BOREAL SHIELD PEATLAND CO2 EXCHANGE: A MULTI-YEAR ANALYSIS AND POST-WILDFIRE RECOVERY ASSESSMENT
Bibliographic record
Abstract
Peatland ecosystems are important as natural climate regulators for their capacity to store carbon over long-time scales. Carbon cycling in peatlands in the boreal ecozone of Canada has been more widely studied than the boreal shield of Ontario, where peat depths are thinner and peatlands spatially smaller. The reliance on fill and spill hydrologic connectivity makes the water table dynamics of peatlands in Ontario’s Eastern Georgian Bay (EGB) region of the Ontario shield ecozone sensitive to rain and drought periods. The drying of wetlands in the EGB region decreases moss productivity and increases the ecosystem’s vulnerability to wildfire through an increase in the water table depth. In an effort to understand how peatlands respond to interannual climate variability and wildfire, we examined the role of regional climate patterns on growing season CO2 exchange from an Ontario shield peatland and completed a post-wildfire assessment of CO2 exchange patterns in a recently burned peatland for the first and second year post-wildfire. Using the eddy covariance technique, we analyzed 5-years of growing season CO2 exchange data from 2016 to 2020 from an unburned peatland and 2-years of growing season CO2 exchange data from a burned peatland (2019-2020) in EGB. Plot-scale CO2 exchange measurements were also completed within the burned peatland jointly with abiotic variables and vegetation community surveys. Water table depth was identified as an important variable to explain total summer CO2 uptake (GPP) and net ecosystem exchange (NEE), where years of considerable rainfall maintained a water table near the peat surface and perpetuated high vegetation productivity. Summer total ecosystem respiration (ER) was greatly influenced by preceding winter and spring air temperature, with warmer winter air temperatures leading to summers of increased total ER. Warmer winter air temperatures also initiated water flow across the landscape, thus reviving plant and microbial activity following snow cover. These findings have important implications for the function of these shallow Ontario shield peatlands in a warming climate, where decreased water availability with projected increased temperatures and evapotranspiration leaves peatlands at risk of a net loss of C over the summer with lower water table. In the burned landscape, there was lower GPP in the summer (2019) compared to the wet summer of 2020, however the burned landscape continued to act as a net CO2 sink for the summer season of both years. The rapid recovery of vegetation across the wildfire-disturbed landscape has important implications for the function of these peatlands over time, with the ability for continued carbon uptake and reinstating peat accumulation processes.
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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 | 0.001 | 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 | 0.000 | 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".