Addition of brackish water to tundra soils does not inhibit methane production: implications for Arctic coastal methane production
Bibliographic record
Abstract
Abstract. In Arctic regions where coastal sediments contain permafrost, global climate change drives processes such as erosion and subsidence. The contribution of these processes to carbon emissions are still uncertain. Relative sea level rise can lead to more waterlogged environments, promoting anoxic degradation of organic matter but it can also lead to a greater exposure of coastal sediments to seawater. This could alter methane (CH4) production dynamics, although the controls remain poorly understood. For instance, sulfates contained in seawater may have a tampering effect on methanogenesis through competitive inhibition but the increase in microbial abundance could enhance methanogenesis. In this study, we present CH4 production rates alongside geochemical analyses in a rapidly evolving coastal landscape near the community of Tuktoyaktuk, NWT, Canada, which is located in the continuous permafrost zone. To better constrain CH4 production dynamics along the land to ocean continuum, sediment profiles were collected from nearshore marine sediments, as well as from the active layer of the coastal (intertidal) zone and inland soils. Anoxic incubations were performed, amended with brackish water to simulate the effect of seawater on the breakdown of organic matter and the production of CH4. We found marine sediments expectedly led to negligible CH4 production rates, while the inland sites showed variable rates between null and 35 nmol cm-3 d-1. The coastal (intertidal) zone had the highest rates reaching 415 nmol cm-3 d-1. Interestingly, sulfate present in brackish water and sediments did not suppress methanogenesis in the incubations of the coastal and inland zones. Analyses of stable carbon isotopes from CH4 produced in the incubation experiment indicated greater acetotrophy and higher organic matter lability in the coastal zone, possibly contributing to higher CH4 production rates. This study highlights the potential for significant CH4 emissions even with high sulfate concentrations which are classically thought to inhibit methanogenesis. This suggests that Arctic coastal microbial CH4 production might be an understudied source to the atmosphere.
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 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 | 0.000 | 0.000 |
| 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".