Impacts of Forest Harvest and Beaver Dams on Hydrological Transport of Mercury in Boreal Headwater Streams
Bibliographic record
Abstract
Boreal soils are notable reservoirs of mercury that originates from both geogenic sources and atmospheric sources derived predominantly from anthropogenic emissions. Following forest disturbances, this pool of mercury may be mobilized and transported to wet areas on the landscape that promote the microbial transformation of mercury to methylmercury, a neurotoxin with the potential to biomagnify up aquatic food chains. Two common disturbances in Canadian boreal forests that may facilitate the mobilization, transport, and methylation of mercury are forest harvest and beaver ponds, as both tend to increase wet area coverage in the watershed and result in increased transport of sediment- and organic matter-bound mercury to aquatic systems. Though both frequently occur in the same watersheds, they are seldom studied in tandem. Thus, there is a need to further our understanding of how ponds and harvest interact and modify the effects of one another. This thesis addressed this objective in four steps. First, a baseline was established by examining mercury concentrations and export in streams draining undisturbed watersheds. Watersheds underlain by near-surface bedrock and dominated by wet soil moisture conditions exported more methylmercury than those with good soil drainage. Second, background mercury concentrations were compared to those during and postharvest to quantify the effect of harvest on stream mercury, which was greatest in watersheds with the most near-stream soil disturbance and minimal otherwise. Third,mercury concentrations downstream of beaver dams in mostly unharvested watersheds were compared to upstream concentrations to isolate the effects of beaver ponds on stream mercury. Landscape characteristics played a significant role in stream response, with the greatest downstream changes observed in watersheds less conducive to mercury methylation. Lastly, a study of watersheds with varying combinations of pond and harvest impacts showed that while ponds had a greater impact on downstream methylmercury than harvest, harvest resulted in upstream changes that affected the degree of methylmercury impact contributable to beaver activity. Understanding the mechanisms behind the impacts of beaver ponds and forest harvest will allow land managers to (a) adjust management practices to minimize the effects of harvest on stream mercury and (b) consider how harvest activity could affect downstream methylmercury in areas with high beaver activity.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".