Identification of areas containing high biological diversity ('hotspots') from species presence-only data has become increasingly important in species and ecosystem management when presence/absence data is unavailable. However, as presence-only data sets lack any information on absences and as they suffer from many biases associated with the ad hoc or non-stratified sampling, they are often assumed problematic and inadequate for most statistical modeling methods. In this paper, this supposition is investigated by comparing generalized additive models (GAM) fitted with 43 native New Zealand fern species presence/absence data, obtained from a survey of 19 875 forested plots, to GAM models and ecological niche factor analysis (ENFA) models fitted with identical presence data and, in the case of GAM models, computer generated 'seudo'absences. Keywords: Biodiversity hotspots, ENFA, GAM, New Zealand ferns, Presence-only data, Spatial predictions.