Failure to detect ecological and evolutionary effects of harvest on exploited fish populations in a managed fisheries ecosystem
Bibliographic record
Abstract
Overexploitation and collapse of major fisheries raises important concerns about effects of harvest on fish populations. We tested for ecological and evolutionary mechanisms by which harvest could affect exploited fish populations in Lake Erie over the last four decades, over most of which intensive fisheries management was implemented. We did not detect evidence of long-term negative effects of harvest on yellow perch (Perca flavescens), walleye (Sander vitreus), white perch (Morone americana), or white bass (Morone chrysops) populations, either through recruitment success or through alteration of maturation schedules. Current fisheries management in Lake Erie has been relatively successful with respect to minimizing negative harvest effects, such that the dynamics of exploited fish populations in Lake Erie were more strongly affected by environment than harvest. Our study adds to the evidence that effective fisheries management is capable of rebuilding depleted fisheries and (or) maintaining healthy fisheries. Nevertheless, fisheries management needs to move beyond the ecological dimension to incorporate economic, social, and institutional aspects for society to be better assured of the sustainability of fisheries in rapidly changing ecosystems.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".