We discourage the practice of excluding certain pseudo-absence data because it involves arbitrary assumptions about what are (un)suitable environments for the species being modelled.
Because absence data are lacking, DIVA-GIS uses a set of random pseudo-absence points [38].
Absence data are even more scarce because it is difficult to determine when a species does not (transiently) exist in an area (Guisan and Thuiller 2005).
Because such techniques require presence and absence data, 5000 pseudo-absences were randomly selected, and because different selections can provide different results, the models were run with 5 different sets of pseudo-absences.
In addition, the 10th percentile threshold has been widely used because the true absence data has not been generally available [73], and other thresholds have usually been based on more complex methods requiring both presence and absence data [71], [72].
Despite its numerous advantages, LRM has been precluded from many studies of species distributions because it requires absence data, which are frequently unavailable and often not reliable.
Logistic regression methods have not been widely used in many ecological contexts because they require absence data which are usually not available.
Multiple logistic regression is precluded from many practical applications in ecology that aim to predict the geographic distributions of species because it requires absence data, which are rarely available or are unreliable.
The Bray-Curtis distance measure is appropriate for assemblage data because it gives no weight to zero/zero (mutual absence) data.
Model output was most sensitive to incomplete absence data, especially when true presence data was included, perhaps because absence data was missing within a specific environmental niche and Adélie penguins do not occupy all available habitats (Supplemental Results).
Because we do not have true absence data, we cannot quantify our commission error rate.
