Selection processes of Arctic seasonal glacier snowpack bacterial communities

• Published in: Microbiome
• Main Authors: Keuschnig, Christoph, Vogel, Timothy M., Barbaro, Elena, Spolaor, Andrea, Koziol, Krystyna, Björkman, Mats P., Zdanowicz, Christian, Gallet, Jean-Charles, Luks, Bartłomiej, Layton, Rose, Larose, Catherine
• Format: Article in Journal/Newspaper
• Language: English
• Published: Uppsala universitet, Luft-, vatten- och landskapslära 2023
• Subjects: Microbial ecology; Snow; Arctic; Niche-based selection; Neutral processes; Biological Sciences; Biologiska vetenskaper; Svalbard; glacier
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-497937; https://doi.org/10.1186/s40168-023-01473-6

Background Arctic snowpack microbial communities are continually subject to dynamic chemical and microbial input from the atmosphere. As such, the factors that contribute to structuring their microbial communities are complex and have yet to be completely resolved. These snowpack communities can be used to evaluate whether they fit niche-based or neutral assembly theories. Methods We sampled snow from 22 glacier sites on 7 glaciers across Svalbard in April during the maximum snow accumulation period and prior to the melt period to evaluate the factors that drive snowpack metataxonomy. These snowpacks were seasonal, accumulating in early winter on bare ice and firn and completely melting out in autumn. Using a Bayesian fitting strategy to evaluate Hubbell’s Unified Neutral Theory of Biodiversity at multiple sites, we tested for neutrality and defined immigration rates at different taxonomic levels. Bacterial abundance and diversity were measured and the amount of potential ice-nucleating bacteria was calculated. The chemical composition (anions, cations, organic acids) and particulate impurity load (elemental and organic carbon) of the winter and spring snowpack were also characterized. We used these data in addition to geographical information to assess possible niche-based effects on snow microbial communities using multivariate and variable partitioning analysis. Results While certain taxonomic signals were found to fit the neutral assembly model, clear evidence of niche-based selection was observed at most sites. Inorganic chemistry was not linked directly to diversity, but helped to identify predominant colonization sources and predict microbial abundance, which was tightly linked to sea spray. Organic acids were the most significant predictors of microbial diversity. At low organic acid concentrations, the snow microbial structure represented the seeding community closely, and evolved away from it at higher organic acid concentrations, with concomitant increases in bacterial numbers. Conclusions These ...