Are There Longitudinal Effects of Forest Harvesting on Carbon Quality and Flow and Methylmercury Bioaccumulation in Primary Consumers of Temperate Stream Networks?
Bibliographic record
Abstract
Abstract Forest harvesting affects dissolved organic matter (DOM) and aqueous mercury inputs as well as the food web structure in small-headwater streams, but how these upstream changes manifest downstream is unclear. To address this uncertainty, we examined DOM quality, autochthony in the caddisfly Hydropsychidae (using δ2H), and methylmercury (MeHg) concentrations in stream water and the caddisfly along a longitudinal gradient (first- to fourth-order streams, subcatchments of 50–1900 ha) in paired partially harvested and reference catchments in central Ontario, Canada. Although measures of DOM quality (specific ultraviolet absorbance at 254 nm 2.20–11.62) and autochthony in caddisflies (4.9%–34.0%) varied among sites, no upstream-to-downstream differences in these measures were observed between the paired harvested and reference catchments. In contrast, MeHg levels in stream water (0.06–0.35 ng/L) and caddisflies (29.7–192 µg/kg dry wt) were significantly higher in the upstream sites but not the farthest downstream sites in the harvested catchments compared to the reference catchments. This suggests that while current mitigation measures used by forestry companies did not prevent elevated MeHg in water and invertebrates at smaller spatial scales (subcatchments of 50–400 ha), these upstream impacts did not manifest at larger spatial scales (subcatchments of 800–1900 ha). The present study advances our understanding of spatially cumulative impacts within harvested catchments, which is critical to help forest managers maintain healthy forest streams and their provisioning of aquatic ecosystem services. Environ Toxicol Chem 2022;41:1490–1507. © 2022 SETAC
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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 | 0.001 | 0.001 |
| 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".