Aim: We evaluate differences between and the applicability of three linear predictive models to determine butterfly hotspots in Belgium for nature conservation purposes. Location: The study is carried out in Belgium for records located to Universal Transverse Mercator (UTM) grid cells of 5 x 5 km. Methods: We first determine the relationship between factors correlated to butterfly diversity by means of modified t-tests and principal components analysis; subsequently, we predict hotspots using linear models based on land use, climate and topographical variables of well-surveyed UTM grid cells (n = 197). The well-surveyed squares are divided into a training set and an evaluation set to test the model predictions. We apply three different models: (1) a 'statistically focused' model where variables are entered in descending order of statistical significance, (2) a 'land use-focused' model where land use variables known to be related to butterfly diversity are forced into the model and (3) a 'hybrid' model where the variables of the 'land use-focused model' are entered first and subsequently complemented by the remaining variables entered in descending order of statistical significance. Results: A principal components analyses reveals that climate, and to a large extent, land use are locked into topography, and that topography and climate are the variables most strongly correlated with butterfly diversity in Belgium. In the statistically focused model, biogeographical region alone explains 65% of the variability; other variables entering the statistically focused model are the area of coniferous and deciduous woodland, elevation and the number of frost days; the statistically focused model explains 77% of the variability in the training set and 66% in the evaluation set. In the land use-focused model, biogeographical region, deciduous and mixed woodland, natural grassland, heathland and bog, woodland edge, urban and agricultural area and biotope diversity are forced into the model; the land use-focused model explains ...