Species Distribution Models (SDMs) have become increasingly useful for conservation issues. Initially designed to predict distributions of species from incomplete datasets, SDMs may also identify environmental conditions associated with higher occurrences and abundances of widely distributed taxa. Using sighting records of 15 widely distributed mammals from French Guiana, including primates, carnivores, rodents and ungulates, we used three SDMs-based on (i) entropy, (ii) genetic algorithm, (iii) Mahalanobis distance-to investigate relationships between species occurrence and predictive variables such as vegetation, biogeographic units, climate, and disturbance index. Maximal entropy procedures resulted in more accurate projected conditions: the accuracy of the predicted distributions was higher than 90% in nine species among the 15 tested, and predicted occurrences were correlated to field-measured abundances for nine species. The Genetic algorithm implemented with GARP had lower accuracy, with predicted occurrences correlated to abundances for three species only. Finally, Mahalanobis distance had a much lower performance and failed to find any correlation between occurrences and abundances. In the case of MaxEnt modelling, since map projection summarized more appropriate environmental conditions and identified areas likely to act as sources and/or corridors, we propose to use those appropriate environmental conditions as a proxy of conductance for landscape connectivity planning. We provide evidence here that SDMs can identify not only more suitable environmental conditions, but also areas hosting higher abundances for a large set of species with key ecological roles. Further management applications of this environmental suitability index could help in designing corridors between protected areas.