Abstract
The global retreat of glaciers is dramatically altering mountain and high-latitude landscapes, with new ecosystems developing from apparently barren substrates1,2,3,4. The study of these emerging ecosystems is critical to understanding how climate change interacts with microhabitat and biotic communities and determines the future of ice-free terrains1,5. Here, using a comprehensive characterization of ecosystems (soil properties, microclimate, productivity and biodiversity by environmental DNA metabarcoding6) across 46 proglacial landscapes worldwide, we found that all the environmental properties change with time since glaciers retreated, and that temperature modulates the accumulation of soil nutrients. The richness of bacteria, fungi, plants and animals increases with time since deglaciation, but their temporal patterns differ. Microorganisms colonized most rapidly in the first decades after glacier retreat, whereas most macroorganisms took longer. Increased habitat suitability, growing complexity of biotic interactions and temporal colonization all contribute to the increase in biodiversity over time. These processes also modify community composition for all the groups of organisms. Plant communities show positive links with all other biodiversity components and have a key role in ecosystem development. These unifying patterns provide new insights into the early dynamics of deglaciated terrains and highlight the need for integrated surveillance of their multiple environmental properties5.
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Data availability
Raw sequence data and filtered sequence data are available at https://doi.org/10.5281/zenodo.6620359 and https://doi.org/10.5281/zenodo.10423968 digital repositories, respectively.
Code availability
All codes used are available at https://doi.org/10.5281/zenodo.10423968.
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Acknowledgements
This study was funded by the European Research Council under the European Community’s Horizon 2020 Programme, grant agreement no. 772284 (IceCommunities), by Biodiversa+, the European Biodiversity Partnership under the 2021–2022 BiodivProtect joint call for research proposals, co-funded by the European Commission (no. 101052342) and with the funding organizations MUR and ANR, and by LabEx OSUG@2020 (Investissement d’Avenir, ANR-10-LABX-56). P.C. was supported by the Science and Engineering Research Board (SERB), Department of Science and Technology (GoI), NPDF project no. PDF/2017/002717. L.T. was also supported by the Australian Research Council Special Research Initiative Securing Antarctica’s Environmental Future (no. SR200100005). Y.Y. was supported by the National Natural Science Foundation of China (41941015). D.F. was supported by the National Biodiversity Future Center of the National Recovery and Resilience Plan (no. CN_00000033).
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G.F.F. conceived the work with the help of W.T., P.T. and J.P. G.F.F., S.M., A.G., A.B., R.A., M.C., F.A., R.S.A., P.A., P.A.G., S.C.-F., J.L.C.L, P.C., M.C.S., J.J.C., J.A.C.R., C.C., R.C.E., O.D., P.D., A.E., S.E., A.F., L.G., F.G., M.G., S.H., R.K., N.K., R.I.M., M.A.M.-M., G.P., F.P., A.R., K.S., L.T., N.U., Y.Y., V.Z., A.Zimmer, G.A.D. and J.P. planned the data collection and performed the sampling. A.G., A.B., C.C., L.G., A.P., A.Zerboni and G.F.F. performed the laboratory analyses. A.C., S.M., A.G., I.C., D.F., W.T. and G.F.F. contributed to data preparation and statistical analyses. A.C. and G.F.F. prepared the first draft of the manuscript, with subsequent contributions from all authors.
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Extended data figures and tables
Extended Data Fig. 1 Study sites and sampling design.
a, Global distribution of the 46 analysed proglacial landscapes. The size of symbols is proportional to the average number of detected MOTUs per plot. The inset shows the landscapes in the European Alps. b, Sampling design. For each proglacial landscape, we selected a number of sites corresponding to the lines representing the position of the glacier forefront at a given date (following ref. 53; four sites are represented in this example). For each site, we established five regularly spaced plots (diamonds; distance between plots: 20 m); at each plot, we collected five soil subsamples within a 1-m radius and pooled subsamples together, resulting in a ~ 200 g composite sample per plot (total: 46 landscapes; 256 sites; 1,256 plots analysed separately).
Extended Data Fig. 2 Pearson’s correlations between the alpha-diversity of eight taxonomic groups of organisms within proglacial landscapes.
Larger dots and more intense colours indicate stronger correlations. N = 1,251 plots.
Extended Data Fig. 3 Relationship between diversity values predicted by chronosequence-based models, and diversity values observed in permanent plots surveyed in multiple years by independent studies.
These studies are refs. 103,104. The bold line indicates values predicted by a mixed model relating observed and predicted diversity (marginal R2 = 0.43, conditional R2 = 0.63); the shaded area represents the 95% credible intervals. Diversity was measured using Hill’s number q = 1. Differences in absolute values are related to methodological differences between our sampling approach and independent data used for validation. For instance, ref. 103 provided total diversity across 10 plots spread over 150 m2 on each site; ref. 104 calculated diversity across 11-13 plots per site, while our diversity predictions refer to one plot per site.
Extended Data Fig. 4 Differences in colonization rate of eight groups of organisms between subpolar, temperate and tropical landscapes.
a–h, The plots represent the relationship between age and diversity of the groups estimated by mixed models (Supplementary Table 2); error bars represent 95% credible intervals. i, Average soil temperature during the growing season in the 1,251 analysed plots.
Extended Data Fig. 5 Conceptual models representing how time and microclimate can drive soil chemistry and (alpha or beta) biodiversity changes in ecological succession following glacier retreat.
To determine the relationships between soil nutrients and biodiversity, we tested three conceptual models assessing three potential causal structures, either (a) soil nutrients and ecosystem productivity shape biodiversity, (b) biodiversity shapes soil nutrients and productivity, or (c) soil nutrients, productivity and biodiversity co-vary. “Soil biodiversity” indicates the biodiversity of all the organisms beside plants (i.e. bacteria, fungi, protists and animals). The detailed structure of models, including the relationships between nutrients, plants and productivity, is shown in Fig. 2.
Extended Data Fig. 6 Alternative structural equation models, assuming different relationships between alpha-diversity, soil nutrients and ecosystem productivity.
The colour of the paths in a–c is proportional to effect size; dashed lines indicate non-significant relationships. Co-variations between soil features and between the biodiversity of different taxonomic groups are not shown. In all the models, N = 793 plots.
Extended Data Fig. 7 Relationship between the number of molecular operational taxonomic units (MOTUs) detected by environmental DNA (eDNA) and the number of species recorded in traditional inventories for plants and insects.
For plants, N = 38 sites from 10 forelands; for insects, N = 44 sites from 13 forelands. We show the partial regression plot of linear mixed models accounting for glacier identity (plants: R2C = 0.86; insects: R2C = 0.83). For eDNA we used the number of MOTUs detected on soil samples across all plots in a site; for traditional detections we used the number of taxa identified at species and genus level. See Supplementary Table 6 for the sources of traditional data.
Supplementary information
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Ficetola, G.F., Marta, S., Guerrieri, A. et al. The development of terrestrial ecosystems emerging after glacier retreat. Nature 632, 336–342 (2024). https://doi.org/10.1038/s41586-024-07778-2
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DOI: https://doi.org/10.1038/s41586-024-07778-2
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