Mangrove Blue Carbon in the Face of Deforestation, Climate Change, and Restoration
Bibliographic record
Abstract
Abstract Coastal wetlands have disproportionately high carbon densities, known as blue carbon, compared to most terrestrial ecosystems. Mangroves and their blue carbon stocks are at risk globally from land‐use and land‐cover change (LULCC) activities such as aquaculture, alongside biophysical disturbances such as sea‐level rise and cyclones. Global estimates of carbon emissions from mangrove loss have been previously unable to differentiate between the variable impacts of different drivers of loss. This article discusses the impacts that different LULCC activities and biophysical disturbances have on carbon stocks (biomass and soil) and greenhouse gas fluxes (CO2and CH4). The dynamics of carbon stocks and fluxes depends on the type of LULCC; aquaculture often results in biomass and soil carbon removal, and some forms of agriculture can substantially increase methane emissions. Natural disturbances have mixed impacts on mangrove carbon; sea‐level rise will drown some mangroves and their carbon stocks but provide opportunities for new carbon accumulation, while cyclones can have immediate negative impacts on stocks but positive impacts on sequestration during recovery. Mangrove rehabilitation practices can actively restore carbon stocks and reduce greenhouse gas emissions from previous land uses. It is critical to consider the type of LULCC when estimating carbon emissions due to mangrove loss or rehabilitation. Mangrove blue carbon is now high on the international conservation policy agenda, and a better understanding of how carbon stocks and fluxes respond to anthropogenic and biophysical disturbance may provide better incentives for mangrove conservation and sustainable management.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".