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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".