Ecosystem change and decadal variation in stock–recruitment relationships of Lake Erie yellow perch (Perca flavescens)
Bibliographic record
Abstract
Abstract Fish stock–recruitment relationships (SRRs) may vary in response to ecosystem change, increasing uncertainty for fisheries management. We defined three periods between 1975 and 2015 over which Lake Erie, a Laurentian Great Lake, underwent significant ecosystem changes: before zebra mussel (Dreissena polymorpha) establishment, after zebra mussel establishment and before re-eutrophication, and after re-eutrophication. To examine the extent to which SRRs of Lake Erie yellow perch (Perca flavescens) also varied over these periods, we compared the performance of Baseline (constant recruitment), Ricker (constant SRR), Periodic Ricker (different SRRs among three periods) and Random-walk Ricker (annually varying SRRs) models fitted to data for yellow perch stocks corresponding to three lake basins. Periodic and Random-walk Ricker models performed better for stocks in the western and eastern basins, but the Baseline model performed best in the central basin. Annual variation in the SRRs coincided with the timing of zebra mussel establishment and re-eutrophication in the shallower western basin, but not in the deeper eastern basin, where quagga mussels (Dreissena bugensis) established later and conditions are less eutrophic. These results underscore that temporally and spatially varying SRRs associated with ecosystem change should be taken into account in models of fish population dynamics.
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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 | 0.001 | 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 | 0.000 | 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".