Effect of eutrophication on mercury (Hg) dynamics in subtropical reservoirs from a high Hg deposition ecoregion
Bibliographic record
Abstract
Eutrophication can have opposite effects on mercury (Hg) bioavailability in aquatic systems, by increasing methylmercury (MeHg) production but reducing Hg biomagnification. Globally, the effect of eutrophication on Hg dynamics remains largely untested at lower latitudes such as eastern China, a productive subtropical ecoregion with Hg emission and deposition rates that are among the highest worldwide. Here, we quantify Hg (both MeHg and total Hg, THg) concentrations, Hg bioaccumulation, and Hg biomagnification rates in reservoir food webs across a gradient of eutrophication indicated by chlorophyll a (Chl a), zooplankton density, and total phosphorus (TP). We also assess the effect of hydrogeomorphic (HGM) features on Hg concentrations in water and biota. Water THg concentrations were strongly correlated with TP and were greater in reservoirs at higher elevations with short water retention times (WRT). Zooplankton and top predator THg concentrations were negatively correlated with Chl a, suggesting algal biodilution; evidence for zooplankton density dilution was also found when subtropical reservoirs were compared at a global scale with temperate lakes. Mercury bioaccumulation and biomagnification factors, respectively, did not correlate with increasing Chl a or zooplankton density suggesting no effect of plankton density on Hg trophic transfer. In subtropical reservoirs, eutrophication is associated with lower Hg concentrations in biota but does not explain Hg biomagnification; HGM features (i.e., elevation, WRT) and land use (i.e., % crop) appear to also influence Hg concentrations and bioaccumulation in reservoir food webs.
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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 | 0.001 |
| 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".