Predicting climate-change impacts on the global glacier-fed stream microbiome

作者：Massimo Bourquin, Hannes Peter, Grégoire Michoud, Susheel Bhanu Busi, Tyler J. Kohler, Andrew L. Robison, Michael Nikolaos Styllas, Leïla Ezzat, Aileen Ute Geers, Matthias Huss, Stilianos Fodelianakis, The Vanishing Glaciers Field Team, Michael Nikolaos Styllas, Martina Schön, Matteo Tolosano, Vincent De Staercke, Tyler J. Kohler, Tom J. Battin · 发表于：Nature Communications · 年份：2025 · DOI：10.1038/s41467-025-56426-4 · 被引用次数：14 · 研究领域：Microbial Community Ecology and Physiology、Cryospheric studies and observations、Polar Research and Ecology

The shrinkage of glaciers and the vanishing of glacier-fed streams (GFSs) are emblematic of climate change. However, forecasts of how GFS microbiome structure and function will change under projected climate change scenarios are lacking. Combining 2,333 prokaryotic metagenome-assembled genomes with climatic, glaciological, and environmental data collected by the Vanishing Glaciers project from 164 GFSs draining Earth’s major mountain ranges, we here predict the future of the GFS microbiome until the end of the century under various climate change scenarios. Our model framework is rooted in a space-for-time substitution design and leverages statistical learning approaches. We predict that declining environmental selection promotes primary production in GFSs, stimulating both bacterial biomass and biodiversity. Concomitantly, predictions suggest that the phylogenetic structure of the GFS microbiome will change and entire bacterial clades are at risk. Furthermore, genomic projections reveal that microbiome functions will shift, with intensified solar energy acquisition pathways, heterotrophy and algal-bacterial interactions. Altogether, we project a ‘greener’ future of the world’s GFSs accompanied by a loss of clades that have adapted to environmental harshness, with consequences for ecosystem functioning. Little is known about how climate change impacts glacier-fed streams (GFSs) microbiomes. Here, using a modelling framework based on global GFS metagenomic, climatic and enviro...