Species distribution models predict rare species occurrences despite significant effects of landscape context

Summary The true status of many endangered plants is uncertain because the locations of all extant populations are not known. Species distribution models ( SDM s) can direct searches for additional populations, but habitat fragmentation may influence the distribution of rare species more than climatic or edaphic factors in human‐dominated landscapes. In this study, I test the ability of SDM s to predict rare plant occurrences in a fragmented landscape and the importance of predicted habitat suitability versus landscape context. I built SDM s for eight rare woodland plants and assessed them using an independent data set including plant community surveys of 51 sites. I used community data to determine whether SDM s predict the right habitat type even when the target rare species was absent. I then modelled rare species presence based on predicted habitat suitability, distance to the nearest known population and the amount of forest habitat within 500 m of the site. SDM s were effective for seven of the eight species, with the degree of predicted habitat suitability positively related to species' occurrence. I found new populations of four of the eight species. However, the amount of forest habitat available in the vicinity of a plot was also a positive predictor of rare plant occurrences. Among sites predicted to be suitable, the distance to the nearest known population was the strongest predictor of rare plant occurrence. Synthesis and applications . Species distribution models ( SDM s) can effectively target searches for populations of rare species even in human‐dominated landscapes. Surveying the plant community at sites predicted to be suitable can help to improve the SDM . SDM s used in conjunction with data on landscape context can maximize the efficiency of searches for rare species and show which species are restricted by dispersal limitation and habitat fragmentation in addition to edaphic and climatic factors.