The area of research called biodiversity informatics, or bioinformatics, has to face the challenge of meeting the demand for technologies to support the conservation of biodiversity, providing computational tools applied to the study of biodiversity. The models of geographic distribution of species have a fundamental implication for understanding the biodiversity and conservation decision making. Researches on modeling species distributions generally do not present clearly the treatments applied to the data in the pre-analysis and criteria for selection of predictor variables, which increases the uncertainty regarding the results and affect the reproducibility of experiment. The objective of this research is to present the process of species distribution modeling with emphasis on the activities of preanalysis and activities selection of the predictor variables, such as to favor its repeatability and reproducibility by other researchers. The process of modeling species distribution proposed is validated on a case study of modeling distribution of pollinator species Centris hyptidis and oilseed Angelonia campestris and Angelonia cornigera, a biotic factor that considers the specialization of these interactions between plants and pollinators. In this case study we can observe one of the main contributions of this work: the application of statistical techniques for data exploration in the pre-analysis of species distribution modeling process, with improved capacity for evaluation and selection of points of occurrence essential to the performance of the predictive model.