Conference Agenda

Satellite imagery is commonly used for deriving snow cover metrics, i.e. snow cover duration and melt-out, at high resolution for large areas, which are important determinants of plant species distributions in cold environments. This has fostered their use as predictors in species distribution models (SDMs) of alpine and arctic plants. Despite their widespread use, little is known about how well remotely-sensed snow cover metrics perform in SDMs compared to those of other sources

Here, we evaluate the use of Sentinel-2 (S2) derived melt-out dates, compared to soil temperature derived, webcam derived and modeled melt-out dates at Schrankogel in the Stubaier Alps (Tyrol, Austria) as predictors in SDMs. The SDMs are based on a set of topographic and climatic predictors (slope, topographic wetness index, potential solar radiation and mean summer air temperature) alongside the melt-out date of one of the four data sources. We compared the impact of melt-out dates on the predictions of the distribution of 70 plant species among models to assess the value of S2 melt-out date as a predictor in SDMs and to identify the most powerful source of snow cover data for species distribution modeling.

Acknowledgements: This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 883669).