Assessing the effects of pseudo-absences on predictive distribution model performance

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.

Thesis abstract. Applying species distribution modeling for the conservation of Iberian protected invertebrates

This article outlines the approaches to modeling the distribution of threatened invertebrates using data from atlases, museums and databases. Species Distribution Models (SDMs) are useful for estimating species’ ranges, identifying suitable habitats, and identifying the primary factors affecting species’ distributions. The study tackles the strategies used to obtain SDMs without reliable absence data while exploring their applications for conservation. I examine the conservation status of Copris species and Graellsia isabelae by delimiting their populations and exploring the effectiveness of protected areas. I show that the method of pseudo‐absence selection strongly determines the model obtained, generating different model predictions along the gradient between potential and realized distributions. After assessing the effects of species’ traits and data characteristics on accuracy, I found that species are modeled more accurately when sample sizes are larger, no matter the technique used.