Carbon sequestration capacity of a prairie pothole wetland under warm and dry conditions
Bibliographic record
Abstract
• Ecosystem net carbon uptake occurred in two growing seasons despite strong drought. • Methane emission had a small impact on the carbon budget due to low soil moisture. • LandSat NDVI showed fluctuations during 1984–2023 due to several wet-dry cycles. • Calculated GEP and NEP in 2022–23 were close to the average during 1984–2023. • The natural wetland was resilient in providing the ecosystem service of C storage. Prairie Pothole wetlands have large temporal changes in water status. The wetlands are often flooded, with water above the soil surface during the early growing season, while becoming dry during the later growing season or for years under strong drought. We used the eddy covariance technique to assess the potential for ecosystem carbon sequestration as a natural climate solution in a large Prairie Pothole wetland in southern Alberta (Frank Lake wetland complex) that was dominated by the emergent macrophyte, Schoenoplectus acutus L. (bulrush). We made ecosystem-scale measurements of CO 2 and CH 4 exchange over two growing seasons during a time-period with environmental conditions that were warmer and drier than the climate normal. In particular, the study was conducted while the wetland had been experiencing a decade-long drought based on the Standardized Precipitation Evapotranspiration Index. To provide perspective on the longer-term temporal variability of ecosystem carbon exchange processes, we also used LandSat NDVI measurements of vegetation greenness, calibrated with eddy covariance measurements of ecosystem CO 2 exchange during 2022–23, to estimate carbon sequestration capacity during 1984–2023, a period that included several wet-dry cycles. Our measured growing season-integrated net CO 2 uptake values were 47 and 70 g C m −2 season −1 in 2022 and 2023, respectively. Including the measured low methane emissions (converted to CO 2 equivalents based on a Sustained Global Warming Potential) only changed the net sink to 40 and 67 g C m −2 season −1 in 2022 and 2023, respectively. Despite drought conditions over the last decade, measured ecosystem carbon sequestration values were close to average values during 1984–2023, based on NDVI measurements and model carbon flux calculations. Our results demonstrated net carbon sequestration as a natural climate solution in a Prairie Pothole wetland, even during a time-period that was not expected to be favourable for carbon sequestration because of the drought conditions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".