Logging disrupts the ecology of molecules in headwater streams
Bibliographic record
Abstract
Abstract Global demand for wood products is increasing forest harvest. One understudied consequence of logging is that it accelerates mobilization of dissolved organic matter (DOM) from soils to aquatic ecosystems. Here, we tested how logging changed DOM in headwaters of hardwood-dominated catchments in northern Ontario, Canada. We apply a before-after control-impact experiment across four catchments for three years. DOM concentration in streams from logged catchments quadrupled, on average, after the first year post-harvest, but resulting changes to the molecular composition of DOM persisted for at least two-years. Ultrahigh-resolution mass spectrometry revealed that DOM within logged catchments was more energy-rich and chemically diverse than in controls, with novel highly unsaturated polyphenols, carboxylic-rich alicyclic, and nitrogen-containing formulae. The molecular composition of stream DOM measured fortnightly post-harvest was most strongly associated with DOM composition within intermediate and deeper layers of contributing soils, likely due to increased hydrological connectivity post-harvest. We estimate logging increased the total annual flux of dissolved organic carbon in streams by 6.4% of extracted wood carbon, and this carbon was more likely to be released into the atmosphere. Carbon accounting of forestry, including as a natural climate solution, must now consider the transport and fate of DOM from land into water.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".