Temporal and regional effects of forest harvesting on mercury bioaccumulation and biomagnification in boreal stream food webs
Bibliographic record
Abstract
Forest harvesting can affect mercury (Hg) dynamics in boreal stream food webs by changing the availability and methylation of Hg and dietary exposure to methylmercury (MeHg) through basal resources. The objectives of this study were to quantify the 1) temporal and 2) regional effects of forest harvest on Hg bioaccumulation and biomagnification in streams. From 2019-2021, 5 headwater streams were sampled (n=2 harvested, n=3 reference) pre-(2019), during (2020), and post-(2021) harvest for basal food sources, macroinvertebrates and fish. In 2021, 7 streams (n=4 harvested, n=3 reference) were also sampled to assess the regional effects of forest harvest. All samples were analysed for total Hg ([THg]) or MeHg ([MeHg]), and stable isotopes of nitrogen (δ 15 N). Temporally, we observed increases in [MeHg] in water and some predatory macroinvertebrates and [THg] in fish post-harvest at the most impacted stream (i.e., 78% of watershed harvested, a stream crossing, and narrow buffer zones), but few differences at the stream where more rigorous best management practices were used (i.e, wider buffer zones and avoidance of wet soils). Regionally, we found significantly higher [MeHg] in food sources and macroinvertebrates in harvested compared to non-harvested landscapes, but no difference in [THg] of fish or Hg biomagnification (log Hg vs. δ 15 N). Overall, results showed that harvesting increased [Hg] in some food web components, and the magnitude of these effects varied with management practices used and proportion of the watershed harvested. • Hg concentrations increased post-harvest in predators and water in the most forestry-impacted stream. • Water and organism Hg did not vary pre- to post- harvest at the site where best management practices were more strictly followed. • Regionally, Hg was higher in primary and secondary consumers but not fish in harvested streams. • Forestry did not affect Hg biomagnification temporally or regionally. • •Effects on Hg appear dependent on amount of protection a stream was provided during forest harvest.
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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 | 0.000 |
| 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".