How fisher behavior can bias stock assessment: insights from an agent-based modeling approach
Bibliographic record
Abstract
During stock assessment, fishery-dependent observations are often used to develop indices of abundance or biomass from catch per unit of effort (CPUE) and contribute catch at size or age information. However, fisher behavior, rather than scientific sampling protocols, determines the spatial and temporal locations of fishery-dependent observations. As a result, trends from fishery-dependent data may be a function of fishing activity rather than fish population changes. This study evaluates whether data collected from commercial fishing fleets in the Gulf of Mexico are representative of trends in fish population size. A coupled bioeconomic agent-based model was developed to generate simulated fishery data, which were used to populate an age-structured stock assessment. Comparison of stock assessment results with simulated fish population dynamics showed that management advice from assessment models based on fishery-dependent data could be biased. Assessment of fish with small home ranges harvested by fishing fleets that frequent the same fishing grounds could cause overestimation of fishing mortality. Not accounting for the spatial structure of the fishers or fish can cause biased estimates of population status.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".