From Sink to Source: Salinity and Water Level Fluctuations Between Years Drive Large Differences in CO <sub>2</sub> Exchange in a Temperate Salt Marsh
Bibliographic record
Abstract
Abstract Coastal salt marshes are important carbon (C) sinks in the global C cycle; however, their sequestration capacity can be disrupted by changes in salinity and water availability, driven by spring precipitation and tidal inundation dynamics. This study examines a temperate salt marsh that transitioned from a carbon dioxide (CO 2 ) sink in 2022 to a source in 2023. Eddy covariance measurements showed that a 35% decline in gross primary productivity (GPP), coupled with a smaller decrease in ecosystem respiration, drove this transition. Random Forest (RF) models revealed shifts in the importance of controls on GPP between years, particularly salinity and water levels. To isolate effects of individual factors, we utilized an RF model trained on 2022 data and replaced each driver with 2023 values one at a time while holding others constant. Results showed that reduced spring precipitation, persistently lower water table during the growing season, together with elevated salinity, increased plant stress and suppressed C uptake. In 2022, sufficient early‐season rainfall and higher water levels supported plant productivity, whereas in 2023, elevated salinity and declining water levels in the latter part of the growing season intensified plant stress. These findings highlight the high sensitivity of salt marsh C dynamics to hydrological and salinity changes, underscoring the need for multi‐year monitoring to capture interannual variability. Accurately representing such variability is critical for informing C accounting frameworks and restoration strategies in the context of climate change.
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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 | 0.001 |
| 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".