Estimating abundance of pelagic fishes using gillnet catch data in data-limited fisheries: a Bayesian approach
Bibliographic record
Abstract
We describe a Bayesian modelling approach to estimate abundance and biomass of pelagic fishes from gillnet catches in data-limited situations. By making a number of simple assumptions, we use fish sustained swimming speed to calculate the effective area fished by a gillnet in a specified soak time to estimate abundance (fish·km–2) from the number of fish caught. We used catch data from various sampling methods in northern Australia and elicited anecdotal information from experts to build a size distribution of the true population to compensate for size classes that were unlikely to be represented in the catch because of size selectivity of the gear. Our final abundance estimates for various-sized scombrids (0.04–4.17 fish·km–2) and istiophorids (0.004–0.005 fish·km–2) were similar to what has been estimated for similar species in more data-rich situations in tropical regions of the Pacific Ocean. The model is particularly useful in data-limited situations in which abundance or biomass estimates are required for pelagic fish species of low economic importance. These data are often required for ecosystem models (e.g., Ecopath) that are increasingly being considered as potential tools for ecosystem-based fisheries management.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".