Temporal Trends in Largemouth Bass Mortality, with Fishery Implications
Bibliographic record
Abstract
Abstract We reviewed estimates of annual exploitation (u) and total mortality (Z) for populations of largemouth bass Micropterus salmoides and used a simulation model to explore how temporal changes in u have influenced those populations. The review produced 32 estimates of u and 30 of Z spanning 51 years. Fishing mortality was roughly parabolic through time, with a mean of 0.35 for 1976–1989 and a mean of 0.18 for 1990–2003. Thus, average fishing mortality rates have declined by about one-half since about 1990. Total mortality declined with the decline in u, suggesting that changes in u caused lower overall total mortality rates. The evidence further suggests that the decline in u was caused by the voluntary release of fish by anglers rather than by changes in overall fishing effort. The simulation model showed that the decline in exploitation increased adult largemouth bass abundance but reduced the ability of size and bag regulations to improve population metrics owing to low rates of directed harvest. Discard mortality (i.e., the mortality of fish caught and released) would not negate the benefits of lower exploitation unless the mortality of fish caught and released was 0.3 or higher. Changes in angler behavior have substantially reduced fishing mortality for largemouth bass fisheries, which should be considered when developing management plans for this species and others with high rates of voluntary release.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".