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Climate change is predicted to alter the hydrological and thermal regimes of high-mountain streams, particularly glacier-fed streams. However, relatively little is known about how these environmental changes impact the microbial communities in glacier-fed streams. Here, we operated streamside flume mesocosms in the Swiss Alps, where benthic biofilms were grown under treatments simulating climate change. Treatments comprised four flow (natural, intermittent, stochastic, and constant) and two temperature (ambient streamwater and warming of +2°C) regimes. We monitored microbial biomass, diversity, community composition, and metabolic diversity in biofilms over 3 months. We found that community composition was largely influenced by successional dynamics independent of the treatments. While stochastic and constant flow regimes did not significantly affect community composition, droughts altered their composition in the intermittent regime, favouring drought-adapted bacteria and decreasing algal biomass. Concomitantly, warming decreased algal biomass and the abundance of some typical glacier-fed stream bacteria and eukaryotes, and stimulated heterotrophic metabolism overall. Our study provides experimental evidence towards potential and hitherto poorly considered impacts of climate change on benthic biofilms in glacier-fed streams., (© The Author(s) 2024. Published by Oxford University Press on behalf of FEMS.)

The rapid melting of mountain glaciers and the vanishing of their streams is emblematic of climate change 1,2 . Glacier-fed streams (GFSs) are cold, oligotrophic and unstable ecosystems in which life is dominated by microbial biofilms 2,3 . However, current knowledge on the GFS microbiome is scarce 4,5 , precluding an understanding of its response to glacier shrinkage. Here, by leveraging metabarcoding and metagenomics, we provide a comprehensive survey of bacteria in the benthic microbiome across 152 GFSs draining the Earth's major mountain ranges. We find that the GFS bacterial microbiome is taxonomically and functionally distinct from other cryospheric microbiomes. GFS bacteria are diverse, with more than half being specific to a given mountain range, some unique to single GFSs and a few cosmopolitan and abundant. We show how geographic isolation and environmental selection shape their biogeography, which is characterized by distinct compositional patterns between mountain ranges and hemispheres. Phylogenetic analyses furthermore uncovered microdiverse clades resulting from environmental selection, probably promoting functional resilience and contributing to GFS bacterial biodiversity and biogeography. Climate-induced glacier shrinkage puts this unique microbiome at risk. Our study provides a global reference for future climate-change microbiology studies on the vanishing GFS ecosystem., Competing Interests: Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

The shrinking of glaciers is among the most iconic consequences of climate change. Despite this, the downstream consequences for ecosystem processes and related microbiome structure and function remain poorly understood. Here, using a space-for-time substitution approach across 101 glacier-fed streams (GFSs) from six major regions worldwide, we investigated how glacier shrinkage is likely to impact the organic matter (OM) decomposition rates of benthic biofilms. To do this, we measured the activities of five common extracellular enzymes and estimated decomposition rates by using enzyme allocation equations based on stoichiometry. We found decomposition rates to average 0.0129 (% d -1 ), and that decreases in glacier influence (estimated by percent glacier catchment coverage, turbidity, and a glacier index) accelerates decomposition rates. To explore mechanisms behind these relationships, we further compared decomposition rates with biofilm and stream water characteristics. We found that chlorophyll-a, temperature, and stream water N:P together explained 61% of the variability in decomposition. Algal biomass, which is also increasing with glacier shrinkage, showed a particularly strong relationship with decomposition, likely indicating their importance in contributing labile organic compounds to these carbon-poor habitats. We also found high relative abundances of chytrid fungi in GFS sediments, which putatively parasitize these algae, promoting decomposition through a fungal shunt. Exploring the biofilm microbiome, we then sought to identify bacterial phylogenetic clades significantly associated with decomposition, and found numerous positively (e.g., Saprospiraceae) and negatively (e.g., Nitrospira) related clades. Lastly, using metagenomics, we found evidence of different bacterial classes possessing different proportions of EEA-encoding genes, potentially informing some of the microbial associations with decomposition rates. Our results, therefore, present new mechanistic insights into OM decomposition in GFSs by demonstrating that an algal-based "green food web" is likely to increase in importance in the future and will promote important biogeochemical shifts in these streams as glaciers vanish., (© 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

Existing mountain permafrost distribution models generally offer a good overview of the potential extent of this phenomenon at a regional scale. They are however not always able to reproduce the high spatial discontinuity of permafrost at the micro-scale (scale of a specific landform; ten to several hundreds of meters). To overcome this lack, we tested an alternative modelling approach using three classification algorithms belonging to statistics and machine learning: Logistic regression, Support Vector Machines and Random forests. These supervised learning techniques infer a classification function from labelled training data (pixels of permafrost absence and presence) with the aim of predicting the permafrost occurrence where it is unknown. The research was carried out in a 588km 2 area of the Western Swiss Alps. Permafrost evidences were mapped from ortho-image interpretation (rock glacier inventorying) and field data (mainly geoelectrical and thermal data). The relationship between selected permafrost evidences and permafrost controlling factors was computed with the mentioned techniques. Classification performances, assessed with AUROC, range between 0.81 for Logistic regression, 0.85 with Support Vector Machines and 0.88 with Random forests. The adopted machine learning algorithms have demonstrated to be efficient for permafrost distribution modelling thanks to consistent results compared to the field reality. The high resolution of the input dataset (10m) allows elaborating maps at the micro-scale with a modelled permafrost spatial distribution less optimistic than classic spatial models. Moreover, the probability output of adopted algorithms offers a more precise overview of the potential distribution of mountain permafrost than proposing simple indexes of the permafrost favorability. These encouraging results also open the way to new possibilities of permafrost data analysis and mapping., (Copyright © 2017 Elsevier B.V. All rights reserved.)