Optimal harvest responses to environmental forecasts depend on resource knowledge and how it can be used
Bibliographic record
Abstract
Managing natural resources under large-scale environmental fluctuations like the El Niño Southern Oscillation (ENSO) is likely to become increasingly important under climate change. Forecasts of environmental conditions are improving, but the best response to an unfavorable forecast remains unclear; many practitioners advocate reducing harvest as a more precautionary approach, while prior economic theory favors increasing harvest. Using logistic and age-structured fisheries models, we show that informational constraints — uncertain stock estimates and restrictions on harvest policies — play a central role in choosing how to respond to a forecasted shock. With perfect knowledge and no policy constraints, risk-neutral managers should increase harvest when a negative shock is forecast. However, informational constraints may drive the optimal response to a forecast of a negative shock toward or away from precaution. Precautionary forecast responses arise when informational constraints make the harvest policy insufficiently sensitive to the true resource status. In contrast, uncertainty about the stock size can lead to more aggressive forecast responses when stock dynamics are nonlinear and not all fish are susceptible to fishing.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".