Exploring dynamic reference points for a short-lived, highly variable species under future climate
Bibliographic record
Abstract
Fish stock recruitment can be highly variable and may change under changing climate. The use of time-varying reference points (e.g., dynamic B0), compared with traditional “static” (fixed) reference points, has increasingly been debated. Few studies have focussed on short-lived, highly variable stocks for which factors other than fishing influence stock productivity. Our Management Strategy Evaluation framework included models with either a stock-climate or catchability-climate relationship for a highly variable prawn ( Penaeus indicus). We tested three different harvest control rules (HCRs) based on static and dynamic reference levels under three climate scenarios representing high, intermediate, and low stock productivity. Performance of HCRs was similar if stock productivity exhibited no trend or was cyclical. However, differences between HCRs were pronounced if productivity declined with the dynamic B0 HCR, on average, maintaining higher catches and reducing fishery closure, but at the risk of decreasing spawning stock biomass and breaching traditional limit reference levels. Further critical valuation of dynamic B0 is therefore recommended before uptake to address climate-related changes in stock productivity, even for species strongly influenced by climate.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".