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


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.

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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.

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.


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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



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.

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The authors declare no competing interests.

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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.

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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

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

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Supplementary Data 1

Supplementary Data 1 Data submitted for species distribution modelling.

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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 (2023).

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