Exploring the Impacts of Methylmercury‐Induced Behavioral Alterations in Larval Yellow Perch in Lake Michigan Using an Individual‐Based Model
Bibliographic record
Abstract
Abstract The inclusion of sublethal behavioral effects in population models has predicted lower estimates of cohort abundance and survival. Methylmercury, a persistent contaminant in the Great Lakes, has been shown to alter foraging and predator avoidance behaviors of larval Yellow Perch Perca flavescens, which may have indirect effects on recruitment. Poor recruitment of Yellow Perch in Lake Michigan has been associated with changes in trophic structure and adverse habitat conditions, but the potential effects of contaminants have not been fully explored. To test this, we adapted existing larval fish individual‐based models to incorporate laboratory‐derived methylmercury behavioral impairments on a larval Yellow Perch cohort in Lake Michigan. Overall, swimming speed reductions following methylmercury exposure did not drastically affect cohort survival. In contrast, the impairment to prey capture success resulted in an additional 8, 45, and 28% starvation of the cohort in simulations with tissue concentrations of 0.21, 0.95, and 3.14 μg/g total mercury whole‐embryo wet weight, respectively. While our experimental methylmercury concentrations were higher than those typically found in Lake Michigan, our findings suggest that contaminants could be an additional factor impacting recruitment of Yellow Perch in systems highly contaminated with mercury.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".