The illusion of plenty: hyperstability masks collapses in two recreational fisheries that target fish spawning aggregations
Bibliographic record
Abstract
Fisheries that target fish spawning aggregations can exhibit hyperstability, in which catch per unit effort (CPUE) remains elevated as stock abundance declines, but empirical support is limited. We compiled several fishery-dependent and fishery-independent data sets to assess stock trends in the barred sand bass ( Paralabrax nebulifer ) and the kelp bass ( Paralabrax clathratus ) in southern California, USA, evaluate the interaction between spawning aggregations and fishing activities, and test for hyperstability. Annual and seasonal trends from fisheries and population data indicate that regional stocks of both species have collapsed in response to overfishing of spawning aggregations and changes in environmental conditions. The aggregating behavior of fish and persistent targeting of spawning aggregations by recreational fisheries combined to produce a hyperstable relationship between CPUE and stock abundance in both species, which created the illusion that population levels were stable and masked fishery collapses. Differences in the rate of decline between the two species may be related to the size, duration, and spatial distribution of their spawning aggregations. Results of this study provide empirical evidence of hyperstability in aggregation-based fisheries and demonstrate that CPUE data be used with caution and given low weight when fishery-independent data are available.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".