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This Sitaxentan study extends this observation by showing that background sites are also not suitable for evaluating models of realized distributions when test presences are biased, meaning they misrepresent the distribution of the species. As test sites are commonly a subset of all possible sites, measures of model performance calculated using test sites only indicate apparent model accuracy. In contrast, actual accuracy is a model's performance against the true distribution of a species. As this is typically unknown, a modeller can only assume apparent accuracy correlates positively with actual accuracy. Modelling generally requires numerous choices regarding inclusion or exclusion of predictors, study region extent, model parameterization and so on. Decisions that increase apparent accuracy are typically preferred as these are assumed to increase actual accuracy. However, if test sites misrepresent habitat occupied by a species, then a modeller may retain decisions that result in a high MK-8776 price test metric but have low performance in reality, leading to a trade-off between apparent and actual model accuracy. Here, I show that a particular type of bias in test presences, the disproportionate representation of suitable areas, combined with the use of background sites in place of absences, can lead to incorrect ranking of site suitability by a model. This tends to occur when AUC is relatively high, causing a trade-off between apparent and actual model accuracy. Disproportionate representation of suitable sites is likely one of the most prevalent forms of bias in test data as only well-designed surveys can obviate such bias. Throughout, I mostly ignore mention http://www.selleckchem.com/products/AC-220.html of training presences, which can be identical to test presences, drawn from the same region and time but mutually exclusive, or from a different region or era. The conclusions of this study are independent of their relationship because AUC, by definition, is only calculated on test presences. AUC measures a models' discriminatory ability, or propensity to assign higher model predictions to presence sites over absence (or background) sites regardless of the absolute difference between them. Provided a set of np test presences with values indexed by i and na test absences with values indexed by j, AUC is given by (1a) AUC calculated with true presences, AUCpa, ranges from 0 to 1, with values above, equal to and below 0.5 indicating that a model discriminates between presences and absences better than a random guess, no better than random and worse than random, respectively. AUCpa also equals the probability that a randomly chosen presence will have a higher model prediction value than a randomly chosen absence (Mason & Graham, 2002). AUC calculated with background sites, AUCbg, is bounded at values
