Impacts of spruce budworm defoliation on stream food webs and mercury cycling
Bibliographic record
Abstract
Forested streams are closely linked to terrestrial catchments which affects their biogeochemical cycling and carbon inputs. Catchment disturbances alter stream water quality and food webs, including changes in productivity. Such changes in stream conditions can potentially alter consumers’ reliance on autochthonous (in-stream) or allochthonous (terrestrial) sources and mercury bioaccumulation. A recent outbreak of the spruce budworm (SBW) that feeds on spruce and fir trees has provided the unique opportunity to examine stream food web responses across watersheds experiencing a range of defoliation in the Gaspé Peninsula, Québec. This project compares streams in twelve watersheds which were selectively sprayed to control SBW and create a gradient in defoliation. Food web samples (food sources, invertebrates, fish) were analyzed for stable isotopes of carbon and nitrogen in 2019 and 2020 to characterize food web structure, and algal productivity was measured in 2019. Hierarchical partitioning models were used throughout the study to compare the contributions of various local and landscape conditions to stream responses. Models indicated that watershed defoliation contributed to increasing autochthonous production, although some invertebrates were more allochthonous in heavily defoliated watersheds, and brook diets were unaffected by defoliation. Next, food web samples were analyzed for methylmercury (food sources, invertebrates) or total mercury (fish) and trophic magnification slopes were determined for each stream food web. Mercury levels in carnivorous invertebrates and brook trout were driven by dissolved organic carbon (DOC), but not consumer autochthony or watershed defoliation. Additionally, rates of trophic magnification were not related to defoliation severity or DOC. This study found that defoliation contributed to increasing autochthonous production and invertebrate consumer allochthony. However, this disturbance did not increase consumer mercury levels or biomagnification in stream food webs. These findings suggest that intervention to reduce defoliation would mitigate algal responses and dietary shifts, but not mercury cycling as it is influenced by DOC levels in the streams of this region.
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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) | 0.001 | 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".