Epiphytic lichens are an important component of forest biodiversity. Many of them are threatened and conservation planning therefore requires comprehensive information about their distribution over space and time. However, besides topo-climatic predictors, the spatial pattern of epiphytic lichens is also closely related to the tree characteristics on which they grow. In this paper, we tested the hypothesis that habitat specialist lichens restricted to specific habitat types and common species with a wide range of ecological preferences may respond differently to the topo-climatic and forest-structure predictors. We applied the generalized linear model (GLM) and maximum entropy (Maxent) for 20 epiphytic lichen species in Switzerland at different spatial scales (1 km and 100 m). To evaluate the influence of forest structure on the epiphytic lichen distribution, models with three sets of predictors (topo-climatic, forest-structure and combined predictor sets) were fitted for each species. Canopy height of forest stands and forest type were tested as a proxy of forest structure. Our results showed that both the techniques applied predicted well the occurrences of habitat specialists but the models of common lichens were overall less accurate. Forest-structure predictors significantly improved the models of rare and threatened species with specific microhabitat requirements, i.e. mainly forest lichens (Arthonia byssacea, Bactrospora dryina, Calicium viride, Gyalecta truncigena and Vulpicida pinastri), but they did not contribute considerably to the models of common species that grow equally well on the bark of different trees within or outside of forests (e.g. Caloplaca cerina, Phaeophyscia orbicularis, Physcia stellaris and Xanthoria parietina). We recommend using high-resolution forest-structure data for robust and reliable predictions of the distribution patterns of threatened epiphytic lichens, most of which are habitat specialists. The inclusion of these data will allow a hypothesis-driven sampling strategy, and will thus increase the effectiveness of the field sampling needed for searching large regions thoroughly to discover new sites where rare and threatened species occur.