Vertical distribution of methane cycling microbial communities in the active layer of degrading permafrost on Herschel Island, Canadian Western Arctic based on T-RFLP analysis of two functional genes.
Bibliographic record
Abstract
In the current context of climate change, we attempt to elucidate the fate of organic carbon stored in permafrost by looking at microbial-driven carbon degradation in active layer samples of permafrost affected soils from Herschel Island in Canadian Western Arctic. The abundance, dynamics and function of microbial communities involved in consuming this organic carbon is analysed, especially those involved in the production and consumption of methane. Microorganisms involved in methane cycling are of particular relevance in frozen environments, as rising permafrost temperatures eventually lead to an increased degradation of previously conserved organic matter. This in turn leads to an increased methanogenic activity, creating a potentially dangerous positive feedback-loop for climate change. The point of focus here is the biodiversity and function of methanogenic and methanotrophic microorganisms thriving in such difficult conditions and their reaction to warming temperatures and a rapidly changing environment. \nTerminal-restriction fragment length polymorphism (T-RFLP) was applied to analyse the methanogenic and methanotrophic communities based on two functional genes (mcrA and pmoA, respectively) in an active layer profile from Herschel Island, showing preferential colonisation of the middle and lower, partly water saturated active layer by methanogenic archaea (5cm to 35cm depth) and of the higher, aerobic layers by methanotrophic bacteria (0cm to 20cm depth). Incubation experiments under controlled variables at 10°C with no added substrate revealed a high methane production rate from middle (3.2 nmol g-1 h-1 at 15cm depth) and lower (3 nmol g-1 h-1 close to the permafrost table) active layer samples, and a lower but nonetheless consequent methanogenic activity in the upper sediment layers. Soil characteristics including soil moisture, C/N ratio, total organic carbon content and grain size were also investigated in order to help elucidate the observed distribution of microorganisms. Active layer samples generally have a high water content (41-88%) and very high organic carbon content (20-42%). The results obtained are being scrutinized to elucidate the adaptability of methane cycling microbial communities and the fate of organic carbon in the active layer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".