Quantification of blue carbon in salt marshes of the Pacific coast of Canada
Bibliographic record
Abstract
Abstract. Tidal salt marshes are known to accumulate “blue carbon” at high rates relative to their surface area, which render these systems among the Earth's most efficient carbon (C) sinks. However, the potential for tidal salt marshes to mitigate global warming remains poorly constrained because of the lack of representative sampling of tidal marshes from around the globe, inadequate areal extent estimations, and inappropriate dating methods for accurately estimating C accumulation rates. Here we provide the first estimates of organic C storage and accumulation rates in salt marshes along the Pacific coast of Canada, within the United Nations Educational, Scientific and Cultural Organization (UNESCO) Clayoquot Sound Biosphere Reserve and Pacific Rim National Park Reserve, a region currently underrepresented in global compilations. Within the context of other sites from the Pacific coast of North America, these young Clayoquot Sound marshes have relatively low C stocks but are accumulating C at rates that are higher than the global average with pronounced differences between high and low marsh habitats. The average C stock calculated during the past 30 years is 54 ± 5 Mg C ha−1 (mean ± standard error), which accounts for 81 % of the C accumulated to the base of the marsh peat layer (67 ± 9 Mg C ha−1). The total C stock is just under one-third of previous global estimates of salt marsh C stocks, likely due to the shallow depth and young age of the marsh. In contrast, the average C accumulation rate (CAR) (184 ± 50 g C m−2 yr−1 to the base of the peat layer) is higher than both CARs from salt marshes along the Pacific coast (112 ± 12 g C m−2 yr−1) and global estimates (91 ± 7 g C m−2 yr−1). This difference was even more pronounced when we considered individual marsh zones: CARs were significantly greater in high marsh (303 ± 45 g C m−2 yr−1) compared to the low marsh sediments (63 ± 6 g C m−2 yr−1), an observation unique to Clayoquot Sound among NE Pacific coast marsh studies. We attribute low CARs in the low marsh zones to shallow-rooting vegetation, reduced terrestrial sediment inputs, negative relative sea level rise in the region, and enhanced erosional processes. Per hectare, CARs in Clayoquot Sound marsh soils are approximately 2–7 times greater than C uptake rates based on net ecosystem productivity in Canadian boreal forests, which highlights their potential importance as C reservoirs and the need to consider their C accumulation capacity as a climate mitigation co-benefit when conserving for other salt marsh ecosystem services.
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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 | 0.001 |
| 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 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".