Field Evaluation and Simulation Modeling of Length Limits and their Effects on Fishery Quality for Muskellunge in the New River, Virginia
Bibliographic record
Abstract
Abstract The trophy fisheries for Muskellunge Esox masquinongy in the northern U.S.A. and Canada often are developed and maintained by using high minimum-length limits (MLLs). However, the effectiveness of using such MLLs on southern-latitude Muskellunge populations, which have different rates of growth and mortality, warrants further research. The Muskellunge fishery in the New River, Virginia, was managed under a 30-in (75 cm) MLL until 2006 when the MLL was increased to 42 in (105 cm) to increase the abundance of large Muskellunge. We measured fishery quality before and after the institution of the 42-in MLL using size structure, average individual condition, rates of growth and mortality, and CPUE. We also assessed the potential of alternative length regulations (other MLLs and a 40–48-in protected-slot limit) to improve the population's size structure and trophy production using simulation models in the Fisheries Analyses and Modeling Simulator (FAMS) program. Following the institution of the 42-in MLL, we observed a 5-in increase in the average size of Muskellunge, an increase in the population's size structure with greater proportions of memorable-size individuals (≥42 in) and an increase in the abundance of memorable-size Muskellunge. However, declines in the average condition, i.e., relative weight (Wr), of large Muskellunge (≥38 in) suggest there is possible stockpiling of individuals just below the 42-in length limit. Higher MLLs (e.g., 48-in MLL) could further improve fishery quality by increasing the survival of Muskellunge to large trophy sizes (≥50 in). However, managers should be wary of stockpiling under alternative MLLs as well. Furthermore, a higher MLL is unlikely to garner broad angler support in this system. Conversely, a protected-slot limit that allows the production of some trophy-sized Muskellunge while reducing the overall number of individuals, and that limits potential for stockpiling, may be a more agreeable regulatory option for New River fishery managers. These findings and the methods described within this study may be useful for fisheries managers working on other Muskellunge fisheries in southern systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".