Increased methylmercury bioaccumulation and community shifts in zooplankton along a boreal multi-reservoir system
Bibliographic record
Abstract
New hydroelectric developments, built to meet rising energy demands, transform natural riverscapes into lentic-like reservoirs while flooding terrestrial systems. This has led to increased bacterial production of methylmercury (MeHg), a neurotoxin with high absorption and biomagnification potential, resulting in high concentrations in predatory fish. Still, lower trophic level processes governing this trophic increase, particularly zooplankton dynamics, are not well understood, hampering accurate predictions, especially for series of reservoirs along a river. We found that in a boreal multi-reservoir complex (Romaine River, QC), bulk zooplankton MeHg and total mercury (THg) bioaccumulation responded rapidly to upstream impoundments, with pronounced increases typically emerging the following year. Linear regression estimated MeHg increases of 25 to 63 ng⋅g -1 ⋅yr -1 in reservoirs (one run-of-river dam, two large reservoirs) and 43 ng⋅g -1 ⋅yr -1 downriver, highlighting their usefulness as indicators of short-term Hg dynamics. Synchronous year-to-year zooplankton MeHg and THg patterns across reservoirs and downriver suggested responses were driven by the timing of seasonal events (ice off, ecological succession), while transient variability between reservoir sub-environments led to higher baseline MeHg within warm, shallow bays. Dissolved MeHg (<0.45 μm) levels partially predicted (∼40%) zooplankton MeHg and THg concentrations and although high bioaccumulation factors (BAFs) indicated an efficient bioaccumulation by algae and zooplankton, BAFs declined with higher dissolved MeHg. Rises in temperature were associated with decreased Cladocera abundance, increased copepod growth, and reduced MeHg aqueous exposure and bioaccumulation levels later in summer. We propose a conceptual model of zooplankton MeHg dynamics in a boreal reservoir series following impoundment, offering valuable insights for environmental managers monitoring recently impounded multi-reservoir systems.
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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) | 0.000 | 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 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".