Methane emissions from aquatic and terrestrial ecosystems play a crucial role in global warming, which is particularly affecting high-latitude ecosystems. As major contributors to methane emissions in natural environments, the microbial communities involved in methane production and oxidation deserve a special attention. Microbial diversity and activity are expected to be strongly affected by the already observed (and further predicted) temperature increase in high-latitude ecosystems, eventually resulting in disrupted feedback methane emissions. The METHANOBASE project has been designed to investigate the intricate relations between microbial diversity and methane emissions in Arctic, Subarctic and Subantarctic ecosystems, under natural (baseline) conditions and in response to simulated temperature increments. We report here a small subunit ribosomal RNA (16S rRNA) analysis of lake, peatland and mineral soil ecosystems.
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Barret M, Thalasso F, Gandois L, Martinez Cruz K, Sepulveda Jaureguy A, Lavergne C, Teisserenc R, Aguilar P, Gerardo-Nieto O, Etchebehere C, Martins B, Fochesatto J, Tananaev N, Svenning M, Seppey C, Tveit A, Chamy R, Soledad Astorga-España M, Mansilla A, Van de Putte A, Sweetlove M, Murray A, Cabrol L (2022): Bacteria and Archaea biodiversity in terrestrial ecosystems of Chilean Patagonia. v1.3. SCAR - Microbial Antarctic Resource System. Dataset/Metadata. https://ipt.biodiversity.aq/resource?r=methanobasepatagonia&v=1.3
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The publisher and rights holder of this work is SCAR - Microbial Antarctic Resource System. This work is licensed under a Creative Commons Attribution (CC-BY) 4.0 License.
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methane; greenhouse gas; bacteria; archaea; procaryote; peatland; wetland; soil; lake; sediment; metabarcoding; 16S rRNA; MiSeq; Occurrence; Metadata
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|Bounding Coordinates||South West [-54.065, -72.038], North East [-52.073, -68.85]|
Bacterial and Archaea diversity was profiled by targeting the V4-V5 region of the 16S SSU rRNA gene for high throughput metabarcode (amplicon) sequencing, using the Illumia MiSeq platform (2x 250bp).
|Domain||Bacteria (Bacteria), Archaea (Archaea)|
METHANOgenic Biodiversity and activity in Arctic, subarctic and Subantarctic Ecosystems affected by climate change
|Funding||ERANET-LAC joint call 2014|
|Study Area Description||Patagonia [-54.94925011 to -52.0728575, -72.03831167 to -67.33177633] Lakes (water, sediments), peatlands (hollows, edges, hummocks) and mineral soils|
|Design Description||The METHANOBASE project has been designed to investigate the intricate relations between microbial diversity and methane emissions in Arctic, Subarctic and Subantarctic ecosystems, under natural (baseline) conditions and in response to simulated temperature increments.|
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Water samples were collected with a Van Dorn bottle. Sediments were sampled thanks to a grab-sampler, peat monoliths (approximately 30*30*30cm) were cut with a bread-knife and soil monoliths with a shovel.
|Study Extent||Samples were collected in summer 2015, without any temporal replication. A total of 19 ecosystems were studied in Alaska, USA. The selected sites are representative of this Subantarctic region: lakes, peatlands, Nothofagus forest, pampa In each site, various samples were collected to take into account the local heterogeneity: different depths in water column and sediments, soil horizons, hollows/edges/hummocks.|
Method step description:
- After collection, samples were stored at 4°C prior to further processing. Liquid samples were filtered at 0.45µm until clogging and the filters were stored at -20°C. DNA was extracted from these filters using the PowerWater DNA isolation kit (MOBIO) while DNA was extracted from solid samples using the PowerSoil DNA isolation kit (MOBIO). DNA extracts were kept at -20°C. The V4-V5 region of 16S rRNA gene was amplified in the following conditions: 515F and 928R primers (Wang & Qian, 2009. doi:10.1371/journal.pone.0007401), 2min at 94°C, 30 cycles of 60s at 94°C, 40s at 65°C and 30s at 72°C, and 10 min at 72°C. Amplicon sequencing was carried out with Illumina MiSeq technology (2x250pb, V3). Denoising of the sequences dataset and OTU clustering was carried using the FROGS pipeline (Auer et al., 2017. doi:10.1093/bioinformatics/btx791). BLAST was used for taxonomic affiliation.