Effects of forest management on mercury bioaccumulation and biomagnification along the river continuum
Bibliographic record
Abstract
Forest management can alter the mobilization of mercury (Hg) into headwater streams and its conversion to methylmercury (MeHg), the form that bioaccumulates in aquatic biota and biomagnifies through food webs. As headwater streams are important sources of organic materials and nutrients to larger systems, this connectivity may also increase MeHg in downstream biota through direct or indirect effects of forestry on water quality or food web structure. In this study, we collected water, seston, food sources (biofilm, leaves, organic matter), five macroinvertebrate taxa and fish (slimy sculpin; Cottus cognata) at 6 sites representing different stream orders (1–5) within three river basins with different total disturbances from forestry (both harvesting and silviculture). Methylmercury levels were highest in water and some food sources from the basin with moderate disturbance (greater clearcutting but less silviculture). Water, leaves, stoneflies and fish increased in MeHg or total Hg along the river continuum in the least disturbed basin, and there were some dissipative effects of forest management on these spatial patterns. Trophic level (δ15N) was a significant predictor of MeHg (and total Hg in fish) within food webs across all 18 sites, and biomagnification slopes were significantly lower in the basin with moderate total disturbance but not different in the other two basins. The elevated MeHg in lower trophic levels but its reduced trophic transfer in the basin with moderate disturbance was likely due to greater inputs of sediments and of dissolved organic carbon that is more humic, as these factors are known to both increase transport of Hg to streams and its uptake in primary producers but to also decrease MeHg bioaccumulation in consumers. Overall, these results suggest that the type of disturbance from forestry affects MeHg bioaccumulation and trophic transfer in stream food webs and some longitudinal patterns along a river continuum.
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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) | 0.000 | 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 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".