Wildfires Change Summertime Dissolved Organic Matter in Boreal Headwater Streams
Bibliographic record
Abstract
Abstract Boreal forests export large amounts of terrestrial carbon into downstream waters as dissolved organic material (DOM), but how increasing wildfire frequency affects this flux remains understudied. If more DOM is exported from land into water after wildfire and/or this DOM is more readily transformed by microbes, the net loss of carbon from terrestrial ecosystems after wildfire may be greater than currently estimated. Here we investigated how wildfire changes DOM exported from boreal forests into headwater streams in northwestern Ontario, Canada over a summer growing season. We compared the concentration and molecular composition of DOM between 10 recently burned and 10 undisturbed catchments using optical spectroscopy and ultra‐high‐resolution mass spectrometry. We found a 29% increase, on average, in DOM concentrations in the streams of burned catchments in August only. DOM in burned catchments appeared less bioavailable, as indicated by a lower H:C and higher modified aromaticity index. As expected, because of wildfire, black carbon was 55% more abundant, on average, in burned catchments compared to controls, contributing to the greater aromaticity of DOM. However, despite the lower bioavailability of DOM, compounds in burned catchments were more thermodynamically favorable for microbial degradation and as likely to be biochemically transformed as unburned DOM during July and August, but not in June. Overall, our results suggest wildfires reduce forest carbon sequestration more than currently estimated because of fluvial DOM losses. If exported DOM is mineralized, carbon sequestration may even decrease, highlighting the need to incorporate the impacts of wildfires on receiving waters into carbon accounting.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".