Effects of Warming, Wildfire, and Permafrost Thaw on Carbon Dioxide Fluxes from Boreal Peat Landscapes in northwestern Canada
Bibliographic record
Abstract
The Taiga Plains ecozone in northwestern Canada is characterized by vast peat landscapes consisting of both mostly tree-less, permafrost-free and forested, permafrost-affected peat landscapes. In response to warming due to ongoing climate change, more frequent and severe wildfires and rapid permafrost thaw affect landscape composition, structure and functioning, whereas more and more ice-rich permafrost peat plateaus transform into water-saturated thermokarst wetlands or lakes. Collectively, these three agents of change, namely warming, wildfire, and thermokarst, could turn these boreal peat landscape from atmospheric carbon and nitrogen sinks into sources with potentially positive climate system feedbacks. We studied net ecosystem exchange (NEE) and its two component fluxes, i.e., gross primary productivity (GPP) and ecosystem respiration (ER), from three sites with five eddy covariance towers near the southern limit of permafrost in western Canada. Around the southernmost site Lutose, both footprint areas around the two towers have completely burned in wildfires in 2007 and 2019, respectively. We hypothesized that these two subsites would act as net CO2 sources, because of the recent disturbance history. This has been confirmed by preliminary results. The two other sites mainly differed in permafrost extent, ranging from sporadic (Scotty Creek) to discontinuous (Smith Creek), and in peat plateau-to-wetland ratio and corresponding forest cover (Scotty Creek < Smith Creek). Between the two sites Scotty Creek and Smith Creek, we hypothesized that the overall landscape GPP and ER will be higher at Scotty Creek compared to the northernmost site Smith Creek, due to both more abundant thermokarst wetlands and higher GPP and ER of the peat plateau areas at this more southern site. We further hypothesized that the effects of warming on GPP are greater than on ER and thus that the warmer Scotty Creek site is a greater net CO2 sink. Contrary to expectations, preliminary results have shown that there is no difference in NEE between Scotty Creek and Smith Creek, whereas both, the overall landscape GPP and ER, are actually higher at Smith Creek. To identify differences in the NEE, GPP, and ER between their peat plateaus and thermokarst wetlands, respectively, we move forward by applying footprint analyses for Smith Creek and Scotty Creek. Through these analyses we will be able to shed light on how each of the drivers, i.e., warming, wildfire, and thermokarst, alters magnitude and direction in greenhouse gas fluxes from rapidly thawing boreal peat landscapes.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".