Cumulative Effects of Watershed Disturbances and Run-of-river Dams on Mercury Cycling: Case Study and Recommendations for Environmental Managers
Bibliographic record
Abstract
Run-of-river power plants (ROR) represent the majority of hydroelectric plants worldwide. Their environmental impacts are not well documented and are believed to be limited, particularly regarding the contamination of food webs by methylmercury (MeHg), a neurotoxin. RORs are typically installed in small rivers where combined effects of watershed disturbances with dam construction can complicate environmental management. We report a multi-year case study on the Saint-Maurice River (Canada) where an unpredicted temporary increase in MeHg accumulation in predator fish was observed after the construction of two ROR plants. The associated pondages acted as sedimentation basins for mercury (Hg) and organic matter from a watershed disturbed by a forest fire and by logging. This fresh organic carbon likely fueled microbial MeHg production. Hg methylation was more associated with environmental conditions than to the presence of Hg, and main methylating microbial groups were identified. A constructed wetland was a site of significant Hg methylation but was not the main source of the fish Hg increase. Organic carbon degradation was the main driver of MeHg accumulation at the base of the food chain whereas trophic levels explained the variations at the top of the food chain. Overall, carbon cycling was a key driver of Hg dynamics in this system, and ROR plants can cause temporary (ca. 12 years) Hg increase in food webs when developed in disturbed watersheds, although this increase is smaller than for large reservoirs. Recommendations for future ROR construction are to establish a good environmental monitoring plan with initial high temporal resolution and to consider recent and potential watershed disturbances in the plan.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".