Spatial and temporal variability in blue carbon accumulation in the largest salt marsh in British Columbia, Canada
Bibliographic record
Abstract
Preserving blue carbon ecosystems, such as salt marshes, for climate change mitigation requires quantifying their carbon (C) dynamics. Boundary Bay (BB) marsh is a 222-ha salt marsh in southern British Columbia, Canada, where construction began in 2023 to enhance the marsh as a natural defense against coastal flooding. This study provides a baseline understanding of C storage and sequestration in Boundary Bay marsh prior to foreshore enhancement. We collected 18 sediment cores and vegetation surveys across the middle (BBM), eastern (BBE), and Mud Bay (MB) areas of the marsh, along with 128 depth profiles (i.e., field measurements of marsh thickness to refusal) from BBM. We combined C measurements with 210 Pb chronologies, in addition to existing data from western Boundary Bay (BBW), to estimate C stocks (g C m -2 ) and accumulation rates (g C m -2 yr -1 ) for the entire marsh. Total C stocks averaged 71 ± 37 Mg C ha -1 for high marsh and 41 ± 36 Mg C ha -1 for low marsh, with higher values in western Boundary Bay (BBW, BBM) compared to the east (BBE, MB). Total C storage (Mg C) at Boundary Bay was 17,360 ± 4,960 Mg C, with the western marsh (BBW, BBM) comprising 84% of the total. The C accumulation rates (CAR) for Boundary Bay marsh averaged 80 ± 45 g C m -2 yr -1 , comparable to regional averages on the Pacific coast of North America. However, large spatial variability exists, with significantly lower average CARs in the east (35 ± 11 g C m -2 yr -1 ). Historical aerial photographs indicate that the eastern marsh area (BBE, MB) decreased by ~35% while BBW expanded by ~20% since 1930. These contrasting trends suggest dynamism in marsh development, likely driven by environmental factors and human influence. This work highlights the high spatial and temporal dynamics of blue C ecosystems, especially in urban settings, and how decadal changes induced by human activities could influence their short-term (years to decades) C storage capacity, with potential consequences for long-term (centuries to millennial) C sequestration.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".