Modelling species distributions with presence data from atlases, museum collections and
databases is challenging. In this paper, we compare seven procedures to generate pseudoabsence
data, which in turn are used to generate GLM-logistic regressed models when
reliable absence data are not available. We use pseudo-absences selected randomly or by
means of presence-only methods (ENFA and MDE) to model the distribution of a threatened
endemic Iberian moth species (Graellsia isabelae). The results show that the pseudo-absence
selection method greatly influences the percentage of explained variability, the scores of
the accuracy measures and, most importantly, the degree of constraint in the distribution
estimated. As we extract pseudo-absences from environmental regions further from the
optimum established by presence data, the models generated obtain better accuracy scores,
and over-prediction increases. When variables other than environmental ones influence the
distribution of the species (i.e., non-equilibrium state) and precise information on absences
is non-existent, the random selection of pseudo-absences or their selection from environmental
localities similar to those of species presence data generates the most constrained
predictive distribution maps, because pseudo-absences can be located within environmentally
suitable areas. This study showsthat ifwe do not have reliable absence data, the method
of pseudo-absence selection strongly conditions the obtained model, generating different
model predictions in the gradient between potential and realized distributions