Effect of wildfire burn severity on dissolved organic carbon concentration and dissolved organic matter composition export from Boreal Shield peatlands 3- to 5-years post-wildfire
Bibliographic record
Abstract
Northern peatlands function as important global carbon sinks. However, due to climate change, there are concerns about whether these peatlands will maintain this net carbon sink function. Climate change is already increasing boreal biome drying, area-burned, wildfire intensity, and burn severity as observed in the unprecedented 2023 wildfire season in Canada (>15 Mha burned). Of particular concern in boreal wildfires are deep burning smouldering peat fires that can switch peatlands to net emitters of atmospheric carbon. However, less studied are the effects of peat fires on water-borne carbon and the potentially deleterious impacts it has on downstream water quality as the burned area recovers 3- to 5-years post-fire. To better understand the impacts of wildfires on northern peatlands, we investigated the effects of varying peat burn severities on the dissolved organic carbon (DOC) concentration and the composition of dissolved organic matter (DOM) exported in the fall from peatlands located in Ontario's Boreal Shield ecozone. A paired peatlands approach was used with seven burned peatlands and six unburned peatlands. Each burned and unburned group contained three peatlands of similar size, average peat depth, and catchment size. The burned peatlands were located within the Parry Sound #33 wildfire footprint roughly 65 km north of the unburned peatlands that are located near Dinner Lake. Vegetation recovery was measured at the burned sites while runoff, water quality, water table depth, and precipitation were measured at both unburned and burned sites. Over a three-year period (2021-2023), exported DOC concentrations decreased significantly with increasing burn severity, but the composition of DOM varied across burn severities. Both the unburned and burned sites experienced fall flushing events in both 2022 and 2023 with the burned sites experiencing an additional flushing event mid-summer in 2023. The burned peatland with the highest percent burn experienced a delayed flushing event late fall due to the lack of discharge earlier in the season. Moss recovery was found to have the largest impact on DOM composition with increased Sphagnum moss regrowth associated with significant increases in DOM molecular size, weight, aromaticity, and degree of humification. The degree of moss recovery varied across high burn severity peatlands indicating a delay in recovery compared to low burn severity peatlands. Considering that climate change is increasing burn severity, future research should investigate the impact burn severity has on DOC concentration immediately following wildfire in landscapes dominated by fill and spill hydrological processes. Additionally, examining the impact of burn severity and average peat depth on vegetation recovery and exported DOM composition could lead to a better understanding of exported DOM composition following future wildfires on peatlands in this landscape.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".