Blue Carbon Storage in Tropical Seagrass Meadows Relates to Carbonate Stock Dynamics, Plant–Sediment Processes, and Landscape Context: Insights from the Western Indian Ocean
Bibliographic record
Abstract
Globally, seagrass ecosystems are considered major blue carbon sinks and thus indirect contributors to climate change mitigation. Quantitative estimates and multi-scale appraisals of sources that underlie long-term storage of sedimentary carbon are vital for understanding coastal carbon dynamics. Across a tropical–subtropical coastal continuum in the Western Indian Ocean, we estimated organic (C org ) and inorganic (C carb ) carbon stocks in seagrass sediment. Quantified levels and variability of the two carbon stocks were evaluated with regard to the relative importance of environmental attributes in terms of plant–sediment properties and landscape configuration. The explored seagrass habitats encompassed low to moderate levels of sedimentary C org (ranging from 0.20 to 1.44% on average depending on species- and site-specific variability) but higher than unvegetated areas (ranging from 0.09 to 0.33% depending on site-specific variability), suggesting that some of the seagrass areas (at tropical Zanzibar in particular) are potentially important as carbon sinks. The amount of sedimentary inorganic carbon as carbonate (C carb ) clearly corresponded to C org levels, and as carbonates may represent a carbon source, this could diminish the strength of seagrass sediments as carbon sinks in the region. Partial least squares modelling indicated that variations in sedimentary C org and C carb stocks in seagrass habitats were primarily predicted by sediment density (indicating a negative relationship with the content of carbon stocks) and landscape configuration (indicating a positive effect of seagrass meadow area, relative to the area of other major coastal habitats, on carbon stocks), while seagrass structural complexity also contributed, though to a lesser extent, to model performance. The findings suggest that accurate carbon sink assessments require an understanding of plant–sediment processes as well as better knowledge of how sedimentary carbon dynamics are driven by cross-habitat links and sink–source relationships in a scale-dependent landscape context, which should be a priority for carbon sink conservation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".