Cumulative Impacts of Forest Management on the Accumulation and Biomagnification of Mercury and its Relationship to Autochthony in Stream Food Webs in New Brunswick, Canada
Bibliographic record
Abstract
Forests provide a multitude of ecological services and are one of Canada’s most important natural resources that support a profitable industry, especially in New Brunswick. The activities associated with harvesting and forest management have documented ecological impacts such as the increased mobilization of mercury from the land to adjacent streams. Methylated mercury bioaccumulates and biomagnifies (concentrates) through food webs and in headwater streams forestry has been shown to change its accumulation. However, not much is known about the spatial trends of mercury accumulation and biomagnification through stream food webs and how different forest management practices affect these trends. To delineate these patterns, food webs were sampled across a spatial gradient from three basins experiencing different levels of forest management intensity. At a basin scale, methylmercury concentrations were greatest in filtered water, food sources, and one invertebrate taxa in a harvested but less intensively managed basin, likely due to increased inorganic sediments and dissolved organic carbon also observed. Biomagnification was lower in this same basin, possibly from inefficient trophic transfer of methylmercury from food sources. Longitudinally this basin also showed differences in fine particulate organic matter (FPOM) and coarse particulate organic matter (CPOM) mercury compared to the other basins, likely due to similar spatial patterns in organic matter. In conclusion, mercury dynamics in stream food webs were impacted by forestry primarily in water and basal food sources at a basin scale, but spatial patterns were inconsistent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".