctic realm does not include North Africa and the Arabian Peninsula because of historically poor sampling and the lack of recent taxonomic revisions for species in those regions. Geographical divisions used in the database were delimited based on either administrative region (GADM, version 2.8, accessed 1st Sep. 2020) or modified areas based on the physical geographic area (e.g., islands and mountains), depending on data availability. Preliminary versions of the dataset were validated by ant experts (co-authors of this study) who identified dubious records and provided additional information (e.g., unpublished or missing records) to complete and provide more accurate ant range maps. Occurrence records were deemed dubious for reasons including nomenclatural changes in recent taxonomic revisions, outdated taxonomy, and misidentifications (which can be numerous in older literature or databases). For all ant taxa in our database, we also verified nomenclature based on AntCat, an online, global catalog of ants (Bolton, 2021), with validation and inclusion of taxa up to July 1st 2021. Here, we treated valid subspecies as species in our analysis, which resulted in a total of 747 valid native species (including 40 subspecies) for regional lists. 2. Binary range maps of the western Palearctic ants (rasters/.tif files): Ranges were estimated for low-data species (<5 occurrence records) with univalue polygons (either buffered [30 km] points or convex/alpha hull, depending on data availability), and for species with sufficient data (≥ 5 occurrence records) using SDMs. We used the presence-background machine-learning algorithm Maxent to train models over a study extent defined by their polygon range estimate (buffered alpha hull) using 19 bioclimatic predictor variables at 10 arcminute resolution (~20 km at the equator) from Worldclim 2.0 (Fick & Hijmans 2017). We tuned models for optimal complexity (i.e., combinations of feature classes and regularization multipliers) using sequential criteria of cross-validation results (based on the 10 percentile omission rate and validation AUC; Radosavljevic & Anderson 2014) with the R package ENMeval 2.0.0 (Kass et al. 2021). We used these tuned models to make predictions of suitability over the species’ study extents, effectively constraining range estimates to the limits of the occurrence data, and made them binary (presence/absence predictions) by thresholding with the 10 percentile omission value. Range estimates represented by polygons for low-data species were converted to 10 arcminute grid cells to align with the modeled range estimates. Detailed metadata of SDMs can be found in the metadata as the ODMAP protocol format (Zurell et al., 2020). Detailed information of methodology can be found in our paper. Note: All those data are part of an unpublished database: the EUropean Ant Distribution (EUAD) database, we are still working on it and its open access. Thus we highly recommend you to contact us first if you want to use this dataset. References: Guénard, B., Weiser, M. D., Gomez, K., Narula, N., & Economo, E. P. (2017). The Global Ant Biodiversity Informatics (GABI) database: synthesizing data on the geographic distribution of ant species (Hymenoptera: Formicidae). Myrmecological News, 24, 83-89. Zurell, D., Franklin, J., König, C., Bouchet, P. J., Dormann, C. F., Elith, J., ... & Merow, C. (2020). A standard protocol for reporting species distribution models. Ecography, 43(9), 1261-1277.