Quantifying soil carbon storage and losses in natural and agriculturally converted salt marsh
Bibliographic record
Abstract
Coastal vegetated ecosystems, such as salt marshes, actively sequester large amounts of carbon from the atmosphere and can store this carbon in their soils for millennia. These ecosystems have been badly degraded from anthropogenic activity over time, with evidence suggesting that their stores of carbon can be released to the atmosphere as a result. There is a general lack of studies which report on the carbon storage in salt marshes over the full depth of the soil deposit, and it is not well established just how much carbon is stored in these systems. Correspondingly, 1 m is often used as a default in estimates. Thus it is largely unknown what geomorphic or environmental parameters drive differences in carbon storage between marshes. How much carbon is lost when drained for agriculture or other land use change has a similar paucity in studies, and those which do exist are geographically biased to warm temperate climates. This thesis reports on two projects which seek to address these gaps in research.The chapter two study sought to estimate the total carbon stock of four salt marshes along the coasts of New Brunswick, Canada, and Maine, USA using GIS interpolation and identify any spatial trends or relations to general climate and geomorphic conditions. The spatial distribution of soil in the marshes was similar to developmental models of developed for similar marsh types in literature, with soil depth the greatest in the center of the marsh and declining towards the upland and seaward margins. The average carbon storage and carbon densities of the marshes were lower than current global averages. Average carbon density with depth was very stable except a single notable decrease which occurred at a breakpoint at 50 cm depth – likely due to carbon losses from the rapid decay of labile carbon in the rooting zone. Thus a very strong linear relationship between soil depth and carbon storage was found, which would allow for estimations of carbon storage using just soil depth in marshes of similar characteristics. This also indicates that assuming a soil depth (such as 1 m) is not an acceptable method when estimating carbon stocks. Comparing interpolation results to simple averages of the cores indicated that a single transect of cores could acceptably estimate soil depth (thus carbon storage) in marshes with simple morphology, and that one transect per axis of soil depth variation may work in more complex marsh systems.The chapter three study measured carbon stocks and calculated losses for a series of drained marshes and paired undrained marsh along the Kamouraska region of the St. Lawrence River, Quebec, Canada. The estimated rate of loss averaged 459 g C m-2 yr-1, with overall losses varying between 15% and 39% of the original amount since drainage. The rate is lower than the IPCC default emission factor and most current rates reported in literature, which are from warm temperate climates, and suggests that rates of carbon loss may vary in different climates. Using the average rate of loss for the St. Lawrence region, the total estimated carbon loss in the region since 1987 (further dyking was banned after this date) is 1,673,055 t C.
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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 | 0.001 |
| 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 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".