Using sulfur stable isotopes to assess mercury bioaccumulation and biomagnification in temperate lake food webs
Bibliographic record
Abstract
Abstract Nitrogen and carbon stable isotopes (δ15N, δ13C) are commonly used to understand mercury (Hg) bioaccumulation and biomagnification in freshwater food webs. Though sulfur isotopes (δ34S) can distinguish between energy sources from the water column (aqueous sulfate) and from sediments to freshwater organisms, little is known about whether δ34S can help interpret variable Hg concentrations in aquatic species or food webs. Seven acidic lakes in Kejimkujik National Park (Nova Scotia, Canada) were sampled for biota, water, and sediments in 2009 and 2010. Fishes, zooplankton, and macroinvertebrates were analyzed for δ34S, δ15N, δ13C, and Hg (methyl Hg in invertebrates, total Hg in fishes); aqueous sulfate and profundal sediments were analyzed for δ34S. Within lakes, mean δ34S values in sediments and sulfate differed between 0.53‰ and 1.98‰, limiting their use as tracers of energy sources to the food webs. However, log-Hg and δ34S values were negatively related (slopes –0.14 to –0.35, R2 0.20–0.39, p < 0.001–0.01) through each food web, and slopes were significantly different among lakes (analysis of covariance, lake × δ34S interaction term p = 0.04). Despite these relationships, multiple regression analyses within each taxon showed that biotic Hg concentrations were generally better predicted by δ15N and/or δ13C. The results indicate that δ34S values are predictive of Hg concentrations in these food webs, although the mechanisms underlying these relationships warrant further study. Environ Toxicol Chem 2017;36:661–670. © 2016 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".