The Effect of Nonnative Salmonids on Social Dominance and Growth of Juvenile Atlantic Salmon
Bibliographic record
Abstract
Abstract Nonnative species have been shown to negatively impact the native community in which they are introduced. In the Great Lakes, competition with nonnative salmonids may be hindering the restoration efforts of Atlantic salmon Salmo salar, a once‐native top predator in Lake Ontario. We examined the effects of brown trout S. trutta and rainbow trout Oncorhynchus mykiss, two nonnative fishes in Lake Ontario, on the social dominance and growth rate of juvenile Atlantic salmon from three strains being used for reintroduction efforts in Lake Ontario. Using seminatural stream channels, we found that the presence of either rainbow trout or brown trout reduced aggression, dominance, and food consumption of the Atlantic salmon. Brown trout had the strongest effect, increasing aggression levels in the channels by a factor of two and sharply reducing the dominance of Atlantic salmon. When both nonnatives were present, initiated aggression by Atlantic salmon decreased by a factor of three and food consumption halved as compared with when the salmon were alone. Consequently, over a 7‐d time period, standard growth rate of the Atlantic salmon dropped from no change in mass when alone to a value of –0.3% per day when with the nonnative species. Of the three strains tested, one strain was least affected by the nonnative trouts, implicating genetic differences among the strains and suggesting that one strain may have greater poststocking success in Lake Ontario tributaries with naturalized populations of brown trout and rainbow trout.
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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 | 0.000 | 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".