Maps predicting habitat suitability across Mexico for 348 of the 370 species were generated using maxent Species Distribution Modelling software version 2·3 (Phillips, Dudík & Schapire 2006). The remaining 22 species were not modelled because they occurred in just one locality with one or two records. In these cases, we assumed that these species are present only in the cells where they were recorded. maxent method was selected because it is considered one of the most accurate when only presence information is available, and it outperforms other, more conventional methods that use presence–absence information (Elith et al. 2006). In brief, maxent uses a deterministic algorithm able to find an optimal probability distribution based on a set of environmental constraints (see Phillips et al. 2006; Phillips & Dudík 2008 for a detailed explanation on the method). Individual maps were made by relating observed presence data to the 19 bioclimatic variables from the worldclim data base version 1·3 (Hijmans, Cameron & Parra 2005): annual mean temperature, annual precipitation, isothermality, maximum temperature of warmest month, mean diurnal range, mean temperature of coldest quarter, mean temperature of warmest quarter, mean temperature of wettest quarter, minimum temperature of coldest month, annual temperature range, temperature seasonality, precipitation of coldest quarter, precipitation of driest month, precipitation of driest quarter, precipitation of warmest quarter, precipitation of wettest month, precipitation of wettest quarter and precipitation seasonality. The resolution of the climate layers used in the analyses was 5 min (i.e. 0·083°, or squares of c. 10 × 10 km). The maps were subsequently exported to obtain a matrix of suitability values for each species in every one of the 24 997 cells or sites of c. 10 × 10 km that cover the whole of Mexico. As suitability values calculated by maxent range from 0 to 100, it is necessary to set a threshold for converting these continuous values to binary ones (presence/absence) for each species, and subsequently overlapping all individual models to derive a possible species richness. Initially, we set 21 thresholds selected from 1 to 100 at intervals of five to obtain the respective presence–absence maps for each species; we assumed presence in the cells with suitability scores equal to or higher than 1, 5, 10 ... and 100 (hereafter T1, T5, T10 ... and T100). Thus, after overlapping all individual models according to the threshold used, we obtained 21 possible scenarios of species richness distribution, whose values would be subsequently compared with observed values of well-surveyed sites to select the threshold that represents better the patterns in species richness.