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 (CH 4 ) and sinks of carbon dioxide (CO 2 ). However, little is known about CH 4 and CO 2 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 CH 4 and CO 2 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 CO 2 but strong sources of CH 4 , particularly during inundation when reducing conditions in soils increase CH 4 production and limit CO 2 release. During inundation when soils were anaerobic, the flooded forest emitted 0.11 ± 0.002 g CH 4 ‐C m −2 d −1 and absorbed 1.6 ± 0.2 g CO 2 ‐C m −2 d −1 (mean ± 95% confidence interval for the entire study period). Following the recession of floodwaters, soils rapidly became aerobic and CH 4 emissions decreased significantly (0.002 ± 0.001 g CH 4 ‐C m −2 d −1 ) but remained a net source, while the net CO 2 flux flipped from being a net sink during anaerobic periods to acting as a source during aerobic periods. CH 4 fluxes were 50 times higher in the wet season; DOC was a minor component in the net ecosystem carbon balance. Daily fluxes of CO 2 and CH 4 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 CH 4 ‐C + CO 2 ‐C) in anaerobic phases and emitting 76 g C m −2 in aerobic phases), high CH 4 effluxes during the anaerobic flooded phase and modest CH 4 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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".