Catch as catch can: markets, availability, and fishery closures drive distinct responses among the U.S. West Coast coastal pelagic species fleet segments
Bibliographic record
Abstract
Fishers often target multiple species. More diverse harvest portfolios may reduce income risk, increasing resilience to climate-driven changes in target species’ spatial distributions and availability. Moreover, different effects can be observed across vessels in response to the same shocks and stressors, as fishers are heterogeneous. Evaluation of climate risk across different vessel groups within a particular fishery requires consideration of heterogeneous climate impacts on the availability of multiple target species and how such changes may impact substitution behavior. Here we analyze how historical climate-driven changes in forage species distribution and the closure of the Pacific sardine fishery affected landings per vessel of three coastal pelagic species (CPS): Pacific sardine ( Sardinops sagax), market squid ( Doryteuthis opalescens), and northern anchovy ( Engraulis mordax) targeted by the U.S. West Coast CPS fleet from 2000 to 2020. Using cluster analysis, we grouped vessels into different fleet segments and estimated heterogeneous responses by fleet segment and port area. Our results show that considering heterogeneity is essential in the development of equitable and effective adaptation policies designed to mitigate the impact of changes on species availability in these fisheries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".