Radiative forcing of methane fluxes offsets net carbon dioxide uptake for a tropical flooded forest
Bibliographic record
Abstract
Abstract Wetlands are important sources of methane (CH4) and sinks of carbon dioxide (CO2). However, little is known about CH4 and CO2 fluxes and dynamics of seasonally flooded tropical forests of South America in relation to local carbon (C) balances and atmospheric exchange. We measured net ecosystem fluxes of CH4 and CO2 in the Pantanal over 2014–2017 using tower‐based eddy covariance along with C measurements in soil, biomass and water. Our data indicate that seasonally flooded tropical forests are potentially large sinks for CO2 but strong sources of CH4, particularly during inundation when reducing conditions in soils increase CH4 production and limit CO2 release. During inundation when soils were anaerobic, the flooded forest emitted 0.11 ± 0.002 g CH4‐C m−2 d−1 and absorbed 1.6 ± 0.2 g CO2‐C m−2 d−1 (mean ± 95% confidence interval for the entire study period). Following the recession of floodwaters, soils rapidly became aerobic and CH4 emissions decreased significantly (0.002 ± 0.001 g CH4‐C m−2 d−1) but remained a net source, while the net CO2 flux flipped from being a net sink during anaerobic periods to acting as a source during aerobic periods. CH4 fluxes were 50 times higher in the wet season; DOC was a minor component in the net ecosystem carbon balance. Daily fluxes of CO2 and CH4 were similar in all years for each season, but annual net fluxes varied primarily in relation to flood duration. While the ecosystem was a net C sink on an annual basis (absorbing 218 g C m−2 (as CH4‐C + CO2‐C) in anaerobic phases and emitting 76 g C m−2in aerobic phases), high CH4 effluxes during the anaerobic flooded phase and modest CH4 effluxes during the aerobic phase indicate that seasonally flooded tropical forests can be a net source of radiative forcings on an annual basis, thus acting as an amplifying feedback on global warming.
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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".