The area under the curve (AUC) of the receiving-operating characteristic (ROC), or certain modifications of it, is almost universally used to assess the performance of species distribution models (SDMs), despite the well-recognized problems encountered with this approach, which are mainly present when dealing with presence-only data. In this work, we present a probabilistic treatment of the presence-only problem and derive a method to assess the performance of SDMs based on the analysis of an accumulation curve of presences (an area-presence plot) and the SDM outputs represented in both geographic and environmental spaces. We applied our method to three SDMs (Maxent, Bioclim, and Mahalanobis distance) and compared their performance.
Results/Conclusions
We show how our method is useful to solve the two main tasks for which the AUC is used: assessing the performance of an SDM and comparing the performance of different SDMs. Our results build on previous work and constitute a rigorous method for assessing the performance of SDM algorithms in relation to a random classifier. We suggest that the performance of an algorithm that classifies presence-only data can be assessed by two factors: (1) the degree of non-randomness of the classification at every step in the accumulation curve of presences, and (2) the amount of uninformative niche space used for the classification.