Abstract
Aim Snow cover persistence (SCP) has significant effects on plants in high‐elevation ecosystems. It determines the length of the growing season, provides insulation against low temperatures and influences water availability, thereby shaping the vegetation mosaic. Despite its importance, SCP is rarely used in plant species distribution modelling. In this study, we examine whether incorporating SCP in plant species distribution models (SDMs) improves their predictive power. We investigate the link between species' ecology and SDM improvements by the addition of various SCP predictors. Location Western Swiss Alps. Taxon 206 alpine flowering plants (angiosperms). Methods We produced three maps of landsat satellite‐based SCP indices over an entire mountain region, one of them using an online open access platform allowing quick and easy replication and used them as a predictor in plant SDMs alongside commonly used predictors. We tested whether this improved the predictive performance of plant SDMs. Results All three SCP indices improved the overall SDM predictive accuracy, but the overall improvement was potentially limited by their correlation with other climatic predictors. Alpine plant species known for their dependence on snow benefited more from the additional snow information. Main Conclusions SCP should be used for predicting at least the distribution of alpine, snow‐related plant species. Given that adding snow cover improves SDMs and that snow duration decreases as climate warms, future predictions of alpine plant distributions should account for both snow predictor and associated snow change scenarios.