Methylmercury bioaccumulation and biomagnification in streams within forested catchments defoliated by spruce budworm
Bibliographic record
Abstract
Though outbreaks of defoliating insects are a widespread, natural disturbance in Canadian boreal forests and their magnitude and duration are increasing in recent years, we have little understanding of the impacts on stream ecosystems. Herein we examined the fate of mercury (Hg), a toxic element affected by other landscape-scale forest disturbances, in twelve stream food webs in Gaspé Peninsula, Québec, Canada, that ranged in their severity of watershed defoliation from spruce budworm ( Choristoneura fumiferana ). Basal food sources (coarse and fine particulate organic matter, and biofilms), several macroinvertebrate taxa, and fish (brook trout [ Salvelinus fontinalis ] and slimy sculpin [ Cottus cognatus ]) were sampled in 2019 and 2020 and analyzed for stable isotopes of C and N, methylmercury (MeHg) or total Hg (THg, fish only). Hierarchical partitioning models were used to identify relative importance among landscape and local water quality variables, and they explained 76 and 65% of variation in brook trout THg and carnivorous invertebrate (Chloroperlidae, Rhyacophila , Parapsyche ) MeHg levels, respectively, with dissolved organic carbon (DOC) as the main driver increasing biotic mercury levels. Trophic magnification slopes (TMS) (calculated as log 10 Hg vs. δ 15 N) ranged from 0.27-0.38 across all watersheds but were not related to defoliation severity or DOC concentrations. Collectively, these findings suggest that local measures of water quality are more important drivers of Hg bioaccumulation in stream biota than landscape disturbances caused by forest defoliation by spruce budworm.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".