The molecular diversity of dissolved organic matter in forest streams across central Canadian boreal watersheds
Bibliographic record
Abstract
Small headwater streams can mobilize large amounts of terrestrially derived dissolved organic matter (DOM). While the molecular composition of DOM has important controls on biogeochemical cycles and carbon cycling, how stationary landscape metrics affect DOM composition is poorly understood, particularly in relation to non-stationary effects from hydrological changes across seasons. Here, we apply a combination of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) and absorbance spectroscopy to characterize stream DOM from 13 diverse watersheds across the central Canadian boreal forests and statistically relate DOM compositional characteristics to landscape topography and hydrological metrics. We found that watershed runoff across different surface physiographies produced DOM with distinctly different chemical compositions related to runoff pH. Specifically, streams in sandy soil watersheds contained more abundant aromatic, nitrogenated and sulfurized fractions of DOM, likely due to a combination of lower soil capacity to absorb DOM than other soil types and high conifer forest coverage that generated acidic litterfall in more sandy watersheds. In contrast, streams with more neutral pH in watersheds with shallow soils had DOM resembling low oxidized phenolic molecules mainly due to increased brush/alder and deciduous vegetation coverage in relatively steeper watersheds. However, as precipitation and flows increased in the fall, the overall water chemistry of streams became more similar as runoff pH increased, the overall chemical diversity of DOM in streams decreased, and stream DOM resembled fresher, lower molecular weight lignin material likely originating from freshly produced leaf litter. Together, our findings show that during hydrologically disconnected periods, pH and landscape characteristics have important controls on the mobilization of aromatic DOM but that many landscape-specific characteristics in the Canadian boreal forest are less influential on DOM processing during wetter conditions where chemically similar, plant-derived DOM signatures are preferentially mobilized. These findings collectively help predict the composition of DOM across diverse watersheds in the Canadian boreal to inform microbial and contaminant biogeochemical processes in downstream ecosystems.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".