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Snow cover is a neglected driver of Arctic biodiversity loss


Snow has far-reaching effects on ecosystem processes and biodiversity in high-latitude ecosystems, but these have been poorly considered in climate change impact models1,2. Here, to forecast future trends in species occurrences and richness, we fitted species–environment models with temperature data from three climate scenarios and simulated up to a 40% decrease in snow cover duration (SCD)3. We used plot-scale data on 273 vascular plant, moss and lichen species in 1,200 study sites spanning a wide range of environmental conditions typical for mountainous Arctic landscapes (within 165 km2). According to the models, a rise in temperature increased overall species richness and caused only one species to lose all suitable habitat. In contrast, a shorter SCD tempered the effect of increasing temperature on species richness and led to accelerated rates of species’ local extinctions after a tipping point at 20–30% SCD decrease. All three species groups showed similar extinction rates but contrasting species richness responses. Our simulations indicate that future biodiversity patterns in Arctic regions are highly dependent on the evolution of snow conditions. Climate impact models that ignore the effects of snow cover change may provide biased biodiversity projections, with potentially erratic implications for Arctic nature conservation planning.

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Fig. 1: The study area and example habitats along the SCD gradient.
Fig. 2: Projected species richness patterns and number of local extinctions.
Fig. 3: The evolution of the rate of extinctions and average species richness.
Fig. 4: The occurrence predictions for three example species under current and future snow and summer temperature conditions.

Data availability

The data that support the findings of this study are available from the corresponding author upon request.


  1. 1.

    Callaghan, T. V. et al. Multiple effects of changes in Arctic snow cover. Ambio 40, 32–45 (2011).

    Article  Google Scholar 

  2. 2.

    Snow, Water, Ice and Permafrost in the Arctic (SWIPA) 2017 (AMAP, 2017).

  3. 3.

    Callaghan, T. V. et al. The changing face of Arctic snow cover: a synthesis of observed and projected changes. Ambio 40, 17–31 (2011).

    Article  Google Scholar 

  4. 4.

    Hartmann, D. L. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 2 (IPCC, Cambridge Univ. Press, 2013).

  5. 5.

    Pearson, R. G. et al. Shifts in Arctic vegetation and associated feedbacks under climate change. Nat. Clim. Change 3, 673–677 (2013).

    Article  Google Scholar 

  6. 6.

    Hobbie, S. E., Schimel, J. P., Trumbore, S. E. & Randerson, J. R. Controls over carbon storage and turnover in high-latitude soils. Glob. Change Biol. 6, 196–210 (2000).

    Article  Google Scholar 

  7. 7.

    Bokhorst, S. et al. Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts. Ambio 45, 516–537 (2016).

    Article  Google Scholar 

  8. 8.

    Bintanja, R. & Andry, O. Towards a rain-dominated Arctic. Nat. Clim. Change 7, 263–267 (2017).

    Article  Google Scholar 

  9. 9.

    Zhang, T. J. Influence of the seasonal snow cover on the ground thermal regime: an overview. Rev. Geophys. 43, RG4002 (2005).

    Google Scholar 

  10. 10.

    Liston, G. E. & Elder, K. A distributed snow-evolution modeling system (SnowModel). J. Hydrometeorol. 7, 1259–1276 (2006).

    Article  Google Scholar 

  11. 11.

    Billings, W. D. & Mooney, H. A. Ecology of Arctic and Alpine Plants. Biol. Rev. Camb. Phil. Soc. 43, 481–529 (1968).

    Article  Google Scholar 

  12. 12.

    Niittynen, P. & Luoto, M. The importance of snow in species distribution models of arctic vegetation. Ecography 41, 1024–1037 (2018).

    Article  Google Scholar 

  13. 13.

    Bokhorst, S. et al. Changing Arctic snow cover: a review of recent developments and assessment of future needs for observations, modelling, and impacts. Ambio 45, 516–537 (2016).

    Article  Google Scholar 

  14. 14.

    Ernakovich, J. G. et al. Predicted responses of arctic and alpine ecosystems to altered seasonality under climate change. Glob. Change Biol. 20, 3256–3269 (2014).

    Article  Google Scholar 

  15. 15.

    Mod, H. K., Scherrer, D., Luoto, M. & Guisan, A. What we use is not what we know: environmental predictors in plant distribution models. J. Veg. Sci. 27, 1308–1322 (2016).

    Article  Google Scholar 

  16. 16.

    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Article  Google Scholar 

  17. 17.

    Guisan, A. & Rahbek, C. SESAM — a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages. J. Biogeogr. 38, 1433–1444 (2011).

    Article  Google Scholar 

  18. 18.

    Carlson, B. Z., Choler, P., Renaud, J., Dedieu, J. P. & Thuiller, W. Modelling snow cover duration improves predictions of functional and taxonomic diversity for alpine plant communities. Ann. Bot. 116, 1023–1034 (2015).

    Article  Google Scholar 

  19. 19.

    Elmendorf, S. C. et al. Global assessment of experimental climate warming on tundra vegetation: heterogeneity over space and time. Ecol. Lett. 15, 164–175 (2012).

    Article  Google Scholar 

  20. 20.

    Lang, S. I. et al. Arctic warming on two continents has consistent negative effects on lichen diversity and mixed effects on bryophyte diversity. Glob. Change Biol. 18, 1096–1107 (2012).

    Article  Google Scholar 

  21. 21.

    Cornelissen, J. H. C. et al. Global change and arctic ecosystems: is lichen decline a function of increases in vascular plant biomass? J. Ecol. 89, 984–994 (2001).

    Article  Google Scholar 

  22. 22.

    Wipf, S. & Rixen, C. A review of snow manipulation experiments in Arctic and alpine tundra ecosystems. Polar Res. 29, 95–109 (2010).

    Article  Google Scholar 

  23. 23.

    Aerts, R., Cornelissen, J. H. C. & Dorrepaal, E. Plant performance in a warmer world: general responses of plants from cold, northern biomes and the importance of winter and spring events. Plant Ecol. 182, 65–77 (2006).

    Google Scholar 

  24. 24.

    Blume-Werry, G., Kreyling, J., Laudon, H. & Milbau, A. Short-term climate change manipulation effects do not scale up to long-term legacies: effects of an absent snow cover on boreal forest plants. J. Ecol. 104, 1638–1648 (2016).

    Article  Google Scholar 

  25. 25.

    Bokhorst, S. F., Bjerke, J. W., Tommervik, H. & Callaghan, T. V. & Phoenix, G. K. Winter warming events damage sub-arctic vegetation: consistent evidence from an experimental manipulation and a natural event. J. Ecol. 97, 1408–1415 (2009).

    Article  Google Scholar 

  26. 26.

    Kreyling, J., Haei, M. & Laudon, H. Absence of snow cover reduces understory plant cover and alters plant community composition in boreal forests. Oecologia 168, 577–587 (2012).

    Article  Google Scholar 

  27. 27.

    Wipf, S. Phenology, growth, and fecundity of eight subarctic tundra species in response to snowmelt manipulations. Plant Ecol. 207, 53–66 (2010).

    Article  Google Scholar 

  28. 28.

    Cornelissen, J. H. C., Lang, S. I., Soudzilovskaia, N. A. & During, H. J. Comparative cryptogam ecology: a review of bryophyte and lichen traits that drive biogeochemistry. Ann. Bot. 99, 987–1001 (2007).

    CAS  Article  Google Scholar 

  29. 29.

    Mankin, J. S. & Diffenbaugh, N. S. Influence of temperature and precipitation variability on near-term snow trends. Clim. Dynam. 45, 1099–1116 (2015).

    Article  Google Scholar 

  30. 30.

    Wipf, S., Rixen, C. & Mulder, C. P. H. Advanced snowmelt causes shift towards positive neighbour interactions in a subarctic tundra community. Glob. Change Biol. 12, 1496–1506 (2006).

    Article  Google Scholar 

  31. 31.

    Aalto, J., Riihimäki, H., Meineri, E., Hylander, K. & Luoto, M. Revealing topoclimatic heterogeneity using meteorological station data. Int. J. Climatol. 37, 544–556 (2017).

    Article  Google Scholar 

  32. 32.

    Virtanen, R. et al. Where do the treeless tundra areas of northern highlands fit in the global biome system: toward an ecologically natural subdivision of the tundra biome. Ecol. Evol. 6, 143–158 (2016).

    Article  Google Scholar 

  33. 33.

    Ryvarden, L. The vascular plants of the Rastigaissa area (Finnmark, Northern Norway). Acta Borealia 26, 1–56 (1969).

    Google Scholar 

  34. 34.

    Hämet-Ahti, L., Suominen, J., Ulvinen, T. & Uotila, P. Retkeilykasvio (Field Flora of Finland) 3rd edn (Finnish Museum of Natural History, Botanical Museum, Helsinki, 1998).

    Google Scholar 

  35. 35.

    Laine, J. et al. The Intricate Beauty of Sphagnum Mosses — A Finnish Guide to Identification Vol. 2 (Department of Forest Sciences, Univ. Helsinki, 2011).

  36. 36.

    Hallinbäck, T., Lönnell, N., Weibull, H., Hedenäs, L. & von Knorring, P. Nationalnyckeln till Sveriges Flora och Fauna. Bladmossor: Sködmossor - Blåmossor. Bryophyta: Buxbaumia – Leucobryum (ArtDatabanken, SLU, 2006).

  37. 37.

    Hallinbäck, T. et al. Nationalnyckeln till Sveriges Flora och Fauna. Bladmossor: Kompaktmossor - kapmossor. Bryophyta: Anoectangium - Orthodontium (ArtDatabanken, SLU, 2008).

  38. 38.

    Hedenäs, L. & Hallinbäck, T. Nationalnyckeln till Sveriges Flora och Fauna, Bladmossor: Skirmossor - Baronmossor. Bryophyta: Hookeria - Anomodon (ArtDatabanken, SLU, 2014).

  39. 39.

    Stenroos, S., Ahti, T., Lohtander, K. & Myllys, L. Suomen jäkäläopas (Finnish Museum of Natural History, Botanical Museum, Helsinki, 2011).

    Google Scholar 

  40. 40.

    Product Guide: Landsat 4–7 Climate Data Record (CDR) Surface Reflectance (Department of the Interior US Geological Survey, 2017).

  41. 41.

    Product Guide: Provisional Landsat 8 Surface Reflectance Code (LASRC) Product (Department of the Interior US Geological Survey, 2016).

  42. 42.

    Macander, M. J., Swingley, C. S., Joly, K. & Raynolds, M. K. Landsat-based snow persistence map for northwest Alaska. Remote Sens. Environ. 163, 23–31 (2015).

    Article  Google Scholar 

  43. 43.

    R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).

  44. 44.

    Hijmans, R. J. Raster: Geographic Data Analysis and Modeling R package v.2.5-8 (R Foundation for Statistical Computing, 2016)..

  45. 45.

    Hall, D. K., Riggs, G. A. & Salomonson, V. V. Development of methods for mapping global snow cover using Moderate Resolution Imaging Spectroradiometer data. Remote Sens. Environ. 54, 127–140 (1995).

    Article  Google Scholar 

  46. 46.

    Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010).

    CAS  Article  Google Scholar 

  47. 47.

    Austin, M. P. & Van Niel, K. P. Improving species distribution models for climate change studies: variable selection and scale. J. Biogeogr. 38, 1–8 (2011).

    Article  Google Scholar 

  48. 48.

    McCune, B. & Keon, D. Equations for potential annual direct incident radiation and heat load. J. Veg. Sci. 13, 603–606 (2002).

    Article  Google Scholar 

  49. 49.

    Beven, K. J. & Kirkby, M. J. A physically based, variable contributing area model of basin hydrology. Hydrolog. Sci. Bull. 24, 43–69 (1979).

    Article  Google Scholar 

  50. 50.

    Böhner, J. & Selige, T. in SAGA — Analysis and Modelling Applications (eds Boehner, J. et al.) (Goettinger Geographische Abhandlungen, 2006).

  51. 51.

    Wang, L. & Liu, H. An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling. Int. J. Geogr. Inf. Sci. 20, 193–213 (2006).

    CAS  Article  Google Scholar 

  52. 52.

    biomod2: Ensemble Platform for Species Distribution Modeling R package v.3.3-7 (2016).

  53. 53.

    Thuiller, W., Lafourcade, B., Engler, R. & Araujo, M. B. BIOMOD - a platform for ensemble forecasting of species distributions. Ecography 32, 369–373 (2009).

    Article  Google Scholar 

  54. 54.

    Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).

    Article  Google Scholar 

  55. 55.

    Fielding, A. H. & Bell, J. F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24, 38–49 (1997).

    Article  Google Scholar 

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The authors are grateful for research support provided by the Academy of Finland (project no. 1286950), Kone Foundation and Societas pro Fauna et Flora Fennica. We thank A. Niskanen for her language check and all members of the Biogeoclimate modelling laboratory for assistance with the field work.

Author information




P.N., M.L. and R.K.H. designed the research. P.N. gathered the data, performed the analysis and wrote the first draft of the paper. All of the authors contributed to writing the paper.

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Correspondence to Pekka Niittynen.

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Supplementary Information

Supplementary Methods (Notes 1–4). Supplementary Results (Notes 1–3). Supplementary Figures 1–11. Supplementary Tables 1–4

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Niittynen, P., Heikkinen, R.K. & Luoto, M. Snow cover is a neglected driver of Arctic biodiversity loss. Nature Clim Change 8, 997–1001 (2018).

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