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Methane-cycling communities in a permafrost-affected soil on Herschel Island, Western Canadian Arctic: active layer profiling of<i>mcrA</i>and<i>pmoA</i>genes

2012· article· en· W2025663672 on OpenAlexafffundabout
Béatrice Barbier, Isabel Dziduch, Susanne Liebner, Lars Ganzert, Hugues Lantuit, Wayne H. Pollard, Dirk Wagner

Bibliographic record

VenueFEMS Microbiology Ecology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsMcGill University
FundersParks CanadaMcGill UniversityUniversitetet i Tromsø
KeywordsPermafrostBiologyEcologyArcticCyclingForestry

Abstract

fetched live from OpenAlex

In Arctic wet tundra, microbial controls on organic matter decomposition are likely to be altered as a result of climatic disruption. Here, we present a study on the activity, diversity and vertical distribution of methane-cycling microbial communities in the active layer of wet polygonal tundra on Herschel Island. We recorded potential methane production rates from 5 to 40 nmol h(-1) g(-1) wet soil at 10 °C and significantly higher methane oxidation rates reaching values of 6-10 μmol h(-1) g(-1) wet soil. Terminal restriction fragment length polymorphism (T-RFLP) and cloning analyses of mcrA and pmoA genes demonstrated that both communities were stratified along the active layer vertical profile. Similar to other wet Arctic tundra, the methanogenic community hosted hydrogenotrophic (Methanobacterium) as well as acetoclastic (Methanosarcina and Methanosaeta) members. A pronounced shift toward a dominance of acetoclastic methanogens was observed in deeper soil layers. In contrast to related circum-Arctic studies, the methane-oxidizing (methanotrophic) community on Herschel Island was dominated by members of the type II group (Methylocystis, Methylosinus, and a cluster related to Methylocapsa). The present study represents the first on methane-cycling communities in the Canadian Western Arctic, thus advancing our understanding of these communities in a changing Arctic.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.234
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations95
Published2012
Admission routes3
Has abstractyes

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