Slow post-fire carbon balance recovery despite increased net uptake rates in Alaskan tundra
Bibliographic record
Abstract
Abstract Increasing wildfire occurrence and intensity have immediate effects on northern ecosystems due to combustion of aboveground vegetation and belowground soil organic matter. These immediate impacts have indirect and longer term effects, including deepening of the active layer, changes in soil decomposition rates, and shifts in plant community composition. Despite the increasing fire impacts across the tundra region, the implications of wildfire on ecosystem carbon balance are not well understood. Using paired eddy covariance towers in unburned and burned tundra, we examined the effects of a 2015 wildfire on carbon dioxide and methane fluxes in a wetland tundra ecosystem in the Yukon–Kuskokwim Delta, Alaska, from 2020 to 2022. Wildfire increased the amplitude and variability of carbon uptake and release on seasonal and annual timescales and increased the temperature sensitivity of soil respiration. Seven years post fire, there was annual net uptake in both unburned and burned tundra based on net ecosystem exchange, with the sink strength of burned tundra exceeding that of the unburned tundra by 1.18–1.64 times. However, when considering emissions, it would take approximately 86 years to recover the carbon lost from the wildfire itself. Soil moisture was a dominant driver of fluxes and positively associated with higher rates of carbon dioxide uptake and release and methane release. This study underscores the importance of understanding the effects of wildfire-induced shifts on tundra carbon cycling, allowing better predictions of long-term landscape-scale climate feedbacks as the climate continues to warm.
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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) | 0.001 | 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".