Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Glacier retreat reorganizes river habitats leaving refugia for Alpine invertebrate biodiversity poorly protected

Abstract

Alpine river biodiversity around the world is under threat from glacier retreat driven by rapid warming, yet our ability to predict the future distributions of specialist cold-water species is currently limited. Here we link future glacier projections, hydrological routing methods and species distribution models to quantify the changing influence of glaciers on population distributions of 15 alpine river invertebrate species across the entire European Alps, from 2020 to 2100. Glacial influence on rivers is projected to decrease steadily, with river networks expanding into higher elevations at a rate of 1% per decade. Species are projected to undergo upstream distribution shifts where glaciers persist but become functionally extinct where glaciers disappear completely. Several alpine catchments are predicted to offer climate refugia for cold-water specialists. However, present-day protected area networks provide relatively poor coverage of these future refugia, suggesting that alpine conservation strategies must change to accommodate the future effects of global warming.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Data overview and example projected river network changes.
Fig. 2: GI on alpine river systems.
Fig. 3: Alpine river invertebrate population responses to glacier retreat.
Fig. 4: Locations of predicted refugia for alpine river invertebrates in 2100.
Fig. 5: Predicted habitat elevation changes.

Similar content being viewed by others

Data availability

Biological data are available in Supplementary Data 1. GloGEM data are available as supplementary material to the original paper18.

Code availability

Code is available in the supplementary material.

References

  1. Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science 354, aaf7671 (2016).

    Article  PubMed  Google Scholar 

  2. Brondizio, E. S., Settele, J., Díaz, S. & Ngo, H. T. Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science–Policy Platform on Biodiversity and Ecosystem Services. https://doi.org/10.5281/ZENODO.6417333 (IPBES, 2019).

  3. Scheffers, B. R. & Pecl, G. Persecuting, protecting or ignoring biodiversity under climate change. Nat. Clim. Chang. 9, 581–586 (2019).

    Article  Google Scholar 

  4. Hock, R. et al. High Mountain Areas: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (Intergovernmental Panel on Climate Change, 2019).

  5. Brown, L. E. et al. Functional diversity and community assembly of river invertebrates show globally consistent responses to decreasing glacier cover. Nat. Ecol. Evol. 2, 325–333 (2017).

    Article  PubMed  Google Scholar 

  6. Shugar, D. H. et al. River piracy and drainage basin reorganization led by climate-driven glacier retreat. Nat. Geosci. 10, 370–375 (2017).

    Article  CAS  Google Scholar 

  7. Milner, A. M. et al. Glacier shrinkage driving global changes in downstream systems. Proc. Natl Acad. Sci. USA 114, 9770–9778 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Giersch, J. J., Hotaling, S., Kovach, R. P., Jones, L. A. & Muhlfeld, C. C. Climate-induced glacier and snow loss imperils alpine stream insects. Glob. Chang. Biol. 23, 2577–2589 (2017).

    Article  PubMed  Google Scholar 

  9. Cauvy-Fraunié, S. & Dangles, O. A global synthesis of biodiversity responses to glacier retreat. Nat. Ecol. Evol. 3, 1675–1685 (2019).

    Article  PubMed  Google Scholar 

  10. Jacobsen, D., Milner, A. M., Brown, L. E. & Dangles, O. Biodiversity under threat in glacier-fed river systems. Nat. Clim. Chang. 2, 361–364 (2012).

    Article  Google Scholar 

  11. Muhlfeld, C. C. et al. Specialized meltwater biodiversity persists despite widespread deglaciation. Proc. Natl Acad. Sci. USA 117, 12208–12214 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Pitman, K. J. et al. Glacier retreat creating new Pacific salmon habitat in western North America. Nat. Commun. 12, 6816 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Milner, A. M. et al. Evolution of a stream ecosystem in recently deglaciated terrain. Ecology 92, 1924–1935 (2011).

    Article  PubMed  Google Scholar 

  14. Brown, L. E., Hannah, D. M. & Milner, A. M. Vulnerability of alpine stream biodiversity to shrinking glaciers and snowpacks. Glob. Chang. Biol. 13, 958–966 (2007).

    Article  Google Scholar 

  15. Fell, S. C., Carrivick, J. L., Kelly, M. G., Füreder, L. & Brown, L. E. Declining glacier cover threatens the biodiversity of alpine river diatom assemblages. Glob. Chang. Biol. 24, 5828–5840 (2018).

    Article  PubMed  Google Scholar 

  16. Finn, D. S., Räsänen, K. & Robinson, C. T. Physical and biological changes to a lengthening stream gradient following a decade of rapid glacial recession. Glob. Chang. Biol. 16, 3314–3326 (2010).

    Article  Google Scholar 

  17. Brown, L. E. & Milner, A. M. Rapid loss of glacial ice reveals stream community assembly processes. Glob. Chang. Biol. 18, 2195–2204 (2012).

    Article  PubMed Central  Google Scholar 

  18. Huss, M. & Hock, R. A new model for global glacier change and sea-level rise. Front. Earth Sci. 3, 54 (2015).

    Article  Google Scholar 

  19. Milner, A. M., Brittain, J. E., Castella, E. & Petts, G. E. Trends of macroinvertebrate community structure in glacier-fed rivers in relation to environmental conditions: a synthesis. Freshw. Biol. 46, 1833–1847 (2001).

    Article  Google Scholar 

  20. Fell, S. C., Carrivick, J. L. & Brown, L. E. The multitrophic effects of climate change and glacier retreat in mountain rivers. Bioscience 67, 897–911 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Zekollari, H., Huss, M. & Farinotti, D. Modelling the future evolution of glaciers in the European Alps under the EURO-CORDEX RCM ensemble. Cryosphere 13, 1125–1146 (2019).

    Article  Google Scholar 

  22. Phillips, S. J. et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).

    Article  PubMed  Google Scholar 

  23. Clappe, S., Dray, S. & Peres-Neto, P. R. Beyond neutrality: disentangling the effects of species sorting and spurious correlations in community analysis. Ecology 99, 1737–1747 (2018).

    Article  PubMed  Google Scholar 

  24. Valavi, R., Elith, J., Lahoz-Monfort, J. J. & Guillera-Arroita, G. blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods Ecol. Evol. 10, 225–232 (2019).

    Article  Google Scholar 

  25. Raes, N. & ter Steege, H. A null-model for significance testing of presence-only species distribution models. Ecography 30, 727–736 (2007).

    Article  Google Scholar 

  26. Desquilbet, M. et al. Comment on ‘Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances’. Science 370, eabd8947 (2020).

    Article  CAS  PubMed  Google Scholar 

  27. Rossaro, B., Montagna, M. & Lencioni, V. Environmental traits affect chironomid communities in glacial areas of the Southern Alps: evidence from a long-lasting case study. Insect Conserv. Divers. 9, 192–201 (2016).

    Article  Google Scholar 

  28. Losapio, G. et al. The consequences of glacier retreat are uneven between plant species. Front. Ecol. Evol. 8, 520 (2021).

    Article  Google Scholar 

  29. Hotaling, S. et al. Demographic modelling reveals a history of divergence with gene flow for a glacially tied stonefly in a changing post-Pleistocene landscape. J. Biogeogr. 45, 304–317 (2018).

    Article  Google Scholar 

  30. Finn, D. S., Theobald, D. M., Black, W. C. IV & Poff, N. L. Spatial population genetic structure and limited dispersal in a Rocky Mountain alpine stream insect. Mol. Ecol. 15, 3553–3566 (2006).

    Article  CAS  PubMed  Google Scholar 

  31. Brighenti, S. et al. Rock glaciers and related cold rocky landforms: overlooked climate refugia for mountain biodiversity. Glob. Chang. Biol. 27, 1504–1517 (2021).

    Article  PubMed  Google Scholar 

  32. Dornelas, M. & Daskalova, G. N. Nuanced changes in insect abundance. Science 368, 368–369 (2020).

    Article  CAS  PubMed  Google Scholar 

  33. Ashcroft, M. B. Identifying refugia from climate change. J. Biogeogr. 37, 1407–1413 (2010).

    Google Scholar 

  34. Brambilla, M. et al. Identifying climate refugia for high-elevation alpine birds under current climate warming predictions. Glob. Chang. Biol. 28, 4276–4291 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Vittoz, P. et al. Climate change impacts on biodiversity in Switzerland: a review. J. Nat. Conserv. 21, 154–162 (2013).

    Article  Google Scholar 

  36. Schai-Braun, S. C., Jenny, H., Ruf, T. & Hackländer, K. Temperature increase and frost decrease driving upslope elevational range shifts in alpine grouse and hares. Glob. Chang. Biol. 27, 6602–6614 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Khamis, K., Brown, L. E., Hannah, D. M. & Milner, A. M. Glacier–groundwater stress gradients control alpine river biodiversity. Ecohydrology 9, 1263–1275 (2016).

    Article  Google Scholar 

  38. Farinotti, D., Pistocchi, A. & Huss, M. From dwindling ice to headwater lakes: could dams replace glaciers in the European Alps? Environ. Res. Lett. 11, 054022 (2016).

    Article  Google Scholar 

  39. Hao, T., Elith, J., Lahoz-Monfort, J. J., Guillera-Arroita, G. & Hao, T. Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models. Ecography 43, 549–558 (2020).

    Article  Google Scholar 

  40. Kaky, E., Nolan, V., Alatawi, A. & Gilbert, F. A comparison between ensemble and MaxEnt species distribution modelling approaches for conservation: a case study with Egyptian medicinal plants. Ecol. Inform. 60, 101150 (2020).

    Article  Google Scholar 

  41. Thompson, P. L. et al. A process-based metacommunity framework linking local and regional scale community ecology. Ecol. Lett. 23, 1314–1329 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Khamis, K., Brown, L. E., Hannah, D. M. & Milner, A. M. Experimental evidence that predator range expansion modifies alpine stream community structure. Freshw. Sci. 34, 66–80 (2015).

    Article  Google Scholar 

  43. Clitherow, L. R., Carrivick, J. L. & Brown, L. E. Food web structure in a harsh glacier-fed river. PLoS ONE 8, e60899 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Niedrist, G. H. & Füreder, L. Trophic ecology of alpine stream invertebrates: current status and future research needs. Freshw. Sci. 36, 466–478 (2017).

    Article  Google Scholar 

  45. Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).

    Article  CAS  PubMed  Google Scholar 

  46. Fell, S. C. et al. Fungal decomposition of river organic matter accelerated by decreasing glacier cover. Nat. Clim. Chang. 11, 349–353 (2021).

    Article  Google Scholar 

  47. Kohler, T. J. et al. Glacier shrinkage will accelerate downstream decomposition of organic matter and alters microbiome structure and function. Glob. Chang. Biol. 28, 3846–3859 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  48. GLIMS: Global Land Ice Measurements from Space; https://www.glims.org/RGI/ (2017).

  49. Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).

    Article  Google Scholar 

  50. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article  Google Scholar 

  51. Meinshausen, M. et al. The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. Geosci. Model Dev. 13, 3571–3605 (2020).

    Article  CAS  Google Scholar 

  52. Ou, Y. N. et al. Can updated climate pledges limit warming well below 2 °C?; Increased ambition and implementation are essential. Science 374, 693–695 (2021).

    Article  CAS  PubMed  Google Scholar 

  53. Marzeion, B. et al. Partitioning the uncertainty of ensemble projections of global glacier mass change. Earth’s Futur. 8, e2019EF001470 (2020).

    Article  Google Scholar 

  54. Tarboton, D. Terrain Analysis Using Digital Elevation Models (TauDEM)(2008); https://hydrology.usu.edu/taudem/taudem3.1/

  55. Carrivick, J., Heckmann, T., Fischer, M. & Davies, B. in Geomorphology of Proglacial Systems. Geography of the Physical Environment (eds. Heckmann, T. & Morche, D.) 43–57 (Springer, 2019); https://doi.org/10.1007/978-3-319-94184-4_3

  56. Carrivick, J. L., Heckmann, T., Turner, A. & Fischer, M. An assessment of landform composition and functioning with the first proglacial systems dataset of the central European Alps. Geomorphology 321, 117–128 (2018).

    Article  Google Scholar 

  57. Farinotti, D. et al. A consensus estimate for the ice thickness distribution of all glaciers on Earth. Nat. Geosci. 12, 168–173 (2019).

    Article  CAS  Google Scholar 

  58. Linke, S. et al. Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution. Sci. Data 6, 283 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Slope Derived from the Digital Elevation Model over Europe from the GSGRDA Project (EU-DEM-PRE Slope, Resolution 25m); https://sdi.eea.europa.eu/catalogue/srv/api/records/b0f63ca4-a269-4769-b384-5eedd64a7522 (2012).

  60. Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Valavi, R., Guillera-Arroita, G., Lahoz-Monfort, J. J. & Elith, J. Predictive performance of presence-only species distribution models: a benchmark study with reproducible code. Ecol. Monogr. 92, e01486 (2022).

    Article  Google Scholar 

  62. Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, 4858–4874 (2019).

    Article  Google Scholar 

  63. van Proosdij, A. S. J., Sosef, M. S. M., Wieringa, J. J. & Raes, N. Minimum required number of specimen records to develop accurate species distribution models. Ecography 39, 542–552 (2016).

    Article  Google Scholar 

  64. Soultan, A. & Safi, K. The interplay of various sources of noise on reliability of species distribution models hinges on ecological specialisation. PLoS ONE 12, e0187906 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  Google Scholar 

Download references

Acknowledgements

The collection of data modelled in this study was funded by multiple organizations. A UK Natural Environment Research Council (NERC) Scholarship (number NE/L002574/1) was awarded to S.C.F. Additional financial support for laboratory overheads was provided to S.C.F. by the River Basin Processes and Management Cluster, School of Geography, University of Leeds. Support for L.E.B. was provided by the Royal Society (International Outgoing grant 2006/R4) and for L.E.B. and J.L.C. by the Royal Geographical Society-Institute of British Geographers with the Royal Institute of Chartered Surveyors (GFG 39/08). Financial support for V.L. was provided by the European Union Environment and Climate Programme, contract number ENV4-CT95-0164/1996, the Autonomous Province of Trento (Italy) (grant 1060/2001; grant 3402/2002) and the protected areas Adamello Brenta Nature Park and Stelvio National Park. Sampling in the French Alps by S.C.-F. has been supported by the Agence Alpes de l’Eau Rhône Méditerranée Corse (grant 722 2017 024), the Région Auvergne-Rhône-Alpes (BERGER project, grant P089O002), the Observatoire des Sciences de l’Univers de Grenoble, the LTSER Zone Atelier Bassin du Rhône, and the protected areas Vanoise National Park and Aiguilles Rouges nature reserve. Financial support for the University of Geneva (E.C.) was provided by the Académie Suisse des Sciences Naturelles, the Société Murithienne, the Société Académique of Geneva and the French Embassy in Switzerland. Partial funding for this project was through an EAWAG Action Field Grant ‘Aquatic Biodiversity in Rapidly Changing Alpine Landscapes’ (C.R.). We are grateful to multiple people, too numerous to list, who have assisted with the collection, identification and analysis of alpine river datasets used in this study. However, special thanks go to B. Maiolini, promoter of research on glacial streams in Italy in the late 1990s; B. Lods-Crozet, who provided invaluable taxonomic expertise about Chironomidae in some of the Rhône basin sites; B. Launay and M. Forcellini for their taxonomic expertise (especially for Ephemeroptera, Plecoptera and Trichoptera); and J. Becquet for significant support in the lab.

Author information

Authors and Affiliations

Authors

Contributions

M.A.W., J.L.C. and L.E.B. led the study and the writing of the manuscript. J.L.C. and M.H. performed the glacial and hydrological modelling. M.A.W. performed the species distribution modelling and all statistical tests. M.A.W., J.L.C., L.E.B. and W.J. designed the methodology. E.C., C.I., S.C-F., S.C.F., L.F., V.L., C.R. and L.E.B. collected field data and acquired funding for their collection. All authors contributed to review and editing.

Corresponding author

Correspondence to L. E. Brown.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Ecology & Evolution thanks Scott Hotaling, Wilfried Thuiller and Rocco Tiberti for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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

Extended data

Extended Data Fig. 1 Projected catchment level environmental changes in selected decades.

Left column shows baseline sub-catchment means of glacial influence (GI), contributing area (CA), pH and slope in 2020. Middle and left columns show changes in 2060 and 2100, respectively, as a percentage of 2020 sub-catchment means. Sub-catchments with no glacial influence under the baseline condition are shown in grey. *Slope is a dimensionless variable.

Extended Data Fig. 2 Distributions of environmental conditions at the river segment level.

Rows show probability densities for each major river basin. Columns show data for contributing area (CA), pH and slope respectively. White marks show the distribution of each variable for biological samples. *Slope is a dimensionless variable.

Extended Data Fig. 3 Projected catchment level environmental conditions in selected decades.

Rows show sub-catchment means of glacial influence (GI), contributing area (CA), pH and slope respectively. Columns show conditions in 2020, 2060 and 2100 respectively. Sub-catchments with no glacial influence under the baseline condition (2020) are shown in grey. *Slope is a dimensionless variable.

Extended Data Fig. 4 Response curves for 15 alpine invertebrate species.

Rows show predictions from the best performing species distribution model for each species. Columns show response curves for each environmental variable, including glacial influence (GI), contributing area (CA), pH and slope. Curves for each variable were generated from models whilst holding all other variables at their mean values from the biological dataset. Colours correspond to the identity of the best performing model for each species, including Artificial Neural Network (ANN), Generalized Additive Model (GAM), Generalized Linear Model (GLM), Maximum Entropy (MAXENT) and an ensemble mean (‘mean’). Solid lines and dashed lines respectively show curves within and outside of the 99% confidence limits of the background environmental data. Shaded areas show 95% confidence intervals from cross-validation. Black marks at the upper edge of each panel show the distribution of the corresponding variable where the species was recorded as present. *Slope is a dimensionless variable.

Extended Data Fig. 5 Predicted change in suitable habitat area for 15 alpine invertebrate species.

Panels for each species show the change in suitability-weighted habitat area per decade relative to a 2020 baseline within major river basins. Lines denote the mean of model predictions and shaded areas show 95% confidence intervals from cross-validation.

Extended Data Fig. 6 Predicted habitat elevation changes.

Distributions of habitat suitability by elevation band across selected decades by major river basin (columns) and species (rows). Boxplots show the median (centre line), interquartile range (box limits) and 1.5 x interquartile range (whiskers). Sample sizes (number of river segments) for the Danube, Po/Adige, Rhine and Rhône basins respectively are n = 2683, n = 2797, n = 1726 and n = 3418 in 2020, n = 2775, n = 2831, n = 2019 and n = 3607 in 2060, and n = 2755, n = 2851, n = 2102 and n = 3609 in 2100.

Extended Data Fig. 7 Species distribution model performance.

Performance metrics of the best performing model for each species, including area under the curve (AUC) of the receiver operating characteristic and mean absolute error (MAE) on the out-of-sample (‘test’) data and training data. Colours correspond to the identity of the best performing model for each species, including Artificial Neural Network (ANN), Generalized Additive Model (GAM), Generalized Linear Model (GLM), Maximum Entropy (MAXENT) and an ensemble mean (‘mean’). The left panel shows the distribution of AUC values from the null model (upper, closed distribution) and from training folds (lower, open distribution). Species shown in ascending order of glacial influence optima. Boxplots show the median (centre line), interquartile range (box limits) and 1.5 x interquartile range (whiskers). P-values shown from a one-sided bootstrap hypothesis test. Sample sizes (n) denote the number of model folds.

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2, Supplementary Figures 1–62 and Supplementary Tables 1 and 2.

Reporting Summary

Peer Review File

Supplementary Data 1

Supplementary Data 1 Data submitted for species distribution modelling.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wilkes, M.A., Carrivick, J.L., Castella, E. et al. Glacier retreat reorganizes river habitats leaving refugia for Alpine invertebrate biodiversity poorly protected. Nat Ecol Evol 7, 841–851 (2023). https://doi.org/10.1038/s41559-023-02061-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-023-02061-5

This article is cited by

Search

Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Anthropocene