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Record W7030093941

Mikrobielle Gemeinschaften des Methankreislaufs in Permafrost beeinflussten Böden auf der Insel Herschel und an der Yukon-Küste, westliche kanadische Arktis

2013· dissertation· en· W7030093941 on OpenAlexaboutno aff

Bibliographic record

Venuepublish.UP (University of Potsdam) · 2013
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostTundraEcosystemPeatOrganic matterWetlandArcticTerrestrial ecosystemAnoxic waters
DOInot available

Abstract

fetched live from OpenAlex

Permafrost-affected ecosystems including peat wetlands are among the most obvious regions in which current microbial controls on organic matter decomposition are likely to change as a result of global warming. Wet tundra ecosystems in particular are ideal sites for increased methane production because of the waterlogged, anoxic conditions that prevail in seasonally increasing thawed layers. The following doctoral research project focused on investigating the abundance and distribution of the methane-cycling microbial communities in four different polygons on Herschel Island and the Yukon Coast. Despite the relevance of the Canadian Western Arctic in the global methane budget, the permafrost microbial communities there have thus far remained insufficiently characterized. Through the study of methanogenic and methanotrophic microbial communities involved in the decomposition of permafrost organic matter and their potential reaction to rising environmental temperatures, the overarching goal of the ensuing thesis is to fill the current gap in understanding the fate of the organic carbon currently stored in Artic environments and its implications regarding the methane cycle in permafrost environments. To attain this goal, a multiproxy approach including community fingerprinting analysis, cloning, quantitative PCR and next generation sequencing was used to describe the bacterial and archaeal community present in the active layer of four polygons and to scrutinize the diversity and distribution of methane-cycling microorganisms at different depths. These methods were combined with soil properties analyses in order to identify the main physico-chemical variables shaping these communities. In addition a climate warming simulation experiment was carried-out on intact active layer cores retrieved from Herschel Island in order to investigate the changes in the methane-cycling communities associated with an increase in soil temperature and to help better predict future methane-fluxes from polygonal wet tundra environments in the context of climate change. Results showed that the microbial community found in the water-saturated and carbon-rich polygons on Herschel Island and the Yukon Coast was diverse and showed a similar distribution with depth in all four polygons sampled. Specifically, the methanogenic community identified resembled the communities found in other similar Arctic study sites and showed comparable potential methane production rates, whereas the methane oxidizing bacterial community differed from what has been found so far, being dominated by type-II rather than type-I methanotrophs. After being subjected to strong increases in soil temperature, the active-layer microbial community demonstrated the ability to quickly adapt and as a result shifts in community composition could be observed. These results contribute to the understanding of carbon dynamics in Arctic permafrost regions and allow an assessment of the potential impact of climate change on methane-cycling microbial communities. This thesis constitutes the first in-depth study of methane-cycling communities in the Canadian Western Arctic, striving to advance our understanding of these communities in degrading permafrost environments by establishing an important new observatory in the Circum-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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.004
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0260.001

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 head0.034
GPT teacher head0.240
Teacher spread0.206 · 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.

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

Citations0
Published2013
Admission routes1
Has abstractyes

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