Developing Recreational Harvest Regulations for an Unexploited Lake Trout Population
Bibliographic record
Abstract
Abstract Developing fishing regulations for previously unexploited populations presents numerous challenges, many of which stem from a scarcity of baseline information about abundance, population productivity, and expected angling pressure. We used simulation models to test the effect of six management strategies (catch and release; trophy, minimum, and maximum length limits; and protected and exploited slot length limits) on an unexploited population of Lake Trout Salvelinus namaycush in Follensby Pond, a 393-ha lake located in New York State's Adirondack Park. We combined field and literature data and mark–recapture abundance estimates to parameterize an age-structured population model and used the model to assess the effects of each management strategy on abundance, catch per unit effort (CPUE), and harvest over a range of angler effort (0–2,000 angler-days/year). Lake Trout density (3.5 fish/ha for fish ≥ age 13, the estimated age at maturity) was similar to densities observed in other unexploited systems, but growth rate was relatively slow. Maximum harvest occurred at levels of effort ≤ 1,000 angler-days/year in all the scenarios considered. Regulations that permitted harvest of large postmaturation fish, such as New York's standard Lake Trout minimum size limit or a trophy size limit, resulted in low harvest and high angler CPUE. Regulations that permitted harvest of small and sometimes immature fish, such as a protected slot or maximum size limit, allowed high harvest but resulted in low angler CPUE and produced rapid declines in harvest with increases in effort beyond the effort consistent with maximum yield. Management agencies can use these results to match regulations to management goals and to assess the risks of different management options for unexploited Lake Trout populations and other fish species with similar life history traits. Received May 18, 2015; accepted November 30, 2015 Published online March 31, 2016
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".