Leaving the area under the receiving operating characteristic curve behind: An evaluation method for species distribution modelling applications based on presence-only data

摘要

The area under the curve (AUC) of the receiving-operating characteristic (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, mainly present when dealing with presence-only data. 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 area-presence plot and the SDM outputs represented in both geographic and environmental spaces. 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 SDMs in relation to a random classifier. We establish comparisons with two of the most popular approaches used to assess the performance of an SDM, the AUC and the Boyce index, and identified cases in which our method has advantages over these two approaches. We suggest that the performance of an algorithm that classifies presence-only data can be assessed by two factors: (a) the degree of non-randomness of the classification at every step in the accumulation curve of presences, and (b) the amount of uninformative niche space used for the classification. The method we developed can be applied to any SDM output by using the R functions available at: .