Spartina alterniflora has the highest methane emissions in a St. Lawrence estuary salt marsh
Bibliographic record
Abstract
Abstract Salt marshes have the ability to store large amounts of ‘blue carbon’, potentially mitigating some of the effects of climate change. Salt marsh carbon storage may be partially offset by emissions of CH4, a highly potent greenhouse gas. Sea level rise and invasive vegetation may cause shifts between different elevation and vegetation zones in salt marsh ecosystems. Elevation zones have distinct soil properties, plant traits and rhizosphere characteristics, which affect CH4 fluxes. We investigated differences in CH4 emissions between four elevation zones (mudflat, Spartina alterniflora, Spartina patens and invasive Phragmites australis) typical of salt marshes in the northern Northwest Atlantic. CH4 emissions were significantly higher from the S. alterniflora zone (17.7 ± 9.7 mg C m−2h−1) compared to the other three zones, where emissions were negligible (<0.3 mg C m−2h−1). These emissions were high for salt marshes and were similar to those typically found in oligohaline marshes with lower salinities. CH4 fluxes were significantly correlated with soil properties (salinity, water table depth, bulk density and temperature), plant traits (rhizome volume and biomass, root volume and dead biomass volume all at 0–15 cm) and CO2 fluxes. The relationships between CH4 emissions, and rhizome and root volume suggest that the aerenchyma tissues in these plants may be a major transport mechanism of CH4 from anoxic soils to the atmosphere. This may have major implications for the mitigation potential carbon sink from salt marshes globally, especially as S. alterniflora is widespread. This study shows CH4 fluxes can vary over orders of magnitude from different vegetation in the same system, therefore, specific emissions factors may need to be used in future climate models and for more accurate carbon budgeting depending on vegetation type.
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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 | 0.000 |
| 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".