Ecosystem-based reference points under varying plankton productivity states and fisheries management strategies
Bibliographic record
Abstract
Abstract In the context of ecosystem-based fisheries management, which should consider changing and uncertain environmental conditions, the development of ecosystem-based biological reference points (EBRPs) to account for important multi-species (MS) interactions, fishery operations, and climate change, is of paramount importance for sustainable fisheries management. However, EBRPs under varying plankton productivity states and fisheries management strategies are seldom developed, and the ecosystem effects of these changes are still largely unknown. In this study, ecosystem-based FMSY (fishing mortality rate at MSY) values were estimated within an end-to-end ecosystem model (OSMOSE) for three focused fish species (Pacific Herring, Clupea pallasii; Pacific Cod, Gadus macrocephalus; Lingcod, Ophiodon elongatus) under three plankton productivity states of differing plankton biomass at high, current, and low levels. In addition, ecosystem effects were compared across different plankton productivity and fisheries management strategies with the latter consisting of two fishery scenarios (i.e. single-species-focused (SS) and MS-focused), various fishing mortality rates, and two harvest policies (with and without harvest control rules, HCRs). Main findings of this study include: (i) plankton productivity change affected the values of ecosystem-based FMSY, which increased as plankton productivity states changed from low to high plankton biomass; (ii) ecosystem-based FMSY for Pacific Herring and Pacific Cod stocks increased when fishery scenarios shifted from SS-focused to MS-focused; (iii) fisheries management incorporating HCR yielded more stable system catch and system biomass; and (iv) high plankton biomass combined with fisheries management using HCR could maintain stable ecosystem production and sustainable fisheries. Based on our findings, we highlight possible adaptive fisheries management strategies in the face of future climate and ocean changes. Overall, EBRPs complement SS stock assessments by incorporating key ecological processes and ecosystem properties, thus providing supporting evidence for better incorporation of ecosystem considerations into scientific advice for sustainable fisheries management.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".