Climate-forcing effects on trophically linked groundfish populations: implications for fisheries management
Bibliographic record
Abstract
Commercially important groundfish populations in the Bering Sea are connected through the food web as predators and prey. In addition to having different trophic roles, the recruitment of these species varies on interdecadal time scales and may be related to climate forcing. We simulate the effects of fishing mortality on eight trophically linked species under two scenarios of climate regimes using the multispecies virtual population analysis (MSVPA) model and the multispecies forecasting model (MSFOR). Species respond differently to climate change assumptions and fishing mortality depending on their position in the food web. Results suggest that the assumptions regarding climate regime shifts on mean recruitment may produce effects comparable to the ones produced by fishing and predation interactions. Therefore, accurate models for fisheries management would require considering these factors and their potential interactions. Because responses are complex and difficult to predict, it is necessary to take a risk-averse approach in managing the species with the largest potential variation. The incorporation of climate regime shifts in fisheries management will require a better understanding of recruitment during a particular regime and a reliable way to identify regime shifts based on biological and (or) physical indices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".