Effects of Competition with Four Nonnative Salmonid Species on Atlantic Salmon from Three Populations
Bibliographic record
Abstract
Abstract The presence of ecologically similar nonnative species may impede recovery efforts for native species. We assessed the survival and growth of juvenile Atlantic Salmon Salmo salar from three populations (LaHave River, Sebago Lake, and Lac Saint‐Jean) in the presence of four naturalized nonnative salmonid competitors. The three populations are being used for reintroduction efforts in Lake Ontario, where Atlantic Salmon are extirpated. Juvenile Atlantic Salmon were placed into artificial stream tanks with combinations of juvenile Brown Trout S. trutta, Rainbow Trout Oncorhynchus mykiss, Chinook Salmon O. tshawytscha, and Coho Salmon O. kisutch. Survival of all three Atlantic Salmon populations was lower in the presence of Brown Trout; growth was lower in the Brown Trout treatment and in the multispecies treatment. In contrast, Atlantic Salmon survival and growth were not negatively impacted by the presence of Chinook Salmon, Rainbow Trout, or Coho Salmon. Based on measurements of circulating hormones, Atlantic Salmon were not chronically stressed and did not show a change in social status after 10 months in the artificial stream tanks. Our results support the theory that differences in aggression and niche overlap can influence competitive outcomes and suggest that tributaries containing Brown Trout should be avoided during Atlantic Salmon reintroduction into Lake Ontario.
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 | 0.001 |
| 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 | 0.001 |
| 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".