Effects of Clearcutting and Residual Biomass Harvesting on Hillslope Mercury Mobilization and Downgradient Mercury Accumulation
Bibliographic record
Abstract
Abstract Mercury (Hg) in forest runoff varies geographically and in relation to silvicultural practices. There is also considerable uncertainty about how forest management practices, such as residual biomass removal, affect Hg mobilization, and downgradient cumulative effects. In this study, total Hg, dissolved organic carbon (DOC), and sulfate (SO42−) mobilization in runoff were compared among unharvested and harvested hillslopes with and without residual biomass removal, and peat soil and invertebrate methylmercury (MeHg) concentrations were assessed in a down‐gradient peatland. Using a before‐after‐control‐impact design, runoff from three adjacent hillslopes was monitored pre‐harvest (2010–2011) and post‐harvest (2012–2013) from snowmelt to freeze‐up at the USDA Forest Service’s Marcell Experimental Forest in northern Minnesota. Dilution, due to increased available hillslope soil water, led to decreased THg and DOC concentrations after harvest at both harvested hillslopes. Sulfate concentrations did not statistically change following harvest. Compared to its removal, leaving residual biomass significantly increased yields of all solutes but only DOC yields significantly increased when the residual biomass was removed. In the adjacent down‐gradient peatland, peat MeHg concentrations decreased, whereas no change was observed in peat below the unharvested hillslope. There was no discernible change post‐harvest in MeHg levels among several peatland macroinvertebrate taxa. This study provides a much‐needed hillslope‐scale understanding of Hg response to forest management practices, highlighting that increased solute yields from hillslopes do not necessarily stimulate Hg methylation or invertebrate bioaccumulation in down‐gradient peatland systems.
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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 | 0.000 |
| 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".