A framework for incorporating climate regime shifts into the management of marine resources
Bibliographic record
Abstract
Abstract It is possible to use an ecosystem‐based management approach to incorporate knowledge of climate regime impacts on ecosystem productivity to manage fishery resources. To do so, it requires the development of a coherent framework that can be built using existing stock assessment and management activities: ecosystem assessment, risk analyses, adaptive management and reference points. This paper builds such a framework and uses two population simulations to illustrate the benefits and tradeoffs of variable regime‐specific harvest rates. The framework does not require prediction of regime shifts, but assumes that detection can occur soon after one has happened. As such, decisions do not need to be coincident to regime shifts, but can be delayed by an appropriate period of time that is linked to a species’ life history, i.e. age of maturity or recruitment. Fisheries scientists should provide harvest recommendations that reflect a range of levels of risk to the stock under different assumptions of productivity. Coupling ecosystem assessment with ecosystem‐based management would allow managers to select appropriate regime‐specific harvest rates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".