Effects of Non-native Fish on Lacustrine Food Web Structure and Mercury Biomagnification along a Dissolved Organic Carbon Gradient
Bibliographic record
Abstract
Abstract Although the introduction of non-native fish species has been shown to alter trophic ecology in aquatic ecosystems, there has been limited research on how invasive species alter methylmercury (MeHg) biomagnification in lacustrine food webs. We sampled surface water and biota from 8 lakes in Quebec, Canada, spanning a range of dissolved organic carbon (DOC) concentrations (2.9–8.4 mg/L); 4 lakes were inhabited by native brook trout (Salvelinus fontinalis), and the remaining lakes contained brook trout and a non-native fish, Allegheny pearl dace (Margariscus margarita). Periphyton, zooplankton, macroinvertebrates, and fish were analyzed for: 1) stable carbon (δ13C) and nitrogen (δ15N) isotope ratios to delineate food webs, and 2) total Hg (THg) or MeHg. Compared with the brook trout from reference lakes, fish from invaded lakes had higher length-standardized THg concentrations as well as a narrower dietary range and elevated trophic level, inferred from unadjusted δ13C and δ15N values, respectively. The rate of Hg biomagnification was similar across invaded and reference lakes, implying little effect of the invasive fish on the trophic transfer of MeHg. Despite differences in food web structure due to pearl dace invasion, DOC was the strongest predictor of brook trout THg levels for all lakes, suggesting that underlying environmental factors exerted a stronger influence on brook trout THg concentrations than the presence of a non-native forage fish. Environ Toxicol Chem 2020;39:2196–2207. © 2020 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".