Aim: We examine whether the addition of snow cover persistence 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 snow cover persistence predictors.
Location: Western Swiss Alps.
Taxon: 206 alpine flowering plants (Angiospermes).
Methods: We produced three maps of landsat satellite-based snow cover persistence 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 snow cover persistence 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: Snow cover persistence 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.