Aggregated yield and fishing effort in multispecies fisheries: an empirical analysis
Bibliographic record
Abstract
Many tropical fisheries are inherently of a multispecies nature, with any given type of fishing gear harvesting a wide range of species. Species-aggregated relationships between fishing effort and yield or catch per unit of effort (CPUE) provide important information for the management of such fisheries, as well as insights into ecosystem-level responses to fisheries exploitation. We used a model selection approach to study species-aggregated, yieldeffort relationships in spatially replicated, multispecies inland fisheries. Of three alternative models considered, the data strongly supported a sigmoid functional form (Akaike weight 0.95) over the alternative, asymptotic exponential or quadratic (Schaefer) models (Akaike weights 0.03 and 0.02, respectively). The sigmoid form implies the occurrence of an inflexion point in the rising part of the yieldeffort curve and no decline in aggregated yield even at the highest effort levels observed. Aggregated CPUE declines steeply near the origin of the effort scale before stabilizing or rising to a local maximum and then declining inversely with further increases in effort. This highly nonlinear response suggests that extreme caution is required when interpreting aggregated CPUE as an indicator of fishing impacts on exploited communities. Further research is required to identify the mechanisms generating the observed relationship.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".