, Volume 23, Issue 2, pp 369-385
Date: 07 Jan 2014

Threshold-dependence as a desirable attribute for discrimination assessment: implications for the evaluation of species distribution models

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Abstract

Species distribution modelling has become a common approach in ecology in the last decades. As in any modelling exercise, evaluation of the predicted suitability surfaces is a key process, and the area under the receiver operating characteristic (ROC) curve (AUC) has become the most popular statistic for this purpose. A close covariation between the AUC and threshold-dependent discrimination measures (sensitivity Se and specificity Sp) raises into question the advantage of the threshold-independence of the AUC. In this study, the relationship between the AUC and several threshold-dependent discrimination measures is characterized in detail, and the sensitivity of the pattern to variations in the shape of the ROC curve is assessed. Hypothetical suitability values, coming from normal and skew-normal distributions, were simulated for both instances of presence and absence. The flexibility of the skew-normal distribution allowed for the simulation of a wide range of ROC curve configurations. The relationship between the AUC and threshold-dependent measures was graphically assessed; independently of the ROC curve shape, a nonlinear asymptotic relationship between the AUC and Se (and Sp) was obtained after applying the threshold that makes Se = Sp. A nonlinear asymptotic relationship between the AUC and the Youden index was also reported. These results imply that the AUC does not appropriately measure changes in the discrimination of models, and it is especially incapable of distinguishing between models with high discrimination capacity. Se or Sp derived from the application of the threshold that makes them equal is a preferred measure of discrimination power. Together with the rate of false positives and negatives, and with the prevalence of the species, these statistics provide more information about the discrimination capacity of the models than the AUC.