Incorporating environmental forcing in developing ecosystem-based fisheries management strategies
Bibliographic record
Abstract
Abstract This study incorporated two pathways of environmental forcing (i.e. “larval mortality forcing” and “somatic growth forcing”) into an end-to-end ecosystem model (Object-oriented Simulator of Marine ecOSystEms, OSMOSE) developed for the Pacific North Coast Integrated Management Area (PNCIMA) off western Canada, in order to evaluate alternative fisheries management strategies under environmental changes. With a suite of ecosystem-level indicators, the present study first compared the ecosystem effects of different pathways of environmental forcing scenarios; and then evaluated the alternative fisheries management strategies which encompassed a series of fishing mortality rates relative to FMSY (the fishing mortality rate that produces maximum sustainable yield) and a set of precautionary harvest control rules (HCRs). The main objectives of this study were to (i) explore the ecosystem effects of different environmental forcing scenarios; (ii) identify the impacts of different fishing mortality rates on marine ecosystem structure and function; and (iii) evaluate the ecosystem-level performance of various levels of precautionary HCRs. Results indicated that different pathways of environmental forcing had different ecosystem effects and incorporating appropriate HCRs in the fisheries management process could help maintain ecosystem health and sustainable fisheries. This study provides important information on future fisheries management options within similar marine ecosystems that are facing global changes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".