Evaluating ecosystem caps on fishery yield in the context of climate stress and predation
Bibliographic record
Abstract
Ecosystem-based fisheries management strives to account for species interactions and ecosystem processes in natural resource management and conservation. In this context, ecosystem-wide caps on total fishery catches have been proposed as one tool to manage multispecies fisheries with an ecosystem approach. However, determining effective ecosystem caps is complicated because fish stock production is influenced by environmental conditions, species interactions, and fishing. Consequently, the implementation of ecosystem caps in fisheries management frameworks remains uncommon. We investigated whether ecosystem caps should account for climate variability and for predator-prey dynamics to achieve management objectives in complex marine ecosystems. We considered the example of the Gulf of Alaska (United States), a North Pacific large marine ecosystem where annual groundfish catches are managed using an "optimum yield" ecosystem cap of 800,000 t. We simulated multispecies yield of the 12 most abundant and commercially valuable groundfish stocks under selected climate and fishing scenarios using an end-to-end marine ecosystem model (Atlantis), which accounts for predator-prey and ecosystem dynamics. We found that total groundfish yield was never projected to exceed the 800,000 mt optimum yield cap across scenarios and fishing mortalities. Projected climate change led to decreased groundfish yield, and predation from the underexploited groundfish predator arrowtooth flounder (Atheresthes stomias) led to foregone catches. Groundfish removals had negative indirect effects on groundfish predators, despite total yield never exceeding the optimum yield cap, highlighting that an ineffective cap may not protect non-target species. These results suggest that the optimum yield cap currently used in the Gulf of Alaska may be too high to constrain groundfish catches under future climate change and low exploitation rates of predators. We propose that ecosystem caps should be reviewed when environmental conditions, stock productivity, or species interactions change.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".