CARBON SEQUESTRATION POTENTIAL OF WETLANDS IN AGRICULTURAL LANDSCAPES
Bibliographic record
Abstract
Wetlands provide crucial ecosystem services, including hydrological regulation, biogeochemical functions, and biodiversity support, which enhance landscape resilience. A key biogeochemical function is carbon sequestration—the long-term storage of atmospheric carbon dioxide (CO₂) as organic carbon (OC) in wetland soils. This natural process mitigates climate change by removing CO₂ from the atmosphere. However, wetland loss, primarily due to agricultural expansion, significantly diminishes their carbon sequestration capacity and associated ecosystem benefits. Urgent and effective policies and management strategies are needed, but a limited understanding of the biogeochemical processes driving carbon sequestration hinders sustainable wetland management. This research investigates the OC sequestration potential of wetlands across Canada’s diverse climatic gradients. The OC sequestration rate of undisturbed wetlands ranged from 0.25 to 1.56 Mg C ha⁻¹ yr⁻¹. Using statistical learning techniques, the study identifies key factors influencing OC sequestration rates, such as inundation probability, human impact, and soil properties. The machine learning model achieved high predictive accuracy (adjusted coefficient of determination [R²] = 0.70). This study shows that statistical learning models, informed by expert knowledge of process controls, can estimate OC sequestration rates within wetlands. Rewetted wetlands demonstrated a steady increase of total OC stock post-wetting, reaching 24.60 Mg C ha⁻¹. Net change of OC sequestration post-rewetting showed an increasing trend from baseline levels, peaking between 4–14 years post-rewetting, followed by a gradual decline to baseline levels within 40 years. These findings underscore the time-dependent nature of wetland carbon sequestration and highlight restoration by rewetting can be a viable strategy to enhance climate mitigation efforts. This study provides crucial evidence to guide the protection and restoration of wetlands as natural climate solutions (NCS). By integrating carbon sequestration data into policy frameworks and national greenhouse gas (GHG) inventories, these ecosystems can contribute meaningfully to global climate targets. Moreover, the results support wetland inclusion in carbon markets and emphasize the necessity of sustainable land-use practices to ensure long-term ecosystem functionality. This work establishes a foundation for future research and policy to leverage wetlands as integral components of climate action.
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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 | 0.001 | 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) | 0.000 | 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".