Zebra mussels (<i>Dreissena polymorpha</i>), habitat alteration, and yellow perch (<i>Perca flavescens</i>) foraging: system-wide effects and behavioural mechanisms
Bibliographic record
Abstract
The aggregate impact of an exotic species introduction, such as the zebra mussel (Dreissena polymorpha), may involve a large number of biotic and abiotic interactions within the recipient ecosystem. We used laboratory experiments and field data to assess effects of zebra mussels on both foraging success of yellow perch (Perca flavescens) and activity of the amphipod Gammarus fasciatus. In two laboratory experiments zebra mussel clusters reduced the rate at which yellow perch captured amphipods. Yellow perch captured fewer amphipods when zebra mussels were present at two light levels (<2.1 and >214 lx) and across a range of prey densities (761500 amphipods·m2). The effect of zebra mussels on amphipod activity depended on light level. Yellow perch captured fewer amphipods in the presence of mussel clusters than when plants were present. The frequency of amphipods in the diets of adult yellow perch in Oneida Lake increased after zebra mussel introduction, but the increase was greater in low mussel density habitats. Our laboratory results and field observations suggest that zebra mussels affect yellow perch foraging on amphipods through increased structural complexity (negative) and increased light penetration ( positive), but not through increased prey density.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".