Shifts in Depth Distributions of Alewives, Rainbow Smelt, and Age-2 Lake Trout in Southern Lake Ontario following Establishment of Dreissenids
Bibliographic record
Abstract
In the mid-1990s, biologists conducting assessments of fish stocks in Lake Ontario reported finding alewives Alosa pseudoharengus, rainbow smelt Osmerus mordax, and juvenile lake trout Salvelinus namaycush at greater depths than in the mid-1980s. To determine if depth distributions shifted coincident with the early 1990s colonization of Lake Ontario by exotic Dreissena mussels, we calculated mean depth of capture for each of the three species during trawl surveys conducted annually during 1978–1997 and examined the means for significant deviations from established patterns. We found that mean capture depth of alewives, rainbow smelt, and age-2 lake trout shifted deeper during the build up of the dreissenid population in Lake Ontario but that timing of the shift varied among seasons and species. Depth shifts occurred first for rainbow smelt and age-2 lake trout in June 1991. In 1992, alewives shifted deeper in June followed by age-2 lake trout in July–August. Finally, in 1993 and 1994, the distribution of lake trout and alewives shifted in April–May. Reasons why the three fishes moved to deeper water are not clear, but changes in distribution were not linked to temperature. Mean temperature of capture after the depth shift was significantly lower than before the depth shift except for alewives in April–May. Movement of alewives, rainbow smelt, and age-2 lake trout to colder, deeper water has the potential to alter growth and reproduction schedules by exposing the fish to different temperature regimes and to alter the food chain, increasing predation on Mysis relicta in deep water and decreasing alewife predation on lake trout fry over nearshore spawning grounds in spring.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".