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. 2013 Nov 25;8(11):e79972.
doi: 10.1371/journal.pone.0079972. eCollection 2013.

Interactions between snow chemistry, mercury inputs and microbial population dynamics in an Arctic snowpack

Catherine Larose  1 , Emmanuel Prestat, Sébastien Cecillon, Sibel Berger, Cédric Malandain, Delina Lyon, Christophe Ferrari, Dominique Schneider, Aurélien Dommergue, Timothy M Vogel
Affiliations

Interactions between snow chemistry, mercury inputs and microbial population dynamics in an Arctic snowpack

Catherine Larose et al. PLoS One. .

Abstract

We investigated the interactions between snowpack chemistry, mercury (Hg) contamination and microbial community structure and function in Arctic snow. Snowpack chemistry (inorganic and organic ions) including mercury (Hg) speciation was studied in samples collected during a two-month field study in a high Arctic site, Svalbard, Norway (79 °N). Shifts in microbial community structure were determined by using a 16S rRNA gene phylogenetic microarray. We linked snowpack and meltwater chemistry to changes in microbial community structure by using co-inertia analyses (CIA) and explored changes in community function due to Hg contamination by q-PCR quantification of Hg-resistance genes in metagenomic samples. Based on the CIA, chemical and microbial data were linked (p = 0.006) with bioavailable Hg (BioHg) and methylmercury (MeHg) contributing significantly to the ordination of samples. Mercury was shown to influence community function with increases in merA gene copy numbers at low BioHg levels. Our results show that snowpacks can be considered as dynamic habitats with microbial and chemical components responding rapidly to environmental changes.

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Conflict of interest statement

Competing Interests: One co-author, Cédric Malandain, involved in the manuscript works at a private consulting company (ENOVEO). He was involved in data interpretation. His company has no rights to any of the methods or data. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.

Figures

Figure 1
Figure 1. Co-inertia analysis of the chemical and microbial data.
(1A) K-means clustering output. Each of the eight groups is numbered inside square boxes and the 39 samples are indicated and linked to the boxes. The ellipses represent group clusters based on K-means clustering. (1B) Main chemical vectors that affect sample ordination. The lengths of the vector arrows represent the influence of the given parameter on the co-structure of the CIA. Anions and cations are represented by their chemical symbols and organic acids are given as: Prop (propionate), Ox (oxalic acid), Ace.Glyc (acetate-glycolate), MSA (methylsulfonic acid) and Glut (glutaric acid). (1C) Probes showing the greatest influence on the ordination. The lengths of the vector arrows represent the influence of the given parameter on the co-structure of the CIA.
Figure 2
Figure 2. Distribution of major phyla/classes among the eight groups of samples.
Figure 3
Figure 3. Distribution of cyanobacterial taxa between Groups 1 and 2.
Figure 4
Figure 4. Metabolic potential of the snowpack.
The proportion of each type of analyzed metabolism (aerobic, anaerobic, facultative and unknown) is given for each of the eight groups.
Figure 5
Figure 5. Linear correlation between merA gene copy number (copies.ngDNA−1) and BioHg concentrations (log ng.L−1).
Crosses represent samples from Group 2, and circles samples from Groups 3 and 7.
See this image and copyright information in PMC

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