Drivers of Soil Carbon Variability in North America’s Prairie Pothole Wetlands: A Review
Bibliographic record
Abstract
There is an ongoing demand for region-specific soil organic carbon estimates to support sustainable land management and inform carbon credit programs. The Prairie Pothole Region is prominent agricultural area that extends through Canada and the United States, and features a significant number of wetlands commonly referred to as prairie potholes. The contribution of these wetlands to landscape-level soil organic carbon storage is complex and may not be consistent across the region as influenced by several environmental and management factors. This study reviews existing literature to identify the main factors that contribute to variability in soil organic carbon stocks in prairie pothole wetlands. Soil organic carbon stock data from 10 studies in the Prairie Pothole Region were summarized through a meta-analysis. Variable importance and regression analyses were used to assess which factors explain variability in soil organic carbon. Wetland class explained up to 26.6% of the variability in soil organic carbon. Other important factors included ecoregion as well as land management. There were significant differences in average wetland soil organic carbon stocks across the ecoregions. Data limitations restricted our ability to accurately estimate the stocks for wetland class and land management. The findings from this study highlighted the need for targeted studies in the Northern short grassland ecoregion as well as studies that consider wetland classes under various land uses. To advance wetland carbon research in the Prairie Pothole Region, recommendations were provided on landscape-level carbon modelling, soil carbon measurement and monitoring, and improved wetland classification systems. Supplementary Information: The online version contains supplementary material available at 10.1007/s13157-025-01898-9.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".