Recruitment, growth, and exploitation as determinants of brook trout size structure in natural lakes
Bibliographic record
Abstract
Recreational fisheries are globally significant both culturally and economically, yet unmanaged harvest can erode population structure and sustainability. This study evaluates the long-term effectiveness of a trait-informed harvest regulation combining reduced daily bag limits with weight-based harvest caps on brook trout (Salvelinus fontinalis) populations in eight unstocked, oligotrophic lakes in Newfoundland, Canada. Over 15 years (1994–2008), standardized index netting and winter creel surveys were used to assess changes in fish size structure and angler behaviour. Mixed-effects models revealed significant increases in maximum length (L max ), mean length at capture (L c ), and proportional size distribution (PSD) following regulation implementation, with no significant changes in catch rate, effort, or release rate. We applied a variance partitioning framework to identify mechanisms driving PSD improvements, attributing 31 % of PSD variation to growth, 18 % to recruitment, and 7 % to exploitation. These findings demonstrate that biologically grounded regulations can enhance fishery quality while maintaining angler engagement. The study offers one of the first empirical demonstrations of PSD driver partitioning in a wild brook trout fishery. It provides evidence to support adaptive, trait-informed management in recreational salmonid systems.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".