Responding to rising waters and temperatures: greenhouse gas flux from a high-latitude coastal wetland
Bibliographic record
Abstract
Abstract Climate change is exposing coastal landscapes to more flooding, in addition to rapidly rising temperatures. These changes are critical in the Arctic where the effects of sea level rise are exacerbated by the loss of sea ice protecting coasts, subsidence as permafrost thaws, and a projected increase in storms. Such changes will likely alter the land-atmosphere gas exchange of high-latitude coastal ecosystems, but the effects of flooding with warming remain unexplored. In this work we use a field experiment to examine the interacting effects of increased tidal flooding and warming on land-atmosphere CO2 and CH4 exchange in the coastal Yukon–Kuskokwim Delta, a large sub-Arctic wetland and tundra complex in western Alaska. We inundated dammed plots to simulate two levels of future flooding: low-intensity flooding represented by one day of flooding per summer-month (June, July and August), and high-intensity flooding represented by three-consecutive days of flooding per summer-month, crossed with a warming treatment of 1.4 °C. We found that both flooding and warming influenced greenhouse gas (GHG) exchange. Low-intensity flooding reduced net CO2 uptake by 20% (0.78 µmol m−2 s−1) regardless of temperature, and marginally increased CH4 emissions 0.83 nmol m−2 s−1 (33%) under ambient temperature, while decreasing CH4 emissions by −1.96 nmol m−2 s−1 (40%) under warming. In contrast, high-intensity flooding restored net CO2 uptake to control levels due to enhanced primary productivity under both temperature treatments. High-intensity flooding decreased CH4 emissions under ambient temperature by 0.76 nmol m−2 s−1 (30%), but greatly increased emissions under warming by 4.68 nmol m−2 s−1 (265%), presumably driven by increased plant-mediated CH4 transport. These findings reveal that GHG exchange responds rapidly and non-linearly to intensifying flooding, and highlight the importance of short-term flooding dynamics and warming in shaping future carbon cycling in this Arctic coastal wetland.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".