Letter | Published:

Snow cover is a neglected driver of Arctic biodiversity loss

Nature Climate Changevolume 8pages9971001 (2018) | Download Citation

Abstract

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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Data availability

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

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.

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

  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).

  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).

  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).

  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).

  8. 8.

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

  9. 9.

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

  10. 10.

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

  11. 11.

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

  12. 12.

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

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  22. 22.

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

  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).

  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).

  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).

  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).

  27. 27.

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

  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).

  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).

  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).

  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).

  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).

  33. 33.

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

  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).

  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).

  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).

  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).

  46. 46.

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

  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).

  48. 48.

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

  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).

  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).

  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).

  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).

  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).

Download references

Acknowledgements

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

Affiliations

  1. Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland

    • Pekka Niittynen
    •  & Miska Luoto
  2. Biodiversity Centre, Finnish Environment Institute, Helsinki, Finland

    • Risto K. Heikkinen

Authors

  1. Search for Pekka Niittynen in:

  2. Search for Risto K. Heikkinen in:

  3. Search for Miska Luoto in:

Contributions

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.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Pekka Niittynen.

Supplementary information

  1. Supplementary Information

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

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/s41558-018-0311-x