Snow information is required in subcontinental scale predictions of mountain plant distributions

• Published in: Global Ecology and Biogeography
• Main Authors: Rissanen, Tuuli, Niittynen, Pekka, Soininen, Janne, Luoto, Miska
• Other Authors: Bjorkman, Anne, Koneen Säätiö
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2021
• Subjects: Arctic; Fennoscandia; Fennoscandian; permafrost
• Online Access: http://dx.doi.org/10.1111/geb.13315; https://onlinelibrary.wiley.com/doi/pdf/10.1111/geb.13315; https://onlinelibrary.wiley.com/doi/full-xml/10.1111/geb.13315

Abstract Aim To examine how snow cover and permafrost affect plant species distributions at a subcontinental extent. Location Mountain realm of Fennoscandia, northern Europe. Time period Species data from 1 January 1990–25 February 2019. Major taxa studied Arctic‐alpine and boreal vascular plants. Methods We examined the effect of snow persistence and permafrost occurrence on the distributions of arctic‐alpine and boreal plant species while controlling for climate, topography and geological factors. Data comprised 475,811 observations from 671 species in the Fennoscandian mountains. We investigated the relationships between species distributions and environmental variables using four modelling methods and ensemble modelling building on both non‐spatial and spatial models. Results Snow persistence was the most important driver of plant species distributions, with the greatest variable importance for both arctic‐alpine (38.2%) and boreal (49.9%) species. Permafrost had a consistent minor effect on the predicted distributions. Arctic‐alpine plants occur in areas with long snow persistence and permafrost, whereas boreal species showed the opposite habitat preferences. Main conclusions Our results highlight the importance of snow persistence in driving the distribution of vascular plant species in cold environments at a subcontinental scale. The notable contribution of the cryosphere to plant species distribution models indicates that the inclusion of snow information in particular may improve our understanding and model predictions of biogeographical patterns in cold regions.