Sources and fate of terrestrial dissolved organic carbon in lakes of a Boreal Plains region recently affected by wildfire
Bibliographic record
Abstract
Abstract. Downstream mineralization and sedimentation of terrestrial dissolved organic carbon (DOC) render lakes important for landscape carbon cycling in the boreal region. The chemical composition of terrestrial DOC, the downstream delivery of terrestrial DOC and its processing within aquatic ecosystem may all be influenced by climate change, including increased occurrence of wildfire. Here, we assessed composition and lability (during both dark- and UV incubations) of DOC from peatland groundwater and mineral soil groundwater, and from shallow lakes within a peatland-rich region on the Boreal Plains of western Canada that was recently affected by wildfire. Wildfire was found to increase aromaticity of DOC in peat pore water above the water table, but had no effect on the concentrations or composition of peatland groundwater DOC or mineral soil DOC. Using a mixing model we estimated that on average 98 and 78% of terrestrial DOC inputs to the lakes in fine- and coarse-textured settings, respectively, originated from peatland groundwater sources. Accordingly, lake DOC composition reflected primarily a mixing of peatland and mineral soil groundwater sources, with no detectable influence of the recent wildfire. Lake and peatland DOC had low biodegradability, lower than that of mineral soil DOC. However, both mineralization and sedimentation of peatland DOC increased substantially during UV incubations through selective removal of aromatic humic and fulvic acids. Similar shifts in DOC composition as observed during the UV incubations were also observed across lakes with longer water residence times. The mixing model estimated that on average 54% (95% confidence interval: 36–64%) of terrestrial DOC had been removed in lakes as a result of mineralization and sedimentation. Meanwhile, the reduction in absorbance at 254 nm was 71% (58–76%), which suggests selective removal of aromatic DOC. Hence, incubation results, patterns of DOC composition among lakes and mixing model results were consistent with significant within-lake removal of terrestrial DOC through UV-mediated processes. Selective removal of highly aromatic DOC through UV-mediated processes implies that organic sources that are considered stabile in terrestrial ecosystems can be readily mineralized once entering aquatic ecosystems. Together, our results suggest that regional characteristics (climate, surface geology and lake morphometry) can prevent wildfire from causing pulse perturbations to the linkages between terrestrial and aquatic C cycling and also regulate the processes that dominate within-lake removal of terrestrial DOC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".