Experimental demonstration of catch hyperstability from habitat aggregation, not effort sorting, in a recreational fishery
Bibliographic record
Abstract
The relationship between angler catch rates and fish abundance can contribute to or hinder sustainable exploitation of fisheries depending on whether catch rates are proportional to fish abundance or are hyperstable. We performed a whole-ecosystem experiment in which fish abundance was manipulated and paired with weekly angler catch rate estimates from controlled experimental fishing. Catch rates were hyperstable (β = 0.47) in response to changes in fish abundance. By excluding effort sorting (i.e., catch rates remaining high because less skilled anglers leave the fishery as abundance declines), our experiment isolated the influence of fish aggregation as a driver of hyperstability. Spatial analysis of catch locations did not identify clustering around specific points, suggesting that loose aggregation to preferred habitat at the scale of the entire littoral zone was enough to maintain stable catch rates. In our study, general, non-spawning, habitat preferences created loose aggregations for anglers to target, which was sufficient to generate hyperstability. Habitat preferences are common to nearly all fishes and widely known to anglers, suggesting that many harvest-oriented recreational fisheries can be expected to exhibit hyperstability.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".