Effect of Stock Size, Climate, Predation, and Trophic Status on Recruitment of Alewives in Lake Ontario, 1978–2000
Bibliographic record
Abstract
Abstract The population of alewives Alosa pseudoharengus in Lake Ontario is of great concern to fishery managers because alewives are the principal prey of introduced salmonines and because alewives negatively influence many endemic fishes. We used spring bottom trawl catches of alewives to investigate the roles of stock size, climate, predation, and lake trophic status on recruitment of alewives to age 2 in Lake Ontario during 1978–2000. Climate was indexed from the temperature of water entering a south‐shore municipal treatment plant, lake trophic status was indexed by the mean concentration of total phosphorus (TP) in surface water in spring, and predation was indexed by the product of the number of salmonines stocked and relative, first‐year survival of Chinook salmon Oncorhynchus tshawytscha. A Ricker‐type parent–progeny model suggested that peak production of age‐1 alewives could occur over a broad range of spawning stock sizes, and the fit of the model was improved most by the addition of terms for spring water temperature and winter duration. With the addition of the two climate terms, the Ricker model indicated that when water was relatively warm in spring and the winter was relatively short, peak potential production of young was nine times higher than when water temperature and winters were average, and 73 times higher than when water was cold in spring and winters were long. Relative survival from age 1 to recruitment at age 2 was best described by a multiple linear regression with terms for adult abundance, TP, and predation. Mean recruitment of age‐2 fish in the 1978–1998 year‐classes predicted by using the two models in sequence was only about 20% greater than the observed mean recruitment. Model estimates fit the measured data exceptionally well for all but the largest four year‐classes, which suggests that the models will facilitate improvement in estimates of trophic transfer due to alewives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".