Investigating three sources of bias in hook-and-line surveys: survey design, gear saturation, and multispecies interactions
Bibliographic record
Abstract
Hook-and-line surveys can be used to estimate population trends in fish species where conventional methods such as trawl, acoustic, visual, or pot surveys cannot be applied. Hook-and-line surveys allow for the collection of biological information, but the resultant indices of abundance may be biased. We designed simulations to address concerns around survey design, hook saturation, and competition among species and found that catch per unit effort (CPUE) declined more slowly than population size across all scenarios. This hyperstability was most prominent when fish were found in high-density patches, and these scenarios have median absolute relative error values roughly three to five times greater than those with more even distributions of fish density. Despite hyperstability, the surveys still had statistical power to detect changes in abundance. Interspecific competition for hooks caused bias in survey results when one species was more aggressive than another. Taken together, our results indicate hook-and-line surveys fill a niche in survey methodologies, but their use and interpretation can be challenged by hyperstability and competition among species.
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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 | 0.079 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".