Mangrove Carbon Stocks and Ecosystem Cover Dynamics in Southwest Madagascar and the Implications for Local Management
Bibliographic record
Abstract
Of the numerous ecosystem services mangroves provide, carbon storage is gaining particular attention for its potential role in climate change mitigation strategies. Madagascar contains 2% of the world’s mangroves, over 20% of which is estimated to have been deforested through charcoal production, timber extraction and agricultural development. This study presents a carbon stock assessment of the mangroves in Helodrano Fagnemotse in southwest Madagascar alongside an analysis of mangrove land-cover change from 2002 to 2014. Similar to other mangrove ecosystems in East Africa, higher stature, closed-canopy mangroves in southwest Madagascar were estimated to contain 454.92 (±26.58) Mg·C·ha−1. Although the mangrove extent in this area is relatively small (1500 ha), these mangroves are of critical importance to local communities and anthropogenic pressures on coastal resources in the area are increasing. This was evident in both field observations and remote sensing analysis, which indicated an overall net loss of 3.18% between 2002 and 2014. Further dynamics analysis highlighted widespread transitions of dense, higher stature mangroves to more sparse mangrove areas indicating extensive degradation. Harnessing the value that the carbon stored within these mangroves holds on the voluntary carbon market could generate revenue to support and incentivise locally-led sustainable mangrove management, improve livelihoods and alleviate anthropogenic pressures.
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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 | 0.000 |
| 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".