Linking the dynamics of harvest effort to recruitment dynamics in a multistock, spatially structured fishery
Bibliographic record
Abstract
A freshwater sport fishery that targets hundreds of geographically isolated stocks is simulated by combining a model of angler behavior with a model of rainbow trout (Oncorhynchus mykiss) population dynamics. Ideal free distribution (IFD) theory, which suggests that angling quality will be similar on all lakes, is used to drive angler effort distribution. Model parameters are based on creel survey data from 53 lakes and empirical relationships between growth, survival, and density derived from whole-lake density manipulations on nine lakes over a period of 10 years. We compared angling quality, population density, fish size, and yield under unfished conditions, harvest rates that maximize sustained yields (MSY), and an IFD equilibrium driven by angler behavior. The IFD equilibrium rarely maximized yields. Stocks with high MSY angling quality are overexploited at the IFD equilibrium because anglers move to take advantage of exceptional angling opportunities. These stocks would often be viewed as more resistant to harvest pressure because they have higher stock productivities and habitat capacities. However, in our model, they are systematically overharvested because their high fish density attracts excessive angling pressure. Conversely, stocks with low MSY angling quality are underexploited because anglers move to take advantage of better angling quality on other lakes.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".