Effect of land‐use and land‐cover change on mangrove blue carbon: A systematic review
Bibliographic record
Abstract
Mangroves shift from carbon sinks to sources when affected by anthropogenic land-use and land-cover change (LULCC). Yet, the magnitude and temporal scale of these impacts are largely unknown. We undertook a systematic review to examine the influence of LULCC on mangrove carbon stocks and soil greenhouse gas (GHG) effluxes. A search of 478 data points from the peer-reviewed literature revealed a substantial reduction of biomass (82% ± 35%) and soil (54% ± 13%) carbon stocks due to LULCC. The relative loss depended on LULCC type, time since LULCC and geographical and climatic conditions of sites. We also observed that the loss of soil carbon stocks was linked to the decreased soil carbon content and increased soil bulk density over the first 100 cm depth. We found no significant effect of LULCC on soil GHG effluxes. Regeneration efforts (i.e. restoration, rehabilitation and afforestation) led to biomass recovery after ~40 years. However, we found no clear patterns of mangrove soil carbon stock re-establishment following biomass recovery. Our findings suggest that regeneration may help restore carbon stocks back to pre-disturbed levels over decadal to century time scales only, with a faster rate for biomass recovery than for soil carbon stocks. Therefore, improved mangrove ecosystem management by preventing further LULCC and promoting rehabilitation is fundamental for effective climate change mitigation policy.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".