Impacts of an invasive benthic predator on macroinvertebrate communities in the St. Lawrence River
Bibliographic record
Abstract
Introduced exotic predators can restructure biotic communities and alter the functioning of ecosystems.The round goby Neogobius melanostomus, a Eurasian fish, has spread throughout the lower Great Lakes and, in recent years, has colonized the upper St. Lawrence River.A few studies have recorded the impacts of this predator on benthic communities in the Great Lakes, highlighting its negative effects on zebra mussels and other macroinvertebrates.In this thesis, I investigate the direct and indirect effects of the round goby on benthic macroinvertebrate communities and algae in the upper St.Lawrence River.Through an analysis of the goby's diet, I link its prey selection to observed changes in mollusc size structure and the composition of macroinvertebrates on rocky substrates.I also quantify post-invasion changes in macroinvertebrate functional feeding groups, body size of dominant taxa, and community richness, evenness and abundance.Finally, I present evidence of a trophic cascade -an increase in algal biomass driven by the negative impacts of gobies on algivorous macroinvertebrates.These changes have significant implications for benthic community stability, ecosystem functioning, and native fishes that rely on benthic macroinvertebrates in the upper St.Lawrence River.
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 | 0.001 | 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".