Wetland Restoration Is Effective but Insufficient to Compensate for Soil Organic Carbon Losses From Degradation
Bibliographic record
Abstract
ABSTRACT Aim To assess the effectiveness of wetland restoration in reversing soil organic carbon (SOC) loss from degradation. Location Global. Time Period 1996–2023. Major Taxa Studied Wetland. Methods We conducted a global meta‐analysis to compare SOC levels in restored, degraded, and natural wetlands across different restoration approaches and wetland types and to examine the dynamic trajectories of SOC recovery and the influence of climatic and edaphic factors. Results We found that passive restoration increased SOC in degraded sites by 141%, compared to an 8% increase from active restoration. Restored inland wetlands showed an increase in SOC of 118%, while coastal wetlands showed a limited improvement of 5%, in comparison with degraded wetlands. Increases in SOC primarily occurred within the first 10 years after restoration and then levelled off. That being said, SOC accumulation in restored wetlands rarely approached the levels found in natural wetlands, highlighting the importance of protecting wetlands from degradation for SOC targets. Key factors for wetland SOC restoration were total soil nitrogen and mean annual temperature. Main Conclusions We conclude that wetland restoration is effective but insufficient to compensate for SOC losses from degradation. This study provides valuable insights for climate change mitigation through wetland restoration, supporting the goals of the United Nations Decade on Ecosystem Restoration and the Paris Agreement.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".