Hierarchical stock assessment methods improve management performance in multi-species, data-limited fisheries
Bibliographic record
Abstract
Management performance of five alternative stock assessment methods was evaluated by using them to set harvest levels targeting multi-species maximum yield in a multi-species flatfish fishery, including single-species and hierarchical multi-species models, and methods that pooled data across species and spatial strata, with catch outcomes of each method under three data scenarios compared to catch under an omniscient manager simulation. Operating models included technical interactions between species intended to produce choke effects often observed in output controlled multi-species fisheries. Hierarchical multi-species models outperformed all other methods under data-poor and data-moderate scenarios, and outperformed single-species models under the data-rich scenario. Hierarchical models were least sensitive to prior precision, sometimes improving in performance when prior precision was reduced. Choke effects were found to both positive and negative effects, sometimes leading to underfishing of non-choke species, but at other times preventing overfishing of non-choke species. We highlight the importance of including technical interactions in multi-species assessment models and management objectives, how choke species can indicate mismatches between management objectives and system dynamics, and recommend hierarchical multi-species models for multi-species fishery management systems.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".