Data on distribution and variability of three pseudocryptic species: Fruticicola fruticum (O.F. Müller, 1774), F. similis Proćków et Sîrbu, 2022, and F. gemina Proćków, 2022 in Europe.

The dataset consists in samples from 60 populations of Fruticicola sp. from its entire range, belonging to 8 mOTU (codified with the letters from A to H), and three pseudocryptic species, denoted as I - Fruticicola fruticum, II - F. similis and III - F. gemina. The location of each sample (and population) was given as latitude (Y), longitude (X), and altitude (Alt). Genetic analysis was based on the previously published p-distances obtained from phylogenetic analysis of COI sequences (cytochrome oxidase c subunit I) and from mitochondrial ribosomal 16S (Hofman et al. 2022). Uncorrected p distances, the proportion of nucleotide sites at which two sequences being compared are different (Nei & Kumar 2000) were calculated in MEGA 7 (Kumar et al. 2016). For the morphometric analysis, nine shell parameters were measured, in adult snails (with completely developed lips) in the 24 populations for which the samples were rich enough to allow biometric study of their shells. As climatic variables, we used 21 parameters for each of the 60 locations: average precipitation in January (Rjan), April (Rapr), July (Rjul), October (Roct), total amount of annual precipitation (Rtot)—all in mm, precipitation coefficient of variation (Rcv), number of wet days in January (Wjan), in April (Wapr), July (Wjul), October (Woct), and in the whole year (Wet), relative humidity in percentage (RHum), average duration of annual sunshine in percent of possible (Sunt), average temperature for January (Tjan), April (Tapr), July (Tjul), October (Toct), for the whole year (Tavg), average maximum temperature in the hottest month (Tmax), and minimum temperature in the coldest month (Tmin), and the number of months with minimum temperatures above 10 °C (Tno10): all temperature values are in Celsius degrees. Climatic parameters were obtained from AQUASTAT Climate Information Tool, FAO's Global Information System on Water and Agriculture (https://www.fao.org/aquastat/en/geospatial-information/climate-information). The study based on this dataset aims to relate variability in, and discrimination of, populations, mOTUs (molecular Operational Taxonomic Units), and species as responses to four classes of predictors, namely genetics (p-distances between populations), morphology (biometrical descriptors), climatic parameters and spatial descriptors.