Using food web model results to inform stock assessment estimates of mortality and production for ecosystem-based fisheries management
Bibliographic record
Abstract
Examining food web relationships for commercially important species enhances fisheries management by identifying sources of variability in mortality and production that are not included in standard single-species stock assessments. We use a static mass-balance model to evaluate relationships between species in a large marine ecosystem, the coastal Gulf of Alaska, USA. We focus on food web relationships for four case-study species: Pacific halibut ( Hippoglossus stenolepis ), longnose skate ( Raja rhina ), walleye pollock ( Theragra chalcogramma ), and squids (order Teuthoidea). For each, we present the species’ position within the food web, evaluate fishing mortality relative to predation mortality, and evaluate diet compositions. We find that high trophic level (TL) species, whether commercially valuable (halibut) or incidentally caught (skates), have mortality patterns consistent with single-species assessment assumptions, where fishing mortality dominates natural mortality. However, assessments for commercially valuable (pollock) or incidentally caught (squids) mid-TL species can be enhanced by including food web derived predation information because fishing mortality is small compared with high and variable predation mortality. Finally, we outline food web relationships that suggest how production of species may change with diet composition or prey availability.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".