Yellow perch consumption of invasive mussels in the St. Lawrence River
Bibliographic record
Abstract
Biological invasions are a global phenomenon that can threaten native species and disrupt ecosystem processes. Exotic species also impact ecosystems in less conspicuous ways by provoking native species to alter their foraging behaviour. Subtle impacts such as diet shifts are frequent, and can have consequences for food web dynamics and the fitness of native predators. Diet shifts involving the consumption of exotic species require native predators to recognize, capture and handle novel prey. In this thesis, I document a diet shift in the St. Lawrence River involving a common native fish and Eurasian mussels that invaded the river in the early 1990s. I conducted diet analysis of yellow perch (Perca flavescens) at multiple sites in the upper St. Lawrence River and discovered that they consumed substantial quantities of zebra and quagga mussels (Dreissena spp.) in the Soulanges Canal, an artificial waterway west of Montreal. This was unexpected because perch lack adaptations for crushing molluscs. This foraging innovation was not observed at the same site in the early 1990s or at other sites at any time. Mussel shells were weaker at this site, probably because of exposure to calcium-poor water. This suggests that water chemistry mediates yellow perch predation on mussels. This study provides an example of diet shifts involving exotic prey and illustrates the influence of abiotic factors on species interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 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".