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At a spatial resolution of 10?km?��?10?km, species richness of all taxa increased with increasing habitat heterogeneity (Fig.?3). Passerine species showed the strongest response to habitat heterogeneity (rho?=?0.47), CGK 733 while reptiles species showed the weakest response (rho?=?0.19). For amphibians and reptiles this increase was close to linear, but at a slower rate in the case of reptiles. On the other hand, the relationship between passerines and habitat heterogeneity seems to slow down or even stabilize at intermediate to higher levels of heterogeneity. At the spatial resolution of 50?km?��?50?km, the richness of amphibians and reptiles did not show a significant relation with habitat heterogeneity (respectively, rho?=?0.06 and rho?=?0.04), while passerine species showed a strong positive relationship (rho?=?0.58). The relationship between single predictor variables and species richness varied among species groups (Table?1). Amphibian and reptiles showed similar responses to Linsitinib mouse predictors, species richness increased with precipitation (rho?=?0.40, rho?=?0.20) but decreased with temperature range (rho?=??0.33, rho?=??0.21) and elevation (rho?=??0.17) ( Table?1). The richness of passerine birds increased with elevation (rho?=?0.48) but decreased with mean radiation index (rho?=??0.43) and both mean temperature and temperature range (rho?=??0.19, rho?=??0.57). In addition, there was an overall positive response of all taxa to natural habitat cover (i.e., forest and uncultivated land; 0.12?MLN0128 are shown in Table?2. Compared with OLS models, AR models significantly reduce spatial autocorrelation (SA) (Fig.?B.1). If we only considered models including one set of variables, that is, climate (MC), topographic (MT) or habitat (MH) variables, the richness of amphibians and passerines was better explained by climatic variables while the richness of reptiles by topographic variables, thus indicating which set of variables have the main explanatory effect ( Table?2, Table?A.5). When the models including two sets (MTC, MCH, MTH) and all sets of variables (MCTH) were also considered, the MCTH model was always the best model (i.e., smaller AIC values; Table?2, Table?A.5). The second best models vary with taxa: for amphibians it was MCH, which includes habitat variables in addition to climate variables; for passerines and reptiles it was MTH, which includes both climatic and topographic variables.