Bacterial communities and greenhouse gas emissions of shallow ponds in the High Arctic.

• Published in: Polar Biology
• Main Authors: Negandhi, Karita, Laurion, Isabelle, Lovejoy, Connie
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: 2014
• Subjects: methanotrophic communities; carbon; thermokarst ponds; permafrost; methane; carbon dioxide; pyrosequencing; Arctic; Polar Biology; Thermokarst; Tundra
• Online Access: https://espace.inrs.ca/id/eprint/3643/; https://doi.org/10.1007/s00300-014-1555-1

Permafrost thawing in lowland Arctic tundra results in a polygonal patterned landscape and the formation of numerous small ponds. These ponds emit biologically mediated carbon dioxide (CO₂) and methane (CH₄). Their greenhouse gas (GHG) emissions are variable, for reasons that are not well understood. Emissions are related to a balance between GHG producers and consumers, as well as to physical properties of the water column controlling gas exchange rates with the atmosphere. Here, we investigated the bacterial diversity of polygonal and runnel ponds, two geomorphologically distinct pond types commonly found in continuous permafrost regions. Using a combination of 16S rRNA Sanger sequencing and high-throughput amplicon sequencing, we found that bacterial communities in overlying waters were clearly dominated by carbon degraders and were similar in both pond types, despite their variable physical and chemical properties. However, surface sediment communities in the two pond types were significantly different. Polygonal pond sediment was colonized by carbon degraders (46–29 %), cyanobacteria (20–27 %) that take up CO₂ and produce oxygen, and methanotrophs (11–20 %) that consume CH4 and require oxygen. In contrast, cyanobacteria were effectively absent from the surface sediment of runnel ponds, which in addition to carbon degraders (65–81 %), were colonized by purple non-sulfur bacteria (5–21 %), and by fewer methanotrophs (1–5 %). The link between the methanotrophic community and the type of ponds could potentially be used to improve upscale estimates of GHG emissions based on landscape morphology in such remote regions.