Hydrologic Export Is a Major Component of Coastal Wetland Carbon Budgets
Bibliographic record
Abstract
Abstract Coastal wetlands are among the most productive habitats on Earth and sequester globally significant amounts of atmospheric carbon (C). Extreme rates of soil C accumulation are widely assumed to reflect efficient C storage. Yet the fraction of wetland C lost via hydrologic export has not been directly quantified, since comprehensive budgets including direct estimates of lateral C loss are lacking. We present a complete net ecosystem C budget (NECB), demonstrating that lateral losses of C are a major component of the NECB for the largest stable brackish tidal marsh on the U.S. Pacific coast. Mean annual net ecosystem exchange of CO 2 with the atmosphere (NEE = −185 g C m 2 year −1 , negative NEE denoting ecosystem uptake) was compared to long‐term soil C burial (87–110 g C m 2 year −1 ), suggesting only 47–59% of fixed atmospheric C accumulates in soils. Consistently, direct monitoring in 2017–2018 showed NEE of −255 g C m −2 year −1 , and hydrologic export of 105 g C m −2 year −1 (59% of NEE remaining on site). Despite their high C sequestration capacity, lateral losses from coastal wetlands are typically a larger fraction of the NECB when compared to other terrestrial ecosystems. Loss of inorganic C (the least measured NECB term) was 91% of hydrologic export and may be the most important term limiting C sequestration. The high productivity of coastal wetlands thus serves a dual function of C burial and estuarine export, and the multiple fates of fixed C must be considered when evaluating wetland capacity for C sequestration.
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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 | 0.001 |
| 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".