Quota use in mixed‐stock fisheries
Bibliographic record
Abstract
Abstract Although most fisheries assessment and management focuses on the status of individual stocks, and regulations are commonly established as single‐species total allowable catch limits (TACs), much of the catch from global fisheries comes from mixed‐stock fisheries where species cannot be harvested separately. We show that in some fisheries where TAC and catch of demersal fish stocks are tracked, the average fraction of TAC harvested ranges from 21% to 68% overall and is declining. This is, in part, related to efforts to protect all species from overfishing, leading to ‘choke species’, which limit fishing pressure on other target species. While some choke species arise from a mix of low and high‐productivity species, others result from allocation processes, which can be aggravated by shifting distributions due to climate change. Underutilization of TACs can also result from market limitations, low value of individual species, undercapacity or management measures. Proposed methods for increasing long‐term yield require species to be managed in stock groups, or allowing the abundance of some stocks to fall below target reference points. We suggest that the observed low and declining aggregate harvests are due, primarily, to the focus on single‐stock sustainability measures, rather than performance of the fisheries in relation to potential overall yield. While there is a growing consensus that single‐species management should be replaced by an ecosystem‐based approach, this will require clear legislative directives regarding management of the trade‐offs involved. Time series considered in this analysis do not extend beyond 2019.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".